Restructure

docs/source/10-min.ipynb

@@ -569,7 +569,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "See: [Expressions](user_guide/basic_concepts/expressions.rst)\n",
+    "See: [Expressions](user_guide/expressions.rst)\n",
     "\n",
     "Expressions are an API for defining computation that needs to happen over your columns.\n",
     "\n",
@@ -1516,7 +1516,7 @@
    "source": [
     "### User-Defined Functions\n",
     "\n",
-    "See: [UDF User Guide](user_guide/daft_in_depth/udf)"
+    "See: [UDF User Guide](user_guide/udf)"
    ]
   },
   {

docs/source/conf.py

@@ -87,11 +87,13 @@
     "learn/user_guides/remote_cluster_execution": "distributed-computing.html",
     "learn/quickstart": "learn/10-min.html",
     "learn/10-min": "../10-min.html",
-    "user_guide/basic_concepts/*": "user_guide/basic_concepts.html",
+    "user_guide/basic_concepts/expressions": "user_guide/expressions",
+    "user_guide/basic_concepts/dataframe_introduction": "user_guide/basic_concepts",
+    "user_guide/basic_concepts/introduction": "user_guide/basic_concepts",
     "user_guide/daft_in_depth/aggregations": "user_guide/aggregations",
     "user_guide/daft_in_depth/dataframe-operations": "user_guide/dataframe-operations",
-    "user_guide/daft_in_depth/datatypes": "user_guide/datatypes",
-    "user_guide/daft_in_depth/udf": "user_guide/udf",
+    "user_guide/datatypes": "user_guide/datatypes",
+    "user_guide/udf": "user_guide/udf",
 }
 
 # Resolving code links to github

docs/source/migration_guides/coming_from_dask.rst

@@ -30,7 +30,7 @@ Daft does not use an index
 
 Dask aims for as much feature-parity with pandas as possible, including maintaining the presence of an Index in the DataFrame. But keeping an Index is difficult when moving to a distributed computing environment. Dask doesn’t support row-based positional indexing (with .iloc) because it does not track the length of its partitions. It also does not support pandas MultiIndex. The argument for keeping the Index is that it makes some operations against the sorted index column very fast. In reality, resetting the Index forces a data shuffle and is an expensive operation.
 
-Daft drops the need for an Index to make queries more readable and consistent. How you write a query should not change because of the state of an index or a reset_index call. In our opinion, eliminating the index makes things simpler, more explicit, more readable and therefore less error-prone. Daft achieves this by using the [Expressions API](../user_guide/basic_concepts/expressions).
+Daft drops the need for an Index to make queries more readable and consistent. How you write a query should not change because of the state of an index or a reset_index call. In our opinion, eliminating the index makes things simpler, more explicit, more readable and therefore less error-prone. Daft achieves this by using the [Expressions API](../user_guide/expressions).
 
 In Dask you would index your DataFrame to return row ``b`` as follows:
 
@@ -80,7 +80,7 @@ For example:
     res = ddf.map_partitions(my_function, **kwargs)
 
 
-Daft implements two APIs for mapping computations over the data in your DataFrame in parallel: :doc:`Expressions <../user_guide/basic_concepts/expressions>` and :doc:`UDFs <../user_guide/daft_in_depth/udf>`. Expressions are most useful when you need to define computation over your columns.
+Daft implements two APIs for mapping computations over the data in your DataFrame in parallel: :doc:`Expressions <../user_guide/expressions>` and :doc:`UDFs <../user_guide/udf>`. Expressions are most useful when you need to define computation over your columns.
 
 .. code:: python
 
@@ -113,7 +113,7 @@ Daft is built as a DataFrame API for distributed Machine learning. You can use D
 Daft supports Multimodal Data Types
 -----------------------------------
 
-Dask supports the same data types as pandas. Daft is built to support many more data types, including Images, nested JSON, tensors, etc. See :doc:`the documentation <../user_guide/daft_in_depth/datatypes>` for a list of all supported data types.
+Dask supports the same data types as pandas. Daft is built to support many more data types, including Images, nested JSON, tensors, etc. See :doc:`the documentation <../user_guide/datatypes>` for a list of all supported data types.
 
 Distributed Computing and Remote Clusters
 -----------------------------------------

docs/source/user_guide/fotw/fotw-001-images.ipynb

@@ -447,7 +447,7 @@
    "metadata": {},
    "source": [
     "### Create Thumbnails\n",
-    "[Expressions](../basic_concepts/expressions) are a Daft API for defining computation that needs to happen over your columns. There are dedicated `image.(...)` Expressions for working with images.\n",
+    "[Expressions](../expressions) are a Daft API for defining computation that needs to happen over your columns. There are dedicated `image.(...)` Expressions for working with images.\n",
     "\n",
     "You can use the `image.resize` Expression to create a thumbnail of each image:"
    ]
@@ -527,7 +527,7 @@
     "\n",
     "We'll define a function that uses a pre-trained PyTorch model [ResNet50](https://pytorch.org/vision/main/models/generated/torchvision.models.resnet50.html) to classify the dog pictures. We'll then pass the `image`  column to this PyTorch model and send the classification predictions to a new column `classify_breed`. \n",
     "\n",
-    "You will use Daft [User-Defined Functions (UDFs)](../daft_in_depth/udf) to do this. Daft UDFs which are the best way to run computations over multiple rows or columns.\n",
+    "You will use Daft [User-Defined Functions (UDFs)](../udf) to do this. Daft UDFs which are the best way to run computations over multiple rows or columns.\n",
     "\n",
     "#### Setting up PyTorch\n",
     "\n",

docs/source/user_guide/index.rst

@@ -12,6 +12,7 @@ Daft User Guide
     dataframe-operations
     sql
     aggregations
+    udf
     poweruser
     integrations
     tutorials
@@ -41,16 +42,23 @@ The Daft User Guide is laid out as follows:
 
 High-level overview of Daft interfaces and usage to give you a better understanding of how Daft will fit into your day-to-day workflow.
 
+Daft in Depth
+*************
+
+Core Daft concepts all Daft users will find useful to understand deeply.
+
+* :doc:`read-and-write`
+* :doc:`expressions`
+* :doc:`datatypes`
+* :doc:`dataframe-operations`
+* :doc:`aggregations`
+* :doc:`udf`
+
 :doc:`Structured Query Language (SQL) <sql>`
 ********************************************
 
 A look into Daft's SQL interface and how it complements Daft's Pythonic DataFrame APIs.
 
-:doc:`Daft in Depth <daft_in_depth>`
-************************************
-
-Core Daft concepts all Daft users will find useful to understand deeply.
-
 :doc:`The Daft Poweruser <poweruser>`
 *************************************