EDA_Pandas_Banking_L1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vdaqf6vgTTg4"
   },
   "source": [
    "<center>\n",
    "    <img src=\"https://gitlab.com/ibm/skills-network/courses/placeholder101/-/raw/master/labs/module%201/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n",
    "</center>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "y2VgPk2mTThF"
   },
   "source": [
    "# **Exploratory data analysis (EDA) with Pandas in banking**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jkRBtAnQTThK"
   },
   "source": [
    "The purpose of this lab is to master the exploratory data analysis (EDA) in banking with Pandas framework.\n",
    "\n",
    "After completing this lab you will be able to\n",
    "\n",
    "1.  Explore a banking dataset with Pandas framework.\n",
    "2.  Build pivot tables.\n",
    "3.  Visualize the dataset with various plot types.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CHwOXFUpTThK"
   },
   "source": [
    "## Outline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bHSc4iksTThP"
   },
   "source": [
    "*   Materials and methods\n",
    "*   General part\n",
    "    *   Libraries import\n",
    "    *   Dataset exploration\n",
    "    *   Pivot tables\n",
    "    *   Visualization in Pandas\n",
    "*   Tasks\n",
    "*   Authors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S4t5rpwITThQ"
   },
   "source": [
    "***\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KoNAvKmglBm-"
   },
   "source": [
    "## Materials and methods\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZOgyr5RSlI40"
   },
   "source": [
    "The data that we are going to use for this is a subset of an open source Bank Marketing Data Set from the UCI ML repository: https://archive.ics.uci.edu/ml/citation_policy.html.\n",
    "\n",
    "> This dataset is publicly available for research. The details are described in \\[Moro et al., 2014].\n",
    "> Please include this citation if you plan to use this database:\n",
    "> \\[Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014\n",
    "\n",
    "During the work, the task of preliminary analysis of a positive response (term deposit) to direct calls from a bank is solved. In essence, the task is a matter of bank scoring, i.e. according to the characteristics of a client (potential client), their behavior is predicted (loan default, a wish to make a deposit, etc.).\n",
    "\n",
    "In this lesson, we will try to give answers to a set of questions that may be relevant when analyzing banking data:\n",
    "\n",
    "1.  What is the share of clients attracted in our source data?\n",
    "2.  What are the mean values ​​of numerical features among the attracted clients?\n",
    "3.  What is the average call duration for the attracted clients?\n",
    "4.  What is the average age among the attracted and unmarried clients?\n",
    "5.  What is the average age and call duration for different types of client employment?\n",
    "\n",
    "In addition, we will make a visual analysis in order to plan marketing banking campaigns more effectively.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t6D_qFGMqUaN"
   },
   "source": [
    "[Pandas](http://pandas.pydata.org/?utm_medium=Exinfluencer\\&utm_source=Exinfluencer\\&utm_content=000026UJ\\&utm_term=10006555\\&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkQuickLabsEDA_Pandas_Banking_L126457256-2021-01-01) is a Python library that provides extensive means for data analysis. Data scientists often work with data stored in table formats like .csv, .tsv, or .xlsx. Pandas makes it very convenient to load, process, and analyze such tabular data using SQL-like queries. In conjunction with Matplotlib and Seaborn, Pandas provides a wide range of opportunities for visual analysis of tabular data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xS8UOiQOTThR"
   },
   "source": [
    "## Libraries import\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "H6YFQWnQHKui"
   },
   "source": [
    "Download data using a URL.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "bN5NPujBTThV"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2021-08-24 05:26:52--  https://archive.ics.uci.edu/ml/machine-learning-databases/00222/bank-additional.zip\n",
      "Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\n",
      "Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 444572 (434K) [application/x-httpd-php]\n",
      "Saving to: ‘bank-additional.zip.2’\n",
      "\n",
      "bank-additional.zip 100%[===================>] 434.15K  1.53MB/s    in 0.3s    \n",
      "\n",
      "2021-08-24 05:26:53 (1.53 MB/s) - ‘bank-additional.zip.2’ saved [444572/444572]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://archive.ics.uci.edu/ml/machine-learning-databases/00222/bank-additional.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-BnB2tiSZh4R"
   },
   "source": [
    "Alternative URL for the dataset downloading.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "ORfD1EUiZuLK"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2021-08-24 05:26:54--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/EDA_Pandas_Banking_L1/bank-additional.zip\n",
      "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.63.118.104\n",
      "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 444572 (434K) [application/zip]\n",
      "Saving to: ‘bank-additional.zip.3’\n",
      "\n",
      "bank-additional.zip 100%[===================>] 434.15K  --.-KB/s    in 0.008s  \n",
      "\n",
      "2021-08-24 05:26:54 (54.2 MB/s) - ‘bank-additional.zip.3’ saved [444572/444572]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/EDA_Pandas_Banking_L1/bank-additional.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AJAUUEruHYTj"
   },
   "source": [
    "Unzipping to a folder. It is a good idea to apply `-o` and `-q`  when unzipping to quiet the process and overwrite any existing folders.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "iZKbsroFTThY"
   },
   "outputs": [],
   "source": [
    "!unzip -o -q bank-additional.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eGH5DhdLHmOp"
   },
   "source": [
    "Import the libraries necessary for this lab. We can add some aliases to make the libraries easier to use in our code and set a default figure size for further plots. Ignore the warnings.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "jdVcijxmTTha"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams[\"figure.figsize\"] = (8, 6)\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "evtbog-C13Z2"
   },
   "source": [
    "Further specify the value of the `precision` parameter equal to 2 to display two decimal signs (instead of 6 as default).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "nwMoaJLV13Z3"
   },
   "outputs": [],
   "source": [
    "pd.set_option(\"precision\", 2)\n",
    "pd.options.display.float_format = '{:.2f}'.format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SbmrZguMTThd"
   },
   "source": [
    "## Dataset exploration\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EvMGO3m4TThd"
   },
   "source": [
    "In this section you will explore the sourse dataset.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lJ-GKs1J13Zz"
   },
   "source": [
    "Let's read the data and look at the first 5 rows using the `head` method. The number of the output rows from the dataset is determined by the `head` method parameter.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "wamAf3HLm_FS"
   },
   "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</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>56</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.10</td>\n",
       "      <td>93.99</td>\n",
       "      <td>-36.40</td>\n",
       "      <td>4.86</td>\n",
       "      <td>5191.00</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.10</td>\n",
       "      <td>93.99</td>\n",
       "      <td>-36.40</td>\n",
       "      <td>4.86</td>\n",
       "      <td>5191.00</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.10</td>\n",
       "      <td>93.99</td>\n",
       "      <td>-36.40</td>\n",
       "      <td>4.86</td>\n",
       "      <td>5191.00</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>40</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.6y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.10</td>\n",
       "      <td>93.99</td>\n",
       "      <td>-36.40</td>\n",
       "      <td>4.86</td>\n",
       "      <td>5191.00</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>56</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.10</td>\n",
       "      <td>93.99</td>\n",
       "      <td>-36.40</td>\n",
       "      <td>4.86</td>\n",
       "      <td>5191.00</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age        job  marital    education  default housing loan    contact  \\\n",
       "0   56  housemaid  married     basic.4y       no      no   no  telephone   \n",
       "1   57   services  married  high.school  unknown      no   no  telephone   \n",
       "2   37   services  married  high.school       no     yes   no  telephone   \n",
       "3   40     admin.  married     basic.6y       no      no   no  telephone   \n",
       "4   56   services  married  high.school       no      no  yes  telephone   \n",
       "\n",
       "  month day_of_week  ...  campaign  pdays  previous     poutcome emp.var.rate  \\\n",
       "0   may         mon  ...         1    999         0  nonexistent         1.10   \n",
       "1   may         mon  ...         1    999         0  nonexistent         1.10   \n",
       "2   may         mon  ...         1    999         0  nonexistent         1.10   \n",
       "3   may         mon  ...         1    999         0  nonexistent         1.10   \n",
       "4   may         mon  ...         1    999         0  nonexistent         1.10   \n",
       "\n",
       "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n",
       "0           93.99         -36.40       4.86      5191.00  no  \n",
       "1           93.99         -36.40       4.86      5191.00  no  \n",
       "2           93.99         -36.40       4.86      5191.00  no  \n",
       "3           93.99         -36.40       4.86      5191.00  no  \n",
       "4           93.99         -36.40       4.86      5191.00  no  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('bank-additional/bank-additional-full.csv', sep = ';')\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NjzxXtUp13Z3"
   },
   "source": [
    "### Let's look at the dataset size, feature names and their types\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "53xc7fWwoOMD"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41188, 21)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HtO5KNTV13Z5"
   },
   "source": [
    "The dataset contains 41188 objects (rows), for each of which 21 features are set (columns), including 1 target feature (`y`).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KVOmrfNH3pRs"
   },
   "source": [
    "### Attributing information\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TSTqzHlbKWoa"
   },
   "source": [
    "Output the column (feature) names:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "3uuAnHe3nvCh"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'job', 'marital', 'education', 'default', 'housing', 'loan',\n",
       "       'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays',\n",
       "       'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx',\n",
       "       'cons.conf.idx', 'euribor3m', 'nr.employed', 'y'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0SDpd1dxKeq4"
   },
   "source": [
    "Input features (column names):\n",
    "\n",
    "1.  `age` - client's age in years (numeric)\n",
    "2.  `job` - type of job (categorical: `admin.`, `blue-collar`, `entrepreneur`, `housemaid`, `management`, `retired`, `self-employed`, `services`, `student`, `technician`, `unemployed`, `unknown`)\n",
    "3.  `marital` - marital status (categorical: `divorced`, `married`, `single`, `unknown`)\n",
    "4.  `education` - client's education (categorical: `basic.4y`, `basic.6y`, `basic.9y`, `high.school`, `illiterate`, `professional.course`, `university.degree`, `unknown`)\n",
    "5.  `default` - has credit in default? (categorical: `no`, `yes`, `unknown`)\n",
    "6.  `housing` - has housing loan? (categorical: `no`, `yes`, `unknown`)\n",
    "7.  `loan` - has personal loan? (categorical: `no`, `yes`, `unknown`)\n",
    "8.  `contact` - contact communication type (categorical: `cellular`, `telephone`)\n",
    "9.  `month` - last contact month of the year (categorical: `jan`, `feb`, `mar`, ..., `nov`, `dec`)\n",
    "10. `day_of_week` - last contact day of the week (categorical: `mon`, `tue`, `wed`, `thu`, `fri`)\n",
    "11. `duration` - last contact duration, in seconds (numeric).\n",
    "12. `campaign` - number of contacts performed and for this client during this campaign (numeric, includes the last contact)\n",
    "13. `pdays` - number of days that have passed after the client was last contacted from the previous campaign (numeric; 999 means the client has not been previously contacted)\n",
    "14. `previous` - number of contacts performed for this client before this campaign (numeric)\n",
    "15. `poutcome` - outcome of the previous marketing campaign (categorical: `failure`, `nonexistent`, `success`)\n",
    "16. `emp.var.rate` - employment variation rate, quarterly indicator (numeric)\n",
    "17. `cons.price.idx` - consumer price index, monthly indicator (numeric)\n",
    "18. `cons.conf.idx` - consumer confidence index, monthly indicator (numeric)\n",
    "19. `euribor3m` - euribor 3 month rate, daily indicator (numeric)\n",
    "20. `nr.employed` - number of employees, quarterly indicator (numeric)\n",
    "\n",
    "Output feature (desired target):\n",
    "\n",
    "21. `y` - has the client subscribed a term deposit? (binary: `yes`,`no`)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x6SaYJgj13Z6"
   },
   "source": [
    "To see the general information on all the DataFrame features (columns), we use the **`info`** method:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "LCV1-dAJ13Z6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 41188 entries, 0 to 41187\n",
      "Data columns (total 21 columns):\n",
      "age               41188 non-null int64\n",
      "job               41188 non-null object\n",
      "marital           41188 non-null object\n",
      "education         41188 non-null object\n",
      "default           41188 non-null object\n",
      "housing           41188 non-null object\n",
      "loan              41188 non-null object\n",
      "contact           41188 non-null object\n",
      "month             41188 non-null object\n",
      "day_of_week       41188 non-null object\n",
      "duration          41188 non-null int64\n",
      "campaign          41188 non-null int64\n",
      "pdays             41188 non-null int64\n",
      "previous          41188 non-null int64\n",
      "poutcome          41188 non-null object\n",
      "emp.var.rate      41188 non-null float64\n",
      "cons.price.idx    41188 non-null float64\n",
      "cons.conf.idx     41188 non-null float64\n",
      "euribor3m         41188 non-null float64\n",
      "nr.employed       41188 non-null float64\n",
      "y                 41188 non-null object\n",
      "dtypes: float64(5), int64(5), object(11)\n",
      "memory usage: 6.6+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(df.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ic2B0eP0PshN"
   },
   "source": [
    "As you can see, the dataset is full, no pass (`non-null`), so there is no need to fill the gaps. The dataset contains 5 integer (`int64`), 5 real (`float64`) and 11 categorical and binary (`object`) features.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z2I_fcSH13Z8"
   },
   "source": [
    "Method **`describe`** shows the main statistical characteristics of the dataset for each numerical feature (`int64` and `float64` types): the existing values number, mean, standard deviation, range, min & max, 0.25, 0.5 and 0.75 quartiles.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 163
    },
    "id": "JGuHLQMl13Z8",
    "outputId": "4f6c347e-6fa7-4c30-fa10-b85b897fd8e1"
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\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</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>41188.00</td>\n",
       "      <td>41188.00</td>\n",
       "      <td>41188.00</td>\n",
       "      <td>41188.00</td>\n",
       "      <td>41188.00</td>\n",
       "      <td>41188.00</td>\n",
       "      <td>41188.00</td>\n",
       "      <td>41188.00</td>\n",
       "      <td>41188.00</td>\n",
       "      <td>41188.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>40.02</td>\n",
       "      <td>258.29</td>\n",
       "      <td>2.57</td>\n",
       "      <td>962.48</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.08</td>\n",
       "      <td>93.58</td>\n",
       "      <td>-40.50</td>\n",
       "      <td>3.62</td>\n",
       "      <td>5167.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>10.42</td>\n",
       "      <td>259.28</td>\n",
       "      <td>2.77</td>\n",
       "      <td>186.91</td>\n",
       "      <td>0.49</td>\n",
       "      <td>1.57</td>\n",
       "      <td>0.58</td>\n",
       "      <td>4.63</td>\n",
       "      <td>1.73</td>\n",
       "      <td>72.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>17.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-3.40</td>\n",
       "      <td>92.20</td>\n",
       "      <td>-50.80</td>\n",
       "      <td>0.63</td>\n",
       "      <td>4963.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>32.00</td>\n",
       "      <td>102.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>999.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-1.80</td>\n",
       "      <td>93.08</td>\n",
       "      <td>-42.70</td>\n",
       "      <td>1.34</td>\n",
       "      <td>5099.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>38.00</td>\n",
       "      <td>180.00</td>\n",
       "      <td>2.00</td>\n",
       "      <td>999.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.10</td>\n",
       "      <td>93.75</td>\n",
       "      <td>-41.80</td>\n",
       "      <td>4.86</td>\n",
       "      <td>5191.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>47.00</td>\n",
       "      <td>319.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>999.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.40</td>\n",
       "      <td>93.99</td>\n",
       "      <td>-36.40</td>\n",
       "      <td>4.96</td>\n",
       "      <td>5228.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>98.00</td>\n",
       "      <td>4918.00</td>\n",
       "      <td>56.00</td>\n",
       "      <td>999.00</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1.40</td>\n",
       "      <td>94.77</td>\n",
       "      <td>-26.90</td>\n",
       "      <td>5.04</td>\n",
       "      <td>5228.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           age  duration  campaign    pdays  previous  emp.var.rate  \\\n",
       "count 41188.00  41188.00  41188.00 41188.00  41188.00      41188.00   \n",
       "mean     40.02    258.29      2.57   962.48      0.17          0.08   \n",
       "std      10.42    259.28      2.77   186.91      0.49          1.57   \n",
       "min      17.00      0.00      1.00     0.00      0.00         -3.40   \n",
       "25%      32.00    102.00      1.00   999.00      0.00         -1.80   \n",
       "50%      38.00    180.00      2.00   999.00      0.00          1.10   \n",
       "75%      47.00    319.00      3.00   999.00      0.00          1.40   \n",
       "max      98.00   4918.00     56.00   999.00      7.00          1.40   \n",
       "\n",
       "       cons.price.idx  cons.conf.idx  euribor3m  nr.employed  \n",
       "count        41188.00       41188.00   41188.00     41188.00  \n",
       "mean            93.58         -40.50       3.62      5167.04  \n",
       "std              0.58           4.63       1.73        72.25  \n",
       "min             92.20         -50.80       0.63      4963.60  \n",
       "25%             93.08         -42.70       1.34      5099.10  \n",
       "50%             93.75         -41.80       4.86      5191.00  \n",
       "75%             93.99         -36.40       4.96      5228.10  \n",
       "max             94.77         -26.90       5.04      5228.10  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vz-xyMpdVC3I"
   },
   "source": [
    "The `Mean` row shows the feature average, `STD` is an RMS (Root Mean Square) deviation, `min`,`  max ` - the minimum and maximum values, `25%`, `50%`, ` 75%  `- quarters that split the dataset (or part of it) into four groups containing approximately an equal number of observations (rows). For example, the duration (`duration`) of about a quarter of calls to customers is around 100 seconds.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pUuf9K8JQ6-c"
   },
   "source": [
    "In general, according to the data, it is impossible to say that there are outliers in the data. However, such an inspection is not enough, it is desirable to still see the charts of the target feature dependence from each input feature. We will do it later when we visualize features and dependencies.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sJO4lJj113Z9"
   },
   "source": [
    "To see the statistics on non-numeric features, you need to explicitly specify the feature types by the `include` parameter. You can also set `include = all` to output statistics on all the existing features.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "MtLmlp9k13Z-",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>41188</td>\n",
       "      <td>41188</td>\n",
       "      <td>41188</td>\n",
       "      <td>41188</td>\n",
       "      <td>41188</td>\n",
       "      <td>41188</td>\n",
       "      <td>41188</td>\n",
       "      <td>41188</td>\n",
       "      <td>41188</td>\n",
       "      <td>41188</td>\n",
       "      <td>41188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>unique</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>top</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>may</td>\n",
       "      <td>thu</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>freq</td>\n",
       "      <td>10422</td>\n",
       "      <td>24928</td>\n",
       "      <td>12168</td>\n",
       "      <td>32588</td>\n",
       "      <td>21576</td>\n",
       "      <td>33950</td>\n",
       "      <td>26144</td>\n",
       "      <td>13769</td>\n",
       "      <td>8623</td>\n",
       "      <td>35563</td>\n",
       "      <td>36548</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           job  marital          education default housing   loan   contact  \\\n",
       "count    41188    41188              41188   41188   41188  41188     41188   \n",
       "unique      12        4                  8       3       3      3         2   \n",
       "top     admin.  married  university.degree      no     yes     no  cellular   \n",
       "freq     10422    24928              12168   32588   21576  33950     26144   \n",
       "\n",
       "        month day_of_week     poutcome      y  \n",
       "count   41188       41188        41188  41188  \n",
       "unique     10           5            3      2  \n",
       "top       may         thu  nonexistent     no  \n",
       "freq    13769        8623        35563  36548  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include = [\"object\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cMel_T0IRPA5"
   },
   "source": [
    "The result shows that the average client refers to administrative staff (`job = admin.`), is married (`marital = married`) and has a university degree (`education = university.degree`).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "l0-sAGXl13Z_"
   },
   "source": [
    "For categorical (type `object`) and boolean (type `bool`) features you can use the **`value_counts`** method. Let's look at the target feature (`y`) distribution:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "pjchGbdx13aA"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     36548\n",
       "yes     4640\n",
       "Name: y, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"y\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8P92yMFY13aB"
   },
   "source": [
    "4640 clients (11.3%) of 41188 issued a term deposit, the value of the variable `y` equals `yes`.\n",
    "\n",
    "Let's look at the client distribution by the variable `marital`. Specify the value of the `normalize = True` parameter to view relative frequencies, but not absolute.\n",
    "\n",
    "Replace `##YOUR CODE GOES HERE##` with your Python code.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "yPqBQPAj13aC"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "married    0.61\n",
       "single     0.28\n",
       "divorced   0.11\n",
       "unknown    0.00\n",
       "Name: marital, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"marital\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "z57YgF-mgjNc"
   },
   "source": [
    "Double-click **here** for the solution.\n",
    "\n",
    "<!-- \n",
    "df[\"marital\"].value_counts(normalize = True)\n",
    "-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DjpEnpfUTaKa"
   },
   "source": [
    "As we can see, 61% (0.61) of clients are married, which must be taken into account when planning marketing campaigns to manage deposit operations.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IuChKGTT13aC"
   },
   "source": [
    "### Sorting\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hVb-TBY486e0"
   },
   "source": [
    "A `DataFrame` can be sorted by a few feature values. In our case, for example, by `duration` (`ascending = False` for sorting in descending order):\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "2cPhMArw13aD"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
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       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
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       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
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       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>y</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>24091</td>\n",
       "      <td>33</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
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       "      <td>...</td>\n",
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       "      <td>-0.10</td>\n",
       "      <td>93.20</td>\n",
       "      <td>-42.00</td>\n",
       "      <td>4.41</td>\n",
       "      <td>5195.80</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22192</td>\n",
       "      <td>52</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>aug</td>\n",
       "      <td>thu</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.40</td>\n",
       "      <td>93.44</td>\n",
       "      <td>-36.10</td>\n",
       "      <td>4.96</td>\n",
       "      <td>5228.10</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40537</td>\n",
       "      <td>27</td>\n",
       "      <td>admin.</td>\n",
       "      <td>single</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>aug</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-1.70</td>\n",
       "      <td>94.03</td>\n",
       "      <td>-38.30</td>\n",
       "      <td>0.89</td>\n",
       "      <td>4991.60</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13820</td>\n",
       "      <td>31</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>jul</td>\n",
       "      <td>thu</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.40</td>\n",
       "      <td>93.92</td>\n",
       "      <td>-42.70</td>\n",
       "      <td>4.96</td>\n",
       "      <td>5228.10</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7727</td>\n",
       "      <td>37</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.10</td>\n",
       "      <td>93.99</td>\n",
       "      <td>-36.40</td>\n",
       "      <td>4.86</td>\n",
       "      <td>5191.00</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age          job  marital            education default housing loan  \\\n",
       "24091   33   technician   single  professional.course      no     yes   no   \n",
       "22192   52  blue-collar  married             basic.4y      no      no   no   \n",
       "40537   27       admin.   single          high.school      no      no   no   \n",
       "13820   31   technician  married  professional.course      no      no   no   \n",
       "7727    37   unemployed  married  professional.course      no     yes   no   \n",
       "\n",
       "         contact month day_of_week  ...  campaign  pdays  previous  \\\n",
       "24091  telephone   nov         mon  ...         1    999         0   \n",
       "22192  telephone   aug         thu  ...         3    999         0   \n",
       "40537  telephone   aug         fri  ...         1    999         0   \n",
       "13820   cellular   jul         thu  ...         1    999         0   \n",
       "7727   telephone   may         fri  ...         2    999         0   \n",
       "\n",
       "          poutcome emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  \\\n",
       "24091  nonexistent        -0.10           93.20         -42.00       4.41   \n",
       "22192  nonexistent         1.40           93.44         -36.10       4.96   \n",
       "40537  nonexistent        -1.70           94.03         -38.30       0.89   \n",
       "13820  nonexistent         1.40           93.92         -42.70       4.96   \n",
       "7727   nonexistent         1.10           93.99         -36.40       4.86   \n",
       "\n",
       "       nr.employed    y  \n",
       "24091      5195.80   no  \n",
       "22192      5228.10  yes  \n",
       "40537      4991.60   no  \n",
       "13820      5228.10  yes  \n",
       "7727       5191.00  yes  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by = \"duration\", ascending = False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0FBBnPZ1Uiy5"
   },
   "source": [
    "The sorting results show that the longest calls exceed one hour, as the value `duration` is more than 3600 seconds or 1 hour. At the same time, it usually was on Mondays and Thursdays (`day_of_week`) and, especially, in November and August (`month`).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OGVjGnbg13aE"
   },
   "source": [
    "Sort by the column group:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "AqfjeNAS13aE"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>38274</td>\n",
       "      <td>17</td>\n",
       "      <td>student</td>\n",
       "      <td>single</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>oct</td>\n",
       "      <td>tue</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>success</td>\n",
       "      <td>-3.40</td>\n",
       "      <td>92.43</td>\n",
       "      <td>-26.90</td>\n",
       "      <td>0.74</td>\n",
       "      <td>5017.50</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37579</td>\n",
       "      <td>17</td>\n",
       "      <td>student</td>\n",
       "      <td>single</td>\n",
       "      <td>basic.9y</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>cellular</td>\n",
       "      <td>aug</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "      <td>-2.90</td>\n",
       "      <td>92.20</td>\n",
       "      <td>-31.40</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5076.20</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37140</td>\n",
       "      <td>17</td>\n",
       "      <td>student</td>\n",
       "      <td>single</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
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       "      <td>aug</td>\n",
       "      <td>wed</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>success</td>\n",
       "      <td>-2.90</td>\n",
       "      <td>92.20</td>\n",
       "      <td>-31.40</td>\n",
       "      <td>0.88</td>\n",
       "      <td>5076.20</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37539</td>\n",
       "      <td>17</td>\n",
       "      <td>student</td>\n",
       "      <td>single</td>\n",
       "      <td>basic.9y</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>aug</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>999</td>\n",
       "      <td>2</td>\n",
       "      <td>failure</td>\n",
       "      <td>-2.90</td>\n",
       "      <td>92.20</td>\n",
       "      <td>-31.40</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5076.20</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37558</td>\n",
       "      <td>17</td>\n",
       "      <td>student</td>\n",
       "      <td>single</td>\n",
       "      <td>basic.9y</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>aug</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>success</td>\n",
       "      <td>-2.90</td>\n",
       "      <td>92.20</td>\n",
       "      <td>-31.40</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5076.20</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age      job marital education default  housing     loan   contact  \\\n",
       "38274   17  student  single   unknown      no       no      yes  cellular   \n",
       "37579   17  student  single  basic.9y      no  unknown  unknown  cellular   \n",
       "37140   17  student  single   unknown      no      yes       no  cellular   \n",
       "37539   17  student  single  basic.9y      no      yes       no  cellular   \n",
       "37558   17  student  single  basic.9y      no      yes       no  cellular   \n",
       "\n",
       "      month day_of_week  ...  campaign  pdays  previous  poutcome  \\\n",
       "38274   oct         tue  ...         1      2         2   success   \n",
       "37579   aug         fri  ...         2    999         1   failure   \n",
       "37140   aug         wed  ...         3      4         2   success   \n",
       "37539   aug         fri  ...         2    999         2   failure   \n",
       "37558   aug         fri  ...         3      4         2   success   \n",
       "\n",
       "      emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  nr.employed    y  \n",
       "38274        -3.40           92.43         -26.90       0.74      5017.50  yes  \n",
       "37579        -2.90           92.20         -31.40       0.87      5076.20  yes  \n",
       "37140        -2.90           92.20         -31.40       0.88      5076.20   no  \n",
       "37539        -2.90           92.20         -31.40       0.87      5076.20   no  \n",
       "37558        -2.90           92.20         -31.40       0.87      5076.20   no  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by = [\"age\", \"duration\"], ascending = [True, False]).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FcV8UApXW0ud"
   },
   "source": [
    "We see that the youngest customers are at the `age` of 17, and the call `duration` exceeded 3 minutes only for three clients, which indicates the ineffectiveness of long-term interaction with such clients.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YHsAC2Vr13aO"
   },
   "source": [
    "### Application of functions: `apply`, `map` etc.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UhFtQbGX13aP"
   },
   "source": [
    "**Apply the function to each column:**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "f9tABUDp13aQ"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                      98\n",
       "job                 unknown\n",
       "marital             unknown\n",
       "education           unknown\n",
       "default                 yes\n",
       "housing                 yes\n",
       "loan                    yes\n",
       "contact           telephone\n",
       "month                   sep\n",
       "day_of_week             wed\n",
       "duration               4918\n",
       "campaign                 56\n",
       "pdays                   999\n",
       "previous                  7\n",
       "poutcome            success\n",
       "emp.var.rate           1.40\n",
       "cons.price.idx        94.77\n",
       "cons.conf.idx        -26.90\n",
       "euribor3m              5.04\n",
       "nr.employed         5228.10\n",
       "y                       yes\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(np.max)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2BqG93kcXW57"
   },
   "source": [
    "The oldest client is 98 years old (`age` = 98), and the number of contacts with one of the customers reached 56 (`campaign` = 56).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MnafwYlU13aR"
   },
   "source": [
    "The `apply` method can also be used to apply the function to each row. To do this, you need to specify the `axis = 1`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RmAMfWTG13aS"
   },
   "source": [
    "**Apply the function to each column cell**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ze8tUYHP13aT"
   },
   "source": [
    "The `map` can also be used for **the values ​​replacement in a column** by passing it as an argument dictionary in form of ` {old_value: new_value}  `.\n",
    "\n",
    "Replace `##YOUR CODE GOES HERE##` with your Python code.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "DchqdQ0_13aU"
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
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       "  <tbody>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
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       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
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       "      <td>1.10</td>\n",
       "      <td>93.99</td>\n",
       "      <td>-36.40</td>\n",
       "      <td>4.86</td>\n",
       "      <td>5191.00</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
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       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.10</td>\n",
       "      <td>93.99</td>\n",
       "      <td>-36.40</td>\n",
       "      <td>4.86</td>\n",
       "      <td>5191.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>40</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.6y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.10</td>\n",
       "      <td>93.99</td>\n",
       "      <td>-36.40</td>\n",
       "      <td>4.86</td>\n",
       "      <td>5191.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>56</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.10</td>\n",
       "      <td>93.99</td>\n",
       "      <td>-36.40</td>\n",
       "      <td>4.86</td>\n",
       "      <td>5191.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age        job  marital    education  default housing loan    contact  \\\n",
       "0   56  housemaid  married     basic.4y       no      no   no  telephone   \n",
       "1   57   services  married  high.school  unknown      no   no  telephone   \n",
       "2   37   services  married  high.school       no     yes   no  telephone   \n",
       "3   40     admin.  married     basic.6y       no      no   no  telephone   \n",
       "4   56   services  married  high.school       no      no  yes  telephone   \n",
       "\n",
       "  month day_of_week  ...  campaign  pdays  previous     poutcome emp.var.rate  \\\n",
       "0   may         mon  ...         1    999         0  nonexistent         1.10   \n",
       "1   may         mon  ...         1    999         0  nonexistent         1.10   \n",
       "2   may         mon  ...         1    999         0  nonexistent         1.10   \n",
       "3   may         mon  ...         1    999         0  nonexistent         1.10   \n",
       "4   may         mon  ...         1    999         0  nonexistent         1.10   \n",
       "\n",
       "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed  y  \n",
       "0           93.99         -36.40       4.86      5191.00  0  \n",
       "1           93.99         -36.40       4.86      5191.00  0  \n",
       "2           93.99         -36.40       4.86      5191.00  0  \n",
       "3           93.99         -36.40       4.86      5191.00  0  \n",
       "4           93.99         -36.40       4.86      5191.00  0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {\"no\": 0, \"yes\": 1}\n",
    "df[\"y\"] = df[\"y\"].map(d)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mFDBIaFrg--8"
   },
   "source": [
    "Double-click **here** for the solution.\n",
    "\n",
    "<!-- \n",
    "df[\"y\"] = df[\"y\"].map(d)\n",
    "-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1XLFcjst13aE"
   },
   "source": [
    "### Indexing and extracting data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5YmO8SzC13aF"
   },
   "source": [
    "A `DataFrame` can be indexed in many ways. In this regard, consider various ways of indexing and extracting data from the DataFrame with simple question examples.\n",
    "\n",
    "You can use the code `dataframe ['name']` to extract a separate column. We use this to answer the question: **What is the share of clients attracted in our DataFrame?**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "f8G8Ce_I13aF"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Share of attracted clients = 11.3%\n"
     ]
    }
   ],
   "source": [
    "print(\"Share of attracted clients =\", '{:.1%}'.format(df[\"y\"].mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "frbJ1GQp13aG"
   },
   "source": [
    "11,3% is a rather bad indicator for a bank, with such a percentage of attracted customers a business can collapse.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cauScC7f13aG"
   },
   "source": [
    "Logical indexation by one column of a `DataFrame` is very convenient. It looks like this: `df [p(df['Name']]`, where`  p ` is a certain logical condition that is checked for each element of the `Name` column. The result of such an indexation is a `DataFrame` consisting only of the rows satisfying the condition `p` by the `Name` column.\n",
    "\n",
    "We use this to answer the question: **What are the mean values ​​of numerical features among the attracted clients?**\n",
    "\n",
    "Replace `##YOUR CODE GOES HERE##` with your Python code.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "TXXcd0mH13aH",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                40.91\n",
       "duration          553.19\n",
       "campaign            2.05\n",
       "pdays             792.04\n",
       "previous            0.49\n",
       "emp.var.rate       -1.23\n",
       "cons.price.idx     93.35\n",
       "cons.conf.idx     -39.79\n",
       "euribor3m           2.12\n",
       "nr.employed      5095.12\n",
       "y                   1.00\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"y\"] == 1].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EEhM_a9KBMnY"
   },
   "source": [
    "Double-click **here** for the solution.\n",
    "\n",
    "<!-- \n",
    "df[df[\"y\"] == 1].mean()\n",
    "-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hqYEmztJY-Vf"
   },
   "source": [
    "Thus, the average age of the attracted clients is about 40 (`age` = 40.91), and 2 calls were required to attract them (`campaign` = 2.05).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ModvYJAq13aH"
   },
   "source": [
    "Combining two previous types of indexation, we will answer the question: **What is the average call duration for the attracted clients**?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "Ubf3zpbk13aH"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average call duration for attracted clients = 9.0 min 13 sec\n"
     ]
    }
   ],
   "source": [
    "acd = round(df[df[\"y\"] == 1][\"duration\"].mean(), 2)\n",
    "acd_in_min = acd // 60\n",
    "print(\"Average call duration for attracted clients =\", acd_in_min, \"min\", int(acd) % 60, \"sec\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C9a34m70ZgRa"
   },
   "source": [
    "So, the average duration of a successful call is almost 553 seconds, that is, nearly 10 minutes.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "95VAbA6Z13aJ"
   },
   "source": [
    "**What is the average age of attracted (`y == 1`) and unmarried (`'marital' == 'single'`) clients?**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "q9Mx-i4r13aK"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average age of attracted clients = 31 years\n"
     ]
    }
   ],
   "source": [
    "print(\"Average age of attracted clients =\", int(df[(df[\"y\"] == 1) & (df[\"marital\"] == \"single\")][\"age\"].mean()), \"years\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5yLFwNHbZ5tm"
   },
   "source": [
    "The average age of unmarried attracted clients is 31, which should be considered when working with such clients.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5-pVIUlf13aN"
   },
   "source": [
    "If we need to get the first or last line of the DataFrame, we can use the code `df[:1]` or `df[-1:]`:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "Q6uHjTaG13aN",
    "scrolled": true
   },
   "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</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>41187</td>\n",
       "      <td>74</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>999</td>\n",
       "      <td>1</td>\n",
       "      <td>failure</td>\n",
       "      <td>-1.10</td>\n",
       "      <td>94.77</td>\n",
       "      <td>-50.80</td>\n",
       "      <td>1.03</td>\n",
       "      <td>4963.60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age      job  marital            education default housing loan  \\\n",
       "41187   74  retired  married  professional.course      no     yes   no   \n",
       "\n",
       "        contact month day_of_week  ...  campaign  pdays  previous  poutcome  \\\n",
       "41187  cellular   nov         fri  ...         3    999         1   failure   \n",
       "\n",
       "      emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  nr.employed  y  \n",
       "41187        -1.10           94.77         -50.80       1.03      4963.60  0  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[-1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1nYYsB2W13aY"
   },
   "source": [
    "## Pivot tables\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AcIkYgoQ13aY"
   },
   "source": [
    "Suppose we want to see how observations in our sample are distributed in the context of two features - `y` and `marital`. To do this, we can build **cross tabulation** by the `crosstab` method.\n",
    "\n",
    "Replace `##YOUR CODE GOES HERE##` with your Python code.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "Xs4C3jZ213aa"
   },
   "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>marital</th>\n",
       "      <th>divorced</th>\n",
       "      <th>married</th>\n",
       "      <th>single</th>\n",
       "      <th>unknown</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>4136</td>\n",
       "      <td>22396</td>\n",
       "      <td>9948</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>476</td>\n",
       "      <td>2532</td>\n",
       "      <td>1620</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "marital  divorced  married  single  unknown\n",
       "y                                          \n",
       "0            4136    22396    9948       68\n",
       "1             476     2532    1620       12"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"y\"], df[\"marital\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "X9DzdWPQh1Fj"
   },
   "source": [
    "Double-click **here** for the solution.\n",
    "\n",
    "<!-- \n",
    "pd.crosstab(df[\"y\"], df[\"marital\"])\n",
    "-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cS4ybt3rai_A"
   },
   "source": [
    "The result shows that the number of attracted married clients is 2532 (`y = 1` for `married`) from the total number.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "4sVTC0mR13aa",
    "scrolled": true
   },
   "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>marital</th>\n",
       "      <th>divorced</th>\n",
       "      <th>married</th>\n",
       "      <th>single</th>\n",
       "      <th>unknown</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "marital  divorced  married  single  unknown\n",
       "y                                          \n",
       "0            0.11     0.61    0.27     0.00\n",
       "1            0.10     0.55    0.35     0.00"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"y\"],\n",
    "            df[\"marital\"],\n",
    "            normalize = 'index')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MONsLf1z13ab"
   },
   "source": [
    "We see that more than half of the clients (61%, column `married`) are married and have not issued a deposit.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "66M5GLW613ab"
   },
   "source": [
    "In `Pandas`, **pivot tables** are implemented by the method `pivot_table` with such parameters:\n",
    "\n",
    "*   `values` – a list of variables to calculate the necessary statistics,\n",
    "*   `index` – a list of variables to group data,\n",
    "*   `aggfunc` — values that we actually need to count by groups - the amount, average, maximum, minimum or something else.\n",
    "\n",
    "Let's find the average age and the call duration for different types of client employment `job`:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "ebAVIoDt13ac"
   },
   "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</th>\n",
       "      <th>duration</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>admin.</td>\n",
       "      <td>38.19</td>\n",
       "      <td>254.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>blue-collar</td>\n",
       "      <td>39.56</td>\n",
       "      <td>264.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>41.72</td>\n",
       "      <td>263.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>housemaid</td>\n",
       "      <td>45.50</td>\n",
       "      <td>250.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>management</td>\n",
       "      <td>42.36</td>\n",
       "      <td>257.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>retired</td>\n",
       "      <td>62.03</td>\n",
       "      <td>273.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>self-employed</td>\n",
       "      <td>39.95</td>\n",
       "      <td>264.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>services</td>\n",
       "      <td>37.93</td>\n",
       "      <td>258.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>student</td>\n",
       "      <td>25.89</td>\n",
       "      <td>283.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>technician</td>\n",
       "      <td>38.51</td>\n",
       "      <td>250.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age  duration\n",
       "job                          \n",
       "admin.        38.19    254.31\n",
       "blue-collar   39.56    264.54\n",
       "entrepreneur  41.72    263.27\n",
       "housemaid     45.50    250.45\n",
       "management    42.36    257.06\n",
       "retired       62.03    273.71\n",
       "self-employed 39.95    264.14\n",
       "services      37.93    258.40\n",
       "student       25.89    283.68\n",
       "technician    38.51    250.23"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(\n",
    "    [\"age\", \"duration\"],\n",
    "    [\"job\"],\n",
    "    aggfunc = \"mean\",\n",
    ").head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7gj2inIUb1U7"
   },
   "source": [
    "The obtained results allow you to plan marketing banking campaigns more effectively.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "omZ1hAP1FVIH"
   },
   "source": [
    "## Visualization in Pandas\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "G0sVMyqlFVIH"
   },
   "source": [
    "Method **scatter_matrix** allows you to visualize the pairwise dependencies between the features (as well as the distribution of each feature on the diagonal). We will do it for numerical features.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "_8CueXbuFVII"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.plotting.scatter_matrix(\n",
    "    df[[\"age\", \"duration\", \"campaign\"]],\n",
    "    figsize = (15, 15),\n",
    "    diagonal = \"kde\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "i1RN2MBidNJX"
   },
   "source": [
    "A scatter matrix (pairs plot) compactly plots all the numeric variables we have in a dataset against each other.\n",
    "The plots on the main diagonal allow you to visually define the type of data distribution: the distribution is similar to normal for age, and for a call duration and the number of contacts, the [geometric distribution](https://en.wikipedia.org/wiki/geometric_distribution?utm_medium=Exinfluencer\\&utm_source=Exinfluencer\\&utm_content=000026UJ\\&utm_term=10006555\\&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkQuickLabsEDA_Pandas_Banking_L126457256-2021-01-01) is more suitable.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zEAaz2PIFVII"
   },
   "source": [
    "**You can build a separate histogram for each feature:**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "SJOQB60_FVII"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfMAAAFlCAYAAAD/MAEVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAZ50lEQVR4nO3df6zd9X3f8edruCUGj18huaI2m5nqpQXcpOGKOs0SXRc2nIACmoLkiBQzsVlDLCWVpWIv2rL+YcnRRtugDCQrZECI4lCaDi+ENMzpVbaJHzP5UWOIh1s8YnBx0iQEp5TF9L0/zpdwaq59r++5P87H9/mQjs73vL8/zuctzuV1v5/v1+emqpAkSe36e/M9AEmSNBjDXJKkxhnmkiQ1zjCXJKlxhrkkSY0zzCVJatyi+R7AdJ199tm1fPny+R4GAD/5yU849dRT53sYAztR+oATpxf7GC72MVwWWh+PP/7496vqLROtazbMly9fzs6dO+d7GACMj48zNjY238MY2InSB5w4vdjHcLGP4bLQ+kjyf4+2zml2SZIaZ5hLktQ4w1ySpMYZ5pIkNc4wlySpcYa5JEmNM8wlSWqcYS5JUuMMc0mSGmeYS5LUOMNckqTGTRrmST6T5GCSJ/pq/zHJd5L8WZI/TnJG37pNSfYm2ZPksr76RUl2detuTZKufnKSL3T1R5Msn9kWJUk6sU3lzPxOYM0RtYeAC6vqV4D/A2wCSHI+sBa4oNvntiQndfvcDqwHVnSP1455PfDDqvpF4PeBT0y3GUmSFqJJ/2paVX39yLPlqvpq38tHgA92y1cC26rqFeCZJHuBi5PsA06rqocBktwNXAU82O3zH7r97wM+lSRVVdPsSfNo+cYH5nsIAGxYeZjrJhjLvi2Xz8NoJGl2ZSqZ2YX5l6rqwgnW/TfgC1V1T5JPAY9U1T3dujvoBfY+YEtVXdrV3wPcXFVXdNP3a6pqf7fuz4Ffq6rvT/Be6+md3TMyMnLRtm3bptHyzDt06BBLliyZ72EMbCb62PXcizM0msGMLIYXXn5jfeXS0+d+MAPwszVc7GO4LLQ+Vq9e/XhVjU60bqC/Z57kY8Bh4HOvlSbYrI5RP9Y+byxWbQW2AoyOjtaw/B3bhfY3dY9lorPh+bBh5WFu2fXGj/e+a8bmfjAD8LM1XOxjuNjH66Z9N3uSdcAVwDV9U+L7gXP7NlsGPN/Vl01Q/zv7JFkEnA78YLrjkiRpoZlWmCdZA9wMfKCq/rpv1XZgbXeH+nn0bnR7rKoOAC8lWdXdxX4tcH/fPuu65Q8CX/N6uSRJUzfpNHuSzwNjwNlJ9gMfp3f3+snAQ92/MHukqv51Ve1Oci/wJL3p9xur6tXuUDfQuzN+Mb3r6A929TuAz3Y3y/2A3t3wkiRpiqZyN/uHJijfcYztNwObJ6jvBN5wA11V/Q1w9WTjkCRJE/Mb4CRJapxhLklS4wxzSZIaZ5hLktQ4w1ySpMYZ5pIkNc4wlySpcYa5JEmNM8wlSWqcYS5JUuMMc0mSGjfQ3zPX3Fo+y38rfMPKw0Pz98glSVPnmbkkSY0zzCVJapxhLklS4wxzSZIaZ5hLktQ4w1ySpMYZ5pIkNc4wlySpcYa5JEmNM8wlSWqcYS5JUuMMc0mSGmeYS5LUOMNckqTGGeaSJDXOMJckqXGGuSRJjTPMJUlqnGEuSVLjDHNJkhpnmEuS1DjDXJKkxhnmkiQ1zjCXJKlxhrkkSY0zzCVJapxhLklS4wxzSZIaZ5hLktQ4w1ySpMZNGuZJPpPkYJIn+mpnJXkoydPd85l96zYl2ZtkT5LL+uoXJdnVrbs1Sbr6yUm+0NUfTbJ8hnuUJOmENpUz8zuBNUfUNgI7qmoFsKN7TZLzgbXABd0+tyU5qdvndmA9sKJ7vHbM64EfVtUvAr8PfGK6zUiStBBNGuZV9XXgB0eUrwTu6pbvAq7qq2+rqleq6hlgL3BxknOA06rq4aoq4O4j9nntWPcBl7x21i5JkiY33WvmI1V1AKB7fmtXXwp8t2+7/V1tabd8ZP3v7FNVh4EXgTdPc1ySJC04i2b4eBOdUdcx6sfa540HT9bTm6pnZGSE8fHxaQxx5h06dGhOxrJh5eFZPf7I4tl/j7lytF6G5TMzVXP12Zpt9jFc7GO4zEQf0w3zF5KcU1UHuin0g119P3Bu33bLgOe7+rIJ6v377E+yCDidN07rA1BVW4GtAKOjozU2NjbN4c+s8fFx5mIs1218YFaPv2HlYW7ZNdO/382Po/Wy75qxuR/MAObqszXb7GO42MdwmYk+pjvNvh1Y1y2vA+7vq6/t7lA/j96Nbo91U/EvJVnVXQ+/9oh9XjvWB4GvddfVJUnSFEx6Gpbk88AYcHaS/cDHgS3AvUmuB54Frgaoqt1J7gWeBA4DN1bVq92hbqB3Z/xi4MHuAXAH8Nkke+mdka+dkc4kSVogJg3zqvrQUVZdcpTtNwObJ6jvBC6coP43dL8MSJKk4+c3wEmS1DjDXJKkxhnmkiQ1zjCXJKlxhrkkSY0zzCVJapxhLklS4wxzSZIaZ5hLktQ4w1ySpMYZ5pIkNc4wlySpcYa5JEmNM8wlSWqcYS5JUuMMc0mSGmeYS5LUOMNckqTGGeaSJDXOMJckqXGGuSRJjTPMJUlqnGEuSVLjDHNJkhpnmEuS1DjDXJKkxhnmkiQ1zjCXJKlxhrkkSY0zzCVJapxhLklS4wxzSZIaZ5hLktQ4w1ySpMYZ5pIkNc4wlySpcYa5JEmNM8wlSWqcYS5JUuMMc0mSGmeYS5LUOMNckqTGGeaSJDVuoDBP8ttJdid5Isnnk7wpyVlJHkrydPd8Zt/2m5LsTbInyWV99YuS7OrW3Zokg4xLkqSFZNF0d0yyFPgt4PyqejnJvcBa4HxgR1VtSbIR2AjcnOT8bv0FwC8A/z3JP66qV4HbgfXAI8CXgTXAgwP0JU1o+cYH5nsIk9q35fL5HoKkxgw6zb4IWJxkEXAK8DxwJXBXt/4u4Kpu+UpgW1W9UlXPAHuBi5OcA5xWVQ9XVQF39+0jSZImkV5+TnPn5CZgM/Ay8NWquibJj6rqjL5tflhVZyb5FPBIVd3T1e+gd/a9D9hSVZd29fcAN1fVFRO833p6Z/CMjIxctG3btmmPfSYdOnSIJUuWzPr77HruxVk9/shieOHlWX2LOdNyLyuXnv6z5bn6bM02+xgu9jFcptrH6tWrH6+q0YnWDTLNfia9s+3zgB8Bf5jkw8faZYJaHaP+xmLVVmArwOjoaI2NjR3HiGfP+Pg4czGW62Z5injDysPcsmvaH4mh0nIv+64Z+9nyXH22Zpt9DBf7GC4z0ccg0+yXAs9U1feq6qfAF4FfB17ops7png922+8Hzu3bfxm9afn93fKRdUmSNAWDhPmzwKokp3R3n18CPAVsB9Z126wD7u+WtwNrk5yc5DxgBfBYVR0AXkqyqjvOtX37SJKkSUx7HrKqHk1yH/AN4DDwTXpT4EuAe5NcTy/wr+62393d8f5kt/2N3Z3sADcAdwKL6V1H9052SZKmaKCLilX1ceDjR5RfoXeWPtH2m+ndMHdkfSdw4SBjkSRpofIb4CRJapxhLklS4wxzSZIaZ5hLktQ4w1ySpMYZ5pIkNc4wlySpcYa5JEmNM8wlSWqcYS5JUuMMc0mSGmeYS5LUOMNckqTGGeaSJDXOMJckqXGGuSRJjTPMJUlqnGEuSVLjDHNJkhpnmEuS1DjDXJKkxhnmkiQ1zjCXJKlxhrkkSY0zzCVJapxhLklS4wxzSZIaZ5hLktQ4w1ySpMYZ5pIkNc4wlySpcYa5JEmNM8wlSWqcYS5JUuMMc0mSGmeYS5LUOMNckqTGGeaSJDXOMJckqXGGuSRJjTPMJUlqnGEuSVLjDHNJkho3UJgnOSPJfUm+k+SpJO9KclaSh5I83T2f2bf9piR7k+xJcllf/aIku7p1tybJIOOSJGkhGfTM/JPAV6rql4C3A08BG4EdVbUC2NG9Jsn5wFrgAmANcFuSk7rj3A6sB1Z0jzUDjkuSpAVj2mGe5DTgvcAdAFX1/6rqR8CVwF3dZncBV3XLVwLbquqVqnoG2AtcnOQc4LSqeriqCri7bx9JkjSJ9PJzGjsm7wC2Ak/SOyt/HLgJeK6qzujb7odVdWaSTwGPVNU9Xf0O4EFgH7Clqi7t6u8Bbq6qKyZ4z/X0zuAZGRm5aNu2bdMa+0w7dOgQS5YsmfX32fXci7N6/JHF8MLLs/oWc6blXlYuPf1ny3P12Zpt9jFc7GO4TLWP1atXP15VoxOtWzTA+y8C3gl8pKoeTfJJuin1o5joOngdo/7GYtVWer9AMDo6WmNjY8c14NkyPj7OXIzluo0PzOrxN6w8zC27BvlIDI+We9l3zdjPlufqszXb7GO42MdwmYk+Brlmvh/YX1WPdq/voxfuL3RT53TPB/u2P7dv/2XA81192QR1SZI0BdMO86r6S+C7Sd7WlS6hN+W+HVjX1dYB93fL24G1SU5Och69G90eq6oDwEtJVnV3sV/bt48kSZrEoPOQHwE+l+Tngb8A/gW9XxDuTXI98CxwNUBV7U5yL73APwzcWFWvdse5AbgTWEzvOvqDA45LkqQFY6Awr6pvARNdjL/kKNtvBjZPUN8JXDjIWKQTxfK+eyM2rDw86/dKHK99Wy6f7yFIOoLfACdJUuMMc0mSGmeYS5LUOMNckqTGGeaSJDXOMJckqXGGuSRJjTPMJUlqnGEuSVLjDHNJkhpnmEuS1DjDXJKkxhnmkiQ1zjCXJKlxhrkkSY0zzCVJapxhLklS4wxzSZIaZ5hLktQ4w1ySpMYZ5pIkNc4wlySpcYa5JEmNM8wlSWqcYS5JUuMMc0mSGmeYS5LUOMNckqTGGeaSJDXOMJckqXGGuSRJjTPMJUlqnGEuSVLjDHNJkhpnmEuS1DjDXJKkxhnmkiQ1zjCXJKlxhrkkSY0zzCVJapxhLklS4wxzSZIaN3CYJzkpyTeTfKl7fVaSh5I83T2f2bftpiR7k+xJcllf/aIku7p1tybJoOOSJGmhmIkz85uAp/pebwR2VNUKYEf3miTnA2uBC4A1wG1JTur2uR1YD6zoHmtmYFySJC0IA4V5kmXA5cCn+8pXAnd1y3cBV/XVt1XVK1X1DLAXuDjJOcBpVfVwVRVwd98+kiRpEoOemf8B8DvA3/bVRqrqAED3/NauvhT4bt92+7va0m75yLokSZqCRdPdMckVwMGqejzJ2FR2maBWx6hP9J7r6U3HMzIywvj4+JTGOtsOHTo0J2PZsPLwrB5/ZPHsv8dcOVF6GcY+pvNZn6ufkdlmH8PFPl437TAH3g18IMn7gTcBpyW5B3ghyTlVdaCbQj/Ybb8fOLdv/2XA81192QT1N6iqrcBWgNHR0RobGxtg+DNnfHycuRjLdRsfmNXjb1h5mFt2DfKRGB4nSi/D2Me+a8aOe5+5+hmZbfYxXOzjddOeZq+qTVW1rKqW07ux7WtV9WFgO7Cu22wdcH+3vB1Ym+TkJOfRu9HtsW4q/qUkq7q72K/t20eSJE1iNn7l3wLcm+R64FngaoCq2p3kXuBJ4DBwY1W92u1zA3AnsBh4sHtIkqQpmJEwr6pxYLxb/ivgkqNstxnYPEF9J3DhTIxFkqSFxm+AkySpcYa5JEmNM8wlSWqcYS5JUuMMc0mSGmeYS5LUOMNckqTGGeaSJDXOMJckqXGGuSRJjTPMJUlqnGEuSVLjDHNJkhpnmEuS1DjDXJKkxhnmkiQ1zjCXJKlxhrkkSY0zzCVJapxhLklS4wxzSZIaZ5hLktQ4w1ySpMYZ5pIkNc4wlySpcYa5JEmNM8wlSWqcYS5JUuMMc0mSGmeYS5LUOMNckqTGGeaSJDXOMJckqXGGuSRJjTPMJUlqnGEuSVLjDHNJkhpnmEuS1DjDXJKkxhnmkiQ1zjCXJKlxhrkkSY0zzCVJaty0wzzJuUn+NMlTSXYnuamrn5XkoSRPd89n9u2zKcneJHuSXNZXvyjJrm7drUkyWFuSJC0cg5yZHwY2VNUvA6uAG5OcD2wEdlTVCmBH95pu3VrgAmANcFuSk7pj3Q6sB1Z0jzUDjEuSpAVl2mFeVQeq6hvd8kvAU8BS4Ergrm6zu4CruuUrgW1V9UpVPQPsBS5Ocg5wWlU9XFUF3N23jyRJmkR6+TngQZLlwNeBC4Fnq+qMvnU/rKozk3wKeKSq7unqdwAPAvuALVV1aVd/D3BzVV0xwfusp3cGz8jIyEXbtm0beOwz4dChQyxZsmTW32fXcy/O6vFHFsMLL8/qW8yZE6WXYexj5dLTj3ufufoZmW32MVwWWh+rV69+vKpGJ1q3aNBBJFkC/BHw0ar68TEud0+0oo5Rf2OxaiuwFWB0dLTGxsaOe7yzYXx8nLkYy3UbH5jV429YeZhbdg38kRgKJ0ovw9jHvmvGjnufufoZmW32MVzs43UD3c2e5OfoBfnnquqLXfmFbuqc7vlgV98PnNu3+zLg+a6+bIK6JEmagkHuZg9wB/BUVf1e36rtwLpueR1wf199bZKTk5xH70a3x6rqAPBSklXdMa/t20eSJE1ikPm7dwO/CexK8q2u9m+BLcC9Sa4HngWuBqiq3UnuBZ6kdyf8jVX1arffDcCdwGJ619EfHGBckiQtKNMO86r6n0x8vRvgkqPssxnYPEF9J72b5yRJ0nHyG+AkSWqcYS5JUuMMc0mSGjdc/4B1Hi0f4N9wb1h5eNb/DbgkSUdjmEs6LtP5xXeuf+Hdt+XyOXsvaRg4zS5JUuMMc0mSGmeYS5LUOMNckqTGGeaSJDXOMJckqXGGuSRJjTPMJUlqnGEuSVLjDHNJkhpnmEuS1DjDXJKkxhnmkiQ1zjCXJKlxhrkkSY0zzCVJapxhLklS4wxzSZIaZ5hLktQ4w1ySpMYZ5pIkNc4wlySpcYa5JEmNM8wlSWqcYS5JUuMMc0mSGmeYS5LUOMNckqTGGeaSJDXOMJckqXGGuSRJjTPMJUlqnGEuSVLjFs33ACRppi3f+MCsHHfDysNcNwPH3rfl8hkYjfQ6z8wlSWqcYS5JUuOcZpekOTZblwGmaiqXC7wU0JahOTNPsibJniR7k2yc7/FIktSKoQjzJCcB/xl4H3A+8KEk58/vqCRJasOwTLNfDOytqr8ASLINuBJ4cl5HJUkL1HxfCpiKO9ecOt9DGBpDcWYOLAW+2/d6f1eTJEmTSFXN9xhIcjVwWVX9y+71bwIXV9VHjthuPbC+e/k2YM+cDvTozga+P9+DmAEnSh9w4vRiH8PFPobLQuvjH1bVWyZaMSzT7PuBc/teLwOeP3KjqtoKbJ2rQU1Vkp1VNTrf4xjUidIHnDi92MdwsY/hYh+vG5Zp9v8NrEhyXpKfB9YC2+d5TJIkNWEozsyr6nCSfwP8CXAS8Jmq2j3Pw5IkqQlDEeYAVfVl4MvzPY5pGrqp/2k6UfqAE6cX+xgu9jFc7KMzFDfASZKk6RuWa+aSJGmaDPPjlOTcJH+a5Kkku5Pc1NXPSvJQkqe75zPne6zHkuRNSR5L8u2uj9/t6k318ZokJyX5ZpIvda+b6yPJviS7knwryc6u1mIfZyS5L8l3up+Td7XWR5K3df8dXnv8OMlHW+sDIMlvdz/jTyT5fPez32IfN3U97E7y0a7WRB9JPpPkYJIn+mpHHXuSTd1Xm+9JctlU3sMwP36HgQ1V9cvAKuDG7qtnNwI7qmoFsKN7PcxeAX6jqt4OvANYk2QV7fXxmpuAp/pet9rH6qp6R98/U2mxj08CX6mqXwLeTu+/S1N9VNWe7r/DO4CLgL8G/pjG+kiyFPgtYLSqLqR3g/Fa2uvjQuBf0fu20LcDVyRZQTt93AmsOaI24di7PFkLXNDtc1v3lefHVlU+BngA9wP/lN4X2JzT1c4B9sz32I6jh1OAbwC/1mIf9L6XYAfwG8CXulqLfewDzj6i1lQfwGnAM3T347TaxxFj/2fA/2qxD17/ds2z6N3w/KWun9b6uBr4dN/rfwf8Tkt9AMuBJ/peTzh2YBOwqW+7PwHeNdnxPTMfQJLlwK8CjwIjVXUAoHt+6zwObUq6qelvAQeBh6qqyT6AP6D3g/23fbUW+yjgq0ke777tENrr4x8B3wP+S3fZ49NJTqW9PvqtBT7fLTfVR1U9B/wn4FngAPBiVX2VxvoAngDem+TNSU4B3k/vi8Za66Pf0cY+ra83N8ynKckS4I+Aj1bVj+d7PNNRVa9WbxpxGXBxN5XVlCRXAAer6vH5HssMeHdVvZPeXw+8Mcl753tA07AIeCdwe1X9KvAThnfqc1Ldl1h9APjD+R7LdHTXYa8EzgN+ATg1yYfnd1THr6qeAj4BPAR8Bfg2vUueJ6JMUJv0n50Z5tOQ5OfoBfnnquqLXfmFJOd068+hd7bbhKr6ETBO7/pMa328G/hAkn3ANuA3ktxDe31QVc93zwfpXZ+9mPb62A/s72Z5AO6jF+6t9fGa9wHfqKoXutet9XEp8ExVfa+qfgp8Efh12uuDqrqjqt5ZVe8FfgA8TYN99Dna2Kf09eZHMsyPU5IAdwBPVdXv9a3aDqzrltfRu5Y+tJK8JckZ3fJiej/036GxPqpqU1Utq6rl9KZDv1ZVH6axPpKcmuTvv7ZM77rmEzTWR1X9JfDdJG/rSpfQ+1PGTfXR50O8PsUO7fXxLLAqySnd/7suoXdDYmt9kOSt3fM/AP45vf8uzfXR52hj3w6sTXJykvOAFcBjkx5tvm8KaO0B/BN6Ux5/Bnyre7wfeDO9m7Ce7p7Pmu+xTtLHrwDf7Pp4Avj3Xb2pPo7oaYzXb4Brqg9615q/3T12Ax9rsY9uzO8Adnafrf8KnNloH6cAfwWc3ldrsY/fpfeL+hPAZ4GTG+3jf9D7xfDbwCUt/feg94vHAeCn9M68rz/W2IGPAX9O7ya5903lPfwGOEmSGuc0uyRJjTPMJUlqnGEuSVLjDHNJkhpnmEuS1DjDXJKkxhnmkiQ1zjCXJKlx/x8xkePaEtrtWgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"age\"].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CjnmCPbFefXl"
   },
   "source": [
    "The histogram shows that most of our clients are between the ages of 25 and 50, which corresponds to the actively working part of the population.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-1A59i55FVIJ"
   },
   "source": [
    "**Or you can build it for all together:**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "H5oIPZa0FVIJ"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(color = \"k\",\n",
    "        bins = 30,\n",
    "        figsize = (15, 10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gb57x-rPe5mb"
   },
   "source": [
    "A visual analysis of the histograms presented allows us to make preliminary assumptions about the variability of the source data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-hQDR1PGFVIK"
   },
   "source": [
    "[**Box Plot** (\"Box and whisker plot\")](https://en.wikipedia.org/wiki/box_plot?utm_medium=Exinfluencer\\&utm_source=Exinfluencer\\&utm_content=000026UJ\\&utm_term=10006555\\&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkQuickLabsEDA_Pandas_Banking_L126457256-2021-01-01) is useful too. It allows you to compactly visualize the main characteristics of the feature distribution (the median, lower and upper quartile, minimal and maximum, outliers).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "9lAwux3g4Q6C",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UU8+ImTNn0tTUxIYNG2hqalJCz1Lt7e08/vjj7N+/n4aGBvbv38/jjz9Oe3t72KGJiABJJnUzu8rMmsxsi5ldHcwbZWbrzWx78Hp0SiIVCVlhYSG1tbU95tXW1lJYWBhSRCIiPSWc1M2sFJgDvBeYCHzYzE4GFgIb3P1kYEMwLZL15syZQ1VVFUuXLqWtrY2lS5dSVVXFnDlzwg5NRARI7pp6CfAHd/8HgJk9AHwEmA5MCdZZCTQCVUkcR2RQWLZsGQCLFi2ivb2dwsJC5s2b1z1fRCRslug9tmZWAqwBzgbeJFYr3wR82t1Hxq33mrsf0ARvZnOBuQDFxcWTfvzjHycURza46aabWLduHfv27WPIkCFcfPHFXHXVVWGHJUlobW1l2LBhYYchKaCyjJZcKM/y8vJH3b2sr2UJ19TdvdnMlgDrgVbgT0DHYWy/HFgOUFZW5lF9znhlZSX33nsvS5YsYfz48WzdupWqqipOOOEE1fCyWGNjI1H9nc01KstoyfXyTKqjnLvXufu/uPv7gF3AdqDFzMYABK8vJx9m9lqxYgVLlixhwYIFFBUVsWDBApYsWcKKFSvCDk1ERCIm2d7vxwavJwIfBeqBtcCsYJVZxJroc1Z7ezvz5s3rMW/evHm6DUpERFIu2cFnfmpmxwD7gM+7+2tmthi4y8wqgB3ApckGmc0KCwsZOnRon/Ml+5x++uls3ry5e3rChAk8+eSTIUYkIvKWZJvfz3P38e4+0d03BPNedfep7n5y8LorNaFmp/ga+Ze//OU+50t26Ero06ZN45577mHatGls3ryZ008/PezQREQAjSiXMYWFhdxwww2qoWexroS+Zs0aRo4cyZo1a7oTu4jIYKCkngF33303bW1tNDQ00NbWxt133x12SJKgurq6g06LiIRJST0DPvaxj1FaWsrUqVMpLS3lYx/7WNghSYIqKioOOi0iEiYl9QzZsmULixYtYsuWLWGHIgmaMGECa9euZfr06ezevZvp06ezdu1aJkyYEHZoIiKAHr2adqeddlp3Iv/617/eY75klyeffJLTTz+dtWvXsnbtWkC930VkcFFNPc2am5uZP39+dwe5wsJC5s+fT3Nzc8iRSSJ279590GnJHvX19T0ui9XX14cdkkjSVFNPs5EjR7J8+XL++7//u3uY2P/8z/9k5MiRYYcmh+nEE0/k+eef55xzzuELX/gC3/72t/nd737HiSeeyI4dO8IOTw5DfX091dXV1NXV0dnZSX5+fnf/iJkzZ4YcnUjiVFNPs7179zJ8+HDOPPNMCgoKOPPMMxk+fDh79+4NOzQ5TF0J/aGHHmL06NE89NBDnHPOOTz//PNhhyaHqaamhrq6OsrLyykoKKC8vJy6ujpqamrCDk0kKUrqadbR0cG3vvUtKisrueCCC6isrORb3/oWHR0DfvaNDCK9b0fU7YnZqbm5mcmTJ/eYN3nyZF0Wk6yn5vc0KywsZPHixWzfvh13Z+vWrSxevFiD0GSpsWPHhh2CpEBJSQnXXXcdq1evprm5mZKSEmbMmEFJSUnYoYkkRTX1NDv22GPZtm0bZ599NqtWreLss89m27ZtHHvssWGHJklQM212Ky8vZ8mSJcyePZt169Yxe/ZslixZQnl5edihiSTF3D3sGCgrK/NNmzaFHUZa5OXlMX78eJ566ina29spLCzkpJNOYuvWrezfvz/s8OQwmFm/ywbD90gGrrS0lBkzZhxQU1+9ejVNTU1hhydJyIXnqZvZo+5e1tcy1dTTzN3ZvXt39wNc2tvb2b17t5JAlrr00kt73J546aU5/RDCrNXc3My1115LU1MTGzZsoKmpiWuvvVbX1CXrKalnwIsvvsg555zDqlWrOOecc3jxxRfDDkkStGrVKq6//np++ctfcv3117Nq1aqwQ5IElJSUsHHjxh7zNm7cqGvqkvWU1DNk4sSJFBUVMXHixLBDkSRVV1fz9NNPU11dHXYokqDq6moqKipoaGigo6ODhoYGKioqVKaS9dT7PQMmT55MbW0tt912G2bG5MmTD6glSPZoa2vjiiuuCDsMSULXADOVlZXd19Rramo08IxkPXWUSzMzw8x6XEPvmh4M514GrqioqLtvRLzCwkLa2tpCiEhSIRc6VuWSXChPdZQLmbszZMgQbrrpJoYMGaJknqW6Enp8WcbPFxEJm5J6BuTl5bFv3z6uuuoq9u3bR16eTnu2KigoIC8vj6uuuoq8vDwKCnQFS0QGD2WXDNi2bRvuTkNDA+7Otm3bwg5JEvTwww/T1tZGQ0MDbW1tPPzww2GHJCLSTdWMDDj55JMPuKYu2WnSpElhhyApUllZyYoVK7oHhZozZw7Lli0LOyyRpKimngHuTn5+PkuXLiU/P1/X1CPgK1/5StghSBIqKyupra3tMeZAbW0tlZWVYYcmkhT1fk8zDS0aHSrL6CgqKuL6669nwYIF3b2lly5dyqJFi3QnQ5ZT73dJu/z8/INOi0hmtbe3M2/evB7z5s2bpzsZJOspqWdAZ2cnRx99NCtWrODoo4+ms7Mz7JAkCUVFRdxyyy0UFRWFHYokqLCwkNra2h7zamtr9UhkyXrqKJchY8eOZdiwYYwdO5bXXnst7HAkCUceeSSFhYUceeSRaqrNUnPmzKGqqgqA8ePHs3TpUqqqqg6ovYtkGyX1DBg+fDhbtmzpHoJy+PDh7N27N+SoJBF5eXm89tprzJkzp3taj9DNPl293BctWtTd+33evHnq/S5ZL6nmdzP7gpltMbMmM6s3syIzG2Vm681se/B6dKqCzVa9E7gSevbqncCV0LPXsmXLeow5oIQuUZBwUjez44ErgTJ3LwXygU8CC4EN7n4ysCGYFuDqq68OOwRJkdmzZ4cdgojIAZLtKFcAHGlmBcDbgJeA6cDKYPlKYEaSx4iMG2+8MewQJEVuv/32sEMQETlAwtfU3f1FM/smsAN4E7jf3e83s2J33xmss9PMju1rezObC8wFKC4uprGxMdFQBr1vfOMblJWV0drayrBhw9i0aRNf+tKXIv2Zo+prX/sa5513XndZ/va3v+Waa65RWWax1tZWlV+E5Hp5Jjz4THCt/KfAJ4DdwCrgbuAWdx8Zt95r7n7Q6+pRH3wmLy+Pzs7O7kER8vPz2b9/vwYsyTIafCaacmGwklySC+WZrsFnzgeecfdX3H0f8DPgHKDFzMYEBx4DvJzEMSJh//795Ofns2nTpu6ELtntU5/6VNghiIgcIJmkvgP4VzN7m8WqMFOBZmAtMCtYZxawJrkQs1tXDW7//v186Utf6k7oqtlltzvvvDPsEEREDpDMNfWHzexu4DGgA3gcWA4MA+4yswpiif/SVASazXrfy6znqYuISDoklV3c/Vp3P9XdS9390+7e7u6vuvtUdz85eN2VqmCzUVdz+7Bhw7jtttsYNmxYd3O8ZK+vfe1rYYcgInIAVRnTrCuhv/7665x66qm8/vrr3YldslNeXh5HHnmkWlxEZNDRX6UMeOCBBw46Ldmld/8IEZHBQkk9AyZNmoSZUV5ejpkxadKksEMSyXn19fWUlpYydepUSktLqa+vDzskkaQpqWeQrsNGx+WXXx52CJKE+vp6qqurWbZsGb/61a9YtmwZ1dXVSuyS9ZTUM+iaa64JOwRJkTvuuCPsECQJNTU11NXVUV5eTkFBAeXl5dTV1VFTUxN2aCJJUVLPgKeeegp3p6GhAXfnqaeeCjskSdDGjRt7lOXGjRvDDkkS0NzczOTJk3vMmzx5Ms3NzSFFJJIaep56Bpx66ql0dHR0TxcU6LRnq96JQLJTSUkJ1113HatXr6a5uZmSkhJmzJhBSUlJ2KGJJEU19TQzMzo6OigqKuKWW26hqKiIjo6Og44jLoPfhRdeGHYIkoTy8nKWLFnC7NmzWbduHbNnz2bJkiWUl5eHHZpIUlRlTDN3Jy8vj7a2Nq644grgwBHmJPvcd999YYcgSWhoaKCqqorbb7+9u6ZeVVXF6tWrww5NJCkJP6UtlaL+lLaCgoIDmt87Ojo0/nuW0VPaoiM/P5+2tjaGDBnS/VSvffv2UVRURGdnZ9jhSRL0lDZJu46ODoqLi/ne975HcXFxjwQv2Um9pLNbSUnJAZ0cN27cqGvqkvWU1DPkrLPOYuTIkZx11llhhyIpsH379rBDkCRUV1dTUVFBQ0MDHR0dNDQ0UFFRQXV1ddihiSRF19Qz4JRTTmHt2rWsXbu2e3rbtm0hRyXJ0H3q2W3mzJkAVFZWdl9Tr6mp6Z4vkq1UU8+APXv29Li3ec+ePWGHJJLzZs6cSVNTExs2bKCpqUkJXSJBST3NCgsLaWlp4bjjjuPZZ5/luOOOo6WlhcLCwrBDkyR8+MMfDjsEEZEDqPk9zdra2igqKqKlpYXPfvazQCzRt7W1hRyZJOPee+8NOwQRkQOopp4gMxvwT3t7e49t29vbD2t7EUk9PaVNokg19QQlcl/yuIXreHbxxWmIRjLtc5/7HLfeemvYYUiCup7SVldXR2dnJ/n5+VRUVADo2rpkNdXURRJwxBFHhB2CJEFPaZOoUlIXScCNN94YdgiSBD2lTaJKze8iknNKSkr4+Mc/zi9/+Uva29spLCzkQx/6kEaUk6ynmrpIAt773veGHYIk4fjjj2f16tXMnj2bn//858yePZvVq1dz/PHHhx2aSFKU1EUS8Mgjj4QdgiThgQce4LLLLuPBBx9k+vTpPPjgg1x22WU88MADYYcmkhQldZHDcPPNN/cYHfDmm28OOyRJQHt7O8uXL+8xotzy5csPuP1UJNsoqYschiuvvPKg05IdCgsLGT16NGZGeXk5Zsbo0aM10qNkPSV1kcNkZqxatUoDA2W5N998s8cjkd98882wQxJJmpK6yADFDzgUP/BMIgMRSbja29sZNWoUu3fv5rOf/Sy7d+9m1KhRan6XrJdwUjezd5vZE3E/e83sajMbZWbrzWx78Hp0KgMWSbVkh+zVkL/Z6fHHH6etrY2Ghgba2tp4/PHHww5JJGkJJ3V3/4u7n+HuZwCTgH8A9wALgQ3ufjKwIZgWGbTc/bB/3ll1b0LbyeBx0UUXHXRaJBulqvl9KvC0uz8HTAdWBvNXAjNSdAwRkZQYNWoUW7ZsobS0lL/97W+UlpayZcsWRo0aFXZoIkmxVNQezOx24DF3v8XMdrv7yLhlr7n7AU3wZjYXmAtQXFw86cc//nHScQx2l9/3BndcODTsMCQFVJbZb9q0abz++uvd00cddRRr164NMSJJhdbWVoYNGxZ2GGlVXl7+qLuX9bUs6WFizewIYBrw5cPZzt2XA8sBysrKfMqUKcmGMvjdt46c+Jy5QGWZ9fbu3QtAY2OjyjJCcr08U9H8/iFitfSWYLrFzMYABK8vp+AYIiIicgipSOozgfq46bXArOD9LGBNCo4hIiIih5BUUjeztwH/BvwsbvZi4N/MbHuwbHEyxxAREZGBSeqaurv/Azim17xXifWGFxEZtE4//XQ2b97cPT1hwgSefPLJECMSSZ5GlBORnNOV0KdNm8Y999zDtGnT2Lx5M6effnrYoYkkRUldRHJOV0Jfs2YNI0eOZM2aNd2JXSSbKamLSE668MILKS0tZerUqZSWlnLhhReGHZJI0pK+T11EJBstWLCAX/ziF3R2dpKfn69hYiUSVFMXkZxTWFhIW1sbN954I62trdx44420tbXpeeqS9VRTF5Gcs2/fPkpLS1m7dm330LClpaVs3bo15MhEkqOauojknJKSElpaWnrMa2lpoaSkJKSIRFJDSV1Ecs7OnTt55ZVXOO2006ivr+e0007jlVdeYefOnWGHJpIUJXURyTm7du3ipJNOAuCyyy4D4KSTTmLXrl1hhiWSNF1TF5Gc9OCDDzJmzJjup3rt3LmTsWPHhh2WxDGzjBwnFY8gHyyU1EUkJ5155pns3r2b9vZ2CgsLGTlyZNghSS+JJNtxC9fx7OKL0xBNdlDzu4jknKFDh9LS0sKYMWP4wQ9+wJgxY2hpaWHo0KFhhyaSFNXURSTndHR0MGLECJ599lk+/elPAzBixAja2tpCjkwkOaqpi0jOaW9v56WXXsLdaWhowN156aWXaG9vDzs0kaQoqYtIziksLKS2trbHvNraWo0oJ1lPze8iknPmzJlDVVUVAOPHj2fp0qVUVVUxb968kCMTSY6SuojknGXLlgGwaNGi7t7v8+bN654vkq3U/C4iOWnZsmW0tbXR0NBAW1ubErpEgpK6iIhIRCipi4iIRISSuojkpPr6ekpLS5k6dSqlpaXU19eHHZJI0tRRTkRyTn19PdXV1dTV1dHZ2Ul+fj4VFRUAzJw5M+ToRBKnmrqI5Jyamhrq6uooLy+noKCA8vJy6urqqKmpCTs0kaQoqYtIzmlubmby5Mk95k2ePJnm5uaQIhJJDTW/i0jOKSkp4ZxzzuHRRx/F3TEzJk2aRElJSdihiSRFNXURyTl5eXls2rSJSy65hHvuuYdLLrmETZs2kZenP4mS3fQbLCI5p6mpialTp/L000/z7//+7zz99NNMnTqVpqamsEMTSYqa30Uk57g7P/3pTxkxYgSNjY1MmTKFPXv2MHLkyLBDE0lKUkndzEYC3wVKAQdmA38BfgKMA54FPu7uryVzHBGRVDKzPhO4mWU+GJEUSrb5/SbgPnc/FZgINAMLgQ3ufjKwIZgWERk03B2A/Px8li5dSn5+fo/5Itkq4aRuZsOB9wF1AO7+v+6+G5gOrAxWWwnMSC5EEZHUy8vLY//+/SxYsID9+/erk5xEQjLN7/8MvAJ8z8wmAo8CVwHF7r4TwN13mtmxfW1sZnOBuQDFxcU0NjYmEUr2yJXPmQtUltlt//793e/dvbuWrnLNfrlchskk9QLgX4BKd3/YzG7iMJra3X05sBygrKzMp0yZkkQoWeK+deTE58wFKsvIuPbaa7nuuuu6p1WuWS7Hv5vJtDe9ALzg7g8H03cTS/ItZjYGIHh9ObkQRUTSY9iwYRx33HEMGzYs7FBEUiLhpO7ufwOeN7N3B7OmAluBtcCsYN4sYE1SEYqIpElrayvz58+ntbU17FBEUiLZ+9QrgR+a2RHAX4HPEvtH4S4zqwB2AJcmeQwREREZgKSSurs/AZT1sWhqMvsVEcmU888/n1//+tdhhyGSErqHQ0RymhK6RImSuojkpJtvvhl3p6GhAXfn5ptvDjskkaRp7HcRyUlXXnklV155ZdhhiKSUauoiktOOOeaYsEMQSRkldRHJaa+++mrYIYikjJK6iIhIRCipi0hOu/zyy8MOQSRllNRFJKdt2rQp7BBEUkZJXURyWlNTU9ghiKSMkrqIiEhEKKmLSE475ZRTwg5BJGWU1EUkp23bti3sEERSRkldRHLSF7/4xR7DxH7xi18MOySRpGmYWBHJSd/85jf55je/GXYYIimlmrqI5LQTTzwx7BBEUkZJXURy2o4dO8IOQSRllNRFREQiQkldRHLa+973vrBDEEkZJXURyWmjRo0KOwSRlFFSF5Gctnr16rBDEEkZJXUREZGIUFIXkZxWUKDhOiQ6lNRFJKd1dHSEHYJIyuhfVGDidfez5819GTnWuIXr0n6MEUcO4U/XfjDtxxERkcFFSR3Y8+Y+nl18cdqP09jYyJQpU9J+nEz84yASFR/84Ae5//77ww5DJCXU/C4iOe3vf/972CGIpIySuojktMceeyzsEERSJqmkbmbPmtlmM3vCzDYF80aZ2Xoz2x68Hp2aUEVEUiv+0asiUZCKmnq5u5/h7mXB9EJgg7ufDGwIpkVEBh0z44YbbsDMwg5FJCXS0fw+HVgZvF8JzEjDMUREEhZfM4/vJKcau2S7ZHu/O3C/mTnwHXdfDhS7+04Ad99pZsf2taGZzQXmAhQXF9PY2JhkKMnJxPFbW1sz9jnDPp+5QOd48CkvL09q+8OpsTc0NCR1rFzz+Q1v8EZm7hxO+x1AQ4fA/0wdmtZjJCrZpH6uu78UJO71ZvbngW4Y/AOwHKCsrMwzcatXv+5bl5FbzTJ1S1umPk9O0zkelBKpaY9buC4jt7Tmujfuy8x5zsTf2XELB+/3P6nmd3d/KXh9GbgHeC/QYmZjAILXl5MNUkRERA4t4aRuZkPN7Kiu98AHgSZgLTArWG0WsCbZIEVEROTQkml+LwbuCa5BFQA/cvf7zOyPwF1mVgHsAC5NPkyRgdGQvyKSyxJO6u7+V2BiH/NfBaYmE5RIojTkr4jkMo0oJyIiEhFK6iIiIhGhpC4iIhIRSuoiIiIRoaQuIiISEUrqIiIiEaGkLiIiEhFK6iIiIhGhpC4iIhIRSuoiIiIRoaQuIiISEUrqIiIiEaGkLiIiEhFK6iIiIhGhpC4iIhIRSuoiIiIRURB2AIPBUSULmbByYWYOtjL9hziqBODi9B9IREQGFSV14PXmxTy7OP1JsLGxkSlTpqT9OOMWrkv7MUREZPBR87uIiEhEKKmLiIhEhJK6iIhIRCipi4iIRISSuoiISEQoqYuIiESEkrqIiEhEKKmLiIhEhJK6iIhIRCQ9opyZ5QObgBfd/cNmNgr4CTAOeBb4uLu/luxxRAZCQ/6KSC5LxTCxVwHNwPBgeiGwwd0Xm9nCYLoqBccROSQN+SsiuSyp5nczO4FYNeK7cbOn81YdZiUwI5ljiIiIyMAkW1O/EfhP4Ki4ecXuvhPA3Xea2bF9bWhmc4G5AMXFxTQ2NiYZSnIycfzW1taMfc6wz2eYVJZyuHSOMyNK383B+juTcFI3sw8DL7v7o2Y25XC3d/flwHKAsrIyz0RTZr/uW5eRptRMNdlm6vMMSipLOVw6x5kRpe/mIP6dSaamfi4wzcwuAoqA4WZ2J9BiZmOCWvoY4OVUBCoiIiIHl/A1dXf/sruf4O7jgE8Cv3H3TwFrgVnBarOANUlHKSIiIoeUjvvUFwP/ZmbbgX8LpkVERCTNUnFLG+7eCDQG718FpqZivyIiIjJwGlFOREQkIpTURUREIkJJXUREJCKU1EVERCJCSV1ERCQiUtL7PQoy9uCM+9J/nBFHDkn7MUTSbeJ197PnzX0ZOVYmvv8jjhzCn679YNqPI7lNSR0y8lQviP3hyNSxRLLdnjf36Yl7IodJze8iIiIRoaQuIiISEUrqIiIiEaGkLiIiEhHqKCciIml3VMlCJqxcmJmDrUzv7o8qARicnZ6V1EVEJO1eb14cmbsZBvOdDGp+FxERiQgldRERkYhQUhcREYkIXVOXyNGQvyKSq5TUJVI05K+I5DI1v4uIiESEkrqIiEhEKKmLiIhEhJK6iIhIRCipi4iIRISSuoiISEToljYRGZSi9AAQGNwPAcmUqIwhMZjHj1BSF5FBKUoPAIHB/RCQTNAYEpmh5ncREZGIUFIXERGJiISTupkVmdkjZvYnM9tiZtcF80eZ2Xoz2x68Hp26cEVERKQ/ydTU24EPuPtE4AzgQjP7V2AhsMHdTwY2BNMiIiKSZgkndY9pDSaHBD8OTOetvqQrgRnJBCgiIiIDk1TvdzPLBx4FTgL+x90fNrNid98J4O47zezYfradC8wFKC4uprGxMZlQskaufM5coLJMv0yc49bW1oyVpX5nMiOXz3NSSd3dO4EzzGwkcI+ZlR7GtsuB5QBlZWWeiVtKQnffuozcOiMZoLJMvwyd40zd0qbfmQzJ8fOckt7v7r4baAQuBFrMbAxA8PpyKo4hIiIiB5dM7/e3BzV0zOxI4Hzgz8BaYFaw2ixgTZIxioiIyAAk0/w+BlgZXFfPA+5y93vN7PfAXWZWAewALk1BnCIiInIICSd1d38SOLOP+a8CU5MJSkRERA6fRpQTERGJCD3QRUQGrag81QsG95O9JDqU1EVkUNJTvUQOn5rfRUREIkJJXUREJCKU1EVERCJCSV1ERCQilNRFREQiQkldREQkIpTURUREIkJJXUREJCKU1EVERCJCI8qJiMigZGaJbbfk8NZ394SOMxippi4iIoOSux/2T0NDw2FvEyVK6iIiIhGhpC4iIhIRuqaeoExd64FoXe8REZH0UU09QZm61qOELiIiA6WkLiIiEhFK6iIiIhGhpC4iIhIRSuoiIiIRoaQuIiISEUrqIiIiEaGkLiIiEhFK6iIiIhGhpC4iIhIRCSd1M3uHmTWYWbOZbTGzq4L5o8xsvZltD16PTl24IiIi0p9kauodwP9z9xLgX4HPm9l4YCGwwd1PBjYE0yIiIpJmCSd1d9/p7o8F718HmoHjgenAymC1lcCMJGMUERGRAUjJU9rMbBxwJvAwUOzuOyGW+M3s2H62mQvMBSguLqaxsTEVoQxqra2tOfE5s015eXlC2yXyxL2GhoaEjiXppe9ldOT639mkk7qZDQN+Clzt7nsH+khSd18OLAcoKyvzKVOmJBvKoNfY2EgufM5sk8iT8FSWEXLfOpVlhOT6dzOp3u9mNoRYQv+hu/8smN1iZmOC5WOAl5MLUURERAYimd7vBtQBze6+NG7RWmBW8H4WsCbx8ERERGSgkml+Pxf4NLDZzJ4I5i0CFgN3mVkFsAO4NKkIRUREZEASTuruvhHo7wL61ET3KyIiIonRiHIiIiIRoaQuIiISEUrqIiIiEaGkLiIiEhFK6iIiIhGRkmFiRUQGg4GOaHnAdgkM+ZvISIQi6aaauohEhrsf9k9DQ0NC24kMRkrqIiIiEaGkLiIiEhFK6iIiIhGhpC4iIhIRSuoiIiIRoaQuIiISEUrqIiIiEaGkLiIiEhFK6iIiIhGhpC4iIhIRSuoiIiIRoaQuIiISEUrqIiIiEWGD4WlDZvYK8FzYcWTAaODvYQchKaGyjA6VZbTkQnm+093f3teCQZHUc4WZbXL3srDjkOSpLKNDZRktuV6ean4XERGJCCV1ERGRiFBSz6zlYQcgKaOyjA6VZbTkdHnqmrqIiEhEqKYuIiISEUrqIiIiEaGkPkBm9lUz+6KZfc3Mzh8E8TxrZqPDjkNizGysmd19mNvcYWYfS1dMAmb2XTMbn+C248ysKdUxSWLM7HIzuyXsOAa7grADyDbufk0q9mNm+e7emYp9SWaZWYG7d/SafglQgh5k3P0/wo5BJJNUUz8IM6s2s7+Y2a+Bdwfz7jCzj5nZh8zsrrh1p5jZz4P3M81ss5k1mdmSuHVag5r+w8DZZvYZM3vSzP5kZj8I1nm7mf3UzP4Y/JwbzD/GzO43s8fN7DuAZfBUREJQ8/pzUHtrMrMfmtn5ZvaQmW03s/cGP78LzvPvzKyr3C83s1VBGd/fx3R3rc7M8s3sG0H5PWlm/zeYb2Z2i5ltNbN1wLGhnYwIMrOhZrYu+D41mdknzKzRzMqC5a1mVhMs/4OZFQfz3xVM/zH4frb2se8+y1QS17slJGgJ/WpQZkvM7BEz22Zm5/Wx7cVm9nszGx38Tb45+L7+tav1K/i+fSP4XdhsZp8I5t9qZtOC9/eY2e3B+woz+3oQV7OZrTCzLcHf3SMzc1aSp6TeDzObBHwSOBP4KPCeXqusB/7VzIYG058AfmJmY4ElwAeAM4D3mNmMYJ2hQJO7nwW8BlQDH3D3icBVwTo3Ad929/cA/w58N5h/LbDR3c8E1gInpu7T5pSTiJ3j04FTgf8DTAa+CCwC/gy8LzjP1wDXx217NjDL3T/Qz3SXCmBPUIbvAeaY2T8BHyH2z+EEYA5wTuo/Xk67EHjJ3Se6eylwX6/lQ4E/BN+3B4mVAcR+H24KyuulfvbdX5lKehS4+3uBq4n97etmZh8BFgIXuXvXcLBjiH2PPwwsDuZ9lNjf4InA+cA3zGwMsbLv+kfheKDr8sxk4LfB+5OB/3H304DdxP4WZwUl9f6dB9zj7v9w973EEmm3oPn1PuASMysALgbWEPvCN7r7K8E6PwTeF2zWCfw0eP8B4O6uX0p33xXMPx+4xcyeCI453MyOCvZxZ7DuOmL/FMjhe8bdN7v7fmALsMFj93VuBsYBI4BVQQ3i28Bpcduujyunvqa7fBD4TFCGDwPHEPsj8T6g3t07g+b636T2o+W8zcD5QS3vPHff02v5/wL3Bu8fJVbeEPvnbFXw/kf97Lu/MpX0+FnwGl9OAOVAFXCxu8f/DVzt7vvdfStQHMybzFvftxbgAWJ/n38LnGexvhZbgZYg2Z8N/C7Y9hl3f6KfGAY1XVM/uEPdxP8T4PPALuCP7v66mR2sWbwt7jq69bP/POBsd38zfmawWw0qkLz2uPf746b3E/s+/BfQ4O4fMbNxQGPc+m/02lfv6S4GVLr7r3rMNLsIlWHauPu2oIXtIuAGM7u/1yr7/K2BOTo5vL9/fZapJKWDnhXLorj3Xd/L3uX0V+CfgVOATX2sD29dmuzzb7G7v2hmRxNr2XkQGAV8HGgN/oYf02t/nYCa3yPgQeAjZnZkUFO+pI91GoF/IdaM95Ng3sPA+4NrPfnATGL/Ifa2Afh48AuEmY0K5t8PXNG1kpmdERfPZcG8DwFHJ/zJ5GBGAC8G7y9PcB+/Auab2RAAMzsluEzzIPDJ4PrsGGK1DkmR4NLXP9z9TuCbxL6bA/EH3mpe/WQ/6/RXppK4FuBYi/UXKiTWdH4ozxFrVv++mZ12iHUfBD4RfN/eTqyl7JFg2e+JNe0/SKzm/kXeanrPakrq/XD3x4gl6ieINZkfUOBBrfte4EPBK+6+E/gy0AD8CXjM3df0se0WoAZ4wMz+BCwNFl0JlAWdcbYC84L51wHvM7PHiDUF7kjNJ5Ve/ptYLe8hID/BfXyXWLPeY0Ez/neI1TbuAbYTaya+jb7/2ZPETQAeCZrIq4GvD3C7q4EFZvYIsWuzvZvtof8ylQS5+z7ga8QqQvcS688ykO3+QqyCs8rM3nWQVe8BniT2d/g3wH+6+9+CZb8ldt3+KeAxYrX1SCR1DRMrIjnNzN4GvOnubmafBGa6+/Sw4xJJhP7TFJFcN4lY51Qj1tN5drjhiCRONXUREZGI0DV1ERGRiFBSFxERiQgldRERkYhQUheRQ7K4p9CZ2RnBQDqH2maKmd17qPVEJHWU1EXkoCx4Cp27dz2F7gxio7aJyCCjpC4SUZbap9KNC/ZxBLEBQz5hZk9Y7Elofe5DRDJP96mLRNtJwKXAXOCPvPVUumnEnkr3GWJPpesws/OJPZWua8jUs4HT3X1XMA4+7v6/ZnYNUObuVwCY2fCD7ENEMkhJXSTannH3zQBm1v1UOjOLfyrdSjM7mdjDZobEbdvfU+h6O9g+RCSD1PwuEm0DfSpdKbGHFsU/Kau/p9D1drB9iEgGKamL5LZEnkr3OnBUkvsQkTRQUhfJbYk8la4BGN/VUS7BfYhIGmjsdxERkYhQTV1ERCQilNRFREQiQkldREQkIpTURUREIkJJXUREJCKU1EVERCJCSV1ERCQi/j/gd3xbZTIV4AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column = \"age\",\n",
    "           by = \"marital\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2Ci3YxUBgDjB"
   },
   "source": [
    "The plot shows that unmarried people are on average younger than divorced and married ones. For the last two groups, there is an outlier zone over 70 years old, and for unmarried - over 50.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yQ5td0XaFVIL"
   },
   "source": [
    "**You can do this by data grouping on any other feature:**\n",
    "\n",
    "Replace `##YOUR CODE GOES HERE##` with your Python code.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "wtnHOQ3sFVIM"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column = \"age\",\n",
    "           by = [\"marital\", \"housing\"],\n",
    "           figsize = (20, 20))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bNzZczbK_jd1"
   },
   "source": [
    "Double-click **here** for the solution.\n",
    "\n",
    "<!-- \n",
    "df.boxplot(column = \"age\",\n",
    "           by = [\"marital\", \"housing\"],\n",
    "           figsize = (20, 20))\n",
    "-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MZB02pxKhReu"
   },
   "source": [
    "As you can see, age and marital status do not have any significant influence on having a housing loan.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VUMjKczqTThq"
   },
   "source": [
    "## Tasks\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jLCT0HXkTThr"
   },
   "source": [
    "In this section, you will solve some tasks with the source bank dataset.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q7C0DMPdTThs"
   },
   "source": [
    "### Question 1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "psvWjxS4TThs"
   },
   "source": [
    "List 10 clients with the largest number of contacts.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "unSAHwIPTTht"
   },
   "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</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>4107</td>\n",
       "      <td>32</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>56</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.10</td>\n",
       "      <td>93.99</td>\n",
       "      <td>-36.40</td>\n",
       "      <td>4.86</td>\n",
       "      <td>5191.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18728</td>\n",
       "      <td>54</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>jul</td>\n",
       "      <td>thu</td>\n",
       "      <td>...</td>\n",
       "      <td>43</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.40</td>\n",
       "      <td>93.92</td>\n",
       "      <td>-42.70</td>\n",
       "      <td>4.97</td>\n",
       "      <td>5228.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13447</td>\n",
       "      <td>32</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>jul</td>\n",
       "      <td>wed</td>\n",
       "      <td>...</td>\n",
       "      <td>43</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.40</td>\n",
       "      <td>93.92</td>\n",
       "      <td>-42.70</td>\n",
       "      <td>4.96</td>\n",
       "      <td>5228.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4168</td>\n",
       "      <td>29</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>42</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.10</td>\n",
       "      <td>93.99</td>\n",
       "      <td>-36.40</td>\n",
       "      <td>4.86</td>\n",
       "      <td>5191.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5304</td>\n",
       "      <td>44</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.9y</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>42</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.10</td>\n",
       "      <td>93.99</td>\n",
       "      <td>-36.40</td>\n",
       "      <td>4.86</td>\n",
       "      <td>5191.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11033</td>\n",
       "      <td>38</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>jun</td>\n",
       "      <td>wed</td>\n",
       "      <td>...</td>\n",
       "      <td>41</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.40</td>\n",
       "      <td>94.47</td>\n",
       "      <td>-41.80</td>\n",
       "      <td>4.96</td>\n",
       "      <td>5228.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18754</td>\n",
       "      <td>36</td>\n",
       "      <td>admin.</td>\n",
       "      <td>single</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>jul</td>\n",
       "      <td>thu</td>\n",
       "      <td>...</td>\n",
       "      <td>40</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.40</td>\n",
       "      <td>93.92</td>\n",
       "      <td>-42.70</td>\n",
       "      <td>4.97</td>\n",
       "      <td>5228.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11769</td>\n",
       "      <td>56</td>\n",
       "      <td>self-employed</td>\n",
       "      <td>married</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>jun</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>40</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.40</td>\n",
       "      <td>94.47</td>\n",
       "      <td>-41.80</td>\n",
       "      <td>4.96</td>\n",
       "      <td>5228.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4114</td>\n",
       "      <td>52</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>39</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.10</td>\n",
       "      <td>93.99</td>\n",
       "      <td>-36.40</td>\n",
       "      <td>4.86</td>\n",
       "      <td>5191.00</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11593</td>\n",
       "      <td>43</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>jun</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>37</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.40</td>\n",
       "      <td>94.47</td>\n",
       "      <td>-41.80</td>\n",
       "      <td>4.96</td>\n",
       "      <td>5228.10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age            job  marital            education  default  housing  \\\n",
       "4107    32         admin.  married    university.degree  unknown  unknown   \n",
       "18728   54         admin.  married    university.degree  unknown      yes   \n",
       "13447   32     technician   single    university.degree       no      yes   \n",
       "4168    29     technician  married  professional.course       no      yes   \n",
       "5304    44        retired  married             basic.9y       no      yes   \n",
       "11033   38    blue-collar  married             basic.4y       no      yes   \n",
       "18754   36         admin.   single    university.degree       no       no   \n",
       "11769   56  self-employed  married  professional.course       no       no   \n",
       "4114    52   entrepreneur  married    university.degree       no       no   \n",
       "11593   43     technician  married          high.school       no      yes   \n",
       "\n",
       "          loan    contact month day_of_week  ...  campaign  pdays  previous  \\\n",
       "4107   unknown  telephone   may         mon  ...        56    999         0   \n",
       "18728       no   cellular   jul         thu  ...        43    999         0   \n",
       "13447      yes  telephone   jul         wed  ...        43    999         0   \n",
       "4168        no  telephone   may         mon  ...        42    999         0   \n",
       "5304        no  telephone   may         fri  ...        42    999         0   \n",
       "11033       no  telephone   jun         wed  ...        41    999         0   \n",
       "18754       no   cellular   jul         thu  ...        40    999         0   \n",
       "11769      yes  telephone   jun         fri  ...        40    999         0   \n",
       "4114        no  telephone   may         mon  ...        39    999         0   \n",
       "11593       no  telephone   jun         fri  ...        37    999         0   \n",
       "\n",
       "          poutcome emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  \\\n",
       "4107   nonexistent         1.10           93.99         -36.40       4.86   \n",
       "18728  nonexistent         1.40           93.92         -42.70       4.97   \n",
       "13447  nonexistent         1.40           93.92         -42.70       4.96   \n",
       "4168   nonexistent         1.10           93.99         -36.40       4.86   \n",
       "5304   nonexistent         1.10           93.99         -36.40       4.86   \n",
       "11033  nonexistent         1.40           94.47         -41.80       4.96   \n",
       "18754  nonexistent         1.40           93.92         -42.70       4.97   \n",
       "11769  nonexistent         1.40           94.47         -41.80       4.96   \n",
       "4114   nonexistent         1.10           93.99         -36.40       4.86   \n",
       "11593  nonexistent         1.40           94.47         -41.80       4.96   \n",
       "\n",
       "       nr.employed  y  \n",
       "4107       5191.00  0  \n",
       "18728      5228.10  0  \n",
       "13447      5228.10  0  \n",
       "4168       5191.00  0  \n",
       "5304       5191.00  0  \n",
       "11033      5228.10  0  \n",
       "18754      5228.10  0  \n",
       "11769      5228.10  0  \n",
       "4114       5191.00  0  \n",
       "11593      5228.10  0  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Code block for students answer. \n",
    "# Replace ##YOUR CODE GOES HERE## with python code.\n",
    "df.sort_values(by = \"campaign\", ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qWKD4lEOmLEo"
   },
   "source": [
    "Double-click **here** for the solution.\n",
    "\n",
    "<!-- \n",
    "df.sort_values(by = \"campaign\", ascending = False).head(10)\n",
    "-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xwaZlbEbTThu"
   },
   "source": [
    "### Question 2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_PNycuTMTThv"
   },
   "source": [
    "Determine the median age and the number of contacts for different levels of client education.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "o2Hxx2FzTThw"
   },
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">mean</th>\n",
       "      <th colspan=\"2\" halign=\"left\">count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>campaign</th>\n",
       "      <th>age</th>\n",
       "      <th>campaign</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>basic.4y</td>\n",
       "      <td>47.60</td>\n",
       "      <td>2.60</td>\n",
       "      <td>4176</td>\n",
       "      <td>4176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>basic.6y</td>\n",
       "      <td>40.45</td>\n",
       "      <td>2.56</td>\n",
       "      <td>2292</td>\n",
       "      <td>2292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>basic.9y</td>\n",
       "      <td>39.06</td>\n",
       "      <td>2.53</td>\n",
       "      <td>6045</td>\n",
       "      <td>6045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>high.school</td>\n",
       "      <td>38.00</td>\n",
       "      <td>2.57</td>\n",
       "      <td>9515</td>\n",
       "      <td>9515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>illiterate</td>\n",
       "      <td>48.50</td>\n",
       "      <td>2.28</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>professional.course</td>\n",
       "      <td>40.08</td>\n",
       "      <td>2.59</td>\n",
       "      <td>5243</td>\n",
       "      <td>5243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>university.degree</td>\n",
       "      <td>38.88</td>\n",
       "      <td>2.56</td>\n",
       "      <td>12168</td>\n",
       "      <td>12168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>unknown</td>\n",
       "      <td>43.48</td>\n",
       "      <td>2.60</td>\n",
       "      <td>1731</td>\n",
       "      <td>1731</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     mean           count         \n",
       "                      age campaign    age campaign\n",
       "education                                         \n",
       "basic.4y            47.60     2.60   4176     4176\n",
       "basic.6y            40.45     2.56   2292     2292\n",
       "basic.9y            39.06     2.53   6045     6045\n",
       "high.school         38.00     2.57   9515     9515\n",
       "illiterate          48.50     2.28     18       18\n",
       "professional.course 40.08     2.59   5243     5243\n",
       "university.degree   38.88     2.56  12168    12168\n",
       "unknown             43.48     2.60   1731     1731"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Code block for students answer\n",
    "# Replace ##YOUR CODE GOES HERE## with python code.\n",
    "df.pivot_table(\n",
    "    [\"age\", \"campaign\"],\n",
    "    [\"education\"],\n",
    "    aggfunc = [\"mean\", \"count\"],\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1xAxHU1HotaC"
   },
   "source": [
    "Double-click **here** for the solution.\n",
    "\n",
    "<!-- \n",
    "df.pivot_table(\n",
    "    [\"age\", \"campaign\"],\n",
    "    [\"education\"],\n",
    "    aggfunc = [\"mean\", \"count\"],\n",
    ")\n",
    "-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Fs2Wf3RQTThw"
   },
   "source": [
    "### Question 3\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QPr0VwEXTThw"
   },
   "source": [
    "Output box plot to analyze the client age distribution by their education level.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "YO648QDYTThy"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+5Us19utqz2J6NCJeDpD9/lY2/xvAwjG3ewXw91dfniRJkiSpVK42IPYDt2WXbwN2jpn/kxFRFxE3A68G/nJqJUqSJEmSSuGyu5hGRB+wErgxIr4BvA+4G/hURLQDR4C3AaSUDkXEp4DHgTPAz6aUzs5Q7ZIkSZKkaXTZgJhSarvEVasucftuoHsqRUmSJEmSSu9qdzGVJEmSJM0yBkRJkiRJEmBAlCRJkiRlDIiSJEmSJMCAKEmSJEnKGBAlSZIkSYABUZIkSZKUMSBKkiRJkgADoiRJkiQpY0CUJEmSJAEGxBnR2dlJfX09hUKB+vp6Ojs7y12SJEmSJF1WbbkLmG06Ozvp6elhy5YtLF68mMcff5yNGzcCsHXr1jJXJ0mSJEmX5hbEabZjxw62bNnCnXfeSX19PXfeeSdbtmxhx44d5S5NkiRJkiZlQJxmp06doqOjY9y8jo4OTp06VaaKJEmSJOnKGBCnWV1dHT09PePm9fT0UFdXV6aKJEmSJOnKeAziNLvjjjvOH3O4ePFi7rnnHjZu3HjRVkVJkiRJqjQGxGk2eiKa9773vZw6dYq6ujo6Ojo8QY0kSZKkiucupjNg69atnDx5koGBAU6ePGk4lCRJkpQLBkRJkiRJEmBAlCRJkiRlDIiSJEmSJMCAKEmSJEnKGBAlSZIkSYABUZIkSZKUMSBKkiRJkgADoiRJkiQpY0CUJEmSJAEGREmSJElSxoAoSZIkSQIMiJIkSZKkjAFRkiRJkgQYECVJkiRJGQOiJEmSJAkwIEqSJEmSMgZESZIkSRJgQJQkSZIkZQyIkiRJkiTAgCjR19dHc3Mzq1atorm5mb6+vnKXJEmSJJVFbbkLkMqpr6+Prq4uent7OXv2LDU1NbS3twPQ1tZW5uokSZKk0nILoqpad3c3vb29FAoFamtrKRQK9Pb20t3dXe7SJEmSpJIzIKqqDQ8Ps2LFinHzVqxYwfDwcJkqkiRJksrHgKiq1tTUxL59+8bN27dvH01NTWWqSJIkSSofA6KqWldXF+3t7QwMDHDmzBkGBgZob2+nq6ur3KVJkiRJJedJalTVRk9E09nZyfDwME1NTXR3d3uCGkmSJFUlA6KqXltbG21tbQwODrJy5cpylyNJkiSVjbuYSpIkSZIAA6KknOnr66O5uZlVq1bR3NxMX19fuUuSJEmaNdzFVFJu9PX10dXVRW9vL2fPnqWmpob29nYAjxuVJEmaBm5BlJQb3d3d9Pb2UigUqK2tpVAo0NvbS3d3d7lLkyRJmhUMiJJyY3h4mBUrVoybt2LFCoaHh8tUkSRJ0uxiQJSUG01NTezbt2/cvH379tHU1FSmiiRJkmYXA6Kk3Ojq6qK9vZ2BgQHOnDnDwMAA7e3tdHV1lbs0SZKkWcGT1EjKjdET0XR2djI8PExTUxPd3d2eoEaSJGmaGBAl5UpbWxttbW0MDg6ycuXKcpcjSZI0q7iLqSRJkiQJMCBKkiRJkjIGREmSJEkSYECUJEmSJGUMiJIkSZIkwIAoSZIkScoYECVJkiRJgAFRkiRJkpQxIEqSJEmSAAOiJEmSJCljQJQkSZKkMVpaWogICoUCEUFLS0u5SyoZA6IkSZIkZVpaWjh48CBr167l/vvvZ+3atRw8eLBqQqIBUZIkSZIyo+Fw586dXH/99ezcufN8SKwGBkRJkiRJGqO3t3fS6dnMgChJkiRJY7S3t086PZsZECVJkiQps3TpUvr7+1m3bh3PPPMM69ato7+/n6VLl5a7tJKoLXcBkiRJklQpDhw4QEtLC/39/fT39wPF0HjgwIEyV1YabkGUJEmSpDEOHDhASomBgQFSSlUTDsGAKEmSJEnKGBAlSZKkGdbZ2Ul9fT2FQoH6+no6OzvLXZI0IY9BlCRJkmZQZ2cnPT09bNmyhcWLF/P444+zceNGALZu3Vrm6qTx3IIoSZIkzaAdO3awZcsW7rzzTurr67nzzjvZsmULO3bsKHdp0kUMiJIkSdIMOnXqFB0dHePmdXR0cOrUqTJVJF2aAVGSJEmaQXV1dfT09Iyb19PTQ11dXZkqki7NYxAlSZKkGXTHHXecP+Zw8eLF3HPPPWzcuPGirYpSJTAgSpIkSTNo9EQ0733vezl16hR1dXV0dHR4ghpVJHcxlSRJkmbY1q1bOXnyJAMDA5w8edJwqIplQJQkSZIkAVMMiBHx7ogYiohDEfHz2bx5EbEnIr6W/b5hWiqVJEmSJM2oqw6IEdEM3AH8a+C1wL+PiFcDdwF7U0qvBvZm05IkSZKkCjeVLYhNwJ+nlP4ppXQGeAT4cWAdcG92m3uBN0+pQkmSJElSSURK6eruGNEE7AR+CHie4tbCx4B3pJSuH3O7Eymli3YzjYh3Au8EWLBgwes/+clPXlUdlWxkZISGhoZyl6ErZL/yxX7li/3KF/uVL/YrX+xXvszWfhUKhS+mlJZNdN1VB0SAiGgHfhYYAR6nGBRvv5KAONayZcvSY489dtV1VKrBwUFWrlxZ7jJ0hexXvtivfLFf+WK/8sV+5Yv9ypfZ2q+IuGRAnNJJalJKvSmlH0gp/TBwHPgacDQiXp4t+OXAt6ayDEmSJElSaUz1LKbflf1uBN4C9AH9wG3ZTW6juBuqJEmSJKnC1U7x/n8UEfOB08DPppRORMTdwKey3U+PAG+bapGSJEmSpJk3pYCYUvq3E8w7BqyayuNKkiRJkkpvSruYamKdnZ3U19dTKBSor6+ns7Oz3CVJkiRJ0mVNdRdTXaCzs5Oenh62bNnC4sWLefzxx9m4cSMAW7duLXN1kiRJknRpbkGcZjt27GDLli3ceeed1NfXc+edd7JlyxZ27NhR7tIkSZIkaVIGxGl26tQpOjo6xs3r6Ojg1KlTZapIkiRJkq6MAXGa1dXV0dPTM25eT08PdXV1ZapIkiRJkq6MxyBOszvuuOP8MYeLFy/mnnvuYePGjRdtVZQkSZKkSmNAnGajJ6J573vfy6lTp6irq6Ojo8MT1EiSJEmqeO5iOgO2bt3KyZMnGRgY4OTJk4ZDSZIkSblgQJQkSZIkAQZESZIkSVLGgDgDIoKIoFAonL8sSdWor6+P5uZmVq1aRXNzM319feUuSZLKwvdD5YUnqZlmY8Pgpk2b2Lx58/n5KaVylSVJJdfX10dXVxe9vb2cPXuWmpoa2tvbAWhraytzdZJUOr4fKk/cgjhDUkqsWbPGUCipanV3d9Pb20uhUKC2tpZCoUBvby/d3d3lLk2SSsr3Q+WJAXEGfPzjH590WpKqwfDwMCtWrBg3b8WKFQwPD5epIkkqD98PlScGxBnw9re/fdJpSaoGTU1N7Nu3b9y8ffv20dTUVKaKJKk8fD9UnhgQZ0hEsHv3bk9QI6lqdXV10d7ezsDAAGfOnGFgYID29na6urrKXZoklZTvh8oTT1IzzVJK50Ph6AlqRudLUjUZPfFCZ2cnw8PDNDU10d3d7QkZJFUd3w+VJ25BnAEpJVJKDAwMnL8sSdWora2NoaEh9u7dy9DQkF+GJFUt3w+VFwZESZIkSRJgQJwRDoQqSZIkKY88BnGaORCqJEmSpLxyC+I0cyBUSZIkSXllQJxmDoQqSZIkKa8MiNPMgVAlSZIk5ZUBcZo5EKokSZKkvPIkNdPMgVAlSZIk5ZUBcQa0tbXR1tbG4OAgK1euLHc5kiRJknRF3MVUkiRJkgQYEGdES0sLEUGhUCAiaGlpKXdJ0qzR19dHc3Mzq1atorm5mb6+vnKXJEll4fuhNHM6Ozupr6+nUChQX19PZ2dnuUsqGXcxnWYtLS0cPHiQtWvXcvvtt/PRj36U/v5+WlpaOHDgQLnLk3Ktr6+Prq4uent7OXv2LDU1NbS3twN4nK+kquL7oTRzOjs76enpYcuWLSxevJjHH3+cjRs3ArB169YyVzfz3II4zUbD4c6dO7n++uvZuXMna9eu5eDBg+UuTcq97u5uent7KRQK1NbWUigU6O3tpbu7u9ylSVJJ+X4ozZwdO3awZcsW7rzzTurr67nzzjvZsmULO3bsKHdpJWFAnAG9vb2TTku6OsPDw6xYsWLcvBUrVjA8PFymiiSpPHw/lGbOqVOn6OjoGDevo6ODU6dOlami0jIgzoDRXTwuNS3p6jQ1NbFv375x8/bt20dTU1OZKpKk8vD9UJo5dXV19PT0jJvX09NDXV1dmSoqLQPiNFu6dCn9/f2sW7eOZ555hnXr1tHf38/SpUvLXZqUe11dXbS3tzMwMMCZM2cYGBigvb2drq6ucpcmSSXl+6E0c+644w42btzIPffcw8mTJ7nnnnvYuHEjd9xxR7lLKwlPUjPNDhw4QEtLC/39/fT39wPF0OgJaqSpGz3xQmdnJ8PDwzQ1NdHd3e0JGSRVHd8PpZkzeiKa9773vZw6dYq6ujo6Ojqq4gQ14BbEGXHgwAFSSgwMDJBSMhxK06itrY2hoSH27t3L0NCQX4YkVS3fD6WZs3XrVk6ePMnAwAAnT56smnAIBkRJkiRJUsaAOAMigoigUCicvyxJkjSdqnkgb0kzx4A4zUbDYE1NDffccw81NTXj5kuSJE3V6EDev/7rv84f//Ef8+u//uv09PQYEiVNmQFxBtTU1HDmzBm+//u/nzNnzpwPiZIkSdOh2gfyljRzDIgzYO/evZNOS5IkTUW1D+QtaeYYEGfAqlWrJp2WJEmaimofyFvSzDEgzoCzZ89SW1vLl770JWprazl79my5S5IkSbNItQ/kLWnm1Ja7gNkmpUREcPbsWe68885x8yVJkqZDtQ/kLWnmuAVxBqSUSCkxMDBw/rIkSdJ0quaBvCXNHAOiJEmSJAkwIM6IlpYWIoJCoUBE0NLSUu6SpFmjr6+P5uZmVq1aRXNzM319feUuSZOwX5KkPKrmzy+PQZxmLS0tHDx4kLVr13L77bfz0Y9+lP7+flpaWjhw4EC5y5Nyra+vj66uLnp7ezl79iw1NTW0t7cD0NbWVubqdCH7JUnKo2r//HIL4jQbDYc7d+7k+uuvZ+fOnaxdu5aDBw+WuzQp97q7u+nt7aVQKFBbW0uhUKC3t5fu7u5yl6YJ2C9JUh5V++eXAXEG9Pb2Tjot6eoMDw+zYsWKcfNWrFjB8PBwmSrSZOyXJCmPqv3zy4A4A0Y3QV9qWtLVaWpqYt++fePm7du3j6ampjJVpMnYL0lSHlX755cBcZotXbqU/v5+1q1bxzPPPMO6devo7+9n6dKl5S5Nyr2uri7a29sZGBjgzJkzDAwM0N7eTldXV7lL0wTslyQpj6r988uT1EyzAwcO0NLSQn9/P/39/UAxNHqCGmnqRg8M7+zsZHh4mKamJrq7u6vigPE8sl+SpDyq9s8vA+IMGA2Dg4ODrFy5srzFSLNMW1sbbW1trl85Yb8kSXlUzZ9f7mIqSZIkSQIMiDOivr6eiKBQKBAR1NfXl7skadbo7Oykvr6eQqFAfX09nZ2d5S5Jk7Bf0syp5oG888j3w3yp5n65i+k0q6+v59SpUyxYsIC7776bu+66i6NHj1JfX8/JkyfLXZ6Ua52dnfT09LBlyxYWL17M448/zsaNGwHYunVrmavTheyXNHOqfSDvvPH9MF+qvl8ppbL/vP71r0+zBZAWLFiQUkppYGAgpZTSggULUvGlViUb7ZcqV11dXfrgBz+YUnqhXx/84AdTXV1dGavSpdiv/PL9sPItWbIkPfzwwymlF/r18MMPpyVLlpSxKl2K74f5Ug39Ah5Ll8hm7mI6AwYHByedlnR1Tp06RUdHx7h5HR0dnDp1qkwVaTL2S5o51T6Qd974fpgv1d4vA+IMuPBMR9V25iNpptTV1dHT0zNuXk9PD3V1dWWqSJOxX9LMqfaBvPPG98N8qfZ+eQziNKurq+Po0aPcdNNN3H333dx0000cPXq0av6gpJl0xx13nD8GYPHixdxzzz1s3Ljxov/yqTLYL2nmjA7kPXoM4uhA3t3d3eUuTRPw/TBfqr1fBsRpdvLkSerr6zl69Ci33347UAyNnqBGmrrRA8Pf+973curUKerq6ujo6KiOA8ZzyH5JM6faB/LOG98P86Xa+xXFYxTLa9myZemxxx4rdxnTrhoH1swz+5Uv9itf7Fe+2K98sV/5Yr/yZbb2KyK+mFJaNtF1HoMoSZIkSQIMiDNi7ty5RASFQoGIYO7cueUuSZLKwoG8pZnj+pUvjY2N474fNjY2lrskTaKa1y+PQZxmc+fO5cyZM9xwww38xm/8Br/0S7/EiRMnmDt3LqdPny53eZJUMg7kLc0c1698aWxs5Mknn+SWW27hPe95Dx/60IfYv38/jY2NHDlypNzl6QLVvn65BXGajYbD48eP86pXvYrjx49zww03cObMmXKXJkkl1d3dTW9vL4VCgdraWgqFAr29vZ5lUZoGrl/5MhoOH330UW688UYeffRRbrnlFp588slyl6YJVPv6ZUCcAY888sik05JUDRzIW5o5rl/5c9999006rcpR7euXAXEGvOENb5h0WpKqgQN5SzPH9St/3vrWt046rcpR7euXAXGa1dbWcuLECebNm8fXv/515s2bx4kTJ6it9XBPSdVldCDvgYEBzpw5c34g766urnKXJuWe61e+LFy4kP3797N8+XKefvppli9fzv79+1m4cGG5S9MEqn39MrVMs9OnTzN37lxOnDjBHXfcARRDoyeokVRtHMhbmjmuX/ly5MgRGhsb2b9/P/v37weKodET1FSmal+/IqVU7hpYtmxZeuyxx8pdxrSbrQNrzlb2K1/sV77Yr3yxX/liv/LFfuXLbO1XRHwxpbRsouvcxVSSJEmSBBgQZ0RNTc24gVBramrKXZImUc0DoeaR/cqXzs5O6uvrKRQK1NfX09nZWe6SpFnD9Stf/PzKl9bWVubMmUOhUGDOnDm0traWu6SS8RjEaVZTU8O5c+doaGjgN3/zN/nFX/xFRkZGqKmp4ezZs+UuTxeo9oFQ88Z+5UtnZyc9PT1s2bKFxYsX8/jjj7Nx40YAtm7dWubqpHxz/coXP7/ypbW1ld27d7N+/XpuvfVWHnroIbZv305rayu7du0qd3kzL6VU9p/Xv/71abYAUkNDQ0oppYGBgZRSSg0NDan4UqvSLFmyJD388MMppRf69fDDD6clS5aUsSpdiv3Kl7q6uvTBD34wpfRCvz74wQ+murq6MlalKzHaL1Uu16988fMrXyIirV+/PqX0Qr/Wr1+fIqKMVU0v4LF0iWzmLqYz4JFHHpl0WpWj2gdCzRv7lS+nTp2io6Nj3LyOjg5OnTpVpoqk2cP1K1/8/MqXlBKbN28eN2/z5s2kCji5ZykYEGfAG97whkmnVTmqfSDUvLFf+VJXV0dPT8+4eT09PdTV1ZWpImn2cP3KFz+/8iUi2LRp07h5mzZtIiLKVFFpGRCn2Zw5cxgZGeG6667jq1/9Ktdddx0jIyPMmeNLXYmqfSDUvLFf+XLHHXewceNG7rnnHk6ePMk999zDxo0bz48RK+nquX7li59f+bJ69Wq2b9/Ohg0bGBkZYcOGDWzfvp3Vq1eXu7TSuNS+p6X8mU3HIKaU0pw5cxJw/mfOnDnlLkmT+MQnPpGWLFmS5syZk5YsWZI+8YlPlLskTcJ+5cu73vWuVFdXl4BUV1eX3vWud5W7JF0Bj0HMB9evfPHzK1/WrFmTIiIBKSLSmjVryl3StGKSYxAjVcC+tMuWLUuPPfZYucuYdrN1YM3Zyn7li/3KF/uVL/YrX+xXvtivfJmt/YqIL6aUlk10nfs9SpIkSZKAKQbEiHhPRByKiKGI6IuI+oiYFxF7IuJr2e8bpqvYvIgIIoJCoXD+siRVo2oeaDiPHMg7Xzo7O6mvr6dQKFBfX09nZ2e5S5JmjWp+P6y92jtGxPcAPwcsTik9HxGfAn4SWAzsTSndHRF3AXcBG6el2hwYGwY7OzvPD1YbEVVzalxJAgcazhsH8s6Xzs5Oenp62LJlC4sXL+bxxx9n48bi163R7x6Srk61vx9OdRfTWuCaiKgFXgr8PbAOuDe7/l7gzVNcRi6llHjLW95iKJRUtfbs2cP69evZtm0bDQ0NbNu2jfXr17Nnz55yl6YJdHd309vbS6FQoLa2lkKhQG9vL93d3eUuTRPYsWMHW7Zs4c4776S+vp4777yTLVu2sGPHjnKXJuVetb8fTukkNRHxbqAbeB7YnVL6qYh4JqV0/ZjbnEgpXbSbaUS8E3gnwIIFC17/yU9+8qrrqCSFQoHOzk7e8pa3MDIyQkNDA5/5zGfYunUrAwMD5S5Pkxjtl/LBflW+QqHAAw88QENDw/l+jYyM8GM/9mO+H1agVatWsWvXLmpra8/368yZM7S2trJ3795yl6cLFAoF/viP/5j6+vrz/Tp58iQ/+qM/6vpV4fz8qnzV8H5YKBQueZKaqx6aArgBeBj4TmAu8H+AtwPPXHC7E5d7rNk0zAXZ0BYpvXCa8LHzVLk8rXu+2K/KFxFp/fr1KaUX+rV+/foUEWWsSpeyZMmS9PDDD6eUXujXww8/nJYsWVLGqnQpdXV16YMf/GBK6YV+ffCDH0x1dXVlrEpXws+vylcN74dMMszFVHYxfSPwRErpH1JKp4HPALcARyPi5QDZ729NYRm5FRF85jOf8QQ1kqpW1Q80nDMO5J0vd9xxBxs3buSee+7h5MmT3HPPPWzcuJE77rij3KVJuVft74dXfZIa4AjwbyLipRR3MV0FPAY8B9wG3J393jnVIvMkpXQ+FI49SDx5LKKkKrNr1y5aW1vp6elh+/btRARr1qzxBDUVavTEC52dnQwPD9PU1ER3d3dVnJAhj0a/Y7z3ve/l1KlT1NXV0dHR4QlqpGlQ7e+HUz0G8f3ATwBngC8B/xloAD4FNFIMkW9LKR2f7HGWLVuWHnvssauuo1LN1oE1Zyv7lS/2K1/sV77Yr3yxX/liv/JltvYrIi55DOJUtiCSUnof8L4LZp+iuDVRkiRJkpQjUx3mQpJKqrGxkYigUCgQETQ2Npa7JE2itbWVOXPmUCgUmDNnDq2treUuSZKky+rr66O5uZlVq1bR3NxMX19fuUsqGQOipNxobGzkySef5JZbbuHTn/40t9xyC08++aQhsUK1traye/duOjo6eOCBB+jo6GD37t2GRElSRevr66Orq4utW7eya9cutm7dSldXV9WERAOipNwYDYePPvooN954I48++uj5kKjKs2fPHtavX8+2bdtoaGhg27ZtrF+/nj179pS7NEmSLqm7u5ve3l4KhQK1tbUUCgV6e3vp7u4ud2klYUC8QhHxon9Gd4G7mh9JE7vvvvsmnVblSCmxefPmcfM2b97sWZ0lSRVteHiYFStWjJu3YsUKhoeHy1RRaRkQr9ClBpKc7OeVGz97Vffzy5N0aW9961snnVbliAg2bdo0bt6mTZv8J5gkqaI1NTWxb9++cfP27dtHU1NTmSoqLQOipNxYuHAh+/fvZ/ny5Tz99NMsX76c/fv3s3DhwnKXpgmsXr2a7du3s2HDBkZGRtiwYQPbt29n9erV5S5NkqRL6urqor29nYGBAc6cOcPAwADt7e10dXWVu7SSmNIwF5JUSkeOHKGxsZH9+/ezf/9+oBgajxw5UubKNJFdu3bR2tpKT08P27dvJyJYs2YNu3btKndpkiRdUltbGwCdnZ0MDw/T1NREd3f3+fmznQFRUq6MhsHZOnDtbDMaBu2XJClP2traaGtrq8rPL3cxlSRJkiQBBkTJgbxzppoHrs2jzs5O6uvrKRQK1NfX09nZWe6SpFnDzy9p5jQ0NIwblaChoaHcJZWMu5iqqo0O5L1+/XpuvfVWHnroIbZv305ra6vHSVWg0YFre3t7OXv2LDU1NbS3twNUzXEBedLZ2UlPTw9btmxh8eLFPP7442zcuBGArVu3lrk6Kd/8/JJmTkNDA8899xyLFi3iAx/4AP/tv/03Dh8+TENDAyMjI+Uub8a5BVFVzYG886XaB67Nmx07drBlyxbuvPNO6uvrufPOO9myZQs7duwod2lS7vn5Jc2c0XD4xBNP8IpXvIInnniCRYsW8dxzz5W7tJIwIKqqOZB3vlT7wLV5c+rUKTo6OsbN6+jo4NSpU2WqSJo9/PySZtbnP//5SadnMwOiqpoDeedLtQ9cmzd1dXX09PSMm9fT00NdXV2ZKpJmDz+/pJn1xje+cdLp2cxjEFXVRgfyBrj11lvPD+S9Zs2aMlemiYwOXDt6DOLowLXuYlqZ7rjjjvPHHC5evJh77rmHjRs3XrRVUdKL5+eXNHOuvfZaDh8+zM0338wHPvABbr75Zg4fPsy1115b7tJKIiphV4Rly5alxx57rNxlTLtFdz3I4bvfVO4ydBmtra3s2bOHlBIRwerVqz3Av4L19fXR3d19fuDarq4uT1BTwTo7O9mxYwenTp2irq6OO+64wxPU5EA1jvuVR35+5ZPrVz6Mnqhm1LXXXjurTlATEV9MKS2b6Dp3MVXV27VrF+fOnWNgYIBz58754Vrh2traGBoaYu/evQwNDRkOK9zWrVs5efIkAwMDnDx50nAoTSM/v6SZMzIyQkqJgYEBUkqzKhxejgFRkiRJkgQYECVJkiRJGQOipFzp6+ujubmZVatW0dzcTF9fX7lL0iTslzRzXL/ypbW1lTlz5lAoFJgzZw6tra3lLkmakGcxlZQbfX19dHV1nT+LaU1NDe3t7QAei1iB7Jc0c1y/8qW1tZXdu3ezfv16br31Vh566CG2b99Oa2urx46q4rgFUVJudHd309vbS6FQoLa2lkKhQG9vr8NcVCj7Jc0c16982bNnD+vXr2fbtm00NDSwbds21q9fz549e8pdmnQRA6Kk3BgeHmbFihXj5q1YsYLh4eEyVaTJ2C9p5rh+5UtKic2bN4+bt3nzZiphuLlqEhEv+qdQKFzV/SKi3E/3qhkQJeVGU1MT+/btGzdv3759NDU1lakiTcZ+STPH9StfIoJNmzaNm7dp06Zch4g8Sim96J9XbvzsVd0vz+HfgCgpN7q6umhvb2dgYIAzZ84wMDBAe3s7XV1d5S5NE7Bf0sxx/cqX1atXs337djZs2MDIyAgbNmxg+/btrF69utylSRfxJDWScmP0xAudnZ0MDw/T1NREd3e3J2SoUPZLmjmuX/mya9cuWltb6enpYfv27UQEa9as8QQ1qkgGREm50tbWRltbG4ODg6xcubLc5egy7Jc0c1y/8mU0DNovVTp3MZUkSZIkAQZEyYGGJUm51NLSMu4siy0tLeUuSZOYM2fOuH7NmePXcFUm/zJV1UYHGt66dSu7du1i69atdHV1GRIlSRWtpaWFgwcPsnbtWu6//37Wrl3LwYMHDYkVas6cOaSUqK+v58Mf/jD19fWklAyJqkj+VaqqOdCwJCmPRsPhzp07uf7669m5c+f5kKjKMxoOn3/+eZYsWcLzzz9/PiRKlcaAqKrmQMOSpLzq7e2ddFqVZXBwcNJpqVIYEFXVHGhYkpRX7e3tk06rslx45lLPZKpKZUBUVXOgYUlSHi1dupT+/n7WrVvHM888w7p16+jv72fp0qXlLk0TiAhOnjzJNddcw6FDh7jmmms4efIkEVHu0qSLOA6iqpoDDUuS8ujAgQO0tLTQ399Pf38/UAyNBw4cKHNlmsi5c+eYM2cOJ0+e5F3vehdQDI3nzp0rc2XSxdyCqKrX1tbG0NAQe/fuZWhoyHAoScqFAwcOkFJiYGCAlJLhsMKdO3duXL8Mh6pUBkRJkiRJEmBAlJQzjY2N4wYabmxsLHdJmkRfXx/Nzc2sWrWK5uZmxxiVVLV8P1ReeAyipNxobGzkySef5JZbbuE973kPH/rQh9i/fz+NjY0cOXKk3OXpAn19fXR1ddHb28vZs2epqak5f5ZFd+WWVE18P1SeuAVRUm6MhsNHH32UG2+8kUcffZRbbrmFJ598stylaQLd3d309vZSKBSora2lUCjQ29tLd3d3uUuTpJLy/VB5YkCUlCv33XffpNOqHMPDw6xYsWLcvBUrVjA8PFymiiSpPHw/VJ4YECXlylvf+tZJp1U5mpqa2Ldv37h5+/bto6mpqUwVSVJ5+H6oPDEgSsqNhQsXsn//fpYvX87TTz/N8uXL2b9/PwsXLix3aZpAV1cX7e3tDAwMcObMGQYGBmhvb6erq6vcpUlSSfl+qDzxJDWScuPIkSM0Njayf/9+9u/fDxRDoyeoqUyjJ17o7OxkeHiYpqYmuru7PSGDpKrj+6HyxIAoKVdGw+Dg4CArV64sbzG6rLa2Ntra2uyXpKrn+6Hywl1MJUmSJEmAAVFSzrS2tjJnzhwKhQJz5syhtbW13CVpEo2NjUQEhUKBiKCxsbHcJUmzhutXvsyfP39cv+bPn1/ukqQJGRAl5UZrayu7d++mo6ODBx54gI6ODnbv3m1IrFCNjY3nx6789Kc/fX7MSr/ESlPn+pUv8+fP5/jx4yxZsoS+vj6WLFnC8ePHDYmqSAZESbmxZ88e1q9fz7Zt22hoaGDbtm2sX7+ePXv2lLs0TWD0y+ujjz7KjTfeyKOPPnr+S6ykqXH9ypfRcDg0NMRNN93E0NDQ+ZAoVRoDoqTcSCmxefPmcfM2b95MSqlMFely7rvvvkmnJV091698eeihhyadliqFAVFSbkQEmzZtGjdv06ZNRESZKtLlvPWtb510WtLVc/3Kl1tvvXXSaalSGBAl5cbq1avZvn07GzZsYGRkhA0bNrB9+3ZWr15d7tI0gYULF7J//36WL1/O008/zfLly9m/fz8LFy4sd2lS7rl+5cu8efM4dOgQzc3NPPXUUzQ3N3Po0CHmzZtX7tKkizgOoqTc2LVrF62trfT09LB9+3YigjVr1rBr165yl6YJHDlyhMbGRvbv38/+/fuB4pfa0bEsJV091698OXbsGPPnz+fQoUO0tbUBxdB47NixMlcmXcwtiJJyZdeuXZw7d46BgQHOnTtnOKxwR44cIaXEwMAAKSW/vErTyPUrX44dOzauX4ZDVSoDoiRJkiQJMCBKypm+vj6am5tZtWoVzc3N9PX1lbskTaK1tZU5c+ZQKBSYM2eOY1ZK06ilpWXcwOstLS3lLkmTmD9//rh+OQaiKpUBUVJu9PX10dXVxdatW9m1axdbt26lq6vLkFihWltb2b17Nx0dHTzwwAN0dHSwe/duQ6I0DVpaWjh48CBr167l/vvvZ+3atRw8eNCQWKHmz59/fizEvr6+82MgGhJViQyIknKju7ub3t5eCoUCtbW1FAoFent76e7uLndpmsCePXtYv34927Zto6GhgW3btrF+/Xr27NlT7tKk3BsNhzt37uT6669n586d50OiKs9oOBwaGuKmm25iaGjofEiUKo0BUVJuDA8Ps2LFinHzVqxYwfDwcJkq0mRSSmzevHncvM2bN5NSKlNF0uzS29s76bQqy0MPPTTptFQpDIiScqOpqYl9+/aNm7dv3z6amprKVJEmExFs2rRp3LxNmzYREWWqSJpd2tvbJ51WZbn11lsnnZYqhQFRUm50dXXR3t7OwMAAZ86cYWBggPb2drq6uspdmiawevVqtm/fzoYNGxgZGWHDhg1s376d1atXl7s0KfeWLl1Kf38/69at45lnnmHdunX09/ezdOnScpemCcybN49Dhw7R3NzMU089RXNzM4cOHWLevHnlLk26SG25C5CkKzU6uHBnZyfDw8M0NTXR3d19fr4qy65du2htbaWnp4ft27cTEaxZs8axK6VpcODAAVpaWujv76e/vx8ohsYDBw6UuTJN5NixY8yfP59Dhw6d/8yaN2+eYyGqIrkFUVKutLW1MTQ0xN69exkaGjIcVrhdu3Zx7tw5BgYGOHfunOFQmkYHDhwYN/C64bCyHTt2bFy/DIeqVAZESZIkSRJgQJQcuDZn+vr6aG5uZtWqVTQ3NzsGYoWzX9LMaW1tZc6cORQKBebMmeMYoxWuvr5+3PeN+vr6cpckTchjEFXVxg5c+8u//Mv82q/9GocOHWL+/Pnu+lGB+vr66Orqore3l7Nnz1JTU3P+rH3ualp57Jc0c1pbW9m9ezfr16/n1ltv5aGHHmL79u20tra6K3cFqq+v59SpUyxYsIC7776bu+66i6NHj1JfX8/JkyfLXZ40jlsQVdUcuDZfuru76e3tpVAoUFtbS6FQoLe3l+7u7nKXpgnYL2nm7Nmzh/Xr17Nt2zYaGhrYtm0b69evZ8+ePeUuTRMYDYdPPfUUixYt4qmnnmLBggWcOnWq3KVJFzEgquo5cG1+DA8Ps2LFinHzVqxYwfDwcJkq0mTslzRzUkps3rx53LzNmzeTUipTRbqcwcHBSaelSmFAVNVz4Nr8aGpqYt++fePm7du3j6ampjJVpMnYL2nmRASbNm0aN2/Tpk1ERJkq0uWsXLly0mmpUhgQVdUcuDZfurq6aG9vZ2BggDNnzjAwMEB7eztdXV3lLk0TsF/SzFm9ejXbt29nw4YNjIyMsGHDBrZv387q1avLXZomUFdXx9GjR7nppps4fPgwN910E0ePHqWurq7cpUkX8SQ1qmoOXJsvoz3q7OxkeHiYpqYmuru7PeFJhbJf0szZtWsXra2t9PT0sH37diKCNWvWeIKaCnXy5Enq6+s5evQot99+O1AMjZ6gRpXILYiqeg5cmy9tbW0MDQ2xd+9ehoaGDBsVzn5JM2fXrl2cO3eOgYEBzp07ZziscCdPnhz3fcNwqEplQJQkSZIkAe5iKilnWltb2bNnDyklIoLVq1f7X/MK1tLSwsGDB89PL126lAMHDpSxImn2GB1bb5S7LFa2iU4g5FlnVYncgigpN0YHhu7o6OCBBx6go6OD3bt309raWu7SNIHRcLh27Vruv/9+1q5dy8GDB2lpaSl3aVLujR14/aMf/ej5MfXq6+vLXZomMBoO586dy2/91m8xd+7ccfOlSmJAlJQbDgydL6PhcOfOnVx//fXs3LnzfEiUNDUOvJ4/c+fO5Z//+Z9paWnhn//5n8+HRKnSGBAl5YYDQ+dPb2/vpNOSrp4Dr+fLwMDApNNSpTAgSsoNB4bOn/b29kmnJV09B17Pl0KhMOm0VCkMiJJyw4Gh82Xp0qX09/ezbt06nnnmGdatW0d/fz9Lly4td2lS7jnwev6cPn2al7zkJRw4cICXvOQlnD59utwlSRPyLKaScsOBofPlwIEDtLS00N/fT39/P+BZTKXp4sDr+TJ65u3Tp0/z7ne/e9x8qdK4BVFSrjgwdL4cOHBg3MDQhkNp+jjwer6klMb1y3CoSmVAlCRJkiQBUwiIEfEvI+LLY37+MSJ+PiLmRcSeiPha9vuG6SxYmm59fX00NzezatUqmpub6evrK3dJmoT9ypeWlhYigkKhQEQ4BqI0jRobG8etX42NjeUuSZOwX8qLqw6IKaW/Tim9LqX0OuD1wD8B9wN3AXtTSq8G9mbTUkXq6+ujq6uLrVu3smvXLrZu3UpXV5eho0LZr3xpaWk5Pxbi/ffff34MREOiNHWNjY08+eST3HLLLXz605/mlltu4cknnzR0VCj7pTyZrl1MVwH/N6X0d8A64N5s/r3Am6dpGdK06+7upre3l0KhQG1tLYVCgd7eXrq7u8tdmiZgv/JlNBzu3LmT66+/np07d54PiZKmZjRsPProo9x44408+uij50OHKo/9Up7EdBwgGxG/B/xVSunDEfFMSun6MdedSCldtJtpRLwTeCfAggULXv/JT35yynVUmp/53HN87EeuLXcZmsSqVavYtWsXtbW1jIyM0NDQwJkzZ2htbWXv3r3lLk8XsF/5UigUuP/++7n++uvP9+uZZ57hx3/8xx0gusKN9kuVq1Ao8OlPf5obb7zxfL+efvpp3va2t7l+VSD7lV+z9ft8oVD4Ykpp2YRXjp5F6Wp/gJcATwMLsulnLrj+xOUe4/Wvf32ajV658bPlLkGXsWTJkvTwww+nlFIaGBhIKaX08MMPpyVLlpSxKl2K/coXIK1duzal9EK/1q5dm4ofPapko/1S5QLSLbfcklJ6oV+33HKL61eFsl/5NVu/zwOPpUtks+nYxfRHKW49PJpNH42IlwNkv781DcuQZkRXVxft7e0MDAxw5swZBgYGaG9vp6urq9ylaQL2K1+WLl1Kf38/69at45lnnmHdunX09/ezdOnScpcm5d7ChQvZv38/y5cv5+mnn2b58uXs37+fhQsXlrs0TcB+KU9qp+Ex2oCxZ4joB24D7s5+75yGZUgzoq2tDYDOzk6Gh4dpamqiu7v7/HxVFvuVLwcOHKClpYX+/n76+/uBYmh0LERp6o4cOUJjYyP79+9n//79QDGEHDlypMyVaSL2S3kypS2IEfFSYDXwmTGz7wZWR8TXsuvunsoypJnW1tbG0NAQe/fuZWhoyLBR4exXvhw4cGDcwNCGQ2n6HDlyZNz6ZdiobPZLeTGlLYgppX8C5l8w7xjFs5pKkiRJknJkuoa5kHLLgdfzxX7lS01NzbiBoWtqaspdkjRrNDQ0jFu/PPNsZZs/f/64fs2fP//yd5LKwICoqubA6/liv/KlpqaGc+fO0dDQwPbt22loaODcuXOGRGkaNDQ08Nxzz7Fo0SL+4A/+gEWLFvHcc88ZEivU/PnzOX78OEuWLKGvr48lS5Zw/PhxQ6IqkgFRVc2B1/PFfuXLaDh89tlnec1rXsOzzz57PiRKmprRcPjEE0/wile8gieeeOJ8SFTlGQ2HQ0ND3HTTTQwNDZ0PiVKlMSCqqg0PD7NixYpx81asWMHw8HCZKtJk7Ff+PPLII5NOS7p6n//85yedVmV56KGHJp2WKoUBUVWtqamJffv2jZu3b98+mpqaylSRJmO/8ucNb3jDpNOSrt4b3/jGSadVWW699dZJp6VKYUBUVXPg9XyxX/kyZ84cRkZGuO666/jqV7/Kddddx8jICHPm+NEjTdW1117L4cOHufnmm/nGN77BzTffzOHDh7n22mvLXZomMG/ePA4dOkRzczNPPfUUzc3NHDp0iHnz5pW7NOkiUxrmQso7B17PF/uVL2fPnqWmpoaRkRHWr18PFEPj2bNny1yZlH8jIyM0NDRw+PBh3vGOdwDF0DgyMlLmyjSRY8eOMX/+fA4dOnT+M2vevHkcO3aszJVJF/PfuKp6DryeL/YrX86ePTtuYGjDoTR9RkZGxq1fhsPKduzYsXH9MhyqUhkQJUmSJEmAAVFSzvT19dHc3MyqVatobm52DMQKV19fP25g6Pr6+nKXJEllMXfu3HHvh3Pnzi13SdKEDIiScqOvr4+uri62bt3Krl272Lp1K11dXYbEClVfX8+pU6dYsGABH/3oR1mwYAGnTp0yJEqqOnPnzuXMmTPccMMN7NixgxtuuIEzZ84YElWRDIiScqO7u5ve3l4KhQK1tbUUCgV6e3vp7u4ud2mawGg4fOqpp1i0aBFPPfXU+ZAoSdVkNBweP36cV73qVRw/fvx8SJQqjWcxlZQbw8PDrFixYty8FStWMDw8XKaKdDmDg4MXTTtupXRpEVHS5aWUSrq8avbII49cNN3S0lKmaqRLcwuipNxoampi37594+bt27fPwFHBVq5cOem0pPFSSi/655UbP3tV9zMcltYb3vCGSaelSmFAlJQbXV1dtLe3MzAwwJkzZxgYGKC9vZ2urq5yl6YJ1NXVcfToUW666SYOHz7MTTfdxNGjR6mrqyt3aZJUUrW1tZw4cYJ58+bx9a9/nXnz5nHixAlqa92ZT5XHv0pJuTE65mFnZyfDw8M0NTXR3d3tWIgV6uTJk9TX13P06FFuv/12oBgaT548WebKJKm0Tp8+zdy5czlx4gR33HEHUAyNp0+fLnNl0sXcgigpV9ra2hgaGmLv3r0MDQ0ZDivcyZMnxw0MbTiUVK1Onz497v3QcKhKZUCUJEmSJAEGRMmB1yVJ0oxrbGwkIigUCkQEjY2N5S5JmpDHIKqqjQ683tvby9mzZ6mpqaG9vR3AXRclSdK0aGxs5Mknn+SWW27hPe95Dx/60IfYv38/jY2NHDlypNzlSeO4BVFVzYHXJUnSTBsNh48++ig33ngjjz76KLfccgtPPvlkuUuTLmJAVFVz4HVJklQK991336TTUqUwIKqqOfC6JEkqhbe+9a2TTkuVwoCoqubA65IkaaYtXLiQ/fv3s3z5cp5++mmWL1/O/v37WbhwYblLky7iSWpU1Rx4XZIkzbQjR47Q2NjI/v372b9/P1AMjZ6gRpXILYiqeg68LkmSZtqRI0dIKTEwMEBKyXCoimVAlCRJkiQBBkRJkiRJUsZjEFX1Wltb2bNnDyklIoLVq1eza9eucpclzQrz58/n+PHj56fnzZvHsWPHyliRJJVHQ0MDzz333Pnpa6+9lpGRkTJWJE3MLYiqaq2trezevZuOjg4eeOABOjo62L17N62treUuTcq90XC4ZMkS+vr6WLJkCcePH2f+/PnlLk2SSmo0HC5atIg/+IM/YNGiRTz33HM0NDSUuzTpIgZEVbU9e/awfv16tm3bRkNDA9u2bWP9+vXs2bOn3KVJuTcaDoeGhrjpppsYGho6HxIlqZqMhsMnnniCV7ziFTzxxBPnQ6JUaQyIqmopJTZv3jxu3ubNm0kplami6hQRL/qnUChc1f0iotxPt6o89NBDk05LUrX4/Oc/P+m0VCkMiKpqEcGmTZvGzdu0aZMhosRSSi/655UbP3tV9zP8l9att9466bQkVYs3vvGNk05LlcKAqKq2evVqtm/fzoYNGxgZGWHDhg1s376d1atXl7s0KffmzZvHoUOHaG5u5qmnnqK5uZlDhw4xb968cpcmSSV17bXXcvjwYW6++Wa+8Y1vcPPNN3P48GGuvfbacpcmXcSzmKqq7dq1i9bWVnp6eti+fTsRwZo1azyLqTQNjh07xvz58zl06BBtbW2AZzGVVJ1GRkZoaGjg8OHDvOMd7wA8i6kql1sQVfV27drFuXPnGBgY4Ny5c4ZDaRodO3aMlBIDAwOklAyHkqrWyMjIuPdDw6EqlQFRkiRJkgQYECX6+vpobm5m1apVNDc309fXV+6SpFlj/vz548466xiIkqpVTU3NuPfDmpqacpckTciAqKrW19dHV1cXW7duZdeuXWzdupWuri5DojQN5s+ff34sxL6+vvNjIBoSJVWbmpoazp07R0NDA9u3b6ehoYFz584ZElWRDIiqat3d3fT29lIoFKitraVQKNDb20t3d3e5S5NybzQcDg0NcdNNNzE0NHQ+JEpSNRkNh88++yyvec1rePbZZ8+HRKnSGBBV1YaHh1mxYsW4eStWrGB4eLhMFUmzy0MPPTTptCRVi0ceeWTSaalSGBBV1Zqamti3b9+4efv27aOpqalMFUmzy6233jrptCRVize84Q2TTkuVwoCoqtbV1UV7ezsDAwOcOXOGgYEB2tvb6erqKndpUu7NmzePQ4cO0dzczFNPPUVzczOHDh1i3rx55S5Nkkpqzpw5jIyMcN111/HVr36V6667jpGREebM8au4Kk9tuQuQyml08O7Ozk6Gh4dpamqiu7v7/HxJV+/YsWPMnz+fQ4cOnV+n5s2b51iIkqrO2bNnqampYWRkhPXr1wPF0Hj27NkyVyZdzH9bqOq1tbUxNDTE3r17GRoaMhxK0+jYsWPjBoY2HEqqVmfPnh33fmg4VKUyIEqSJEmSAHcxlSTNoLlz53LmzJnz07W1tZw+fbqMFUmSpMm4BVGSNCNGw+ENN9zAjh07uOGGGzhz5gxz584td2mSJOkSDIiSpBkxGg6PHz/Oq171Ko4fP34+JEqSpMrkLqaalSKipMtLKZV0eVJeTDQwdEtLS5mqkaTp5fcNzUZuQdSslFJ60T+v3PjZq7qfb9bSpTkwtKTZzO8bmo0MiJKkGVFbW8uJEyeYN28eX//615k3bx4nTpygttadVyRJqlR+SkuSZsTp06eZO3cuJ06c4I477gA8i6kkSZXOLYiSpBlz+vTpcQNDGw4lSapsBkRJkiRJEmBAlCRJkiRlDIiSJEmSJMCAKEmSJEnKeBZTSdKL4sDQkiTNXm5BlCS9KA4MLUnS7GVAlCRJkiQBBkRJkiRJUsaAKEmSJEkCDIiSJEmSpIwBUZIkSZIEGBAlSZIkSRkDoiRJkiQJMCBKkiRJkjIGREmSJEkSYECUJEmSJGUMiJIkSZIkwIAoSZIkScoYECVJkiRJgAFRkiRJkpQxIEqSJEmSAAOiJEmSJCljQJQkSZIkAQZESZIkSVLGgChJkiRJAgyIkiRJkqSMAVGSJEmSBBgQJUmSJEmZKQXEiLg+Iu6LiK9GxHBE/FBEzIuIPRHxtez3DdNVrCRJkiRp5kx1C+JvAZ9LKb0GeC0wDNwF7E0pvRrYm01LkiRJkircVQfEiPgO4IeBXoCU0j+nlJ4B1gH3Zje7F3jz1EqUJEmSJJVC7RTu+73APwAfjYjXAl8E3g0sSCl9EyCl9M2I+K6J7hwR7wTeCbBgwQIGBwenUErlmq3Pa7ayX/liv/LFfpVHoVAo6fIGBgZKujwVuX7li/3Kl2rr11QCYi3wA0BnSukvIuK3eBG7k6aUPgJ8BGDZsmVp5cqVUyilQn3uQWbl85qt7Fe+2K98sV9lk1J60fdZdNeDHL77TTNQjWaE61e+2K98qcJ+TeUYxG8A30gp/UU2fR/FwHg0Il4OkP3+1tRKlCRJkiSVwlUHxJTSU8CTEfEvs1mrgMeBfuC2bN5twM4pVShJkiRJKomp7GIK0An874h4CfC3wO0UQ+enIqIdOAK8bYrLkCRJkiSVwJQCYkrpy8CyCa5aNZXHlSRJkiSV3lTHQZQkSZIkzRIGREmSJEkSYECUJEmSJGUMiJIkSZIkwIAoSZIkScoYECVJkiRJgAFRkiRJkpQxIEqSJEmSAAOiJEmSJCljQJQkSZIkAQZESZIkSVLGgChJkiRJAgyIkiRJkqSMAVGSJEmSBBgQJUmSJEkZA6IkSZIkCTAgSpIkSZIyBkRJkiRJEmBAlCRJkiRlDIiSJEmSJMCAKEmSJEnKGBAlSZIkSYABUZIkSZKUMSBKkiRJkgADoiRJkiQpY0CUJEmSJAEGREmSJElSxoAoSZIkSQIMiJIkSZKkjAFRkiRJkgQYECVJkiRJGQOiJEmSJAkwIEqSJEmSMgZESZIkSRJgQJQkSZIkZQyIkiRJkiTAgChJkiRJyhgQJUmSJEmAAVGSJEmSlDEgSpIkSZIAqC13AaX22vfv5tvPny7Z8hbd9WBJlvOya+bylfetKcmyJEmSJM1OVRcQv/38aQ7f/aaSLGtwcJCVK1eWZFmlCqKSJEmSZi93MZUkSZIkAQZESZIkSVLGgChJkiRJAgyIkiRJkqSMAVGSJEmSBBgQJUmSJEkZA6IkSZIkCTAgSpIkSZIyBkRJkiRJEmBAlCRJkiRlDIiSJEmSJMCAKEmSJEnKGBAlSZIkSQDUlrsASZJ0ea99/26+/fzpki1v0V0PlmQ5L7tmLl9535qSLEuSdHkGREmScuDbz5/m8N1vKsmyBgcHWblyZUmWVaogKkm6Mu5iKkmSJEkCDIiSJEmSpIwBUZIkSZIEGBAlSZIkSRkDoiRJkiQJMCBKkiRJkjIGREmSJEkSYECUJEmSJGUMiJIkSZIkwIAoSZIkScrUlrsASZIkSbpSr33/br79/OmSLW/RXQ+WZDkvu2YuX3nfmpIsazIGREmSJEm58e3nT3P47jeVZFmDg4OsXLmyJMsqVRC9HHcxlSRJkiQBBkRJkiRJUsaAKEmSJEkCDIiSJEmSpIwBUZIkSZIEGBAlSZIkSRkDoiRJkiQJMCBKkiRJkjIGREmSJEkSYECUJEmSJGUMiJIkSZIkwIAoSZIkScoYECVJkiRJANRO5c4RcRh4FjgLnEkpLYuIecAfAouAw8B/TCmdmFqZkiRJkqSZNh1bEAsppdellJZl03cBe1NKrwb2ZtOSJEmSpAo3E7uYrgPuzS7fC7x5BpYhSZIkSZpmU9rFFEjA7ohIwO+klD4CLEgpfRMgpfTNiPiuie4YEe8E3gmwYMECBgcHp1jKlSvVskZGRmbl85rNfA3zxX7li/2aOj+/dCm+hvliv6bO98OZM9WAuDyl9PdZCNwTEV+90jtmYfIjAMuWLUsrV66cYilX6HMPUqplDQ4OlmxZpXxes5avYb7Yr3yxX1Pn55cuxdcwX+zX1Pl+OKOmtItpSunvs9/fAu4H/jVwNCJeDpD9/tZUi5QkSZIkzbyrDogRcW1EXDd6GVgDDAH9wG3ZzW4Ddk61SEmSJEnSzJvKLqYLgPsjYvRxPpFS+lxEfAH4VES0A0eAt029TEmSJEnSTLvqgJhS+lvgtRPMPwasmkpRkiRJkqTSm4lhLiRJkiRJOWRAlCRJkiQBBkRJkiRJUsaAKEmSJEkCDIiSJEmSpIwBUZIkSZIEGBAlSZIkSRkDoiRJkiQJMCBKkiRJkjIGREmSJEkSYECUJEmSJGUMiJIkSZIkAGrLXYAkSbq865ruYum9d5VugfeWZjHXNQG8qTQLkyRdlgFRkqQceHb4bg7fXZogNTg4yMqVK0uyrEV3PViS5UiSroy7mEqSJEmSAAOiJEmSJCljQJQkSZIkAQZESZIkSVLGgChJkiRJAgyIkiRJkqSMAVGSJEmSBFThOIgONCxJkmbaa9+/m28/f7pkyyvVeJIvu2YuX3nfmpIsS1J5VF1AdKBhSZI00779/Gm/b0jKJXcxlSRJkiQBBkRJkiRJUsaAKEmSJEkCDIiSJEmSpIwBUZIkSZIEGBAlSZIkSRkDoiRJkiQJqMJxEJUvDjScL/ZLkiQp3wyIqmgONJwv9kuSJCnf3MVUkiRJkgQYECVJkiRJGQOiJEmSJAkwIEqSJEmSMgZESZIkSRJgQJQkSZIkZQyIkiRJkiTAcRAlSZJU5V77/t18+/nTJVteqcbXfdk1c/nK+9aUZFmaPQyIkiRJqmrffv40h+9+U0mWNTg4yMqVK0uyrFIFUc0u7mIqSZIkSQIMiJIkSZKkjAFRkiRJkgQYECVJkiRJGQOiJEmSJAkwIEqSJEmSMgZESZIkSRJgQJQkSZIkZWrLXYAkqTxe+/7dfPv50yVbXqkGbH7ZNXP5yvvWlGRZkiTNNgZESapS337+NIfvflNJljU4OMjKlStLsqxSBVFJkmYjdzGVJEmSJAEGREmSJElSxoAoSZIkSQIMiJIkSZKkjAFRkiRJkgQYECVJkiRJGQOiJEmSJAlwHERJkiRJOXJd010svfeu0i3w3tIs5romgNKMTzwZA6IkSZKk3Hh2+G4O312aIDU4OMjKlStLsqxFdz1YkuVcjruYSpIkSZIAA6IkSZIkKeMuppIk5URJdz/6XGmW9bJr5pZkOZKkK2NAlCQpB0p1vA0Ug2gplydJqhzuYipJkiRJAgyIkiRJkqSMAVGSJEmSBBgQJUmSJEkZA6IkSZIkCTAgSpIkSZIyBkRJkiRJEmBAlCRJkiRlDIiSJEmSJMCAKEmSJEnKGBAlSZIkSYABUZIkSZKUMSBKkiRJkgADoiRJkiQpY0CUJEmSJAEGREmSJElSxoAoSZIkSQIMiJIkSZKkzJQDYkTURMSXIuKz2fS8iNgTEV/Lft8w9TIlSZIkSTNtOrYgvhsYHjN9F7A3pfRqYG82LUmSJEmqcFMKiBHxCuBNwO+Omb0OuDe7fC/w5qksQ5IkSZJUGrVTvP//BH4JuG7MvAUppW8CpJS+GRHfNdEdI+KdwDsBFixYwODg4BRLuXKlWtbIyMisfF6lZr/yxX7li/3SpfgaTp3rV77Yr3yxXzPnqgNiRPx74FsppS9GxMoXe/+U0keAjwAsW7YsrVz5oh/i6nzuQUq1rMHBwZItq5TPq6TsV77Yr3yxX7oUX8Opc/3KF/uVL/ZrRk1lC+JyYG1E3ArUA98RER8HjkbEy7Othy8HvjUdhUqSJEmSZtZVH4OYUtqUUnpFSmkR8JPAwymltwP9wG3ZzW4Ddk65SkmSJEnSjJuJcRDvBlZHxNeA1dm0JEmSJKnCTfUkNQCklAaBwezyMWDVdDyuJEmSJKl0ZmILoiRJkiQphwyIkiRJkiTAgChJkiRJykzLMYh5s+iuB0u3sM+VZlkvu2ZuSZZTatc13cXSe+8q3QLvLc1irmsCeFNpFiZJkiRdoaoLiIfvLt2X8kV3PVjS5c1Gzw7fXbLXsJQDoZb0nxSSJEnSFXIXU0mSJEkSYECUJEmSJGUMiJIkSZIkwIAoSZIkScoYECVJkiRJgAFRkiRJkpQxIEqSJEmSgCocB1HSzLmu6S6W3ntX6RZ4b2kWc10TgGOaSrpyvh9KyisDoqRp8+zw3Ry+uzRfHAYHB1m5cmVJlrXorgdLshxJs4fvh5Lyyl1MJUmSJEmAAVGSJEmSlDEgSpIkSZIAA6IkSZIkKWNAlCRJkiQBBkRJkiRJUsaAKEmSJEkCDIiSJEmSpIwBUZIkSZIEGBAlSZIkSRkDoiRJkiQJMCBKkiRJkjIGREmSJEkSYECUJEmSJGUMiJIkSZIkwIAoSZIkScrUlrsASVJ5XNd0F0vvvat0C7y3NIu5rgngTaVZmKRZwfdD6QUGREmqUs8O383hu0vzxWFwcJCVK1eWZFmL7nqwJMuRNHv4fii9wF1MJUmSJEmAAVGSJEmSlDEgSpIkSZIAA6IkSZIkKWNAlCRJkiQBBkRJkiRJUsaAKEmSJEkCHAdRkiRJUs6UdIzHz5VmWS+7Zm5JlnM5BkRJkiRJuXH47jeVbFmL7nqwpMurBO5iKkmSJEkCDIiSJEmSpIwBUZIkSZIEGBAlSZIkSRkDoiRJkiQJMCBKkiRJkjIGREmSJEkS4DiIygEHQpWkqxcRV3e/LVe3vJTS1d1RklQRDIiqaA6EKklTczWBbXBwkJUrV05/MZKkiucuppIkSZIkwIAoSZIkScoYECVJkiRJgAFRkiRJkpQxIEqSJEmSAAOiJEmSJCljQJQkSZIkAQZESZIkSVKmttwFSJIkzUaL7nqwdAv7XGmW9bJr5pZkOZLKx4AoSZI0zQ7f/aaSLWvRXQ+WdHmSZjd3MZUkSZIkAQZESZIkSVLGgChJkiRJAgyIkiRJkqSMAVGSJEmSBBgQJUmSJEkZA6IkSZIkCXAcREnTzIGhJUl55OeXVGRAlDRtHBhakpRHfn5JL3AXU0mSJEkSYECUJEmSJGUMiJIkSZIkwIAoSZIkScoYECVJkiRJgAFRkiRJkpQxIEqSJEmSAMdBlKSq5sDQkiRpLAOiJFUpB4aWJEkXchdTSZIkSRJgQJQkSZIkZQyIkiRJkiTAgChJkiRJylx1QIyI+oj4y4j4SkQcioj3Z/PnRcSeiPha9vuG6StXkiRJkjRTprIF8RTw71JKrwVeB/xIRPwb4C5gb0rp1cDebFqSJEmSVOGuOiCmopFscm72k4B1wL3Z/HuBN0+lQEmSJElSaUxpHMSIqAG+CLwK+F8ppb+IiAUppW8CpJS+GRHfdYn7vhN4J8CCBQsYHBycSikVa7Y+r9nKfuWL/coX+5UfIyMj9itn7Fe+2K98qbZ+TSkgppTOAq+LiOuB+yOi+UXc9yPARwCWLVuWVq5cOZVSKtPnHmRWPq/Zyn7li/3KF/uVK4ODg/YrT1y/8sV+5UsV9mtazmKaUnoGGAR+BDgaES8HyH5/azqWIUmSJEmaWVM5i+l3ZlsOiYhrgDcCXwX6gduym90G7JxijZIkSZKkEpjKLqYvB+7NjkOcA3wqpfTZiPgz4FMR0Q4cAd42DXVKkiRJkmbYVQfElNIB4PsnmH8MWDWVoiRJkiRJpTctxyBKkiRJkvLPgChJkiRJAgyIkiRJkqSMAVGSJEmSBBgQJUmSJEkZA6IkSZIkCTAgSpIkSZIyBkRJkiRJEmBAlCRJkiRlDIiSJEmSJMCAKEmSJEnKGBAlSZIkSYABUZIkSZKUMSBKkiRJkgADoiRJkiQpY0CUJEmSJAEGREmSJElSxoAoSZIkSQIMiJIkSZKkjAFRkiRJkgQYECVJkiRJGQOiJEmSJAkwIEqSJEmSMgZESZIkSRJgQJQkSZIkZQyIkiRJkiTAgChJkiRJyhgQJUmSJEmAAVGSJEmSlDEgSpIkSZIAA6IkSZIkKWNAlCRJkiQBBkRJkiRJUsaAKEmSJEkCDIiSJEmSpIwBUZIkSZIEGBAlSZIkSRkDoiRJkiQJMCBKkiRJkjIGREmSJEkSYECUJEmSJGUMiJIkSZIkwIAoSZIkScoYECVJkiRJgAFRkiRJkpQxIEqSJEmSAAOiJEmSJCljQJQkSZIkAQZESZIkSVLGgChJkiRJAgyIkiRJkqSMAVGSJEmSBBgQJUmSJEkZA6IkSZIkCTAgSpIkSZIyBkRJkiRJEmBAlCRJkiRlDIiSJEmSJMCAKEmSJEnKGBAlSZIkSYABUZIkSZKUMSBKkiRJkgADoiRJkiQpY0CUJEmSJAEGREmSJElSxoAoSZIkSQIMiJIkSZKkjAFRkiRJkgQYECVJkiRJGQOiJEmSJAkwIEqSJEmSMgZESZIkSRJgQJQkSZIkZQyIkiRJkiTAgChJkiRJyhgQJUmSJEmAAVGSJEmSlDEgSpIkSZIAA6IkSZIkKWNAlCRJkiQBBkRJkiRJUsaAKEmSJEkCphAQI2JhRAxExHBEHIqId2fz50XEnoj4Wvb7hukrV5IkSZI0U6ayBfEM8F9TSk3AvwF+NiIWA3cBe1NKrwb2ZtOSJEmSpAp31QExpfTNlNJfZZefBYaB7wHWAfdmN7sXePMUa5QkSZIklUDtdDxIRCwCvh/4C2BBSumbUAyREfFdl7jPO4F3AixYsIDBwcHpKKXizNbnNVvZr3yxX/liv/JjZGTEfpVJoVC4qvvFlqtb3sDAwNXdUYD9qhbV9n445YAYEQ3AHwE/n1L6x4i4ovullD4CfARg2bJlaeXKlVMtpfJ87kFm5fOarexXvtivfLFfuTI4OGi/yiSl9KLvY7/Kx35VgSr8/JrSWUwjYi7FcPi/U0qfyWYfjYiXZ9e/HPjW1EqUJEmSJJXCVM5iGkAvMJxSumfMVf3Abdnl24CdV1+eJEmSJKlUprKL6XLgHcDBiPhyNu+9wN3ApyKiHTgCvG1KFUqSJEmSSuKqA2JKaR9wqQMOV13t40qSJEmSymNKxyBKkiRJkmYPA6IkSZIkCTAgSpIkSZIyBkRJkiRJEmBAlCRJkiRlDIiSJEmSJMCAKEmSJEnKGBAlSZIkSYABUZIkSZKUMSBKkiRJkgADoiRJkiQpY0CUJEmSJAFQW+4C8iIiru5+W65ueSmlq7ujAPslzSTXL0mSZi+3IF6hlNKL/hkYGLiq+/llaOrslzRzXL8kSZq9DIiSJEmSJMCAKEmSJEnKGBAlSZIkSYABUZIkSZKUMSBKkiRJkgADoiRJkiQpY0CUJEmSJAFQW+4CJMmB1yVJ0kzz+8aVcQuipLJz4HVJkjTT/L5xZQyIkiRJkiTAgChJkiRJyhgQJUmSJEmAAVGSJEmSlDEgSpIkSZIAA6IkSZIkKWNAlCRJkiQBBkRJkiRJUsaAKEmSJEkCDIiSJEmSpIwBUZIkSZIEGBAlSZIkSRkDoiRJkiQJMCBKkiRJkjIGREmSJEkSYECUJEmSJGUMiJIkSZIkwIAoSZIkScoYECVJkiRJgAFRkiRJkpQxIEqSJEmSAAOiJEmSJCljQJQkSZIkAQZESZIkSVLGgChJkiRJAgyIkiRJkqSMAVGSJEmSBBgQJUmSJEkZA6IkSZIkCTAgSpIkSZIyBkRJkiRJEmBAlCRJkiRlDIiSJEmSJMCAKEmSJEnKGBAlSZIkSQBESqncNRAR/wD8XbnrmAE3Ak+XuwhdMfuVL/YrX+xXvtivfLFf+WK/8mW29uuVKaXvnOiKigiIs1VEPJZSWlbuOnRl7Fe+2K98sV/5Yr/yxX7li/3Kl2rsl7uYSpIkSZIAA6IkSZIkKWNAnFkfKXcBelHsV77Yr3yxX/liv/LFfuWL/cqXquuXxyBKkiRJkgC3IEqSJEmSMgZESZIkSRJgQBwnIhZFxNAUH2NtRNx1FffbGhEjU1l2tSlHv6KoOyL+JiKGI+LnprL8alKmfv27iPiriBiKiHsjonYqy59tLtWTiPjViHjjZe77KxHxCzNdy1U+1sci4q3T8Vh5ERH7s9/nX8eIWBkRn80un193IuLNEbF4Gpf9uoi4dboeL68i4m3Z58LAND3eZdfDq3jM838TgohYFhG/PcPLGLtu/n9TeBy/I86AiPiZiPhwueuoNH5ZmmYppX6g/8XcJyKWAdfPSEGa1FX062eAhcBrUkrnIuK7ZqQwTejF9Csi5gD3AqtSSn8TEb8K3Ab0zmCJs0JK6b+Xuwa9OCmlWy5z/dh1583AZ4HHr/TxI6I2pXTmEle/DlgGPHSlj5dXEVGTUjp7iavbgQ0ppWkJiHlfDy/zN1MRUkqPAY9N9XEme65j1s1FwP8HfGKqy5uKPPRF5ecWxIvVZlsaDkTEfRHx0oj47xHxhWwrxEciIgAi4uci4vHstp/M5p3/T0RELIiI+yPiK9nPRR/gEVED/CbwS2PmzYmIr0XEd46Z/npE3FiKFyBnStovYD3wqymlcwAppW/ZrxellP2aD5xKKf1NNr0H+A/26yI1EbEjIg5FxO6IuCbGbIGLiFsj4qsRsS8ifvuCrQ+LI2IwIv42LrE1PSLuHtPH/5HNu1TvLqolu/3rIuLPs8e4PyJumGx+NYrLbF0YXXey13ot8JsR8eWI+L7s53MR8cWI+NOIeE12n49FxD1R3CK2JSL+dUTsj4gvZb//ZUS8BPhV4Ceyx/uJiLg2In4vW6+/FBHrZvwFmAZR3MLz1Qneow5n71P7gLdFRFtEHMzes7Zk9/3vwAqgJyJ+MyJqst9fyB7rv2S3e3lE/En2Wg1FxL/NbvuxbPpgRLwnu+3Y9XBV9loezF7bumz+4Yh4fxT3lDg4pncX9eoKnv8vZY/xlYi4O5t3qXVvMIr/3CYiboyIw9nln4mIT0fEA8DuiZ5vdrs1EfFnWd2fjoiGaezh0JjpX4ji3g6DEbElIv4yinsAjdaxMiI+G8XPgcMRcf2Y+349iu9V3xkRf5T18gsRsTy7/lei+Jm1G/j9iFiSPf6Xs9fr1dntRtfNu4F/m13/nmxde92Y5T0aES1jpm/OXqMvRMQHLnievzjmb+v9Y+b/t+xveE9E9EW2l0f2/H89Ih4B3h0Rr4+IR6K4zu+KiJdnt5vwvSBPXuzfwAX3fVP2mt+YrX+/na0/fztmXYwortuj6+tPZPO3RcTa7PL9EfF72eX2iPi1rK7hmOAzriKllPzJfij+dycBy7Pp3wN+AZg35jZ/APxYdvnvgbrs8vXZ758BPpxd/kPg57PLNcDLJljmu4H3ZJdHxsx/35j7rgH+qNyvT6X9lKlfx4Auiv9x/GPg1farMvsFBPB3wLJs+reAg/brop6cAV6XTX8KeDvwMeCtQD3wJHBzdn0f8Nns8q8A+4E64MZs3Zh7wePPA/6aF86YPdrHi3p3qVqyyweAN2SXfxX4n5eZ/zHgreV+fUvcy5ExPR3KLq8c06+x68641wfYO+a97AeBh8fc7rNATTb9HUBtdvmNo+vN2MfOpn99TO+uB/4GuLbcr9EVvIaLmPg96jDwS9m87waOAN9JcS+sh4E3Z9cN8sL7zTuBX84u11H8zLgZ+K9A15i//euA1wN7xtQxup58jPHr4b/I5v/+mPXnMNCZXd4A/O5lenX+b+KC5/6jFNfnl2bT87Lfl1rHxj7XG4HDY/4WvjHm/hM93xuBPxn9mwA2Av99Gns4NGb6Fyi+Vw0CH8zm3Qp8foJ15LeA28esB6O3+QSwIrvcCAxnl38F+CJwTTa9Ffip7PJLxswfuXBZ2fRtY17PfwE8dsFz6Qd+Orv8s2MeZw3FYReC4oaezwI/THEr/peBa7LX+WvAL4zp17bs8tys19+ZTf8E8HuTvRfk6ecq/gZ+Bvgw8OPAnwI3jFn/Pp29xouBr2fz/wPFfzjXAAsovh+8HPhJ4Dez2/wl8OfZ5Y8CrUzyGVeJP25BvNiTKaVHs8sfp/gfwUJE/EVEHAT+HbAku/4A8L8j4u0Um36hfwdsB0gpnU0pfXvslRHx3cDbKL6pXOj3gJ/OLv8nin9guljJ+pWpA06mlJYBOyj2CezXlSpZv1LxHfgngQ9FxF8Cz455HPv1gidSSl/OLn+R4ofYqNcAf5tSeiKb7rvgvg+mlE6llJ4GvkXxw3KsfwROAr8bEW8B/imbf6neXVRLRLyM4hfmR7L59wI/fKn5V/60BZBtubkF+HREfBn4HYpfdkZ9Or2wS+XLstsNAR/ihXX1QmuAu7LHG6QYcBqnvfiZMdF7FBT/qQHwr4DBlNI/pOJuev+bif/u1gA/nb0Gf0Fxj4ZXA18Abo+IXwGWppSeBf4W+N4onovgRyiuN2P9S4rrxujeEBf+rX8m+z12/b3SXo16I/DRlNI/AaSUjk9hHduTUjqeXZ7o+f4bil+4H81en9uAV17B407VRK/TWH9IMSxB8bNjtOdvBD6c1doPfEdEXJdd159Sej67/GfAeyNiI/DKMfMv5dPAv4+IuRQ/hz52wfXLeeE99w/GzF+T/XwJ+CuK79Ovpvi3ujOl9Hz2Oj8wwfOD4t9TM7Ane06/DLziCt4LZoNL/Q0UKP6j4k0ppRNj5v+flNK5lNLjvPD5tgLoyz67jgKPUHxf+FOKW4gXU9x9/2i2ZfaHKAZymPzztqJ4DOLF0gTT2yj+p+zJ7E2uPrvuTRTfLNcC/y0iLvcGfKHvB14FfD2Ke9W9NCK+nlJ6VbasoxHx7yj+F+enru7pzHql7BcU/zP6R9nl+8mChf26YiXtV0rpz4DzuzRR/C+t/Rrv1JjLZyn+93lUvMj7jvtMSSmdiYh/Dayi+IXrXRTD4dXUopkxB3gmpfS6S1z/3JjLHwAGUko/HhGLKIa/iQTwH1JKfz1dRZbQRO9R8MLrcLl1YlRQ3LK366IrIn6Y4vvbH0TEb6aUfj8iXktxK8PPAv+RYmAY+1iTGV1vxq6DV9qrscu48LlP5gwvHKZUf8F15/9mUkp/cuHzBU5QDJFtL2J5V1PXhbVN9DqN9WfAq6J4+MGbgV/L5s8BfujCwJd9bxv7XD8REX9B8bnuioj/nFJ6+FKFppT+KSL2AOso9nzZRDebYF4Am1NKv3NBPe+51LIyY/+GD6WUfuiC+38Hk78X5MXV/A38LfC9ZFtyJ7g9vLAeTrg+ppT+XxR3wf4RilvI51Hs60hK6dmImE+OPuPcgnixxogYXWnagH3Z5aez/66M7oM8B1iYigej/xLF3Wgu3Id+L8Vj1ojiMQbfMfbKlNKDKaWbUkqLUkqLgH9KKb1qzE1+l+J/MD+VLn1QfLUrWb8y/4cXvuC+geKuU6Ps1+WVtF+RnUQoisfrbAR6xlxtvy7vqxS3bCzKpn9iktteJOvpy1JKDwE/T/FkJnBl6xoA2dbFE2OOF3kH8Mil5r+Y+qrYsxR3QSOl9I/AExHxNjh/fM1rL3G/lwH/L7v8MxM9XmYX0Blx/nji75++0mfcpd6jRv0F8IYoHqNUk91mor+7XcD6bOsQEfEvonhs5iuBb6WUdlA8YdYPRPH45zkppT8C/hvwAxc81lcpbk0f/X5wJX/rl+rVpewG/lNEvDSrd95l1rHDFHeNhex9eyITPV/gz4Hlo88nisd5/osrqPFKHAW+KyLmZ+/7//5K75jtdXI/cA/F3UiPZVftpvjPLbJ6XzfR/SPieynucfHbFLc0tlxwkwvXEyh+Dv028IUxW11HPUrxH2sw/p+Yuyj2qiFb7vdkn3X7gB+LiPrsujdd4qn+NfCdo3/nETE3Ipa8yPeCSnY1fwN/B7yF7FjSy9z2Tygec12T/TPhhynuUgrFfzL8fHabP6W4e+ufvvinUH4GxIsNA7dFxAGK6X87xV0JD1IMB1/IblcDfDyKu8V9CfhQSumZCx7r3RR3nztIcVPyEoCIeCiKu5deTj/FL8XVvPvb5ZS6X3dTPNHJQWAz8J/H3N9+XV6p+/WLETFMcXfVBy74b679uozsP+YbgM9F8QQdR4GJdr0eZ0wPrgM+m/X7EWD0P9wT9m4St1E8qcoBiiHzVy8zX5P7JMV140sR8X0Uv3y2R8RXgEMUt2hM5DeAzRHxKMV1dNQAxRMWfTmKJ2z4AMXjnA5EcRfHD1z8UBVroveo81JK3wQ2UXzOXwH+KqW0c4LH+V2Ku5n9VfYa/A7FLRYrgS9HxJcoHsv0W8D3AINR3K3vY9njj13mSeB2irv+HQTOMf6fXRO5VK/Oi+IQD7+bLeNzFN8TH8vqGB3C5lLr2P+gGID3Uzym8FIuer4ppX+gGFr7ssf9c4q7SU5ZSul0VuNfUDw276sv8iH+kOJx2H84Zt7PAcuieEKYx4GOS9z3J4Ch7PV7DcVjRcc6AJyJ4kmA3pPV+0WKuxR/FM4PbbI2u/27gZ+NiC9QDPyjz3E3xeMi/yz7e7gPuC6l9AWKPfwKxV0pH2OC9+uU0j9TDPVbsnX+yxR3LYUrfy+oWFf7N5Dt8fBTFNez75vkpvdT7OVXKB6D/Esppaey6/6U4rG/X6e4++88choQR08coAoUxTOEfSildNGZllR57Fe+2K8rExENKaWRbGvQ/wK+llL6ULnrkqZbtqX8syml5nLXouqQ/SNtkGzorGl4vNH365dS3Ir1zpTSX031cVV93IJYoaI4oPEfccF/ElWZ7Fe+2K8X5Y7sP+KHKP4X+3cmv7kk6XIi4qcpbuXqmo5wmPlI9n79VxTPXGs41FVxC6IkSZIkCXALoiRJkiQpY0CUJEmSJAEGREmSJElSxoAoSapqEfEzEfHhaX7MN0fE4jHTvxoRb5zOZUiSNBMMiJIkTb83A+cDYkrpv6eUPl++ciRJujIGREnSrBYRb4+Iv8wGcv+diKiJiNsj4m8i4hFg+Zjbfiwi3jpmemTM5V+KiIPZQNd3Z/PuiIgvZPP+KCJeGhG3AGspDjD+5Yj4vrGPGxGrskHqD0bE70VEXTb/cES8PyL+KrtuWgYPlyTpxTAgSpJmrYhoAn4CWJ5Seh1wFng78H6KwXA1Y7b0TfI4P0pxq+APppReC/xGdtVnUkr/Kps3DLSnlPYD/cAvppRel1L6v2Mepx74GPATKaWlQC2wfsyink4p/QCwHfiFq33ekiRdLQOiJGk2WwW8HvhCNoD0KuA9wGBK6R9SSv8M/OEVPM4bgY+mlP4JIKX0/7dz/y5VhXEcx9+f4UoILQmBm1DgUEObW/UftNgkDTa3thnofyG45BYEzXGrIRwi0QSFuLTU1tYSQUH1ONxvcBDNi14abu/Xcn5/ec50+PB9nvOlzl9PspXkAFgCrp1SZx742Fr7UMebwM3O9We13QXmRhiXJEljZUCUJE2yAJvVybvRWpsHVoF2wv0/qW9jkgBTnTrHPfMYeFDdwDXgwgjj+Zsftf3FsLsoSdI/ZUCUJE2yV8BikssASS4Be8DtJDNJesDdzv2fGHYcAe4AvdrvA/eTTHfqAFwEPledpU6dr3XtqAEwl+RqHd8DXp/99SRJGi8DoiRpYrXW3gMrQD/JPvACmGXYRXwDvATedR7ZAG4l2QYWgG9V5znDdYU7NVX1z/rAR8Dbqjvo1HkCPKyf0VzpjOc7sAw8rWmpv4H1Mb6yJEnnktZOmmUjSZIkSfqf2EGUJEmSJAEGREmSJElSMSBKkiRJkgADoiRJkiSpGBAlSZIkSYABUZIkSZJUDIiSJEmSJAAOATKGT6laMCLlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Code block for students answer\n",
    "# Replace ##YOUR CODE GOES HERE## with python code.\n",
    "\n",
    "##YOUR CODE GOES HERE##\n",
    "df.boxplot(column = \"age\",\n",
    "  by = \"education\",\n",
    "  figsize = (15, 15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qvUFCih5phqI"
   },
   "source": [
    "Double-click **here** for the solution.\n",
    "\n",
    "<!-- \n",
    "df.boxplot(column = \"age\",\n",
    "  by = \"education\",\n",
    "  figsize = (15, 15))\n",
    "-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iEbNXiqmTTh0"
   },
   "source": [
    "## Authors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6_1ZOvXKTTh0"
   },
   "source": [
    "[Roman Yatsenko](https://www.linkedin.com/in/yatsenkorn/?utm_medium=Exinfluencer\\&utm_source=Exinfluencer\\&utm_content=000026UJ\\&utm_term=10006555\\&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkQuickLabsEDA_Pandas_Banking_L126457256-2021-01-01)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bHpM3GyTTTh2"
   },
   "source": [
    "## Change Log\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mrgMUa_ETTh2"
   },
   "source": [
    "|  Date (YYYY-MM-DD) |  Version | Changed By  |  Change Description |\n",
    "|---|---|---|---|\n",
    "| 2021-05-05  | 0.3  | Kavun, Sergii  |  Code refactoring-2 |\n",
    "| 2021-05-05  | 0.22  | Yatsenko, Roman  |  Task refactoring |\n",
    "| 2021-04-28  | 0.21  | Kavun, Sergii  |  Code refactoring-1 |\n",
    "| 2021-04-28  | 0.2  | Yatsenko, Roman  |  Translate to english |\n",
    "| 2021-04-23  | 0.1  | Yatsenko, Roman  |  Created Lab |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ma5EMufCTTh2"
   },
   "source": [
    "Copyright © 2021 IBM Corporation. This notebook and its source code are released under the terms of the [MIT License](https://cognitiveclass.ai/mit-license/?utm_medium=Exinfluencer\\&utm_source=Exinfluencer\\&utm_content=000026UJ\\&utm_term=10006555\\&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkQuickLabsEDA_Pandas_Banking_L126457256-2021-01-01).\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "EDA_Pandas_Banking_L1_edited.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}