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@@ -1 +1,5248 @@
-{"cells":[{"cell_type":"markdown","metadata":{},"source":["# Part-2 Data IO with SeDF - Accessing Data\n"]},{"cell_type":"markdown","metadata":{"toc":true},"source":["<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n","<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Introduction\" data-toc-modified-id=\"Introduction-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Introduction</a></span></li><li><span><a href=\"#Accessing-GIS-Data\" data-toc-modified-id=\"Accessing-GIS-Data-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Accessing GIS Data</a></span><ul class=\"toc-item\"><li><span><a href=\"#Read-in-Web-Feature-Layers\" data-toc-modified-id=\"Read-in-Web-Feature-Layers-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Read in Web Feature Layers</a></span><ul class=\"toc-item\"><li><span><a href=\"#Memory-usage-and-the-query()-operation\" data-toc-modified-id=\"Memory-usage-and-the-query()-operation-2.1.1\"><span class=\"toc-item-num\">2.1.1&nbsp;&nbsp;</span>Memory usage and the <code>query()</code> operation</a></span></li><li><span><a href=\"#Create-SeDF-from-FeatureSet\" data-toc-modified-id=\"Create-SeDF-from-FeatureSet-2.1.2\"><span class=\"toc-item-num\">2.1.2&nbsp;&nbsp;</span>Create SeDF from FeatureSet</a></span></li><li><span><a href=\"#Create-SeDF-from-FeatureCollection\" data-toc-modified-id=\"Create-SeDF-from-FeatureCollection-2.1.3\"><span class=\"toc-item-num\">2.1.3&nbsp;&nbsp;</span>Create SeDF from FeatureCollection</a></span></li></ul></li><li><span><a href=\"#Read-in-local-GIS-data\" data-toc-modified-id=\"Read-in-local-GIS-data-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Read in local GIS data</a></span><ul class=\"toc-item\"><li><span><a href=\"#Reading-a-Shapefile\" data-toc-modified-id=\"Reading-a-Shapefile-2.2.1\"><span class=\"toc-item-num\">2.2.1&nbsp;&nbsp;</span>Reading a Shapefile</a></span><ul class=\"toc-item\"><li><span><a href=\"#Shapefile-from-a-URL\" data-toc-modified-id=\"Shapefile-from-a-URL-2.2.1.1\"><span class=\"toc-item-num\">2.2.1.1&nbsp;&nbsp;</span>Shapefile from a URL</a></span></li></ul></li><li><span><a href=\"#Reading-a-Featureclass\" data-toc-modified-id=\"Reading-a-Featureclass-2.2.2\"><span class=\"toc-item-num\">2.2.2&nbsp;&nbsp;</span>Reading a Featureclass</a></span><ul class=\"toc-item\"><li><span><a href=\"#Subset-data-for-specific-fields\" data-toc-modified-id=\"Subset-data-for-specific-fields-2.2.2.1\"><span class=\"toc-item-num\">2.2.2.1&nbsp;&nbsp;</span>Subset data for specific fields</a></span></li><li><span><a href=\"#Subset-using-where_clause\" data-toc-modified-id=\"Subset-using-where_clause-2.2.2.2\"><span class=\"toc-item-num\">2.2.2.2&nbsp;&nbsp;</span>Subset using <code>where_clause</code></a></span></li><li><span><a href=\"#Subset-using-fields-and-where_clause\" data-toc-modified-id=\"Subset-using-fields-and-where_clause-2.2.2.3\"><span class=\"toc-item-num\">2.2.2.3&nbsp;&nbsp;</span>Subset using <code>fields</code> and <code>where_clause</code></a></span></li><li><span><a href=\"#Subset-using-sql_clause\" data-toc-modified-id=\"Subset-using-sql_clause-2.2.2.4\"><span class=\"toc-item-num\">2.2.2.4&nbsp;&nbsp;</span>Subset using <code>sql_clause</code></a></span></li><li><span><a href=\"#Using-spatial_filter\" data-toc-modified-id=\"Using-spatial_filter-2.2.2.5\"><span class=\"toc-item-num\">2.2.2.5&nbsp;&nbsp;</span>Using <code>spatial_filter</code></a></span></li></ul></li></ul></li><li><span><a href=\"#Read-in-DataFrame-with-Addresses\" data-toc-modified-id=\"Read-in-DataFrame-with-Addresses-2.3\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>Read in DataFrame with Addresses</a></span></li><li><span><a href=\"#Read-in-DataFrame-with-Lat/Long-Information\" data-toc-modified-id=\"Read-in-DataFrame-with-Lat/Long-Information-2.4\"><span class=\"toc-item-num\">2.4&nbsp;&nbsp;</span>Read in DataFrame with Lat/Long Information</a></span></li><li><span><a href=\"#Read-in-GeoPandas-DataFrame\" data-toc-modified-id=\"Read-in-GeoPandas-DataFrame-2.5\"><span class=\"toc-item-num\">2.5&nbsp;&nbsp;</span>Read in GeoPandas DataFrame</a></span><ul class=\"toc-item\"><li><span><a href=\"#Create-a-GeoDataFrame\" data-toc-modified-id=\"Create-a-GeoDataFrame-2.5.1\"><span class=\"toc-item-num\">2.5.1&nbsp;&nbsp;</span>Create a GeoDataFrame</a></span></li><li><span><a href=\"#Create-a-SeDF-from-GeoDataFrame\" data-toc-modified-id=\"Create-a-SeDF-from-GeoDataFrame-2.5.2\"><span class=\"toc-item-num\">2.5.2&nbsp;&nbsp;</span>Create a SeDF from GeoDataFrame</a></span></li></ul></li><li><span><a href=\"#Read-in-feather-format-data\" data-toc-modified-id=\"Read-in-feather-format-data-2.6\"><span class=\"toc-item-num\">2.6&nbsp;&nbsp;</span>Read in feather format data</a></span></li><li><span><a href=\"#Read-in-Non-spatial-Table-data\" data-toc-modified-id=\"Read-in-Non-spatial-Table-data-2.7\"><span class=\"toc-item-num\">2.7&nbsp;&nbsp;</span>Read in Non-spatial Table data</a></span><ul class=\"toc-item\"><li><span><a href=\"#Using-the-from_table()-method\" data-toc-modified-id=\"Using-the-from_table()-method-2.7.1\"><span class=\"toc-item-num\">2.7.1&nbsp;&nbsp;</span>Using the <code>from_table()</code> method</a></span><ul class=\"toc-item\"><li><span><a href=\"#Reading-a-csv-file\" data-toc-modified-id=\"Reading-a-csv-file-2.7.1.1\"><span class=\"toc-item-num\">2.7.1.1&nbsp;&nbsp;</span>Reading a csv file</a></span></li><li><span><a href=\"#Reading-table-from-a-File-Geodatabase\" data-toc-modified-id=\"Reading-table-from-a-File-Geodatabase-2.7.1.2\"><span class=\"toc-item-num\">2.7.1.2&nbsp;&nbsp;</span>Reading table from a File Geodatabase</a></span></li></ul></li><li><span><a href=\"#Using-the-from_layer()-method\" data-toc-modified-id=\"Using-the-from_layer()-method-2.7.2\"><span class=\"toc-item-num\">2.7.2&nbsp;&nbsp;</span>Using the <code>from_layer()</code> method</a></span></li></ul></li><li><span><a href=\"#Read-in-data-from-'lite-and-portable'-databases\" data-toc-modified-id=\"Read-in-data-from-'lite-and-portable'-databases-2.8\"><span class=\"toc-item-num\">2.8&nbsp;&nbsp;</span>Read in data from '<em>lite and portable</em>' databases</a></span><ul class=\"toc-item\"><li><span><a href=\"#Read-from-a-mobile-geodatabase\" data-toc-modified-id=\"Read-from-a-mobile-geodatabase-2.8.1\"><span class=\"toc-item-num\">2.8.1&nbsp;&nbsp;</span>Read from a mobile geodatabase</a></span></li><li><span><a href=\"#Read-from-a-SQLite-database\" data-toc-modified-id=\"Read-from-a-SQLite-database-2.8.2\"><span class=\"toc-item-num\">2.8.2&nbsp;&nbsp;</span>Read from a SQLite database</a></span></li></ul></li></ul></li><li><span><a href=\"#Conclusion\" data-toc-modified-id=\"Conclusion-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Conclusion</a></span></li></ul></div>\n"]},{"cell_type":"markdown","metadata":{},"source":["## Introduction\n"]},{"cell_type":"markdown","metadata":{},"source":["In _part-1_ of this guide series, we started with an introduction to the [**Spatially enabled DataFrame**](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#geoaccessor) (SeDF), the `spatial` and `geom` namespaces, and looked at a quick example of SeDF in action. In this part of the guide series, we will look at how GIS data can be accessed from various data formats using SeDF.\n","\n","GIS users work with different vector-based spatial data formats, like published layers on remote servers (web layers) and local data. The [**Spatially enabled DataFrame**](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#geoaccessor) allows the users to read, write, and manipulate spatial data by bringing the data in-memory.\n","\n","The _SeDF_ integrates with Esri's [**ArcPy** site-package](http://pro.arcgis.com/en/pro-app/arcpy/get-started/what-is-arcpy-.htm), as well as the open source [pyshp](https://github.com/GeospatialPython/pyshp/), [shapely](https://github.com/Toblerity/Shapely) and [fiona](https://github.com/Toblerity/Fiona) packages. This means that the _SeDF_ can use either [shapely](https://pypi.org/project/Shapely/) or [arcpy](https://www.esri.com/en-us/arcgis/products/arcgis-python-libraries/libraries/arcpy) geometry engines to provide you with options for easily working with geospatial data, regardless of your platform. The _SeDF_ transforms the data into the formats you desire, allowing you to use Python functionality to analyze and visualize geographic information.\n","\n","Data can be read and scripted to automate workflows and be visualized on maps in a [Jupyter notebooks](../using-the-jupyter-notebook-environment/). Let's explore the options available for accessing GIS data with the versatile _Spatially enabled DataFrame_.\n"]},{"cell_type":"markdown","metadata":{},"source":["The data used in this guide is available as an [item](https://www.arcgis.com/home/item.html?id=c7140ae3d7ae4fd0817181461019aa75). We will start by importing some libraries and downloading and extracting the data needed for the analysis in this guide.\n"]},{"cell_type":"code","execution_count":1,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:51:37.257478Z","start_time":"2021-11-22T19:51:12.679381Z"}},"outputs":[],"source":["# Import Libraries\n","import pandas as pd\n","from arcgis.features import GeoAccessor, GeoSeriesAccessor\n","from arcgis.gis import GIS\n","from IPython.display import display\n","import zipfile\n","import os\n","import shutil"]},{"cell_type":"code","execution_count":2,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:51:38.872324Z","start_time":"2021-11-22T19:51:37.261479Z"}},"outputs":[],"source":["# Create a GIS connection\n","gis = GIS()\n","agol_gis = GIS(\"https://www.arcgis.com\", \"arcgis_python\", \"amazing_arcgis_123\")"]},{"cell_type":"code","execution_count":3,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:51:38.980325Z","start_time":"2021-11-22T19:51:38.876325Z"}},"outputs":[{"data":{"text/html":["<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n","                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n","                       <a href='https://www.arcgis.com/home/item.html?id=c7140ae3d7ae4fd0817181461019aa75' target='_blank'>\n","                        <img src='https://www.arcgis.com/sharing/rest//content/items/c7140ae3d7ae4fd0817181461019aa75/info/thumbnail/ago_downloaded.png' class=\"itemThumbnail\">\n","                       </a>\n","                    </div>\n","\n","                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n","                        <a href='https://www.arcgis.com/home/item.html?id=c7140ae3d7ae4fd0817181461019aa75' target='_blank'><b>sedf_guide_data</b>\n","                        </a>\n","                        <br/>Data for Spatially enabled DataFrame Guides<img src='https://www.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/datafiles16.png' style=\"vertical-align:middle;\">Shapefile by api_data_owner\n","                        <br/>Last Modified: November 11, 2021\n","                        <br/>0 comments, 4 views\n","                    </div>\n","                </div>\n","                "],"text/plain":["<Item title:\"sedf_guide_data\" type:Shapefile owner:api_data_owner>"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["# Get the data item\n","data_item = gis.content.get('c7140ae3d7ae4fd0817181461019aa75')\n","data_item"]},{"cell_type":"markdown","metadata":{},"source":["The cell below downloads and extracts the data from the data item to your machine.\n"]},{"cell_type":"code","execution_count":4,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:51:45.305934Z","start_time":"2021-11-22T19:51:42.206937Z"}},"outputs":[{"name":"stdout","output_type":"stream","text":["Removed existing data directory\n","Dataset unzipped at: sedf_data\\cities\n"]}],"source":["# Download and extract the data\n","def unzip_data():\n","    \"\"\"\n","    This function:\n","    - creates a directory `sedf_data` to download the data from the item\n","    - downloads the item as `sedf_guide_data.zip` file in the sedf_data directory\n","    - unzips and extracts the data to '.\\sedf_data\\cities'.\n","    \"\"\"\n","    try:\n","\n","        # path to downloaded data folder\n","        data_dir = os.path.join(os.getcwd(), 'sedf_data')\n","\n","        # remove existing cities directory if exists\n","        if os.path.isdir(data_dir):\n","            shutil.rmtree(data_dir)\n","            print(f'Removed existing data directory')\n","        else:\n","            os.makedirs(data_dir)\n","\n","        data_item.download(data_dir)    # download the data item\n","        # path to zipped file inside data folder\n","        zipped_file_path = os.path.join(data_dir, 'sedf_guide_data.zip')\n","\n","        # unzip the data\n","        zip_ref = zipfile.ZipFile(zipped_file_path, 'r')\n","        zip_ref.extractall(data_dir)\n","        zip_ref.close()\n","\n","        # path to new cities directory\n","        cities_dir = os.path.join(data_dir, 'cities')\n","        print(f'Dataset unzipped at: {os.path.relpath(cities_dir)}')\n","\n","    except Exception as e:\n","        print(f'Error unzipping file: {e}')\n","\n","\n","# Extract data\n","unzip_data()"]},{"cell_type":"markdown","metadata":{},"source":["## Accessing GIS Data\n"]},{"cell_type":"markdown","metadata":{},"source":["The [**Spatially enabled DataFrame**](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#geoaccessor) reads from many **sources**, including [Feature layers](https://doc.arcgis.com/en/arcgis-online/share-maps/hosted-web-layers.htm), [Feature classes](http://desktop.arcgis.com/en/arcmap/latest/manage-data/feature-classes/a-quick-tour-of-feature-classes.htm), [Shapefiles](http://desktop.arcgis.com/en/arcmap/latest/manage-data/shapefiles/what-is-a-shapefile.htm), Pandas [DataFrames](http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe) and more. Let's dive into the details of accessing GIS data from various sources.\n"]},{"cell_type":"markdown","metadata":{},"source":["### Read in Web Feature Layers\n","\n","[Feature layers](https://doc.arcgis.com/en/arcgis-online/share-maps/hosted-web-layers.htm) hosted on [**ArcGIS Online**](https://www.arcgis.com) or [**ArcGIS Enterprise**](http://enterprise.arcgis.com/en/) can be easily read into a Spatially enabled DataFrame using the [`from_layer()`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?highlight=from_layer#arcgis.features.GeoAccessor.from_layer) method.\n","\n","The example below shows how the [`get()`](https://developers.arcgis.com/python/api-reference/arcgis.gis.toc.html?highlight=gis%20content%20get#arcgis.gis.ContentManager.get) method can be used to retrieve an ArcGIS Online [`item`](https://developers.arcgis.com/python/api-reference/arcgis.gis.toc.html?highlight=gis%20content%20get#item) and how the [`layers`](https://developers.arcgis.com/python/api-reference/arcgis.gis.toc.html#layer) property of an `item` can be used to access the data.\n"]},{"cell_type":"code","execution_count":5,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:51:52.373464Z","start_time":"2021-11-22T19:51:51.851896Z"},"scrolled":true},"outputs":[{"data":{"text/html":["<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n","                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n","                       <a href='https://www.arcgis.com/home/item.html?id=85d0ca4ea1ca4b9abf0c51b9bd34de2e' target='_blank'>\n","                        <img src='https://www.arcgis.com/sharing/rest//content/items/85d0ca4ea1ca4b9abf0c51b9bd34de2e/info/thumbnail/thumbnail1578438561290.jpeg' class=\"itemThumbnail\">\n","                       </a>\n","                    </div>\n","\n","                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n","                        <a href='https://www.arcgis.com/home/item.html?id=85d0ca4ea1ca4b9abf0c51b9bd34de2e' target='_blank'><b>USA Major Cities</b>\n","                        </a>\n","                        <br/>This layer presents the locations of cities within the United States with populations of approximately 10,000 or greater, all state capitals, and the national capital.<img src='https://www.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by esri_dm\n","                        <br/>Last Modified: May 19, 2020\n","                        <br/>1 comments, 33,841,105 views\n","                    </div>\n","                </div>\n","                "],"text/plain":["<Item title:\"USA Major Cities\" type:Feature Layer Collection owner:esri_dm>"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["gis = GIS()\n","item = gis.content.search(\n","    \"USA Major Cities\", item_type=\"Feature layer\", outside_org=True)[0]\n","item"]},{"cell_type":"code","execution_count":6,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:51:56.288612Z","start_time":"2021-11-22T19:51:52.376465Z"}},"outputs":[{"data":{"text/plain":["(3886, 50)"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["# Obtain the first feature layer from the item\n","flayer = item.layers[0]\n","\n","# Use the `from_layer` static method in the 'spatial' namespace on the Pandas' DataFrame\n","sdf = pd.DataFrame.spatial.from_layer(flayer)\n","\n","# Check shape\n","sdf.shape"]},{"cell_type":"code","execution_count":7,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:51:56.317613Z","start_time":"2021-11-22T19:51:56.291617Z"}},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>AGE_10_14</th>\n","      <th>AGE_15_19</th>\n","      <th>AGE_20_24</th>\n","      <th>AGE_25_34</th>\n","      <th>AGE_35_44</th>\n","      <th>AGE_45_54</th>\n","      <th>AGE_55_64</th>\n","      <th>AGE_5_9</th>\n","      <th>AGE_65_74</th>\n","      <th>AGE_75_84</th>\n","      <th>...</th>\n","      <th>PLACEFIPS</th>\n","      <th>POP2010</th>\n","      <th>POPULATION</th>\n","      <th>POP_CLASS</th>\n","      <th>RENTER_OCC</th>\n","      <th>SHAPE</th>\n","      <th>ST</th>\n","      <th>STFIPS</th>\n","      <th>VACANT</th>\n","      <th>WHITE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1313</td>\n","      <td>1058</td>\n","      <td>734</td>\n","      <td>2031</td>\n","      <td>1767</td>\n","      <td>1446</td>\n","      <td>1136</td>\n","      <td>1503</td>\n","      <td>665</td>\n","      <td>486</td>\n","      <td>...</td>\n","      <td>1601990</td>\n","      <td>13816</td>\n","      <td>15181</td>\n","      <td>6</td>\n","      <td>1271</td>\n","      <td>{\"x\": -12462673.723706165, \"y\": 5384674.994080...</td>\n","      <td>ID</td>\n","      <td>16</td>\n","      <td>271</td>\n","      <td>13002</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>890</td>\n","      <td>817</td>\n","      <td>818</td>\n","      <td>1799</td>\n","      <td>1235</td>\n","      <td>1330</td>\n","      <td>1143</td>\n","      <td>1099</td>\n","      <td>721</td>\n","      <td>579</td>\n","      <td>...</td>\n","      <td>1607840</td>\n","      <td>11899</td>\n","      <td>11946</td>\n","      <td>6</td>\n","      <td>1441</td>\n","      <td>{\"x\": -12506251.313993266, \"y\": 5341537.793529...</td>\n","      <td>ID</td>\n","      <td>16</td>\n","      <td>318</td>\n","      <td>9893</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>12750</td>\n","      <td>13959</td>\n","      <td>16966</td>\n","      <td>32135</td>\n","      <td>27048</td>\n","      <td>29595</td>\n","      <td>24177</td>\n","      <td>12933</td>\n","      <td>12176</td>\n","      <td>7087</td>\n","      <td>...</td>\n","      <td>1608830</td>\n","      <td>205671</td>\n","      <td>225405</td>\n","      <td>8</td>\n","      <td>33359</td>\n","      <td>{\"x\": -12938676.6836459, \"y\": 5403597.04949123...</td>\n","      <td>ID</td>\n","      <td>16</td>\n","      <td>6996</td>\n","      <td>182991</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>790</td>\n","      <td>768</td>\n","      <td>699</td>\n","      <td>1445</td>\n","      <td>1136</td>\n","      <td>1134</td>\n","      <td>935</td>\n","      <td>959</td>\n","      <td>679</td>\n","      <td>464</td>\n","      <td>...</td>\n","      <td>1611260</td>\n","      <td>10345</td>\n","      <td>10727</td>\n","      <td>6</td>\n","      <td>1461</td>\n","      <td>{\"x\": -12667411.402393516, \"y\": 5241722.820606...</td>\n","      <td>ID</td>\n","      <td>16</td>\n","      <td>241</td>\n","      <td>7984</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>3803</td>\n","      <td>3779</td>\n","      <td>3687</td>\n","      <td>7571</td>\n","      <td>5559</td>\n","      <td>4744</td>\n","      <td>3624</td>\n","      <td>4397</td>\n","      <td>2296</td>\n","      <td>1222</td>\n","      <td>...</td>\n","      <td>1612250</td>\n","      <td>46237</td>\n","      <td>53942</td>\n","      <td>7</td>\n","      <td>5196</td>\n","      <td>{\"x\": -12989383.674504515, \"y\": 5413226.487333...</td>\n","      <td>ID</td>\n","      <td>16</td>\n","      <td>1428</td>\n","      <td>35856</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 50 columns</p>\n","</div>"],"text/plain":["   AGE_10_14  AGE_15_19  AGE_20_24  AGE_25_34  AGE_35_44  AGE_45_54  \\\n","0       1313       1058        734       2031       1767       1446   \n","1        890        817        818       1799       1235       1330   \n","2      12750      13959      16966      32135      27048      29595   \n","3        790        768        699       1445       1136       1134   \n","4       3803       3779       3687       7571       5559       4744   \n","\n","   AGE_55_64  AGE_5_9  AGE_65_74  AGE_75_84  ...  PLACEFIPS  POP2010  \\\n","0       1136     1503        665        486  ...    1601990    13816   \n","1       1143     1099        721        579  ...    1607840    11899   \n","2      24177    12933      12176       7087  ...    1608830   205671   \n","3        935      959        679        464  ...    1611260    10345   \n","4       3624     4397       2296       1222  ...    1612250    46237   \n","\n","   POPULATION  POP_CLASS  RENTER_OCC  \\\n","0       15181          6        1271   \n","1       11946          6        1441   \n","2      225405          8       33359   \n","3       10727          6        1461   \n","4       53942          7        5196   \n","\n","                                               SHAPE  ST STFIPS VACANT   WHITE  \n","0  {\"x\": -12462673.723706165, \"y\": 5384674.994080...  ID     16    271   13002  \n","1  {\"x\": -12506251.313993266, \"y\": 5341537.793529...  ID     16    318    9893  \n","2  {\"x\": -12938676.6836459, \"y\": 5403597.04949123...  ID     16   6996  182991  \n","3  {\"x\": -12667411.402393516, \"y\": 5241722.820606...  ID     16    241    7984  \n","4  {\"x\": -12989383.674504515, \"y\": 5413226.487333...  ID     16   1428   35856  \n","\n","[5 rows x 50 columns]"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["# Check first few records\n","sdf.head()"]},{"cell_type":"code","execution_count":8,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:51:56.324128Z","start_time":"2021-11-22T19:51:56.320614Z"}},"outputs":[{"data":{"text/plain":["pandas.core.frame.DataFrame"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["# Check type of sdf\n","type(sdf)"]},{"cell_type":"code","execution_count":9,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:51:56.343129Z","start_time":"2021-11-22T19:51:56.326129Z"}},"outputs":[{"data":{"text/plain":["['point']"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["# Access spatial namespace\n","sdf.spatial.geometry_type"]},{"cell_type":"markdown","metadata":{},"source":["> We can see that the dataset has 3886 records and 50 columns. Inspecting the `type` of `sdf` object and accessing the `spatial` namespace shows us that a _Spatially enabled DataFrame_ has been created from all the data in the layer.\n"]},{"cell_type":"markdown","metadata":{},"source":["#### Memory usage and the `query()` operation\n"]},{"cell_type":"markdown","metadata":{},"source":["The [**`from_layer()`**](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?highlight=from_layer#arcgis.features.GeoAccessor.from_layer) method will attempt to read all the data from the layer into the memory. This approach works when you are dealing with small datasets. However, when it comes to large datasets, it becomes imperative to use the memory efficiently and query for only what is necessary.\n","\n","Let's take a look at the memory usage of the existing _SeDF_ using the [`memory_usage()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.memory_usage.html) method from Pandas.\n"]},{"cell_type":"code","execution_count":10,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:51:56.731373Z","start_time":"2021-11-22T19:51:56.722376Z"}},"outputs":[{"name":"stdout","output_type":"stream","text":["Shape of data: (3886, 50)\n","Memory used: 1.48 MB\n"]}],"source":["# Check memory usage of current sdf\n","mem_used = sdf.memory_usage().sum() / (1024**2)  # converting to megabytes\n","print(f'Shape of data: {sdf.shape}')\n","print(f'Memory used: {round(mem_used, 2)} MB')"]},{"cell_type":"markdown","metadata":{},"source":["> We can see that a `SeDF` created using the `from_layer()` method reads all the data into the memory. So, the `sdf` object has 3886 records and 50 columns, and uses 1.48MB memory.\n","\n","But what if we only needed a small amount of data for our analysis and did not need to bring everything from the layer into the memory? Good question... let's see how we can achieve that.\n"]},{"cell_type":"markdown","metadata":{},"source":["The [**`query()`**](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#arcgis.features.FeatureLayer.query) method is a powerful operation that allows you to use [SQL](https://en.wikipedia.org/wiki/SQL) like queries to return only a subset of records. **Since the processing is performed on the server, this operation is not restricted by the capacity of your computer.**\n","\n","The method returns a [`FeatureSet`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?highlight=query#arcgis.features.FeatureSet) object; however, the return type can be changed to a _Spatially enabled DataFrame_ object by specifying the parameter `as_df=True`.\n","\n","Let's subset the data using `query()`, create a new _SeDF_, and check the memory usage. We'll use the `AGE_45_54` column to query the layer and get a subset of records.\n"]},{"cell_type":"code","execution_count":11,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:51:58.344586Z","start_time":"2021-11-22T19:51:57.814005Z"}},"outputs":[{"data":{"text/plain":["(316, 50)"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["# Filter feature layer records with a query.\n","sub_sdf = flayer.query(where=\"AGE_45_54 < 1500\", as_df=True)\n","sub_sdf.shape"]},{"cell_type":"code","execution_count":12,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:51:58.354587Z","start_time":"2021-11-22T19:51:58.346589Z"}},"outputs":[{"name":"stdout","output_type":"stream","text":["Memory used is: 0.12 MB\n"]}],"source":["# Check memory usage of current sdf\n","mem_used = sub_sdf.memory_usage().sum() / (1024**2)  # converting to megabytes\n","print(f'Memory used is: {round(mem_used, 2)} MB')"]},{"cell_type":"markdown","metadata":{},"source":["> Now that we are only querying for records where `AGE_45_54 < 1500`, the result is a smaller DataFrame with 316 records and 50 columns. Since the processing is performed on the server side, only a subset of data is being saved in the memory reducing usage from **1.48 MB to 0.12 MB**.\n"]},{"cell_type":"markdown","metadata":{},"source":["The [`query()`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#arcgis.features.FeatureLayer.query) method allows you to specify a number of optional parameters that may further refine and transform the results. One such key parameter is `out_fields`. With `out_fields`, you can subset your data by specifying a list of field names to return.\n"]},{"cell_type":"code","execution_count":13,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:51:59.855435Z","start_time":"2021-11-22T19:51:59.601357Z"}},"outputs":[{"data":{"text/plain":["(316, 6)"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["# Filter feature layer with where and out_fields\n","out_fields = ['NAME', 'ST', 'POP_CLASS', 'AGE_45_54']\n","sub_sdf2 = flayer.query(where=\"AGE_45_54 < 1500\",\n","                        out_fields=out_fields,\n","                        as_df=True)\n","sub_sdf2.shape"]},{"cell_type":"code","execution_count":14,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:51:59.868435Z","start_time":"2021-11-22T19:51:59.858435Z"}},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>FID</th>\n","      <th>NAME</th>\n","      <th>ST</th>\n","      <th>POP_CLASS</th>\n","      <th>AGE_45_54</th>\n","      <th>SHAPE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>Ammon</td>\n","      <td>ID</td>\n","      <td>6</td>\n","      <td>1446</td>\n","      <td>{\"x\": -12462673.723706165, \"y\": 5384674.994080...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>Blackfoot</td>\n","      <td>ID</td>\n","      <td>6</td>\n","      <td>1330</td>\n","      <td>{\"x\": -12506251.313993266, \"y\": 5341537.793529...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>4</td>\n","      <td>Burley</td>\n","      <td>ID</td>\n","      <td>6</td>\n","      <td>1134</td>\n","      <td>{\"x\": -12667411.402393516, \"y\": 5241722.820606...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>6</td>\n","      <td>Chubbuck</td>\n","      <td>ID</td>\n","      <td>6</td>\n","      <td>1494</td>\n","      <td>{\"x\": -12520053.904151963, \"y\": 5300220.333409...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>12</td>\n","      <td>Jerome</td>\n","      <td>ID</td>\n","      <td>6</td>\n","      <td>1155</td>\n","      <td>{\"x\": -12747828.64784961, \"y\": 5269214.8197742...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   FID       NAME  ST  POP_CLASS  AGE_45_54  \\\n","0    1      Ammon  ID          6       1446   \n","1    2  Blackfoot  ID          6       1330   \n","2    4     Burley  ID          6       1134   \n","3    6   Chubbuck  ID          6       1494   \n","4   12     Jerome  ID          6       1155   \n","\n","                                               SHAPE  \n","0  {\"x\": -12462673.723706165, \"y\": 5384674.994080...  \n","1  {\"x\": -12506251.313993266, \"y\": 5341537.793529...  \n","2  {\"x\": -12667411.402393516, \"y\": 5241722.820606...  \n","3  {\"x\": -12520053.904151963, \"y\": 5300220.333409...  \n","4  {\"x\": -12747828.64784961, \"y\": 5269214.8197742...  "]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["# Check head\n","sub_sdf2.head()"]},{"cell_type":"code","execution_count":15,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:52:01.302923Z","start_time":"2021-11-22T19:52:01.295930Z"}},"outputs":[{"name":"stdout","output_type":"stream","text":["Memory used is: 0.01 MB\n"]}],"source":["# Check memory usage of current sdf\n","mem_used = sub_sdf2.memory_usage().sum() / (1024**2)  # converting to megabytes\n","print(f'Memory used is: {round(mem_used, 2)} MB')"]},{"cell_type":"markdown","metadata":{},"source":["> Using `out_fields`, we have further reduced memory usage by subsetting the data and bringing only necessary information into the memory.\n"]},{"cell_type":"markdown","metadata":{},"source":["#### Create SeDF from FeatureSet\n"]},{"cell_type":"markdown","metadata":{},"source":["As mentioned earlier, the [**`query()`**](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#arcgis.features.FeatureLayer.query) method returns a [`FeatureSet`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?highlight=query#arcgis.features.FeatureSet) object. The `FeatureSet` object contains useful information about the data that can be accessed through its various properties.\n","\n","Let's use the `AGE_45_54` column to query the layer to get the result as a `FeatureSet` and check some its properties.\n"]},{"cell_type":"code","execution_count":16,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:52:03.250472Z","start_time":"2021-11-22T19:52:02.945753Z"}},"outputs":[],"source":["# Filter feature layer to return a feature set.\n","fset = flayer.query(where=\"AGE_45_54 < 1500\")"]},{"cell_type":"code","execution_count":17,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:52:03.259475Z","start_time":"2021-11-22T19:52:03.253475Z"}},"outputs":[{"data":{"text/plain":["arcgis.features.feature.FeatureSet"]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["# Check type\n","type(fset)"]},{"cell_type":"code","execution_count":18,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:52:03.411404Z","start_time":"2021-11-22T19:52:03.406402Z"}},"outputs":[{"data":{"text/plain":["316"]},"execution_count":18,"metadata":{},"output_type":"execute_result"}],"source":["# Check length\n","len(fset.features)"]},{"cell_type":"code","execution_count":19,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:52:03.633193Z","start_time":"2021-11-22T19:52:03.627193Z"}},"outputs":[{"data":{"text/plain":["{'x': -12462673.723706165,\n"," 'y': 5384674.994080178,\n"," 'spatialReference': {'wkid': 102100, 'latestWkid': 3857}}"]},"execution_count":19,"metadata":{},"output_type":"execute_result"}],"source":["# Check geometry of a feature in the featureset\n","fset.features[0].geometry"]},{"cell_type":"markdown","metadata":{},"source":["The `fields` property of a `FeatureSet` returns a list containing information about each column recorded as a dictionary. Let's use the `fields` property to access information about the first column.\n"]},{"cell_type":"code","execution_count":20,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:52:04.088356Z","start_time":"2021-11-22T19:52:04.083359Z"}},"outputs":[{"data":{"text/plain":["{'name': 'FID',\n"," 'type': 'esriFieldTypeOID',\n"," 'alias': 'FID',\n"," 'sqlType': 'sqlTypeInteger',\n"," 'domain': None,\n"," 'defaultValue': None}"]},"execution_count":20,"metadata":{},"output_type":"execute_result"}],"source":["# Check details of a column in the feature set\n","fset.fields[0]"]},{"cell_type":"markdown","metadata":{},"source":["Let's get the names of the columns in the data.\n"]},{"cell_type":"code","execution_count":21,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:52:04.548802Z","start_time":"2021-11-22T19:52:04.542798Z"}},"outputs":[{"data":{"text/plain":["['FID', 'NAME', 'CLASS', 'ST', 'STFIPS']"]},"execution_count":21,"metadata":{},"output_type":"execute_result"}],"source":["# Get column names\n","f_names = [f['name'] for f in fset.fields]\n","f_names[:5]"]},{"cell_type":"markdown","metadata":{},"source":["Now, let's create a _Spatially enabled DataFrame_ from a `FeatureSet` using the [`.sdf`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?highlight=sdf#arcgis.features.FeatureSet.sdf) property.\n"]},{"cell_type":"code","execution_count":22,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:52:05.026469Z","start_time":"2021-11-22T19:52:05.006466Z"}},"outputs":[{"data":{"text/plain":["(316, 50)"]},"execution_count":22,"metadata":{},"output_type":"execute_result"}],"source":["# Create SeDF from FeatureSet\n","fset_df = fset.sdf\n","fset_df.shape"]},{"cell_type":"code","execution_count":23,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:52:05.276147Z","start_time":"2021-11-22T19:52:05.258149Z"}},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>FID</th>\n","      <th>NAME</th>\n","      <th>CLASS</th>\n","      <th>ST</th>\n","      <th>STFIPS</th>\n","      <th>PLACEFIPS</th>\n","      <th>CAPITAL</th>\n","      <th>POP_CLASS</th>\n","      <th>POPULATION</th>\n","      <th>POP2010</th>\n","      <th>...</th>\n","      <th>MARHH_NO_C</th>\n","      <th>MHH_CHILD</th>\n","      <th>FHH_CHILD</th>\n","      <th>FAMILIES</th>\n","      <th>AVE_FAM_SZ</th>\n","      <th>HSE_UNITS</th>\n","      <th>VACANT</th>\n","      <th>OWNER_OCC</th>\n","      <th>RENTER_OCC</th>\n","      <th>SHAPE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>Ammon</td>\n","      <td>city</td>\n","      <td>ID</td>\n","      <td>16</td>\n","      <td>1601990</td>\n","      <td></td>\n","      <td>6</td>\n","      <td>15181</td>\n","      <td>13816</td>\n","      <td>...</td>\n","      <td>1131</td>\n","      <td>106</td>\n","      <td>335</td>\n","      <td>3352</td>\n","      <td>3.61</td>\n","      <td>4747</td>\n","      <td>271</td>\n","      <td>3205</td>\n","      <td>1271</td>\n","      <td>{\"x\": -12462673.723706165, \"y\": 5384674.994080...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>Blackfoot</td>\n","      <td>city</td>\n","      <td>ID</td>\n","      <td>16</td>\n","      <td>1607840</td>\n","      <td></td>\n","      <td>6</td>\n","      <td>11946</td>\n","      <td>11899</td>\n","      <td>...</td>\n","      <td>1081</td>\n","      <td>174</td>\n","      <td>381</td>\n","      <td>2958</td>\n","      <td>3.31</td>\n","      <td>4547</td>\n","      <td>318</td>\n","      <td>2788</td>\n","      <td>1441</td>\n","      <td>{\"x\": -12506251.313993266, \"y\": 5341537.793529...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>2 rows × 50 columns</p>\n","</div>"],"text/plain":["   FID       NAME CLASS  ST STFIPS PLACEFIPS CAPITAL  POP_CLASS  POPULATION  \\\n","0    1      Ammon  city  ID     16   1601990                  6       15181   \n","1    2  Blackfoot  city  ID     16   1607840                  6       11946   \n","\n","   POP2010  ...  MARHH_NO_C  MHH_CHILD  FHH_CHILD  FAMILIES  AVE_FAM_SZ  \\\n","0    13816  ...        1131        106        335      3352        3.61   \n","1    11899  ...        1081        174        381      2958        3.31   \n","\n","   HSE_UNITS  VACANT  OWNER_OCC  RENTER_OCC  \\\n","0       4747     271       3205        1271   \n","1       4547     318       2788        1441   \n","\n","                                               SHAPE  \n","0  {\"x\": -12462673.723706165, \"y\": 5384674.994080...  \n","1  {\"x\": -12506251.313993266, \"y\": 5341537.793529...  \n","\n","[2 rows x 50 columns]"]},"execution_count":23,"metadata":{},"output_type":"execute_result"}],"source":["# Check head\n","fset_df.head(2)"]},{"cell_type":"code","execution_count":24,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:52:05.502653Z","start_time":"2021-11-22T19:52:05.496653Z"}},"outputs":[{"data":{"text/plain":["['point']"]},"execution_count":24,"metadata":{},"output_type":"execute_result"}],"source":["# Check geometry type\n","fset_df.spatial.geometry_type"]},{"cell_type":"markdown","metadata":{},"source":["> The `spatial` namespace shows that a _Spatially enabled DataFrame_ has been created from a `FeatureSet`.\n"]},{"cell_type":"markdown","metadata":{},"source":["#### Create SeDF from FeatureCollection\n"]},{"cell_type":"markdown","metadata":{},"source":["Tools within the ArcGIS API for Python often return a [FeatureCollection](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#featurecollection) object as a result of some analysis. A `FeatureCollection` is an in-memory collection of [Feature](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#arcgis.features.Feature) objects with rendering information. Similar to feature layers, feature collections can also be used to store features. With a feature collection, a service is not created to serve out the feature data.\n","\n","Let's create a `SeDF` from a FeatureCollection. Here, we:\n","\n","- Import the [Major Ports](https://www.arcgis.com/home/item.html?id=405963eaea24428c9db236ec289760eb) feature layer.\n","- Create 5 mile buffers using [`create_buffers()`](https://developers.arcgis.com/python/api-reference/arcgis.features.use_proximity.html#create-buffers) tool resulting in a FeatureCollection.\n","- Using the [query()](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#arcgis.features.FeatureLayer.query) method on a FeatureCollection returns a [FeatureSet](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?highlight=query#arcgis.features.FeatureSet) object. We will create a `SeDF` from the buffered FeatureCollection using the the [`.sdf`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?highlight=sdf#arcgis.features.FeatureSet.sdf) property of a FeatureSet object returned from `query()`.\n"]},{"cell_type":"code","execution_count":25,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:52:10.071650Z","start_time":"2021-11-22T19:52:09.984656Z"}},"outputs":[{"data":{"text/html":["<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n","                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n","                       <a href='https://www.arcgis.com/home/item.html?id=405963eaea24428c9db236ec289760eb' target='_blank'>\n","                        <img src='https://www.arcgis.com/sharing/rest//content/items/405963eaea24428c9db236ec289760eb/info/thumbnail/thumbnail1629922346175.png' class=\"itemThumbnail\">\n","                       </a>\n","                    </div>\n","\n","                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n","                        <a href='https://www.arcgis.com/home/item.html?id=405963eaea24428c9db236ec289760eb' target='_blank'><b>Major Ports</b>\n","                        </a>\n","                        <br/>This feature layer,  utilizing data from the U.S. Department of Transportation, depicts Major Ports in the United States by total tonnage.<img src='https://www.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by Federal_User_Community\n","                        <br/>Last Modified: October 27, 2021\n","                        <br/>0 comments, 157,223 views\n","                    </div>\n","                </div>\n","                "],"text/plain":["<Item title:\"Major Ports\" type:Feature Layer Collection owner:Federal_User_Community>"]},"execution_count":25,"metadata":{},"output_type":"execute_result"}],"source":["# Get the ports item\n","ports_item = gis.content.get(\"405963eaea24428c9db236ec289760eb\")\n","ports_item"]},{"cell_type":"code","execution_count":26,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:52:10.203674Z","start_time":"2021-11-22T19:52:10.197678Z"}},"outputs":[{"data":{"text/plain":["<FeatureLayer url:\"https://geo.dot.gov/server/rest/services/NTAD/Ports_Major/MapServer/0\">"]},"execution_count":26,"metadata":{},"output_type":"execute_result"}],"source":["# Get the ports layer\n","ports_lyr = ports_item.layers[0]\n","ports_lyr"]},{"cell_type":"code","execution_count":27,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:52:40.150295Z","start_time":"2021-11-22T19:52:10.430626Z"}},"outputs":[],"source":["# Create buffers\n","from arcgis.features.use_proximity import create_buffers\n","ports_buffer50 = create_buffers(\n","    ports_lyr, distances=[5], units='Miles', gis=agol_gis)"]},{"cell_type":"code","execution_count":28,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:52:40.159300Z","start_time":"2021-11-22T19:52:40.154296Z"}},"outputs":[{"data":{"text/plain":["arcgis.features.feature.FeatureCollection"]},"execution_count":28,"metadata":{},"output_type":"execute_result"}],"source":["# Check type of result from the analysis\n","type(ports_buffer50)"]},{"cell_type":"markdown","metadata":{},"source":["> The `create_buffers()` tool resulted in a `FeatureCollection`.\n","\n","Now, we will create a `SeDF` from the `FeatureCollection` object.\n"]},{"cell_type":"code","execution_count":29,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:52:40.197297Z","start_time":"2021-11-22T19:52:40.162296Z"}},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>OBJECTID_1</th>\n","      <th>OBJECTID</th>\n","      <th>ID</th>\n","      <th>PORT</th>\n","      <th>PORT_NAME</th>\n","      <th>GRAND_TOTA</th>\n","      <th>FOREIGN_TO</th>\n","      <th>IMPORTS</th>\n","      <th>EXPORTS</th>\n","      <th>DOMESTIC</th>\n","      <th>BUFF_DIST</th>\n","      <th>ORIG_FID</th>\n","      <th>AnalysisArea</th>\n","      <th>SHAPE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>124</td>\n","      <td>C4947</td>\n","      <td>Unalaska Island, AK</td>\n","      <td>1652281</td>\n","      <td>1236829</td>\n","      <td>426251</td>\n","      <td>810578</td>\n","      <td>415452</td>\n","      <td>5</td>\n","      <td>1</td>\n","      <td>78.528402</td>\n","      <td>{\"rings\": [[[-18806114.3995, 7138385.537799999...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>85</td>\n","      <td>C4410</td>\n","      <td>Kahului, Maui, HI</td>\n","      <td>3615449</td>\n","      <td>20391</td>\n","      <td>20391</td>\n","      <td>0</td>\n","      <td>3595058</td>\n","      <td>5</td>\n","      <td>2</td>\n","      <td>78.528402</td>\n","      <td>{\"rings\": [[[-17418472.419, 2388455.4312999994...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   OBJECTID_1  OBJECTID   ID   PORT            PORT_NAME  GRAND_TOTA  \\\n","0           1         1  124  C4947  Unalaska Island, AK     1652281   \n","1           2         2   85  C4410    Kahului, Maui, HI     3615449   \n","\n","   FOREIGN_TO  IMPORTS  EXPORTS  DOMESTIC  BUFF_DIST  ORIG_FID  AnalysisArea  \\\n","0     1236829   426251   810578    415452          5         1     78.528402   \n","1       20391    20391        0   3595058          5         2     78.528402   \n","\n","                                               SHAPE  \n","0  {\"rings\": [[[-18806114.3995, 7138385.537799999...  \n","1  {\"rings\": [[[-17418472.419, 2388455.4312999994...  "]},"execution_count":29,"metadata":{},"output_type":"execute_result"}],"source":["# Create SeDF\n","sedf_fc = ports_buffer50.query().sdf\n","sedf_fc.head(2)"]},{"cell_type":"code","execution_count":30,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:52:40.205296Z","start_time":"2021-11-22T19:52:40.199296Z"}},"outputs":[{"data":{"text/plain":["['polygon']"]},"execution_count":30,"metadata":{},"output_type":"execute_result"}],"source":["# Check geometry type\n","sedf_fc.spatial.geometry_type"]},{"cell_type":"markdown","metadata":{},"source":["> The `spatial` namespace shows that a _Spatially enabled DataFrame_ has been created from a `FeatureCollection`.\n"]},{"cell_type":"markdown","metadata":{},"source":["### Read in local GIS data\n"]},{"cell_type":"markdown","metadata":{},"source":["Local geospatial data, such as [`Feature classes`](http://desktop.arcgis.com/en/arcmap/latest/manage-data/feature-classes/a-quick-tour-of-feature-classes.htm) and [`shapefiles`](http://desktop.arcgis.com/en/arcmap/latest/manage-data/shapefiles/what-is-a-shapefile.htm) can be easily accessed using the [**Spatially enabled DataFrame**](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#geoaccessor). The [`from_featureclass()`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?arcgis.features.GeoAccessor.from_featureclass#arcgis.features.GeoAccessor.from_featureclass) method can be used to access local data. Let's look at some examples.\n"]},{"cell_type":"markdown","metadata":{},"source":["#### Reading a Shapefile\n","\n","A locally stored [`shapefile`](http://desktop.arcgis.com/en/arcmap/latest/manage-data/shapefiles/what-is-a-shapefile.htm) can be accessed by passing the location of the file in the [`from_featureclass()`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?arcgis.features.GeoAccessor.from_featureclass#arcgis.features.GeoAccessor.from_featureclass) method.\n"]},{"cell_type":"markdown","metadata":{},"source":["<div class=\"alert alert-info\">\n","    <b>Note:</b> In the absence of <code>arcpy</code>, the <a href=\"https://pypi.org/project/pyshp/\">PyShp</a> package must be present in your current conda environment in order to read shapefiles. To check if PyShp is present, you can run the following in a cell:\n","         \n","    !conda list pyshp\n","        \n","To install PyShp, you can run the following in a cell:\n","    \n","    !conda install pyshp\n","</div>\n"]},{"cell_type":"code","execution_count":31,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:52:41.031721Z","start_time":"2021-11-22T19:52:40.207294Z"}},"outputs":[{"data":{"text/plain":["(3886, 51)"]},"execution_count":31,"metadata":{},"output_type":"execute_result"}],"source":["# Reading from shape file\n","shp_df = pd.DataFrame.spatial.from_featureclass(\n","    location=\"./sedf_data/cities/cities.shp\")\n","shp_df.shape"]},{"cell_type":"code","execution_count":32,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:52:41.058722Z","start_time":"2021-11-22T19:52:41.034722Z"}},"outputs":[{"data":{"text/plain":["['point']"]},"execution_count":32,"metadata":{},"output_type":"execute_result"}],"source":["shp_df.spatial.geometry_type"]},{"cell_type":"markdown","metadata":{},"source":["> The `spatial` namespace shows that a _Spatially enabled DataFrame_ has been created from the `shapefile` stored locally.\n"]},{"cell_type":"markdown","metadata":{},"source":["##### Shapefile from a URL\n","\n","The url of a zipped `shapefile` can be used to create a `SeDF` by passing the url as location in the `from_featureclass()` method. The image below shows how the operation can be performed.\n"]},{"cell_type":"markdown","metadata":{},"source":["<div class=\"alert alert-info\">\n","    <b>Note:</b> This operation requires <code>PyShp</code> to be available in the environment.\n","\n","</div>\n"]},{"attachments":{"image.png":{"image/png":"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"}},"cell_type":"markdown","metadata":{},"source":["![image.png](attachment:image.png)\n"]},{"cell_type":"markdown","metadata":{},"source":["#### Reading a Featureclass\n","\n","A [featureclass](http://desktop.arcgis.com/en/arcmap/latest/manage-data/feature-classes/a-quick-tour-of-feature-classes.htm) can be accessed from a File Geodatabase by passing its location in the [`from_featureclass()`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?arcgis.features.GeoAccessor.from_featureclass#arcgis.features.GeoAccessor.from_featureclass) method.\n"]},{"cell_type":"markdown","metadata":{},"source":["<div class=\"alert alert-info\">\n","    <b>Note:</b> In the absence of <code>arcpy</code>, the <a href=\"https://pypi.org/project/Fiona/\">Fiona</a> package must be present in your current conda environment in order to read a featureclass.\n","    To check if Fiona is present, you can run the following in a cell:\n","         \n","    !conda list fiona\n","        \n","To install Fiona, you can run the following in a cell:\n","    \n","    !conda install fiona\n","</div>\n"]},{"cell_type":"code","execution_count":33,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:52:41.480724Z","start_time":"2021-11-22T19:52:41.062722Z"}},"outputs":[{"data":{"text/plain":["(3886, 51)"]},"execution_count":33,"metadata":{},"output_type":"execute_result"}],"source":["# Reading from FGDB\n","fcls_df = pd.DataFrame.spatial.from_featureclass(\n","    location=\"./sedf_data/cities/cities.gdb/cities\")\n","fcls_df.shape"]},{"cell_type":"code","execution_count":34,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:52:41.506721Z","start_time":"2021-11-22T19:52:41.485728Z"}},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>OBJECTID</th>\n","      <th>age_10_14</th>\n","      <th>age_15_19</th>\n","      <th>age_20_24</th>\n","      <th>age_25_34</th>\n","      <th>age_35_44</th>\n","      <th>age_45_54</th>\n","      <th>age_55_64</th>\n","      <th>age_5_9</th>\n","      <th>age_65_74</th>\n","      <th>...</th>\n","      <th>placefips</th>\n","      <th>pop2010</th>\n","      <th>population</th>\n","      <th>pop_class</th>\n","      <th>renter_occ</th>\n","      <th>st</th>\n","      <th>stfips</th>\n","      <th>vacant</th>\n","      <th>white</th>\n","      <th>SHAPE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>1313</td>\n","      <td>1058</td>\n","      <td>734</td>\n","      <td>2031</td>\n","      <td>1767</td>\n","      <td>1446</td>\n","      <td>1136</td>\n","      <td>1503</td>\n","      <td>665</td>\n","      <td>...</td>\n","      <td>1601990</td>\n","      <td>13816</td>\n","      <td>15181</td>\n","      <td>6</td>\n","      <td>1271</td>\n","      <td>ID</td>\n","      <td>16</td>\n","      <td>271</td>\n","      <td>13002</td>\n","      <td>{\"x\": -12462673.7237, \"y\": 5384674.994099997, ...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>890</td>\n","      <td>817</td>\n","      <td>818</td>\n","      <td>1799</td>\n","      <td>1235</td>\n","      <td>1330</td>\n","      <td>1143</td>\n","      <td>1099</td>\n","      <td>721</td>\n","      <td>...</td>\n","      <td>1607840</td>\n","      <td>11899</td>\n","      <td>11946</td>\n","      <td>6</td>\n","      <td>1441</td>\n","      <td>ID</td>\n","      <td>16</td>\n","      <td>318</td>\n","      <td>9893</td>\n","      <td>{\"x\": -12506251.314, \"y\": 5341537.793499999, \"...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>2 rows × 51 columns</p>\n","</div>"],"text/plain":["   OBJECTID  age_10_14  age_15_19  age_20_24  age_25_34  age_35_44  age_45_54  \\\n","0         1       1313       1058        734       2031       1767       1446   \n","1         2        890        817        818       1799       1235       1330   \n","\n","   age_55_64  age_5_9  age_65_74  ...  placefips  pop2010  population  \\\n","0       1136     1503        665  ...    1601990    13816       15181   \n","1       1143     1099        721  ...    1607840    11899       11946   \n","\n","   pop_class  renter_occ  st  stfips  vacant  white  \\\n","0          6        1271  ID      16     271  13002   \n","1          6        1441  ID      16     318   9893   \n","\n","                                               SHAPE  \n","0  {\"x\": -12462673.7237, \"y\": 5384674.994099997, ...  \n","1  {\"x\": -12506251.314, \"y\": 5341537.793499999, \"...  \n","\n","[2 rows x 51 columns]"]},"execution_count":34,"metadata":{},"output_type":"execute_result"}],"source":["# Check head\n","fcls_df.head(2)"]},{"cell_type":"code","execution_count":35,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:52:41.525725Z","start_time":"2021-11-22T19:52:41.510728Z"}},"outputs":[{"data":{"text/plain":["['point']"]},"execution_count":35,"metadata":{},"output_type":"execute_result"}],"source":["# Check geometry type\n","fcls_df.spatial.geometry_type"]},{"cell_type":"markdown","metadata":{},"source":["> The `spatial` namespace shows that a _Spatially enabled DataFrame_ has been created from the `featureclass` stored locally.\n"]},{"cell_type":"markdown","metadata":{},"source":["**Specify optional parameters**\n"]},{"cell_type":"markdown","metadata":{},"source":["The **`from_featureclass()`** method allows users to specify optional parameters when the `ArcPy` library is available in the current environment. These parameters are:\n","\n","- `sql_clause`: a pair of SQL prefix and postfix clauses, `sql_clause=(prefix,postfix)`, organized in a list or a tuple can be passed to query specific data. The parameter allows only a small set of operations to be performed. Learn more about the allowed operations [here](https://pro.arcgis.com/en/pro-app/latest/arcpy/data-access/searchcursor-class.htm).\n","- `where_clause`: where statement to subset the data. Learn more about it [here](https://pro.arcgis.com/en/pro-app/latest/help/mapping/navigation/sql-reference-for-elements-used-in-query-expressions.htm).\n","- `fields`: to subset the data for specific fields.\n","- `spatial_filter`: a geometry object to filter the results.\n"]},{"cell_type":"markdown","metadata":{},"source":["<div class=\"alert alert-info\">\n","    <b>Note:</b> The operations below can only be performed in an environment that <b>contains</b> <code>arcpy</code>.\n","\n","</div>\n"]},{"cell_type":"markdown","metadata":{},"source":["##### Subset data for specific fields\n"]},{"cell_type":"code","execution_count":36,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:55:03.504998Z","start_time":"2021-11-22T19:55:03.352999Z"}},"outputs":[{"data":{"text/plain":["(3886, 3)"]},"execution_count":36,"metadata":{},"output_type":"execute_result"}],"source":["# Subset for fields\n","fcls_flds = pd.DataFrame.spatial.from_featureclass(location=\"./sedf_data/cities/cities.gdb/cities\",\n","                                                   fields=['st', 'pop_class'])\n","fcls_flds.shape"]},{"cell_type":"code","execution_count":37,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:55:04.378124Z","start_time":"2021-11-22T19:55:04.367127Z"}},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>st</th>\n","      <th>pop_class</th>\n","      <th>SHAPE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>ID</td>\n","      <td>6</td>\n","      <td>{\"x\": -12462673.7237, \"y\": 5384674.994099997, ...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>ID</td>\n","      <td>6</td>\n","      <td>{\"x\": -12506251.314, \"y\": 5341537.793499999, \"...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   st  pop_class                                              SHAPE\n","0  ID          6  {\"x\": -12462673.7237, \"y\": 5384674.994099997, ...\n","1  ID          6  {\"x\": -12506251.314, \"y\": 5341537.793499999, \"..."]},"execution_count":37,"metadata":{},"output_type":"execute_result"}],"source":["# Check head\n","fcls_flds.head(2)"]},{"cell_type":"markdown","metadata":{},"source":["##### Subset using `where_clause`\n","\n","Learn more about how to use `where_clause` [here](https://pro.arcgis.com/en/pro-app/latest/help/mapping/navigation/sql-reference-for-elements-used-in-query-expressions.htm).\n"]},{"cell_type":"code","execution_count":38,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:55:06.957737Z","start_time":"2021-11-22T19:55:06.876084Z"}},"outputs":[{"data":{"text/plain":["(15, 51)"]},"execution_count":38,"metadata":{},"output_type":"execute_result"}],"source":["# Subset using where_clause\n","fcls_whr = pd.DataFrame.spatial.from_featureclass(location=\"./sedf_data/cities/cities.gdb/cities\",\n","                                                  where_clause=\"st='ID' and pop_class=6\")\n","fcls_whr.shape"]},{"cell_type":"code","execution_count":39,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:55:07.111124Z","start_time":"2021-11-22T19:55:07.093125Z"}},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>OBJECTID</th>\n","      <th>age_10_14</th>\n","      <th>age_15_19</th>\n","      <th>age_20_24</th>\n","      <th>age_25_34</th>\n","      <th>age_35_44</th>\n","      <th>age_45_54</th>\n","      <th>age_55_64</th>\n","      <th>age_5_9</th>\n","      <th>age_65_74</th>\n","      <th>...</th>\n","      <th>placefips</th>\n","      <th>pop2010</th>\n","      <th>population</th>\n","      <th>pop_class</th>\n","      <th>renter_occ</th>\n","      <th>st</th>\n","      <th>stfips</th>\n","      <th>vacant</th>\n","      <th>white</th>\n","      <th>SHAPE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>1313</td>\n","      <td>1058</td>\n","      <td>734</td>\n","      <td>2031</td>\n","      <td>1767</td>\n","      <td>1446</td>\n","      <td>1136</td>\n","      <td>1503</td>\n","      <td>665</td>\n","      <td>...</td>\n","      <td>1601990</td>\n","      <td>13816</td>\n","      <td>15181</td>\n","      <td>6</td>\n","      <td>1271</td>\n","      <td>ID</td>\n","      <td>16</td>\n","      <td>271</td>\n","      <td>13002</td>\n","      <td>{\"x\": -12462673.7237, \"y\": 5384674.994099997, ...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>890</td>\n","      <td>817</td>\n","      <td>818</td>\n","      <td>1799</td>\n","      <td>1235</td>\n","      <td>1330</td>\n","      <td>1143</td>\n","      <td>1099</td>\n","      <td>721</td>\n","      <td>...</td>\n","      <td>1607840</td>\n","      <td>11899</td>\n","      <td>11946</td>\n","      <td>6</td>\n","      <td>1441</td>\n","      <td>ID</td>\n","      <td>16</td>\n","      <td>318</td>\n","      <td>9893</td>\n","      <td>{\"x\": -12506251.314, \"y\": 5341537.793499999, \"...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>2 rows × 51 columns</p>\n","</div>"],"text/plain":["   OBJECTID  age_10_14  age_15_19  age_20_24  age_25_34  age_35_44  age_45_54  \\\n","0         1       1313       1058        734       2031       1767       1446   \n","1         2        890        817        818       1799       1235       1330   \n","\n","   age_55_64  age_5_9  age_65_74  ...  placefips  pop2010  population  \\\n","0       1136     1503        665  ...    1601990    13816       15181   \n","1       1143     1099        721  ...    1607840    11899       11946   \n","\n","   pop_class  renter_occ  st  stfips  vacant  white  \\\n","0          6        1271  ID      16     271  13002   \n","1          6        1441  ID      16     318   9893   \n","\n","                                               SHAPE  \n","0  {\"x\": -12462673.7237, \"y\": 5384674.994099997, ...  \n","1  {\"x\": -12506251.314, \"y\": 5341537.793499999, \"...  \n","\n","[2 rows x 51 columns]"]},"execution_count":39,"metadata":{},"output_type":"execute_result"}],"source":["# Check head\n","fcls_whr.head(2)"]},{"cell_type":"markdown","metadata":{},"source":["##### Subset using `fields` and `where_clause`\n"]},{"cell_type":"code","execution_count":40,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:55:08.852159Z","start_time":"2021-11-22T19:55:08.788159Z"}},"outputs":[{"data":{"text/plain":["(15, 5)"]},"execution_count":40,"metadata":{},"output_type":"execute_result"}],"source":["# Subset using where_clause\n","flds_whr = pd.DataFrame.spatial.from_featureclass(location=\"./sedf_data/cities/cities.gdb/cities\",\n","                                                  fields=[\n","                                                      'st', 'pop_class', 'age_10_14', 'age_15_19'],\n","                                                  where_clause=\"st='ID' and pop_class=6\")\n","flds_whr.shape"]},{"cell_type":"code","execution_count":41,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:55:09.717600Z","start_time":"2021-11-22T19:55:09.706606Z"}},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>st</th>\n","      <th>pop_class</th>\n","      <th>age_10_14</th>\n","      <th>age_15_19</th>\n","      <th>SHAPE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>ID</td>\n","      <td>6</td>\n","      <td>1313</td>\n","      <td>1058</td>\n","      <td>{\"x\": -12462673.7237, \"y\": 5384674.994099997, ...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>ID</td>\n","      <td>6</td>\n","      <td>890</td>\n","      <td>817</td>\n","      <td>{\"x\": -12506251.314, \"y\": 5341537.793499999, \"...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   st  pop_class  age_10_14  age_15_19  \\\n","0  ID          6       1313       1058   \n","1  ID          6        890        817   \n","\n","                                               SHAPE  \n","0  {\"x\": -12462673.7237, \"y\": 5384674.994099997, ...  \n","1  {\"x\": -12506251.314, \"y\": 5341537.793499999, \"...  "]},"execution_count":41,"metadata":{},"output_type":"execute_result"}],"source":["# Check head\n","flds_whr.head(2)"]},{"cell_type":"markdown","metadata":{},"source":["##### Subset using `sql_clause`\n","\n","`sql_clause` can be combined with `fields` and `where_clause` to further subset the data. You can learn more about the allowed operations [here](https://pro.arcgis.com/en/pro-app/latest/arcpy/data-access/searchcursor-class.htm). Now let's look at some examples.\n"]},{"cell_type":"markdown","metadata":{},"source":["###### Prefix `sql_clause` - DISTINCT operation\n"]},{"cell_type":"code","execution_count":42,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:55:12.045052Z","start_time":"2021-11-22T19:55:11.704053Z"}},"outputs":[{"data":{"text/plain":["(3886, 51)"]},"execution_count":42,"metadata":{},"output_type":"execute_result"}],"source":["# Prefix Sql clause - DISTINCT operation\n","fcls_sql1 = pd.DataFrame.spatial.from_featureclass(location=\"./sedf_data/cities/cities.gdb/cities\",\n","                                                   sql_clause=(\"DISTINCT pop_class\", None))\n","\n","# Check shape\n","fcls_sql1.shape"]},{"cell_type":"code","execution_count":43,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:55:12.590891Z","start_time":"2021-11-22T19:55:12.570891Z"}},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>OBJECTID</th>\n","      <th>age_10_14</th>\n","      <th>age_15_19</th>\n","      <th>age_20_24</th>\n","      <th>age_25_34</th>\n","      <th>age_35_44</th>\n","      <th>age_45_54</th>\n","      <th>age_55_64</th>\n","      <th>age_5_9</th>\n","      <th>age_65_74</th>\n","      <th>...</th>\n","      <th>placefips</th>\n","      <th>pop2010</th>\n","      <th>population</th>\n","      <th>pop_class</th>\n","      <th>renter_occ</th>\n","      <th>st</th>\n","      <th>stfips</th>\n","      <th>vacant</th>\n","      <th>white</th>\n","      <th>SHAPE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>941</td>\n","      <td>1247</td>\n","      <td>1213</td>\n","      <td>1043</td>\n","      <td>2022</td>\n","      <td>1692</td>\n","      <td>2116</td>\n","      <td>1827</td>\n","      <td>1187</td>\n","      <td>1037</td>\n","      <td>...</td>\n","      <td>0507330</td>\n","      <td>15620</td>\n","      <td>14771</td>\n","      <td>6</td>\n","      <td>3006</td>\n","      <td>AR</td>\n","      <td>05</td>\n","      <td>1303</td>\n","      <td>6216</td>\n","      <td>{\"x\": -10006810.091, \"y\": 4290154.581699997, \"...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1405</td>\n","      <td>796</td>\n","      <td>748</td>\n","      <td>754</td>\n","      <td>1999</td>\n","      <td>1717</td>\n","      <td>2062</td>\n","      <td>1450</td>\n","      <td>760</td>\n","      <td>851</td>\n","      <td>...</td>\n","      <td>2466850</td>\n","      <td>12677</td>\n","      <td>13188</td>\n","      <td>6</td>\n","      <td>814</td>\n","      <td>MD</td>\n","      <td>24</td>\n","      <td>281</td>\n","      <td>11613</td>\n","      <td>{\"x\": -8517714.7855, \"y\": 4744316.880199999, \"...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>2 rows × 51 columns</p>\n","</div>"],"text/plain":["   OBJECTID  age_10_14  age_15_19  age_20_24  age_25_34  age_35_44  age_45_54  \\\n","0       941       1247       1213       1043       2022       1692       2116   \n","1      1405        796        748        754       1999       1717       2062   \n","\n","   age_55_64  age_5_9  age_65_74  ...  placefips  pop2010  population  \\\n","0       1827     1187       1037  ...    0507330    15620       14771   \n","1       1450      760        851  ...    2466850    12677       13188   \n","\n","   pop_class  renter_occ  st  stfips  vacant  white  \\\n","0          6        3006  AR      05    1303   6216   \n","1          6         814  MD      24     281  11613   \n","\n","                                               SHAPE  \n","0  {\"x\": -10006810.091, \"y\": 4290154.581699997, \"...  \n","1  {\"x\": -8517714.7855, \"y\": 4744316.880199999, \"...  \n","\n","[2 rows x 51 columns]"]},"execution_count":43,"metadata":{},"output_type":"execute_result"}],"source":["# Check head\n","fcls_sql1.head(2)"]},{"cell_type":"markdown","metadata":{},"source":["###### Postfix `sql_clause` with specific fields\n","\n","Here, we will subset the data for the state and population class fields and apply a postfix clause.\n"]},{"cell_type":"code","execution_count":44,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:55:13.456845Z","start_time":"2021-11-22T19:55:13.280450Z"}},"outputs":[{"data":{"text/plain":["(3886, 3)"]},"execution_count":44,"metadata":{},"output_type":"execute_result"}],"source":["# Postfix Sql clause with specific fields\n","fcls_sql2 = pd.DataFrame.spatial.from_featureclass(location=\"./sedf_data/cities/cities.gdb/cities\",\n","                                                   fields=['st', 'pop_class'],\n","                                                   sql_clause=(None, \"ORDER BY st, pop_class\"))\n","# Check shape\n","fcls_sql2.shape"]},{"cell_type":"code","execution_count":45,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:55:14.111586Z","start_time":"2021-11-22T19:55:14.100594Z"}},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>st</th>\n","      <th>pop_class</th>\n","      <th>SHAPE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>AK</td>\n","      <td>6</td>\n","      <td>{\"x\": -16417572.1606, \"y\": 9562359.403800003, ...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>AK</td>\n","      <td>6</td>\n","      <td>{\"x\": -16455422.2224, \"y\": 9574022.0224, \"spat...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>AK</td>\n","      <td>6</td>\n","      <td>{\"x\": -16444303.0276, \"y\": 9568008.9705, \"spat...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>AK</td>\n","      <td>6</td>\n","      <td>{\"x\": -14962313.3618, \"y\": 8031014.926600002, ...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>AK</td>\n","      <td>6</td>\n","      <td>{\"x\": -16657118.680399999, \"y\": 8746757.662600...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   st  pop_class                                              SHAPE\n","0  AK          6  {\"x\": -16417572.1606, \"y\": 9562359.403800003, ...\n","1  AK          6  {\"x\": -16455422.2224, \"y\": 9574022.0224, \"spat...\n","2  AK          6  {\"x\": -16444303.0276, \"y\": 9568008.9705, \"spat...\n","3  AK          6  {\"x\": -14962313.3618, \"y\": 8031014.926600002, ...\n","4  AK          6  {\"x\": -16657118.680399999, \"y\": 8746757.662600..."]},"execution_count":45,"metadata":{},"output_type":"execute_result"}],"source":["# Check head\n","fcls_sql2.head()"]},{"cell_type":"markdown","metadata":{},"source":["###### Prefix and Postfix `sql_clause` with specific fields and `where_clause`\n","\n","Here, we will subset the data using `where_clause`, keep specific fields, and then apply both prefix and postfix clause.\n"]},{"cell_type":"code","execution_count":48,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:55:51.001847Z","start_time":"2021-11-22T19:55:50.922841Z"}},"outputs":[{"data":{"text/plain":["(22, 5)"]},"execution_count":48,"metadata":{},"output_type":"execute_result"}],"source":["# Prefix and Postfix sql_clause\n","fcls_sql3_df = pd.DataFrame.spatial.from_featureclass(location=\"./sedf_data/cities/cities.gdb/cities\",\n","                                                      fields=[\n","                                                          'st', 'name', 'pop_class', 'age_10_14'],\n","                                                      where_clause=\"st='ID'\",\n","                                                      sql_clause=(\"DISTINCT pop_class\", \"ORDER BY name\"))\n","\n","# Check Shape\n","fcls_sql3_df.shape"]},{"cell_type":"code","execution_count":49,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T19:55:51.761628Z","start_time":"2021-11-22T19:55:51.749637Z"}},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>st</th>\n","      <th>name</th>\n","      <th>pop_class</th>\n","      <th>age_10_14</th>\n","      <th>SHAPE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>ID</td>\n","      <td>Ammon</td>\n","      <td>6</td>\n","      <td>1313</td>\n","      <td>{\"x\": -12462673.7237, \"y\": 5384674.994099997, ...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>ID</td>\n","      <td>Blackfoot</td>\n","      <td>6</td>\n","      <td>890</td>\n","      <td>{\"x\": -12506251.314, \"y\": 5341537.793499999, \"...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>ID</td>\n","      <td>Boise City</td>\n","      <td>8</td>\n","      <td>12750</td>\n","      <td>{\"x\": -12938676.683600001, \"y\": 5403597.049500...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>ID</td>\n","      <td>Burley</td>\n","      <td>6</td>\n","      <td>790</td>\n","      <td>{\"x\": -12667411.4024, \"y\": 5241722.820600003, ...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>ID</td>\n","      <td>Caldwell</td>\n","      <td>7</td>\n","      <td>3803</td>\n","      <td>{\"x\": -12989383.6745, \"y\": 5413226.487300001, ...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   st        name  pop_class  age_10_14  \\\n","0  ID       Ammon          6       1313   \n","1  ID   Blackfoot          6        890   \n","2  ID  Boise City          8      12750   \n","3  ID      Burley          6        790   \n","4  ID    Caldwell          7       3803   \n","\n","                                               SHAPE  \n","0  {\"x\": -12462673.7237, \"y\": 5384674.994099997, ...  \n","1  {\"x\": -12506251.314, \"y\": 5341537.793499999, \"...  \n","2  {\"x\": -12938676.683600001, \"y\": 5403597.049500...  \n","3  {\"x\": -12667411.4024, \"y\": 5241722.820600003, ...  \n","4  {\"x\": -12989383.6745, \"y\": 5413226.487300001, ...  "]},"execution_count":49,"metadata":{},"output_type":"execute_result"}],"source":["# Check head\n","fcls_sql3_df.head()"]},{"cell_type":"markdown","metadata":{},"source":["##### Using `spatial_filter`\n"]},{"cell_type":"markdown","metadata":{},"source":["`spatial_filter` can be used to query the results by using a spatial relationship with another geometry. The spatial filtering is even more powerful when integrated with [Geoenrichment](https://developers.arcgis.com/python/guide/part1-introduction-to-geoenrichment/). Let's use this approach to filter our results for the state of Idaho. In this example, we will:\n","\n","- use `arcgis.geoenrichment.Country` to derive the geometries for the state of Idaho.\n","- use `arcgis.geometry.filters.intersects(geometry, sr=None)` to create a geometry filter object that filters results whose geometry intersects with the specified geometry (i.e. filter data points within the boundary of Idaho).\n","- pass the geometry filter object to `spatial_filter` to get desired results.\n"]},{"cell_type":"markdown","metadata":{},"source":["<div class=\"alert alert-warning\">\n","    <b>Note:</b> To perform enrichment operations, GeoEnrichment must be configured in your GIS organization. GeoEnrichment consumes credits, and you can learn more about credit consumption <a href=\"https://doc.arcgis.com/en/arcgis-online/administer/credits.htm\">here</a>. \n","</div>\n"]},{"cell_type":"code","execution_count":51,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T20:03:11.171059Z","start_time":"2021-11-22T20:03:11.154058Z"}},"outputs":[],"source":["# Basic Imports\n","from arcgis.geometry import Geometry\n","from arcgis.geometry.filters import intersects\n","from arcgis.geoenrichment import Country"]},{"cell_type":"code","execution_count":59,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T20:08:43.643602Z","start_time":"2021-11-22T20:08:43.139513Z"}},"outputs":[{"data":{"text/plain":["arcgis.geoenrichment.enrichment.Country"]},"execution_count":59,"metadata":{},"output_type":"execute_result"}],"source":["# Create country object\n","usa = Country.get('US', gis=agol_gis)\n","type(usa)"]},{"cell_type":"code","execution_count":62,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T20:08:49.034325Z","start_time":"2021-11-22T20:08:47.854467Z"}},"outputs":[{"data":{"text/plain":["<NamedArea name:\"Idaho\" area_id=\"16\", level=\"US.States\", country=\"147\">"]},"metadata":{},"output_type":"display_data"},{"data":{"image/svg+xml":["<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"-117.52352955978306 41.707675439871714 6.760541119830947 7.57367711997864\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,90.98902799972207)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.1514735423995728\" opacity=\"0.6\" d=\" M -116.04908600049573,49.000845999851144 L -116.04913199962819,48.97278399990284 L -116.04907299917866,48.90699899950651 L -116.0490970003664,48.82259499975923 L -116.04905899893495,48.76047200030775 L 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-116.1263009997627,49.0006939999602 L -116.04908600049573,49.000845999851144 z\" /></g></svg>"],"text/plain":["<Polygon object at 0x1ba9211a208[0x1ba90451750]>"]},"execution_count":62,"metadata":{},"output_type":"execute_result"}],"source":["# Get boundaries for Idaho\n","named_area_ID = usa.search(query='Idaho', layers=['US.States'])\n","display(named_area_ID[0])\n","named_area_ID[0].geometry.as_arcpy"]},{"cell_type":"code","execution_count":64,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T20:10:38.529463Z","start_time":"2021-11-22T20:10:38.524455Z"}},"outputs":[{"data":{"text/plain":["{'wkid': 4326, 'latestWkid': 4326}"]},"execution_count":64,"metadata":{},"output_type":"execute_result"}],"source":["# Create spatial reference\n","sr_id = named_area_ID[0].geometry[\"spatialReference\"]\n","sr_id"]},{"cell_type":"code","execution_count":66,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T20:12:24.265943Z","start_time":"2021-11-22T20:12:24.259940Z"}},"outputs":[{"data":{"text/plain":["dict"]},"execution_count":66,"metadata":{},"output_type":"execute_result"}],"source":["# Construct a geometry filter using the filter geometry\n","id_state_filter = intersects(named_area_ID[0].geometry,\n","                             sr=sr_id)\n","type(id_state_filter)"]},{"cell_type":"code","execution_count":71,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T20:19:12.104168Z","start_time":"2021-11-22T20:19:10.973170Z"},"code_folding":[]},"outputs":[{"data":{"text/plain":["(22, 5)"]},"execution_count":71,"metadata":{},"output_type":"execute_result"}],"source":["# Pass geometry filter object as a spatial_filter\n","fcls_spfl_df = pd.DataFrame.spatial.from_featureclass(location=\"./sedf_data/cities/cities.gdb/cities\",\n","                                                      fields=[\n","                                                          'st', 'name', 'pop_class', 'age_10_14'],\n","                                                      spatial_filter=id_state_filter)\n","# Check shape\n","fcls_spfl_df.shape"]},{"cell_type":"code","execution_count":73,"metadata":{"ExecuteTime":{"end_time":"2021-11-22T20:26:39.851895Z","start_time":"2021-11-22T20:26:39.840893Z"}},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>st</th>\n","      <th>name</th>\n","      <th>pop_class</th>\n","      <th>age_10_14</th>\n","      <th>SHAPE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>ID</td>\n","      <td>Ammon</td>\n","      <td>6</td>\n","      <td>1313</td>\n","      <td>{\"x\": -12462673.7237, \"y\": 5384674.994099997, ...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>ID</td>\n","      <td>Blackfoot</td>\n","      <td>6</td>\n","      <td>890</td>\n","      <td>{\"x\": -12506251.314, \"y\": 5341537.793499999, \"...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>ID</td>\n","      <td>Boise City</td>\n","      <td>8</td>\n","      <td>12750</td>\n","      <td>{\"x\": -12938676.683600001, \"y\": 5403597.049500...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>ID</td>\n","      <td>Burley</td>\n","      <td>6</td>\n","      <td>790</td>\n","      <td>{\"x\": -12667411.4024, \"y\": 5241722.820600003, ...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>ID</td>\n","      <td>Caldwell</td>\n","      <td>7</td>\n","      <td>3803</td>\n","      <td>{\"x\": -12989383.6745, \"y\": 5413226.487300001, ...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   st        name  pop_class  age_10_14  \\\n","0  ID       Ammon          6       1313   \n","1  ID   Blackfoot          6        890   \n","2  ID  Boise City          8      12750   \n","3  ID      Burley          6        790   \n","4  ID    Caldwell          7       3803   \n","\n","                                               SHAPE  \n","0  {\"x\": -12462673.7237, \"y\": 5384674.994099997, ...  \n","1  {\"x\": -12506251.314, \"y\": 5341537.793499999, \"...  \n","2  {\"x\": -12938676.683600001, \"y\": 5403597.049500...  \n","3  {\"x\": -12667411.4024, \"y\": 5241722.820600003, ...  \n","4  {\"x\": -12989383.6745, \"y\": 5413226.487300001, ...  "]},"execution_count":73,"metadata":{},"output_type":"execute_result"}],"source":["# Check head\n","fcls_spfl_df.head()"]},{"cell_type":"markdown","metadata":{},"source":["> The result shows the data points filtered for Idaho as defined by the spatial filter.\n","\n","You can learn more about applying spatial filters in our [Working with geometries](https://developers.arcgis.com/python/guide/part4-spatial-filters/#arcgis.geometry.filters-module) guide series.\n"]},{"cell_type":"markdown","metadata":{},"source":["### Read in DataFrame with Addresses\n"]},{"cell_type":"markdown","metadata":{},"source":["A `SeDF` can be easily created from a DataFrame with address information using the [`from_df()`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?highlight=from_featureclass#arcgis.features.GeoAccessor.from_df) method. This method geocodes the addresses using the first configured geocoder in your GIS. The locations generated after geocoding are used as the geometry of the SeDF.\n","\n","You can learn more about geocoding in our [Finding Places with geocoding](https://developers.arcgis.com/python/guide/part1-what-is-geocoding/) guide series.\n"]},{"cell_type":"markdown","metadata":{},"source":["<div class=\"alert alert-warning\">\n","    <b>Note:</b> The <code>from_df()</code> method performs a batch geocoding operation which consumes <a href=\"https://doc.arcgis.com/en/arcgis-online/administer/credits.htm\">credits</a>. If a <code>geocoder</code> is not specified, then the first configured geocoder in your GIS organization will be used. Learn more about credit consumption <a href=\"https://developers.arcgis.com/python/guide/part1-what-is-geocoding/#Cost-of-geocoding\">here</a>.\n","\n","To avoid credit consumption, you may specify your own `geocoder`.\n","\n","</div>\n"]},{"cell_type":"markdown","metadata":{},"source":["Let's look at an example of using `from_df()`. We will read addresses into a DataFrame using the [`pd.read_csv()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html) method. Next, we will create a SeDF by passing the DataFrame and address column as parameters to the `from_df()` method.\n"]},{"cell_type":"code","execution_count":48,"metadata":{"ExecuteTime":{"end_time":"2021-11-11T22:34:12.278791Z","start_time":"2021-11-11T22:34:12.267792Z"}},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Address</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>602 Murray Cir, Sausalito, CA 94965</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>340 Stockton St, San Francisco, CA 94108</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3619 Balboa St, San Francisco, CA 94121</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1274 El Camino Real, San Bruno, CA 94066</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>625 Monterey Blvd, San Francisco, CA 94127</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                      Address\n","0         602 Murray Cir, Sausalito, CA 94965\n","1    340 Stockton St, San Francisco, CA 94108\n","2     3619 Balboa St, San Francisco, CA 94121\n","3    1274 El Camino Real, San Bruno, CA 94066\n","4  625 Monterey Blvd, San Francisco, CA 94127"]},"execution_count":48,"metadata":{},"output_type":"execute_result"}],"source":["# Read the csv file with address into a DataFrame\n","orders_df = pd.read_csv(\"./sedf_data/cities/orders.csv\")\n","\n","# Check head\n","orders_df.head()"]},{"cell_type":"markdown","metadata":{},"source":["> The DataFrame shows a column with address information.\n"]},{"cell_type":"code","execution_count":53,"metadata":{"ExecuteTime":{"end_time":"2021-11-11T22:35:55.437412Z","start_time":"2021-11-11T22:35:53.956939Z"}},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Address</th>\n","      <th>SHAPE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>602 Murray Cir, Sausalito, CA 94965</td>\n","      <td>{\"x\": -122.47885242199999, \"y\": 37.83735920100...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>340 Stockton St, San Francisco, CA 94108</td>\n","      <td>{\"x\": -122.44955096499996, \"y\": 37.73152250200...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3619 Balboa St, San Francisco, CA 94121</td>\n","      <td>{\"x\": -122.49772620499999, \"y\": 37.77567413500...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1274 El Camino Real, San Bruno, CA 94066</td>\n","      <td>{\"x\": -122.40685153899994, \"y\": 37.78910429100...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>625 Monterey Blvd, San Francisco, CA 94127</td>\n","      <td>{\"x\": -122.42218381299995, \"y\": 37.63856151200...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                                      Address  \\\n","0         602 Murray Cir, Sausalito, CA 94965   \n","1    340 Stockton St, San Francisco, CA 94108   \n","2     3619 Balboa St, San Francisco, CA 94121   \n","3    1274 El Camino Real, San Bruno, CA 94066   \n","4  625 Monterey Blvd, San Francisco, CA 94127   \n","\n","                                               SHAPE  \n","0  {\"x\": -122.47885242199999, \"y\": 37.83735920100...  \n","1  {\"x\": -122.44955096499996, \"y\": 37.73152250200...  \n","2  {\"x\": -122.49772620499999, \"y\": 37.77567413500...  \n","3  {\"x\": -122.40685153899994, \"y\": 37.78910429100...  \n","4  {\"x\": -122.42218381299995, \"y\": 37.63856151200...  "]},"execution_count":53,"metadata":{},"output_type":"execute_result"}],"source":["# Use from_df to create SeDF\n","orders_sdf = pd.DataFrame.spatial.from_df(\n","    df=orders_df, address_column=\"Address\")\n","orders_sdf.head()"]},{"cell_type":"code","execution_count":54,"metadata":{"ExecuteTime":{"end_time":"2021-11-11T22:35:57.704801Z","start_time":"2021-11-11T22:35:57.697804Z"}},"outputs":[{"data":{"text/plain":["['point']"]},"execution_count":54,"metadata":{},"output_type":"execute_result"}],"source":["# Check geometry type\n","orders_sdf.spatial.geometry_type"]},{"cell_type":"markdown","metadata":{},"source":["> The `spatial` namespace shows that a _Spatially enabled DataFrame_ has been created from a Pandas DataFrame with address information.\n"]},{"cell_type":"markdown","metadata":{},"source":["### Read in DataFrame with Lat/Long Information\n"]},{"cell_type":"markdown","metadata":{},"source":["As we saw in part-1 of this guide series, a SeDF can be created from any Pandas DataFrame with location information (Latitude and Longitude) using the [`from_xy()`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#arcgis.features.GeoAccessor.from_xy) method.\n","\n","Let's look at an example. We will read the data with latitude and longitude information into a DataFrame using the [`pd.read_csv()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html) method. Then, we will create a SeDF by passing the DataFrame, latitude, and longitude as parameters to the `from_xy()` method.\n"]},{"cell_type":"code","execution_count":55,"metadata":{"ExecuteTime":{"end_time":"2021-11-11T22:36:07.669481Z","start_time":"2021-11-11T22:36:07.650485Z"}},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Provider Name</th>\n","      <th>Provider City</th>\n","      <th>Provider State</th>\n","      <th>Residents Total Admissions COVID-19</th>\n","      <th>Residents Total COVID-19 Cases</th>\n","      <th>Residents Total COVID-19 Deaths</th>\n","      <th>Number of All Beds</th>\n","      <th>Total Number of Occupied Beds</th>\n","      <th>LONGITUDE</th>\n","      <th>LATITUDE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>GROSSE POINTE MANOR</td>\n","      <td>NILES</td>\n","      <td>IL</td>\n","      <td>5</td>\n","      <td>56</td>\n","      <td>12</td>\n","      <td>99</td>\n","      <td>61</td>\n","      <td>-87.792973</td>\n","      <td>42.012012</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>MILLER'S MERRY MANOR</td>\n","      <td>DUNKIRK</td>\n","      <td>IN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>46</td>\n","      <td>43</td>\n","      <td>-85.197651</td>\n","      <td>40.392722</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>PARKWAY MANOR</td>\n","      <td>MARION</td>\n","      <td>IL</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>131</td>\n","      <td>84</td>\n","      <td>-88.982944</td>\n","      <td>37.750143</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>AVANTARA LONG GROVE</td>\n","      <td>LONG GROVE</td>\n","      <td>IL</td>\n","      <td>6</td>\n","      <td>141</td>\n","      <td>0</td>\n","      <td>195</td>\n","      <td>131</td>\n","      <td>-87.986442</td>\n","      <td>42.160843</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>HARMONY NURSING &amp; REHAB CENTER</td>\n","      <td>CHICAGO</td>\n","      <td>IL</td>\n","      <td>19</td>\n","      <td>75</td>\n","      <td>16</td>\n","      <td>180</td>\n","      <td>116</td>\n","      <td>-87.726353</td>\n","      <td>41.975505</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                    Provider Name Provider City Provider State  \\\n","0             GROSSE POINTE MANOR         NILES             IL   \n","1            MILLER'S MERRY MANOR       DUNKIRK             IN   \n","2                   PARKWAY MANOR        MARION             IL   \n","3             AVANTARA LONG GROVE    LONG GROVE             IL   \n","4  HARMONY NURSING & REHAB CENTER       CHICAGO             IL   \n","\n","   Residents Total Admissions COVID-19  Residents Total COVID-19 Cases  \\\n","0                                    5                              56   \n","1                                    0                               0   \n","2                                    0                               0   \n","3                                    6                             141   \n","4                                   19                              75   \n","\n","   Residents Total COVID-19 Deaths  Number of All Beds  \\\n","0                               12                  99   \n","1                                0                  46   \n","2                                0                 131   \n","3                                0                 195   \n","4                               16                 180   \n","\n","   Total Number of Occupied Beds  LONGITUDE   LATITUDE  \n","0                             61 -87.792973  42.012012  \n","1                             43 -85.197651  40.392722  \n","2                             84 -88.982944  37.750143  \n","3                            131 -87.986442  42.160843  \n","4                            116 -87.726353  41.975505  "]},"execution_count":55,"metadata":{},"output_type":"execute_result"}],"source":["# Read the data\n","cms_df = pd.read_csv('./sedf_data/cities/sample_cms_data.csv')\n","\n","# Return the first 5 records\n","cms_df.head()"]},{"cell_type":"code","execution_count":56,"metadata":{"ExecuteTime":{"end_time":"2021-11-11T22:36:07.692482Z","start_time":"2021-11-11T22:36:07.672485Z"}},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Provider Name</th>\n","      <th>Provider City</th>\n","      <th>Provider State</th>\n","      <th>Residents Total Admissions COVID-19</th>\n","      <th>Residents Total COVID-19 Cases</th>\n","      <th>Residents Total COVID-19 Deaths</th>\n","      <th>Number of All Beds</th>\n","      <th>Total Number of Occupied Beds</th>\n","      <th>LONGITUDE</th>\n","      <th>LATITUDE</th>\n","      <th>SHAPE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>GROSSE POINTE MANOR</td>\n","      <td>NILES</td>\n","      <td>IL</td>\n","      <td>5</td>\n","      <td>56</td>\n","      <td>12</td>\n","      <td>99</td>\n","      <td>61</td>\n","      <td>-87.792973</td>\n","      <td>42.012012</td>\n","      <td>{\"spatialReference\": {\"wkid\": 4326}, \"x\": -87....</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>MILLER'S MERRY MANOR</td>\n","      <td>DUNKIRK</td>\n","      <td>IN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>46</td>\n","      <td>43</td>\n","      <td>-85.197651</td>\n","      <td>40.392722</td>\n","      <td>{\"spatialReference\": {\"wkid\": 4326}, \"x\": -85....</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>PARKWAY MANOR</td>\n","      <td>MARION</td>\n","      <td>IL</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>131</td>\n","      <td>84</td>\n","      <td>-88.982944</td>\n","      <td>37.750143</td>\n","      <td>{\"spatialReference\": {\"wkid\": 4326}, \"x\": -88....</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>AVANTARA LONG GROVE</td>\n","      <td>LONG GROVE</td>\n","      <td>IL</td>\n","      <td>6</td>\n","      <td>141</td>\n","      <td>0</td>\n","      <td>195</td>\n","      <td>131</td>\n","      <td>-87.986442</td>\n","      <td>42.160843</td>\n","      <td>{\"spatialReference\": {\"wkid\": 4326}, \"x\": -87....</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>HARMONY NURSING &amp; REHAB CENTER</td>\n","      <td>CHICAGO</td>\n","      <td>IL</td>\n","      <td>19</td>\n","      <td>75</td>\n","      <td>16</td>\n","      <td>180</td>\n","      <td>116</td>\n","      <td>-87.726353</td>\n","      <td>41.975505</td>\n","      <td>{\"spatialReference\": {\"wkid\": 4326}, \"x\": -87....</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                    Provider Name Provider City Provider State  \\\n","0             GROSSE POINTE MANOR         NILES             IL   \n","1            MILLER'S MERRY MANOR       DUNKIRK             IN   \n","2                   PARKWAY MANOR        MARION             IL   \n","3             AVANTARA LONG GROVE    LONG GROVE             IL   \n","4  HARMONY NURSING & REHAB CENTER       CHICAGO             IL   \n","\n","   Residents Total Admissions COVID-19  Residents Total COVID-19 Cases  \\\n","0                                    5                              56   \n","1                                    0                               0   \n","2                                    0                               0   \n","3                                    6                             141   \n","4                                   19                              75   \n","\n","   Residents Total COVID-19 Deaths  Number of All Beds  \\\n","0                               12                  99   \n","1                                0                  46   \n","2                                0                 131   \n","3                                0                 195   \n","4                               16                 180   \n","\n","   Total Number of Occupied Beds  LONGITUDE   LATITUDE  \\\n","0                             61 -87.792973  42.012012   \n","1                             43 -85.197651  40.392722   \n","2                             84 -88.982944  37.750143   \n","3                            131 -87.986442  42.160843   \n","4                            116 -87.726353  41.975505   \n","\n","                                               SHAPE  \n","0  {\"spatialReference\": {\"wkid\": 4326}, \"x\": -87....  \n","1  {\"spatialReference\": {\"wkid\": 4326}, \"x\": -85....  \n","2  {\"spatialReference\": {\"wkid\": 4326}, \"x\": -88....  \n","3  {\"spatialReference\": {\"wkid\": 4326}, \"x\": -87....  \n","4  {\"spatialReference\": {\"wkid\": 4326}, \"x\": -87....  "]},"execution_count":56,"metadata":{},"output_type":"execute_result"}],"source":["# Create a SeDF\n","cms_sedf = pd.DataFrame.spatial.from_xy(\n","    df=cms_df, x_column='LONGITUDE', y_column='LATITUDE', sr=4326)\n","\n","# Check head\n","cms_sedf.head()"]},{"cell_type":"markdown","metadata":{},"source":["> The `SHAPE` feature shows that a _Spatially enabled DataFrame_ has been created from a Pandas DataFrame with latitude and longitude information.\n"]},{"cell_type":"markdown","metadata":{},"source":["### Read in GeoPandas DataFrame\n"]},{"cell_type":"markdown","metadata":{},"source":["A `SeDF` can be easily created from a [GeoPandas's](https://geopandas.org/index.html) [GeoDataFrame](https://geopandas.org/docs/reference/geodataframe.html) using the [`from_geodataframe()`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#arcgis.features.GeoAccessor.from_geodataframe) method. We will:\n","\n","- Import Geopandas and create a GeoDataFrame.\n","- Create a [**Spatially enabled DataFrame**](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#geoaccessor) from a GeoDataFrame.\n"]},{"cell_type":"markdown","metadata":{},"source":["#### Create a GeoDataFrame\n","\n","Here, we will create a `GeoDataFrame` from a Pandas DataFrame, `cms_df`, defined above.\n"]},{"cell_type":"code","execution_count":57,"metadata":{"ExecuteTime":{"end_time":"2021-11-11T22:36:07.724482Z","start_time":"2021-11-11T22:36:07.694483Z"}},"outputs":[],"source":["# Import libraries\n","from geopandas import GeoDataFrame\n","from shapely.geometry import Point"]},{"cell_type":"code","execution_count":58,"metadata":{"ExecuteTime":{"end_time":"2021-11-11T22:36:07.743482Z","start_time":"2021-11-11T22:36:07.726483Z"}},"outputs":[{"data":{"text/plain":["(124, 9)"]},"execution_count":58,"metadata":{},"output_type":"execute_result"}],"source":["# Read the data\n","cms_df = pd.read_csv('./sedf_data/cities/sample_cms_data.csv')\n","\n","# Create Geopandas DataFrame\n","gdf = GeoDataFrame(cms_df.drop(['LONGITUDE', 'LATITUDE'], axis=1),\n","                   crs={'init': 'epsg:4326'},\n","                   geometry=[Point(xy) for xy in zip(cms_df.LONGITUDE, cms_df.LATITUDE)])\n","gdf.shape"]},{"cell_type":"code","execution_count":59,"metadata":{"ExecuteTime":{"end_time":"2021-11-11T22:36:07.758480Z","start_time":"2021-11-11T22:36:07.745481Z"}},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Provider Name</th>\n","      <th>Provider City</th>\n","      <th>Provider State</th>\n","      <th>Residents Total Admissions COVID-19</th>\n","      <th>Residents Total COVID-19 Cases</th>\n","      <th>Residents Total COVID-19 Deaths</th>\n","      <th>Number of All Beds</th>\n","      <th>Total Number of Occupied Beds</th>\n","      <th>geometry</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>GROSSE POINTE MANOR</td>\n","      <td>NILES</td>\n","      <td>IL</td>\n","      <td>5</td>\n","      <td>56</td>\n","      <td>12</td>\n","      <td>99</td>\n","      <td>61</td>\n","      <td>POINT (-87.79297 42.01201)</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>MILLER'S MERRY MANOR</td>\n","      <td>DUNKIRK</td>\n","      <td>IN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>46</td>\n","      <td>43</td>\n","      <td>POINT (-85.19765 40.39272)</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["          Provider Name Provider City Provider State  \\\n","0   GROSSE POINTE MANOR         NILES             IL   \n","1  MILLER'S MERRY MANOR       DUNKIRK             IN   \n","\n","   Residents Total Admissions COVID-19  Residents Total COVID-19 Cases  \\\n","0                                    5                              56   \n","1                                    0                               0   \n","\n","   Residents Total COVID-19 Deaths  Number of All Beds  \\\n","0                               12                  99   \n","1                                0                  46   \n","\n","   Total Number of Occupied Beds                    geometry  \n","0                             61  POINT (-87.79297 42.01201)  \n","1                             43  POINT (-85.19765 40.39272)  "]},"execution_count":59,"metadata":{},"output_type":"execute_result"}],"source":["# Check head\n","gdf.head(2)"]},{"cell_type":"markdown","metadata":{},"source":["> A GeoDataFrame has been created with a `geometry` column that stores the geometry of the dataset.\n"]},{"cell_type":"markdown","metadata":{},"source":["#### Create a SeDF from GeoDataFrame\n","\n","Here, we will create a `SeDF` from the `gdf` GeoDataFrame created above using the [`from_geodataframe()`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#arcgis.features.GeoAccessor.from_geodataframe) method.\n"]},{"cell_type":"code","execution_count":60,"metadata":{"ExecuteTime":{"end_time":"2021-11-11T22:36:07.777481Z","start_time":"2021-11-11T22:36:07.760480Z"}},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Provider Name</th>\n","      <th>Provider City</th>\n","      <th>Provider State</th>\n","      <th>Residents Total Admissions COVID-19</th>\n","      <th>Residents Total COVID-19 Cases</th>\n","      <th>Residents Total COVID-19 Deaths</th>\n","      <th>Number of All Beds</th>\n","      <th>Total Number of Occupied Beds</th>\n","      <th>SHAPE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>GROSSE POINTE MANOR</td>\n","      <td>NILES</td>\n","      <td>IL</td>\n","      <td>5</td>\n","      <td>56</td>\n","      <td>12</td>\n","      <td>99</td>\n","      <td>61</td>\n","      <td>{\"x\": -87.792973, \"y\": 42.012012, \"spatialRefe...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>MILLER'S MERRY MANOR</td>\n","      <td>DUNKIRK</td>\n","      <td>IN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>46</td>\n","      <td>43</td>\n","      <td>{\"x\": -85.197651, \"y\": 40.392722, \"spatialRefe...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["          Provider Name Provider City Provider State  \\\n","0   GROSSE POINTE MANOR         NILES             IL   \n","1  MILLER'S MERRY MANOR       DUNKIRK             IN   \n","\n","   Residents Total Admissions COVID-19  Residents Total COVID-19 Cases  \\\n","0                                    5                              56   \n","1                                    0                               0   \n","\n","   Residents Total COVID-19 Deaths  Number of All Beds  \\\n","0                               12                  99   \n","1                                0                  46   \n","\n","   Total Number of Occupied Beds  \\\n","0                             61   \n","1                             43   \n","\n","                                               SHAPE  \n","0  {\"x\": -87.792973, \"y\": 42.012012, \"spatialRefe...  \n","1  {\"x\": -85.197651, \"y\": 40.392722, \"spatialRefe...  "]},"execution_count":60,"metadata":{},"output_type":"execute_result"}],"source":["# Create a SeDF\n","sedf_gpd = pd.DataFrame.spatial.from_geodataframe(gdf)\n","sedf_gpd.head(2)"]},{"cell_type":"code","execution_count":61,"metadata":{"ExecuteTime":{"end_time":"2021-11-11T22:36:07.784482Z","start_time":"2021-11-11T22:36:07.779484Z"}},"outputs":[{"data":{"text/plain":["['point']"]},"execution_count":61,"metadata":{},"output_type":"execute_result"}],"source":["# Check geometry type\n","sedf_gpd.spatial.geometry_type"]},{"cell_type":"markdown","metadata":{},"source":["> The `spatial` namespace shows that a _Spatially enabled DataFrame_ has been created from a GeoDataFrame.\n"]},{"cell_type":"markdown","metadata":{},"source":["### Read in feather format data\n"]},{"cell_type":"markdown","metadata":{},"source":["A `SeDF` can be easily created from the data in [feather](https://arrow.apache.org/docs/python/feather.html) format using the [`from_feather()`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?highlight=from_feather#arcgis.features.GeoAccessor.from_feather) method. The method's defaults _SHAPE_ is the `spatial_column` for geo-spatial information, but any other column with spatial information can be specified.\n"]},{"cell_type":"code","execution_count":62,"metadata":{"ExecuteTime":{"end_time":"2021-11-11T22:36:07.798483Z","start_time":"2021-11-11T22:36:07.786483Z"}},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Provider Name</th>\n","      <th>Provider City</th>\n","      <th>Provider State</th>\n","      <th>Residents Total Admissions COVID-19</th>\n","      <th>Residents Total COVID-19 Cases</th>\n","      <th>Residents Total COVID-19 Deaths</th>\n","      <th>Number of All Beds</th>\n","      <th>Total Number of Occupied Beds</th>\n","      <th>LONGITUDE</th>\n","      <th>LATITUDE</th>\n","      <th>SHAPE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>GROSSE POINTE MANOR</td>\n","      <td>NILES</td>\n","      <td>IL</td>\n","      <td>5</td>\n","      <td>56</td>\n","      <td>12</td>\n","      <td>99</td>\n","      <td>61</td>\n","      <td>-87.792973</td>\n","      <td>42.012012</td>\n","      <td>{\"spatialReference\": {\"wkid\": 4326}, \"x\": -87....</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>MILLER'S MERRY MANOR</td>\n","      <td>DUNKIRK</td>\n","      <td>IN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>46</td>\n","      <td>43</td>\n","      <td>-85.197651</td>\n","      <td>40.392722</td>\n","      <td>{\"spatialReference\": {\"wkid\": 4326}, \"x\": -85....</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["          Provider Name Provider City Provider State  \\\n","0   GROSSE POINTE MANOR         NILES             IL   \n","1  MILLER'S MERRY MANOR       DUNKIRK             IN   \n","\n","   Residents Total Admissions COVID-19  Residents Total COVID-19 Cases  \\\n","0                                    5                              56   \n","1                                    0                               0   \n","\n","   Residents Total COVID-19 Deaths  Number of All Beds  \\\n","0                               12                  99   \n","1                                0                  46   \n","\n","   Total Number of Occupied Beds  LONGITUDE   LATITUDE  \\\n","0                             61 -87.792973  42.012012   \n","1                             43 -85.197651  40.392722   \n","\n","                                               SHAPE  \n","0  {\"spatialReference\": {\"wkid\": 4326}, \"x\": -87....  \n","1  {\"spatialReference\": {\"wkid\": 4326}, \"x\": -85....  "]},"execution_count":62,"metadata":{},"output_type":"execute_result"}],"source":["# Check head\n","cms_sedf.head(2)"]},{"cell_type":"code","execution_count":63,"metadata":{"ExecuteTime":{"end_time":"2021-11-11T22:36:07.854484Z","start_time":"2021-11-11T22:36:07.802481Z"}},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Provider Name</th>\n","      <th>Provider City</th>\n","      <th>Provider State</th>\n","      <th>Residents Total Admissions COVID-19</th>\n","      <th>Residents Total COVID-19 Cases</th>\n","      <th>Residents Total COVID-19 Deaths</th>\n","      <th>Number of All Beds</th>\n","      <th>Total Number of Occupied Beds</th>\n","      <th>LONGITUDE</th>\n","      <th>LATITUDE</th>\n","      <th>SHAPE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>GROSSE POINTE MANOR</td>\n","      <td>NILES</td>\n","      <td>IL</td>\n","      <td>5</td>\n","      <td>56</td>\n","      <td>12</td>\n","      <td>99</td>\n","      <td>61</td>\n","      <td>-87.792973</td>\n","      <td>42.012012</td>\n","      <td>{\"x\": -87.792973, \"y\": 42.012012, \"spatialRefe...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>MILLER'S MERRY MANOR</td>\n","      <td>DUNKIRK</td>\n","      <td>IN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>46</td>\n","      <td>43</td>\n","      <td>-85.197651</td>\n","      <td>40.392722</td>\n","      <td>{\"x\": -85.197651, \"y\": 40.392722, \"spatialRefe...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["          Provider Name Provider City Provider State  \\\n","0   GROSSE POINTE MANOR         NILES             IL   \n","1  MILLER'S MERRY MANOR       DUNKIRK             IN   \n","\n","   Residents Total Admissions COVID-19  Residents Total COVID-19 Cases  \\\n","0                                    5                              56   \n","1                                    0                               0   \n","\n","   Residents Total COVID-19 Deaths  Number of All Beds  \\\n","0                               12                  99   \n","1                                0                  46   \n","\n","   Total Number of Occupied Beds  LONGITUDE   LATITUDE  \\\n","0                             61 -87.792973  42.012012   \n","1                             43 -85.197651  40.392722   \n","\n","                                               SHAPE  \n","0  {\"x\": -87.792973, \"y\": 42.012012, \"spatialRefe...  \n","1  {\"x\": -85.197651, \"y\": 40.392722, \"spatialRefe...  "]},"execution_count":63,"metadata":{},"output_type":"execute_result"}],"source":["# Create SeDf by reading from feather\n","sedf_fthr = pd.DataFrame.spatial.from_feather(\n","    './sedf_data/cities/sample_cms_data.feather')\n","sedf_fthr.head(2)"]},{"cell_type":"code","execution_count":64,"metadata":{"ExecuteTime":{"end_time":"2021-11-11T22:36:07.861486Z","start_time":"2021-11-11T22:36:07.856482Z"}},"outputs":[{"data":{"text/plain":["['point']"]},"execution_count":64,"metadata":{},"output_type":"execute_result"}],"source":["# Check geometry type\n","sedf_fthr.spatial.geometry_type"]},{"cell_type":"markdown","metadata":{},"source":["> The `spatial` namespace shows that a _Spatially enabled DataFrame_ has been created from _feather_ format data.\n"]},{"cell_type":"markdown","metadata":{},"source":["### Read in Non-spatial Table data\n","\n","Non-spatial table data can be hosted on [**ArcGIS Online**](https://www.arcgis.com) or [**ArcGIS Enterprise**](http://enterprise.arcgis.com/en/), or it can be stored locally in a File Geodatabase. A `SeDF` can be easily created from such non-spatial table data using the following methods:\n","\n","- [`from_table()`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?highlight=from_feather#arcgis.features.GeoAccessor.from_table) - for local data\n","- [`from_layer()`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?highlight=from_layer#arcgis.features.GeoAccessor.from_layer) - for data hosted on ArcGIS Online or Enterprise\n"]},{"cell_type":"markdown","metadata":{},"source":["#### Using the `from_table()` method\n","\n","A `SeDF` can be created from local non-spatial data using the [`from_table()`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?highlight=from_feather#arcgis.features.GeoAccessor.from_table) method. The method can read a csv file (in any environment) or a table stored in a File Geodatabase (with ArcPy only).\n"]},{"cell_type":"markdown","metadata":{},"source":["##### Reading a csv file\n"]},{"cell_type":"code","execution_count":65,"metadata":{"ExecuteTime":{"end_time":"2021-11-11T22:36:08.546484Z","start_time":"2021-11-11T22:36:07.863481Z"}},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Provider Name</th>\n","      <th>Provider City</th>\n","      <th>Provider State</th>\n","      <th>Residents Total Admissions COVID-19</th>\n","      <th>Residents Total COVID-19 Cases</th>\n","      <th>Residents Total COVID-19 Deaths</th>\n","      <th>Number of All Beds</th>\n","      <th>Total Number of Occupied Beds</th>\n","      <th>LONGITUDE</th>\n","      <th>LATITUDE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>GROSSE POINTE MANOR</td>\n","      <td>NILES</td>\n","      <td>IL</td>\n","      <td>5</td>\n","      <td>56</td>\n","      <td>12</td>\n","      <td>99</td>\n","      <td>61</td>\n","      <td>-87.792973</td>\n","      <td>42.012012</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>MILLER'S MERRY MANOR</td>\n","      <td>DUNKIRK</td>\n","      <td>IN</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>46</td>\n","      <td>43</td>\n","      <td>-85.197651</td>\n","      <td>40.392722</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["          Provider Name Provider City Provider State  \\\n","0   GROSSE POINTE MANOR         NILES             IL   \n","1  MILLER'S MERRY MANOR       DUNKIRK             IN   \n","\n","   Residents Total Admissions COVID-19  Residents Total COVID-19 Cases  \\\n","0                                    5                              56   \n","1                                    0                               0   \n","\n","   Residents Total COVID-19 Deaths  Number of All Beds  \\\n","0                               12                  99   \n","1                                0                  46   \n","\n","   Total Number of Occupied Beds  LONGITUDE   LATITUDE  \n","0                             61 -87.792973  42.012012  \n","1                             43 -85.197651  40.392722  "]},"execution_count":65,"metadata":{},"output_type":"execute_result"}],"source":["# Create SeDF\n","tbl_df = pd.DataFrame.spatial.from_table(\n","    filename='./sedf_data/cities/sample_cms_data.csv')\n","tbl_df.head(2)"]},{"cell_type":"markdown","metadata":{},"source":["> A Pandas DataFrame without any spatial information is returned.\n"]},{"cell_type":"markdown","metadata":{},"source":["##### Reading table from a File Geodatabase\n"]},{"cell_type":"markdown","metadata":{},"source":["<div class=\"alert alert-info\">\n","    <b>Note:</b> The operation below can only be performed in an environment that <b>contains</b> <code>arcpy</code>.\n","\n","</div>\n"]},{"cell_type":"code","execution_count":66,"metadata":{"ExecuteTime":{"end_time":"2021-11-11T22:36:08.615484Z","start_time":"2021-11-11T22:36:08.548486Z"}},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>OBJECTID</th>\n","      <th>NAME</th>\n","      <th>OTHER</th>\n","      <th>OWNER_OCC</th>\n","      <th>PLACEFIPS</th>\n","      <th>POP2010</th>\n","      <th>POPULATION</th>\n","      <th>POP_CLASS</th>\n","      <th>RENTER_OCC</th>\n","      <th>ST</th>\n","      <th>STFIPS</th>\n","      <th>VACANT</th>\n","      <th>WHITE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>Ammon</td>\n","      <td>307</td>\n","      <td>3205</td>\n","      <td>1601990</td>\n","      <td>13816</td>\n","      <td>15181</td>\n","      <td>6</td>\n","      <td>1271</td>\n","      <td>ID</td>\n","      <td>16</td>\n","      <td>271</td>\n","      <td>13002</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>Blackfoot</td>\n","      <td>1077</td>\n","      <td>2788</td>\n","      <td>1607840</td>\n","      <td>11899</td>\n","      <td>11946</td>\n","      <td>6</td>\n","      <td>1441</td>\n","      <td>ID</td>\n","      <td>16</td>\n","      <td>318</td>\n","      <td>9893</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   OBJECTID       NAME  OTHER  OWNER_OCC PLACEFIPS  POP2010  POPULATION  \\\n","0         1      Ammon    307       3205   1601990    13816       15181   \n","1         2  Blackfoot   1077       2788   1607840    11899       11946   \n","\n","   POP_CLASS  RENTER_OCC  ST STFIPS  VACANT  WHITE  \n","0          6        1271  ID     16     271  13002  \n","1          6        1441  ID     16     318   9893  "]},"execution_count":66,"metadata":{},"output_type":"execute_result"}],"source":["# Create SeDF\n","tbl_df2 = pd.DataFrame.spatial.from_table(\n","    filename=\"./sedf_data/cities/cities.gdb/cities_table_export\")\n","tbl_df2.head(2)"]},{"cell_type":"markdown","metadata":{},"source":["> A Pandas DataFrame without any spatial information is returned.\n"]},{"cell_type":"markdown","metadata":{},"source":["#### Using the `from_layer()` method\n","\n","A `SeDF` can be created from hosted non-spatial data using the[`from_layer()`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?highlight=from_layer#arcgis.features.GeoAccessor.from_layer) method.\n"]},{"cell_type":"code","execution_count":67,"metadata":{"ExecuteTime":{"end_time":"2021-11-11T22:36:08.892350Z","start_time":"2021-11-11T22:36:08.618492Z"}},"outputs":[{"data":{"text/html":["<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n","                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n","                       <a href='https://geosaurus.maps.arcgis.com/home/item.html?id=b022d30f881f478f8155153b9205ce12' target='_blank'>\n","                        <img src='data:image/png;base64,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' width='200' height='133' class=\"itemThumbnail\">\n","                       </a>\n","                    </div>\n","\n","                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n","                        <a href='https://geosaurus.maps.arcgis.com/home/item.html?id=b022d30f881f478f8155153b9205ce12' target='_blank'><b>sedf_major_cities_table</b>\n","                        </a>\n","                        <br/><img src='https://geosaurus.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/table16.png' style=\"vertical-align:middle;\">Table Layer by api_data_owner\n","                        <br/>Last Modified: November 11, 2021\n","                        <br/>0 comments, 4 views\n","                    </div>\n","                </div>\n","                "],"text/plain":["<Item title:\"sedf_major_cities_table\" type:Table Layer owner:api_data_owner>"]},"execution_count":67,"metadata":{},"output_type":"execute_result"}],"source":["# Get table item\n","tbl_item = agol_gis.content.get(\"b022d30f881f478f8155153b9205ce12\")\n","tbl_item"]},{"cell_type":"code","execution_count":68,"metadata":{"ExecuteTime":{"end_time":"2021-11-11T22:36:09.836988Z","start_time":"2021-11-11T22:36:08.897350Z"}},"outputs":[{"data":{"text/plain":["<Table url:\"https://services7.arcgis.com/JEwYeAy2cc8qOe3o/arcgis/rest/services/major_cities_table/FeatureServer/0\">"]},"execution_count":68,"metadata":{},"output_type":"execute_result"}],"source":["# Get table url\n","tbl = tbl_item.tables[0]\n","tbl"]},{"cell_type":"code","execution_count":69,"metadata":{"ExecuteTime":{"end_time":"2021-11-11T22:36:11.687513Z","start_time":"2021-11-11T22:36:09.841640Z"}},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>CLASS</th>\n","      <th>OBJECTID</th>\n","      <th>PLACEFIPS</th>\n","      <th>POP2010</th>\n","      <th>POPULATION</th>\n","      <th>POP_CLASS</th>\n","      <th>STFIPS</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>city</td>\n","      <td>1</td>\n","      <td>1601990</td>\n","      <td>13816</td>\n","      <td>15181</td>\n","      <td>6</td>\n","      <td>16</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>city</td>\n","      <td>2</td>\n","      <td>1607840</td>\n","      <td>11899</td>\n","      <td>11946</td>\n","      <td>6</td>\n","      <td>16</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  CLASS  OBJECTID PLACEFIPS  POP2010  POPULATION  POP_CLASS STFIPS\n","0  city         1   1601990    13816       15181          6     16\n","1  city         2   1607840    11899       11946          6     16"]},"execution_count":69,"metadata":{},"output_type":"execute_result"}],"source":["tbl_df2 = pd.DataFrame.spatial.from_layer(tbl)\n","tbl_df2.head(2)"]},{"cell_type":"markdown","metadata":{},"source":["> A Pandas DataFrame without any spatial information is returned.\n"]},{"cell_type":"markdown","metadata":{},"source":["### Read in data from '_lite and portable_' databases\n"]},{"cell_type":"markdown","metadata":{},"source":["Geospatial data stored in a [mobile geodatabase](https://pro.arcgis.com/en/pro-app/latest/help/data/geodatabases/manage-mobile-gdb/mobile-geodatabases.htm) (.geodatabase) or a [SQLite Database](https://pro.arcgis.com/en/pro-app/latest/tool-reference/data-management/create-sqlite-database.htm) can be easily accessed using the [**Spatially enabled DataFrame**](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#geoaccessor).\n","\n","- A mobile geodatabase (.geodatabase) is a collection of various types of GIS datasets contained in a single file on disk that can store, query, and manage spatial and nonspatial data. Mobile geodatabases are stored in an SQLite database.\n","\n","- SQLite is a full-featured relational database with the advantage of being portable and interoperable making it ubiquitous in mobile app development.\n","\n","The [`from_featureclass()`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?arcgis.features.GeoAccessor.from_featureclass#arcgis.features.GeoAccessor.from_featureclass) method can be used to create a `SeDF` by reading in data from these databases. Let's look at some examples.\n"]},{"cell_type":"markdown","metadata":{},"source":["<div class=\"alert alert-info\">\n","    <b>Note:</b> The operations below can only be performed in an environment that <b>contains</b> <code>arcpy</code>.\n","\n","</div>\n"]},{"cell_type":"markdown","metadata":{},"source":["#### Read from a mobile geodatabase\n"]},{"cell_type":"code","execution_count":70,"metadata":{"ExecuteTime":{"end_time":"2021-11-11T22:36:12.166989Z","start_time":"2021-11-11T22:36:11.692511Z"}},"outputs":[{"data":{"text/plain":["(3886, 51)"]},"execution_count":70,"metadata":{},"output_type":"execute_result"}],"source":["# Reading from mobile geodatabase\n","mobile_gdb_df = pd.DataFrame.spatial.from_featureclass(\n","    location=\"./sedf_data/cities/cities_mobile.geodatabase/main.cities\")\n","mobile_gdb_df.shape"]},{"cell_type":"code","execution_count":71,"metadata":{"ExecuteTime":{"end_time":"2021-11-11T22:36:12.212993Z","start_time":"2021-11-11T22:36:12.171999Z"}},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>OBJECTID</th>\n","      <th>age_10_14</th>\n","      <th>age_15_19</th>\n","      <th>age_20_24</th>\n","      <th>age_25_34</th>\n","      <th>age_35_44</th>\n","      <th>age_45_54</th>\n","      <th>age_55_64</th>\n","      <th>age_5_9</th>\n","      <th>age_65_74</th>\n","      <th>...</th>\n","      <th>placefips</th>\n","      <th>pop2010</th>\n","      <th>population</th>\n","      <th>pop_class</th>\n","      <th>renter_occ</th>\n","      <th>st</th>\n","      <th>stfips</th>\n","      <th>vacant</th>\n","      <th>white</th>\n","      <th>SHAPE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>1313</td>\n","      <td>1058</td>\n","      <td>734</td>\n","      <td>2031</td>\n","      <td>1767</td>\n","      <td>1446</td>\n","      <td>1136</td>\n","      <td>1503</td>\n","      <td>665</td>\n","      <td>...</td>\n","      <td>1601990</td>\n","      <td>13816</td>\n","      <td>15181</td>\n","      <td>6</td>\n","      <td>1271</td>\n","      <td>ID</td>\n","      <td>16</td>\n","      <td>271</td>\n","      <td>13002</td>\n","      <td>{\"x\": -12462673.7237, \"y\": 5384674.994099997, ...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>890</td>\n","      <td>817</td>\n","      <td>818</td>\n","      <td>1799</td>\n","      <td>1235</td>\n","      <td>1330</td>\n","      <td>1143</td>\n","      <td>1099</td>\n","      <td>721</td>\n","      <td>...</td>\n","      <td>1607840</td>\n","      <td>11899</td>\n","      <td>11946</td>\n","      <td>6</td>\n","      <td>1441</td>\n","      <td>ID</td>\n","      <td>16</td>\n","      <td>318</td>\n","      <td>9893</td>\n","      <td>{\"x\": -12506251.314, \"y\": 5341537.793499999, \"...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>2 rows × 51 columns</p>\n","</div>"],"text/plain":["   OBJECTID  age_10_14  age_15_19  age_20_24  age_25_34  age_35_44  age_45_54  \\\n","0         1       1313       1058        734       2031       1767       1446   \n","1         2        890        817        818       1799       1235       1330   \n","\n","   age_55_64  age_5_9  age_65_74  ...  placefips  pop2010  population  \\\n","0       1136     1503        665  ...    1601990    13816       15181   \n","1       1143     1099        721  ...    1607840    11899       11946   \n","\n","   pop_class  renter_occ  st  stfips  vacant  white  \\\n","0          6        1271  ID      16     271  13002   \n","1          6        1441  ID      16     318   9893   \n","\n","                                               SHAPE  \n","0  {\"x\": -12462673.7237, \"y\": 5384674.994099997, ...  \n","1  {\"x\": -12506251.314, \"y\": 5341537.793499999, \"...  \n","\n","[2 rows x 51 columns]"]},"execution_count":71,"metadata":{},"output_type":"execute_result"}],"source":["# Check head\n","mobile_gdb_df.head(2)"]},{"cell_type":"code","execution_count":72,"metadata":{"ExecuteTime":{"end_time":"2021-11-11T22:36:12.230989Z","start_time":"2021-11-11T22:36:12.216993Z"}},"outputs":[{"data":{"text/plain":["['point']"]},"execution_count":72,"metadata":{},"output_type":"execute_result"}],"source":["# Check geometry type\n","mobile_gdb_df.spatial.geometry_type"]},{"cell_type":"markdown","metadata":{},"source":["> The `spatial` namespace shows that a _Spatially enabled DataFrame_ has been created.\n"]},{"cell_type":"markdown","metadata":{},"source":["#### Read from a SQLite database\n"]},{"cell_type":"code","execution_count":73,"metadata":{"ExecuteTime":{"end_time":"2021-11-11T22:36:12.660988Z","start_time":"2021-11-11T22:36:12.232993Z"}},"outputs":[{"data":{"text/plain":["(3886, 51)"]},"execution_count":73,"metadata":{},"output_type":"execute_result"}],"source":["# Reading from sqlite database\n","sqlite_df = pd.DataFrame.spatial.from_featureclass(\n","    location=\"./sedf_data/cities/cities.sqlite/main.cities\")\n","sqlite_df.shape"]},{"cell_type":"code","execution_count":74,"metadata":{"ExecuteTime":{"end_time":"2021-11-11T22:36:12.681991Z","start_time":"2021-11-11T22:36:12.664992Z"}},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>OBJECTID</th>\n","      <th>age_10_14</th>\n","      <th>age_15_19</th>\n","      <th>age_20_24</th>\n","      <th>age_25_34</th>\n","      <th>age_35_44</th>\n","      <th>age_45_54</th>\n","      <th>age_55_64</th>\n","      <th>age_5_9</th>\n","      <th>age_65_74</th>\n","      <th>...</th>\n","      <th>placefips</th>\n","      <th>pop2010</th>\n","      <th>population</th>\n","      <th>pop_class</th>\n","      <th>renter_occ</th>\n","      <th>st</th>\n","      <th>stfips</th>\n","      <th>vacant</th>\n","      <th>white</th>\n","      <th>SHAPE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>1313</td>\n","      <td>1058</td>\n","      <td>734</td>\n","      <td>2031</td>\n","      <td>1767</td>\n","      <td>1446</td>\n","      <td>1136</td>\n","      <td>1503</td>\n","      <td>665</td>\n","      <td>...</td>\n","      <td>1601990</td>\n","      <td>13816</td>\n","      <td>15181</td>\n","      <td>6</td>\n","      <td>1271</td>\n","      <td>ID</td>\n","      <td>16</td>\n","      <td>271</td>\n","      <td>13002</td>\n","      <td>{\"x\": -12462673.7237, \"y\": 5384674.994099997, ...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>890</td>\n","      <td>817</td>\n","      <td>818</td>\n","      <td>1799</td>\n","      <td>1235</td>\n","      <td>1330</td>\n","      <td>1143</td>\n","      <td>1099</td>\n","      <td>721</td>\n","      <td>...</td>\n","      <td>1607840</td>\n","      <td>11899</td>\n","      <td>11946</td>\n","      <td>6</td>\n","      <td>1441</td>\n","      <td>ID</td>\n","      <td>16</td>\n","      <td>318</td>\n","      <td>9893</td>\n","      <td>{\"x\": -12506251.314, \"y\": 5341537.793499999, \"...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>2 rows × 51 columns</p>\n","</div>"],"text/plain":["   OBJECTID  age_10_14  age_15_19  age_20_24  age_25_34  age_35_44  age_45_54  \\\n","0         1       1313       1058        734       2031       1767       1446   \n","1         2        890        817        818       1799       1235       1330   \n","\n","   age_55_64  age_5_9  age_65_74  ...  placefips  pop2010  population  \\\n","0       1136     1503        665  ...    1601990    13816       15181   \n","1       1143     1099        721  ...    1607840    11899       11946   \n","\n","   pop_class  renter_occ  st  stfips  vacant  white  \\\n","0          6        1271  ID      16     271  13002   \n","1          6        1441  ID      16     318   9893   \n","\n","                                               SHAPE  \n","0  {\"x\": -12462673.7237, \"y\": 5384674.994099997, ...  \n","1  {\"x\": -12506251.314, \"y\": 5341537.793499999, \"...  \n","\n","[2 rows x 51 columns]"]},"execution_count":74,"metadata":{},"output_type":"execute_result"}],"source":["# Check head\n","sqlite_df.head(2)"]},{"cell_type":"code","execution_count":75,"metadata":{"ExecuteTime":{"end_time":"2021-11-11T22:36:12.697990Z","start_time":"2021-11-11T22:36:12.685991Z"}},"outputs":[{"data":{"text/plain":["['point']"]},"execution_count":75,"metadata":{},"output_type":"execute_result"}],"source":["# Check geometry type\n","sqlite_df.spatial.geometry_type"]},{"cell_type":"markdown","metadata":{},"source":["> The `spatial` namespace shows that a _Spatially enabled DataFrame_ has been created.\n"]},{"cell_type":"markdown","metadata":{},"source":["## Conclusion\n"]},{"cell_type":"markdown","metadata":{},"source":["In this guide, we explored how [**Spatially enabled DataFrame**](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#geoaccessor) (SeDF) can be used to read spatial data from various formats. We started by reading data from web feature layers and using the `query()` operation to optimize performance and results. We explored reading data from various local data sources such as file geodatabase and shapefile. Next, we explained how data with address or coordinate information, in a geopandas dataframe, or in feather format can be used to create a SeDF. We also discussed creating SeDF from non-spatial table data. Towards the end, we also discussed how SeDF can be created using data from lite and portable databases.\n","\n","In the next part of the guide series, you will learn about exporting data using [**Spatially enabled DataFrame**](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#geoaccessor).\n"]},{"cell_type":"markdown","metadata":{},"source":["<div class=\"alert alert-info\">\n","    <b>Note:</b> Given the importance and popularity of <i>Spatially enabled DataFrame</i>, we are revisiting our documentation for this topic. Our goal is to enhance the existing documentation to showcase the various capabilities of Spatially enabled DataFrame in detail with even more examples this time.\n","\n","Creating quality documentation is time-consuming and exhaustive, but we are committed to providing you with the best experience possible. With that in mind, we will be rolling out the revamped guides on this topic as different parts of a guide series (like the <a href=\"https://developers.arcgis.com/python/guide/part1-introduction-to-dataengineering/\">Data Engineering</a> or <a href=\"https://developers.arcgis.com/python/guide/part1-introduction-what-is-geometry/\">Geometry</a> guide series). This is \"part-2\" of the guide series for Spatially Enabled DataFrame. You will continue to see the existing documentation as we revamp it to add new parts. Stay tuned for more on this topic.\n","\n","</div>\n"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.10"},"toc":{"base_numbering":1,"nav_menu":{},"number_sections":true,"sideBar":true,"skip_h1_title":true,"title_cell":"Table of Contents","title_sidebar":"Contents","toc_cell":true,"toc_position":{"height":"calc(100% - 180px)","left":"10px","top":"150px","width":"360.188px"},"toc_section_display":true,"toc_window_display":true}},"nbformat":4,"nbformat_minor":4}
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Part-2 Data IO with SeDF - Accessing Data\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "toc": true
+   },
+   "source": [
+    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
+    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Introduction\" data-toc-modified-id=\"Introduction-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Introduction</a></span></li><li><span><a href=\"#Accessing-GIS-Data\" data-toc-modified-id=\"Accessing-GIS-Data-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Accessing GIS Data</a></span><ul class=\"toc-item\"><li><span><a href=\"#Read-in-Web-Feature-Layers\" data-toc-modified-id=\"Read-in-Web-Feature-Layers-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Read in Web Feature Layers</a></span><ul class=\"toc-item\"><li><span><a href=\"#Memory-usage-and-the-query()-operation\" data-toc-modified-id=\"Memory-usage-and-the-query()-operation-2.1.1\"><span class=\"toc-item-num\">2.1.1&nbsp;&nbsp;</span>Memory usage and the <code>query()</code> operation</a></span></li><li><span><a href=\"#Create-SeDF-from-FeatureSet\" data-toc-modified-id=\"Create-SeDF-from-FeatureSet-2.1.2\"><span class=\"toc-item-num\">2.1.2&nbsp;&nbsp;</span>Create SeDF from FeatureSet</a></span></li><li><span><a href=\"#Create-SeDF-from-FeatureCollection\" data-toc-modified-id=\"Create-SeDF-from-FeatureCollection-2.1.3\"><span class=\"toc-item-num\">2.1.3&nbsp;&nbsp;</span>Create SeDF from FeatureCollection</a></span></li></ul></li><li><span><a href=\"#Read-in-local-GIS-data\" data-toc-modified-id=\"Read-in-local-GIS-data-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Read in local GIS data</a></span><ul class=\"toc-item\"><li><span><a href=\"#Reading-a-Shapefile\" data-toc-modified-id=\"Reading-a-Shapefile-2.2.1\"><span class=\"toc-item-num\">2.2.1&nbsp;&nbsp;</span>Reading a Shapefile</a></span><ul class=\"toc-item\"><li><span><a href=\"#Shapefile-from-a-URL\" data-toc-modified-id=\"Shapefile-from-a-URL-2.2.1.1\"><span class=\"toc-item-num\">2.2.1.1&nbsp;&nbsp;</span>Shapefile from a URL</a></span></li></ul></li><li><span><a href=\"#Reading-a-Featureclass\" data-toc-modified-id=\"Reading-a-Featureclass-2.2.2\"><span class=\"toc-item-num\">2.2.2&nbsp;&nbsp;</span>Reading a Featureclass</a></span><ul class=\"toc-item\"><li><span><a href=\"#Subset-data-for-specific-fields\" data-toc-modified-id=\"Subset-data-for-specific-fields-2.2.2.1\"><span class=\"toc-item-num\">2.2.2.1&nbsp;&nbsp;</span>Subset data for specific fields</a></span></li><li><span><a href=\"#Subset-using-where_clause\" data-toc-modified-id=\"Subset-using-where_clause-2.2.2.2\"><span class=\"toc-item-num\">2.2.2.2&nbsp;&nbsp;</span>Subset using <code>where_clause</code></a></span></li><li><span><a href=\"#Subset-using-fields-and-where_clause\" data-toc-modified-id=\"Subset-using-fields-and-where_clause-2.2.2.3\"><span class=\"toc-item-num\">2.2.2.3&nbsp;&nbsp;</span>Subset using <code>fields</code> and <code>where_clause</code></a></span></li><li><span><a href=\"#Subset-using-sql_clause\" data-toc-modified-id=\"Subset-using-sql_clause-2.2.2.4\"><span class=\"toc-item-num\">2.2.2.4&nbsp;&nbsp;</span>Subset using <code>sql_clause</code></a></span></li><li><span><a href=\"#Using-spatial_filter\" data-toc-modified-id=\"Using-spatial_filter-2.2.2.5\"><span class=\"toc-item-num\">2.2.2.5&nbsp;&nbsp;</span>Using <code>spatial_filter</code></a></span></li></ul></li></ul></li><li><span><a href=\"#Read-in-DataFrame-with-Addresses\" data-toc-modified-id=\"Read-in-DataFrame-with-Addresses-2.3\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>Read in DataFrame with Addresses</a></span></li><li><span><a href=\"#Read-in-DataFrame-with-Lat/Long-Information\" data-toc-modified-id=\"Read-in-DataFrame-with-Lat/Long-Information-2.4\"><span class=\"toc-item-num\">2.4&nbsp;&nbsp;</span>Read in DataFrame with Lat/Long Information</a></span></li><li><span><a href=\"#Read-in-GeoPandas-DataFrame\" data-toc-modified-id=\"Read-in-GeoPandas-DataFrame-2.5\"><span class=\"toc-item-num\">2.5&nbsp;&nbsp;</span>Read in GeoPandas DataFrame</a></span><ul class=\"toc-item\"><li><span><a href=\"#Create-a-GeoDataFrame\" data-toc-modified-id=\"Create-a-GeoDataFrame-2.5.1\"><span class=\"toc-item-num\">2.5.1&nbsp;&nbsp;</span>Create a GeoDataFrame</a></span></li><li><span><a href=\"#Create-a-SeDF-from-GeoDataFrame\" data-toc-modified-id=\"Create-a-SeDF-from-GeoDataFrame-2.5.2\"><span class=\"toc-item-num\">2.5.2&nbsp;&nbsp;</span>Create a SeDF from GeoDataFrame</a></span></li></ul></li><li><span><a href=\"#Read-in-feather-format-data\" data-toc-modified-id=\"Read-in-feather-format-data-2.6\"><span class=\"toc-item-num\">2.6&nbsp;&nbsp;</span>Read in feather format data</a></span></li><li><span><a href=\"#Read-in-Non-spatial-Table-data\" data-toc-modified-id=\"Read-in-Non-spatial-Table-data-2.7\"><span class=\"toc-item-num\">2.7&nbsp;&nbsp;</span>Read in Non-spatial Table data</a></span><ul class=\"toc-item\"><li><span><a href=\"#Using-the-from_table()-method\" data-toc-modified-id=\"Using-the-from_table()-method-2.7.1\"><span class=\"toc-item-num\">2.7.1&nbsp;&nbsp;</span>Using the <code>from_table()</code> method</a></span><ul class=\"toc-item\"><li><span><a href=\"#Reading-a-csv-file\" data-toc-modified-id=\"Reading-a-csv-file-2.7.1.1\"><span class=\"toc-item-num\">2.7.1.1&nbsp;&nbsp;</span>Reading a csv file</a></span></li><li><span><a href=\"#Reading-table-from-a-File-Geodatabase\" data-toc-modified-id=\"Reading-table-from-a-File-Geodatabase-2.7.1.2\"><span class=\"toc-item-num\">2.7.1.2&nbsp;&nbsp;</span>Reading table from a File Geodatabase</a></span></li></ul></li><li><span><a href=\"#Using-the-from_layer()-method\" data-toc-modified-id=\"Using-the-from_layer()-method-2.7.2\"><span class=\"toc-item-num\">2.7.2&nbsp;&nbsp;</span>Using the <code>from_layer()</code> method</a></span></li></ul></li><li><span><a href=\"#Read-in-data-from-'lite-and-portable'-databases\" data-toc-modified-id=\"Read-in-data-from-'lite-and-portable'-databases-2.8\"><span class=\"toc-item-num\">2.8&nbsp;&nbsp;</span>Read in data from '<em>lite and portable</em>' databases</a></span><ul class=\"toc-item\"><li><span><a href=\"#Read-from-a-mobile-geodatabase\" data-toc-modified-id=\"Read-from-a-mobile-geodatabase-2.8.1\"><span class=\"toc-item-num\">2.8.1&nbsp;&nbsp;</span>Read from a mobile geodatabase</a></span></li><li><span><a href=\"#Read-from-a-SQLite-database\" data-toc-modified-id=\"Read-from-a-SQLite-database-2.8.2\"><span class=\"toc-item-num\">2.8.2&nbsp;&nbsp;</span>Read from a SQLite database</a></span></li></ul></li></ul></li><li><span><a href=\"#Conclusion\" data-toc-modified-id=\"Conclusion-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Conclusion</a></span></li></ul></div>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In _part-1_ of this guide series, we started with an introduction to the [**Spatially enabled DataFrame**](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#geoaccessor) (SeDF), the `spatial` and `geom` namespaces, and looked at a quick example of SeDF in action. In this part of the guide series, we will look at how GIS data can be accessed from various data formats using SeDF.\n",
+    "\n",
+    "GIS users work with different vector-based spatial data formats, like published layers on remote servers (web layers) and local data. The [**Spatially enabled DataFrame**](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#geoaccessor) allows the users to read, write, and manipulate spatial data by bringing the data in-memory.\n",
+    "\n",
+    "The _SeDF_ integrates with Esri's [**ArcPy** site-package](http://pro.arcgis.com/en/pro-app/arcpy/get-started/what-is-arcpy-.htm), as well as the open source [pyshp](https://github.com/GeospatialPython/pyshp/), [shapely](https://github.com/Toblerity/Shapely) and [fiona](https://github.com/Toblerity/Fiona) packages. This means that the _SeDF_ can use either [shapely](https://pypi.org/project/Shapely/) or [arcpy](https://www.esri.com/en-us/arcgis/products/arcgis-python-libraries/libraries/arcpy) geometry engines to provide you with options for easily working with geospatial data, regardless of your platform. The _SeDF_ transforms the data into the formats you desire, allowing you to use Python functionality to analyze and visualize geographic information.\n",
+    "\n",
+    "Data can be read and scripted to automate workflows and be visualized on maps in a [Jupyter notebooks](../using-the-jupyter-notebook-environment/). Let's explore the options available for accessing GIS data with the versatile _Spatially enabled DataFrame_.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data used in this guide is available as an [item](https://www.arcgis.com/home/item.html?id=c7140ae3d7ae4fd0817181461019aa75). We will start by importing some libraries and downloading and extracting the data needed for the analysis in this guide.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:51:37.257478Z",
+     "start_time": "2021-11-22T19:51:12.679381Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Import Libraries\n",
+    "import pandas as pd\n",
+    "from arcgis.features import GeoAccessor, GeoSeriesAccessor\n",
+    "from arcgis.gis import GIS\n",
+    "from IPython.display import display\n",
+    "import zipfile\n",
+    "import os\n",
+    "import shutil"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:51:38.872324Z",
+     "start_time": "2021-11-22T19:51:37.261479Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create a GIS connection\n",
+    "gis = GIS()\n",
+    "agol_gis = GIS(\"https://www.arcgis.com\", \"arcgis_python\", \"amazing_arcgis_123\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:51:38.980325Z",
+     "start_time": "2021-11-22T19:51:38.876325Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
+       "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
+       "                       <a href='https://www.arcgis.com/home/item.html?id=c7140ae3d7ae4fd0817181461019aa75' target='_blank'>\n",
+       "                        <img src='https://www.arcgis.com/sharing/rest//content/items/c7140ae3d7ae4fd0817181461019aa75/info/thumbnail/ago_downloaded.png' class=\"itemThumbnail\">\n",
+       "                       </a>\n",
+       "                    </div>\n",
+       "\n",
+       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
+       "                        <a href='https://www.arcgis.com/home/item.html?id=c7140ae3d7ae4fd0817181461019aa75' target='_blank'><b>sedf_guide_data</b>\n",
+       "                        </a>\n",
+       "                        <br/>Data for Spatially enabled DataFrame Guides<img src='https://www.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/datafiles16.png' style=\"vertical-align:middle;\">Shapefile by api_data_owner\n",
+       "                        <br/>Last Modified: November 11, 2021\n",
+       "                        <br/>0 comments, 4 views\n",
+       "                    </div>\n",
+       "                </div>\n",
+       "                "
+      ],
+      "text/plain": [
+       "<Item title:\"sedf_guide_data\" type:Shapefile owner:api_data_owner>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get the data item\n",
+    "data_item = gis.content.get('c7140ae3d7ae4fd0817181461019aa75')\n",
+    "data_item"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The cell below downloads and extracts the data from the data item to your machine.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:51:45.305934Z",
+     "start_time": "2021-11-22T19:51:42.206937Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Removed existing data directory\n",
+      "Dataset unzipped at: sedf_data\\cities\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Download and extract the data\n",
+    "def unzip_data():\n",
+    "    \"\"\"\n",
+    "    This function:\n",
+    "    - creates a directory `sedf_data` to download the data from the item\n",
+    "    - downloads the item as `sedf_guide_data.zip` file in the sedf_data directory\n",
+    "    - unzips and extracts the data to '.\\sedf_data\\cities'.\n",
+    "    \"\"\"\n",
+    "    try:\n",
+    "\n",
+    "        # path to downloaded data folder\n",
+    "        data_dir = os.path.join(os.getcwd(), 'sedf_data')\n",
+    "\n",
+    "        # remove existing cities directory if exists\n",
+    "        if os.path.isdir(data_dir):\n",
+    "            shutil.rmtree(data_dir)\n",
+    "            print(f'Removed existing data directory')\n",
+    "        else:\n",
+    "            os.makedirs(data_dir)\n",
+    "\n",
+    "        data_item.download(data_dir)    # download the data item\n",
+    "        # path to zipped file inside data folder\n",
+    "        zipped_file_path = os.path.join(data_dir, 'sedf_guide_data.zip')\n",
+    "\n",
+    "        # unzip the data\n",
+    "        zip_ref = zipfile.ZipFile(zipped_file_path, 'r')\n",
+    "        zip_ref.extractall(data_dir)\n",
+    "        zip_ref.close()\n",
+    "\n",
+    "        # path to new cities directory\n",
+    "        cities_dir = os.path.join(data_dir, 'cities')\n",
+    "        print(f'Dataset unzipped at: {os.path.relpath(cities_dir)}')\n",
+    "\n",
+    "    except Exception as e:\n",
+    "        print(f'Error unzipping file: {e}')\n",
+    "\n",
+    "\n",
+    "# Extract data\n",
+    "unzip_data()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Accessing GIS Data\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The [**Spatially enabled DataFrame**](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#geoaccessor) reads from many **sources**, including [Feature layers](https://doc.arcgis.com/en/arcgis-online/share-maps/hosted-web-layers.htm), [Feature classes](http://desktop.arcgis.com/en/arcmap/latest/manage-data/feature-classes/a-quick-tour-of-feature-classes.htm), [Shapefiles](http://desktop.arcgis.com/en/arcmap/latest/manage-data/shapefiles/what-is-a-shapefile.htm), Pandas [DataFrames](http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe) and more. Let's dive into the details of accessing GIS data from various sources.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Read in Web Feature Layers\n",
+    "\n",
+    "[Feature layers](https://doc.arcgis.com/en/arcgis-online/share-maps/hosted-web-layers.htm) hosted on [**ArcGIS Online**](https://www.arcgis.com) or [**ArcGIS Enterprise**](http://enterprise.arcgis.com/en/) can be easily read into a Spatially enabled DataFrame using the [`from_layer()`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?highlight=from_layer#arcgis.features.GeoAccessor.from_layer) method.\n",
+    "\n",
+    "The example below shows how the [`get()`](https://developers.arcgis.com/python/api-reference/arcgis.gis.toc.html?highlight=gis%20content%20get#arcgis.gis.ContentManager.get) method can be used to retrieve an ArcGIS Online [`item`](https://developers.arcgis.com/python/api-reference/arcgis.gis.toc.html?highlight=gis%20content%20get#item) and how the [`layers`](https://developers.arcgis.com/python/api-reference/arcgis.gis.toc.html#layer) property of an `item` can be used to access the data.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:51:52.373464Z",
+     "start_time": "2021-11-22T19:51:51.851896Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
+       "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
+       "                       <a href='https://www.arcgis.com/home/item.html?id=85d0ca4ea1ca4b9abf0c51b9bd34de2e' target='_blank'>\n",
+       "                        <img src='https://www.arcgis.com/sharing/rest//content/items/85d0ca4ea1ca4b9abf0c51b9bd34de2e/info/thumbnail/thumbnail1578438561290.jpeg' class=\"itemThumbnail\">\n",
+       "                       </a>\n",
+       "                    </div>\n",
+       "\n",
+       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
+       "                        <a href='https://www.arcgis.com/home/item.html?id=85d0ca4ea1ca4b9abf0c51b9bd34de2e' target='_blank'><b>USA Major Cities</b>\n",
+       "                        </a>\n",
+       "                        <br/>This layer presents the locations of cities within the United States with populations of approximately 10,000 or greater, all state capitals, and the national capital.<img src='https://www.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by esri_dm\n",
+       "                        <br/>Last Modified: May 19, 2020\n",
+       "                        <br/>1 comments, 33,841,105 views\n",
+       "                    </div>\n",
+       "                </div>\n",
+       "                "
+      ],
+      "text/plain": [
+       "<Item title:\"USA Major Cities\" type:Feature Layer Collection owner:esri_dm>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gis = GIS()\n",
+    "item = gis.content.search(\n",
+    "    \"USA Major Cities\", item_type=\"Feature layer\", outside_org=True)[0]\n",
+    "item"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:51:56.288612Z",
+     "start_time": "2021-11-22T19:51:52.376465Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3886, 50)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Obtain the first feature layer from the item\n",
+    "flayer = item.layers[0]\n",
+    "\n",
+    "# Use the `from_layer` static method in the 'spatial' namespace on the Pandas' DataFrame\n",
+    "sdf = pd.DataFrame.spatial.from_layer(flayer)\n",
+    "\n",
+    "# Check shape\n",
+    "sdf.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:51:56.317613Z",
+     "start_time": "2021-11-22T19:51:56.291617Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>AGE_10_14</th>\n",
+       "      <th>AGE_15_19</th>\n",
+       "      <th>AGE_20_24</th>\n",
+       "      <th>AGE_25_34</th>\n",
+       "      <th>AGE_35_44</th>\n",
+       "      <th>AGE_45_54</th>\n",
+       "      <th>AGE_55_64</th>\n",
+       "      <th>AGE_5_9</th>\n",
+       "      <th>AGE_65_74</th>\n",
+       "      <th>AGE_75_84</th>\n",
+       "      <th>...</th>\n",
+       "      <th>PLACEFIPS</th>\n",
+       "      <th>POP2010</th>\n",
+       "      <th>POPULATION</th>\n",
+       "      <th>POP_CLASS</th>\n",
+       "      <th>RENTER_OCC</th>\n",
+       "      <th>SHAPE</th>\n",
+       "      <th>ST</th>\n",
+       "      <th>STFIPS</th>\n",
+       "      <th>VACANT</th>\n",
+       "      <th>WHITE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1313</td>\n",
+       "      <td>1058</td>\n",
+       "      <td>734</td>\n",
+       "      <td>2031</td>\n",
+       "      <td>1767</td>\n",
+       "      <td>1446</td>\n",
+       "      <td>1136</td>\n",
+       "      <td>1503</td>\n",
+       "      <td>665</td>\n",
+       "      <td>486</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1601990</td>\n",
+       "      <td>13816</td>\n",
+       "      <td>15181</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1271</td>\n",
+       "      <td>{\"x\": -12462673.723706165, \"y\": 5384674.994080...</td>\n",
+       "      <td>ID</td>\n",
+       "      <td>16</td>\n",
+       "      <td>271</td>\n",
+       "      <td>13002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>890</td>\n",
+       "      <td>817</td>\n",
+       "      <td>818</td>\n",
+       "      <td>1799</td>\n",
+       "      <td>1235</td>\n",
+       "      <td>1330</td>\n",
+       "      <td>1143</td>\n",
+       "      <td>1099</td>\n",
+       "      <td>721</td>\n",
+       "      <td>579</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1607840</td>\n",
+       "      <td>11899</td>\n",
+       "      <td>11946</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1441</td>\n",
+       "      <td>{\"x\": -12506251.313993266, \"y\": 5341537.793529...</td>\n",
+       "      <td>ID</td>\n",
+       "      <td>16</td>\n",
+       "      <td>318</td>\n",
+       "      <td>9893</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>12750</td>\n",
+       "      <td>13959</td>\n",
+       "      <td>16966</td>\n",
+       "      <td>32135</td>\n",
+       "      <td>27048</td>\n",
+       "      <td>29595</td>\n",
+       "      <td>24177</td>\n",
+       "      <td>12933</td>\n",
+       "      <td>12176</td>\n",
+       "      <td>7087</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1608830</td>\n",
+       "      <td>205671</td>\n",
+       "      <td>225405</td>\n",
+       "      <td>8</td>\n",
+       "      <td>33359</td>\n",
+       "      <td>{\"x\": -12938676.6836459, \"y\": 5403597.04949123...</td>\n",
+       "      <td>ID</td>\n",
+       "      <td>16</td>\n",
+       "      <td>6996</td>\n",
+       "      <td>182991</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>790</td>\n",
+       "      <td>768</td>\n",
+       "      <td>699</td>\n",
+       "      <td>1445</td>\n",
+       "      <td>1136</td>\n",
+       "      <td>1134</td>\n",
+       "      <td>935</td>\n",
+       "      <td>959</td>\n",
+       "      <td>679</td>\n",
+       "      <td>464</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1611260</td>\n",
+       "      <td>10345</td>\n",
+       "      <td>10727</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1461</td>\n",
+       "      <td>{\"x\": -12667411.402393516, \"y\": 5241722.820606...</td>\n",
+       "      <td>ID</td>\n",
+       "      <td>16</td>\n",
+       "      <td>241</td>\n",
+       "      <td>7984</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3803</td>\n",
+       "      <td>3779</td>\n",
+       "      <td>3687</td>\n",
+       "      <td>7571</td>\n",
+       "      <td>5559</td>\n",
+       "      <td>4744</td>\n",
+       "      <td>3624</td>\n",
+       "      <td>4397</td>\n",
+       "      <td>2296</td>\n",
+       "      <td>1222</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1612250</td>\n",
+       "      <td>46237</td>\n",
+       "      <td>53942</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5196</td>\n",
+       "      <td>{\"x\": -12989383.674504515, \"y\": 5413226.487333...</td>\n",
+       "      <td>ID</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1428</td>\n",
+       "      <td>35856</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 50 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   AGE_10_14  AGE_15_19  AGE_20_24  AGE_25_34  AGE_35_44  AGE_45_54  \\\n",
+       "0       1313       1058        734       2031       1767       1446   \n",
+       "1        890        817        818       1799       1235       1330   \n",
+       "2      12750      13959      16966      32135      27048      29595   \n",
+       "3        790        768        699       1445       1136       1134   \n",
+       "4       3803       3779       3687       7571       5559       4744   \n",
+       "\n",
+       "   AGE_55_64  AGE_5_9  AGE_65_74  AGE_75_84  ...  PLACEFIPS  POP2010  \\\n",
+       "0       1136     1503        665        486  ...    1601990    13816   \n",
+       "1       1143     1099        721        579  ...    1607840    11899   \n",
+       "2      24177    12933      12176       7087  ...    1608830   205671   \n",
+       "3        935      959        679        464  ...    1611260    10345   \n",
+       "4       3624     4397       2296       1222  ...    1612250    46237   \n",
+       "\n",
+       "   POPULATION  POP_CLASS  RENTER_OCC  \\\n",
+       "0       15181          6        1271   \n",
+       "1       11946          6        1441   \n",
+       "2      225405          8       33359   \n",
+       "3       10727          6        1461   \n",
+       "4       53942          7        5196   \n",
+       "\n",
+       "                                               SHAPE  ST STFIPS VACANT   WHITE  \n",
+       "0  {\"x\": -12462673.723706165, \"y\": 5384674.994080...  ID     16    271   13002  \n",
+       "1  {\"x\": -12506251.313993266, \"y\": 5341537.793529...  ID     16    318    9893  \n",
+       "2  {\"x\": -12938676.6836459, \"y\": 5403597.04949123...  ID     16   6996  182991  \n",
+       "3  {\"x\": -12667411.402393516, \"y\": 5241722.820606...  ID     16    241    7984  \n",
+       "4  {\"x\": -12989383.674504515, \"y\": 5413226.487333...  ID     16   1428   35856  \n",
+       "\n",
+       "[5 rows x 50 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check first few records\n",
+    "sdf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:51:56.324128Z",
+     "start_time": "2021-11-22T19:51:56.320614Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.frame.DataFrame"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check type of sdf\n",
+    "type(sdf)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:51:56.343129Z",
+     "start_time": "2021-11-22T19:51:56.326129Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['point']"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Access spatial namespace\n",
+    "sdf.spatial.geometry_type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> We can see that the dataset has 3886 records and 50 columns. Inspecting the `type` of `sdf` object and accessing the `spatial` namespace shows us that a _Spatially enabled DataFrame_ has been created from all the data in the layer.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Memory usage and the `query()` operation\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The [**`from_layer()`**](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?highlight=from_layer#arcgis.features.GeoAccessor.from_layer) method will attempt to read all the data from the layer into the memory. This approach works when you are dealing with small datasets. However, when it comes to large datasets, it becomes imperative to use the memory efficiently and query for only what is necessary.\n",
+    "\n",
+    "Let's take a look at the memory usage of the existing _SeDF_ using the [`memory_usage()`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.memory_usage.html) method from Pandas.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:51:56.731373Z",
+     "start_time": "2021-11-22T19:51:56.722376Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of data: (3886, 50)\n",
+      "Memory used: 1.48 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check memory usage of current sdf\n",
+    "mem_used = sdf.memory_usage().sum() / (1024**2)  # converting to megabytes\n",
+    "print(f'Shape of data: {sdf.shape}')\n",
+    "print(f'Memory used: {round(mem_used, 2)} MB')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> We can see that a `SeDF` created using the `from_layer()` method reads all the data into the memory. So, the `sdf` object has 3886 records and 50 columns, and uses 1.48MB memory.\n",
+    "\n",
+    "But what if we only needed a small amount of data for our analysis and did not need to bring everything from the layer into the memory? Good question... let's see how we can achieve that.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The [**`query()`**](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#arcgis.features.FeatureLayer.query) method is a powerful operation that allows you to use [SQL](https://en.wikipedia.org/wiki/SQL) like queries to return only a subset of records. **Since the processing is performed on the server, this operation is not restricted by the capacity of your computer.**\n",
+    "\n",
+    "The method returns a [`FeatureSet`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?highlight=query#arcgis.features.FeatureSet) object; however, the return type can be changed to a _Spatially enabled DataFrame_ object by specifying the parameter `as_df=True`.\n",
+    "\n",
+    "Let's subset the data using `query()`, create a new _SeDF_, and check the memory usage. We'll use the `AGE_45_54` column to query the layer and get a subset of records.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:51:58.344586Z",
+     "start_time": "2021-11-22T19:51:57.814005Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(316, 50)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Filter feature layer records with a query.\n",
+    "sub_sdf = flayer.query(where=\"AGE_45_54 < 1500\", as_df=True)\n",
+    "sub_sdf.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:51:58.354587Z",
+     "start_time": "2021-11-22T19:51:58.346589Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Memory used is: 0.12 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check memory usage of current sdf\n",
+    "mem_used = sub_sdf.memory_usage().sum() / (1024**2)  # converting to megabytes\n",
+    "print(f'Memory used is: {round(mem_used, 2)} MB')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> Now that we are only querying for records where `AGE_45_54 < 1500`, the result is a smaller DataFrame with 316 records and 50 columns. Since the processing is performed on the server side, only a subset of data is being saved in the memory reducing usage from **1.48 MB to 0.12 MB**.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The [`query()`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#arcgis.features.FeatureLayer.query) method allows you to specify a number of optional parameters that may further refine and transform the results. One such key parameter is `out_fields`. With `out_fields`, you can subset your data by specifying a list of field names to return.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:51:59.855435Z",
+     "start_time": "2021-11-22T19:51:59.601357Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(316, 6)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Filter feature layer with where and out_fields\n",
+    "out_fields = ['NAME', 'ST', 'POP_CLASS', 'AGE_45_54']\n",
+    "sub_sdf2 = flayer.query(where=\"AGE_45_54 < 1500\",\n",
+    "                        out_fields=out_fields,\n",
+    "                        as_df=True)\n",
+    "sub_sdf2.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:51:59.868435Z",
+     "start_time": "2021-11-22T19:51:59.858435Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>FID</th>\n",
+       "      <th>NAME</th>\n",
+       "      <th>ST</th>\n",
+       "      <th>POP_CLASS</th>\n",
+       "      <th>AGE_45_54</th>\n",
+       "      <th>SHAPE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Ammon</td>\n",
+       "      <td>ID</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1446</td>\n",
+       "      <td>{\"x\": -12462673.723706165, \"y\": 5384674.994080...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Blackfoot</td>\n",
+       "      <td>ID</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1330</td>\n",
+       "      <td>{\"x\": -12506251.313993266, \"y\": 5341537.793529...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Burley</td>\n",
+       "      <td>ID</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1134</td>\n",
+       "      <td>{\"x\": -12667411.402393516, \"y\": 5241722.820606...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>6</td>\n",
+       "      <td>Chubbuck</td>\n",
+       "      <td>ID</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1494</td>\n",
+       "      <td>{\"x\": -12520053.904151963, \"y\": 5300220.333409...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>12</td>\n",
+       "      <td>Jerome</td>\n",
+       "      <td>ID</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1155</td>\n",
+       "      <td>{\"x\": -12747828.64784961, \"y\": 5269214.8197742...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   FID       NAME  ST  POP_CLASS  AGE_45_54  \\\n",
+       "0    1      Ammon  ID          6       1446   \n",
+       "1    2  Blackfoot  ID          6       1330   \n",
+       "2    4     Burley  ID          6       1134   \n",
+       "3    6   Chubbuck  ID          6       1494   \n",
+       "4   12     Jerome  ID          6       1155   \n",
+       "\n",
+       "                                               SHAPE  \n",
+       "0  {\"x\": -12462673.723706165, \"y\": 5384674.994080...  \n",
+       "1  {\"x\": -12506251.313993266, \"y\": 5341537.793529...  \n",
+       "2  {\"x\": -12667411.402393516, \"y\": 5241722.820606...  \n",
+       "3  {\"x\": -12520053.904151963, \"y\": 5300220.333409...  \n",
+       "4  {\"x\": -12747828.64784961, \"y\": 5269214.8197742...  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check head\n",
+    "sub_sdf2.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:52:01.302923Z",
+     "start_time": "2021-11-22T19:52:01.295930Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Memory used is: 0.01 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check memory usage of current sdf\n",
+    "mem_used = sub_sdf2.memory_usage().sum() / (1024**2)  # converting to megabytes\n",
+    "print(f'Memory used is: {round(mem_used, 2)} MB')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> Using `out_fields`, we have further reduced memory usage by subsetting the data and bringing only necessary information into the memory.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Create SeDF from FeatureSet\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As mentioned earlier, the [**`query()`**](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#arcgis.features.FeatureLayer.query) method returns a [`FeatureSet`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?highlight=query#arcgis.features.FeatureSet) object. The `FeatureSet` object contains useful information about the data that can be accessed through its various properties.\n",
+    "\n",
+    "Let's use the `AGE_45_54` column to query the layer to get the result as a `FeatureSet` and check some its properties.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:52:03.250472Z",
+     "start_time": "2021-11-22T19:52:02.945753Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Filter feature layer to return a feature set.\n",
+    "fset = flayer.query(where=\"AGE_45_54 < 1500\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:52:03.259475Z",
+     "start_time": "2021-11-22T19:52:03.253475Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "arcgis.features.feature.FeatureSet"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check type\n",
+    "type(fset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:52:03.411404Z",
+     "start_time": "2021-11-22T19:52:03.406402Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "316"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check length\n",
+    "len(fset.features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:52:03.633193Z",
+     "start_time": "2021-11-22T19:52:03.627193Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'x': -12462673.723706165,\n",
+       " 'y': 5384674.994080178,\n",
+       " 'spatialReference': {'wkid': 102100, 'latestWkid': 3857}}"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check geometry of a feature in the featureset\n",
+    "fset.features[0].geometry"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `fields` property of a `FeatureSet` returns a list containing information about each column recorded as a dictionary. Let's use the `fields` property to access information about the first column.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:52:04.088356Z",
+     "start_time": "2021-11-22T19:52:04.083359Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'name': 'FID',\n",
+       " 'type': 'esriFieldTypeOID',\n",
+       " 'alias': 'FID',\n",
+       " 'sqlType': 'sqlTypeInteger',\n",
+       " 'domain': None,\n",
+       " 'defaultValue': None}"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check details of a column in the feature set\n",
+    "fset.fields[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's get the names of the columns in the data.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:52:04.548802Z",
+     "start_time": "2021-11-22T19:52:04.542798Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['FID', 'NAME', 'CLASS', 'ST', 'STFIPS']"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get column names\n",
+    "f_names = [f['name'] for f in fset.fields]\n",
+    "f_names[:5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's create a _Spatially enabled DataFrame_ from a `FeatureSet` using the [`.sdf`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?highlight=sdf#arcgis.features.FeatureSet.sdf) property.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:52:05.026469Z",
+     "start_time": "2021-11-22T19:52:05.006466Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(316, 50)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create SeDF from FeatureSet\n",
+    "fset_df = fset.sdf\n",
+    "fset_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:52:05.276147Z",
+     "start_time": "2021-11-22T19:52:05.258149Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>FID</th>\n",
+       "      <th>NAME</th>\n",
+       "      <th>CLASS</th>\n",
+       "      <th>ST</th>\n",
+       "      <th>STFIPS</th>\n",
+       "      <th>PLACEFIPS</th>\n",
+       "      <th>CAPITAL</th>\n",
+       "      <th>POP_CLASS</th>\n",
+       "      <th>POPULATION</th>\n",
+       "      <th>POP2010</th>\n",
+       "      <th>...</th>\n",
+       "      <th>MARHH_NO_C</th>\n",
+       "      <th>MHH_CHILD</th>\n",
+       "      <th>FHH_CHILD</th>\n",
+       "      <th>FAMILIES</th>\n",
+       "      <th>AVE_FAM_SZ</th>\n",
+       "      <th>HSE_UNITS</th>\n",
+       "      <th>VACANT</th>\n",
+       "      <th>OWNER_OCC</th>\n",
+       "      <th>RENTER_OCC</th>\n",
+       "      <th>SHAPE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Ammon</td>\n",
+       "      <td>city</td>\n",
+       "      <td>ID</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1601990</td>\n",
+       "      <td></td>\n",
+       "      <td>6</td>\n",
+       "      <td>15181</td>\n",
+       "      <td>13816</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1131</td>\n",
+       "      <td>106</td>\n",
+       "      <td>335</td>\n",
+       "      <td>3352</td>\n",
+       "      <td>3.61</td>\n",
+       "      <td>4747</td>\n",
+       "      <td>271</td>\n",
+       "      <td>3205</td>\n",
+       "      <td>1271</td>\n",
+       "      <td>{\"x\": -12462673.723706165, \"y\": 5384674.994080...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Blackfoot</td>\n",
+       "      <td>city</td>\n",
+       "      <td>ID</td>\n",
+       "      <td>16</td>\n",
+       "      <td>1607840</td>\n",
+       "      <td></td>\n",
+       "      <td>6</td>\n",
+       "      <td>11946</td>\n",
+       "      <td>11899</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1081</td>\n",
+       "      <td>174</td>\n",
+       "      <td>381</td>\n",
+       "      <td>2958</td>\n",
+       "      <td>3.31</td>\n",
+       "      <td>4547</td>\n",
+       "      <td>318</td>\n",
+       "      <td>2788</td>\n",
+       "      <td>1441</td>\n",
+       "      <td>{\"x\": -12506251.313993266, \"y\": 5341537.793529...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 50 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   FID       NAME CLASS  ST STFIPS PLACEFIPS CAPITAL  POP_CLASS  POPULATION  \\\n",
+       "0    1      Ammon  city  ID     16   1601990                  6       15181   \n",
+       "1    2  Blackfoot  city  ID     16   1607840                  6       11946   \n",
+       "\n",
+       "   POP2010  ...  MARHH_NO_C  MHH_CHILD  FHH_CHILD  FAMILIES  AVE_FAM_SZ  \\\n",
+       "0    13816  ...        1131        106        335      3352        3.61   \n",
+       "1    11899  ...        1081        174        381      2958        3.31   \n",
+       "\n",
+       "   HSE_UNITS  VACANT  OWNER_OCC  RENTER_OCC  \\\n",
+       "0       4747     271       3205        1271   \n",
+       "1       4547     318       2788        1441   \n",
+       "\n",
+       "                                               SHAPE  \n",
+       "0  {\"x\": -12462673.723706165, \"y\": 5384674.994080...  \n",
+       "1  {\"x\": -12506251.313993266, \"y\": 5341537.793529...  \n",
+       "\n",
+       "[2 rows x 50 columns]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check head\n",
+    "fset_df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:52:05.502653Z",
+     "start_time": "2021-11-22T19:52:05.496653Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['point']"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check geometry type\n",
+    "fset_df.spatial.geometry_type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> The `spatial` namespace shows that a _Spatially enabled DataFrame_ has been created from a `FeatureSet`.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Create SeDF from FeatureCollection\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Tools within the ArcGIS API for Python often return a [FeatureCollection](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#featurecollection) object as a result of some analysis. A `FeatureCollection` is an in-memory collection of [Feature](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#arcgis.features.Feature) objects with rendering information. Similar to feature layers, feature collections can also be used to store features. With a feature collection, a service is not created to serve out the feature data.\n",
+    "\n",
+    "Let's create a `SeDF` from a FeatureCollection. Here, we:\n",
+    "\n",
+    "- Import the [Major Ports](https://www.arcgis.com/home/item.html?id=405963eaea24428c9db236ec289760eb) feature layer.\n",
+    "- Create 5 mile buffers using [`create_buffers()`](https://developers.arcgis.com/python/api-reference/arcgis.features.use_proximity.html#create-buffers) tool resulting in a FeatureCollection.\n",
+    "- Using the [query()](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#arcgis.features.FeatureLayer.query) method on a FeatureCollection returns a [FeatureSet](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?highlight=query#arcgis.features.FeatureSet) object. We will create a `SeDF` from the buffered FeatureCollection using the the [`.sdf`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?highlight=sdf#arcgis.features.FeatureSet.sdf) property of a FeatureSet object returned from `query()`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:52:10.071650Z",
+     "start_time": "2021-11-22T19:52:09.984656Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
+       "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
+       "                       <a href='https://www.arcgis.com/home/item.html?id=405963eaea24428c9db236ec289760eb' target='_blank'>\n",
+       "                        <img src='https://www.arcgis.com/sharing/rest//content/items/405963eaea24428c9db236ec289760eb/info/thumbnail/thumbnail1629922346175.png' class=\"itemThumbnail\">\n",
+       "                       </a>\n",
+       "                    </div>\n",
+       "\n",
+       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
+       "                        <a href='https://www.arcgis.com/home/item.html?id=405963eaea24428c9db236ec289760eb' target='_blank'><b>Major Ports</b>\n",
+       "                        </a>\n",
+       "                        <br/>This feature layer,  utilizing data from the U.S. Department of Transportation, depicts Major Ports in the United States by total tonnage.<img src='https://www.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\">Feature Layer Collection by Federal_User_Community\n",
+       "                        <br/>Last Modified: October 27, 2021\n",
+       "                        <br/>0 comments, 157,223 views\n",
+       "                    </div>\n",
+       "                </div>\n",
+       "                "
+      ],
+      "text/plain": [
+       "<Item title:\"Major Ports\" type:Feature Layer Collection owner:Federal_User_Community>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get the ports item\n",
+    "ports_item = gis.content.get(\"405963eaea24428c9db236ec289760eb\")\n",
+    "ports_item"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:52:10.203674Z",
+     "start_time": "2021-11-22T19:52:10.197678Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<FeatureLayer url:\"https://geo.dot.gov/server/rest/services/NTAD/Ports_Major/MapServer/0\">"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get the ports layer\n",
+    "ports_lyr = ports_item.layers[0]\n",
+    "ports_lyr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:52:40.150295Z",
+     "start_time": "2021-11-22T19:52:10.430626Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create buffers\n",
+    "from arcgis.features.use_proximity import create_buffers\n",
+    "ports_buffer50 = create_buffers(\n",
+    "    ports_lyr, distances=[5], units='Miles', gis=agol_gis)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:52:40.159300Z",
+     "start_time": "2021-11-22T19:52:40.154296Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "arcgis.features.feature.FeatureCollection"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check type of result from the analysis\n",
+    "type(ports_buffer50)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> The `create_buffers()` tool resulted in a `FeatureCollection`.\n",
+    "\n",
+    "Now, we will create a `SeDF` from the `FeatureCollection` object.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:52:40.197297Z",
+     "start_time": "2021-11-22T19:52:40.162296Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>OBJECTID_1</th>\n",
+       "      <th>OBJECTID</th>\n",
+       "      <th>ID</th>\n",
+       "      <th>PORT</th>\n",
+       "      <th>PORT_NAME</th>\n",
+       "      <th>GRAND_TOTA</th>\n",
+       "      <th>FOREIGN_TO</th>\n",
+       "      <th>IMPORTS</th>\n",
+       "      <th>EXPORTS</th>\n",
+       "      <th>DOMESTIC</th>\n",
+       "      <th>BUFF_DIST</th>\n",
+       "      <th>ORIG_FID</th>\n",
+       "      <th>AnalysisArea</th>\n",
+       "      <th>SHAPE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>124</td>\n",
+       "      <td>C4947</td>\n",
+       "      <td>Unalaska Island, AK</td>\n",
+       "      <td>1652281</td>\n",
+       "      <td>1236829</td>\n",
+       "      <td>426251</td>\n",
+       "      <td>810578</td>\n",
+       "      <td>415452</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>78.528402</td>\n",
+       "      <td>{\"rings\": [[[-18806114.3995, 7138385.537799999...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>85</td>\n",
+       "      <td>C4410</td>\n",
+       "      <td>Kahului, Maui, HI</td>\n",
+       "      <td>3615449</td>\n",
+       "      <td>20391</td>\n",
+       "      <td>20391</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3595058</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>78.528402</td>\n",
+       "      <td>{\"rings\": [[[-17418472.419, 2388455.4312999994...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   OBJECTID_1  OBJECTID   ID   PORT            PORT_NAME  GRAND_TOTA  \\\n",
+       "0           1         1  124  C4947  Unalaska Island, AK     1652281   \n",
+       "1           2         2   85  C4410    Kahului, Maui, HI     3615449   \n",
+       "\n",
+       "   FOREIGN_TO  IMPORTS  EXPORTS  DOMESTIC  BUFF_DIST  ORIG_FID  AnalysisArea  \\\n",
+       "0     1236829   426251   810578    415452          5         1     78.528402   \n",
+       "1       20391    20391        0   3595058          5         2     78.528402   \n",
+       "\n",
+       "                                               SHAPE  \n",
+       "0  {\"rings\": [[[-18806114.3995, 7138385.537799999...  \n",
+       "1  {\"rings\": [[[-17418472.419, 2388455.4312999994...  "
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create SeDF\n",
+    "sedf_fc = ports_buffer50.query().sdf\n",
+    "sedf_fc.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:52:40.205296Z",
+     "start_time": "2021-11-22T19:52:40.199296Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['polygon']"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check geometry type\n",
+    "sedf_fc.spatial.geometry_type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> The `spatial` namespace shows that a _Spatially enabled DataFrame_ has been created from a `FeatureCollection`.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Read in local GIS data\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Local geospatial data, such as [`Feature classes`](http://desktop.arcgis.com/en/arcmap/latest/manage-data/feature-classes/a-quick-tour-of-feature-classes.htm) and [`shapefiles`](http://desktop.arcgis.com/en/arcmap/latest/manage-data/shapefiles/what-is-a-shapefile.htm) can be easily accessed using the [**Spatially enabled DataFrame**](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#geoaccessor). The [`from_featureclass()`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?arcgis.features.GeoAccessor.from_featureclass#arcgis.features.GeoAccessor.from_featureclass) method can be used to access local data. Let's look at some examples.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Reading a Shapefile\n",
+    "\n",
+    "A locally stored [`shapefile`](http://desktop.arcgis.com/en/arcmap/latest/manage-data/shapefiles/what-is-a-shapefile.htm) can be accessed by passing the location of the file in the [`from_featureclass()`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?arcgis.features.GeoAccessor.from_featureclass#arcgis.features.GeoAccessor.from_featureclass) method.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\">\n",
+    "    <b>Note:</b> In the absence of <code>arcpy</code>, the <a href=\"https://pypi.org/project/pyshp/\">PyShp</a> package must be present in your current conda environment in order to read shapefiles. To check if PyShp is present, you can run the following in a cell:\n",
+    "         \n",
+    "    !conda list pyshp\n",
+    "        \n",
+    "To install PyShp, you can run the following in a cell:\n",
+    "    \n",
+    "    !conda install pyshp\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:52:41.031721Z",
+     "start_time": "2021-11-22T19:52:40.207294Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3886, 51)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Reading from shape file\n",
+    "shp_df = pd.DataFrame.spatial.from_featureclass(\n",
+    "    location=\"./sedf_data/cities/cities.shp\")\n",
+    "shp_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:52:41.058722Z",
+     "start_time": "2021-11-22T19:52:41.034722Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['point']"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shp_df.spatial.geometry_type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> The `spatial` namespace shows that a _Spatially enabled DataFrame_ has been created from the `shapefile` stored locally.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Shapefile from a URL\n",
+    "\n",
+    "The url of a zipped `shapefile` can be used to create a `SeDF` by passing the url as location in the `from_featureclass()` method. The image below shows how the operation can be performed.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\">\n",
+    "    <b>Note:</b> This operation requires <code>PyShp</code> to be available in the environment.\n",
+    "\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Reading a Featureclass\n",
+    "\n",
+    "A [featureclass](http://desktop.arcgis.com/en/arcmap/latest/manage-data/feature-classes/a-quick-tour-of-feature-classes.htm) can be accessed from a File Geodatabase by passing its location in the [`from_featureclass()`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?arcgis.features.GeoAccessor.from_featureclass#arcgis.features.GeoAccessor.from_featureclass) method.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\">\n",
+    "    <b>Note:</b> In the absence of <code>arcpy</code>, the <a href=\"https://pypi.org/project/Fiona/\">Fiona</a> package must be present in your current conda environment in order to read a featureclass.\n",
+    "    To check if Fiona is present, you can run the following in a cell:\n",
+    "         \n",
+    "    !conda list fiona\n",
+    "        \n",
+    "To install Fiona, you can run the following in a cell:\n",
+    "    \n",
+    "    !conda install fiona\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:52:41.480724Z",
+     "start_time": "2021-11-22T19:52:41.062722Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3886, 51)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Reading from FGDB\n",
+    "fcls_df = pd.DataFrame.spatial.from_featureclass(\n",
+    "    location=\"./sedf_data/cities/cities.gdb/cities\")\n",
+    "fcls_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:52:41.506721Z",
+     "start_time": "2021-11-22T19:52:41.485728Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>OBJECTID</th>\n",
+       "      <th>age_10_14</th>\n",
+       "      <th>age_15_19</th>\n",
+       "      <th>age_20_24</th>\n",
+       "      <th>age_25_34</th>\n",
+       "      <th>age_35_44</th>\n",
+       "      <th>age_45_54</th>\n",
+       "      <th>age_55_64</th>\n",
+       "      <th>age_5_9</th>\n",
+       "      <th>age_65_74</th>\n",
+       "      <th>...</th>\n",
+       "      <th>placefips</th>\n",
+       "      <th>pop2010</th>\n",
+       "      <th>population</th>\n",
+       "      <th>pop_class</th>\n",
+       "      <th>renter_occ</th>\n",
+       "      <th>st</th>\n",
+       "      <th>stfips</th>\n",
+       "      <th>vacant</th>\n",
+       "      <th>white</th>\n",
+       "      <th>SHAPE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1313</td>\n",
+       "      <td>1058</td>\n",
+       "      <td>734</td>\n",
+       "      <td>2031</td>\n",
+       "      <td>1767</td>\n",
+       "      <td>1446</td>\n",
+       "      <td>1136</td>\n",
+       "      <td>1503</td>\n",
+       "      <td>665</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1601990</td>\n",
+       "      <td>13816</td>\n",
+       "      <td>15181</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1271</td>\n",
+       "      <td>ID</td>\n",
+       "      <td>16</td>\n",
+       "      <td>271</td>\n",
+       "      <td>13002</td>\n",
+       "      <td>{\"x\": -12462673.7237, \"y\": 5384674.994099997, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>890</td>\n",
+       "      <td>817</td>\n",
+       "      <td>818</td>\n",
+       "      <td>1799</td>\n",
+       "      <td>1235</td>\n",
+       "      <td>1330</td>\n",
+       "      <td>1143</td>\n",
+       "      <td>1099</td>\n",
+       "      <td>721</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1607840</td>\n",
+       "      <td>11899</td>\n",
+       "      <td>11946</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1441</td>\n",
+       "      <td>ID</td>\n",
+       "      <td>16</td>\n",
+       "      <td>318</td>\n",
+       "      <td>9893</td>\n",
+       "      <td>{\"x\": -12506251.314, \"y\": 5341537.793499999, \"...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 51 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   OBJECTID  age_10_14  age_15_19  age_20_24  age_25_34  age_35_44  age_45_54  \\\n",
+       "0         1       1313       1058        734       2031       1767       1446   \n",
+       "1         2        890        817        818       1799       1235       1330   \n",
+       "\n",
+       "   age_55_64  age_5_9  age_65_74  ...  placefips  pop2010  population  \\\n",
+       "0       1136     1503        665  ...    1601990    13816       15181   \n",
+       "1       1143     1099        721  ...    1607840    11899       11946   \n",
+       "\n",
+       "   pop_class  renter_occ  st  stfips  vacant  white  \\\n",
+       "0          6        1271  ID      16     271  13002   \n",
+       "1          6        1441  ID      16     318   9893   \n",
+       "\n",
+       "                                               SHAPE  \n",
+       "0  {\"x\": -12462673.7237, \"y\": 5384674.994099997, ...  \n",
+       "1  {\"x\": -12506251.314, \"y\": 5341537.793499999, \"...  \n",
+       "\n",
+       "[2 rows x 51 columns]"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check head\n",
+    "fcls_df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:52:41.525725Z",
+     "start_time": "2021-11-22T19:52:41.510728Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['point']"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check geometry type\n",
+    "fcls_df.spatial.geometry_type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> The `spatial` namespace shows that a _Spatially enabled DataFrame_ has been created from the `featureclass` stored locally.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Specify optional parameters**\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The **`from_featureclass()`** method allows users to specify optional parameters when the `ArcPy` library is available in the current environment. These parameters are:\n",
+    "\n",
+    "- `sql_clause`: a pair of SQL prefix and postfix clauses, `sql_clause=(prefix,postfix)`, organized in a list or a tuple can be passed to query specific data. The parameter allows only a small set of operations to be performed. Learn more about the allowed operations [here](https://pro.arcgis.com/en/pro-app/latest/arcpy/data-access/searchcursor-class.htm).\n",
+    "- `where_clause`: where statement to subset the data. Learn more about it [here](https://pro.arcgis.com/en/pro-app/latest/help/mapping/navigation/sql-reference-for-elements-used-in-query-expressions.htm).\n",
+    "- `fields`: to subset the data for specific fields.\n",
+    "- `spatial_filter`: a geometry object to filter the results.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\">\n",
+    "    <b>Note:</b> The operations below can only be performed in an environment that <b>contains</b> <code>arcpy</code>.\n",
+    "\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Subset data for specific fields\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:55:03.504998Z",
+     "start_time": "2021-11-22T19:55:03.352999Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3886, 3)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Subset for fields\n",
+    "fcls_flds = pd.DataFrame.spatial.from_featureclass(location=\"./sedf_data/cities/cities.gdb/cities\",\n",
+    "                                                   fields=['st', 'pop_class'])\n",
+    "fcls_flds.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:55:04.378124Z",
+     "start_time": "2021-11-22T19:55:04.367127Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>st</th>\n",
+       "      <th>pop_class</th>\n",
+       "      <th>SHAPE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ID</td>\n",
+       "      <td>6</td>\n",
+       "      <td>{\"x\": -12462673.7237, \"y\": 5384674.994099997, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ID</td>\n",
+       "      <td>6</td>\n",
+       "      <td>{\"x\": -12506251.314, \"y\": 5341537.793499999, \"...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   st  pop_class                                              SHAPE\n",
+       "0  ID          6  {\"x\": -12462673.7237, \"y\": 5384674.994099997, ...\n",
+       "1  ID          6  {\"x\": -12506251.314, \"y\": 5341537.793499999, \"..."
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check head\n",
+    "fcls_flds.head(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Subset using `where_clause`\n",
+    "\n",
+    "Learn more about how to use `where_clause` [here](https://pro.arcgis.com/en/pro-app/latest/help/mapping/navigation/sql-reference-for-elements-used-in-query-expressions.htm).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:55:06.957737Z",
+     "start_time": "2021-11-22T19:55:06.876084Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(15, 51)"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Subset using where_clause\n",
+    "fcls_whr = pd.DataFrame.spatial.from_featureclass(location=\"./sedf_data/cities/cities.gdb/cities\",\n",
+    "                                                  where_clause=\"st='ID' and pop_class=6\")\n",
+    "fcls_whr.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:55:07.111124Z",
+     "start_time": "2021-11-22T19:55:07.093125Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>OBJECTID</th>\n",
+       "      <th>age_10_14</th>\n",
+       "      <th>age_15_19</th>\n",
+       "      <th>age_20_24</th>\n",
+       "      <th>age_25_34</th>\n",
+       "      <th>age_35_44</th>\n",
+       "      <th>age_45_54</th>\n",
+       "      <th>age_55_64</th>\n",
+       "      <th>age_5_9</th>\n",
+       "      <th>age_65_74</th>\n",
+       "      <th>...</th>\n",
+       "      <th>placefips</th>\n",
+       "      <th>pop2010</th>\n",
+       "      <th>population</th>\n",
+       "      <th>pop_class</th>\n",
+       "      <th>renter_occ</th>\n",
+       "      <th>st</th>\n",
+       "      <th>stfips</th>\n",
+       "      <th>vacant</th>\n",
+       "      <th>white</th>\n",
+       "      <th>SHAPE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1313</td>\n",
+       "      <td>1058</td>\n",
+       "      <td>734</td>\n",
+       "      <td>2031</td>\n",
+       "      <td>1767</td>\n",
+       "      <td>1446</td>\n",
+       "      <td>1136</td>\n",
+       "      <td>1503</td>\n",
+       "      <td>665</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1601990</td>\n",
+       "      <td>13816</td>\n",
+       "      <td>15181</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1271</td>\n",
+       "      <td>ID</td>\n",
+       "      <td>16</td>\n",
+       "      <td>271</td>\n",
+       "      <td>13002</td>\n",
+       "      <td>{\"x\": -12462673.7237, \"y\": 5384674.994099997, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>890</td>\n",
+       "      <td>817</td>\n",
+       "      <td>818</td>\n",
+       "      <td>1799</td>\n",
+       "      <td>1235</td>\n",
+       "      <td>1330</td>\n",
+       "      <td>1143</td>\n",
+       "      <td>1099</td>\n",
+       "      <td>721</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1607840</td>\n",
+       "      <td>11899</td>\n",
+       "      <td>11946</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1441</td>\n",
+       "      <td>ID</td>\n",
+       "      <td>16</td>\n",
+       "      <td>318</td>\n",
+       "      <td>9893</td>\n",
+       "      <td>{\"x\": -12506251.314, \"y\": 5341537.793499999, \"...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 51 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   OBJECTID  age_10_14  age_15_19  age_20_24  age_25_34  age_35_44  age_45_54  \\\n",
+       "0         1       1313       1058        734       2031       1767       1446   \n",
+       "1         2        890        817        818       1799       1235       1330   \n",
+       "\n",
+       "   age_55_64  age_5_9  age_65_74  ...  placefips  pop2010  population  \\\n",
+       "0       1136     1503        665  ...    1601990    13816       15181   \n",
+       "1       1143     1099        721  ...    1607840    11899       11946   \n",
+       "\n",
+       "   pop_class  renter_occ  st  stfips  vacant  white  \\\n",
+       "0          6        1271  ID      16     271  13002   \n",
+       "1          6        1441  ID      16     318   9893   \n",
+       "\n",
+       "                                               SHAPE  \n",
+       "0  {\"x\": -12462673.7237, \"y\": 5384674.994099997, ...  \n",
+       "1  {\"x\": -12506251.314, \"y\": 5341537.793499999, \"...  \n",
+       "\n",
+       "[2 rows x 51 columns]"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check head\n",
+    "fcls_whr.head(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Subset using `fields` and `where_clause`\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:55:08.852159Z",
+     "start_time": "2021-11-22T19:55:08.788159Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(15, 5)"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Subset using where_clause\n",
+    "flds_whr = pd.DataFrame.spatial.from_featureclass(location=\"./sedf_data/cities/cities.gdb/cities\",\n",
+    "                                                  fields=[\n",
+    "                                                      'st', 'pop_class', 'age_10_14', 'age_15_19'],\n",
+    "                                                  where_clause=\"st='ID' and pop_class=6\")\n",
+    "flds_whr.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:55:09.717600Z",
+     "start_time": "2021-11-22T19:55:09.706606Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>st</th>\n",
+       "      <th>pop_class</th>\n",
+       "      <th>age_10_14</th>\n",
+       "      <th>age_15_19</th>\n",
+       "      <th>SHAPE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ID</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1313</td>\n",
+       "      <td>1058</td>\n",
+       "      <td>{\"x\": -12462673.7237, \"y\": 5384674.994099997, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ID</td>\n",
+       "      <td>6</td>\n",
+       "      <td>890</td>\n",
+       "      <td>817</td>\n",
+       "      <td>{\"x\": -12506251.314, \"y\": 5341537.793499999, \"...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   st  pop_class  age_10_14  age_15_19  \\\n",
+       "0  ID          6       1313       1058   \n",
+       "1  ID          6        890        817   \n",
+       "\n",
+       "                                               SHAPE  \n",
+       "0  {\"x\": -12462673.7237, \"y\": 5384674.994099997, ...  \n",
+       "1  {\"x\": -12506251.314, \"y\": 5341537.793499999, \"...  "
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check head\n",
+    "flds_whr.head(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Subset using `sql_clause`\n",
+    "\n",
+    "`sql_clause` can be combined with `fields` and `where_clause` to further subset the data. You can learn more about the allowed operations [here](https://pro.arcgis.com/en/pro-app/latest/arcpy/data-access/searchcursor-class.htm). Now let's look at some examples.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###### Prefix `sql_clause` - DISTINCT operation\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:55:12.045052Z",
+     "start_time": "2021-11-22T19:55:11.704053Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3886, 51)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Prefix Sql clause - DISTINCT operation\n",
+    "fcls_sql1 = pd.DataFrame.spatial.from_featureclass(location=\"./sedf_data/cities/cities.gdb/cities\",\n",
+    "                                                   sql_clause=(\"DISTINCT pop_class\", None))\n",
+    "\n",
+    "# Check shape\n",
+    "fcls_sql1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:55:12.590891Z",
+     "start_time": "2021-11-22T19:55:12.570891Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>OBJECTID</th>\n",
+       "      <th>age_10_14</th>\n",
+       "      <th>age_15_19</th>\n",
+       "      <th>age_20_24</th>\n",
+       "      <th>age_25_34</th>\n",
+       "      <th>age_35_44</th>\n",
+       "      <th>age_45_54</th>\n",
+       "      <th>age_55_64</th>\n",
+       "      <th>age_5_9</th>\n",
+       "      <th>age_65_74</th>\n",
+       "      <th>...</th>\n",
+       "      <th>placefips</th>\n",
+       "      <th>pop2010</th>\n",
+       "      <th>population</th>\n",
+       "      <th>pop_class</th>\n",
+       "      <th>renter_occ</th>\n",
+       "      <th>st</th>\n",
+       "      <th>stfips</th>\n",
+       "      <th>vacant</th>\n",
+       "      <th>white</th>\n",
+       "      <th>SHAPE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>941</td>\n",
+       "      <td>1247</td>\n",
+       "      <td>1213</td>\n",
+       "      <td>1043</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>1692</td>\n",
+       "      <td>2116</td>\n",
+       "      <td>1827</td>\n",
+       "      <td>1187</td>\n",
+       "      <td>1037</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0507330</td>\n",
+       "      <td>15620</td>\n",
+       "      <td>14771</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3006</td>\n",
+       "      <td>AR</td>\n",
+       "      <td>05</td>\n",
+       "      <td>1303</td>\n",
+       "      <td>6216</td>\n",
+       "      <td>{\"x\": -10006810.091, \"y\": 4290154.581699997, \"...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1405</td>\n",
+       "      <td>796</td>\n",
+       "      <td>748</td>\n",
+       "      <td>754</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>1717</td>\n",
+       "      <td>2062</td>\n",
+       "      <td>1450</td>\n",
+       "      <td>760</td>\n",
+       "      <td>851</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2466850</td>\n",
+       "      <td>12677</td>\n",
+       "      <td>13188</td>\n",
+       "      <td>6</td>\n",
+       "      <td>814</td>\n",
+       "      <td>MD</td>\n",
+       "      <td>24</td>\n",
+       "      <td>281</td>\n",
+       "      <td>11613</td>\n",
+       "      <td>{\"x\": -8517714.7855, \"y\": 4744316.880199999, \"...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 51 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   OBJECTID  age_10_14  age_15_19  age_20_24  age_25_34  age_35_44  age_45_54  \\\n",
+       "0       941       1247       1213       1043       2022       1692       2116   \n",
+       "1      1405        796        748        754       1999       1717       2062   \n",
+       "\n",
+       "   age_55_64  age_5_9  age_65_74  ...  placefips  pop2010  population  \\\n",
+       "0       1827     1187       1037  ...    0507330    15620       14771   \n",
+       "1       1450      760        851  ...    2466850    12677       13188   \n",
+       "\n",
+       "   pop_class  renter_occ  st  stfips  vacant  white  \\\n",
+       "0          6        3006  AR      05    1303   6216   \n",
+       "1          6         814  MD      24     281  11613   \n",
+       "\n",
+       "                                               SHAPE  \n",
+       "0  {\"x\": -10006810.091, \"y\": 4290154.581699997, \"...  \n",
+       "1  {\"x\": -8517714.7855, \"y\": 4744316.880199999, \"...  \n",
+       "\n",
+       "[2 rows x 51 columns]"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check head\n",
+    "fcls_sql1.head(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###### Postfix `sql_clause` with specific fields\n",
+    "\n",
+    "Here, we will subset the data for the state and population class fields and apply a postfix clause.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:55:13.456845Z",
+     "start_time": "2021-11-22T19:55:13.280450Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3886, 3)"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Postfix Sql clause with specific fields\n",
+    "fcls_sql2 = pd.DataFrame.spatial.from_featureclass(location=\"./sedf_data/cities/cities.gdb/cities\",\n",
+    "                                                   fields=['st', 'pop_class'],\n",
+    "                                                   sql_clause=(None, \"ORDER BY st, pop_class\"))\n",
+    "# Check shape\n",
+    "fcls_sql2.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:55:14.111586Z",
+     "start_time": "2021-11-22T19:55:14.100594Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>st</th>\n",
+       "      <th>pop_class</th>\n",
+       "      <th>SHAPE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>AK</td>\n",
+       "      <td>6</td>\n",
+       "      <td>{\"x\": -16417572.1606, \"y\": 9562359.403800003, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>AK</td>\n",
+       "      <td>6</td>\n",
+       "      <td>{\"x\": -16455422.2224, \"y\": 9574022.0224, \"spat...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AK</td>\n",
+       "      <td>6</td>\n",
+       "      <td>{\"x\": -16444303.0276, \"y\": 9568008.9705, \"spat...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>AK</td>\n",
+       "      <td>6</td>\n",
+       "      <td>{\"x\": -14962313.3618, \"y\": 8031014.926600002, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>AK</td>\n",
+       "      <td>6</td>\n",
+       "      <td>{\"x\": -16657118.680399999, \"y\": 8746757.662600...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   st  pop_class                                              SHAPE\n",
+       "0  AK          6  {\"x\": -16417572.1606, \"y\": 9562359.403800003, ...\n",
+       "1  AK          6  {\"x\": -16455422.2224, \"y\": 9574022.0224, \"spat...\n",
+       "2  AK          6  {\"x\": -16444303.0276, \"y\": 9568008.9705, \"spat...\n",
+       "3  AK          6  {\"x\": -14962313.3618, \"y\": 8031014.926600002, ...\n",
+       "4  AK          6  {\"x\": -16657118.680399999, \"y\": 8746757.662600..."
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check head\n",
+    "fcls_sql2.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "###### Prefix and Postfix `sql_clause` with specific fields and `where_clause`\n",
+    "\n",
+    "Here, we will subset the data using `where_clause`, keep specific fields, and then apply both prefix and postfix clause.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:55:51.001847Z",
+     "start_time": "2021-11-22T19:55:50.922841Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(22, 5)"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Prefix and Postfix sql_clause\n",
+    "fcls_sql3_df = pd.DataFrame.spatial.from_featureclass(location=\"./sedf_data/cities/cities.gdb/cities\",\n",
+    "                                                      fields=[\n",
+    "                                                          'st', 'name', 'pop_class', 'age_10_14'],\n",
+    "                                                      where_clause=\"st='ID'\",\n",
+    "                                                      sql_clause=(\"DISTINCT pop_class\", \"ORDER BY name\"))\n",
+    "\n",
+    "# Check Shape\n",
+    "fcls_sql3_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T19:55:51.761628Z",
+     "start_time": "2021-11-22T19:55:51.749637Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>st</th>\n",
+       "      <th>name</th>\n",
+       "      <th>pop_class</th>\n",
+       "      <th>age_10_14</th>\n",
+       "      <th>SHAPE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ID</td>\n",
+       "      <td>Ammon</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1313</td>\n",
+       "      <td>{\"x\": -12462673.7237, \"y\": 5384674.994099997, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ID</td>\n",
+       "      <td>Blackfoot</td>\n",
+       "      <td>6</td>\n",
+       "      <td>890</td>\n",
+       "      <td>{\"x\": -12506251.314, \"y\": 5341537.793499999, \"...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>ID</td>\n",
+       "      <td>Boise City</td>\n",
+       "      <td>8</td>\n",
+       "      <td>12750</td>\n",
+       "      <td>{\"x\": -12938676.683600001, \"y\": 5403597.049500...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>ID</td>\n",
+       "      <td>Burley</td>\n",
+       "      <td>6</td>\n",
+       "      <td>790</td>\n",
+       "      <td>{\"x\": -12667411.4024, \"y\": 5241722.820600003, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>ID</td>\n",
+       "      <td>Caldwell</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3803</td>\n",
+       "      <td>{\"x\": -12989383.6745, \"y\": 5413226.487300001, ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   st        name  pop_class  age_10_14  \\\n",
+       "0  ID       Ammon          6       1313   \n",
+       "1  ID   Blackfoot          6        890   \n",
+       "2  ID  Boise City          8      12750   \n",
+       "3  ID      Burley          6        790   \n",
+       "4  ID    Caldwell          7       3803   \n",
+       "\n",
+       "                                               SHAPE  \n",
+       "0  {\"x\": -12462673.7237, \"y\": 5384674.994099997, ...  \n",
+       "1  {\"x\": -12506251.314, \"y\": 5341537.793499999, \"...  \n",
+       "2  {\"x\": -12938676.683600001, \"y\": 5403597.049500...  \n",
+       "3  {\"x\": -12667411.4024, \"y\": 5241722.820600003, ...  \n",
+       "4  {\"x\": -12989383.6745, \"y\": 5413226.487300001, ...  "
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check head\n",
+    "fcls_sql3_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Using `spatial_filter`\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`spatial_filter` can be used to query the results by using a spatial relationship with another geometry. The spatial filtering is even more powerful when integrated with [Geoenrichment](https://developers.arcgis.com/python/guide/part1-introduction-to-geoenrichment/). Let's use this approach to filter our results for the state of Idaho. In this example, we will:\n",
+    "\n",
+    "- use `arcgis.geoenrichment.Country` to derive the geometries for the state of Idaho.\n",
+    "- use `arcgis.geometry.filters.intersects(geometry, sr=None)` to create a geometry filter object that filters results whose geometry intersects with the specified geometry (i.e. filter data points within the boundary of Idaho).\n",
+    "- pass the geometry filter object to `spatial_filter` to get desired results.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-warning\">\n",
+    "    <b>Note:</b> To perform enrichment operations, GeoEnrichment must be configured in your GIS organization. GeoEnrichment consumes credits, and you can learn more about credit consumption <a href=\"https://doc.arcgis.com/en/arcgis-online/administer/credits.htm\">here</a>. \n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T20:03:11.171059Z",
+     "start_time": "2021-11-22T20:03:11.154058Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Basic Imports\n",
+    "from arcgis.geometry import Geometry\n",
+    "from arcgis.geometry.filters import intersects\n",
+    "from arcgis.geoenrichment import Country"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T20:08:43.643602Z",
+     "start_time": "2021-11-22T20:08:43.139513Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "arcgis.geoenrichment.enrichment.Country"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create country object\n",
+    "usa = Country.get('US', gis=agol_gis)\n",
+    "type(usa)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T20:08:49.034325Z",
+     "start_time": "2021-11-22T20:08:47.854467Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<NamedArea name:\"Idaho\" area_id=\"16\", level=\"US.States\", country=\"147\">"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/svg+xml": [
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+      ],
+      "text/plain": [
+       "<Polygon object at 0x1ba9211a208[0x1ba90451750]>"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get boundaries for Idaho\n",
+    "named_area_ID = usa.search(query='Idaho', layers=['US.States'])\n",
+    "display(named_area_ID[0])\n",
+    "named_area_ID[0].geometry.as_arcpy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T20:10:38.529463Z",
+     "start_time": "2021-11-22T20:10:38.524455Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'wkid': 4326, 'latestWkid': 4326}"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create spatial reference\n",
+    "sr_id = named_area_ID[0].geometry[\"spatialReference\"]\n",
+    "sr_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T20:12:24.265943Z",
+     "start_time": "2021-11-22T20:12:24.259940Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Construct a geometry filter using the filter geometry\n",
+    "id_state_filter = intersects(named_area_ID[0].geometry,\n",
+    "                             sr=sr_id)\n",
+    "type(id_state_filter)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T20:19:12.104168Z",
+     "start_time": "2021-11-22T20:19:10.973170Z"
+    },
+    "code_folding": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(22, 5)"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Pass geometry filter object as a spatial_filter\n",
+    "fcls_spfl_df = pd.DataFrame.spatial.from_featureclass(location=\"./sedf_data/cities/cities.gdb/cities\",\n",
+    "                                                      fields=[\n",
+    "                                                          'st', 'name', 'pop_class', 'age_10_14'],\n",
+    "                                                      spatial_filter=id_state_filter)\n",
+    "# Check shape\n",
+    "fcls_spfl_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-22T20:26:39.851895Z",
+     "start_time": "2021-11-22T20:26:39.840893Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>st</th>\n",
+       "      <th>name</th>\n",
+       "      <th>pop_class</th>\n",
+       "      <th>age_10_14</th>\n",
+       "      <th>SHAPE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ID</td>\n",
+       "      <td>Ammon</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1313</td>\n",
+       "      <td>{\"x\": -12462673.7237, \"y\": 5384674.994099997, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>ID</td>\n",
+       "      <td>Blackfoot</td>\n",
+       "      <td>6</td>\n",
+       "      <td>890</td>\n",
+       "      <td>{\"x\": -12506251.314, \"y\": 5341537.793499999, \"...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>ID</td>\n",
+       "      <td>Boise City</td>\n",
+       "      <td>8</td>\n",
+       "      <td>12750</td>\n",
+       "      <td>{\"x\": -12938676.683600001, \"y\": 5403597.049500...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>ID</td>\n",
+       "      <td>Burley</td>\n",
+       "      <td>6</td>\n",
+       "      <td>790</td>\n",
+       "      <td>{\"x\": -12667411.4024, \"y\": 5241722.820600003, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>ID</td>\n",
+       "      <td>Caldwell</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3803</td>\n",
+       "      <td>{\"x\": -12989383.6745, \"y\": 5413226.487300001, ...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   st        name  pop_class  age_10_14  \\\n",
+       "0  ID       Ammon          6       1313   \n",
+       "1  ID   Blackfoot          6        890   \n",
+       "2  ID  Boise City          8      12750   \n",
+       "3  ID      Burley          6        790   \n",
+       "4  ID    Caldwell          7       3803   \n",
+       "\n",
+       "                                               SHAPE  \n",
+       "0  {\"x\": -12462673.7237, \"y\": 5384674.994099997, ...  \n",
+       "1  {\"x\": -12506251.314, \"y\": 5341537.793499999, \"...  \n",
+       "2  {\"x\": -12938676.683600001, \"y\": 5403597.049500...  \n",
+       "3  {\"x\": -12667411.4024, \"y\": 5241722.820600003, ...  \n",
+       "4  {\"x\": -12989383.6745, \"y\": 5413226.487300001, ...  "
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check head\n",
+    "fcls_spfl_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> The result shows the data points filtered for Idaho as defined by the spatial filter.\n",
+    "\n",
+    "You can learn more about applying spatial filters in our [Working with geometries](https://developers.arcgis.com/python/guide/part4-spatial-filters/#arcgis.geometry.filters-module) guide series.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Read in DataFrame with Addresses\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A `SeDF` can be easily created from a DataFrame with address information using the [`from_df()`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?highlight=from_featureclass#arcgis.features.GeoAccessor.from_df) method. This method geocodes the addresses using the first configured geocoder in your GIS. The locations generated after geocoding are used as the geometry of the SeDF.\n",
+    "\n",
+    "You can learn more about geocoding in our [Finding Places with geocoding](https://developers.arcgis.com/python/guide/part1-what-is-geocoding/) guide series.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-warning\">\n",
+    "    <b>Note:</b> The <code>from_df()</code> method performs a batch geocoding operation which consumes <a href=\"https://doc.arcgis.com/en/arcgis-online/administer/credits.htm\">credits</a>. If a <code>geocoder</code> is not specified, then the first configured geocoder in your GIS organization will be used. Learn more about credit consumption <a href=\"https://developers.arcgis.com/python/guide/part1-what-is-geocoding/#Cost-of-geocoding\">here</a>.\n",
+    "\n",
+    "To avoid credit consumption, you may specify your own `geocoder`.\n",
+    "\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's look at an example of using `from_df()`. We will read addresses into a DataFrame using the [`pd.read_csv()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html) method. Next, we will create a SeDF by passing the DataFrame and address column as parameters to the `from_df()` method.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-11T22:34:12.278791Z",
+     "start_time": "2021-11-11T22:34:12.267792Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Address</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>602 Murray Cir, Sausalito, CA 94965</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>340 Stockton St, San Francisco, CA 94108</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3619 Balboa St, San Francisco, CA 94121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1274 El Camino Real, San Bruno, CA 94066</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>625 Monterey Blvd, San Francisco, CA 94127</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                      Address\n",
+       "0         602 Murray Cir, Sausalito, CA 94965\n",
+       "1    340 Stockton St, San Francisco, CA 94108\n",
+       "2     3619 Balboa St, San Francisco, CA 94121\n",
+       "3    1274 El Camino Real, San Bruno, CA 94066\n",
+       "4  625 Monterey Blvd, San Francisco, CA 94127"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Read the csv file with address into a DataFrame\n",
+    "orders_df = pd.read_csv(\"./sedf_data/cities/orders.csv\")\n",
+    "\n",
+    "# Check head\n",
+    "orders_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> The DataFrame shows a column with address information.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-11T22:35:55.437412Z",
+     "start_time": "2021-11-11T22:35:53.956939Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Address</th>\n",
+       "      <th>SHAPE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>602 Murray Cir, Sausalito, CA 94965</td>\n",
+       "      <td>{\"x\": -122.47885242199999, \"y\": 37.83735920100...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>340 Stockton St, San Francisco, CA 94108</td>\n",
+       "      <td>{\"x\": -122.44955096499996, \"y\": 37.73152250200...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3619 Balboa St, San Francisco, CA 94121</td>\n",
+       "      <td>{\"x\": -122.49772620499999, \"y\": 37.77567413500...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1274 El Camino Real, San Bruno, CA 94066</td>\n",
+       "      <td>{\"x\": -122.40685153899994, \"y\": 37.78910429100...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>625 Monterey Blvd, San Francisco, CA 94127</td>\n",
+       "      <td>{\"x\": -122.42218381299995, \"y\": 37.63856151200...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                      Address  \\\n",
+       "0         602 Murray Cir, Sausalito, CA 94965   \n",
+       "1    340 Stockton St, San Francisco, CA 94108   \n",
+       "2     3619 Balboa St, San Francisco, CA 94121   \n",
+       "3    1274 El Camino Real, San Bruno, CA 94066   \n",
+       "4  625 Monterey Blvd, San Francisco, CA 94127   \n",
+       "\n",
+       "                                               SHAPE  \n",
+       "0  {\"x\": -122.47885242199999, \"y\": 37.83735920100...  \n",
+       "1  {\"x\": -122.44955096499996, \"y\": 37.73152250200...  \n",
+       "2  {\"x\": -122.49772620499999, \"y\": 37.77567413500...  \n",
+       "3  {\"x\": -122.40685153899994, \"y\": 37.78910429100...  \n",
+       "4  {\"x\": -122.42218381299995, \"y\": 37.63856151200...  "
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Use from_df to create SeDF\n",
+    "orders_sdf = pd.DataFrame.spatial.from_df(\n",
+    "    df=orders_df, address_column=\"Address\")\n",
+    "orders_sdf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-11T22:35:57.704801Z",
+     "start_time": "2021-11-11T22:35:57.697804Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['point']"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check geometry type\n",
+    "orders_sdf.spatial.geometry_type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> The `spatial` namespace shows that a _Spatially enabled DataFrame_ has been created from a Pandas DataFrame with address information.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Read in DataFrame with Lat/Long Information\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we saw in part-1 of this guide series, a SeDF can be created from any Pandas DataFrame with location information (Latitude and Longitude) using the [`from_xy()`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#arcgis.features.GeoAccessor.from_xy) method.\n",
+    "\n",
+    "Let's look at an example. We will read the data with latitude and longitude information into a DataFrame using the [`pd.read_csv()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html) method. Then, we will create a SeDF by passing the DataFrame, latitude, and longitude as parameters to the `from_xy()` method.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-11T22:36:07.669481Z",
+     "start_time": "2021-11-11T22:36:07.650485Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Provider Name</th>\n",
+       "      <th>Provider City</th>\n",
+       "      <th>Provider State</th>\n",
+       "      <th>Residents Total Admissions COVID-19</th>\n",
+       "      <th>Residents Total COVID-19 Cases</th>\n",
+       "      <th>Residents Total COVID-19 Deaths</th>\n",
+       "      <th>Number of All Beds</th>\n",
+       "      <th>Total Number of Occupied Beds</th>\n",
+       "      <th>LONGITUDE</th>\n",
+       "      <th>LATITUDE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>GROSSE POINTE MANOR</td>\n",
+       "      <td>NILES</td>\n",
+       "      <td>IL</td>\n",
+       "      <td>5</td>\n",
+       "      <td>56</td>\n",
+       "      <td>12</td>\n",
+       "      <td>99</td>\n",
+       "      <td>61</td>\n",
+       "      <td>-87.792973</td>\n",
+       "      <td>42.012012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>MILLER'S MERRY MANOR</td>\n",
+       "      <td>DUNKIRK</td>\n",
+       "      <td>IN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>46</td>\n",
+       "      <td>43</td>\n",
+       "      <td>-85.197651</td>\n",
+       "      <td>40.392722</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>PARKWAY MANOR</td>\n",
+       "      <td>MARION</td>\n",
+       "      <td>IL</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>131</td>\n",
+       "      <td>84</td>\n",
+       "      <td>-88.982944</td>\n",
+       "      <td>37.750143</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>AVANTARA LONG GROVE</td>\n",
+       "      <td>LONG GROVE</td>\n",
+       "      <td>IL</td>\n",
+       "      <td>6</td>\n",
+       "      <td>141</td>\n",
+       "      <td>0</td>\n",
+       "      <td>195</td>\n",
+       "      <td>131</td>\n",
+       "      <td>-87.986442</td>\n",
+       "      <td>42.160843</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>HARMONY NURSING &amp; REHAB CENTER</td>\n",
+       "      <td>CHICAGO</td>\n",
+       "      <td>IL</td>\n",
+       "      <td>19</td>\n",
+       "      <td>75</td>\n",
+       "      <td>16</td>\n",
+       "      <td>180</td>\n",
+       "      <td>116</td>\n",
+       "      <td>-87.726353</td>\n",
+       "      <td>41.975505</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    Provider Name Provider City Provider State  \\\n",
+       "0             GROSSE POINTE MANOR         NILES             IL   \n",
+       "1            MILLER'S MERRY MANOR       DUNKIRK             IN   \n",
+       "2                   PARKWAY MANOR        MARION             IL   \n",
+       "3             AVANTARA LONG GROVE    LONG GROVE             IL   \n",
+       "4  HARMONY NURSING & REHAB CENTER       CHICAGO             IL   \n",
+       "\n",
+       "   Residents Total Admissions COVID-19  Residents Total COVID-19 Cases  \\\n",
+       "0                                    5                              56   \n",
+       "1                                    0                               0   \n",
+       "2                                    0                               0   \n",
+       "3                                    6                             141   \n",
+       "4                                   19                              75   \n",
+       "\n",
+       "   Residents Total COVID-19 Deaths  Number of All Beds  \\\n",
+       "0                               12                  99   \n",
+       "1                                0                  46   \n",
+       "2                                0                 131   \n",
+       "3                                0                 195   \n",
+       "4                               16                 180   \n",
+       "\n",
+       "   Total Number of Occupied Beds  LONGITUDE   LATITUDE  \n",
+       "0                             61 -87.792973  42.012012  \n",
+       "1                             43 -85.197651  40.392722  \n",
+       "2                             84 -88.982944  37.750143  \n",
+       "3                            131 -87.986442  42.160843  \n",
+       "4                            116 -87.726353  41.975505  "
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Read the data\n",
+    "cms_df = pd.read_csv('./sedf_data/cities/sample_cms_data.csv')\n",
+    "\n",
+    "# Return the first 5 records\n",
+    "cms_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-11T22:36:07.692482Z",
+     "start_time": "2021-11-11T22:36:07.672485Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Provider Name</th>\n",
+       "      <th>Provider City</th>\n",
+       "      <th>Provider State</th>\n",
+       "      <th>Residents Total Admissions COVID-19</th>\n",
+       "      <th>Residents Total COVID-19 Cases</th>\n",
+       "      <th>Residents Total COVID-19 Deaths</th>\n",
+       "      <th>Number of All Beds</th>\n",
+       "      <th>Total Number of Occupied Beds</th>\n",
+       "      <th>LONGITUDE</th>\n",
+       "      <th>LATITUDE</th>\n",
+       "      <th>SHAPE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>GROSSE POINTE MANOR</td>\n",
+       "      <td>NILES</td>\n",
+       "      <td>IL</td>\n",
+       "      <td>5</td>\n",
+       "      <td>56</td>\n",
+       "      <td>12</td>\n",
+       "      <td>99</td>\n",
+       "      <td>61</td>\n",
+       "      <td>-87.792973</td>\n",
+       "      <td>42.012012</td>\n",
+       "      <td>{\"spatialReference\": {\"wkid\": 4326}, \"x\": -87....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>MILLER'S MERRY MANOR</td>\n",
+       "      <td>DUNKIRK</td>\n",
+       "      <td>IN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>46</td>\n",
+       "      <td>43</td>\n",
+       "      <td>-85.197651</td>\n",
+       "      <td>40.392722</td>\n",
+       "      <td>{\"spatialReference\": {\"wkid\": 4326}, \"x\": -85....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>PARKWAY MANOR</td>\n",
+       "      <td>MARION</td>\n",
+       "      <td>IL</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>131</td>\n",
+       "      <td>84</td>\n",
+       "      <td>-88.982944</td>\n",
+       "      <td>37.750143</td>\n",
+       "      <td>{\"spatialReference\": {\"wkid\": 4326}, \"x\": -88....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>AVANTARA LONG GROVE</td>\n",
+       "      <td>LONG GROVE</td>\n",
+       "      <td>IL</td>\n",
+       "      <td>6</td>\n",
+       "      <td>141</td>\n",
+       "      <td>0</td>\n",
+       "      <td>195</td>\n",
+       "      <td>131</td>\n",
+       "      <td>-87.986442</td>\n",
+       "      <td>42.160843</td>\n",
+       "      <td>{\"spatialReference\": {\"wkid\": 4326}, \"x\": -87....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>HARMONY NURSING &amp; REHAB CENTER</td>\n",
+       "      <td>CHICAGO</td>\n",
+       "      <td>IL</td>\n",
+       "      <td>19</td>\n",
+       "      <td>75</td>\n",
+       "      <td>16</td>\n",
+       "      <td>180</td>\n",
+       "      <td>116</td>\n",
+       "      <td>-87.726353</td>\n",
+       "      <td>41.975505</td>\n",
+       "      <td>{\"spatialReference\": {\"wkid\": 4326}, \"x\": -87....</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    Provider Name Provider City Provider State  \\\n",
+       "0             GROSSE POINTE MANOR         NILES             IL   \n",
+       "1            MILLER'S MERRY MANOR       DUNKIRK             IN   \n",
+       "2                   PARKWAY MANOR        MARION             IL   \n",
+       "3             AVANTARA LONG GROVE    LONG GROVE             IL   \n",
+       "4  HARMONY NURSING & REHAB CENTER       CHICAGO             IL   \n",
+       "\n",
+       "   Residents Total Admissions COVID-19  Residents Total COVID-19 Cases  \\\n",
+       "0                                    5                              56   \n",
+       "1                                    0                               0   \n",
+       "2                                    0                               0   \n",
+       "3                                    6                             141   \n",
+       "4                                   19                              75   \n",
+       "\n",
+       "   Residents Total COVID-19 Deaths  Number of All Beds  \\\n",
+       "0                               12                  99   \n",
+       "1                                0                  46   \n",
+       "2                                0                 131   \n",
+       "3                                0                 195   \n",
+       "4                               16                 180   \n",
+       "\n",
+       "   Total Number of Occupied Beds  LONGITUDE   LATITUDE  \\\n",
+       "0                             61 -87.792973  42.012012   \n",
+       "1                             43 -85.197651  40.392722   \n",
+       "2                             84 -88.982944  37.750143   \n",
+       "3                            131 -87.986442  42.160843   \n",
+       "4                            116 -87.726353  41.975505   \n",
+       "\n",
+       "                                               SHAPE  \n",
+       "0  {\"spatialReference\": {\"wkid\": 4326}, \"x\": -87....  \n",
+       "1  {\"spatialReference\": {\"wkid\": 4326}, \"x\": -85....  \n",
+       "2  {\"spatialReference\": {\"wkid\": 4326}, \"x\": -88....  \n",
+       "3  {\"spatialReference\": {\"wkid\": 4326}, \"x\": -87....  \n",
+       "4  {\"spatialReference\": {\"wkid\": 4326}, \"x\": -87....  "
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a SeDF\n",
+    "cms_sedf = pd.DataFrame.spatial.from_xy(\n",
+    "    df=cms_df, x_column='LONGITUDE', y_column='LATITUDE', sr=4326)\n",
+    "\n",
+    "# Check head\n",
+    "cms_sedf.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> The `SHAPE` feature shows that a _Spatially enabled DataFrame_ has been created from a Pandas DataFrame with latitude and longitude information.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Read in GeoPandas DataFrame\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A `SeDF` can be easily created from a [GeoPandas's](https://geopandas.org/index.html) [GeoDataFrame](https://geopandas.org/docs/reference/geodataframe.html) using the [`from_geodataframe()`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#arcgis.features.GeoAccessor.from_geodataframe) method. We will:\n",
+    "\n",
+    "- Import Geopandas and create a GeoDataFrame.\n",
+    "- Create a [**Spatially enabled DataFrame**](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#geoaccessor) from a GeoDataFrame.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Create a GeoDataFrame\n",
+    "\n",
+    "Here, we will create a `GeoDataFrame` from a Pandas DataFrame, `cms_df`, defined above.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-11T22:36:07.724482Z",
+     "start_time": "2021-11-11T22:36:07.694483Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Import libraries\n",
+    "from geopandas import GeoDataFrame\n",
+    "from shapely.geometry import Point"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-11T22:36:07.743482Z",
+     "start_time": "2021-11-11T22:36:07.726483Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(124, 9)"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Read the data\n",
+    "cms_df = pd.read_csv('./sedf_data/cities/sample_cms_data.csv')\n",
+    "\n",
+    "# Create Geopandas DataFrame\n",
+    "gdf = GeoDataFrame(cms_df.drop(['LONGITUDE', 'LATITUDE'], axis=1),\n",
+    "                   crs={'init': 'epsg:4326'},\n",
+    "                   geometry=[Point(xy) for xy in zip(cms_df.LONGITUDE, cms_df.LATITUDE)])\n",
+    "gdf.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-11T22:36:07.758480Z",
+     "start_time": "2021-11-11T22:36:07.745481Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Provider Name</th>\n",
+       "      <th>Provider City</th>\n",
+       "      <th>Provider State</th>\n",
+       "      <th>Residents Total Admissions COVID-19</th>\n",
+       "      <th>Residents Total COVID-19 Cases</th>\n",
+       "      <th>Residents Total COVID-19 Deaths</th>\n",
+       "      <th>Number of All Beds</th>\n",
+       "      <th>Total Number of Occupied Beds</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>GROSSE POINTE MANOR</td>\n",
+       "      <td>NILES</td>\n",
+       "      <td>IL</td>\n",
+       "      <td>5</td>\n",
+       "      <td>56</td>\n",
+       "      <td>12</td>\n",
+       "      <td>99</td>\n",
+       "      <td>61</td>\n",
+       "      <td>POINT (-87.79297 42.01201)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>MILLER'S MERRY MANOR</td>\n",
+       "      <td>DUNKIRK</td>\n",
+       "      <td>IN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>46</td>\n",
+       "      <td>43</td>\n",
+       "      <td>POINT (-85.19765 40.39272)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Provider Name Provider City Provider State  \\\n",
+       "0   GROSSE POINTE MANOR         NILES             IL   \n",
+       "1  MILLER'S MERRY MANOR       DUNKIRK             IN   \n",
+       "\n",
+       "   Residents Total Admissions COVID-19  Residents Total COVID-19 Cases  \\\n",
+       "0                                    5                              56   \n",
+       "1                                    0                               0   \n",
+       "\n",
+       "   Residents Total COVID-19 Deaths  Number of All Beds  \\\n",
+       "0                               12                  99   \n",
+       "1                                0                  46   \n",
+       "\n",
+       "   Total Number of Occupied Beds                    geometry  \n",
+       "0                             61  POINT (-87.79297 42.01201)  \n",
+       "1                             43  POINT (-85.19765 40.39272)  "
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check head\n",
+    "gdf.head(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> A GeoDataFrame has been created with a `geometry` column that stores the geometry of the dataset.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Create a SeDF from GeoDataFrame\n",
+    "\n",
+    "Here, we will create a `SeDF` from the `gdf` GeoDataFrame created above using the [`from_geodataframe()`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#arcgis.features.GeoAccessor.from_geodataframe) method.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-11T22:36:07.777481Z",
+     "start_time": "2021-11-11T22:36:07.760480Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Provider Name</th>\n",
+       "      <th>Provider City</th>\n",
+       "      <th>Provider State</th>\n",
+       "      <th>Residents Total Admissions COVID-19</th>\n",
+       "      <th>Residents Total COVID-19 Cases</th>\n",
+       "      <th>Residents Total COVID-19 Deaths</th>\n",
+       "      <th>Number of All Beds</th>\n",
+       "      <th>Total Number of Occupied Beds</th>\n",
+       "      <th>SHAPE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>GROSSE POINTE MANOR</td>\n",
+       "      <td>NILES</td>\n",
+       "      <td>IL</td>\n",
+       "      <td>5</td>\n",
+       "      <td>56</td>\n",
+       "      <td>12</td>\n",
+       "      <td>99</td>\n",
+       "      <td>61</td>\n",
+       "      <td>{\"x\": -87.792973, \"y\": 42.012012, \"spatialRefe...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>MILLER'S MERRY MANOR</td>\n",
+       "      <td>DUNKIRK</td>\n",
+       "      <td>IN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>46</td>\n",
+       "      <td>43</td>\n",
+       "      <td>{\"x\": -85.197651, \"y\": 40.392722, \"spatialRefe...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Provider Name Provider City Provider State  \\\n",
+       "0   GROSSE POINTE MANOR         NILES             IL   \n",
+       "1  MILLER'S MERRY MANOR       DUNKIRK             IN   \n",
+       "\n",
+       "   Residents Total Admissions COVID-19  Residents Total COVID-19 Cases  \\\n",
+       "0                                    5                              56   \n",
+       "1                                    0                               0   \n",
+       "\n",
+       "   Residents Total COVID-19 Deaths  Number of All Beds  \\\n",
+       "0                               12                  99   \n",
+       "1                                0                  46   \n",
+       "\n",
+       "   Total Number of Occupied Beds  \\\n",
+       "0                             61   \n",
+       "1                             43   \n",
+       "\n",
+       "                                               SHAPE  \n",
+       "0  {\"x\": -87.792973, \"y\": 42.012012, \"spatialRefe...  \n",
+       "1  {\"x\": -85.197651, \"y\": 40.392722, \"spatialRefe...  "
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a SeDF\n",
+    "sedf_gpd = pd.DataFrame.spatial.from_geodataframe(gdf)\n",
+    "sedf_gpd.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-11T22:36:07.784482Z",
+     "start_time": "2021-11-11T22:36:07.779484Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['point']"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check geometry type\n",
+    "sedf_gpd.spatial.geometry_type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> The `spatial` namespace shows that a _Spatially enabled DataFrame_ has been created from a GeoDataFrame.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Read in feather format data\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A `SeDF` can be easily created from the data in [feather](https://arrow.apache.org/docs/python/feather.html) format using the [`from_feather()`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?highlight=from_feather#arcgis.features.GeoAccessor.from_feather) method. The method's defaults _SHAPE_ is the `spatial_column` for geo-spatial information, but any other column with spatial information can be specified.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-11T22:36:07.798483Z",
+     "start_time": "2021-11-11T22:36:07.786483Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Provider Name</th>\n",
+       "      <th>Provider City</th>\n",
+       "      <th>Provider State</th>\n",
+       "      <th>Residents Total Admissions COVID-19</th>\n",
+       "      <th>Residents Total COVID-19 Cases</th>\n",
+       "      <th>Residents Total COVID-19 Deaths</th>\n",
+       "      <th>Number of All Beds</th>\n",
+       "      <th>Total Number of Occupied Beds</th>\n",
+       "      <th>LONGITUDE</th>\n",
+       "      <th>LATITUDE</th>\n",
+       "      <th>SHAPE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>GROSSE POINTE MANOR</td>\n",
+       "      <td>NILES</td>\n",
+       "      <td>IL</td>\n",
+       "      <td>5</td>\n",
+       "      <td>56</td>\n",
+       "      <td>12</td>\n",
+       "      <td>99</td>\n",
+       "      <td>61</td>\n",
+       "      <td>-87.792973</td>\n",
+       "      <td>42.012012</td>\n",
+       "      <td>{\"spatialReference\": {\"wkid\": 4326}, \"x\": -87....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>MILLER'S MERRY MANOR</td>\n",
+       "      <td>DUNKIRK</td>\n",
+       "      <td>IN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>46</td>\n",
+       "      <td>43</td>\n",
+       "      <td>-85.197651</td>\n",
+       "      <td>40.392722</td>\n",
+       "      <td>{\"spatialReference\": {\"wkid\": 4326}, \"x\": -85....</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Provider Name Provider City Provider State  \\\n",
+       "0   GROSSE POINTE MANOR         NILES             IL   \n",
+       "1  MILLER'S MERRY MANOR       DUNKIRK             IN   \n",
+       "\n",
+       "   Residents Total Admissions COVID-19  Residents Total COVID-19 Cases  \\\n",
+       "0                                    5                              56   \n",
+       "1                                    0                               0   \n",
+       "\n",
+       "   Residents Total COVID-19 Deaths  Number of All Beds  \\\n",
+       "0                               12                  99   \n",
+       "1                                0                  46   \n",
+       "\n",
+       "   Total Number of Occupied Beds  LONGITUDE   LATITUDE  \\\n",
+       "0                             61 -87.792973  42.012012   \n",
+       "1                             43 -85.197651  40.392722   \n",
+       "\n",
+       "                                               SHAPE  \n",
+       "0  {\"spatialReference\": {\"wkid\": 4326}, \"x\": -87....  \n",
+       "1  {\"spatialReference\": {\"wkid\": 4326}, \"x\": -85....  "
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check head\n",
+    "cms_sedf.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-11T22:36:07.854484Z",
+     "start_time": "2021-11-11T22:36:07.802481Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Provider Name</th>\n",
+       "      <th>Provider City</th>\n",
+       "      <th>Provider State</th>\n",
+       "      <th>Residents Total Admissions COVID-19</th>\n",
+       "      <th>Residents Total COVID-19 Cases</th>\n",
+       "      <th>Residents Total COVID-19 Deaths</th>\n",
+       "      <th>Number of All Beds</th>\n",
+       "      <th>Total Number of Occupied Beds</th>\n",
+       "      <th>LONGITUDE</th>\n",
+       "      <th>LATITUDE</th>\n",
+       "      <th>SHAPE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>GROSSE POINTE MANOR</td>\n",
+       "      <td>NILES</td>\n",
+       "      <td>IL</td>\n",
+       "      <td>5</td>\n",
+       "      <td>56</td>\n",
+       "      <td>12</td>\n",
+       "      <td>99</td>\n",
+       "      <td>61</td>\n",
+       "      <td>-87.792973</td>\n",
+       "      <td>42.012012</td>\n",
+       "      <td>{\"x\": -87.792973, \"y\": 42.012012, \"spatialRefe...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>MILLER'S MERRY MANOR</td>\n",
+       "      <td>DUNKIRK</td>\n",
+       "      <td>IN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>46</td>\n",
+       "      <td>43</td>\n",
+       "      <td>-85.197651</td>\n",
+       "      <td>40.392722</td>\n",
+       "      <td>{\"x\": -85.197651, \"y\": 40.392722, \"spatialRefe...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Provider Name Provider City Provider State  \\\n",
+       "0   GROSSE POINTE MANOR         NILES             IL   \n",
+       "1  MILLER'S MERRY MANOR       DUNKIRK             IN   \n",
+       "\n",
+       "   Residents Total Admissions COVID-19  Residents Total COVID-19 Cases  \\\n",
+       "0                                    5                              56   \n",
+       "1                                    0                               0   \n",
+       "\n",
+       "   Residents Total COVID-19 Deaths  Number of All Beds  \\\n",
+       "0                               12                  99   \n",
+       "1                                0                  46   \n",
+       "\n",
+       "   Total Number of Occupied Beds  LONGITUDE   LATITUDE  \\\n",
+       "0                             61 -87.792973  42.012012   \n",
+       "1                             43 -85.197651  40.392722   \n",
+       "\n",
+       "                                               SHAPE  \n",
+       "0  {\"x\": -87.792973, \"y\": 42.012012, \"spatialRefe...  \n",
+       "1  {\"x\": -85.197651, \"y\": 40.392722, \"spatialRefe...  "
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create SeDf by reading from feather\n",
+    "sedf_fthr = pd.DataFrame.spatial.from_feather(\n",
+    "    './sedf_data/cities/sample_cms_data.feather')\n",
+    "sedf_fthr.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-11T22:36:07.861486Z",
+     "start_time": "2021-11-11T22:36:07.856482Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['point']"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check geometry type\n",
+    "sedf_fthr.spatial.geometry_type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> The `spatial` namespace shows that a _Spatially enabled DataFrame_ has been created from _feather_ format data.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Read in Non-spatial Table data\n",
+    "\n",
+    "Non-spatial table data can be hosted on [**ArcGIS Online**](https://www.arcgis.com) or [**ArcGIS Enterprise**](http://enterprise.arcgis.com/en/), or it can be stored locally in a File Geodatabase. A `SeDF` can be easily created from such non-spatial table data using the following methods:\n",
+    "\n",
+    "- [`from_table()`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?highlight=from_feather#arcgis.features.GeoAccessor.from_table) - for local data\n",
+    "- [`from_layer()`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?highlight=from_layer#arcgis.features.GeoAccessor.from_layer) - for data hosted on ArcGIS Online or Enterprise\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Using the `from_table()` method\n",
+    "\n",
+    "A `SeDF` can be created from local non-spatial data using the [`from_table()`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?highlight=from_feather#arcgis.features.GeoAccessor.from_table) method. The method can read a csv file (in any environment) or a table stored in a File Geodatabase (with ArcPy only).\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Reading a csv file\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-11T22:36:08.546484Z",
+     "start_time": "2021-11-11T22:36:07.863481Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Provider Name</th>\n",
+       "      <th>Provider City</th>\n",
+       "      <th>Provider State</th>\n",
+       "      <th>Residents Total Admissions COVID-19</th>\n",
+       "      <th>Residents Total COVID-19 Cases</th>\n",
+       "      <th>Residents Total COVID-19 Deaths</th>\n",
+       "      <th>Number of All Beds</th>\n",
+       "      <th>Total Number of Occupied Beds</th>\n",
+       "      <th>LONGITUDE</th>\n",
+       "      <th>LATITUDE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>GROSSE POINTE MANOR</td>\n",
+       "      <td>NILES</td>\n",
+       "      <td>IL</td>\n",
+       "      <td>5</td>\n",
+       "      <td>56</td>\n",
+       "      <td>12</td>\n",
+       "      <td>99</td>\n",
+       "      <td>61</td>\n",
+       "      <td>-87.792973</td>\n",
+       "      <td>42.012012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>MILLER'S MERRY MANOR</td>\n",
+       "      <td>DUNKIRK</td>\n",
+       "      <td>IN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>46</td>\n",
+       "      <td>43</td>\n",
+       "      <td>-85.197651</td>\n",
+       "      <td>40.392722</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          Provider Name Provider City Provider State  \\\n",
+       "0   GROSSE POINTE MANOR         NILES             IL   \n",
+       "1  MILLER'S MERRY MANOR       DUNKIRK             IN   \n",
+       "\n",
+       "   Residents Total Admissions COVID-19  Residents Total COVID-19 Cases  \\\n",
+       "0                                    5                              56   \n",
+       "1                                    0                               0   \n",
+       "\n",
+       "   Residents Total COVID-19 Deaths  Number of All Beds  \\\n",
+       "0                               12                  99   \n",
+       "1                                0                  46   \n",
+       "\n",
+       "   Total Number of Occupied Beds  LONGITUDE   LATITUDE  \n",
+       "0                             61 -87.792973  42.012012  \n",
+       "1                             43 -85.197651  40.392722  "
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create SeDF\n",
+    "tbl_df = pd.DataFrame.spatial.from_table(\n",
+    "    filename='./sedf_data/cities/sample_cms_data.csv')\n",
+    "tbl_df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> A Pandas DataFrame without any spatial information is returned.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Reading table from a File Geodatabase\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\">\n",
+    "    <b>Note:</b> The operation below can only be performed in an environment that <b>contains</b> <code>arcpy</code>.\n",
+    "\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-11T22:36:08.615484Z",
+     "start_time": "2021-11-11T22:36:08.548486Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>OBJECTID</th>\n",
+       "      <th>NAME</th>\n",
+       "      <th>OTHER</th>\n",
+       "      <th>OWNER_OCC</th>\n",
+       "      <th>PLACEFIPS</th>\n",
+       "      <th>POP2010</th>\n",
+       "      <th>POPULATION</th>\n",
+       "      <th>POP_CLASS</th>\n",
+       "      <th>RENTER_OCC</th>\n",
+       "      <th>ST</th>\n",
+       "      <th>STFIPS</th>\n",
+       "      <th>VACANT</th>\n",
+       "      <th>WHITE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Ammon</td>\n",
+       "      <td>307</td>\n",
+       "      <td>3205</td>\n",
+       "      <td>1601990</td>\n",
+       "      <td>13816</td>\n",
+       "      <td>15181</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1271</td>\n",
+       "      <td>ID</td>\n",
+       "      <td>16</td>\n",
+       "      <td>271</td>\n",
+       "      <td>13002</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Blackfoot</td>\n",
+       "      <td>1077</td>\n",
+       "      <td>2788</td>\n",
+       "      <td>1607840</td>\n",
+       "      <td>11899</td>\n",
+       "      <td>11946</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1441</td>\n",
+       "      <td>ID</td>\n",
+       "      <td>16</td>\n",
+       "      <td>318</td>\n",
+       "      <td>9893</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   OBJECTID       NAME  OTHER  OWNER_OCC PLACEFIPS  POP2010  POPULATION  \\\n",
+       "0         1      Ammon    307       3205   1601990    13816       15181   \n",
+       "1         2  Blackfoot   1077       2788   1607840    11899       11946   \n",
+       "\n",
+       "   POP_CLASS  RENTER_OCC  ST STFIPS  VACANT  WHITE  \n",
+       "0          6        1271  ID     16     271  13002  \n",
+       "1          6        1441  ID     16     318   9893  "
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create SeDF\n",
+    "tbl_df2 = pd.DataFrame.spatial.from_table(\n",
+    "    filename=\"./sedf_data/cities/cities.gdb/cities_table_export\")\n",
+    "tbl_df2.head(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> A Pandas DataFrame without any spatial information is returned.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Using the `from_layer()` method\n",
+    "\n",
+    "A `SeDF` can be created from hosted non-spatial data using the[`from_layer()`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?highlight=from_layer#arcgis.features.GeoAccessor.from_layer) method.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
+       "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
+       "                       <a href='https://geosaurus.maps.arcgis.com/home/item.html?id=019215fdda4b4b3eb5b4712f3b06f544' target='_blank'>\n",
+       "                        <img 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' width='200' height='133' class=\"itemThumbnail\">\n",
+       "                       </a>\n",
+       "                    </div>\n",
+       "\n",
+       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
+       "                        <a href='https://geosaurus.maps.arcgis.com/home/item.html?id=019215fdda4b4b3eb5b4712f3b06f544' target='_blank'><b>sedf_major_cities_table</b>\n",
+       "                        </a>\n",
+       "                        <br/><br/><img src='https://geosaurus.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/table16.png' style=\"vertical-align:middle;\" width=16 height=16>Table Layer by api_data_owner\n",
+       "                        <br/>Last Modified: September 30, 2024\n",
+       "                        <br/>0 comments, 3 views\n",
+       "                    </div>\n",
+       "                </div>\n",
+       "                "
+      ],
+      "text/plain": [
+       "<Item title:\"sedf_major_cities_table\" type:Table Layer owner:api_data_owner>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tbl_item = agol_gis.content.get(\"019215fdda4b4b3eb5b4712f3b06f544\")\n",
+    "tbl_item"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Table url:\"https://services7.arcgis.com/JEwYeAy2cc8qOe3o/arcgis/rest/services/sedf_major_cities_table/FeatureServer/0\">"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get table url\n",
+    "tbl = tbl_item.tables[0]\n",
+    "tbl"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>OBJECTID</th>\n",
+       "      <th>PLACEFIPS</th>\n",
+       "      <th>POP2010</th>\n",
+       "      <th>POPULATION</th>\n",
+       "      <th>POP_CLASS</th>\n",
+       "      <th>STFIPS</th>\n",
+       "      <th>CLASS</th>\n",
+       "      <th>ObjectId2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1601990</td>\n",
+       "      <td>13816</td>\n",
+       "      <td>15181</td>\n",
+       "      <td>6</td>\n",
+       "      <td>16</td>\n",
+       "      <td>city</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1607840</td>\n",
+       "      <td>11899</td>\n",
+       "      <td>11946</td>\n",
+       "      <td>6</td>\n",
+       "      <td>16</td>\n",
+       "      <td>city</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   OBJECTID  PLACEFIPS  POP2010  POPULATION  POP_CLASS  STFIPS CLASS  \\\n",
+       "0         0    1601990    13816       15181          6      16  city   \n",
+       "1         1    1607840    11899       11946          6      16  city   \n",
+       "\n",
+       "   ObjectId2  \n",
+       "0          1  \n",
+       "1          2  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "tbl_df2 = pd.DataFrame.spatial.from_layer(tbl)\n",
+    "tbl_df2.head(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> A Pandas DataFrame without any spatial information is returned.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Read in data from '_lite and portable_' databases\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Geospatial data stored in a [mobile geodatabase](https://pro.arcgis.com/en/pro-app/latest/help/data/geodatabases/manage-mobile-gdb/mobile-geodatabases.htm) (.geodatabase) or a [SQLite Database](https://pro.arcgis.com/en/pro-app/latest/tool-reference/data-management/create-sqlite-database.htm) can be easily accessed using the [**Spatially enabled DataFrame**](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#geoaccessor).\n",
+    "\n",
+    "- A mobile geodatabase (.geodatabase) is a collection of various types of GIS datasets contained in a single file on disk that can store, query, and manage spatial and nonspatial data. Mobile geodatabases are stored in an SQLite database.\n",
+    "\n",
+    "- SQLite is a full-featured relational database with the advantage of being portable and interoperable making it ubiquitous in mobile app development.\n",
+    "\n",
+    "The [`from_featureclass()`](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html?arcgis.features.GeoAccessor.from_featureclass#arcgis.features.GeoAccessor.from_featureclass) method can be used to create a `SeDF` by reading in data from these databases. Let's look at some examples.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\">\n",
+    "    <b>Note:</b> The operations below can only be performed in an environment that <b>contains</b> <code>arcpy</code>.\n",
+    "\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Read from a mobile geodatabase\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-11T22:36:12.166989Z",
+     "start_time": "2021-11-11T22:36:11.692511Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3886, 51)"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Reading from mobile geodatabase\n",
+    "mobile_gdb_df = pd.DataFrame.spatial.from_featureclass(\n",
+    "    location=\"./sedf_data/cities/cities_mobile.geodatabase/main.cities\")\n",
+    "mobile_gdb_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-11T22:36:12.212993Z",
+     "start_time": "2021-11-11T22:36:12.171999Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>OBJECTID</th>\n",
+       "      <th>age_10_14</th>\n",
+       "      <th>age_15_19</th>\n",
+       "      <th>age_20_24</th>\n",
+       "      <th>age_25_34</th>\n",
+       "      <th>age_35_44</th>\n",
+       "      <th>age_45_54</th>\n",
+       "      <th>age_55_64</th>\n",
+       "      <th>age_5_9</th>\n",
+       "      <th>age_65_74</th>\n",
+       "      <th>...</th>\n",
+       "      <th>placefips</th>\n",
+       "      <th>pop2010</th>\n",
+       "      <th>population</th>\n",
+       "      <th>pop_class</th>\n",
+       "      <th>renter_occ</th>\n",
+       "      <th>st</th>\n",
+       "      <th>stfips</th>\n",
+       "      <th>vacant</th>\n",
+       "      <th>white</th>\n",
+       "      <th>SHAPE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1313</td>\n",
+       "      <td>1058</td>\n",
+       "      <td>734</td>\n",
+       "      <td>2031</td>\n",
+       "      <td>1767</td>\n",
+       "      <td>1446</td>\n",
+       "      <td>1136</td>\n",
+       "      <td>1503</td>\n",
+       "      <td>665</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1601990</td>\n",
+       "      <td>13816</td>\n",
+       "      <td>15181</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1271</td>\n",
+       "      <td>ID</td>\n",
+       "      <td>16</td>\n",
+       "      <td>271</td>\n",
+       "      <td>13002</td>\n",
+       "      <td>{\"x\": -12462673.7237, \"y\": 5384674.994099997, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>890</td>\n",
+       "      <td>817</td>\n",
+       "      <td>818</td>\n",
+       "      <td>1799</td>\n",
+       "      <td>1235</td>\n",
+       "      <td>1330</td>\n",
+       "      <td>1143</td>\n",
+       "      <td>1099</td>\n",
+       "      <td>721</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1607840</td>\n",
+       "      <td>11899</td>\n",
+       "      <td>11946</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1441</td>\n",
+       "      <td>ID</td>\n",
+       "      <td>16</td>\n",
+       "      <td>318</td>\n",
+       "      <td>9893</td>\n",
+       "      <td>{\"x\": -12506251.314, \"y\": 5341537.793499999, \"...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 51 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   OBJECTID  age_10_14  age_15_19  age_20_24  age_25_34  age_35_44  age_45_54  \\\n",
+       "0         1       1313       1058        734       2031       1767       1446   \n",
+       "1         2        890        817        818       1799       1235       1330   \n",
+       "\n",
+       "   age_55_64  age_5_9  age_65_74  ...  placefips  pop2010  population  \\\n",
+       "0       1136     1503        665  ...    1601990    13816       15181   \n",
+       "1       1143     1099        721  ...    1607840    11899       11946   \n",
+       "\n",
+       "   pop_class  renter_occ  st  stfips  vacant  white  \\\n",
+       "0          6        1271  ID      16     271  13002   \n",
+       "1          6        1441  ID      16     318   9893   \n",
+       "\n",
+       "                                               SHAPE  \n",
+       "0  {\"x\": -12462673.7237, \"y\": 5384674.994099997, ...  \n",
+       "1  {\"x\": -12506251.314, \"y\": 5341537.793499999, \"...  \n",
+       "\n",
+       "[2 rows x 51 columns]"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check head\n",
+    "mobile_gdb_df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-11T22:36:12.230989Z",
+     "start_time": "2021-11-11T22:36:12.216993Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['point']"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check geometry type\n",
+    "mobile_gdb_df.spatial.geometry_type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> The `spatial` namespace shows that a _Spatially enabled DataFrame_ has been created.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Read from a SQLite database\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-11T22:36:12.660988Z",
+     "start_time": "2021-11-11T22:36:12.232993Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3886, 51)"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Reading from sqlite database\n",
+    "sqlite_df = pd.DataFrame.spatial.from_featureclass(\n",
+    "    location=\"./sedf_data/cities/cities.sqlite/main.cities\")\n",
+    "sqlite_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-11T22:36:12.681991Z",
+     "start_time": "2021-11-11T22:36:12.664992Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>OBJECTID</th>\n",
+       "      <th>age_10_14</th>\n",
+       "      <th>age_15_19</th>\n",
+       "      <th>age_20_24</th>\n",
+       "      <th>age_25_34</th>\n",
+       "      <th>age_35_44</th>\n",
+       "      <th>age_45_54</th>\n",
+       "      <th>age_55_64</th>\n",
+       "      <th>age_5_9</th>\n",
+       "      <th>age_65_74</th>\n",
+       "      <th>...</th>\n",
+       "      <th>placefips</th>\n",
+       "      <th>pop2010</th>\n",
+       "      <th>population</th>\n",
+       "      <th>pop_class</th>\n",
+       "      <th>renter_occ</th>\n",
+       "      <th>st</th>\n",
+       "      <th>stfips</th>\n",
+       "      <th>vacant</th>\n",
+       "      <th>white</th>\n",
+       "      <th>SHAPE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1313</td>\n",
+       "      <td>1058</td>\n",
+       "      <td>734</td>\n",
+       "      <td>2031</td>\n",
+       "      <td>1767</td>\n",
+       "      <td>1446</td>\n",
+       "      <td>1136</td>\n",
+       "      <td>1503</td>\n",
+       "      <td>665</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1601990</td>\n",
+       "      <td>13816</td>\n",
+       "      <td>15181</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1271</td>\n",
+       "      <td>ID</td>\n",
+       "      <td>16</td>\n",
+       "      <td>271</td>\n",
+       "      <td>13002</td>\n",
+       "      <td>{\"x\": -12462673.7237, \"y\": 5384674.994099997, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>890</td>\n",
+       "      <td>817</td>\n",
+       "      <td>818</td>\n",
+       "      <td>1799</td>\n",
+       "      <td>1235</td>\n",
+       "      <td>1330</td>\n",
+       "      <td>1143</td>\n",
+       "      <td>1099</td>\n",
+       "      <td>721</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1607840</td>\n",
+       "      <td>11899</td>\n",
+       "      <td>11946</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1441</td>\n",
+       "      <td>ID</td>\n",
+       "      <td>16</td>\n",
+       "      <td>318</td>\n",
+       "      <td>9893</td>\n",
+       "      <td>{\"x\": -12506251.314, \"y\": 5341537.793499999, \"...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 51 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   OBJECTID  age_10_14  age_15_19  age_20_24  age_25_34  age_35_44  age_45_54  \\\n",
+       "0         1       1313       1058        734       2031       1767       1446   \n",
+       "1         2        890        817        818       1799       1235       1330   \n",
+       "\n",
+       "   age_55_64  age_5_9  age_65_74  ...  placefips  pop2010  population  \\\n",
+       "0       1136     1503        665  ...    1601990    13816       15181   \n",
+       "1       1143     1099        721  ...    1607840    11899       11946   \n",
+       "\n",
+       "   pop_class  renter_occ  st  stfips  vacant  white  \\\n",
+       "0          6        1271  ID      16     271  13002   \n",
+       "1          6        1441  ID      16     318   9893   \n",
+       "\n",
+       "                                               SHAPE  \n",
+       "0  {\"x\": -12462673.7237, \"y\": 5384674.994099997, ...  \n",
+       "1  {\"x\": -12506251.314, \"y\": 5341537.793499999, \"...  \n",
+       "\n",
+       "[2 rows x 51 columns]"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check head\n",
+    "sqlite_df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2021-11-11T22:36:12.697990Z",
+     "start_time": "2021-11-11T22:36:12.685991Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['point']"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check geometry type\n",
+    "sqlite_df.spatial.geometry_type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> The `spatial` namespace shows that a _Spatially enabled DataFrame_ has been created.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this guide, we explored how [**Spatially enabled DataFrame**](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#geoaccessor) (SeDF) can be used to read spatial data from various formats. We started by reading data from web feature layers and using the `query()` operation to optimize performance and results. We explored reading data from various local data sources such as file geodatabase and shapefile. Next, we explained how data with address or coordinate information, in a geopandas dataframe, or in feather format can be used to create a SeDF. We also discussed creating SeDF from non-spatial table data. Towards the end, we also discussed how SeDF can be created using data from lite and portable databases.\n",
+    "\n",
+    "In the next part of the guide series, you will learn about exporting data using [**Spatially enabled DataFrame**](https://developers.arcgis.com/python/api-reference/arcgis.features.toc.html#geoaccessor).\n"
+   ]
+  },
+  {
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+    "<div class=\"alert alert-info\">\n",
+    "    <b>Note:</b> Given the importance and popularity of <i>Spatially enabled DataFrame</i>, we are revisiting our documentation for this topic. Our goal is to enhance the existing documentation to showcase the various capabilities of Spatially enabled DataFrame in detail with even more examples this time.\n",
+    "\n",
+    "Creating quality documentation is time-consuming and exhaustive, but we are committed to providing you with the best experience possible. With that in mind, we will be rolling out the revamped guides on this topic as different parts of a guide series (like the <a href=\"https://developers.arcgis.com/python/guide/part1-introduction-to-dataengineering/\">Data Engineering</a> or <a href=\"https://developers.arcgis.com/python/guide/part1-introduction-what-is-geometry/\">Geometry</a> guide series). This is \"part-2\" of the guide series for Spatially Enabled DataFrame. You will continue to see the existing documentation as we revamp it to add new parts. Stay tuned for more on this topic.\n",
+    "\n",
+    "</div>\n"
+   ]
+  }
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