Assignment-Coding-for-Economists2024.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11a00e82-b892-46d7-b8f3-1a401878c5a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   country city_actual  rating_count center1label center2label neighbourhood  \\\n",
      "0  Austria      Vienna          36.0  City centre    Donauturm   17. Hernals   \n",
      "1  Austria      Vienna         189.0  City centre    Donauturm   17. Hernals   \n",
      "2  Austria      Vienna          53.0  City centre    Donauturm    Alsergrund   \n",
      "3  Austria      Vienna          55.0  City centre    Donauturm    Alsergrund   \n",
      "4  Austria      Vienna          33.0  City centre    Donauturm    Alsergrund   \n",
      "\n",
      "   price    city  stars  ratingta  ...     offer_cat  year  month  weekend  \\\n",
      "0     81  Vienna    4.0       4.5  ...  15-50% offer  2017     11        0   \n",
      "1     81  Vienna    4.0       3.5  ...   1-15% offer  2017     11        0   \n",
      "2     85  Vienna    4.0       3.5  ...  15-50% offer  2017     11        0   \n",
      "3     83  Vienna    3.0       4.0  ...  15-50% offer  2017     11        0   \n",
      "4     82  Vienna    4.0       3.5  ...  15-50% offer  2017     11        0   \n",
      "\n",
      "  holiday  distance  distance_alter  accommodation_type  nnights  rating  \n",
      "0       0       2.7             4.4           Apartment        1     4.4  \n",
      "1       0       1.7             3.8               Hotel        1     3.9  \n",
      "2       0       1.4             2.5               Hotel        1     3.7  \n",
      "3       0       1.7             2.5               Hotel        1     4.0  \n",
      "4       0       1.2             2.8               Hotel        1     3.9  \n",
      "\n",
      "[5 rows x 24 columns]\n"
     ]
    }
   ],
   "source": [
    "# Task 5\n",
    "## Import Data\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('data/hotels-vienna.csv')\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "320580b7-331b-48d5-81ac-e4a002976046",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "country                object\n",
      "city_actual            object\n",
      "rating_count          float64\n",
      "center1label           object\n",
      "center2label           object\n",
      "neighbourhood          object\n",
      "price                   int64\n",
      "city                   object\n",
      "stars                 float64\n",
      "ratingta              float64\n",
      "ratingta_count        float64\n",
      "scarce_room             int64\n",
      "hotel_id                int64\n",
      "offer                   int64\n",
      "offer_cat              object\n",
      "year                    int64\n",
      "month                   int64\n",
      "weekend                 int64\n",
      "holiday                 int64\n",
      "distance              float64\n",
      "distance_alter        float64\n",
      "accommodation_type     object\n",
      "nnights                 int64\n",
      "rating                float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "## Check Variable Types\n",
    "print(df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "54b9befb-4869-4316-a1d9-473fada5f11a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "country                 0\n",
      "city_actual             0\n",
      "rating_count           35\n",
      "center1label            0\n",
      "center2label            0\n",
      "neighbourhood           0\n",
      "price                   0\n",
      "city                    0\n",
      "stars                   0\n",
      "ratingta              103\n",
      "ratingta_count        103\n",
      "scarce_room             0\n",
      "hotel_id                0\n",
      "offer                   0\n",
      "offer_cat               0\n",
      "year                    0\n",
      "month                   0\n",
      "weekend                 0\n",
      "holiday                 0\n",
      "distance                0\n",
      "distance_alter          0\n",
      "accommodation_type      0\n",
      "nnights                 0\n",
      "rating                 35\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "## Check Errors\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ffa22723-9806-4790-a2d0-a3d0af3f8e3a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>city_actual</th>\n",
       "      <th>rating_count</th>\n",
       "      <th>center1label</th>\n",
       "      <th>center2label</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>price</th>\n",
       "      <th>city</th>\n",
       "      <th>stars</th>\n",
       "      <th>ratingta</th>\n",
       "      <th>...</th>\n",
       "      <th>offer_cat</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>weekend</th>\n",
       "      <th>holiday</th>\n",
       "      <th>distance</th>\n",
       "      <th>distance_alter</th>\n",
       "      <th>accommodation_type</th>\n",
       "      <th>nnights</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>36.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>17. Hernals</td>\n",
       "      <td>81</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>...</td>\n",
       "      <td>15-50% offer</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>189.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>17. Hernals</td>\n",
       "      <td>81</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>...</td>\n",
       "      <td>1-15% offer</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>53.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Alsergrund</td>\n",
       "      <td>85</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>...</td>\n",
       "      <td>15-50% offer</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>55.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Alsergrund</td>\n",
       "      <td>83</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15-50% offer</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>33.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Alsergrund</td>\n",
       "      <td>82</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>...</td>\n",
       "      <td>15-50% offer</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>3.9</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>317</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>53.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Wieden</td>\n",
       "      <td>73</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1-15% offer</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>2.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Wieden</td>\n",
       "      <td>109</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>50%-75% offer</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>145.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Wieden</td>\n",
       "      <td>185</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15-50% offer</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3.6</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>112.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Wieden</td>\n",
       "      <td>100</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0% no offer</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>169.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Wieden</td>\n",
       "      <td>58</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15-50% offer</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>322 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     country city_actual  rating_count center1label center2label  \\\n",
       "0    Austria      Vienna          36.0  City centre    Donauturm   \n",
       "1    Austria      Vienna         189.0  City centre    Donauturm   \n",
       "2    Austria      Vienna          53.0  City centre    Donauturm   \n",
       "3    Austria      Vienna          55.0  City centre    Donauturm   \n",
       "4    Austria      Vienna          33.0  City centre    Donauturm   \n",
       "..       ...         ...           ...          ...          ...   \n",
       "317  Austria      Vienna          53.0  City centre    Donauturm   \n",
       "318  Austria      Vienna           2.0  City centre    Donauturm   \n",
       "319  Austria      Vienna         145.0  City centre    Donauturm   \n",
       "320  Austria      Vienna         112.0  City centre    Donauturm   \n",
       "321  Austria      Vienna         169.0  City centre    Donauturm   \n",
       "\n",
       "    neighbourhood  price    city  stars  ratingta  ...      offer_cat  year  \\\n",
       "0     17. Hernals     81  Vienna    4.0       4.5  ...   15-50% offer  2017   \n",
       "1     17. Hernals     81  Vienna    4.0       3.5  ...    1-15% offer  2017   \n",
       "2      Alsergrund     85  Vienna    4.0       3.5  ...   15-50% offer  2017   \n",
       "3      Alsergrund     83  Vienna    3.0       4.0  ...   15-50% offer  2017   \n",
       "4      Alsergrund     82  Vienna    4.0       3.5  ...   15-50% offer  2017   \n",
       "..            ...    ...     ...    ...       ...  ...            ...   ...   \n",
       "317        Wieden     73  Vienna    3.0       3.0  ...    1-15% offer  2017   \n",
       "318        Wieden    109  Vienna    3.0       3.0  ...  50%-75% offer  2017   \n",
       "319        Wieden    185  Vienna    5.0       4.0  ...   15-50% offer  2017   \n",
       "320        Wieden    100  Vienna    4.0       4.5  ...    0% no offer  2017   \n",
       "321        Wieden     58  Vienna    3.0       3.0  ...   15-50% offer  2017   \n",
       "\n",
       "     month  weekend holiday  distance  distance_alter  accommodation_type  \\\n",
       "0       11        0       0       2.7             4.4           Apartment   \n",
       "1       11        0       0       1.7             3.8               Hotel   \n",
       "2       11        0       0       1.4             2.5               Hotel   \n",
       "3       11        0       0       1.7             2.5               Hotel   \n",
       "4       11        0       0       1.2             2.8               Hotel   \n",
       "..     ...      ...     ...       ...             ...                 ...   \n",
       "317     11        0       0       1.5             4.0               Hotel   \n",
       "318     11        0       0       1.5             3.8           Apartment   \n",
       "319     11        0       0       0.8             3.6               Hotel   \n",
       "320     11        0       0       1.0             3.7               Hotel   \n",
       "321     11        0       0       1.4             4.1               Hotel   \n",
       "\n",
       "     nnights  rating  \n",
       "0          1     4.4  \n",
       "1          1     3.9  \n",
       "2          1     3.7  \n",
       "3          1     4.0  \n",
       "4          1     3.9  \n",
       "..       ...     ...  \n",
       "317        1     3.4  \n",
       "318        1     5.0  \n",
       "319        1     4.3  \n",
       "320        1     4.4  \n",
       "321        1     3.2  \n",
       "\n",
       "[322 rows x 24 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Delete data inclusing missing values\n",
    "df_cleaned = df.dropna().reset_index(drop=True)\n",
    "df_cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e5f765cf-cc5e-4107-9bbb-b50ec6229100",
   "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>country</th>\n",
       "      <th>city_actual</th>\n",
       "      <th>rating_count</th>\n",
       "      <th>center1label</th>\n",
       "      <th>center2label</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>price</th>\n",
       "      <th>city</th>\n",
       "      <th>stars</th>\n",
       "      <th>ratingta</th>\n",
       "      <th>...</th>\n",
       "      <th>offer_cat</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>weekend</th>\n",
       "      <th>holiday</th>\n",
       "      <th>distance</th>\n",
       "      <th>distance_alter</th>\n",
       "      <th>accommodation_type</th>\n",
       "      <th>nnights</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>36.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Hernals</td>\n",
       "      <td>81</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>...</td>\n",
       "      <td>15-50% offer</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>189.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Hernals</td>\n",
       "      <td>81</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>...</td>\n",
       "      <td>1-15% offer</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>53.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Alsergrund</td>\n",
       "      <td>85</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>...</td>\n",
       "      <td>15-50% offer</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>55.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Alsergrund</td>\n",
       "      <td>83</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15-50% offer</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>33.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Alsergrund</td>\n",
       "      <td>82</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>...</td>\n",
       "      <td>15-50% offer</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>3.9</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>317</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>53.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Wieden</td>\n",
       "      <td>73</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1-15% offer</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>2.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Wieden</td>\n",
       "      <td>109</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>50%-75% offer</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>145.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Wieden</td>\n",
       "      <td>185</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15-50% offer</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3.6</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>112.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Wieden</td>\n",
       "      <td>100</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0% no offer</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>169.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Wieden</td>\n",
       "      <td>58</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15-50% offer</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>322 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     country city_actual  rating_count center1label center2label  \\\n",
       "0    Austria      Vienna          36.0  City centre    Donauturm   \n",
       "1    Austria      Vienna         189.0  City centre    Donauturm   \n",
       "2    Austria      Vienna          53.0  City centre    Donauturm   \n",
       "3    Austria      Vienna          55.0  City centre    Donauturm   \n",
       "4    Austria      Vienna          33.0  City centre    Donauturm   \n",
       "..       ...         ...           ...          ...          ...   \n",
       "317  Austria      Vienna          53.0  City centre    Donauturm   \n",
       "318  Austria      Vienna           2.0  City centre    Donauturm   \n",
       "319  Austria      Vienna         145.0  City centre    Donauturm   \n",
       "320  Austria      Vienna         112.0  City centre    Donauturm   \n",
       "321  Austria      Vienna         169.0  City centre    Donauturm   \n",
       "\n",
       "    neighbourhood  price    city  stars  ratingta  ...      offer_cat  year  \\\n",
       "0         Hernals     81  Vienna    4.0       4.5  ...   15-50% offer  2017   \n",
       "1         Hernals     81  Vienna    4.0       3.5  ...    1-15% offer  2017   \n",
       "2      Alsergrund     85  Vienna    4.0       3.5  ...   15-50% offer  2017   \n",
       "3      Alsergrund     83  Vienna    3.0       4.0  ...   15-50% offer  2017   \n",
       "4      Alsergrund     82  Vienna    4.0       3.5  ...   15-50% offer  2017   \n",
       "..            ...    ...     ...    ...       ...  ...            ...   ...   \n",
       "317        Wieden     73  Vienna    3.0       3.0  ...    1-15% offer  2017   \n",
       "318        Wieden    109  Vienna    3.0       3.0  ...  50%-75% offer  2017   \n",
       "319        Wieden    185  Vienna    5.0       4.0  ...   15-50% offer  2017   \n",
       "320        Wieden    100  Vienna    4.0       4.5  ...    0% no offer  2017   \n",
       "321        Wieden     58  Vienna    3.0       3.0  ...   15-50% offer  2017   \n",
       "\n",
       "     month  weekend holiday  distance  distance_alter  accommodation_type  \\\n",
       "0       11        0       0       2.7             4.4           Apartment   \n",
       "1       11        0       0       1.7             3.8               Hotel   \n",
       "2       11        0       0       1.4             2.5               Hotel   \n",
       "3       11        0       0       1.7             2.5               Hotel   \n",
       "4       11        0       0       1.2             2.8               Hotel   \n",
       "..     ...      ...     ...       ...             ...                 ...   \n",
       "317     11        0       0       1.5             4.0               Hotel   \n",
       "318     11        0       0       1.5             3.8           Apartment   \n",
       "319     11        0       0       0.8             3.6               Hotel   \n",
       "320     11        0       0       1.0             3.7               Hotel   \n",
       "321     11        0       0       1.4             4.1               Hotel   \n",
       "\n",
       "     nnights  rating  \n",
       "0          1     4.4  \n",
       "1          1     3.9  \n",
       "2          1     3.7  \n",
       "3          1     4.0  \n",
       "4          1     3.9  \n",
       "..       ...     ...  \n",
       "317        1     3.4  \n",
       "318        1     5.0  \n",
       "319        1     4.3  \n",
       "320        1     4.4  \n",
       "321        1     3.2  \n",
       "\n",
       "[322 rows x 24 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Task 2\n",
    "## Organize neighbourhood name with loop\n",
    "import re\n",
    "\n",
    "for i, value in enumerate(df_cleaned['neighbourhood']):\n",
    "    df_cleaned.at[i, 'neighbourhood'] = re.sub(r'^\\d+\\.\\s*', '', value)\n",
    "\n",
    "df_cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "04ab4b4a-7d01-4e1b-8049-6da10780fa42",
   "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>country</th>\n",
       "      <th>city_actual</th>\n",
       "      <th>rating_count</th>\n",
       "      <th>center1label</th>\n",
       "      <th>center2label</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>price</th>\n",
       "      <th>city</th>\n",
       "      <th>stars</th>\n",
       "      <th>ratingta</th>\n",
       "      <th>...</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>weekend</th>\n",
       "      <th>holiday</th>\n",
       "      <th>distance</th>\n",
       "      <th>distance_alter</th>\n",
       "      <th>accommodation_type</th>\n",
       "      <th>nnights</th>\n",
       "      <th>rating</th>\n",
       "      <th>female_manager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>36.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Hernals</td>\n",
       "      <td>81</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>189.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Hernals</td>\n",
       "      <td>81</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>53.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Alsergrund</td>\n",
       "      <td>85</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>55.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Alsergrund</td>\n",
       "      <td>83</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>33.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Alsergrund</td>\n",
       "      <td>82</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>3.9</td>\n",
       "      <td>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>317</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>53.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Wieden</td>\n",
       "      <td>73</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>2.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Wieden</td>\n",
       "      <td>109</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>145.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Wieden</td>\n",
       "      <td>185</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3.6</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>112.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Wieden</td>\n",
       "      <td>100</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>169.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Wieden</td>\n",
       "      <td>58</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>322 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     country city_actual  rating_count center1label center2label  \\\n",
       "0    Austria      Vienna          36.0  City centre    Donauturm   \n",
       "1    Austria      Vienna         189.0  City centre    Donauturm   \n",
       "2    Austria      Vienna          53.0  City centre    Donauturm   \n",
       "3    Austria      Vienna          55.0  City centre    Donauturm   \n",
       "4    Austria      Vienna          33.0  City centre    Donauturm   \n",
       "..       ...         ...           ...          ...          ...   \n",
       "317  Austria      Vienna          53.0  City centre    Donauturm   \n",
       "318  Austria      Vienna           2.0  City centre    Donauturm   \n",
       "319  Austria      Vienna         145.0  City centre    Donauturm   \n",
       "320  Austria      Vienna         112.0  City centre    Donauturm   \n",
       "321  Austria      Vienna         169.0  City centre    Donauturm   \n",
       "\n",
       "    neighbourhood  price    city  stars  ratingta  ...  year  month  weekend  \\\n",
       "0         Hernals     81  Vienna    4.0       4.5  ...  2017     11        0   \n",
       "1         Hernals     81  Vienna    4.0       3.5  ...  2017     11        0   \n",
       "2      Alsergrund     85  Vienna    4.0       3.5  ...  2017     11        0   \n",
       "3      Alsergrund     83  Vienna    3.0       4.0  ...  2017     11        0   \n",
       "4      Alsergrund     82  Vienna    4.0       3.5  ...  2017     11        0   \n",
       "..            ...    ...     ...    ...       ...  ...   ...    ...      ...   \n",
       "317        Wieden     73  Vienna    3.0       3.0  ...  2017     11        0   \n",
       "318        Wieden    109  Vienna    3.0       3.0  ...  2017     11        0   \n",
       "319        Wieden    185  Vienna    5.0       4.0  ...  2017     11        0   \n",
       "320        Wieden    100  Vienna    4.0       4.5  ...  2017     11        0   \n",
       "321        Wieden     58  Vienna    3.0       3.0  ...  2017     11        0   \n",
       "\n",
       "     holiday distance  distance_alter  accommodation_type  nnights  rating  \\\n",
       "0          0      2.7             4.4           Apartment        1     4.4   \n",
       "1          0      1.7             3.8               Hotel        1     3.9   \n",
       "2          0      1.4             2.5               Hotel        1     3.7   \n",
       "3          0      1.7             2.5               Hotel        1     4.0   \n",
       "4          0      1.2             2.8               Hotel        1     3.9   \n",
       "..       ...      ...             ...                 ...      ...     ...   \n",
       "317        0      1.5             4.0               Hotel        1     3.4   \n",
       "318        0      1.5             3.8           Apartment        1     5.0   \n",
       "319        0      0.8             3.6               Hotel        1     4.3   \n",
       "320        0      1.0             3.7               Hotel        1     4.4   \n",
       "321        0      1.4             4.1               Hotel        1     3.2   \n",
       "\n",
       "     female_manager  \n",
       "0                 1  \n",
       "1                 1  \n",
       "2                 1  \n",
       "3                 0  \n",
       "4                 0  \n",
       "..              ...  \n",
       "317               1  \n",
       "318               0  \n",
       "319               0  \n",
       "320               1  \n",
       "321               1  \n",
       "\n",
       "[322 rows x 25 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Task 3\n",
    "## Create New Variable by Using \"lists\"\n",
    "import numpy as np\n",
    "\n",
    "a = [np.random.choice([0, 1]) for _ in range(322)]\n",
    "df_cleaned['female_manager'] = a\n",
    "df_cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "12ff05ac-4b0b-40a5-834e-8932f4996cf5",
   "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>country</th>\n",
       "      <th>city_actual</th>\n",
       "      <th>rating_count</th>\n",
       "      <th>center1label</th>\n",
       "      <th>center2label</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>price</th>\n",
       "      <th>city</th>\n",
       "      <th>stars</th>\n",
       "      <th>ratingta</th>\n",
       "      <th>...</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>weekend</th>\n",
       "      <th>holiday</th>\n",
       "      <th>distance</th>\n",
       "      <th>distance_alter</th>\n",
       "      <th>accommodation_type</th>\n",
       "      <th>nnights</th>\n",
       "      <th>rating</th>\n",
       "      <th>female_manager</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>36.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Hernals</td>\n",
       "      <td>81</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>Apartment</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>189.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Hernals</td>\n",
       "      <td>81</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>53.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Alsergrund</td>\n",
       "      <td>85</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>55.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Alsergrund</td>\n",
       "      <td>83</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>33.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Alsergrund</td>\n",
       "      <td>82</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>3.9</td>\n",
       "      <td>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>316</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>572.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Wieden</td>\n",
       "      <td>95</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>53.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Wieden</td>\n",
       "      <td>73</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>145.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Wieden</td>\n",
       "      <td>185</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>3.6</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>112.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Wieden</td>\n",
       "      <td>100</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>Austria</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>169.0</td>\n",
       "      <td>City centre</td>\n",
       "      <td>Donauturm</td>\n",
       "      <td>Wieden</td>\n",
       "      <td>58</td>\n",
       "      <td>Vienna</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2017</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>4.1</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>1</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>311 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     country city_actual  rating_count center1label center2label  \\\n",
       "0    Austria      Vienna          36.0  City centre    Donauturm   \n",
       "1    Austria      Vienna         189.0  City centre    Donauturm   \n",
       "2    Austria      Vienna          53.0  City centre    Donauturm   \n",
       "3    Austria      Vienna          55.0  City centre    Donauturm   \n",
       "4    Austria      Vienna          33.0  City centre    Donauturm   \n",
       "..       ...         ...           ...          ...          ...   \n",
       "316  Austria      Vienna         572.0  City centre    Donauturm   \n",
       "317  Austria      Vienna          53.0  City centre    Donauturm   \n",
       "319  Austria      Vienna         145.0  City centre    Donauturm   \n",
       "320  Austria      Vienna         112.0  City centre    Donauturm   \n",
       "321  Austria      Vienna         169.0  City centre    Donauturm   \n",
       "\n",
       "    neighbourhood  price    city  stars  ratingta  ...  year  month  weekend  \\\n",
       "0         Hernals     81  Vienna    4.0       4.5  ...  2017     11        0   \n",
       "1         Hernals     81  Vienna    4.0       3.5  ...  2017     11        0   \n",
       "2      Alsergrund     85  Vienna    4.0       3.5  ...  2017     11        0   \n",
       "3      Alsergrund     83  Vienna    3.0       4.0  ...  2017     11        0   \n",
       "4      Alsergrund     82  Vienna    4.0       3.5  ...  2017     11        0   \n",
       "..            ...    ...     ...    ...       ...  ...   ...    ...      ...   \n",
       "316        Wieden     95  Vienna    4.0       4.0  ...  2017     11        0   \n",
       "317        Wieden     73  Vienna    3.0       3.0  ...  2017     11        0   \n",
       "319        Wieden    185  Vienna    5.0       4.0  ...  2017     11        0   \n",
       "320        Wieden    100  Vienna    4.0       4.5  ...  2017     11        0   \n",
       "321        Wieden     58  Vienna    3.0       3.0  ...  2017     11        0   \n",
       "\n",
       "     holiday distance  distance_alter  accommodation_type  nnights  rating  \\\n",
       "0          0      2.7             4.4           Apartment        1     4.4   \n",
       "1          0      1.7             3.8               Hotel        1     3.9   \n",
       "2          0      1.4             2.5               Hotel        1     3.7   \n",
       "3          0      1.7             2.5               Hotel        1     4.0   \n",
       "4          0      1.2             2.8               Hotel        1     3.9   \n",
       "..       ...      ...             ...                 ...      ...     ...   \n",
       "316        0      1.5             3.9               Hotel        1     4.1   \n",
       "317        0      1.5             4.0               Hotel        1     3.4   \n",
       "319        0      0.8             3.6               Hotel        1     4.3   \n",
       "320        0      1.0             3.7               Hotel        1     4.4   \n",
       "321        0      1.4             4.1               Hotel        1     3.2   \n",
       "\n",
       "     female_manager  \n",
       "0                 1  \n",
       "1                 1  \n",
       "2                 1  \n",
       "3                 0  \n",
       "4                 0  \n",
       "..              ...  \n",
       "316               1  \n",
       "317               1  \n",
       "319               0  \n",
       "320               1  \n",
       "321               1  \n",
       "\n",
       "[311 rows x 25 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Task 6\n",
    "## Filter observations\n",
    "df_filtered = df_cleaned[df_cleaned['rating_count'] >= 10]\n",
    "\n",
    "df_filtered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "7c1967fd-5bea-48c8-af48-c3726f657b73",
   "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>hotel_id</th>\n",
       "      <th>stars</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>price</th>\n",
       "      <th>rating_count</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21894</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Hernals</td>\n",
       "      <td>81</td>\n",
       "      <td>36.0</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21897</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Hernals</td>\n",
       "      <td>81</td>\n",
       "      <td>189.0</td>\n",
       "      <td>3.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21901</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Alsergrund</td>\n",
       "      <td>85</td>\n",
       "      <td>53.0</td>\n",
       "      <td>3.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21902</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Alsergrund</td>\n",
       "      <td>83</td>\n",
       "      <td>55.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21903</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Alsergrund</td>\n",
       "      <td>82</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3.9</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>22402</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Wieden</td>\n",
       "      <td>95</td>\n",
       "      <td>572.0</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>22403</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Wieden</td>\n",
       "      <td>73</td>\n",
       "      <td>53.0</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>22406</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Wieden</td>\n",
       "      <td>185</td>\n",
       "      <td>145.0</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>22407</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Wieden</td>\n",
       "      <td>100</td>\n",
       "      <td>112.0</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>22408</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Wieden</td>\n",
       "      <td>58</td>\n",
       "      <td>169.0</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>311 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     hotel_id  stars neighbourhood  price  rating_count  rating\n",
       "0       21894    4.0       Hernals     81          36.0     4.4\n",
       "1       21897    4.0       Hernals     81         189.0     3.9\n",
       "2       21901    4.0    Alsergrund     85          53.0     3.7\n",
       "3       21902    3.0    Alsergrund     83          55.0     4.0\n",
       "4       21903    4.0    Alsergrund     82          33.0     3.9\n",
       "..        ...    ...           ...    ...           ...     ...\n",
       "316     22402    4.0        Wieden     95         572.0     4.1\n",
       "317     22403    3.0        Wieden     73          53.0     3.4\n",
       "319     22406    5.0        Wieden    185         145.0     4.3\n",
       "320     22407    4.0        Wieden    100         112.0     4.4\n",
       "321     22408    3.0        Wieden     58         169.0     3.2\n",
       "\n",
       "[311 rows x 6 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Filter variables\n",
    "df_less_variables = df_filtered[['hotel_id', 'stars', 'neighbourhood', 'price', 'rating_count', 'rating']]\n",
    "\n",
    "df_less_variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "4803928d-1341-4ce7-bf17-564baa208528",
   "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>hotel_id</th>\n",
       "      <th>stars</th>\n",
       "      <th>neighbourhood</th>\n",
       "      <th>price</th>\n",
       "      <th>rating_count</th>\n",
       "      <th>rating</th>\n",
       "      <th>log_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21894</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Hernals</td>\n",
       "      <td>81</td>\n",
       "      <td>36.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>4.394449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21897</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Hernals</td>\n",
       "      <td>81</td>\n",
       "      <td>189.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>4.394449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21901</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Alsergrund</td>\n",
       "      <td>85</td>\n",
       "      <td>53.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>4.442651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21902</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Alsergrund</td>\n",
       "      <td>83</td>\n",
       "      <td>55.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.418841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21903</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Alsergrund</td>\n",
       "      <td>82</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>4.406719</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>22402</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Wieden</td>\n",
       "      <td>95</td>\n",
       "      <td>572.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.553877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>22403</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Wieden</td>\n",
       "      <td>73</td>\n",
       "      <td>53.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.290459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>22406</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Wieden</td>\n",
       "      <td>185</td>\n",
       "      <td>145.0</td>\n",
       "      <td>4.3</td>\n",
       "      <td>5.220356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>22407</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Wieden</td>\n",
       "      <td>100</td>\n",
       "      <td>112.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>4.605170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>22408</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Wieden</td>\n",
       "      <td>58</td>\n",
       "      <td>169.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.060443</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>311 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     hotel_id  stars neighbourhood  price  rating_count  rating  log_price\n",
       "0       21894    4.0       Hernals     81          36.0     4.4   4.394449\n",
       "1       21897    4.0       Hernals     81         189.0     3.9   4.394449\n",
       "2       21901    4.0    Alsergrund     85          53.0     3.7   4.442651\n",
       "3       21902    3.0    Alsergrund     83          55.0     4.0   4.418841\n",
       "4       21903    4.0    Alsergrund     82          33.0     3.9   4.406719\n",
       "..        ...    ...           ...    ...           ...     ...        ...\n",
       "316     22402    4.0        Wieden     95         572.0     4.1   4.553877\n",
       "317     22403    3.0        Wieden     73          53.0     3.4   4.290459\n",
       "319     22406    5.0        Wieden    185         145.0     4.3   5.220356\n",
       "320     22407    4.0        Wieden    100         112.0     4.4   4.605170\n",
       "321     22408    3.0        Wieden     58         169.0     3.2   4.060443\n",
       "\n",
       "[311 rows x 7 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Create transformations of variables\n",
    "import numpy as np\n",
    "\n",
    "df_new = df_less_variables.copy()\n",
    "\n",
    "df_new['log_price'] = np.log(df_log['price'])\n",
    "\n",
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "6f01becc-2fd3-4841-892f-62f6a0313163",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Task 7\n",
    "## Save Data\n",
    "df_new.to_csv('df_new.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "806d8ce1-05d8-478c-8ecd-f07b2a25fe50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/uco/Library/CloudStorage/OneDrive-CentralEuropeanUniversity/09. CEU_Fall/03. Coding for Economists/Assignment-Coding-for-Economists2024/data/df_new.csv'"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Task 1\n",
    "## Move the new data CSV file to a different folder\n",
    "import shutil\n",
    "import os\n",
    "\n",
    "current_directory = '/Users/uco/Library/CloudStorage/OneDrive-CentralEuropeanUniversity/09. CEU_Fall/03. Coding for Economists/Assignment-Coding-for-Economists2024'\n",
    "\n",
    "source_path = os.path.join(current_directory, 'df_new.csv')\n",
    "\n",
    "destination_path = os.path.join(current_directory, 'data', 'df_new.csv')\n",
    "\n",
    "shutil.move(source_path, destination_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "4c3ce89e-1c1b-46d3-9753-d94af4aae6ab",
   "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>hotel_id</th>\n",
       "      <th>stars</th>\n",
       "      <th>price</th>\n",
       "      <th>rating_count</th>\n",
       "      <th>rating</th>\n",
       "      <th>log_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>311.000000</td>\n",
       "      <td>311.000000</td>\n",
       "      <td>311.000000</td>\n",
       "      <td>311.000000</td>\n",
       "      <td>311.000000</td>\n",
       "      <td>311.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>22158.578778</td>\n",
       "      <td>3.543408</td>\n",
       "      <td>122.990354</td>\n",
       "      <td>193.241158</td>\n",
       "      <td>4.050804</td>\n",
       "      <td>4.680604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>145.947713</td>\n",
       "      <td>0.760229</td>\n",
       "      <td>86.274486</td>\n",
       "      <td>197.944786</td>\n",
       "      <td>0.415651</td>\n",
       "      <td>0.471075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>21894.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.295837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>22035.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>3.900000</td>\n",
       "      <td>4.382027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>22159.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>4.100000</td>\n",
       "      <td>4.605170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>22288.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>261.000000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>4.927254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>22408.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1012.000000</td>\n",
       "      <td>1541.000000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>6.919684</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           hotel_id       stars        price  rating_count      rating  \\\n",
       "count    311.000000  311.000000   311.000000    311.000000  311.000000   \n",
       "mean   22158.578778    3.543408   122.990354    193.241158    4.050804   \n",
       "std      145.947713    0.760229    86.274486    197.944786    0.415651   \n",
       "min    21894.000000    1.000000    27.000000     10.000000    2.000000   \n",
       "25%    22035.500000    3.000000    80.000000     69.000000    3.900000   \n",
       "50%    22159.000000    4.000000   100.000000    125.000000    4.100000   \n",
       "75%    22288.500000    4.000000   138.000000    261.000000    4.400000   \n",
       "max    22408.000000    5.000000  1012.000000   1541.000000    4.900000   \n",
       "\n",
       "        log_price  \n",
       "count  311.000000  \n",
       "mean     4.680604  \n",
       "std      0.471075  \n",
       "min      3.295837  \n",
       "25%      4.382027  \n",
       "50%      4.605170  \n",
       "75%      4.927254  \n",
       "max      6.919684  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Task 8 \n",
    "## Create a summary statistics table\n",
    "\n",
    "df_new.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "d7dd5e2a-567a-4c34-9352-1ed3a2a6bb99",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from plotnine import *\n",
    "import warnings\n",
    "\n",
    "%matplotlib inline\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "7f44b4be-d872-4cc7-b229-9165371a5345",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABQAAAAPACAYAAABq3NR5AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAB7CAAAewgFu0HU+AABRVklEQVR4nO3de5ycdX33//fsZBOSTTbZHEk4n8PhpgUCBO+gcrAqIEittIRUwRPYlghqPfw8AMJdCvSu3CgWKYhVoFoRQSsVxWCQQwqEqoRjAsVgyIEkSxIWSMju/P7AbEGyIQmTDPPN8/l4+HDZ67omnx0mXyavXHNdlVqtVgsAAAAAUKSWRg8AAAAAAGw6AiAAAAAAFEwABAAAAICCCYAAAAAAUDABEAAAAAAKJgACAAAAQMEEQAAAAAAomAAIAAAAAAUTAAEAAACgYAIgAAAAABRMAAQAAACAggmAAAAAAFCwfo0egI2zePHiRo9QpI6OjlSr1XR3d6ezs7PR48AGqVar6ejoSGdnZ7q7uxs9Dqw3ay/NzNpLs7L20qysuzSzeq29I0eO3OBjnAEIAAAAAAUTAAEAAACgYAIgAAAAABRMAAQAAACAggmAAAAAAFAwARAAAAAACiYAAgAAAEDBBEAAAAAAKJgACAAAAAAFEwABAAAAoGACIAAAAAAUTAAEAAAAgIIJgAAAAABQMAEQAAAAAAomAAIAAABAwQRAAAAAACiYAAgAAAAABRMAAQAAAKBgAiAAAAAAFEwABAAAAICCCYAAAAAAUDABEAAAAAAKJgACAAAAQMEEQAAAAAAomAAIAAAAAAUTAAEAAACgYAIgAAAAABRMAAQAAACAggmAAAAAAFAwARAAAAAACiYAAgAAAEDBBEAAAAAAKJgACAAAAAAFEwABAAAAoGACIAAAAAAUTAAEAAAAgIIJgAAAAABQMAEQAAAAAArWr9EDAAAAbCmmTp3a6BHekC655JJGjwBQNGcAAgAAAEDBBEAAAAAAKJgACAAAAAAFEwABAAAAoGACIAAAAAAUTAAEAAAAgIIJgAAAAABQMAEQAAAAAAomAAIAAABAwQRAAAAAACiYAAgAAAAABRMAAQAAAKBgAiAAAAAAFEwABAAAAICCCYAAAAAAUDABEAAAAAAKJgACAAAAQMEEQAAAAAAomAAIAAAAAAUTAAEAAACgYAIgAAAAABRMAAQAAACAggmAAAAAAFAwARAAAAAACiYAAgAAAEDBBEAAAAAAKJgACAAAAAAFEwABAAAAoGACIAAAAAAUTAAEAAAAgIIJgAAAAABQMAEQAAAAAAomAAIAAABAwQRAAAAAACiYAAgAAAAABRMAAQAAAKBgAiAAAAAAFEwABAAAAICCCYAAAAAAUDABEAAAAAAK1q/RA7BxqtVqo0conueYZrPmNeu1SzPz+qXZWHuhPvweYn1ZdynF5n4NC4BNqqOjo9EjFK1arXqOaVrt7e2NHgE2irWXZmbthdfH+s+Gsu7SzBrxvlcAbFKdnZ2NHqFI7e3tqVar6e7uzvLlyxs9DmyQarWa9vb2LF++PN3d3Y0eB9abtZdmZu2F+vDnG9aXdZdmVq/3vRsTDwXAJmWh2/Q8xzSr7u5ur1+altcuzcraC6+P3z9sKOsuzW5zv37dBAQAAAAACiYAAgAAAEDBBEAAAAAAKJgACAAAAAAFEwABAAAAoGACIAAAAAAUTAAEAAAAgIIJgAAAAABQMAEQAAAAAAomAAIAAABAwQRAAAAAACiYAAgAAAAABRMAAQAAAKBgAiAAAAAAFEwABAAAAICCCYAAAAAAUDABEAAAAAAKJgACAAAAQMEEQAAAAAAomAAIAAAAAAUTAAEAAACgYAIgAAAAABRMAAQAAACAggmAAAAAAFAwARAAAAAACiYAAgAAAEDBBEAAAAAAKJgACAAAAAAFEwABAAAAoGACIAAAAAAUTAAEAAAAgIIJgAAAAABQMAEQAAAAAAomAAIAAABAwQRAAAAAACiYAAgAAAAABRMAAQAAAKBgAiAAAAAAFEwABAAAAICCCYAAAAAAUDABEAAAAAAKJgACAAAAQMEEQAAAAAAomAAIAAAAAAUTAAEAAACgYAIgAAAAABRMAAQAAACAggmAAAAAAFAwARAAAAAACiYAAgAAAEDBBEAAAAAAKJgACAAAAAAFEwABAAAAoGACIAAAAAAUTAAEAAAAgIIJgAAAAABQMAEQAAAAAAomAAIAAABAwQRAAAAAACiYAAgAAAAABRMAAQAAAKBgAiAAAAAAFEwABAAAAICCCYAAAAAAUDABEAAAAAAKJgACAAAAQMEEQAAAAAAomAAIAAAAAAUTAAEAAACgYAIgAAAAABRMAAQAAACAgvVr9ACN8thjj+XMM8/MO97xjvzVX/1Vn/s98MADuf766/P4449nxYoVGT58eA444IC8+93vzpgxY9Z6zKpVq/LTn/4006ZNy1NPPZWWlpaMGzcub33rW/POd74z1Wp1U/1YAAAAAPAKW2wA/OlPf/qa+9x000257LLLkiQDBgzIsGHDsnjx4vz4xz/O9OnT8/nPfz577bXXK45ZuXJlzjnnnMyaNStJMnTo0LS0tOTRRx/No48+mjvuuCNnn312BgwYUP8fCgAAAAD+wBYXADs7O/PTn/40N9988zr3mz17dr7+9a8nSU444YSccMIJ6d+/f1asWJGvfe1rueOOO3LhhRfmq1/9agYPHtx73FVXXZVZs2Zl6NCh+du//dvsu+++SZJHHnkk559/fh544IFcddVVOe200zbdDwkAAAAAv7fFXAPw3/7t3/KBD3wg73//+3PNNdekp6dnnftfc801qdVqOfDAAzNlypT0798/STJkyJCcccYZGTp0aJYuXZqf/OQnvcc8/fTTvWcWfvSjH+2Nf0myxx575MMf/nCS5Oabb87ixYvr/SMCAAAAwKtsMQFw2bJlWb16dYYNG5Zhw4b1Br216erqyq9+9askydFHH/2q7QMGDMj++++fJLnzzjt7v/+f//mfWb16dUaMGJGJEye+6rgJEyaktbU13d3dmTFjxuv8iQAAAADgtW0xAfDDH/5wvvWtb/X+b9KkSX3uO2vWrPT09KSlpSV77733WvdZc3bf448/nhdffDFJ8pvf/CZJsvfee6el5dVP7YABA7LHHnskeekjwQAAAACwqW0xAXBDzJ07N0kycuTIPm/WMWrUqCRJT09PFi5cmCR58sknkyTjxo3r87HXHLdgwYK6zQsAAAAAfdnibgKyPp599tkkL93Bty9Dhgzp/bqrqytJsmLFiiTJsGHDXvO4Ncf05eqrr861117b5/YTTzwxkydPXudjsOHWnLnZ0tKSjo6OBk8DG6ZSqSR5ae2q1WoNngbWn7WXZmbthfqw/rO+rLs0s0a+7xUA12JNnGttbe1zn6222qr36+7u7g0+bvXq1a85w6JFi/rc/txzz6Vara7zMdh4lUrF80vTWtslCKAZWHtpZtZeeH2s/2wo6y7NrBHvewXAtVjzL2Fddwpec92/JL0fE65Wq+nu7l6v4/r6aPEabW1tGT16dJ/bBw0a1BseqZ+WlpZUKpXUarXXvFM0vNFUKpW0tLSkp6fH34bSVKy9NDNrL9SHP9uwvqy7NLN6ve/dmHgoAK7Fmo/prvko8Nq8fNua0zaHDBmSJUuWrPO4NWcJDh8+fJ0zTJkyJVOmTOlz++LFi9PZ2bnOx2DDdXR0pFqtpqenx/NL06lWq+no6MiyZcu8iaapWHtpZtZeqA/rP+vLukszq9f73pEjR27wMc6ZXYs1N/FYtGhRnwvKmht/tLW19QbANcfNnz+/z8dec9y2225bt3kBAAAAoC8C4FrstddeSZJVq1blkUceWes+Dz30UJJk77337r0I6d57750kuf/++9d6zOrVq/Poo48mSfbZZ5+6zgwAAAAAayMArsXWW2+d3XffPUnyox/96FXbly9fnl/+8pdJksMOO6z3+5MmTUqlUsn8+fNz7733vuq4adOm5fnnn8+QIUMyYcKETTQ9AAAAAPwPAbAPJ510UiqVSu64445897vf7f0o8IIFC3Luueemq6sr48ePzyGHHNJ7zPbbb583v/nNSZJLLrkkDz/8cJKkVqvlzjvvzBVXXJHkpev7retOwQAAAABQL24C0of99tsvJ510Uq6++upcc801+f73v58hQ4Zk8eLFqdVq2XrrrfPxj3/8Vbce/+hHP5p58+Zlzpw5+dSnPpXhw4fnxRdfzIoVK5IkRxxxRN7xjnc04kcCAAAAYAskAK7DCSeckF133TU//OEPM2fOnCxbtizjxo3Lm970phx33HFpb29/1TGDBg3KBRdckBtuuCG33XZbFixYkP79+2evvfbKO9/5zrzlLW9pwE8CAAAAwJZqiw2AZ5xxRs4444zX3G///ffP/vvvv0GP3dramve+971573vfu5HTAQAAAEB9uAYgAAAAABRMAAQAAACAggmAAAAAAFAwARAAAAAACiYAAgAAAEDBBEAAAAAAKJgACAAAAAAFEwABAAAAoGACIAAAAAAUTAAEAAAAgIIJgAAAAABQMAEQAAAAAAomAAIAAABAwQRAAAAAACiYAAgAAAAABRMAAQAAAKBgAiAAAAAAFEwABAAAAICCCYAAAAAAUDABEAAAAAAKJgACAAAAQMEEQAAAAAAomAAIAAAAAAUTAAEAAACgYAIgAAAAABRMAAQAAACAggmAAAAAAFAwARAAAAAACiYAAgAAAEDBBEAAAAAAKJgACAAAAAAFEwABAAAAoGACIAAAAAAUTAAEAAAAgIIJgAAAAABQMAEQAAAAAAomAAIAAABAwQRAAAAAACiYAAgAAAAABRMAAQAAAKBgAiAAAAAAFEwABAAAAICCCYAAAAAAUDABEAAAAAAKJgACAAAAQMEEQAAAAAAomAAIAAAAAAUTAAEAAACgYAIgAAAAABRMAAQAAACAggmAAAAAAFAwARAAAAAACiYAAgAAAEDBBEAAAAAAKJgACAAAAAAFEwABAAAAoGACIAAAAAAUTAAEAAAAgIIJgAAAAABQMAEQAAAAAAomAAIAAABAwQRAAAAAACiYAAgAAAAABRMAAQAAAKBgAiAAAAAAFEwABAAAAICCCYAAAAAAUDABEAAAAAAKJgACAAAAQMEEQAAAAAAomAAIAAAAAAXr1+gB2DjVarXRIxTPc0yzWfOa9dqlmXn90mysvVAffg+xvqy7lGJzv4YFwCbV0dHR6BGKVq1WPcc0rfb29kaPABvF2kszs/bC62P9Z0NZd2lmjXjfKwA2qc7OzkaPUKT29vZUq9V0d3dn+fLljR4HNki1Wk17e3uWL1+e7u7uRo8D683aSzOz9kJ9+PMN68u6SzOr1/vejYmHAmCTstBtep5jmlV3d7fXL03La5dmZe2F18fvHzaUdZdmt7lfv24CAgAAAAAFEwABAAAAoGACIAAAAAAUTAAEAAAAgIIJgAAAAABQMAEQAAAAAAomAAIAAABAwQRAAAAAACiYAAgAAAAABRMAAQAAAKBgAiAAAAAAFEwABAAAAICCCYAAAAAAUDABEAAAAAAKJgACAAAAQMEEQAAAAAAomAAIAAAAAAUTAAEAAACgYAIgAAAAABRMAAQAAACAggmAAAAAAFAwARAAAAAACiYAAgAAAEDBBEAAAAAAKJgACAAAAAAFEwABAAAAoGACIAAAAAAUTAAEAAAAgIIJgAAAAABQMAEQAAAAAAomAAIAAABAwQRAAAAAACiYAAgAAAAABRMAAQAAAKBgAiAAAAAAFEwABAAAAICCCYAAAAAAUDABEAAAAAAKJgACAAAAQMEEQAAAAAAomAAIAAAAAAUTAAEAAACgYAIgAAAAABRMAAQAAACAggmAAAAAAFAwARAAAAAACiYAAgAAAEDBBEAAAAAAKJgACAAAAAAFEwABAAAAoGACIAAAAAAUTAAEAAAAgIIJgAAAAABQMAEQAAAAAAomAAIAAABAwQRAAAAAACiYAAgAAAAABRMAAQAAAKBgAiAAAAAAFEwABAAAAICCCYAAAAAAUDABEAAAAAAKJgACAAAAQMEEQAAAAAAomAAIAAAAAAUTAAEAAACgYAIgAAAAABRMAAQAAACAggmAAAAAAFAwARAAAAAACtav0QO8US1cuDAf/vCH13v/ffbZJ3/3d3+XJPnqV7+aBx54YJ37f+xjH8v48eNf14wAAAAA8FoEwD7069cv22yzzWvut3jx4qxcuTLt7e2933vggQcyb968dR73wgsvvO4ZAQAAAOC1CIB9GDFiRP7pn/5pnfvMnj07n/70pzNy5MicdtppSZLu7u4sXLgwo0aNypVXXrk5RgUAAACAPrkG4Ebq6urKRRddlJ6ennziE5/IsGHDkiRPP/10Vq9enXHjxjV2QAAAAACIALjRLr/88ixYsCDHHnts9t57797vz58/P0kyduzYRo0GAAAAAL0EwI0wa9as3HrrrRk5cmROPPHEV2wTAAEAAAB4I3ENwA20evXq3msDnnLKKRk4cOArtq8JgEny5S9/Ob/+9a+zfPnytLW1Zffdd8/RRx+d/ffff7PODAAAAMCWSwDcQLfcckuefPLJ7LDDDpk0adKrtq8JgFdddVWSZOjQoRkyZEg6Oztzzz335J577slRRx2VU089NZVKZbPODgAAAMCWRwDcAC+++GK+973vJUlOOOGEtQa8NQFw7733zqmnnpodd9wxSbJkyZJ8+9vfzrRp03LTTTdl1KhRec973tPnr3X11Vfn2muv7XP7iSeemMmTJ7+On4a1aWlp6f3/jo6OBk8DG2bNmjR06NDUarUGTwPrz9pLM7P2Qn1Y/1lf1l2aWSPf9wqAG+DWW2/N008/nVGjRuVNb3rTWveZMmVKuru7c/DBB6e1tbX3+yNGjMgZZ5yR7u7uTJ8+Pdddd12OO+649Ou39n8FXV1dWbRoUZ+zPPfcc6lWq6/vB6JPlUrF80vTWvMfFWg21l6ambUXXh/rPxvKuksza8T7XgFwA/zHf/xHkuQd73hHn/+iDjnkkHU+xtvf/vZMnz49XV1defLJJ7PTTjutdb+2traMHj26z8cZNGhQuru713Ny1ldLS0sqlUpqtVp6enoaPQ5skEqlkpaWlvT09PjbUJqKtZdmZu2F+vBnG9aXdZdmVq/3vRsTDwXA9TR79uw89thjqVQqOfzwwzf6cYYOHdr79apVq/rcb8qUKZkyZUqf2xcvXpzOzs6NnoO16+joSLVaTU9Pj+eXplOtVtPR0ZFly5Z5E01TsfbSzKy9UB/Wf9aXdZdmVq/3vSNHjtzgYwTA9XTbbbclSfbcc8+MGDFirfvceeedmTlzZrbffvscd9xxa93nySef7P16zJgx9R8UAAAAAF5GAFxP//mf/5kkOfDAA/vcZ+XKlfnZz36Wtra2HHHEERk8ePCr9vnhD3+YJBk/fnyGDRu2SWYFAAAAgDVcNXM9/O53v8uCBQuSvHR3375MnDgxHR0d6erqyrnnnvuKs/1WrFiRr33ta3nwwQdTqVTW+fFeAAAAAKgXZwCuh0ceeSRJ0traml122aXP/QYOHJhPf/rTOe+88/LQQw/lr//6r9PR0ZHW1tYsXrw4PT09qVQq+eAHP5h99913c40PAAAAwBZMAFwPs2fPTpLsuOOOaW1tXee+e+21Vy699NLceOONueeee7JgwYJUKpWMHj06e+21V4455pjsuuuum2NsAAAAABAA18dpp52W0047bb337+joyMknn5yTTz550w0FAAAAAOvBNQABAAAAoGACIAAAAAAUTAAEAAAAgIIJgAAAAABQMAEQAAAAAAomAAIAAABAwQRAAAAAACiYAAgAAAAABRMAAQAAAKBgAiAAAAAAFEwABAAAAICCCYAAAAAAUDABEAAAAAAKJgACAAAAQMEEQAAAAAAomAAIAAAAAAUTAAEAAACgYAIgAAAAABRMAAQAAACAggmAAAAAAFAwARAAAAAACiYAAgAAAEDBBEAAAAAAKJgACAAAAAAFEwABAAAAoGACIAAAAAAUTAAEAAAAgIIJgAAAAABQMAEQAAAAAAomAAIAAABAwQRAAAAAACiYAAgAAAAABRMAAQAAAKBgAiAAAAAAFEwABAAAAICCCYAAAAAAUDABEAAAAAAKJgACAAAAQMEEQAAAAAAomAAIAAAAAAUTAAEAAACgYAIgAAAAABRMAAQAAACAggmAAAAAAFAwARAAAAAACiYAAgAAAEDBBEAAAAAAKJgACAAAAAAFEwABAAAAoGD9Gj0AAAAA8GpTp05t9AhvSNdee22jR4Cm4wxAAAAAACiYAAgAAAAABRMAAQAAAKBgAiAAAAAAFEwABAAAAICCCYAAAAAAUDABEAAAAAAKVrcA2NLSkn79+uXyyy/foOP+6Z/+KdVqNfvss0+9RgEAAAAAfq+uZwDWarUNPuaZZ55JrVbL448/Xs9RAAAAAIAk/TbmoJ///Of5+c9/vtZtN9xwQ5544on1epwVK1bk29/+dpKkUqlszCgAAAAAwDpsVAC8/fbbc8EFF7zq+7VaLTfffHNuvvnmDXq8SqWSgw46aGNGAQAAAADWYaMCYNL3x3035mPAEyZMyKWXXrqxowAAAAAAfdioAHjyySfnrW99a+8/12q1HH744alUKjnzzDPzrne9a70ep3///tl2222z3XbbbcwYAAAAAMBr2KgAuMMOO2SHHXZY67bdd989b3nLW17XUAAAAABAfWz0R4D/0FVXXZUkOeSQQ+r1kAAAAADA61S3APj+97+/Xg8FAAAAANRJ3QLgy/X09GTOnDlZuHBhXnjhhfW+Mcif/MmfbIpxAAAAAGCLVdcAuGLFinz2s5/Nt7/97Tz77LMbdGylUsnq1avrOQ4AAAAAbPHqFgC7urpyyCGH5KGHHlrvM/4AAAAAgE2rbgHwoosuyoMPPphKpZJKpZKDDz44EyZMyPDhw1OpVOr1ywAAAAAAG6BuAfD6669PkgwcODA//OEPc/jhh9froQEAAACAjVS3APjEE0+kUqnk5JNPFv82g2q12ugRiuc5ptmsec167dLMvH5pNtZeqA+/h9hQXjM0u839Gq5bAFzzMd8999yzXg/JOnR0dDR6hKJVq1XPMU2rvb290SPARrH20sysvfD6WP/ZUNZdmlkj3vfWLQDuvPPO+c1vfpNnnnmmXg/JOnR2djZ6hCK1t7enWq2mu7s7y5cvb/Q4sEGq1Wra29uzfPnydHd3N3ocWG/WXpqZtRfqw59v2FDWXZpRvd73bkw8rFsAPO644/LrX/86N954Yz73uc/V62Hpg4Vu0/Mc06y6u7u9fmlaXrs0K2svvD5+/7ChrLs0u839+m2p1wOdeeaZ2XrrrTNz5sxcdNFF9XpYAAAAAOB1qFsAHDp0aG644YYMHTo0n/nMZ/LhD384jzzySL0eHgAAAADYCHX7CPCaO/8OHTo0zzzzTL7xjW/kG9/4RgYOHJhhw4alf//+6zy+Uqnkscceq9c4AAAAAEDqGAB/8Ytf9N4JOElqtVqS5Lnnnstzzz33mse//FgAAAAAoD7qFgDf/OY3i3gAAAAA8AZT1zMAAQAAAIA3lrrdBAQAAAAAeOMRAAEAAACgYAIgAAAAABSsbtcArFarr+v4SqWS1atX12kaAAAAACCpYwCs1Wr1eigAAAAAoE7qFgDf//73r3N7rVbLCy+8kIULF2bWrFlZsmRJkmTo0KE5/fTT09bWVq9RAAAAAIDfq1sAvOqqqzZo/5///Of59Kc/nfvuuy833HBDpk2bVq9RAAAAAIDfa9hNQI444ojcfvvtedOb3pQHHnggH/nIRxo1CgAAAAAUq6F3Ad5qq63y93//96nVarnxxhvzyCOPNHIcAAAAAChOQwNgkhx88MFpaXlpjOnTpzd4GgAAAAAoS8MDYLVa7f16zY1BAAAAAID6aHgAvPnmm9PT05MkGTFiRIOnAQAAAICyNCwArlq1Ktdff30++MEP9n7vgAMOaNQ4AAAAAFCkfvV6oHHjxq33vqtXr05nZ2fvmX+VSiUHHnigAAgAAAAAdVa3ALhgwYJUKpXUarUNPnb33XfPd77znXqNAgAAAAD8Xt0C4Pbbb59KpbJe+7a0tGT48OHZbrvtcvTRR2fy5MkZOHBgvUYBAAAAAH6vbgHwiSeeqNdDAQAAAAB10vC7AAMAAAAAm44ACAAAAAAF2yQBsFar5d/+7d/yl3/5lxk/fnyGDx+eAQMGZNSoUfmjP/qjfOADH8j111+f1atXb4pfHgAAAAD4vbpdA3CNX//61/nzP//zzJ49u/d7a+4MvGTJkixdujSzZs3Kv/zLv2Ts2LG58MILM3ny5HqPAQAAAACkzmcAzpw5M5MmTcrs2bNTq9VSq9XS1taWXXbZJfvtt1/GjRuXlpaW3m1PPfVU/vIv/zKf+MQn6jkGAAAAAPB7dQuAL7zwQt7znvekq6srtVotJ598cu68884sX748s2fPzsyZM/O73/0uK1euzM9//vN8+MMfTltbW2q1Wi6++OL8y7/8S71GAQAAAAB+r24B8PLLL8/cuXNTqVRy6aWX5hvf+EYmTpz46l+wpSWHHXZYvv71r+fXv/51tttuu9RqtXzuc5+r1ygAAAAAwO/VLQB+//vfT5JMmDAhH/3oR9frmJ133jlf+cpXkiTz58/PjBkz6jUOAAAAAJA6BsAHH3wwlUol73znOzfouKOOOirVajVJMmvWrHqNAwAAAACkjgFwxYoVSZIRI0Zs0HH9+vVLe3t7kpfuEgwAAAAA1E/dAuDo0aOTJE8++eQGHffCCy9k2bJlSZK2trZ6jQMAAAAApI4B8KCDDkqtVsv3vve9rFy5cr2Pu+6669LT05Mk2WOPPeo1DgAAAACQOgbAk046KUkyd+7cTJkyJc8///xrHjNr1qx88pOfTJJ0dHTkrW99a73GAQAAAABSxwB4/PHH5/DDD0+tVsv111+f3XbbLeeff35+85vfpLu7u3e/VatWZcaMGZk6dWoOOuigLFq0KJVKJWeddVZaW1vrNQ4AAAAAkKRfPR/s+9//fo488sjMnDkz8+fPz+c///l8/vOfT7VazdChQ1Or1bJs2bLej/zWarUkySmnnJLTTz+9nqMAAAAAAKnjGYBJMnTo0Nxxxx359Kc/nYEDB6ZWq6VWq2X16tVZsmRJli5dmu7u7t7vDxs2LF/96ldzxRVX1HMMAAAAAOD36noGYJL0798/559/fj7zmc/kuuuuyx133JFHHnkknZ2dqdVq6ejoyJ577pm3vOUtee9735uBAwfWewQAAAAA4PfqHgCTl67zd+edd6azszPf+MY3XrX9sssuy9Zbb+2afwAAAACwidX1I8BJ8nd/93cZPXp0jjnmmJx11llr3eeCCy7IO9/5zowZMybnn39+Vq9eXe8xAAAAAIDUOQB+6EMfyhe+8IWsWLGi9zp/a7NmW2dnZz7/+c/n6KOPzosvvljPUQAAAACA1DEA/vznP883vvGN1Gq1bLXVVvnCF76Qu+++e637/sd//EfOOuustLe3p1ar5ZZbbsk555xTr1EAAAAAgN+rWwD853/+5yRJW1tb7r777pxzzjnZZ5991rrvnnvumbPOOiv3339/9tlnn9RqtVxyySVZsWJFvcYBAAAAAFLHADhjxoxUKpWcdNJJ2XvvvdfrmO222y5f//rXkyRdXV2ZPn16vcYBAAAAAFLHALhgwYIkWe/4t8YhhxyStra2JMns2bPrNQ4AAAAAkDoGwEGDBiVJnnnmmY1+DDcCAQAAAID6qlsA3G233VKr1XLTTTdt0HEPPPBAurq6kiTjxo2r1zgAAAAAQOoYAP/0T/80SXL33Xfn//yf/7Nex6xcuTJTp05NklQqlRxxxBH1GgcAAAAASB0D4Omnn55tt902SfLFL34xRxxxRG688cY8++yzr9r3+eefz3e/+91MmDAhv/jFL3pvHjJ27Nh6jQMAAAAAJOlXrwcaNGhQbrzxxhx55JHp7OzML37xi/ziF79Iv379Mm7cuIwcOTIDBw7MkiVLMmfOnKxevTpJUqvVsu++++biiy+u1ygAAAAAwO/V7QzAJNlvv/1y55135qCDDkqtVkutVsuLL76YuXPn5r777ssdd9yRhx9+OC+++GLv9ve85z2ZNm1aOjo66jkKAAAAAJA6B8Ak2WOPPTJjxozcfPPNmTJlSnbeeefe2Lfmf9tvv31OPvnk3H777fne976X4cOH13sMAAAAACB1/AjwH3rb296Wt73tbUmS1atX55lnnsmLL76YYcOGZeDAgZvqlwUAAAAAXmaTBcBX/CL9+mXkyJGb45cCAAAAAF6m7h8BBgAAAADeOARAAAAAACiYAAgAAAAABRMAAQAAAKBgAiAAAAAAFEwABAAAAICCCYAAAAAAUDABEAAAAAAKJgACAAAAQMEEQAAAAAAomAAIAAAAAAUTAAEAAACgYAIgAAAAABRMAAQAAACAggmAAAAAAFCwfo0e4I1u5syZueKKK9a5z0EHHZRTTjnlFd974IEHcv311+fxxx/PihUrMnz48BxwwAF597vfnTFjxmzKkQEAAACglwD4Gh5//PHMmzdvnfssXbr0Ff9800035bLLLkuSDBgwIMOGDcvixYvz4x//ONOnT8/nP//57LXXXptsZgAAAABYQwB8DfPnz0+SnHfeedl3331fc//Zs2fn61//epLkhBNOyAknnJD+/ftnxYoV+drXvpY77rgjF154Yb761a9m8ODBm3R2AAAAAHANwNewJgCOGzduvfa/5pprUqvVcuCBB2bKlCnp379/kmTIkCE544wzMnTo0CxdujQ/+clPNtnMAAAAALCGAPga5s+fn/79+2fEiBGvuW9XV1d+9atfJUmOPvroV20fMGBA9t9//yTJnXfeWdc5AQAAAGBtBMB1WLlyZZYuXZqxY8emUqm85v6zZs1KT09PWlpasvfee691nzUfI3788cfz4osv1nVeAAAAAPhDrgG4Dk899VSSZMSIEbnuuusyffr0zJ8/Py0tLdlmm20yadKkHHPMMRkwYECSZO7cuUmSkSNH9n7vD40aNSpJ0tPTk4ULF2bbbbfdDD8JAAAAAFsqAXAd1lz/77777st9993Xe0ffJUuW5LHHHstjjz2WW265Jeecc05Gjx6dZ599NkkydOjQPh9zyJAhvV93dXX1ud/VV1+da6+9ts/tJ554YiZPnryhPxKvoaWlpff/Ozo6GjwNbJg1ZyoPHTo0tVqtwdPA+rP20sysvVAf1n82lHWXZtTI970C4DqsCYCDBw/ORz7ykUyaNCn9+vXL6tWrc8stt+Rf/uVfMm/evJx77rm5+OKLe4Nea2trn4+51VZb9X7d3d3d535dXV1ZtGhRn9ufe+65VKvVDf2RWE+VSsXzS9Na8x8VaDbWXpqZtRdeH+s/G8q6SzNrxPteAXAd9ttvv4waNSo777zzKz6q269fv7zjHe/ImDFjctZZZ+W3v/1t7rrrrt5/eT09PX0+5suv+9fXx4STpK2tLaNHj+5z+6BBg9YZENk4LS0tqVQqqdVq6/z3CG9ElUolLS0t6enp8behNBVrL83M2gv14c82bCjrLs2oXu97NyYeCoDrsPPOO2fnnXfuc/t+++2XMWPGZOHChXn44Yd7P9675qPAa/Pybes63XPKlCmZMmVKn9sXL16czs7OdY3PRujo6Ei1Wk1PT4/nl6ZTrVbT0dGRZcuWeRNNU7H20sysvVAf1n82lHWXZlSv970jR47c4GOcM/s6rbne36pVqzJu3LgkyaJFi/pciBYuXJjkpTP8XOcCAAAAgE1NAOxDZ2dnvvKVr+QrX/lKnzfr6Onpybx585IkY8eOzV577ZXkpRj4yCOPrPWYhx56KEmy99579140GgAAAAA2FQGwD4MHD85tt92Wn/3sZ7nzzjvXus8tt9ySrq6uVCqVHHjggdl6662z++67J0l+9KMfvWr/5cuX55e//GWS5LDDDtt0wwMAAADA7wmAfWhtbc2RRx6ZJLniiivyy1/+svdjvd3d3bnjjjty5ZVXJkkOP/zw3puEnHTSSalUKrnjjjvy3e9+t/eYBQsW5Nxzz01XV1fGjx+fQw45pAE/FQAAAABbGjcBWYf3ve99mTt3bu6///5cdNFFGThwYIYOHZply5bl+eefT5Lss88++chHPtJ7zH777ZeTTjopV199da655pp8//vfz5AhQ7J48eLUarVsvfXW+fjHP+6W5QAAAABsFgLgOgwcODDnnntubr311kybNi2PP/54nn766QwePDh77LFHDj300Bx++OGvuv3yCSeckF133TU//OEPM2fOnCxbtizjxo3Lm970phx33HFpb29v0E8EAAAAwJZGAHwNLS0tOeKII3LEEUds0HH7779/9t9//000FQAAAACsH59DBQAAAICCCYAAAAAAUDABEAAAAAAKJgACAAAAQMEEQAAAAAAomAAIAAAAAAUTAAEAAACgYAIgAAAAABRMAAQAAACAggmAAAAAAFAwARAAAAAACiYAAgAAAEDBBEAAAAAAKJgACAAAAAAFEwABAAAAoGACIAAAAAAUTAAEAAAAgIIJgAAAAABQMAEQAAAAAAomAAIAAABAwQRAAAAAACiYAAgAAAAABRMAAQAAAKBgAiAAAAAAFEwABAAAAICCCYAAAAAAUDABEAAAAAAKJgACAAAAQMEEQAAAAAAomAAIAAAAAAUTAAEAAACgYAIgAAAAABRMAAQAAACAggmAAAAAAFAwARAAAAAACiYAAgAAAEDBBEAAAAAAKJgACAAAAAAFEwABAAAAoGACIAAAAAAUTAAEAAAAgIIJgAAAAABQMAEQAAAAAAomAAIAAABAwQRAAAAAACiYAAgAAAAABRMAAQAAAKBgAiAAAAAAFEwABAAAAICCCYAAAAAAUDABEAAAAAAKJgACAAAAQMEEQAAAAAAomAAIAAAAAAUTAAEAAACgYAIgAAAAABRMAAQAAACAggmAAAAAAFAwARAAAAAACiYAAgAAAEDBBEAAAAAAKJgACAAAAAAFEwABAAAAoGACIAAAAAAUTAAEAAAAgIIJgAAAAABQMAEQAAAAAAomAAIAAABAwQRAAAAAACiYAAgAAAAABRMAAQAAAKBg/Ro9ABunWq02eoTieY5pNmtes167NDOvX5qNtRfqw+8hNpTXDM1uc7+GBcAm1dHR0egRilatVj3HNK329vZGjwAbxdpLM7P2wutj/WdDWXdpZo143ysANqnOzs5Gj1Ck9vb2VKvVdHd3Z/ny5Y0eBzZItVpNe3t7li9fnu7u7kaPA+vN2kszs/ZCffjzDRvKukszqtf73o2JhwJgk7LQbXqeY5pVd3e31y9Ny2uXZmXthdfH7x82lHWXZre5X79uAgIAAAAABRMAAQAAAKBgAiAAAAAAFEwABAAAAICCCYAAAAAAUDABEAAAAAAKJgACAAAAQMEEQAAAAAAomAAIAAAAAAUTAAEAAACgYAIgAAAAABRMAAQAAACAggmAAAAAAFAwARAAAAAACiYAAgAAAEDBBEAAAAAAKJgACAAAAAAFEwABAAAAoGACIAAAAAAUTAAEAAAAgIL1a/QAAABAeaZOndroEQCA33MGIAAAAAAUTAAEAAAAgIIJgAAAAABQMAEQAAAAAAomAAIAAABAwQRAAAAAACiYAAgAAAAABRMAAQAAAKBgAiAAAAAAFEwABAAAAICCCYAAAAAAUDABEAAAAAAKJgACAAAAQMEEQAAAAAAomAAIAAAAAAUTAAEAAACgYAIgAAAAABRMAAQAAACAggmAAAAAAFAwARAAAAAACiYAAgAAAEDBBEAAAAAAKJgACAAAAAAFEwABAAAAoGACIAAAAAAUTAAEAAAAgIIJgAAAAABQMAEQAAAAAAomAAIAAABAwQRAAAAAACiYAAgAAAAABRMAAQAAAKBg/Ro9AAAAb3xTp05t9AhvWNdee22jR4CmZ40B2LScAQgAAAAABRMAAQAAAKBgAiAAAAAAFEwABAAAAICCCYAAAAAAUDABEAAAAAAKJgACAAAAQMEEQAAAAAAomAAIAAAAAAUTAAEAAACgYAIgAAAAABRMAAQAAACAggmAAAAAAFAwARAAAAAACiYAAgAAAEDBBEAAAAAAKFi/Rg/QDJ5++unccMMNue+++7JkyZLUarWMHDky++23X44//viMGjXqFft/9atfzQMPPLDOx/zYxz6W8ePHb8qxAQAAAEAAfC0PPvhgzj333HR1daVSqWT48OFZuXJl5s2bl3nz5mX69On50pe+lF122aX3mAceeCDz5s1b5+O+8MILm3p0AAAAABAA12XVqlW58MIL09XVlX333Tenn356xowZkySZM2dOvvzlL+fJJ5/MBRdckEsvvTStra3p7u7OwoULM2rUqFx55ZUN/gkAAAAA2NK5BuA63HXXXVm6dGmGDBmSz372s73xL0l23XXXfOYzn0lLS0sWLFiQGTNmJHnp48KrV6/OuHHjGjU2AAAAAPQSANdhzXX8JkyYkLa2tldt32677TJ27NgkycMPP5wkmT9/fpL0fh8AAAAAGkkAXIelS5cmSUaPHt3nPi0tLz2Fq1evTiIAAgAAAPDG4hqA6/CpT30q3d3daW1tXev2uXPn9t7sY/vtt0/yPwEwSb785S/n17/+dZYvX562trbsvvvuOfroo7P//vtv+uEBAAAAIALgOvXv37/PbYsXL86FF16Ynp6etLW15dBDD03yPwHwqquuSpIMHTo0Q4YMSWdnZ+65557cc889Oeqoo3LqqaemUqls+h8CAAAAgC2aALiBarVafv7zn+eqq67KihUr0q9fv5xxxhlpb29P8j8BcO+9986pp56aHXfcMUmyZMmSfPvb3860adNy0003ZdSoUXnPe97T569z9dVX59prr+1z+4knnpjJkyfX7wcjyf98pLulpSUdHR0NngY2zJq/VBg6dGhqtVqDp4H1Z+2lBNZegM3LukszauT7XgFwA8yePTuXX355HnnkkSTJqFGjcuaZZ2afffbp3WfKlCnp7u7OwQcf/IqPDo8YMSJnnHFGuru7M3369Fx33XU57rjj0q/f2v8VdHV1ZdGiRX3O8txzz6VardbpJ+MPVSoVzy9Na81/VKDZWHtpZtZegM3Luksza8T7XgFwPXR1deWqq67Kz372s9RqtbS2tubYY4/NCSeckIEDB75i30MOOWSdj/X2t78906dPT1dXV5588snstNNOa92vra1tnTcfGTRoULq7uzf8h2GdWlpaUqlUUqvV0tPT0+hxYINUKpW0tLSkp6fH34bSVKy9lMDaC7B5WXdpRvV637sx8VAAfA0LFizIWWed1fvR3kmTJuX9739/xowZs1GPN3To0N6vV61a1ed+U6ZMyZQpU/rcvnjx4nR2dm7UDPSto6Mj1Wo1PT09nl+aTrVaTUdHR5YtW+YvCGgq1l5KYO0F2LysuzSjer3vHTly5AYfIwCuw8qVK3P22Wdn/vz5aW9vz5lnnpkDDjigz/3vvPPOzJw5M9tvv32OO+64te7z5JNP9n69sRERAAAAANaXALgON998c5566qlstdVWOfvss7Prrruuc/+VK1fmZz/7Wdra2nLEEUdk8ODBr9rnhz/8YZJk/PjxGTZs2KYYGwAAAAB6uWrmOtx+++1Jkne9612vGf+SZOLEieno6EhXV1fOPffcV5ztt2LFinzta1/Lgw8+mEqlss6P9wIAAABAvTgDsA+1Wi1z5sxJktx66625884717n/0UcfnWOOOSaf/vSnc9555+Whhx7KX//1X6ejoyOtra1ZvHhxenp6UqlU8sEPfjD77rvv5vgxAAAAANjCCYB9WLFiRVavXp3kpRtuvJbly5cnSfbaa69ceumlufHGG3PPPfdkwYIFqVQqGT16dPbaa68cc8wx63U2IQAAAADUgwDYh/b29t7r9W2ojo6OnHzyyTn55JPrOxQAAAAAbCDXAAQAAACAggmAAAAAAFAwARAAAAAACiYAAgAAAEDBBEAAAAAAKJgACAAAAAAFEwABAAAAoGACIAAAAAAUTAAEAAAAgIIJgAAAAABQMAEQAAAAAAomAAIAAABAwQRAAAAAACiYAAgAAAAABRMAAQAAAKBgAiAAAAAAFEwABAAAAICCCYAAAAAAUDABEAAAAAAKJgACAAAAQMEEQAAAAAAomAAIAAAAAAUTAAEAAACgYAIgAAAAABRMAAQAAACAggmAAAAAAFAwARAAAAAACiYAAgAAAEDBBEAAAAAAKJgACAAAAAAFEwABAAAAoGACIAAAAAAUTAAEAAAAgIIJgAAAAABQMAEQAAAAAAomAAIAAABAwQRAAAAAACiYAAgAAAAABRMAAQAAAKBgAiAAAAAAFEwABAAAAICCCYAAAAAAUDABEAAAAAAKJgACAAAAQMEEQAAAAAAomAAIAAAAAAUTAAEAAACgYAIgAAAAABRMAAQAAACAggmAAAAAAFAwARAAAAAACiYAAgAAAEDBBEAAAAAAKJgACAAAAAAFEwABAAAAoGACIAAAAAAUTAAEAAAAgIIJgAAAAABQMAEQAAAAAAomAAIAAABAwQRAAAAAACiYAAgAAAAABRMAAQAAAKBgAiAAAAAAFEwABAAAAICCCYAAAAAAUDABEAAAAAAKJgACAAAAQMEEQAAAAAAomAAIAAAAAAUTAAEAAACgYP0aPQAbp1qtNnqE4nmOaTZrXrNeuzQzr1+aldcuwOZl3aXZbe7XsADYpDo6Oho9QtGq1arnmKbV3t7e6BFgo1h7aWbWXoDNy7pLM2vE+14BsEl1dnY2eoQitbe3p1qtpru7O8uXL2/0OLBBqtVq2tvbs3z58nR3dzd6HFhv1l5KYO0F2LysuzSjer3v3Zh4KAA2KQvdpuc5pll1d3d7/dK0vHZpVtZegM3Lukuz29yvXzcBAQAAAICCCYAAAAAAUDABEAAAAAAKJgACAAAAQMEEQAAAAAAomAAIAAAAAAUTAAEAAACgYAIgAAAAABRMAAQAAACAggmAAAAAAFAwARAAAAAACiYAAgAAAEDBBEAAAAAAKJgACAAAAAAF69foAQAAAAB4faZOndroEd6wLrnkkkaP0HDOAAQAAACAggmAAAAAAFAwARAAAAAACiYAAgAAAEDBBEAAAAAAKJgACAAAAAAFEwABAAAAoGACIAAAAAAUTAAEAAAAgIIJgAAAAABQMAEQAAAAAAomAAIAAABAwfo1egAAgDeSqVOnNnoEAGAdJk+e3OgRoOk4AxAAAAAACiYAAgAAAEDBBEAAAAAAKJgACAAAAAAFEwABAAAAoGACIAAAAAAUTAAEAAAAgIIJgAAAAABQMAEQAAAAAAomAAIAAABAwQRAAAAAACiYAAgAAAAABRMAAQAAAKBgAiAAAAAAFEwABAAAAICCCYAAAAAAUDABEAAAAAAKJgACAAAAQMEEQAAAAAAomAAIAAAAAAUTAAEAAACgYAIgAAAAABRMAAQAAACAggmAAAAAAFAwARAAAAAACiYAAgAAAEDBBEAAAAAAKJgACAAAAAAFEwABAAAAoGACIAAAAAAUTAAEAAAAgIIJgAAAAABQsH6NHgAAaIypU6c2egQAAGAzcAYgAAAAABRMAAQAAACAggmAAAAAAFAwARAAAAAACiYAAgAAAEDB3AUYoCCTJ09u9AhvSJdcckmjRwAAAGgYZwACAAAAQMEEQAAAAAAomAAIAAAAAAUTAAEAAACgYG4Csgl1dnbmBz/4Qe69994sWrQogwYNyi677JKjjjoqBx54YKPHAwAAAGALIABuIk899VT+v//v/8vSpUvT0tKS4cOH5/nnn8/MmTMzc+bMHH/88TnllFMaPSYAAAAAhRMAN4FarZbzzz8/S5cuzW677Za//du/zdZbb52enp7ceuutufTSS/ODH/wgu+22WyZNmtTocd8Qpk6d2ugR3pAuueSSRo8AAAAANDnXANwEbr/99vz2t79N//7989nPfjZbb711kqSlpSVHHHFE/uRP/iRJcu211zZyTAAAAAC2AALgJnD77bcnSQ4++OCMHDnyVdvf9KY3JUl+97vf5be//e1mnQ0AAACALYsAWGe1Wi2zZs1Kkvyv//W/1rrPnnvumdbW1iTJI488stlmAwAAAGDLIwDWWWdnZ1asWJEk2Wabbda6T2tra4YNG5YkmT9//uYaDQAAAIAtkJuA1Nmzzz7b+/XQoUP73G/w4MF5+umn09XVtdbtV1999TqvEXjiiSdm8uTJGz8oTaGjo6PRI9BEKpVKo0d4w/J7CdjUhg4dmlqt1ugxAIC1eKP8eaClpaX3/zf3TAJgnb08APbv37/P/QYOHJgk6e7uXuv2rq6uLFq0qM/jn3vuuVSr1Y2c8o3HDVGgPvxeYkN4vUD9rHlDz/+wxgDA2lUqlc3edATAOuvX73+e0p6enj73e/HFF5MkAwYMWOv2tra2jB49us/jBw0a1Gc8ZOO1tLSkUqmkVqut898fvBFVKpW0tLSkp6fHWSg0FWsvzczaS7Oy9tKsrLs0s3qtvRsTDwXAOhs8eHDv12uuBbg2a84U7OuUzylTpmTKlCl9Hr948eJ0dnZu5JT0paOjI9VqNT09PZ5fmk61Wk1HR0eWLVvmLwhoKtZempm1l2Zl7aVZWXdpZvVae0eOHLnBx/isQp2NGTOm9yzAvm7w0d3dncWLFydJtt122802GwAAAABbHgGwzqrVavbYY48kyaxZs9a6z+zZs/Piiy+mpaUle++99+YcDwAAAIAtjAC4CRx66KFJkttuuy3PPPPMq7b/+Mc/TpLsv//+aW9v35yjAQAAALCFEQA3gSOPPDJjx47NCy+8kPPPP7/3476rVq3Ktddem+nTp6dfv3553/ve1+BJAQAAACidm4BsAv3798+nP/3pnHXWWXnooYfyoQ99KCNGjMjy5cuzcuXKtLS05LTTTsuOO+7Y6FEBAAAAKJwAuInsvPPOufjii/O9730v9957bzo7OzNkyJDsv//+Of744zN+/PhGjwgAAADAFkAA3IRGjBiR0047rdFjAAAAALAFcw1AAAAAACiYAAgAAAAABRMAAQAAAKBgAiAAAAAAFEwABAAAAICCCYAAAAAAUDABEAAAAAAKJgACAAAAQMEEQAAAAAAomAAIAAAAAAUTAAEAAACgYAIgAAAAABRMAAQAAACAggmAAAAAAFAwARAAAAAACiYAAgAAAEDBBEAAAAAAKJgACAAAAAAFEwABAAAAoGACIAAAAAAUTAAEAAAAgIIJgAAAAABQMAEQAAAAAAomAAIAAABAwQRAAAAAACiYAAgAAAAABRMAAQAAAKBgAiAAAAAAFEwABAAAAICCVWq1Wq3RQ8AbxdVXX52urq60tbVlypQpjR4HYItg7QXY/Ky9AJtfI9deARBe5qijjsqiRYsyevTo3HTTTY0eB2CLYO0F2PysvQCbXyPXXh8BBgAAAICCCYAAAAAAUDABEAAAAAAKJgACAAAAQMEEQAAAAAAomAAIAAAAAAXr1+gB4I1k8uTJ6erqSltbW6NHAdhiWHsBNj9rL8Dm18i1t1Kr1Wqb/VcFAAAAADYLHwEGAAAAgIIJgAAAAABQMAEQAAAAAArmJiCQ5Omnn84NN9yQ++67L0uWLEmtVsvIkSOz33775fjjj8+oUaMaPSJAcaZPn55bbrklTzzxRJ599tkMGjQoO+64Yw499NC87W1vS7VabfSIAMVbunRppk6dmuXLl+fCCy/M+PHjGz0SQFFmzpyZK664Yp37HHTQQTnllFM26RwCIFu8Bx98MOeee266urpSqVQyfPjwrFy5MvPmzcu8efMyffr0fOlLX8ouu+zS6FEBivHVr341P/3pT5MkAwYMyIgRI/LMM8/k/vvvz/3335/bbrstZ599dvr379/gSQHKVavV8uUvfznLly9v9CgAxXr88cczb968de6zdOnSTT6HAMgWbdWqVbnwwgvT1dWVfffdN6effnrGjBmTJJkzZ06+/OUv58knn8wFF1yQSy+9NK2trQ2eGKD53XXXXfnpT3+aarWaU089NUcccURaW1vT3d2dm2++OZdffnlmzZqVH/3oR3nPe97T6HEBinX99dfn17/+daPHACja/PnzkyTnnXde9t1334bN4RqAbNHuuuuuLF26NEOGDMlnP/vZ3viXJLvuums+85nPpKWlJQsWLMiMGTMaOClAOX7yk58kSY466qi84x3v6P3LlWq1mqOOOipvf/vbkyT33ntvw2YEKN3s2bNzzTXXZPvtt2/0KABFWxMAx40b19A5BEC2aA888ECSZMKECWlra3vV9u222y5jx45Nkjz88MObdTaAUj3xxBNJkj/+4z9e6/YddtghSfLCCy9spokAtizPP/98/uEf/iGVSiWf+MQnGj0OQNHmz5+f/v37Z8SIEQ2dw0eA2aKt+Zz96NGj+9ynpeWlTr569erNMhNA6T784Q+np6cne+yxx1q3z549O8lLfwkDQP19/etfz/z58/PBD34wO+20U6PHASjWypUrs3Tp0uywww6pVCoNnUUAZIv2qU99Kt3d3X1e22/u3Lm9F+v08QiA+pg0adIr/rm7uzvJS3dkv+WWWzJt2rS0tra6/h/AJjB9+vRMmzYtf/zHf5xjjz220eMAFO2pp55KkowYMSLXXXddpk+fnvnz56elpSXbbLNNJk2alGOOOSYDBgzY5LMIgGzR1nV3ycWLF+fCCy9MT09P2tracuihh27GyQC2HF/60pfyX//1X73/3NbWli984Qu9HwUGoD4WLlyYyy67LO3t7TnjjDMafjYKQOnWXP/vvvvuy3333ZcBAwZk2LBhWbJkSR577LE89thjueWWW3LOOees85OJ9eAagPAHarVabrnllnzsYx/L3Llz069fv5xxxhlpb29v9GgARRo1alRGjhyZarWaJOnq6srf//3f59FHH23wZADl6O7uzv/9v/83XV1d+Zu/+ZsMHz680SMBFG9NABw8eHA+/vGP51//9V9zxRVX5Hvf+17+6q/+Km1tbZk3b17OPffc3k/FbCrOAISXmT17di6//PI88sgjSV76Q+mZZ56ZffbZp8GTAZTrb/7mb5K89IfTe+65J9/85jfz1FNP5dxzz80VV1yxWT4SAVC6f/3Xf83DDz+ct7/97Zk4cWKjxwHYIuy3334ZNWpUdt5552y77ba93+/Xr1/e8Y53ZMyYMTnrrLPy29/+NnfddderLpVTTwIg5KWzTa666qr87Gc/S61WS2tra4499ticcMIJGThwYKPHA9giVKvVTJw4MVtvvXWmTp2aZcuW5e6773YJBoDX6aGHHsp1112XbbbZJh/60IcaPQ7AFmPnnXfOzjvv3Of2/fbbL2PGjMnChQvz8MMPC4CwKS1YsCBnnXVW76m5kyZNyvvf//6MGTOmwZMBlOehhx7KXXfdla222iqTJ09e6z477rhjhg4dmmXLlmXRokWbeUKA8jz66KPp6enJvHnz8t73vrfP/T71qU8lSQ4++OB87nOf21zjAWzRhg4dmoULF2bVqlWb9NcRANmirVy5MmeffXbmz5+f9vb2nHnmmTnggAMaPRZAsZ5++unccMMNqVQqOf7449d6lnWtVsvq1auTxFnYAHUwZMiQbLPNNn1unzdvXpJk9OjRaW1tzYgRIzbXaADF6uzszNVXX50k+cAHPpC2trZX7bPmL2eSZOzYsZt0HgGQLdrNN9+cp556KltttVXOPvvs7Lrrro0eCaBoe+65ZyqVSmq1WmbMmJHDDjvsVfvce++96erqShLXYAWog8MPPzyHH354n9uPPfbYJMknP/nJjB8/fnONBVC0wYMH57bbbsvKlSszfvz4vO1tb3vVPrfccku6urpSqVRy4IEHbtJ53AWYLdrtt9+eJHnXu94l/gFsBqNGjcqb3vSmJMmVV16ZmTNnplarJXnpJiA/+9nP8o//+I9JkokTJ2b77bdv2KwAALCxWltbc+SRRyZJrrjiivzyl7/svdNvd3d37rjjjlx55ZVJXvqLmpffJGRTcAYgW6xarZY5c+YkSW699dbceeed69z/6KOPzjHHHLM5RgMo2qmnnpq5c+fmySefzDnnnJO2tra0tbVlyZIlvW+K9txzz3zsYx9r8KQAALDx3ve+92Xu3Lm5//77c9FFF2XgwIG917p+/vnnk7z0iZePfOQjm3wWAZAt1ooVK3qvMbV48eLX3H/58uWbeiSALcKwYcNy0UUX5d///d8zY8aMzJs3L0uWLMngwYOz00475c1vfnMOO+ywVKvVRo8KAAAbbeDAgTn33HNz6623Ztq0aXn88cfz9NNPZ/Dgwdljjz1y6KGH5vDDD98s73srtTWfuwEAAAAAiuMagAAAAABQMAEQAAAAAAomAAIAAABAwQRAAAAAACiYAAgAAAAABRMAAQAAAKBgAiAAAAAAFEwABAAAAICCCYAAAAAAUDABEAAAAAAKJgACAAAAQMEEQAAAAAAomAAIAAAAAAUTAAEAAACgYAIgAAAAABRMAAQAAACAggmAAAAAAFAwARAAgKbxzW9+M5VKJZVKJZdddlmjxwEAaAr9Gj0AAACs8atf/So33HBDkuSYY47JhAkTGjsQAEABBEAAAN4wfvWrX+Wcc85Jkmy99dYCIABAHQiAAAA0jZNPPjknn3xyo8cAAGgqrgEIAAAAAAUTAAEAAACgYAIgAADrtOauu2effXaS5N///d8zadKkDBw4MG9961tftf+iRYvypS99KQcddFCGDh2a1tbWjBgxIocccki++MUvZt68ea865uyzz06lUskpp5zS+72PfvSjqVQq2XHHHXu/t667AK95jEqlkiTp7u7OP//zP+etb31rRo4cmYEDB2bnnXfOqaeemjlz5qzzZ+7p6ck3vvGNHHrooeno6MiwYcMyYcKE/L//9//y3HPP5Yknnuj9tb75zW+u3xMJANAgrgEIAMB6O++88/KFL3yhz+0/+tGPctJJJ2XFihWv+P7SpUszY8aMzJgxIxdddFGuvPLKTJ48eZPNuXjx4hxzzDH5z//8z1d8/7//+79z+eWX5zvf+U5uueWWHHjgga86dvny5Tn22GMzffr0V3x/5syZmTlzZi677LJcfvnlm2x2AIB6EwABAFgv119/fe6///5stdVW+Yu/+Ivsu+++2W677Xq3P/HEE/nzP//zPP/880mSI488Mocddlja2tqycOHC/OQnP8l//dd/5YUXXsgpp5ySAw88MLvttluS5JhjjsnWW2+du+66K9/61reSJJMnT86hhx6aIUOGbPCsb3vb2/KrX/0q48aNy5QpU7LttttmwYIF+eY3v5mnnnoqy5cvz8knn5xZs2b1njGYvHTm37ve9a7cdtttSV66E/GJJ56YHXbYIfPmzct3vvOdPPzww/mLv/iLjX4eAQA2NwEQAID1cv/992fs2LGZNm1axo8f/6rtV111VW/8u/jii/Oxj33sFdv/7u/+Ll/84hdz7rnnZtWqVfnud7+bz3/+80mSCRMmZMKECdlqq616A+Chhx6a0047baNm/dWvfpV3v/vd+dd//ddstdVWvd8//fTT80d/9EdZtGhRHnzwwdx99905+OCDe7d/5Stf6Y1/Rx55ZG688cYMGjSod/tZZ52Vv/iLv8i///u/b9RcAACN4BqAAACst8svv3yt8S956SOySbLNNtvk9NNPX+s+n/rUp9LS8tJb0N/+9rebZsgk48ePz3e+851XxL/kpTP6PvCBD/T+83/913/1ft3T05N//Md/TJIMGTIk11577SviX5K0tbXlmmuuydZbb73JZgcAqDdnAAIAsF522mmnHHPMMX1uP+GEEzJx4sTssssuvZHvD7W1tWWrrbbKc889lxdffHFTjZqpU6dmwIABa902YcKE3q8XLlzY+/Xdd9+duXPnJnnpZxk1atRaj29vb88pp5yS888/v44TAwBsOgIgAADr5YADDljn9ve9733r3N7V1ZWvfOUree655+o51lrtv//+fW4bPnx479cvvPBC79d3331379dvfvOb1/n4kyZNeh3TAQBsXgIgAADrpa2tbb32W7lyZe6666788pe/zJw5c/L444/nv//7v/PUU0+lVqtt4ilf0tHR0ee2l9/04+XzrDn7L0l22GGHdT7+y29+AgDwRicAAgBQF7VaLZdddlk++9nPZtmyZWvd55BDDsldd921yWfp37//Bh+zfPny3q+HDRu2zn3/8NqCAABvZAIgAAB18cEPfjBXXXVV7z/vu+++mTBhQnbdddfsueeeOfDAA7PNNtu84gy8N5LW1tber19rxsWLF2/qcQAA6kYABADgdZsxY0Zv/Nt+++1zww03ZL/99mvwVBvm5Wf9vfzmIGszZ86cTTwNAED9rP32bAAAsAFuuumm3q8vuOCCPuPfa4W1Rtpjjz16v77nnnvWue+Pf/zjTT0OAEDdCIAAALxuCxYs6P365SHtD/3kJz/ZHONslP/9v/9379ff+ta3snr16rXu9+ijj+YHP/jB5hoLAOB1EwABAHjdXn7X3XvvvXet+zz++OP5zGc+s87HefnNO1asWFGf4dbTLrvsksMOOyxJ8sgjj+QTn/hEenp6XrHP/Pnz82d/9mdZtWrVZp0NAOD1cA1AAABet7e//e258MILkySf+tSnMnLkyBx77LGpVqt54okncvXVV+fCCy9MV1dXBg0alOeeey7z5s1LkvT09KSl5aW/l9522217H/Piiy9OpVLJ6NGj8773vW+z/BwXX3xxJk6cmOeffz6XXHJJbr/99hx//PEZOHBgHnrooXz3u9/Ns88+m9133z2PPvroZpkJAOD1cgYgAACv2+GHH56jjz46SfLMM8/kT//0TzNo0KAMGjQoO+20U77whS/k+eefzxVXXJEjjjgiSXLLLbeko6Mj5513Xu/jHHLIIRk7dmyS5Kmnnsrf/u3f5otf/OJm+zn23Xff/OhHP8rw4cOTJPfdd1++8IUv5JOf/GSuvPLKPPvss9lvv/3yD//wD73HrImXAABvVN6tAABQF9/5znfy53/+573/vGrVqjz//PNJXgp7d911V0455ZR89KMfTaVSSfJSLHz5x2xbW1vzne98JwcccED69++fgQMHrvOagpvCEUcckUceeSSf/exns9dee2WrrbZKe3t7/uiP/ijnnXde7rjjjt75k2TgwIGbdT4AgA1VqdVqtUYPAQBAOR588MH84he/yLJlyzJ27NgcfPDB2XPPPV+xz4wZMzJt2rS0tbXl2GOPzU477dSgaTfOlVdemQ996ENJkltvvTVvfetbGzsQAMA6uAYgAAB1tddee2WvvfZa5z4TJ07MxIkTN9NE66e7uzvz589PkrS1tb3ixiZ/6P777+/9erfddtvkswEAvB7OAAQAgCTPP/982traUqvVMnHixNx111197rfbbrtl3rx52WWXXTJnzpzNPCkAwIZxDUAAAMhL1/KbMGFCkpc+onzDDTe8ap8XX3wxH/nIR3rvYLzmY8AAAG9kzgAEAIDf+8EPfpA//dM/TZJUq9Uce+yxOeSQQ9La2pq5c+fm+9//fubOnZvkpTsG33vvvWltbW3kyAAAr0kABACAl7ngggvyuc99Lt3d3X3uM3HixHzve9/LtttuuxknAwDYOAIgAAD8gQcffDBf+9rXMm3atDzxxBOp1WoZO3Zs9t5770yePDl/9md/5sw/AKBpCIAAAAAAUDA3AQEAAACAggmAAAAAAFAwARAAAAAACiYAAgAAAEDBBEAAAAAAKJgACAAAAAAFEwABAAAAoGACIAAAAAAUTAAEAAAAgIIJgAAAAABQMAEQAAAAAAomAAIAAABAwf5/C2mWAolJ0zkAAAAASUVORK5CYII="
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 640
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Task 9\n",
    "## Create histgram of rating\n",
    "from plotnine import ggplot, aes, geom_histogram\n",
    "\n",
    "ggplot(df_new, aes(x=\"rating\")) + geom_histogram(binwidth=0.2)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}