Solution_practice_exercise+(cleaning_transforming).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Practice Exercise: Cleaning data & Transforming columns/features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Context:\n",
    "- The data is based on real anonymized Czech bank transactions and account info.\n",
    "- We'll be focusing on practicing the data cleaning, columns transformations, and other techniques that we've learned in the course. \n",
    "- But here is the original task description of the dataset publishers:\n",
    "\n",
    "*The bank wants to improve their services. For instance, the bank managers have only vague idea, who is a good client (whom to offer some additional services)   and who is a bad client (whom to watch carefully to minimize the bank losses). Fortunately, the bank stores data about their clients, the accounts (transactions within several months), the loans already granted, the credit cards issued. The bank managers hope to improve their understanding of customers and seek specific actions to improve services.*\n",
    "\n",
    "- We've made minor changes on the data to fit this exercise, such as changing the column names. Check out the original source if you are interested in using this data for other purposes (https://data.world/lpetrocelli/czech-financial-dataset-real-anonymized-transactions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset Description:\n",
    "\n",
    "We'll work on three datasets (in three separate csv files):\n",
    "\n",
    "  - **account**: each record describes static characteristics of an account\n",
    "  - **transaction**: each record describes one transaction on an account\n",
    "  - **district**: each record describes demographic characteristics of a district\n",
    "  \n",
    "In reality, the organizations like banks often have data stored in multiple datasets. Assume we want to study the transactional level data, we'll need to combine these three datasets together to have transactions data with account and district data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objective: \n",
    "   - Examine/clean the individual dataset\n",
    "   - Combine them into a single dataset, which is subject to more cleaning\n",
    "   - Create new columns based on existing columns\n",
    "\n",
    "By the end, the new dataset is ready for more analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Import the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Import the data from three csv files as DataFrames `account`, `district`, `trans`\n",
    "Hint: \n",
    "- the `read_csv` function can automatically infer and load zip file, read its documentation of parameter `compression` if you are interested in details\n",
    "- you may ignore the warning when reading the `trans.csv.zip` file. It is optional to follow the warning instructions to remove it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "account = pd.read_csv('account.csv')\n",
    "district = pd.read_csv('district.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\liann\\anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3147: DtypeWarning: Columns (8) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "trans = pd.read_csv('trans.csv.zip')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to remove the warning message, you can specify dtype for the column `partner_bank` as below. But it's not mandatory.\n",
    "\n",
    "`trans = pd.read_csv('trans.csv.zip', dtype={'partner_bank': 'string'})`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Look at the info summary, head of each DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4500 entries, 0 to 4499\n",
      "Data columns (total 5 columns):\n",
      " #   Column             Non-Null Count  Dtype \n",
      "---  ------             --------------  ----- \n",
      " 0   account_id         4500 non-null   int64 \n",
      " 1   district_id        4500 non-null   int64 \n",
      " 2   frequency          4500 non-null   object\n",
      " 3   account_open_date  4500 non-null   object\n",
      " 4   client_id          4500 non-null   int64 \n",
      "dtypes: int64(3), object(2)\n",
      "memory usage: 175.9+ KB\n"
     ]
    }
   ],
   "source": [
    "account.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>account_id</th>\n",
       "      <th>district_id</th>\n",
       "      <th>frequency</th>\n",
       "      <th>account_open_date</th>\n",
       "      <th>client_id</th>\n",
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       "      <th>0</th>\n",
       "      <td>576</td>\n",
       "      <td>55</td>\n",
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       "      <td>1993-01-01</td>\n",
       "      <td>692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3818</td>\n",
       "      <td>74</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1993-01-01</td>\n",
       "      <td>4601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>704</td>\n",
       "      <td>55</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1993-01-01</td>\n",
       "      <td>844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2378</td>\n",
       "      <td>16</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1993-01-01</td>\n",
       "      <td>2873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2632</td>\n",
       "      <td>24</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1993-01-02</td>\n",
       "      <td>3177</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   account_id  district_id           frequency account_open_date  client_id\n",
       "0         576           55  MONTHLY STATEMENTS        1993-01-01        692\n",
       "1        3818           74  MONTHLY STATEMENTS        1993-01-01       4601\n",
       "2         704           55  MONTHLY STATEMENTS        1993-01-01        844\n",
       "3        2378           16  MONTHLY STATEMENTS        1993-01-01       2873\n",
       "4        2632           24  MONTHLY STATEMENTS        1993-01-02       3177"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "account.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 76 entries, 0 to 75\n",
      "Data columns (total 7 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   district_id           76 non-null     int64  \n",
      " 1   district_name         76 non-null     object \n",
      " 2   region                76 non-null     object \n",
      " 3   population            76 non-null     int64  \n",
      " 4   average_salary        66 non-null     float64\n",
      " 5   unemployment_rate     66 non-null     float64\n",
      " 6   num_committed_crimes  67 non-null     float64\n",
      "dtypes: float64(3), int64(2), object(2)\n",
      "memory usage: 4.3+ KB\n"
     ]
    }
   ],
   "source": [
    "district.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>district_id</th>\n",
       "      <th>district_name</th>\n",
       "      <th>region</th>\n",
       "      <th>population</th>\n",
       "      <th>average_salary</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>num_committed_crimes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Hl.m. Praha</td>\n",
       "      <td>Prague</td>\n",
       "      <td>1204953</td>\n",
       "      <td>12541.0</td>\n",
       "      <td>0.43</td>\n",
       "      <td>99107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Benesov</td>\n",
       "      <td>central Bohemia</td>\n",
       "      <td>88884</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2674.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Beroun</td>\n",
       "      <td>central Bohemia</td>\n",
       "      <td>75232</td>\n",
       "      <td>8980.0</td>\n",
       "      <td>2.21</td>\n",
       "      <td>2813.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Kladno</td>\n",
       "      <td>central Bohemia</td>\n",
       "      <td>149893</td>\n",
       "      <td>9753.0</td>\n",
       "      <td>5.05</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Kolin</td>\n",
       "      <td>central Bohemia</td>\n",
       "      <td>95616</td>\n",
       "      <td>9307.0</td>\n",
       "      <td>4.43</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   district_id district_name           region  population  average_salary  \\\n",
       "0            1   Hl.m. Praha           Prague     1204953         12541.0   \n",
       "1            2       Benesov  central Bohemia       88884             NaN   \n",
       "2            3        Beroun  central Bohemia       75232          8980.0   \n",
       "3            4        Kladno  central Bohemia      149893          9753.0   \n",
       "4            5         Kolin  central Bohemia       95616          9307.0   \n",
       "\n",
       "   unemployment_rate  num_committed_crimes  \n",
       "0               0.43               99107.0  \n",
       "1                NaN                2674.0  \n",
       "2               2.21                2813.0  \n",
       "3               5.05                   NaN  \n",
       "4               4.43                   NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "district.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1056320 entries, 0 to 1056319\n",
      "Data columns (total 10 columns):\n",
      " #   Column           Non-Null Count    Dtype  \n",
      "---  ------           --------------    -----  \n",
      " 0   trans_id         1056320 non-null  int64  \n",
      " 1   account_id       1056320 non-null  int64  \n",
      " 2   date             1056320 non-null  object \n",
      " 3   type             1056320 non-null  object \n",
      " 4   operation_type   873206 non-null   object \n",
      " 5   amount           1056320 non-null  float64\n",
      " 6   balance          1056320 non-null  float64\n",
      " 7   description      574439 non-null   object \n",
      " 8   partner_bank     273508 non-null   object \n",
      " 9   partner_account  295389 non-null   float64\n",
      "dtypes: float64(3), int64(2), object(5)\n",
      "memory usage: 80.6+ MB\n"
     ]
    }
   ],
   "source": [
    "trans.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "      <th>type</th>\n",
       "      <th>operation_type</th>\n",
       "      <th>amount</th>\n",
       "      <th>balance</th>\n",
       "      <th>description</th>\n",
       "      <th>partner_bank</th>\n",
       "      <th>partner_account</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>695247</td>\n",
       "      <td>2378</td>\n",
       "      <td>1993-01-01</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>700.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>171812</td>\n",
       "      <td>576</td>\n",
       "      <td>1993-01-01</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>900.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>207264</td>\n",
       "      <td>704</td>\n",
       "      <td>1993-01-01</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1117247</td>\n",
       "      <td>3818</td>\n",
       "      <td>1993-01-01</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>600.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>579373</td>\n",
       "      <td>1972</td>\n",
       "      <td>1993-01-02</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>400.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   trans_id  account_id        date    type  operation_type  amount  balance  \\\n",
       "0    695247        2378  1993-01-01  CREDIT  CREDIT IN CASH   700.0    700.0   \n",
       "1    171812         576  1993-01-01  CREDIT  CREDIT IN CASH   900.0    900.0   \n",
       "2    207264         704  1993-01-01  CREDIT  CREDIT IN CASH  1000.0   1000.0   \n",
       "3   1117247        3818  1993-01-01  CREDIT  CREDIT IN CASH   600.0    600.0   \n",
       "4    579373        1972  1993-01-02  CREDIT  CREDIT IN CASH   400.0    400.0   \n",
       "\n",
       "  description partner_bank  partner_account  \n",
       "0         NaN          NaN              NaN  \n",
       "1         NaN          NaN              NaN  \n",
       "2         NaN          NaN              NaN  \n",
       "3         NaN          NaN              NaN  \n",
       "4         NaN          NaN              NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A few key things to notice: \n",
    "- We must fix the data types for some columns\n",
    "- There is missing data\n",
    "- These datasets have connections among them:\n",
    "  - `account` and `trans` have common column named `account_id`\n",
    "  - `account` and `district` have common column named `district_id`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Check for the unique values and their counts in each column for the three DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "account_id           4500\n",
       "district_id            77\n",
       "frequency               3\n",
       "account_open_date    1535\n",
       "client_id            4500\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "account.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "district_id             76\n",
       "district_name           76\n",
       "region                   8\n",
       "population              76\n",
       "average_salary          65\n",
       "unemployment_rate       63\n",
       "num_committed_crimes    66\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "district.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "trans_id           1056320\n",
       "account_id            4500\n",
       "date                  2191\n",
       "type                     2\n",
       "operation_type           5\n",
       "amount               40400\n",
       "balance             542739\n",
       "description              8\n",
       "partner_bank            13\n",
       "partner_account       7665\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All columns have at least 2 unique values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Check for duplicates in the three DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "<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>account_id</th>\n",
       "      <th>district_id</th>\n",
       "      <th>frequency</th>\n",
       "      <th>account_open_date</th>\n",
       "      <th>client_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [account_id, district_id, frequency, account_open_date, client_id]\n",
       "Index: []"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "account[account.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>district_id</th>\n",
       "      <th>district_name</th>\n",
       "      <th>region</th>\n",
       "      <th>population</th>\n",
       "      <th>average_salary</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>num_committed_crimes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [district_id, district_name, region, population, average_salary, unemployment_rate, num_committed_crimes]\n",
       "Index: []"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "district[district.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trans_id</th>\n",
       "      <th>account_id</th>\n",
       "      <th>date</th>\n",
       "      <th>type</th>\n",
       "      <th>operation_type</th>\n",
       "      <th>amount</th>\n",
       "      <th>balance</th>\n",
       "      <th>description</th>\n",
       "      <th>partner_bank</th>\n",
       "      <th>partner_account</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [trans_id, account_id, date, type, operation_type, amount, balance, description, partner_bank, partner_account]\n",
       "Index: []"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans[trans.duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No duplicated rows in the DataFrames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Convert column `account_open_date` in `account` and column `date` in `trans` into datetime dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "account['account_open_date'] = pd.to_datetime(account['account_open_date'])\n",
    "trans['date'] = pd.to_datetime(trans['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "account_id                    int64\n",
       "district_id                   int64\n",
       "frequency                    object\n",
       "account_open_date    datetime64[ns]\n",
       "client_id                     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "account.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "trans_id                    int64\n",
       "account_id                  int64\n",
       "date               datetime64[ns]\n",
       "type                       object\n",
       "operation_type             object\n",
       "amount                    float64\n",
       "balance                   float64\n",
       "description                object\n",
       "partner_bank               object\n",
       "partner_account           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Convert the columns `region` and `district_name` in `district` to all uppercase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "district['region'] = district['region'].str.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "district['district_name'] = district['district_name'].str.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>district_id</th>\n",
       "      <th>district_name</th>\n",
       "      <th>region</th>\n",
       "      <th>population</th>\n",
       "      <th>average_salary</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>num_committed_crimes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>HL.M. PRAHA</td>\n",
       "      <td>PRAGUE</td>\n",
       "      <td>1204953</td>\n",
       "      <td>12541.0</td>\n",
       "      <td>0.43</td>\n",
       "      <td>99107.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>BENESOV</td>\n",
       "      <td>CENTRAL BOHEMIA</td>\n",
       "      <td>88884</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2674.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>BEROUN</td>\n",
       "      <td>CENTRAL BOHEMIA</td>\n",
       "      <td>75232</td>\n",
       "      <td>8980.0</td>\n",
       "      <td>2.21</td>\n",
       "      <td>2813.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>KLADNO</td>\n",
       "      <td>CENTRAL BOHEMIA</td>\n",
       "      <td>149893</td>\n",
       "      <td>9753.0</td>\n",
       "      <td>5.05</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>KOLIN</td>\n",
       "      <td>CENTRAL BOHEMIA</td>\n",
       "      <td>95616</td>\n",
       "      <td>9307.0</td>\n",
       "      <td>4.43</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   district_id district_name           region  population  average_salary  \\\n",
       "0            1   HL.M. PRAHA           PRAGUE     1204953         12541.0   \n",
       "1            2       BENESOV  CENTRAL BOHEMIA       88884             NaN   \n",
       "2            3        BEROUN  CENTRAL BOHEMIA       75232          8980.0   \n",
       "3            4        KLADNO  CENTRAL BOHEMIA      149893          9753.0   \n",
       "4            5         KOLIN  CENTRAL BOHEMIA       95616          9307.0   \n",
       "\n",
       "   unemployment_rate  num_committed_crimes  \n",
       "0               0.43               99107.0  \n",
       "1                NaN                2674.0  \n",
       "2               2.21                2813.0  \n",
       "3               5.05                   NaN  \n",
       "4               4.43                   NaN  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "district.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Check for missing data by columns in `account` using the `isna` method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "account_id           0\n",
       "district_id          0\n",
       "frequency            0\n",
       "account_open_date    0\n",
       "client_id            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "account.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No missing data in this dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 9. Check for missing data by columns in `district` using the `isna` method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "district_id              0\n",
       "district_name            0\n",
       "region                   0\n",
       "population               0\n",
       "average_salary          10\n",
       "unemployment_rate       10\n",
       "num_committed_crimes     9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "district.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`district` has numeric features that could have relationships with each other. Let's use iterative imputation on them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use `IterativeImputer` in `sklearn` to impute based on columns `population`, `average_salary`, `unemployment_rate`, `num_committed_crimes`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.experimental import enable_iterative_imputer\n",
    "from sklearn.impute import IterativeImputer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Build a list of columns that will be used for imputation, which are `population`, `average_salary`, `unemployment_rate`, `num_committed_crimes`\n",
    "These are the columns that might be related to each other "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_to_impute = ['population', 'average_salary', 'unemployment_rate', 'num_committed_crimes']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Create `IterativeImputer` object and set its `min_value` and `max_value` parameters to be the minumum and maximum of corresponding columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "iter_imp = IterativeImputer(min_value=district[cols_to_impute].min(), max_value=district[cols_to_impute].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Apply the imputer to fit and transform the columns to an imputed NumPy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "imputed_cols = iter_imp.fit_transform(district[cols_to_impute])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Assign the imputed array back to the original DataFrame's columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "district[cols_to_impute] = imputed_cols"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Double check that the columns are imputed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "district_id             0\n",
       "district_name           0\n",
       "region                  0\n",
       "population              0\n",
       "average_salary          0\n",
       "unemployment_rate       0\n",
       "num_committed_crimes    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "district.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 10. Check for missing data by columns in `trans` using the `isna` method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "trans_id                0\n",
       "account_id              0\n",
       "date                    0\n",
       "type                    0\n",
       "operation_type     183114\n",
       "amount                  0\n",
       "balance                 0\n",
       "description        481881\n",
       "partner_bank       782812\n",
       "partner_account    760931\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Divide the columns into numeric columns and categorical columns, then use the `fillna` method to fill numeric columns with -999, fill categorical columns with 'UNKNOWN'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_cols = trans.select_dtypes(include='number').columns\n",
    "cat_cols = trans.select_dtypes(exclude='number').columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "trans[numeric_cols] = trans[numeric_cols].fillna(-999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "trans[cat_cols] = trans[cat_cols].fillna('UNKNOWN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1056320 entries, 0 to 1056319\n",
      "Data columns (total 10 columns):\n",
      " #   Column           Non-Null Count    Dtype         \n",
      "---  ------           --------------    -----         \n",
      " 0   trans_id         1056320 non-null  int64         \n",
      " 1   account_id       1056320 non-null  int64         \n",
      " 2   date             1056320 non-null  datetime64[ns]\n",
      " 3   type             1056320 non-null  object        \n",
      " 4   operation_type   1056320 non-null  object        \n",
      " 5   amount           1056320 non-null  float64       \n",
      " 6   balance          1056320 non-null  float64       \n",
      " 7   description      1056320 non-null  object        \n",
      " 8   partner_bank     1056320 non-null  object        \n",
      " 9   partner_account  1056320 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(3), int64(2), object(4)\n",
      "memory usage: 80.6+ MB\n"
     ]
    }
   ],
   "source": [
    "trans.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11. Check for outliers in `district` using the `describe` method, then look at the histograms of the suspicious columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>district_id</th>\n",
       "      <th>population</th>\n",
       "      <th>average_salary</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>num_committed_crimes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>76.000000</td>\n",
       "      <td>7.600000e+01</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>76.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.500000</td>\n",
       "      <td>1.336920e+05</td>\n",
       "      <td>8983.707247</td>\n",
       "      <td>3.864003</td>\n",
       "      <td>5054.492038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>22.083176</td>\n",
       "      <td>1.378127e+05</td>\n",
       "      <td>740.959314</td>\n",
       "      <td>1.721597</td>\n",
       "      <td>11392.279917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.282100e+04</td>\n",
       "      <td>8110.000000</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>888.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>19.750000</td>\n",
       "      <td>8.472500e+04</td>\n",
       "      <td>8533.750000</td>\n",
       "      <td>2.650000</td>\n",
       "      <td>1918.454646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>38.500000</td>\n",
       "      <td>1.083910e+05</td>\n",
       "      <td>8792.984750</td>\n",
       "      <td>3.761936</td>\n",
       "      <td>2864.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>57.250000</td>\n",
       "      <td>1.382770e+05</td>\n",
       "      <td>9280.750000</td>\n",
       "      <td>4.570000</td>\n",
       "      <td>4562.413240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>76.000000</td>\n",
       "      <td>1.204953e+06</td>\n",
       "      <td>12541.000000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>99107.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       district_id    population  average_salary  unemployment_rate  \\\n",
       "count    76.000000  7.600000e+01       76.000000          76.000000   \n",
       "mean     38.500000  1.336920e+05     8983.707247           3.864003   \n",
       "std      22.083176  1.378127e+05      740.959314           1.721597   \n",
       "min       1.000000  4.282100e+04     8110.000000           0.430000   \n",
       "25%      19.750000  8.472500e+04     8533.750000           2.650000   \n",
       "50%      38.500000  1.083910e+05     8792.984750           3.761936   \n",
       "75%      57.250000  1.382770e+05     9280.750000           4.570000   \n",
       "max      76.000000  1.204953e+06    12541.000000           9.400000   \n",
       "\n",
       "       num_committed_crimes  \n",
       "count             76.000000  \n",
       "mean            5054.492038  \n",
       "std            11392.279917  \n",
       "min              888.000000  \n",
       "25%             1918.454646  \n",
       "50%             2864.000000  \n",
       "75%             4562.413240  \n",
       "max            99107.000000  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "district.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'population'}>,\n",
       "        <AxesSubplot:title={'center':'average_salary'}>],\n",
       "       [<AxesSubplot:title={'center':'unemployment_rate'}>,\n",
       "        <AxesSubplot:title={'center':'num_committed_crimes'}>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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J6jgLO0mSJEnqOAs7SZIkSeo4CztJkiRJ6ji/x06SpA5Lsh24CbgF2FlVm8YbkSRpHCzsJEnqvodX1XfGHYQkaXwciilJkiRJHbfgGbskpwCPB66rqvu10/4v8KvAj4GvAc+uqhvmWHc7Dg+RJGklFfDRJAX8XVVtm71Aks3AZoB169YxPT3dd2MzMzPzzl9uWzbu7Dvvdad+YIjtLSWava2mXHWVeRqcuRqcudrbIEMx3wq8Hnh7z7RzgJdW1c4kfwm8FHhJn/UdHiJJ0sp5aFVdneRQ4JwkX66qc3sXaIu9bQCbNm2qqampvhubnp5mvvnL7aStZ42srWFsP3Gq77xR56qrzNPgzNXgzNXeFhyK2e4crp817aNVtesQ22eBI1YgNkmStICqurr9eR1wBnDMeCOSJI3Dclxj9xzgQ33m7RoeckE7DESSJC2TJPsnudOu58CjgUvGG5UkaRyWdFfMJH8I7ARO7bPIgsNDerbVd/z/co6hnW8s/zCWK65JGCdsH9cG+7g2TEIfJ8Q64Iwk0OzT31VVHx5vSJKkcRi6sEvyLJqbqhxbVTXXMr3DQ5LsGh4yZ2E33/j/5RxDu9xj+ecbe78YkzBO2D6uDfZxbZiEPk6CqroCuP+445Akjd9QQzGTHEdzs5QnVNUP+izj8BBJkiRJGoEFC7skpwGfATYk2ZHkuTR3ybwTzfDKC5O8qV327knOblddB3wqyReBzwFnOTxEkiRJkpbfgkMxq+ppc0x+S59lrwaOb587PESSJEmSRmA57oopSZIkSRojCztJkiRJ6jgLO0mSJEnqOAs7SZIkSeo4CztJkiRJ6jgLO0mSJEnqOAs7SZIkSeo4CztJkiRJ6jgLO0mSJEnqOAs7SZIkSeo4CztJkiRJ6jgLO0mSJEnqOAs7SZIkSeo4CztJkjouyT5J/i3JB8cdiyRpPCzsJEnqvhcBl407CEnS+FjYSZLUYUmOAB4HvHncsUiSxmfBwi7JKUmuS3JJz7S7JjknyVfbn3fps+5xSb6S5PIkW5czcEmSBMBrgD8AfjrmOCRJY7TvAMu8FXg98PaeaVuBj1XVyW3BthV4Se9KSfYB3gA8CtgBfD7JmVX1peUIXJKkSZfk8cB1VXVBkql5ltsMbAZYt24d09PTfbc5MzMz7/yLr7qx77yNhx+46HW2bOw7a1V43akfmHP6xsMPXDBXizFMXrtiOfO01pmrwZmrvS1Y2FXVuUnWz5p8AjDVPn8bMM2swg44Bri8qq4ASHJ6u56FnSRJy+OhwBOSHA/cAbhzkndW1dN7F6qqbcA2gE2bNtXU1FTfDU5PTzPf/JO2ntV33vYT515vvnW6avuJUwvmajGGyWtXLGee1jpzNThztbdhr7FbV1XXALQ/D51jmcOBK3te72inSZKkZVBVL62qI6pqPfBU4OOzizpJ0mQYZCjmsDLHtOq78DzDRJbzVOuWjTuXZTu7LFdck3A62T6uDfZxbZiEPkqSNEmGLeyuTXJYVV2T5DDgujmW2QEc2fP6CODqfhucb5jIqIY6DGO5hkdMwulk+7g22Me1YRL6OGmqaprm0ghJ0gQadijmmcCz2ufPAua6svjzwFFJ7pXkdjRDRM4csj1JkiRJUh+DfN3BacBngA1JdiR5LnAy8KgkX6W56+XJ7bJ3T3I2QFXtBF4AfITmS1PfU1WXrkw3JEmSJGlyDXJXzKf1mXXsHMteDRzf8/ps4Oyho5MkSZIkLWjYoZiSJEmSpFXCwk6SJEmSOs7CTpIkSZI6zsJOkiRJkjrOwk6SJEmSOs7CTpIkSZI6zsJOkiRJkjrOwk6SJEmSOs7CTpIkSZI6zsJOkiRJkjrOwk6SJEmSOs7CTpIkSZI6zsJOkiRJkjrOwk6SJEmSOs7CTpKkjkpyhySfS/LFJJcmecW4Y5Ikjce+4w5grVq/9ay+87af/LgRRiJJWsN+BDyiqmaS3Bb4VJIPVdVnxx2YJGm0hj5jl2RDkgt7Ht9P8uJZy0wlubFnmT9ecsSSJAmAasy0L2/bPmqMIUmSxmToM3ZV9RXgaIAk+wBXAWfMsegnq+rxw7YjSZL6a/fBFwD3Ad5QVeeNOSRJ0hgs11DMY4GvVdU3lml7kiRpAFV1C3B0koOAM5Lcr6ou6V0myWZgM8C6deuYnp7uu72ZmZl552/ZuLPvvNed+oE+6/RdpbNed+oHWLff3H3eePiBi97efHmd7/cxjIuvunHO6cPEPYiF3lPazVwNzlztbbkKu6cCp/WZ95AkXwSuBn6vqi5dpjYlSVKrqm5IMg0cB1wya942YBvApk2bampqqu92pqenmW/+SfNcQz5ptmzcyasu3vtfqe0nTi16W/PldZjtDdPWcrezy0LvKe1mrgZnrva25MIuye2AJwAvnWP2F4B7thd1Hw/8E3BUn+30PZq4nBX5fEfEhtEvrsUeeZuEow72cW2wj2vDJPRxEiQ5BPhJW9TtBzwS+MsxhyVJGoPlOGP3WOALVXXt7BlV9f2e52cn+dskB1fVd+ZYtu/RxOWsyJf7SGO/o1uLPfI2CUcd7OPaYB/Xhkno44Q4DHhbe53dbYD3VNUHxxyTJGkMlqOwexp9hmEm+Rng2qqqJMfQ7HS+uwxtSpI08arqIuAB445DkjR+SyrsktwReBTwvJ5pzweoqjcBTwL+e5KdwH8AT60qb8MsSZIkSctoSYVdVf0AuNusaW/qef564PVLaUOSJEmSNL/luivmqrLeO3ZJkiRJmiC3GXcAkiRJkqSlsbCTJEmSpI6zsJMkSZKkjrOwkyRJkqSOs7CTJEmSpI6zsJMkSZKkjrOwkyRJkqSOs7CTJEmSpI6zsJMkSZKkjrOwkyRJkqSOs7CTJEmSpI6zsJMkSZKkjrOwkyRJkqSOs7CTJEmSpI6zsJMkqaOSHJnkE0kuS3JpkheNOyZJ0njsu5SVk2wHbgJuAXZW1aZZ8wO8Fjge+AFwUlV9YSltSpKkW+0EtlTVF5LcCbggyTlV9aVxByZJGq0lFXath1fVd/rMeyxwVPv4ReCN7U9JkrREVXUNcE37/KYklwGHAxZ2kjRhVnoo5gnA26vxWeCgJIetcJuSJE2cJOuBBwDnjTkUSdIYLPWMXQEfTVLA31XVtlnzDweu7Hm9o512zRLblSRJrSQHAO8DXlxV359j/mZgM8C6deuYnp7uu62ZmZl552/ZuHOJ0a4d6/abOx/z5a+f+fI6zPaGaWvYdi6+6sY5p288/EBg4feUdjNXgzNXe0tVDb9ycvequjrJocA5wAur6tye+WcBf1FVn2pffwz4g6q6YI5t9e50HnT66affOm9mZoYDDjhg4Lj6fcCsFrs+6Hotto9dZB/XBvu4NixHHx/+8IdfMPvaao1ektsCHwQ+UlWvXmj5TZs21fnnn993/vT0NFNTU33nr9961hBRrk1bNu7kVRfvfYx8+8mPW/S25svrMNsbpq1h21loewu9p7SbuRrcJOQqyaL2s0s6Y1dVV7c/r0tyBnAMcG7PIjuAI3teHwFc3Wdb24Bt0Ox0en9Ri/3FnbTKdzrbT5zaa9okvDnt49pgH9eGSejjJGhvUvYW4LJBijpJ0to19DV2SfZv78BFkv2BRwOXzFrsTOCZaTwYuLG90FuSJC3dQ4FnAI9IcmH7OH7cQUmSRm8pZ+zWAWc0BwvZF3hXVX04yfMBqupNwNk0X3VwOc3XHTx7aeFKkqRd2ksdMu44JEnjN3RhV1VXAPefY/qbep4X8LvDtiFJkiRJWthKf92BJEmSJGmFWdhJkiRJUsdZ2EmSJElSx1nYSZIkSVLHWdhJkiRJUsdZ2EmSJElSx1nYSZIkSVLHWdhJkiRJUsdZ2EmSJElSx1nYSZIkSVLHWdhJkiRJUsdZ2EmSJElSx1nYSZIkSVLHWdhJkiRJUsdZ2EmSJElSx1nYSZIkSVLHDV3YJTkyySeSXJbk0iQvmmOZqSQ3Jrmwffzx0sKVJEm7JDklyXVJLhl3LJKk8dp3CevuBLZU1ReS3Am4IMk5VfWlWct9sqoev4R2JEnS3N4KvB54+5jjkCSN2dBn7Krqmqr6Qvv8JuAy4PDlCkySJM2vqs4Frh93HJKk8VvKGbtbJVkPPAA4b47ZD0nyReBq4Peq6tLlaFOSJA0myWZgM8C6deuYnp7uu+zMzMy887ds3LnM0XXXuv3mzsfrTv3Aore1ZWP/ef22t/HwA/uuc/FVNy66rWHiHmR76/Ybftu95utvP/Plod/2hllnvvUWE/dCf3/abTXkatj3ykpJVS1tA8kBwL8Af1ZV7581787AT6tqJsnxwGur6qg+2+nd6Tzo9NNPv3XezMwMBxxwwMAxzZfk1WCuX/Ri+9hF9nFtsI9rw3L08eEPf/gFVbVpmULSkNqDqx+sqvsNsvymTZvq/PPP7zt/enqaqampvvPXbz1rkRGuXVs27uRVFy/LMfKhbD/5cX3nrabf03Llab7+9jNfHvptb5h15ltvMXEv9Pen3VZDroZ9rwwqyaL2s0v6K0tyW+B9wKmzizqAqvp+z/Ozk/xtkoOr6jtzLLsN2AbNTqf3F7XYX9xJq+jDbC7bT5zaa9pqeHOuNPu4NtjHtWES+ihJ0iRZyl0xA7wFuKyqXt1nmZ9plyPJMW173x22TUmSJEnS3pZyxu6hwDOAi5Nc2E57GXAPgKp6E/Ak4L8n2Qn8B/DUWurYT0mSBECS04Ap4OAkO4A/qaq3jDcqSdI4DF3YVdWngCywzOtpbsMsSZKWWVU9bdwxSJJWh6GHYkqSJEmSVgcLO0mSJEnqOAs7SZIkSeo4CztJkiRJ6jgLO0mSJEnqOAs7SZIkSeo4CztJkiRJ6jgLO0mSJEnqOAs7SZIkSeo4CztJkiRJ6rh9xx3AJFq/9ay9pm3ZuJOT5pg+iO0nP27gdhZaZ771lnsdSSuv39/mlo07mRptKJIkaQV5xk6SJEmSOs7CTpIkSZI6zsJOkiRJkjrOwk6SJEmSOs7CTpIkSZI6bkmFXZLjknwlyeVJts4xP0n+pp1/UZIHLqU9SZK0p4X2xZKkyTB0YZdkH+ANwGOB+wJPS3LfWYs9FjiqfWwG3jhse5IkaU8D7oslSRNgKWfsjgEur6orqurHwOnACbOWOQF4ezU+CxyU5LAltClJknYbZF8sSZoASynsDgeu7Hm9o5222GUkSdJw3M9KkgBIVQ23YvJk4DFV9Vvt62cAx1TVC3uWOQv4i6r6VPv6Y8AfVNUFc2xvM81wTYANwFd6Zh8MfGeoQLvDPq4N9nFtsI+DuWdVHbIcwWg4g+yL2+nz7WNnm4T3/3IxV4MxT4MzV4ObhFwtaj+77xIa2gEc2fP6CODqIZYBoKq2Advmmpfk/KraNHyoq599XBvs49pgH9UhA+1n59vHzuZ7Y3DmajDmaXDmanDmam9LGYr5eeCoJPdKcjvgqcCZs5Y5E3hme3fMBwM3VtU1S2hTkiTtNsi+WJI0AYY+Y1dVO5O8APgIsA9wSlVdmuT57fw3AWcDxwOXAz8Anr30kCVJEvTfF485LEnSGCxlKCZVdTZN8dY77U09zwv43aW00Rpo+EjH2ce1wT6uDfZRnTHXvniJfG8MzlwNxjwNzlwNzlzNMvTNUyRJkiRJq8NSrrGTJEmSJK0Cq6qwS3Jckq8kuTzJ1jnmJ8nftPMvSvLAccQ5rAH6d2Lbr4uS/GuS+48jzqVYqI89y/2XJLckedIo41sOg/QxyVSSC5NcmuRfRh3jUg3wXj0wyf9L8sW2j527fjbJKUmuS3JJn/md/ryBgfrY+c8cDSbJ/2z/Vi9JclqSOyS5a5Jzkny1/XmXnuVf2r73v5LkMT3TH5Tk4nbe3yTJeHq0cpK8qM3TpUle3E4zV8z9mbKcuUly+yTvbqefl2T9SDu4TPrk6cnte+qnSTbNWn4i8wR9c/V/k3y53TedkeSgnnkTm6uBVNWqeNBc9P014N7A7YAvAvedtczxwIeAAA8Gzht33Mvcv18C7tI+f2yX+jdoH3uW+zjNNSFPGnfcK/B7PAj4EnCP9vWh4457Bfr4MuAv2+eHANcDtxt37Ivs58OABwKX9Jnf2c+bRfSx0585PgZ+HxwOfB3Yr339HuAk4P8AW9tpW3v+pu/b/t3fHrhX+3mwTzvvc8BD2r+LDwGPHXf/ljlX9wMuAe5Icx+CfwaOMle35mevz5TlzA3wO8Cb2udPBd497j4vY55+nuY7JKeBTT3TJzZP8+Tq0cC+7fO/9D01+GM1nbE7Bri8qq6oqh8DpwMnzFrmBODt1fgscFCSw0Yd6JAW7F9V/WtVfa99+Vma7yPqkkF+hwAvBN4HXDfK4JbJIH38TeD9VfVNgKrqWj8H6WMBd2qPiB1AU9jtHG2YS1NV59LE3U+XP2+Ahfu4Bj5zNLh9gf2S7EtTtFxN8x5/Wzv/bcAT2+cnAKdX1Y+q6us0d7Y+pn3/37mqPlPNf0lv71lnrfh54LNV9YOq2gn8C/BrmCug72fKcuamd1vvBY7t4pnOufJUVZdV1VfmWHxi8wR9c/XR9u8P9tw3TXSuBrGaCrvDgSt7Xu9opy12mdVqsbE/l+aIQ5cs2Mckh9PsJN9ENw3ye/w54C5JppNckOSZI4tueQzSx9fT/AN0NXAx8KKq+ulowhuZLn/eDKOLnzkaQFVdBfwV8E3gGprvlP0osK7a75Ztfx7artLvvX94+3z29LXkEuBhSe6W5I40Z+6PxFzNZzlzc+s67T/2NwJ3W7HIVwfzNL/nsHvfZK4WsKSvO1hmc1XPs2/ZOcgyq9XAsSd5OM0/Wb+8ohEtv0H6+BrgJVV1S0cPmAzSx32BBwHHAvsBn0ny2ar695UObpkM0sfHABcCjwB+FjgnySer6vsrHNsodfnzZlE6/JmjAbTXPJ1AM3TpBuAfkzx9vlXmmFbzTF8zquqyJH8JnAPM0Az7mm80wsTmagDD5GYS82ae+kjyhzR/f6fumjTHYuaqx2o6Y7eD5qjYLkfQnA1Y7DKr1UCxJ/nPwJuBE6rquyOKbbkM0sdNwOlJtgNPAv42yRNHEt3yGPR9+uGqurmqvgOcC3TpphSD9PHZNMNNq6oup7l+5z+NKL5R6fLnzcA6/pmjwTwS+HpVfbuqfgK8n+b6ymt3DS9uf+4aNt7vvb+DPYfrrsm/iap6S1U9sKoeRjNE7KuYq/ksZ25uXacdNnwg8w+ZXwvM0xySPAt4PHBiO7wSzNWCVlNh93ngqCT3SnI7mgscz5y1zJnAM9N4MM1wkmtGHeiQFuxfknvQ7HCf0aGzO70W7GNV3auq1lfVepqxzr9TVf808kiHN8j79APAryTZtx3K84vAZSOOcykG6eM3ac5IkmQdzQXhV4w0ypXX5c+bgayBzxwN5pvAg5Pcsb225Fiaz6QzgWe1yzyL5rOLdvpT27vJ3Yvm5iGfa9//NyV5cLudZ/ass2YkObT9eQ/g14HTMFfzWc7c9G7rScDHe/6pX6vM0yxJjgNeAjyhqn7QM8tcLWTQu6yM4kEzlv3fae5y84fttOcDz2+fB3hDO/9ieu4q1IXHAP17M/A9miFuFwLnjzvm5e7jrGXfSsfuijloH4Hfp7kz5iXAi8cd83L3Ebg78NH27/AS4OnjjnmIPp5Gc73RT2iO6D13LX3eDNjHzn/m+Bj4vfAK4Mvt3+s7aO4qdzfgYzRnpD4G3LVn+T9s3/tfoedujjSjLi5p570eyLj7tgK5+mT7+f1F4Nh2mrmqvp8py5Yb4A7AP9LcFONzwL3H3edlzNOvtc9/BFwLfGTS8zRPri6nuS5u177pTeZqsMeuTkuSJEmSOmo1DcWUJEmSJA3Bwk6SJEmSOs7CTpIkSZI6zsJOkiRJkjrOwk6SJlCSU5Jcl+SSAZd/SpIvJbk0ybtWOj5JkrQ43hVTkiZQkocBM8Dbq+p+Cyx7FPAe4BFV9b0kh1bVdfOtI0mSRsszdpI0garqXOD63mlJfjbJh5NckOSTSf5TO+u3gTdU1ffadS3qJElaZSzsJEm7bANeWFUPAn4P+Nt2+s8BP5fk00k+m+S4sUUoSZLmtO+4A5AkjV+SA4BfAv4xya7Jt29/7gscBUwBRwCfTHK/qrphxGFKkqQ+LOwkSdCM4Lihqo6eY94O4LNV9RPg60m+QlPofX6E8UmSpHk4FFOSRFV9n6ZoezJAGvdvZ/8T8PB2+sE0QzOvGEeckiRpbhZ2kjSBkpwGfAbYkGRHkucCJwLPTfJF4FLghHbxjwDfTfIl4BPA71fVd8cRtyRJmptfdyBJkiRJHecZO0mSJEnqOAs7SZIkSeo4CztJkiRJ6jgLO0mSJEnqOAs7SZIkSeo4CztJkiRJ6jgLO0mSJEnqOAs7SZIkSeo4CztJkiRJ6jgLO0mSJEnqOAs7SZIkSeo4CztJkiRJ6jgLO2kBSbYneeS445AkSZL6sbCT1pgkJyX51Apte32SSrLvSmxfkqRBJXlTkv+1gttf9n1ekl9J8pXl2p7Uy8JO0q2S7DPuGCRJGkRVPb+qXgmQZCrJjt75SV6e5J3jiW5uVfXJqtow7ji0NlnYaWzao2D36Xn91iR/2j6fSrIjyZYk1yW5Jsmze5a9fZK/SvLNJNe2R+32m7XuH/Ss+8Qkxyf59yTXJ3lZz7ZenuS9Sd6d5KYkX0hy/z4x3z7Ja5Jc3T5ek+T27bxLkvxqz7K3TfKdJEf3HPV7dpIrk3wvyfOT/JckFyW5IcnrZ7X1nCSXtct+JMk9Z+Xu+Um+2s5/Qxo/D7wJeEiSmSQ3LPA7eGuSNyY5O8nNwMOTPC7JvyX5fhvry3tWObf9eUO7/YcsFKskSQJHu2ilWdhpNfsZ4EDgcOC5wBuS3KWd95fAzwFHA/dpl/njWeveoWf63wNPBx4E/Arwx0nu3bP8CcA/AncF3gX8U5LbzhHTHwIPbtu9P3AM8EftvLe3bexyPHBNVV3YM+0XgaOA3wBe027vkcAvAE9J8l8BkjwReBnw68AhwCeB02bF8njgv7RxPAV4TFVdBjwf+ExVHVBVB83Rh9l+E/gz4E7Ap4CbgWcCBwGPA/57Gw/Aw9qfB7Xb/8yAsUqSlqC93vv32oOBN7YHI+8w1/D73gOn7QG8v03yofaA3KeT/Ex7YPJ7Sb6c5AEDtH9kkvcn+XaS7+46GJnkNkn+KMk32oOpb09yYDtvUQc12758Oslft/OuSPJL7fQr2+0/q2f5tyb50yT7Ax8C7t72cSbJb9Lsm36jff3Fdp0Dk7wlzUHfq9r192nn7ZPmoPF3klxBsw8c5Hdz1yT/kOaA7/eS/FM7fdeB5pck+RbwD5l1ZrH9vf5+m4+b29jWtb+vm5L8c8//PiR5cJJ/bfPzxSRTs/J3Rbve15OcOEj8Wjss7LSa/QT431X1k6o6G5gBNiQJ8NvA/6yq66vqJuDPgafOWvfPquonwOnAwcBrq+qmqroUuBT4zz3LX1BV722XfzVNUfjgOWI6sY3puqr6NvAK4BntvHcCxye5c/v6GcA7Zq3/yqr6YVV9lKaAOq3d1lU0BdGunevzgL+oqsuqamfbv6NnnQk7uapuqKpvAp+gKTaH8YGq+nRV/bSNbbqqLm5fX0RTpP3XedYfJFZJ0tI9BTgOuBfNPuykRaz3RzT7wh8BnwG+0L5+L81+r6+28Pkg8A1gPc1B09Pb2Se1j4cD9wYOAF4/axMDHdTsWfYi4G40B1pPpzmIeR+ag6evT3JA78ar6mbgscDV7UHHA6rqXTT7o3e3r3eNxHkbsLPd3gOARwO/1c77bZqDpg8ANgFPmi8vPd4B3LHtz6HAX/fM+xmag8b3BDb3Wf+/AY+iOWD9qzRF6stofj+3Af4HQJLDgbOAP223+XvA+5Ic0ha3fwM8tqruBPwScOGA8WuNsLDTavbdtlDY5Qc0O4xDaD5AL2iPWN0AfLid3rvuLe3z/2h/Xtsz/z/abe1y5a4nVfVTYAdw9zliujvNjm2Xb+xarqquBj4N/LckB9HsZE6dtf7sGPrFdE/gtT39ux4Izc50l2/1PP8Be/ZnMa7sfZHkF5N8oj0qeyPNGcCD51l/kFglSUv3N1V1dVVdD/w/Bj+gd0ZVXVBVPwTOAH5YVW9v95PvZvdBxX6OodnX/X5V3dweBNx1lvBE4NVVdUVVzQAvBZ6aPYcdDnpQE+DrVfUPPbEdSXNA9Uft+j+mKcoWLck6mn3zi9t+XEdThO06MPwU4DVVdWWb478YYJuHtdt8flV9rz0Y/S89i/wU+JM2/v+Yeyu8rqqu7cnHeVX1b1X1I5rf1678PB04u6rObg++ngOcTzNCaFdb90uyX1Vd0x7I1gSxsNM4/YCmQNvlZwZc7zs0RdAvVNVB7ePAqhq2sIFmxwE0w0qAI4Cr51juappCZpd7zFrubTQfvE+mGQ551ZDxXAk8r6d/B1XVflX1rwOsW4tsa/by7wLOBI6sqgNprtnLPNteSqySpMENe0Bv0IOK/RwJfGPWwdZd5jrguS+wbsj2Z8+jqhYbbz/3BG4LXNNzMPLvaM6yQdOX3oOd32BhRwLXV9X3+sz/dltQz2cxB32fvCv2Nv5fBg5rz1r+Bs3B2GuSnJXkPw0Qv9YQCzuN04XAb7Zj2o9j/uF+t2rPqP098NdJDoVmeEKSxywhlgcl+fX2COOLaYaqfHaO5U4D/qgd9nAwzfV7vXfc+ifggcCLaK65G9abgJcm+QW49ZqAJw+47rXAEUluN2Tbd6LZSf0wyTE01+Dt8m2aI4K91ycuJVZJ0tLcTM9B0iSDHiRdjCuBe2Tum3/MdcBzJ3sWJ6Mw14HH2dOupNm/H9xzIPLOVfUL7fxr6DnQS9OXhVwJ3LUdqTNoXMO6EnjHrAOp+1fVyQBV9ZGqehRwGPBlmv+VNEEs7DROL6IZS34DzVCOf1rEui8BLgc+m+T7wD8DS7l98AdojnR9j+bauF9vr7eb7U9phj1cBFxMc43Cn+6a2Q6zeB/N9Q/vHzaYqjqD5gYxp7f9u4RmqMcgPk5zDeG3knxniOZ/B/jfSW6iKVzf0xPXD2hutPLp9mjhg5cYqyRpab4I/EKaOzDfAXj5CrTxOZqi5+Qk+6e5actD23mnAf8zyb3aa992Xdc219m9lXQtcLe0N27pmba+HYlDVV0DfBR4VZI7p7nxy8/2XOP3HuB/JDmivWHJ1oUabbf5IeBvk9wlzR2xH7bQekN6J/CrSR7THhS/Q3szliPaG648ob3W7kc09yW4Zf7Naa3xtqsam6o6n+ZC47nmTdMMh+ydtr7n+Q9pLix+GbPMXrfduWTWMr88a7UfVtXTmcMc7f6P9tHPN2muZ5jpWW/7HDHM7t/TZ71+B3vffGXXvNnbOqnn+Y8Z8E5evev1THsvzcX0/db5Y/a8A+m8sUqSVk5V/XuS/01zgPM/aK5xe94yt3FLmq/z+RuafVzRDNv/NHAKzRDGc2luPPYR4IXL2f6AMX45yWnAFe3NXu5Lc7frpwPfTfL1qnogzV2fTwa+RDNC5Qqag5PQnOH6OZpi+fvAXwGPGKD5Z9Bcq/dl4HY0NzQ7d941hlBVVyY5Afg/NAX1LTRF93+nOVmzhWZfXDSjon5nuWPQ6paq5TxDLHVPmu9pu0+/wm6R27or8G/AM6pq2T/UJUmSpLk4FFNaJkl+m2b8+4dWU1GX5NLs/l6f3offbyNJkrRGeMZOkiRpwiW5B83wxLnct5rvTJ1YSWb6zHpsVX1ypMFIfVjYSZIkSVLHORRTkiRJkjpuVd4V86CDDqr73Oc+4w5j1bv55pvZf//9xx3GqmeeBmOeFmaO9nTBBRd8p6oOGXccWpyDDz641q9fv6h1fO/vyXzsZi52Mxd7Mh+7DZuLxe5nV2Vht27dOs4///xxh7HqTU9PMzU1Ne4wVj3zNBjztDBztKck3xh3DFq89evXL3of63t/T+ZjN3Oxm7nYk/nYbdhcLHY/61BMSZIkSeo4CztJkiRJ6jgLO0mSJEnquBUv7JJsSHJhz+P7SV680u1KkiRJ0qRY8ZunVNVXgKMBkuwDXAWcsdLtSpIkSdKkGPVQzGOBr1WVd1KTJEmSpGUy6sLuqcBpI25TkiRJkta0kX2PXZLbAU8AXtpn/mZgM8AhhxzC9PT0qELrrJmZmTWbp4uvunHO6RsPP3DR21rLeVpO5mlh5kiC9VvP6jtv+8mPG2EkkqReo/yC8scCX6iqa+eaWVXbgG0AGzZsKL/QcGFr+YsfT+rzj8P2E6cWva21nKflZJ4WZo4kSdJqNcqhmE/DYZiSJEmStOxGUtgluSPwKOD9o2hPkiRJkibJSIZiVtUPgLuNoi1JkiRJmjSjviumJEmSJGmZWdhJkiRJUsdZ2EmSJElSx1nYSZIkSVLHWdhJkiRJUsdZ2EmStMoluUOSzyX5YpJLk7yinf7yJFclubB9HD/uWCVJ4zGSrzuQJElL8iPgEVU1k+S2wKeSfKid99dV9VdjjE2StApY2EmStMpVVQEz7cvbto8aX0SSpNXGoZiSJHVAkn2SXAhcB5xTVee1s16Q5KIkpyS5y/gilCSNk2fsJEnqgKq6BTg6yUHAGUnuB7wReCXN2btXAq8CnjN73SSbgc0A69atY3p6elFtz8zM3LrOlo07+y632O12VW8+Jp252M1c7Ml87DaqXFjYSZLUIVV1Q5Jp4Ljea+uS/D3wwT7rbAO2AWzatKmmpqYW1eb09DS71jlp61l9l9t+4uK221W9+Zh05mI3c7En87HbqHLhUExJkla5JIe0Z+pIsh/wSODLSQ7rWezXgEvGEJ4kaRXwjJ0kSavfYcDbkuxDc1D2PVX1wSTvSHI0zVDM7cDzxheiJGmcLOwkSVrlquoi4AFzTH/GGMKRJK1CDsWUJEmSpI6zsJMkSZKkjrOwkyRJkqSOG0lhl+SgJO9N8uUklyV5yCjalSRJkqRJMKqbp7wW+HBVPSnJ7YA7jqhdSZIkSVrzVrywS3Jn4GHASQBV9WPgxyvdriRJkiRNilGcsbs38G3gH5LcH7gAeFFV3dy7UJLNwGaAQw45hOnp6RGE1m0zMzNrNk9bNu6cc/rrTv1A33U2Hn7gnNPXcp6Wk3lamDmSJEmr1SgKu32BBwIvrKrzkrwW2Ar8r96FqmobsA1gw4YNNTU1NYLQum16epq1mqeTtp616HW2nzg15/S1nKflZJ4WZo4kSdJqNYqbp+wAdlTVee3r99IUepIkSZKkZbDihV1VfQu4MsmGdtKxwJdWul1JkiRJmhSjuivmC4FT2ztiXgE8e0TtSpIkSdKaN5LCrqouBDaNoi1JkiRJmjQj+YJySZIkSdLKsbCTJEmSpI6zsJMkSZKkjrOwkyRplUtyhySfS/LFJJcmeUU7/a5Jzkny1fbnXcYdqyRpPCzsJEla/X4EPKKq7g8cDRyX5MHAVuBjVXUU8LH2tSRpAlnYSZK0ylVjpn152/ZRwAnA29rpbwOeOProJEmrgYWdJEkdkGSfJBcC1wHnVNV5wLqqugag/XnoGEOUJI3RqL6gXJIkLUFV3QIcneQg4Iwk9xt03SSbgc0A69atY3p6elFtz8zM3LrOlo07+y632O12VW8+Jp252M1c7Ml87DaqXFjYSZLUIVV1Q5Jp4Djg2iSHVdU1SQ6jOZs31zrbgG0AmzZtqqmpqUW1OT09za51Ttp6Vt/ltp+4uO12VW8+Jp252M1c7Ml87DaqXDgUU5KkVS7JIe2ZOpLsBzwS+DJwJvCsdrFnAR8YS4CSpLHzjJ0kSavfYcDbkuxDc1D2PVX1wSSfAd6T5LnAN4EnjzNISdL4WNhJkrTKVdVFwAPmmP5d4NjRRyRJWm0ciilJkiRJHWdhJ0mSJEkdZ2EnSZIkSR1nYSdJkiRJHTeSm6ck2Q7cBNwC7KyqTaNoV5IkSZImwSjvivnwqvrOCNuTJEmSpIngUExJkiRJ6rhRFXYFfDTJBUk2j6hNSZIkSZoIoxqK+dCqujrJocA5Sb5cVef2LtAWfJsBDjnkEKanp0cUWnfNzMys2Txt2bhz0eu87tQPzDn9XgfuM1SeLr7qxjmnbzz8wEVvqwvW8vtpuZgjSZK0Wo2ksKuqq9uf1yU5AzgGOHfWMtuAbQAbNmyoqampUYTWadPT06zVPJ209axl29Zbj9t/qDz1i2H7iYvfVhes5ffTcjFHkiRptVrxoZhJ9k9yp13PgUcDl6x0u5IkSZI0KUZxxm4dcEaSXe29q6o+PIJ2JUmSJGkirHhhV1VXAPdf6XYkSZIkaVL5dQeSJEmS1HEWdpIkSZLUcRZ2kiStckmOTPKJJJcluTTJi9rpL09yVZIL28fx445VkjQeo/oeO0mSNLydwJaq+kJ7p+kLkpzTzvvrqvqrMcYmSVoFLOwkSVrlquoa4Jr2+U1JLgMOH29UkqTVxMJOkqQOSbIeeABwHvBQ4AVJngmcT3NW73tzrLMZ2Aywbt06pqenF9XmzMzMrets2biz73KL3W5X9eZj0pmL3czFnszHbqPKhYWdJEkdkeQA4H3Ai6vq+0neCLwSqPbnq4DnzF6vqrYB2wA2bdpUU1NTi2p3enqaXeuctPWsvsttP3Fx2+2q3nxMOnOxm7nYk/nYbVS58OYpkiR1QJLb0hR1p1bV+wGq6tqquqWqfgr8PXDMOGOUJI2PhZ0kSatckgBvAS6rqlf3TD+sZ7FfAy4ZdWySpNXBoZiSJK1+DwWeAVyc5MJ22suApyU5mmYo5nbgeeMITpI0fhZ2kiStclX1KSBzzDp71LFIklYnh2JKkiRJUsdZ2EmSJElSx1nYSZIkSVLHWdhJkiRJUsdZ2EmSJElSx1nYSZIkSVLHjaywS7JPkn9L8sFRtSlJkiRJk2CUZ+xeBFw2wvYkSZIkaSKMpLBLcgTwOODNo2hPkiRJkibJqM7YvQb4A+CnI2pPkiRJkibGvivdQJLHA9dV1QVJpuZZbjOwGeCQQw5henp6pUPrvJmZmTWbpy0bdy7btubL08VX3ThPDHNPX6s5X8vvp+VijiRJ0mq14oUd8FDgCUmOB+4A3DnJO6vq6b0LVdU2YBvAhg0bampqagShddv09DRrNU8nbT1r2bb11uP275unYdrZfuLc2+q6tfx+Wi7mSJIkrVYrPhSzql5aVUdU1XrgqcDHZxd1kiRJkqThjeKMnSRJmgDr+4yC2H7y40YciSRNnpF+QXlVTVfV40fZpiRJXZfkyCSfSHJZkkuTvKidftck5yT5avvzLuOOVZI0HiMt7CRJ0lB2Aluq6ueBBwO/m+S+wFbgY1V1FPCx9rUkaQJZ2EmStMpV1TVV9YX2+U3AZcDhwAnA29rF3gY8cSwBSpLGzsJOkqQOSbIeeABwHrCuqq6BpvgDDh1jaJKkMfLmKZIkdUSSA4D3AS+uqu8nGXS9W78rdt26dYv+Psbe73Ac5ntG19r3P/qdlruZi93MxZ7Mx26jyoWFnSRJHZDktjRF3alV9f528rVJDquqa5IcBlw317q93xW7adOmRX9XbO93OPr9n36nZS9zsZu52JP52G1UuXAopiRJq1yaU3NvAS6rqlf3zDoTeFb7/FnAB0YdmyRpdfCMnSRJq99DgWcAFye5sJ32MuBk4D1Jngt8E3jyeMKTJI2bhZ0kSatcVX0K6HdB3bGjjEWStDo5FFOSJEmSOs7CTpIkSZI6zsJOkiRJkjrOwk6SJEmSOs7CTpIkSZI6zsJOkiRJkjrOwk6SJEmSOs7CTpIkSZI6zsJOkiRJkjpuxQu7JHdI8rkkX0xyaZJXrHSbkiRJkjRJ9h1BGz8CHlFVM0luC3wqyYeq6rMjaFuSJEmS1rwVL+yqqoCZ9uVt20etdLuSJEmSNClGco1dkn2SXAhcB5xTVeeNol1JkiRJmgSjGIpJVd0CHJ3kIOCMJPerqkt6l0myGdgMcMghhzA9PT2K0Drj4qtu3Gvauv3gdad+gI2HH7gs21vIMO0Ma8vGncu2rZmZmb7vp2Hame+9OV9el/P3NN+2ho1hvjytNcPkFSYrR5IkqVtGUtjtUlU3JJkGjgMumTVvG7ANYMOGDTU1NTXK0Fa9k7aetde0LRt38qqL92X7iVPLsr2FDNPOsIaJr5+3Hrc//d5Py52H+ba3nL+nlYhhenq6b57WmmHyCpOVI0mS1C2juCvmIe2ZOpLsBzwS+PJKtytJ0lqR5JQk1yW5pGfay5NcleTC9nH8OGOUJI3XKK6xOwz4RJKLgM/TXGP3wRG0K0nSWvFWmtEus/11VR3dPs4ecUySpFVkFHfFvAh4wEq3I0nSWlVV5yZZP+44JEmr10juiilJklbEC5Jc1A7VvMu4g5Ekjc9Ib54iSZKWzRuBV9J8N+wrgVcBz5lrwd47T69bt27Rd3ftvSPsct9NuIu8Q+5u5mI3c7En87HbqHJhYSdJUgdV1bW7nif5e6Dv9eu9d57etGnTou883XtH2NV+V+VR8A65u5mL3czFnszHbqPKhUMxJUnqoCSH9bz8NWZ9jZAkabJ4xk6SpFUuyWnAFHBwkh3AnwBTSY6mGYq5HXjeuOKTJI2fhZ0kSatcVT1tjslvGXkgkqRVy6GYkiRJktRxFnaSJEmS1HEWdpIkSZLUcRZ2kiRJktRxFnaSJEmS1HEWdpIkSZLUcRZ2kiRJktRxFnaSJEmS1HEWdpIkSZLUcRZ2kiRJktRxFnaSJEmS1HErXtglOTLJJ5JcluTSJC9a6TYlSZIkaZLsO4I2dgJbquoLSe4EXJDknKr60gjaliRJkqQ1b8XP2FXVNVX1hfb5TcBlwOEr3a4kSZIkTYpRnLG7VZL1wAOA8+aYtxnYDHDIIYcwPT09dDsXX3Vj33kbDz9w6O0upq1h2pkv7i0b9562bj/YsnEnrzv1A4tua67tLWS+dvr1d74+zWeY+Pq57vob+8Y+TDvzvTe3bNw51HqL3d5KxDAzM7Okv7suGSavMFk5kpbT+q1n9Z23/eTHjTASSVq7RlbYJTkAeB/w4qr6/uz5VbUN2AawYcOGmpqaGrqtk+bbgZw4/HYX09Yw7cwX91y2bNzJqy4eaW3eV7/+LrZPK2G58zTf73a533vDvL+GjWF6epql/N11ybB/t5OUI60uSU4BHg9cV1X3a6fdFXg3sB7YDjylqr43rhglSeM1krtiJrktTVF3alW9fxRtSpK0hrwVOG7WtK3Ax6rqKOBj7WtJ0oQaxV0xA7wFuKyqXr3S7UmStNZU1bnA9bMmnwC8rX3+NuCJo4xJkrS6jGIc30OBZwAXJ7mwnfayqjp7BG1LkrRWrauqa6C5UVmSQ/st2Hsd+7p16xZ9rWjv9aXzXcc7jC5et+r1truZi93MxZ7Mx26jysWKF3ZV9SkgK92OJEmaW+917Js2bVr0dey915cu97XTy33t+yh4ve1u5mI3c7En87HbqHIxkmvsJEnSsrs2yWEA7c/rxhyPJGmMLOwkSeqmM4Fntc+fBSz+u28kSWuGhZ0kSatcktOAzwAbkuxI8lzgZOBRSb4KPKp9LUmaUKvjS9AkSVJfVfW0PrOOHWkgkqRVyzN2kiRJktRxFnaSJEmS1HEWdpIkSZLUcRZ2kiRJktRxFnaSJEmS1HEWdpIkSZLUcRZ2kiRJktRxFnaSJEmS1HEWdpIkSZLUcRZ2kiRJktRxFnaSJEmS1HEWdpIkSZLUcSte2CU5Jcl1SS5Z6bYkSZIkaRKN4ozdW4HjRtCOJEmSJE2kfVe6gao6N8n6lW5HkqRJlGQ7cBNwC7CzqjaNNyJJ0jiseGEnSZJW3MOr6jvjDkKSND6rprBLshnYDHDIIYcwPT099La2bNzZd95StruYtl536geG2Nbill+33/x9HaV+/V1sn1bCcudpvt/tfP3tt97Gww+cZ3uLf3/NF8N87/+ZmZll+/u4+Kob+87r19/51pnPfPnrp19eF+r/cuZoVIb5XUiSpO5ZNYVdVW0DtgFs2LChpqamht7WSVvP6jtv+4nDb3exba20LRt38qqLV82vcNVa7Xma7z253O+v+dqanp5mKX93vYb5Gxy2r8P8Tfdra6FtLWeORmWUn4camwI+mqSAv2v3p5KkCbN6/9uVJEmDeGhVXZ3kUOCcJF+uqnN7F+gdFbNu3bpFn3nuPVu93CNFunYWHLp59n6lmIvdzMWezMduo8rFihd2SU4DpoCDk+wA/qSq3rLS7UqSNAmq6ur253VJzgCOAc6dtcyto2I2bdq06FExvWerRzmSYLXq4tn7lWIudjMXezIfu40qF6O4K+bTVroNSZImUZL9gdtU1U3t80cD/3vMYUmSxsChmJIkddc64Iwk0OzT31VVHx5vSJKkcbCwkySpo6rqCuD+445DkjR+txl3AJIkSZKkpbGwkyRJkqSOs7CTJEmSpI6zsJMkSZKkjrOwkyRJkqSOs7CTJEmSpI6zsJMkSZKkjrOwkyRJkqSOs7CTJEmSpI7bd9wBSJIkzWX91rPmnL795MeNOBJJWv08YydJkiRJHWdhJ0mSJEkdZ2EnSZIkSR1nYSdJkiRJHefNUyRJ0prR74Yr0P+mK/OtM5ctG3cytag1JK1Fw3zerKSRnLFLclySryS5PMnWUbQpSdIkcB8rSYIRFHZJ9gHeADwWuC/wtCT3Xel2JUla69zHSpJ2GcUZu2OAy6vqiqr6MXA6cMII2pUkaa1zHytJAkZT2B0OXNnzekc7TZIkLY37WEkSAKmqlW0geTLwmKr6rfb1M4BjquqFs5bbDGxuX94PuGRFA1sbDga+M+4gOsA8DcY8Lcwc7emeVXXIuIOYZEPuYzcAX1lkU77392Q+djMXu5mLPZmP3YbNxaL2s6O4K+YO4Mie10cAV89eqKq2AdsAkpxfVZtGEFunmafBmKfBmKeFmSOtQovexw7D9/6ezMdu5mI3c7En87HbqHIxiqGYnweOSnKvJLcDngqcOYJ2JUla69zHSpKAEZyxq6qdSV4AfATYBzilqi5d6XYlSVrr3MdKknYZyReUV9XZwNmLWGXo4SITxjwNxjwNxjwtzBxp1RliHzsM3/t7Mh+7mYvdzMWezMduI8nFit88RZIkSZK0skZxjZ0kSZIkaQWtusIuyXFJvpLk8iRbxx3PapTkyCSfSHJZkkuTvGjcMa1WSfZJ8m9JPjjuWFarJAcleW+SL7fvqYeMO6bVKMn/bP/eLklyWpI7jDsmaRTW4n653340yV2TnJPkq+3Pu/Ss89I2B19J8pie6Q9KcnE772+SpJ1++yTvbqefl2T9yDu6CLP3lxOei732i5Oaj7n2fZOUiySnJLkuySU900bS/yTPatv4apJnDRRwVa2aB82F318D7g3cDvgicN9xx7XaHsBhwAPb53cC/t089c3V/we8C/jguGNZrQ/gbcBvtc9vBxw07phW24PmC5+/DuzXvn4PcNK44/LhY6Ufa3W/3G8/CvwfYGs7fSvwl+3z+7Z9vz1wrzYn+7TzPgc8BAjwIeCx7fTfAd7UPn8q8O5x93uBnOyxv5zwXOy1X5zEfPTb901SLoCHAQ8ELumZtuL9B+4KXNH+vEv7/C4LxbvaztgdA1xeVVdU1Y+B04ETxhzTqlNV11TVF9rnNwGX0fzxqUeSI4DHAW8edyyrVZI703xovQWgqn5cVTeMNajVa19gvyT7Andkju8Kk9agNblfnmc/egLNP/W0P5/YPj8BOL2qflRVXwcuB45Jchhw56r6TDX/jb191jq7tvVe4NhdR+lXmz77y0nNRb/94kTmg7n3fROTi6o6F7h+1uRR9P8xwDlVdX1VfQ84BzhuoXhXW2F3OHBlz+sdWLDMqz1l+wDgvDGHshq9BvgD4KdjjmM1uzfwbeAf2iE4b06y/7iDWm2q6irgr4BvAtcAN1bVR8cblTQSa36/PGs/uq6qroGm+AMObRfrl4fD2+ezp++xTlXtBG4E7rYinVi617D3/nJSc9Fvvzhx+Zhn3zdxuZhlFP0f6rN3tRV2c1Xo3razjyQHAO8DXlxV3x93PKtJkscD11XVBeOOZZXbl2aIwRur6gHAzTTDCtSjHT9/As3QirsD+yd5+nijkkZiTe+XF7Ef7ZeH+fLTidwNsb9cs7loLXa/uGbzMcS+b83mYkDL2f+h8rLaCrsdwJE9r4/A4U5zSnJbmp3RqVX1/nHHswo9FHhCku00Q4cekeSd4w1pVdoB7KiqXWd830uzQ9OeHgl8vaq+XVU/Ad4P/NKYY5JGYc3ul/vsR69th03R/ryund4vDzva57On77FOO4ztQPYe0rUa9NtfTmIuoP9+cRLz0W/fN4m56DWK/g/12bvaCrvPA0cluVeS29FcRHjmmGNaddqxt28BLquqV487ntWoql5aVUdU1Xqa99HHq8ozLLNU1beAK5NsaCcdC3xpjCGtVt8EHpzkju3f37E01+RIa92a3C/Psx89E9h197lnAR/omf7U9g529wKOAj7XDsO6KcmD220+c9Y6u7b1JJr90Ko7EzHP/nLicgHz7hcnMR/99n2TmIteo+j/R4BHJ7lLe+b00e20+S10d5VRP4Djae5O9TXgD8cdz2p8AL9Mczr2IuDC9nH8uONarQ9gCu+KOV9+jgbOb99P/8QAd12axAfwCuDLwCXAO4DbjzsmHz5G8ViL++V++1Gaa1s+Bny1/XnXnnX+sM3BV2jvaNdO39R+LnwNeD2QdvodgH+kuYHC54B7j7vfA+Tl1v3lJOdirv3ipOZjrn3fJOUCOI3m+sKf0JxFe+6o+g88p51+OfDsQeLdtVFJkiRJUkettqGYkiRJkqRFsrCTJEmSpI6zsJMkSZKkjrOwkyRJkqSOs7CTJEmSpI6zsJMkSZKkjrOwkyRJkqSOs7CTJEmSpI77/wHOxf0Z9LT5PgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "district[['population', 'average_salary', 'unemployment_rate', 'num_committed_crimes']].hist(bins=50, figsize=(15, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explore the outliers in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>district_id</th>\n",
       "      <th>district_name</th>\n",
       "      <th>region</th>\n",
       "      <th>population</th>\n",
       "      <th>average_salary</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>num_committed_crimes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>HL.M. PRAHA</td>\n",
       "      <td>PRAGUE</td>\n",
       "      <td>1204953.0</td>\n",
       "      <td>12541.0</td>\n",
       "      <td>0.43</td>\n",
       "      <td>99107.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   district_id district_name  region  population  average_salary  \\\n",
       "0            1   HL.M. PRAHA  PRAGUE   1204953.0         12541.0   \n",
       "\n",
       "   unemployment_rate  num_committed_crimes  \n",
       "0               0.43               99107.0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "district.nlargest(1, columns='population')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most obvious outlier seems to come from the same district. It is a district with large `population`, higher `average_salary`, as well as higher `num_committed_crimes`. Since we don't have enough reasons to remove it, we'll leave it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 12. Check for outliers in `trans` using the `describe` method, then look at the histograms of the suspicious columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trans_id</th>\n",
       "      <th>account_id</th>\n",
       "      <th>amount</th>\n",
       "      <th>balance</th>\n",
       "      <th>partner_account</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.056320e+06</td>\n",
       "      <td>1.056320e+06</td>\n",
       "      <td>1.056320e+06</td>\n",
       "      <td>1.056320e+06</td>\n",
       "      <td>1.056320e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.335311e+06</td>\n",
       "      <td>2.936867e+03</td>\n",
       "      <td>5.924146e+03</td>\n",
       "      <td>3.851833e+04</td>\n",
       "      <td>1.277068e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.227487e+06</td>\n",
       "      <td>2.477345e+03</td>\n",
       "      <td>9.522735e+03</td>\n",
       "      <td>2.211787e+04</td>\n",
       "      <td>2.613658e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-4.112570e+04</td>\n",
       "      <td>-9.990000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.302628e+05</td>\n",
       "      <td>1.204000e+03</td>\n",
       "      <td>1.359000e+02</td>\n",
       "      <td>2.240250e+04</td>\n",
       "      <td>-9.990000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.585065e+05</td>\n",
       "      <td>2.434000e+03</td>\n",
       "      <td>2.100000e+03</td>\n",
       "      <td>3.314340e+04</td>\n",
       "      <td>-9.990000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.060979e+06</td>\n",
       "      <td>3.660000e+03</td>\n",
       "      <td>6.800000e+03</td>\n",
       "      <td>4.960362e+04</td>\n",
       "      <td>3.410566e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.682987e+06</td>\n",
       "      <td>1.138200e+04</td>\n",
       "      <td>8.740000e+04</td>\n",
       "      <td>2.096370e+05</td>\n",
       "      <td>9.999420e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           trans_id    account_id        amount       balance  partner_account\n",
       "count  1.056320e+06  1.056320e+06  1.056320e+06  1.056320e+06     1.056320e+06\n",
       "mean   1.335311e+06  2.936867e+03  5.924146e+03  3.851833e+04     1.277068e+07\n",
       "std    1.227487e+06  2.477345e+03  9.522735e+03  2.211787e+04     2.613658e+07\n",
       "min    1.000000e+00  1.000000e+00  0.000000e+00 -4.112570e+04    -9.990000e+02\n",
       "25%    4.302628e+05  1.204000e+03  1.359000e+02  2.240250e+04    -9.990000e+02\n",
       "50%    8.585065e+05  2.434000e+03  2.100000e+03  3.314340e+04    -9.990000e+02\n",
       "75%    2.060979e+06  3.660000e+03  6.800000e+03  4.960362e+04     3.410566e+06\n",
       "max    3.682987e+06  1.138200e+04  8.740000e+04  2.096370e+05     9.999420e+07"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'amount'}>,\n",
       "        <AxesSubplot:title={'center':'balance'}>]], dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trans[['amount', 'balance']].hist(bins=50, figsize=(15,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explore the outliers in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trans_id</th>\n",
       "      <th>account_id</th>\n",
       "      <th>date</th>\n",
       "      <th>type</th>\n",
       "      <th>operation_type</th>\n",
       "      <th>amount</th>\n",
       "      <th>balance</th>\n",
       "      <th>description</th>\n",
       "      <th>partner_bank</th>\n",
       "      <th>partner_account</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>1313408</td>\n",
       "      <td>4484</td>\n",
       "      <td>1993-02-23</td>\n",
       "      <td>DEBIT</td>\n",
       "      <td>WITHDRAWAL IN CASH</td>\n",
       "      <td>62100.0</td>\n",
       "      <td>41183.0</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td>1450519</td>\n",
       "      <td>4937</td>\n",
       "      <td>1993-04-04</td>\n",
       "      <td>DEBIT</td>\n",
       "      <td>WITHDRAWAL IN CASH</td>\n",
       "      <td>60500.0</td>\n",
       "      <td>74071.2</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3565</th>\n",
       "      <td>303136</td>\n",
       "      <td>1032</td>\n",
       "      <td>1993-06-05</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>COLLECTION FROM ANOTHER BANK</td>\n",
       "      <td>74648.0</td>\n",
       "      <td>112843.8</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>GH</td>\n",
       "      <td>43027007.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3635</th>\n",
       "      <td>655858</td>\n",
       "      <td>2242</td>\n",
       "      <td>1993-06-06</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>COLLECTION FROM ANOTHER BANK</td>\n",
       "      <td>65240.0</td>\n",
       "      <td>126832.2</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>CD</td>\n",
       "      <td>7862908.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3783</th>\n",
       "      <td>894506</td>\n",
       "      <td>3050</td>\n",
       "      <td>1993-06-08</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>COLLECTION FROM ANOTHER BANK</td>\n",
       "      <td>62168.0</td>\n",
       "      <td>73568.0</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>GH</td>\n",
       "      <td>69871258.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1049226</th>\n",
       "      <td>602639</td>\n",
       "      <td>2051</td>\n",
       "      <td>1998-12-21</td>\n",
       "      <td>DEBIT</td>\n",
       "      <td>WITHDRAWAL IN CASH</td>\n",
       "      <td>62000.0</td>\n",
       "      <td>71187.7</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1049504</th>\n",
       "      <td>302367</td>\n",
       "      <td>1029</td>\n",
       "      <td>1998-12-22</td>\n",
       "      <td>DEBIT</td>\n",
       "      <td>WITHDRAWAL IN CASH</td>\n",
       "      <td>63800.0</td>\n",
       "      <td>57501.1</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1049791</th>\n",
       "      <td>335369</td>\n",
       "      <td>1139</td>\n",
       "      <td>1998-12-23</td>\n",
       "      <td>DEBIT</td>\n",
       "      <td>WITHDRAWAL IN CASH</td>\n",
       "      <td>62600.0</td>\n",
       "      <td>90537.9</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1050834</th>\n",
       "      <td>414144</td>\n",
       "      <td>1407</td>\n",
       "      <td>1998-12-26</td>\n",
       "      <td>DEBIT</td>\n",
       "      <td>WITHDRAWAL IN CASH</td>\n",
       "      <td>62700.0</td>\n",
       "      <td>73836.1</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1051694</th>\n",
       "      <td>378184</td>\n",
       "      <td>1286</td>\n",
       "      <td>1998-12-30</td>\n",
       "      <td>DEBIT</td>\n",
       "      <td>WITHDRAWAL IN CASH</td>\n",
       "      <td>63400.0</td>\n",
       "      <td>55245.6</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1575 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         trans_id  account_id       date    type  \\\n",
       "422       1313408        4484 1993-02-23   DEBIT   \n",
       "1287      1450519        4937 1993-04-04   DEBIT   \n",
       "3565       303136        1032 1993-06-05  CREDIT   \n",
       "3635       655858        2242 1993-06-06  CREDIT   \n",
       "3783       894506        3050 1993-06-08  CREDIT   \n",
       "...           ...         ...        ...     ...   \n",
       "1049226    602639        2051 1998-12-21   DEBIT   \n",
       "1049504    302367        1029 1998-12-22   DEBIT   \n",
       "1049791    335369        1139 1998-12-23   DEBIT   \n",
       "1050834    414144        1407 1998-12-26   DEBIT   \n",
       "1051694    378184        1286 1998-12-30   DEBIT   \n",
       "\n",
       "                       operation_type   amount   balance description  \\\n",
       "422                WITHDRAWAL IN CASH  62100.0   41183.0     UNKNOWN   \n",
       "1287               WITHDRAWAL IN CASH  60500.0   74071.2     UNKNOWN   \n",
       "3565     COLLECTION FROM ANOTHER BANK  74648.0  112843.8     UNKNOWN   \n",
       "3635     COLLECTION FROM ANOTHER BANK  65240.0  126832.2     UNKNOWN   \n",
       "3783     COLLECTION FROM ANOTHER BANK  62168.0   73568.0     UNKNOWN   \n",
       "...                               ...      ...       ...         ...   \n",
       "1049226            WITHDRAWAL IN CASH  62000.0   71187.7     UNKNOWN   \n",
       "1049504            WITHDRAWAL IN CASH  63800.0   57501.1     UNKNOWN   \n",
       "1049791            WITHDRAWAL IN CASH  62600.0   90537.9     UNKNOWN   \n",
       "1050834            WITHDRAWAL IN CASH  62700.0   73836.1     UNKNOWN   \n",
       "1051694            WITHDRAWAL IN CASH  63400.0   55245.6     UNKNOWN   \n",
       "\n",
       "        partner_bank  partner_account  \n",
       "422          UNKNOWN           -999.0  \n",
       "1287         UNKNOWN           -999.0  \n",
       "3565              GH       43027007.0  \n",
       "3635              CD        7862908.0  \n",
       "3783              GH       69871258.0  \n",
       "...              ...              ...  \n",
       "1049226      UNKNOWN           -999.0  \n",
       "1049504      UNKNOWN           -999.0  \n",
       "1049791      UNKNOWN           -999.0  \n",
       "1050834      UNKNOWN           -999.0  \n",
       "1051694      UNKNOWN           -999.0  \n",
       "\n",
       "[1575 rows x 10 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans[trans['amount'] > 60000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WITHDRAWAL IN CASH              837\n",
       "COLLECTION FROM ANOTHER BANK    738\n",
       "Name: operation_type, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans.loc[trans['amount'] > 60000, 'operation_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trans_id</th>\n",
       "      <th>account_id</th>\n",
       "      <th>date</th>\n",
       "      <th>type</th>\n",
       "      <th>operation_type</th>\n",
       "      <th>amount</th>\n",
       "      <th>balance</th>\n",
       "      <th>description</th>\n",
       "      <th>partner_bank</th>\n",
       "      <th>partner_account</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2832</th>\n",
       "      <td>695256</td>\n",
       "      <td>2378</td>\n",
       "      <td>1993-05-19</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>43637.0</td>\n",
       "      <td>151483.0</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2872</th>\n",
       "      <td>576769</td>\n",
       "      <td>1965</td>\n",
       "      <td>1993-05-21</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>33711.0</td>\n",
       "      <td>156483.5</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3037</th>\n",
       "      <td>1100776</td>\n",
       "      <td>3760</td>\n",
       "      <td>1993-05-29</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>38405.0</td>\n",
       "      <td>169292.1</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7797</th>\n",
       "      <td>637133</td>\n",
       "      <td>2176</td>\n",
       "      <td>1993-08-02</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>47024.0</td>\n",
       "      <td>193909.9</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16229</th>\n",
       "      <td>615555</td>\n",
       "      <td>2099</td>\n",
       "      <td>1993-10-18</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>36684.0</td>\n",
       "      <td>184168.4</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1050134</th>\n",
       "      <td>3317727</td>\n",
       "      <td>11021</td>\n",
       "      <td>1998-12-24</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>42890.0</td>\n",
       "      <td>160348.0</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1050758</th>\n",
       "      <td>2675921</td>\n",
       "      <td>8856</td>\n",
       "      <td>1998-12-26</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>46170.0</td>\n",
       "      <td>192662.5</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1050912</th>\n",
       "      <td>3215715</td>\n",
       "      <td>10670</td>\n",
       "      <td>1998-12-27</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>44890.0</td>\n",
       "      <td>173945.9</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1051009</th>\n",
       "      <td>157025</td>\n",
       "      <td>525</td>\n",
       "      <td>1998-12-27</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>46726.0</td>\n",
       "      <td>158444.3</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1051504</th>\n",
       "      <td>656921</td>\n",
       "      <td>2245</td>\n",
       "      <td>1998-12-29</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>40462.0</td>\n",
       "      <td>161952.7</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>354 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         trans_id  account_id       date    type  operation_type   amount  \\\n",
       "2832       695256        2378 1993-05-19  CREDIT  CREDIT IN CASH  43637.0   \n",
       "2872       576769        1965 1993-05-21  CREDIT  CREDIT IN CASH  33711.0   \n",
       "3037      1100776        3760 1993-05-29  CREDIT  CREDIT IN CASH  38405.0   \n",
       "7797       637133        2176 1993-08-02  CREDIT  CREDIT IN CASH  47024.0   \n",
       "16229      615555        2099 1993-10-18  CREDIT  CREDIT IN CASH  36684.0   \n",
       "...           ...         ...        ...     ...             ...      ...   \n",
       "1050134   3317727       11021 1998-12-24  CREDIT  CREDIT IN CASH  42890.0   \n",
       "1050758   2675921        8856 1998-12-26  CREDIT  CREDIT IN CASH  46170.0   \n",
       "1050912   3215715       10670 1998-12-27  CREDIT  CREDIT IN CASH  44890.0   \n",
       "1051009    157025         525 1998-12-27  CREDIT  CREDIT IN CASH  46726.0   \n",
       "1051504    656921        2245 1998-12-29  CREDIT  CREDIT IN CASH  40462.0   \n",
       "\n",
       "          balance description partner_bank  partner_account  \n",
       "2832     151483.0     UNKNOWN      UNKNOWN           -999.0  \n",
       "2872     156483.5     UNKNOWN      UNKNOWN           -999.0  \n",
       "3037     169292.1     UNKNOWN      UNKNOWN           -999.0  \n",
       "7797     193909.9     UNKNOWN      UNKNOWN           -999.0  \n",
       "16229    184168.4     UNKNOWN      UNKNOWN           -999.0  \n",
       "...           ...         ...          ...              ...  \n",
       "1050134  160348.0     UNKNOWN      UNKNOWN           -999.0  \n",
       "1050758  192662.5     UNKNOWN      UNKNOWN           -999.0  \n",
       "1050912  173945.9     UNKNOWN      UNKNOWN           -999.0  \n",
       "1051009  158444.3     UNKNOWN      UNKNOWN           -999.0  \n",
       "1051504  161952.7     UNKNOWN      UNKNOWN           -999.0  \n",
       "\n",
       "[354 rows x 10 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans[trans['balance'] > 150000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CREDIT IN CASH                  312\n",
       "WITHDRAWAL IN CASH               18\n",
       "COLLECTION FROM ANOTHER BANK     14\n",
       "REMITTANCE TO ANOTHER BANK        4\n",
       "UNKNOWN                           4\n",
       "CREDIT CARD WITHDRAWAL            2\n",
       "Name: operation_type, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trans.loc[trans['balance'] > 150000, 'operation_type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "They look like real data. So we'll just keep them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The DataFrame `account` doesn't have any columns that could have outliers, so we are not exploring it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 13. Merge (left join) `account` and `district` into a new DataFrame called `account_district` using their common columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "account_district = pd.merge(account, district, on='district_id', how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 14. Check the information summary of `account_district`, any missing data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 4500 entries, 0 to 4499\n",
      "Data columns (total 11 columns):\n",
      " #   Column                Non-Null Count  Dtype         \n",
      "---  ------                --------------  -----         \n",
      " 0   account_id            4500 non-null   int64         \n",
      " 1   district_id           4500 non-null   int64         \n",
      " 2   frequency             4500 non-null   object        \n",
      " 3   account_open_date     4500 non-null   datetime64[ns]\n",
      " 4   client_id             4500 non-null   int64         \n",
      " 5   district_name         4460 non-null   object        \n",
      " 6   region                4460 non-null   object        \n",
      " 7   population            4460 non-null   float64       \n",
      " 8   average_salary        4460 non-null   float64       \n",
      " 9   unemployment_rate     4460 non-null   float64       \n",
      " 10  num_committed_crimes  4460 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4), int64(3), object(3)\n",
      "memory usage: 421.9+ KB\n"
     ]
    }
   ],
   "source": [
    "account_district.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After doing the left join, there is more missing data. That means there were some `district_id`s in `account`, but not in `district`. It is common that after combining the datasets we need to clean the data more"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Look at the rows with missing data in `account_district`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>5228</td>\n",
       "      <td>77</td>\n",
       "      <td>STATEMENTS AFTER TRANSACTION</td>\n",
       "      <td>1993-06-23</td>\n",
       "      <td>6317</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>368</td>\n",
       "      <td>77</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1993-07-06</td>\n",
       "      <td>448</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>790</th>\n",
       "      <td>3157</td>\n",
       "      <td>77</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1993-09-10</td>\n",
       "      <td>3817</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>857</td>\n",
       "      <td>77</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1993-09-16</td>\n",
       "      <td>1032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>832</th>\n",
       "      <td>153</td>\n",
       "      <td>77</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1993-09-19</td>\n",
       "      <td>185</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>4119</td>\n",
       "      <td>77</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1993-10-18</td>\n",
       "      <td>4959</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1161</th>\n",
       "      <td>742</td>\n",
       "      <td>77</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1994-01-28</td>\n",
       "      <td>890</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1331</th>\n",
       "      <td>1634</td>\n",
       "      <td>77</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1994-06-04</td>\n",
       "      <td>1980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1526</th>\n",
       "      <td>2377</td>\n",
       "      <td>77</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1994-11-13</td>\n",
       "      <td>2872</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1622</th>\n",
       "      <td>217</td>\n",
       "      <td>77</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1995-01-20</td>\n",
       "      <td>262</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1716</th>\n",
       "      <td>3750</td>\n",
       "      <td>77</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1995-03-18</td>\n",
       "      <td>4524</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1747</th>\n",
       "      <td>1590</td>\n",
       "      <td>77</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1995-04-07</td>\n",
       "      <td>1927</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1806</th>\n",
       "      <td>2919</td>\n",
       "      <td>77</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1995-05-10</td>\n",
       "      <td>3527</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1847</th>\n",
       "      <td>53</td>\n",
       "      <td>77</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1995-06-07</td>\n",
       "      <td>62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2134</th>\n",
       "      <td>2157</td>\n",
       "      <td>77</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1995-11-08</td>\n",
       "      <td>2611</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2336</th>\n",
       "      <td>976</td>\n",
       "      <td>77</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1996-01-27</td>\n",
       "      <td>1177</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2392</th>\n",
       "      <td>1737</td>\n",
       "      <td>77</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1996-02-13</td>\n",
       "      <td>2108</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2422</th>\n",
       "      <td>184</td>\n",
       "      <td>77</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1996-02-21</td>\n",
       "      <td>224</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2438</th>\n",
       "      <td>1962</td>\n",
       "      <td>77</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1996-02-24</td>\n",
       "      <td>2387</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2451</th>\n",
       "      <td>1216</td>\n",
       "      <td>77</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1996-03-01</td>\n",
       "      <td>1468</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2684</th>\n",
       "      <td>2994</td>\n",
       "      <td>77</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1996-05-03</td>\n",
       "      <td>3616</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2709</th>\n",
       "      <td>3248</td>\n",
       "      <td>77</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1996-05-11</td>\n",
       "      <td>3928</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2829</th>\n",
       "      <td>3647</td>\n",
       "      <td>77</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1996-06-17</td>\n",
       "      <td>4398</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2881</th>\n",
       "      <td>7181</td>\n",
       "      <td>77</td>\n",
       "      <td>STATEMENTS AFTER TRANSACTION</td>\n",
       "      <td>1996-07-02</td>\n",
       "      <td>8667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3002</th>\n",
       "      <td>4389</td>\n",
       "      <td>77</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1996-07-28</td>\n",
       "      <td>5287</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3232</th>\n",
       "      <td>2332</td>\n",
       "      <td>77</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1996-09-24</td>\n",
       "      <td>2820</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3372</th>\n",
       "      <td>1683</td>\n",
       "      <td>77</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1996-11-01</td>\n",
       "      <td>2043</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3514</th>\n",
       "      <td>5817</td>\n",
       "      <td>77</td>\n",
       "      <td>STATEMENTS AFTER TRANSACTION</td>\n",
       "      <td>1996-12-10</td>\n",
       "      <td>7043</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3520</th>\n",
       "      <td>4631</td>\n",
       "      <td>77</td>\n",
       "      <td>STATEMENTS AFTER TRANSACTION</td>\n",
       "      <td>1996-12-12</td>\n",
       "      <td>5579</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3677</th>\n",
       "      <td>3990</td>\n",
       "      <td>77</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1997-02-04</td>\n",
       "      <td>4811</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3723</th>\n",
       "      <td>4268</td>\n",
       "      <td>77</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1997-02-20</td>\n",
       "      <td>5143</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3969</th>\n",
       "      <td>3172</td>\n",
       "      <td>77</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1997-05-27</td>\n",
       "      <td>3836</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4050</th>\n",
       "      <td>2883</td>\n",
       "      <td>77</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1997-06-29</td>\n",
       "      <td>3485</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4092</th>\n",
       "      <td>3833</td>\n",
       "      <td>77</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1997-07-13</td>\n",
       "      <td>4619</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4137</th>\n",
       "      <td>3686</td>\n",
       "      <td>77</td>\n",
       "      <td>MONTHLY STATEMENTS</td>\n",
       "      <td>1997-08-03</td>\n",
       "      <td>4444</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      account_id  district_id                     frequency account_open_date  \\\n",
       "5           1972           77            MONTHLY STATEMENTS        1993-01-02   \n",
       "77          1000           77            MONTHLY STATEMENTS        1993-01-25   \n",
       "308          299           77            MONTHLY STATEMENTS        1993-04-05   \n",
       "322         8321           77            MONTHLY STATEMENTS        1993-04-12   \n",
       "394          551           77            MONTHLY STATEMENTS        1993-05-07   \n",
       "537         5228           77  STATEMENTS AFTER TRANSACTION        1993-06-23   \n",
       "574          368           77            MONTHLY STATEMENTS        1993-07-06   \n",
       "790         3157           77            MONTHLY STATEMENTS        1993-09-10   \n",
       "817          857           77            MONTHLY STATEMENTS        1993-09-16   \n",
       "832          153           77            MONTHLY STATEMENTS        1993-09-19   \n",
       "902         4119           77            MONTHLY STATEMENTS        1993-10-18   \n",
       "1161         742           77            MONTHLY STATEMENTS        1994-01-28   \n",
       "1331        1634           77            MONTHLY STATEMENTS        1994-06-04   \n",
       "1526        2377           77            MONTHLY STATEMENTS        1994-11-13   \n",
       "1622         217           77            MONTHLY STATEMENTS        1995-01-20   \n",
       "1716        3750           77            MONTHLY STATEMENTS        1995-03-18   \n",
       "1747        1590           77            MONTHLY STATEMENTS        1995-04-07   \n",
       "1806        2919           77            MONTHLY STATEMENTS        1995-05-10   \n",
       "1847          53           77            MONTHLY STATEMENTS        1995-06-07   \n",
       "2134        2157           77            MONTHLY STATEMENTS        1995-11-08   \n",
       "2336         976           77            MONTHLY STATEMENTS        1996-01-27   \n",
       "2392        1737           77            MONTHLY STATEMENTS        1996-02-13   \n",
       "2422         184           77            MONTHLY STATEMENTS        1996-02-21   \n",
       "2438        1962           77            MONTHLY STATEMENTS        1996-02-24   \n",
       "2451        1216           77            MONTHLY STATEMENTS        1996-03-01   \n",
       "2684        2994           77            MONTHLY STATEMENTS        1996-05-03   \n",
       "2709        3248           77            MONTHLY STATEMENTS        1996-05-11   \n",
       "2829        3647           77            MONTHLY STATEMENTS        1996-06-17   \n",
       "2881        7181           77  STATEMENTS AFTER TRANSACTION        1996-07-02   \n",
       "3002        4389           77            MONTHLY STATEMENTS        1996-07-28   \n",
       "3232        2332           77            MONTHLY STATEMENTS        1996-09-24   \n",
       "3372        1683           77            MONTHLY STATEMENTS        1996-11-01   \n",
       "3514        5817           77  STATEMENTS AFTER TRANSACTION        1996-12-10   \n",
       "3520        4631           77  STATEMENTS AFTER TRANSACTION        1996-12-12   \n",
       "3677        3990           77            MONTHLY STATEMENTS        1997-02-04   \n",
       "3723        4268           77            MONTHLY STATEMENTS        1997-02-20   \n",
       "3969        3172           77            MONTHLY STATEMENTS        1997-05-27   \n",
       "4050        2883           77            MONTHLY STATEMENTS        1997-06-29   \n",
       "4092        3833           77            MONTHLY STATEMENTS        1997-07-13   \n",
       "4137        3686           77            MONTHLY STATEMENTS        1997-08-03   \n",
       "\n",
       "      client_id district_name region  population  average_salary  \\\n",
       "5          2397           NaN    NaN         NaN             NaN   \n",
       "77         1204           NaN    NaN         NaN             NaN   \n",
       "308         364           NaN    NaN         NaN             NaN   \n",
       "322       10275           NaN    NaN         NaN             NaN   \n",
       "394         664           NaN    NaN         NaN             NaN   \n",
       "537        6317           NaN    NaN         NaN             NaN   \n",
       "574         448           NaN    NaN         NaN             NaN   \n",
       "790        3817           NaN    NaN         NaN             NaN   \n",
       "817        1032           NaN    NaN         NaN             NaN   \n",
       "832         185           NaN    NaN         NaN             NaN   \n",
       "902        4959           NaN    NaN         NaN             NaN   \n",
       "1161        890           NaN    NaN         NaN             NaN   \n",
       "1331       1980           NaN    NaN         NaN             NaN   \n",
       "1526       2872           NaN    NaN         NaN             NaN   \n",
       "1622        262           NaN    NaN         NaN             NaN   \n",
       "1716       4524           NaN    NaN         NaN             NaN   \n",
       "1747       1927           NaN    NaN         NaN             NaN   \n",
       "1806       3527           NaN    NaN         NaN             NaN   \n",
       "1847         62           NaN    NaN         NaN             NaN   \n",
       "2134       2611           NaN    NaN         NaN             NaN   \n",
       "2336       1177           NaN    NaN         NaN             NaN   \n",
       "2392       2108           NaN    NaN         NaN             NaN   \n",
       "2422        224           NaN    NaN         NaN             NaN   \n",
       "2438       2387           NaN    NaN         NaN             NaN   \n",
       "2451       1468           NaN    NaN         NaN             NaN   \n",
       "2684       3616           NaN    NaN         NaN             NaN   \n",
       "2709       3928           NaN    NaN         NaN             NaN   \n",
       "2829       4398           NaN    NaN         NaN             NaN   \n",
       "2881       8667           NaN    NaN         NaN             NaN   \n",
       "3002       5287           NaN    NaN         NaN             NaN   \n",
       "3232       2820           NaN    NaN         NaN             NaN   \n",
       "3372       2043           NaN    NaN         NaN             NaN   \n",
       "3514       7043           NaN    NaN         NaN             NaN   \n",
       "3520       5579           NaN    NaN         NaN             NaN   \n",
       "3677       4811           NaN    NaN         NaN             NaN   \n",
       "3723       5143           NaN    NaN         NaN             NaN   \n",
       "3969       3836           NaN    NaN         NaN             NaN   \n",
       "4050       3485           NaN    NaN         NaN             NaN   \n",
       "4092       4619           NaN    NaN         NaN             NaN   \n",
       "4137       4444           NaN    NaN         NaN             NaN   \n",
       "\n",
       "      unemployment_rate  num_committed_crimes  \n",
       "5                   NaN                   NaN  \n",
       "77                  NaN                   NaN  \n",
       "308                 NaN                   NaN  \n",
       "322                 NaN                   NaN  \n",
       "394                 NaN                   NaN  \n",
       "537                 NaN                   NaN  \n",
       "574                 NaN                   NaN  \n",
       "790                 NaN                   NaN  \n",
       "817                 NaN                   NaN  \n",
       "832                 NaN                   NaN  \n",
       "902                 NaN                   NaN  \n",
       "1161                NaN                   NaN  \n",
       "1331                NaN                   NaN  \n",
       "1526                NaN                   NaN  \n",
       "1622                NaN                   NaN  \n",
       "1716                NaN                   NaN  \n",
       "1747                NaN                   NaN  \n",
       "1806                NaN                   NaN  \n",
       "1847                NaN                   NaN  \n",
       "2134                NaN                   NaN  \n",
       "2336                NaN                   NaN  \n",
       "2392                NaN                   NaN  \n",
       "2422                NaN                   NaN  \n",
       "2438                NaN                   NaN  \n",
       "2451                NaN                   NaN  \n",
       "2684                NaN                   NaN  \n",
       "2709                NaN                   NaN  \n",
       "2829                NaN                   NaN  \n",
       "2881                NaN                   NaN  \n",
       "3002                NaN                   NaN  \n",
       "3232                NaN                   NaN  \n",
       "3372                NaN                   NaN  \n",
       "3514                NaN                   NaN  \n",
       "3520                NaN                   NaN  \n",
       "3677                NaN                   NaN  \n",
       "3723                NaN                   NaN  \n",
       "3969                NaN                   NaN  \n",
       "4050                NaN                   NaN  \n",
       "4092                NaN                   NaN  \n",
       "4137                NaN                   NaN  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msk = account_district.isna().sum(axis=1) > 0\n",
    "account_district[msk]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "They are from the same district of 77. But there aren't any obvious features to impute the missing numeric district features, so we'll use mean imputation for them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use `SimpleImputer` from `sklearn` to impute the missing data in columns `population`, `average_salary`, `unemployment_rate`, `num_committed_crimes` with their means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "imp_mean = SimpleImputer(strategy='mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_to_impute = ['population', 'average_salary', 'unemployment_rate', 'num_committed_crimes']\n",
    "account_district[cols_to_impute] = imp_mean.fit_transform(account_district[cols_to_impute])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 4500 entries, 0 to 4499\n",
      "Data columns (total 11 columns):\n",
      " #   Column                Non-Null Count  Dtype         \n",
      "---  ------                --------------  -----         \n",
      " 0   account_id            4500 non-null   int64         \n",
      " 1   district_id           4500 non-null   int64         \n",
      " 2   frequency             4500 non-null   object        \n",
      " 3   account_open_date     4500 non-null   datetime64[ns]\n",
      " 4   client_id             4500 non-null   int64         \n",
      " 5   district_name         4460 non-null   object        \n",
      " 6   region                4460 non-null   object        \n",
      " 7   population            4500 non-null   float64       \n",
      " 8   average_salary        4500 non-null   float64       \n",
      " 9   unemployment_rate     4500 non-null   float64       \n",
      " 10  num_committed_crimes  4500 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4), int64(3), object(3)\n",
      "memory usage: 421.9+ KB\n"
     ]
    }
   ],
   "source": [
    "account_district.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Use `fillna` method to impute the missing data in columns `district_name` and `region` with 'UNKNOWN'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "account_district = account_district.fillna('UNKNOWN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 4500 entries, 0 to 4499\n",
      "Data columns (total 11 columns):\n",
      " #   Column                Non-Null Count  Dtype         \n",
      "---  ------                --------------  -----         \n",
      " 0   account_id            4500 non-null   int64         \n",
      " 1   district_id           4500 non-null   int64         \n",
      " 2   frequency             4500 non-null   object        \n",
      " 3   account_open_date     4500 non-null   datetime64[ns]\n",
      " 4   client_id             4500 non-null   int64         \n",
      " 5   district_name         4500 non-null   object        \n",
      " 6   region                4500 non-null   object        \n",
      " 7   population            4500 non-null   float64       \n",
      " 8   average_salary        4500 non-null   float64       \n",
      " 9   unemployment_rate     4500 non-null   float64       \n",
      " 10  num_committed_crimes  4500 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4), int64(3), object(3)\n",
      "memory usage: 421.9+ KB\n"
     ]
    }
   ],
   "source": [
    "account_district.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 15. Merge (left join) `trans` and `account_district` into a new DataFrame called `all_data` using their common columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data = pd.merge(trans, account_district, on='account_id', how='left')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check the information summary of `all_data`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1056320 entries, 0 to 1056319\n",
      "Data columns (total 20 columns):\n",
      " #   Column                Non-Null Count    Dtype         \n",
      "---  ------                --------------    -----         \n",
      " 0   trans_id              1056320 non-null  int64         \n",
      " 1   account_id            1056320 non-null  int64         \n",
      " 2   date                  1056320 non-null  datetime64[ns]\n",
      " 3   type                  1056320 non-null  object        \n",
      " 4   operation_type        1056320 non-null  object        \n",
      " 5   amount                1056320 non-null  float64       \n",
      " 6   balance               1056320 non-null  float64       \n",
      " 7   description           1056320 non-null  object        \n",
      " 8   partner_bank          1056320 non-null  object        \n",
      " 9   partner_account       1056320 non-null  float64       \n",
      " 10  district_id           1056320 non-null  int64         \n",
      " 11  frequency             1056320 non-null  object        \n",
      " 12  account_open_date     1056320 non-null  datetime64[ns]\n",
      " 13  client_id             1056320 non-null  int64         \n",
      " 14  district_name         1056320 non-null  object        \n",
      " 15  region                1056320 non-null  object        \n",
      " 16  population            1056320 non-null  float64       \n",
      " 17  average_salary        1056320 non-null  float64       \n",
      " 18  unemployment_rate     1056320 non-null  float64       \n",
      " 19  num_committed_crimes  1056320 non-null  float64       \n",
      "dtypes: datetime64[ns](2), float64(7), int64(4), object(7)\n",
      "memory usage: 169.2+ MB\n"
     ]
    }
   ],
   "source": [
    "all_data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no missing data. This is a dataset combined from the three DataFrames `account`, `district`, `trans`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 16. Create a new column `account_open_year` and assign it as the year from column `account_open_date`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data['account_open_year'] = all_data['account_open_date'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1993\n",
       "1    1993\n",
       "2    1993\n",
       "3    1993\n",
       "4    1993\n",
       "Name: account_open_year, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data['account_open_year'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 17. Calculate the difference between columns `date` (transaction date) and `account_open_date`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            0 days\n",
       "1            0 days\n",
       "2            0 days\n",
       "3            0 days\n",
       "4            0 days\n",
       "             ...   \n",
       "1056315    832 days\n",
       "1056316   1326 days\n",
       "1056317    825 days\n",
       "1056318   1939 days\n",
       "1056319   2163 days\n",
       "Length: 1056320, dtype: timedelta64[ns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data['date'] - all_data['account_open_date']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 18. Create a new column `account_age_days` and assign it as the difference in days between columns `date` (transaction date) and `account_open_date`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data['account_age_days'] = (all_data['date'] - all_data['account_open_date'])/pd.Timedelta(days=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trans_id</th>\n",
       "      <th>account_id</th>\n",
       "      <th>date</th>\n",
       "      <th>type</th>\n",
       "      <th>operation_type</th>\n",
       "      <th>amount</th>\n",
       "      <th>balance</th>\n",
       "      <th>description</th>\n",
       "      <th>partner_bank</th>\n",
       "      <th>partner_account</th>\n",
       "      <th>...</th>\n",
       "      <th>account_open_date</th>\n",
       "      <th>client_id</th>\n",
       "      <th>district_name</th>\n",
       "      <th>region</th>\n",
       "      <th>population</th>\n",
       "      <th>average_salary</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>num_committed_crimes</th>\n",
       "      <th>account_open_year</th>\n",
       "      <th>account_age_days</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>695247</td>\n",
       "      <td>2378</td>\n",
       "      <td>1993-01-01</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>700.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1993-01-01</td>\n",
       "      <td>2873</td>\n",
       "      <td>JINDRICHUV HRADEC</td>\n",
       "      <td>SOUTH BOHEMIA</td>\n",
       "      <td>93931.000000</td>\n",
       "      <td>8427.000000</td>\n",
       "      <td>1.540000</td>\n",
       "      <td>1913.000000</td>\n",
       "      <td>1993</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>171812</td>\n",
       "      <td>576</td>\n",
       "      <td>1993-01-01</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>900.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1993-01-01</td>\n",
       "      <td>692</td>\n",
       "      <td>BRNO - VENKOV</td>\n",
       "      <td>SOUTH MORAVIA</td>\n",
       "      <td>157042.000000</td>\n",
       "      <td>9072.274638</td>\n",
       "      <td>2.430000</td>\n",
       "      <td>3894.000000</td>\n",
       "      <td>1993</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>207264</td>\n",
       "      <td>704</td>\n",
       "      <td>1993-01-01</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1993-01-01</td>\n",
       "      <td>844</td>\n",
       "      <td>BRNO - VENKOV</td>\n",
       "      <td>SOUTH MORAVIA</td>\n",
       "      <td>157042.000000</td>\n",
       "      <td>9072.274638</td>\n",
       "      <td>2.430000</td>\n",
       "      <td>3894.000000</td>\n",
       "      <td>1993</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1117247</td>\n",
       "      <td>3818</td>\n",
       "      <td>1993-01-01</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>600.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1993-01-01</td>\n",
       "      <td>4601</td>\n",
       "      <td>OSTRAVA - MESTO</td>\n",
       "      <td>NORTH MORAVIA</td>\n",
       "      <td>323870.000000</td>\n",
       "      <td>10673.000000</td>\n",
       "      <td>5.440000</td>\n",
       "      <td>18347.000000</td>\n",
       "      <td>1993</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>579373</td>\n",
       "      <td>1972</td>\n",
       "      <td>1993-01-02</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>400.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1993-01-02</td>\n",
       "      <td>2397</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>270325.692377</td>\n",
       "      <td>9482.017843</td>\n",
       "      <td>3.571654</td>\n",
       "      <td>16486.170933</td>\n",
       "      <td>1993</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056315</th>\n",
       "      <td>3626622</td>\n",
       "      <td>2906</td>\n",
       "      <td>1998-12-31</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>62.3</td>\n",
       "      <td>13729.4</td>\n",
       "      <td>INTEREST CREDIT</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1996-09-20</td>\n",
       "      <td>3512</td>\n",
       "      <td>SEMILY</td>\n",
       "      <td>EAST BOHEMIA</td>\n",
       "      <td>75685.000000</td>\n",
       "      <td>8208.000000</td>\n",
       "      <td>2.260000</td>\n",
       "      <td>888.000000</td>\n",
       "      <td>1996</td>\n",
       "      <td>832.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056316</th>\n",
       "      <td>3627616</td>\n",
       "      <td>2935</td>\n",
       "      <td>1998-12-31</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>81.3</td>\n",
       "      <td>19544.9</td>\n",
       "      <td>INTEREST CREDIT</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1995-05-15</td>\n",
       "      <td>3545</td>\n",
       "      <td>FRYDEK - MISTEK</td>\n",
       "      <td>NORTH MORAVIA</td>\n",
       "      <td>228848.000000</td>\n",
       "      <td>9893.000000</td>\n",
       "      <td>4.720000</td>\n",
       "      <td>12703.935555</td>\n",
       "      <td>1995</td>\n",
       "      <td>1326.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056317</th>\n",
       "      <td>3625403</td>\n",
       "      <td>2869</td>\n",
       "      <td>1998-12-31</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>60.2</td>\n",
       "      <td>14638.2</td>\n",
       "      <td>INTEREST CREDIT</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1996-09-27</td>\n",
       "      <td>3468</td>\n",
       "      <td>FRYDEK - MISTEK</td>\n",
       "      <td>NORTH MORAVIA</td>\n",
       "      <td>228848.000000</td>\n",
       "      <td>9893.000000</td>\n",
       "      <td>4.720000</td>\n",
       "      <td>12703.935555</td>\n",
       "      <td>1996</td>\n",
       "      <td>825.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056318</th>\n",
       "      <td>3626683</td>\n",
       "      <td>2907</td>\n",
       "      <td>1998-12-31</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>107.5</td>\n",
       "      <td>23453.0</td>\n",
       "      <td>INTEREST CREDIT</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1993-09-09</td>\n",
       "      <td>3513</td>\n",
       "      <td>KARVINA</td>\n",
       "      <td>NORTH MORAVIA</td>\n",
       "      <td>285387.000000</td>\n",
       "      <td>10177.000000</td>\n",
       "      <td>7.750000</td>\n",
       "      <td>10108.000000</td>\n",
       "      <td>1993</td>\n",
       "      <td>1939.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1056319</th>\n",
       "      <td>3626540</td>\n",
       "      <td>2902</td>\n",
       "      <td>1998-12-31</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>164.1</td>\n",
       "      <td>41642.9</td>\n",
       "      <td>INTEREST CREDIT</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1993-01-28</td>\n",
       "      <td>3507</td>\n",
       "      <td>CESKA LIPA</td>\n",
       "      <td>NORTH BOHEMIA</td>\n",
       "      <td>105058.000000</td>\n",
       "      <td>9272.000000</td>\n",
       "      <td>3.220000</td>\n",
       "      <td>4505.000000</td>\n",
       "      <td>1993</td>\n",
       "      <td>2163.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1056320 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         trans_id  account_id       date    type  operation_type  amount  \\\n",
       "0          695247        2378 1993-01-01  CREDIT  CREDIT IN CASH   700.0   \n",
       "1          171812         576 1993-01-01  CREDIT  CREDIT IN CASH   900.0   \n",
       "2          207264         704 1993-01-01  CREDIT  CREDIT IN CASH  1000.0   \n",
       "3         1117247        3818 1993-01-01  CREDIT  CREDIT IN CASH   600.0   \n",
       "4          579373        1972 1993-01-02  CREDIT  CREDIT IN CASH   400.0   \n",
       "...           ...         ...        ...     ...             ...     ...   \n",
       "1056315   3626622        2906 1998-12-31  CREDIT         UNKNOWN    62.3   \n",
       "1056316   3627616        2935 1998-12-31  CREDIT         UNKNOWN    81.3   \n",
       "1056317   3625403        2869 1998-12-31  CREDIT         UNKNOWN    60.2   \n",
       "1056318   3626683        2907 1998-12-31  CREDIT         UNKNOWN   107.5   \n",
       "1056319   3626540        2902 1998-12-31  CREDIT         UNKNOWN   164.1   \n",
       "\n",
       "         balance      description partner_bank  partner_account  ...  \\\n",
       "0          700.0          UNKNOWN      UNKNOWN           -999.0  ...   \n",
       "1          900.0          UNKNOWN      UNKNOWN           -999.0  ...   \n",
       "2         1000.0          UNKNOWN      UNKNOWN           -999.0  ...   \n",
       "3          600.0          UNKNOWN      UNKNOWN           -999.0  ...   \n",
       "4          400.0          UNKNOWN      UNKNOWN           -999.0  ...   \n",
       "...          ...              ...          ...              ...  ...   \n",
       "1056315  13729.4  INTEREST CREDIT      UNKNOWN           -999.0  ...   \n",
       "1056316  19544.9  INTEREST CREDIT      UNKNOWN           -999.0  ...   \n",
       "1056317  14638.2  INTEREST CREDIT      UNKNOWN           -999.0  ...   \n",
       "1056318  23453.0  INTEREST CREDIT      UNKNOWN           -999.0  ...   \n",
       "1056319  41642.9  INTEREST CREDIT      UNKNOWN           -999.0  ...   \n",
       "\n",
       "         account_open_date client_id      district_name         region  \\\n",
       "0               1993-01-01      2873  JINDRICHUV HRADEC  SOUTH BOHEMIA   \n",
       "1               1993-01-01       692      BRNO - VENKOV  SOUTH MORAVIA   \n",
       "2               1993-01-01       844      BRNO - VENKOV  SOUTH MORAVIA   \n",
       "3               1993-01-01      4601    OSTRAVA - MESTO  NORTH MORAVIA   \n",
       "4               1993-01-02      2397            UNKNOWN        UNKNOWN   \n",
       "...                    ...       ...                ...            ...   \n",
       "1056315         1996-09-20      3512             SEMILY   EAST BOHEMIA   \n",
       "1056316         1995-05-15      3545    FRYDEK - MISTEK  NORTH MORAVIA   \n",
       "1056317         1996-09-27      3468    FRYDEK - MISTEK  NORTH MORAVIA   \n",
       "1056318         1993-09-09      3513            KARVINA  NORTH MORAVIA   \n",
       "1056319         1993-01-28      3507         CESKA LIPA  NORTH BOHEMIA   \n",
       "\n",
       "            population average_salary  unemployment_rate  \\\n",
       "0         93931.000000    8427.000000           1.540000   \n",
       "1        157042.000000    9072.274638           2.430000   \n",
       "2        157042.000000    9072.274638           2.430000   \n",
       "3        323870.000000   10673.000000           5.440000   \n",
       "4        270325.692377    9482.017843           3.571654   \n",
       "...                ...            ...                ...   \n",
       "1056315   75685.000000    8208.000000           2.260000   \n",
       "1056316  228848.000000    9893.000000           4.720000   \n",
       "1056317  228848.000000    9893.000000           4.720000   \n",
       "1056318  285387.000000   10177.000000           7.750000   \n",
       "1056319  105058.000000    9272.000000           3.220000   \n",
       "\n",
       "         num_committed_crimes  account_open_year  account_age_days  \n",
       "0                 1913.000000               1993               0.0  \n",
       "1                 3894.000000               1993               0.0  \n",
       "2                 3894.000000               1993               0.0  \n",
       "3                18347.000000               1993               0.0  \n",
       "4                16486.170933               1993               0.0  \n",
       "...                       ...                ...               ...  \n",
       "1056315            888.000000               1996             832.0  \n",
       "1056316          12703.935555               1995            1326.0  \n",
       "1056317          12703.935555               1996             825.0  \n",
       "1056318          10108.000000               1993            1939.0  \n",
       "1056319           4505.000000               1993            2163.0  \n",
       "\n",
       "[1056320 rows x 22 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 19. Create a new column `amount_category` by cutting the column `amount` into 3 equal-sized bins, and label the bins as 'low_amount', 'medium_amount', 'high_amount'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data['amount_category'] = pd.qcut(all_data['amount'], q=3, labels=['low_amount', 'medium_amount', 'high_amount'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Verify the categories and their counts in `amount_category`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "medium_amount    352394\n",
       "low_amount       352118\n",
       "high_amount      351808\n",
       "Name: amount_category, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data['amount_category'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 20. Create a new column `account_age_days_category` by cutting the column `account_age_days` into 5 equal-width bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data['account_age_days_category'] = pd.cut(all_data['account_age_days'], bins=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Verify the categories and their counts in `account_age_days_category`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(438.0, 876.0]      333012\n",
       "(-2.19, 438.0]      330815\n",
       "(876.0, 1314.0]     204014\n",
       "(1314.0, 1752.0]    131926\n",
       "(1752.0, 2190.0]     56553\n",
       "Name: account_age_days_category, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data['account_age_days_category'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Print out the first 20 rows of `all_data` to look at the newly added columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trans_id</th>\n",
       "      <th>account_id</th>\n",
       "      <th>date</th>\n",
       "      <th>type</th>\n",
       "      <th>operation_type</th>\n",
       "      <th>amount</th>\n",
       "      <th>balance</th>\n",
       "      <th>description</th>\n",
       "      <th>partner_bank</th>\n",
       "      <th>partner_account</th>\n",
       "      <th>...</th>\n",
       "      <th>district_name</th>\n",
       "      <th>region</th>\n",
       "      <th>population</th>\n",
       "      <th>average_salary</th>\n",
       "      <th>unemployment_rate</th>\n",
       "      <th>num_committed_crimes</th>\n",
       "      <th>account_open_year</th>\n",
       "      <th>account_age_days</th>\n",
       "      <th>amount_category</th>\n",
       "      <th>account_age_days_category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>695247</td>\n",
       "      <td>2378</td>\n",
       "      <td>1993-01-01</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>700.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>JINDRICHUV HRADEC</td>\n",
       "      <td>SOUTH BOHEMIA</td>\n",
       "      <td>9.393100e+04</td>\n",
       "      <td>8427.000000</td>\n",
       "      <td>1.540000</td>\n",
       "      <td>1913.000000</td>\n",
       "      <td>1993</td>\n",
       "      <td>0.0</td>\n",
       "      <td>medium_amount</td>\n",
       "      <td>(-2.19, 438.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>171812</td>\n",
       "      <td>576</td>\n",
       "      <td>1993-01-01</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>900.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>BRNO - VENKOV</td>\n",
       "      <td>SOUTH MORAVIA</td>\n",
       "      <td>1.570420e+05</td>\n",
       "      <td>9072.274638</td>\n",
       "      <td>2.430000</td>\n",
       "      <td>3894.000000</td>\n",
       "      <td>1993</td>\n",
       "      <td>0.0</td>\n",
       "      <td>medium_amount</td>\n",
       "      <td>(-2.19, 438.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>207264</td>\n",
       "      <td>704</td>\n",
       "      <td>1993-01-01</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>BRNO - VENKOV</td>\n",
       "      <td>SOUTH MORAVIA</td>\n",
       "      <td>1.570420e+05</td>\n",
       "      <td>9072.274638</td>\n",
       "      <td>2.430000</td>\n",
       "      <td>3894.000000</td>\n",
       "      <td>1993</td>\n",
       "      <td>0.0</td>\n",
       "      <td>medium_amount</td>\n",
       "      <td>(-2.19, 438.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1117247</td>\n",
       "      <td>3818</td>\n",
       "      <td>1993-01-01</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>600.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>OSTRAVA - MESTO</td>\n",
       "      <td>NORTH MORAVIA</td>\n",
       "      <td>3.238700e+05</td>\n",
       "      <td>10673.000000</td>\n",
       "      <td>5.440000</td>\n",
       "      <td>18347.000000</td>\n",
       "      <td>1993</td>\n",
       "      <td>0.0</td>\n",
       "      <td>medium_amount</td>\n",
       "      <td>(-2.19, 438.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>579373</td>\n",
       "      <td>1972</td>\n",
       "      <td>1993-01-02</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>400.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>2.703257e+05</td>\n",
       "      <td>9482.017843</td>\n",
       "      <td>3.571654</td>\n",
       "      <td>16486.170933</td>\n",
       "      <td>1993</td>\n",
       "      <td>0.0</td>\n",
       "      <td>medium_amount</td>\n",
       "      <td>(-2.19, 438.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>771035</td>\n",
       "      <td>2632</td>\n",
       "      <td>1993-01-02</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>KARLOVY VARY</td>\n",
       "      <td>WEST BOHEMIA</td>\n",
       "      <td>1.226030e+05</td>\n",
       "      <td>8991.000000</td>\n",
       "      <td>2.010000</td>\n",
       "      <td>5273.000000</td>\n",
       "      <td>1993</td>\n",
       "      <td>0.0</td>\n",
       "      <td>medium_amount</td>\n",
       "      <td>(-2.19, 438.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>452728</td>\n",
       "      <td>1539</td>\n",
       "      <td>1993-01-03</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>600.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>HL.M. PRAHA</td>\n",
       "      <td>PRAGUE</td>\n",
       "      <td>1.204953e+06</td>\n",
       "      <td>12541.000000</td>\n",
       "      <td>0.430000</td>\n",
       "      <td>99107.000000</td>\n",
       "      <td>1993</td>\n",
       "      <td>0.0</td>\n",
       "      <td>medium_amount</td>\n",
       "      <td>(-2.19, 438.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>725751</td>\n",
       "      <td>2484</td>\n",
       "      <td>1993-01-03</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>OSTRAVA - MESTO</td>\n",
       "      <td>NORTH MORAVIA</td>\n",
       "      <td>3.238700e+05</td>\n",
       "      <td>10673.000000</td>\n",
       "      <td>5.440000</td>\n",
       "      <td>18347.000000</td>\n",
       "      <td>1993</td>\n",
       "      <td>0.0</td>\n",
       "      <td>medium_amount</td>\n",
       "      <td>(-2.19, 438.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>497211</td>\n",
       "      <td>1695</td>\n",
       "      <td>1993-01-03</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>SUMPERK</td>\n",
       "      <td>NORTH MORAVIA</td>\n",
       "      <td>1.273690e+05</td>\n",
       "      <td>8369.000000</td>\n",
       "      <td>3.818541</td>\n",
       "      <td>2807.000000</td>\n",
       "      <td>1993</td>\n",
       "      <td>0.0</td>\n",
       "      <td>low_amount</td>\n",
       "      <td>(-2.19, 438.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>232960</td>\n",
       "      <td>793</td>\n",
       "      <td>1993-01-03</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>800.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>PARDUBICE</td>\n",
       "      <td>EAST BOHEMIA</td>\n",
       "      <td>1.625800e+05</td>\n",
       "      <td>9538.000000</td>\n",
       "      <td>4.315642</td>\n",
       "      <td>5410.000000</td>\n",
       "      <td>1993</td>\n",
       "      <td>0.0</td>\n",
       "      <td>medium_amount</td>\n",
       "      <td>(-2.19, 438.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>505240</td>\n",
       "      <td>1726</td>\n",
       "      <td>1993-01-03</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>RYCHNOV NAD KNEZNOU</td>\n",
       "      <td>EAST BOHEMIA</td>\n",
       "      <td>7.895500e+04</td>\n",
       "      <td>9060.000000</td>\n",
       "      <td>2.440000</td>\n",
       "      <td>1717.000000</td>\n",
       "      <td>1993</td>\n",
       "      <td>0.0</td>\n",
       "      <td>medium_amount</td>\n",
       "      <td>(-2.19, 438.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>144541</td>\n",
       "      <td>485</td>\n",
       "      <td>1993-01-04</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>300.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>KUTNA HORA</td>\n",
       "      <td>CENTRAL BOHEMIA</td>\n",
       "      <td>7.796300e+04</td>\n",
       "      <td>8546.000000</td>\n",
       "      <td>4.020000</td>\n",
       "      <td>3120.000000</td>\n",
       "      <td>1993</td>\n",
       "      <td>0.0</td>\n",
       "      <td>medium_amount</td>\n",
       "      <td>(-2.19, 438.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>637741</td>\n",
       "      <td>2177</td>\n",
       "      <td>1993-01-04</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>800.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>UHERSKE HRADISTE</td>\n",
       "      <td>SOUTH MORAVIA</td>\n",
       "      <td>1.456880e+05</td>\n",
       "      <td>8544.000000</td>\n",
       "      <td>1.860000</td>\n",
       "      <td>2906.000000</td>\n",
       "      <td>1993</td>\n",
       "      <td>0.0</td>\n",
       "      <td>medium_amount</td>\n",
       "      <td>(-2.19, 438.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>689827</td>\n",
       "      <td>2357</td>\n",
       "      <td>1993-01-04</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>800.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>PRACHATICE</td>\n",
       "      <td>SOUTH BOHEMIA</td>\n",
       "      <td>5.142800e+04</td>\n",
       "      <td>8402.000000</td>\n",
       "      <td>3.980000</td>\n",
       "      <td>1099.000000</td>\n",
       "      <td>1993</td>\n",
       "      <td>0.0</td>\n",
       "      <td>medium_amount</td>\n",
       "      <td>(-2.19, 438.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>846006</td>\n",
       "      <td>2881</td>\n",
       "      <td>1993-01-04</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>700.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>KARVINA</td>\n",
       "      <td>NORTH MORAVIA</td>\n",
       "      <td>2.853870e+05</td>\n",
       "      <td>10177.000000</td>\n",
       "      <td>7.750000</td>\n",
       "      <td>10108.000000</td>\n",
       "      <td>1993</td>\n",
       "      <td>0.0</td>\n",
       "      <td>medium_amount</td>\n",
       "      <td>(-2.19, 438.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>637742</td>\n",
       "      <td>2177</td>\n",
       "      <td>1993-01-05</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>COLLECTION FROM ANOTHER BANK</td>\n",
       "      <td>5123.0</td>\n",
       "      <td>5923.0</td>\n",
       "      <td>PENSION</td>\n",
       "      <td>YZ</td>\n",
       "      <td>62457513.0</td>\n",
       "      <td>...</td>\n",
       "      <td>UHERSKE HRADISTE</td>\n",
       "      <td>SOUTH MORAVIA</td>\n",
       "      <td>1.456880e+05</td>\n",
       "      <td>8544.000000</td>\n",
       "      <td>1.860000</td>\n",
       "      <td>2906.000000</td>\n",
       "      <td>1993</td>\n",
       "      <td>1.0</td>\n",
       "      <td>high_amount</td>\n",
       "      <td>(-2.19, 438.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2908688</td>\n",
       "      <td>9635</td>\n",
       "      <td>1993-01-05</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>400.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>KARVINA</td>\n",
       "      <td>NORTH MORAVIA</td>\n",
       "      <td>2.853870e+05</td>\n",
       "      <td>10177.000000</td>\n",
       "      <td>7.750000</td>\n",
       "      <td>10108.000000</td>\n",
       "      <td>1993</td>\n",
       "      <td>0.0</td>\n",
       "      <td>medium_amount</td>\n",
       "      <td>(-2.19, 438.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>232961</td>\n",
       "      <td>793</td>\n",
       "      <td>1993-01-05</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>COLLECTION FROM ANOTHER BANK</td>\n",
       "      <td>3401.0</td>\n",
       "      <td>4201.0</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>IJ</td>\n",
       "      <td>6149286.0</td>\n",
       "      <td>...</td>\n",
       "      <td>PARDUBICE</td>\n",
       "      <td>EAST BOHEMIA</td>\n",
       "      <td>1.625800e+05</td>\n",
       "      <td>9538.000000</td>\n",
       "      <td>4.315642</td>\n",
       "      <td>5410.000000</td>\n",
       "      <td>1993</td>\n",
       "      <td>2.0</td>\n",
       "      <td>medium_amount</td>\n",
       "      <td>(-2.19, 438.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>192096</td>\n",
       "      <td>652</td>\n",
       "      <td>1993-01-05</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>700.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>TABOR</td>\n",
       "      <td>SOUTH BOHEMIA</td>\n",
       "      <td>1.033470e+05</td>\n",
       "      <td>9104.000000</td>\n",
       "      <td>2.070000</td>\n",
       "      <td>2354.000000</td>\n",
       "      <td>1993</td>\n",
       "      <td>0.0</td>\n",
       "      <td>medium_amount</td>\n",
       "      <td>(-2.19, 438.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>542215</td>\n",
       "      <td>1844</td>\n",
       "      <td>1993-01-06</td>\n",
       "      <td>CREDIT</td>\n",
       "      <td>CREDIT IN CASH</td>\n",
       "      <td>500.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>UNKNOWN</td>\n",
       "      <td>-999.0</td>\n",
       "      <td>...</td>\n",
       "      <td>CHRUDIM</td>\n",
       "      <td>EAST BOHEMIA</td>\n",
       "      <td>1.056060e+05</td>\n",
       "      <td>8254.000000</td>\n",
       "      <td>3.760000</td>\n",
       "      <td>2325.000000</td>\n",
       "      <td>1993</td>\n",
       "      <td>0.0</td>\n",
       "      <td>medium_amount</td>\n",
       "      <td>(-2.19, 438.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    trans_id  account_id       date    type                operation_type  \\\n",
       "0     695247        2378 1993-01-01  CREDIT                CREDIT IN CASH   \n",
       "1     171812         576 1993-01-01  CREDIT                CREDIT IN CASH   \n",
       "2     207264         704 1993-01-01  CREDIT                CREDIT IN CASH   \n",
       "3    1117247        3818 1993-01-01  CREDIT                CREDIT IN CASH   \n",
       "4     579373        1972 1993-01-02  CREDIT                CREDIT IN CASH   \n",
       "5     771035        2632 1993-01-02  CREDIT                CREDIT IN CASH   \n",
       "6     452728        1539 1993-01-03  CREDIT                CREDIT IN CASH   \n",
       "7     725751        2484 1993-01-03  CREDIT                CREDIT IN CASH   \n",
       "8     497211        1695 1993-01-03  CREDIT                CREDIT IN CASH   \n",
       "9     232960         793 1993-01-03  CREDIT                CREDIT IN CASH   \n",
       "10    505240        1726 1993-01-03  CREDIT                CREDIT IN CASH   \n",
       "11    144541         485 1993-01-04  CREDIT                CREDIT IN CASH   \n",
       "12    637741        2177 1993-01-04  CREDIT                CREDIT IN CASH   \n",
       "13    689827        2357 1993-01-04  CREDIT                CREDIT IN CASH   \n",
       "14    846006        2881 1993-01-04  CREDIT                CREDIT IN CASH   \n",
       "15    637742        2177 1993-01-05  CREDIT  COLLECTION FROM ANOTHER BANK   \n",
       "16   2908688        9635 1993-01-05  CREDIT                CREDIT IN CASH   \n",
       "17    232961         793 1993-01-05  CREDIT  COLLECTION FROM ANOTHER BANK   \n",
       "18    192096         652 1993-01-05  CREDIT                CREDIT IN CASH   \n",
       "19    542215        1844 1993-01-06  CREDIT                CREDIT IN CASH   \n",
       "\n",
       "    amount  balance description partner_bank  partner_account  ...  \\\n",
       "0    700.0    700.0     UNKNOWN      UNKNOWN           -999.0  ...   \n",
       "1    900.0    900.0     UNKNOWN      UNKNOWN           -999.0  ...   \n",
       "2   1000.0   1000.0     UNKNOWN      UNKNOWN           -999.0  ...   \n",
       "3    600.0    600.0     UNKNOWN      UNKNOWN           -999.0  ...   \n",
       "4    400.0    400.0     UNKNOWN      UNKNOWN           -999.0  ...   \n",
       "5   1100.0   1100.0     UNKNOWN      UNKNOWN           -999.0  ...   \n",
       "6    600.0    600.0     UNKNOWN      UNKNOWN           -999.0  ...   \n",
       "7   1100.0   1100.0     UNKNOWN      UNKNOWN           -999.0  ...   \n",
       "8    200.0    200.0     UNKNOWN      UNKNOWN           -999.0  ...   \n",
       "9    800.0    800.0     UNKNOWN      UNKNOWN           -999.0  ...   \n",
       "10  1000.0   1000.0     UNKNOWN      UNKNOWN           -999.0  ...   \n",
       "11   300.0    300.0     UNKNOWN      UNKNOWN           -999.0  ...   \n",
       "12   800.0    800.0     UNKNOWN      UNKNOWN           -999.0  ...   \n",
       "13   800.0    800.0     UNKNOWN      UNKNOWN           -999.0  ...   \n",
       "14   700.0    700.0     UNKNOWN      UNKNOWN           -999.0  ...   \n",
       "15  5123.0   5923.0     PENSION           YZ       62457513.0  ...   \n",
       "16   400.0    400.0     UNKNOWN      UNKNOWN           -999.0  ...   \n",
       "17  3401.0   4201.0     UNKNOWN           IJ        6149286.0  ...   \n",
       "18   700.0    700.0     UNKNOWN      UNKNOWN           -999.0  ...   \n",
       "19   500.0    500.0     UNKNOWN      UNKNOWN           -999.0  ...   \n",
       "\n",
       "          district_name           region    population  average_salary  \\\n",
       "0     JINDRICHUV HRADEC    SOUTH BOHEMIA  9.393100e+04     8427.000000   \n",
       "1         BRNO - VENKOV    SOUTH MORAVIA  1.570420e+05     9072.274638   \n",
       "2         BRNO - VENKOV    SOUTH MORAVIA  1.570420e+05     9072.274638   \n",
       "3       OSTRAVA - MESTO    NORTH MORAVIA  3.238700e+05    10673.000000   \n",
       "4               UNKNOWN          UNKNOWN  2.703257e+05     9482.017843   \n",
       "5          KARLOVY VARY     WEST BOHEMIA  1.226030e+05     8991.000000   \n",
       "6           HL.M. PRAHA           PRAGUE  1.204953e+06    12541.000000   \n",
       "7       OSTRAVA - MESTO    NORTH MORAVIA  3.238700e+05    10673.000000   \n",
       "8               SUMPERK    NORTH MORAVIA  1.273690e+05     8369.000000   \n",
       "9             PARDUBICE     EAST BOHEMIA  1.625800e+05     9538.000000   \n",
       "10  RYCHNOV NAD KNEZNOU     EAST BOHEMIA  7.895500e+04     9060.000000   \n",
       "11           KUTNA HORA  CENTRAL BOHEMIA  7.796300e+04     8546.000000   \n",
       "12     UHERSKE HRADISTE    SOUTH MORAVIA  1.456880e+05     8544.000000   \n",
       "13           PRACHATICE    SOUTH BOHEMIA  5.142800e+04     8402.000000   \n",
       "14              KARVINA    NORTH MORAVIA  2.853870e+05    10177.000000   \n",
       "15     UHERSKE HRADISTE    SOUTH MORAVIA  1.456880e+05     8544.000000   \n",
       "16              KARVINA    NORTH MORAVIA  2.853870e+05    10177.000000   \n",
       "17            PARDUBICE     EAST BOHEMIA  1.625800e+05     9538.000000   \n",
       "18                TABOR    SOUTH BOHEMIA  1.033470e+05     9104.000000   \n",
       "19              CHRUDIM     EAST BOHEMIA  1.056060e+05     8254.000000   \n",
       "\n",
       "   unemployment_rate num_committed_crimes  account_open_year  \\\n",
       "0           1.540000          1913.000000               1993   \n",
       "1           2.430000          3894.000000               1993   \n",
       "2           2.430000          3894.000000               1993   \n",
       "3           5.440000         18347.000000               1993   \n",
       "4           3.571654         16486.170933               1993   \n",
       "5           2.010000          5273.000000               1993   \n",
       "6           0.430000         99107.000000               1993   \n",
       "7           5.440000         18347.000000               1993   \n",
       "8           3.818541          2807.000000               1993   \n",
       "9           4.315642          5410.000000               1993   \n",
       "10          2.440000          1717.000000               1993   \n",
       "11          4.020000          3120.000000               1993   \n",
       "12          1.860000          2906.000000               1993   \n",
       "13          3.980000          1099.000000               1993   \n",
       "14          7.750000         10108.000000               1993   \n",
       "15          1.860000          2906.000000               1993   \n",
       "16          7.750000         10108.000000               1993   \n",
       "17          4.315642          5410.000000               1993   \n",
       "18          2.070000          2354.000000               1993   \n",
       "19          3.760000          2325.000000               1993   \n",
       "\n",
       "    account_age_days  amount_category  account_age_days_category  \n",
       "0                0.0    medium_amount             (-2.19, 438.0]  \n",
       "1                0.0    medium_amount             (-2.19, 438.0]  \n",
       "2                0.0    medium_amount             (-2.19, 438.0]  \n",
       "3                0.0    medium_amount             (-2.19, 438.0]  \n",
       "4                0.0    medium_amount             (-2.19, 438.0]  \n",
       "5                0.0    medium_amount             (-2.19, 438.0]  \n",
       "6                0.0    medium_amount             (-2.19, 438.0]  \n",
       "7                0.0    medium_amount             (-2.19, 438.0]  \n",
       "8                0.0       low_amount             (-2.19, 438.0]  \n",
       "9                0.0    medium_amount             (-2.19, 438.0]  \n",
       "10               0.0    medium_amount             (-2.19, 438.0]  \n",
       "11               0.0    medium_amount             (-2.19, 438.0]  \n",
       "12               0.0    medium_amount             (-2.19, 438.0]  \n",
       "13               0.0    medium_amount             (-2.19, 438.0]  \n",
       "14               0.0    medium_amount             (-2.19, 438.0]  \n",
       "15               1.0      high_amount             (-2.19, 438.0]  \n",
       "16               0.0    medium_amount             (-2.19, 438.0]  \n",
       "17               2.0    medium_amount             (-2.19, 438.0]  \n",
       "18               0.0    medium_amount             (-2.19, 438.0]  \n",
       "19               0.0    medium_amount             (-2.19, 438.0]  \n",
       "\n",
       "[20 rows x 24 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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