11)Categorical_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with Categorical Data in Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What does it mean to be \"categorical\"?\n",
    "- Finite number of groups (or categories)\n",
    "- These categories are ususally fixed or known (eye color, hair color)\n",
    "- Known as qualitative data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Categorical data can be broke down into two main types:\n",
    "- **ordinal** (having a natural rank order - Strongly Disagree / Disagree / Neutral / Agree / Strongly Agree)\n",
    "- **nominal** (cannot be placed into a natural order - Blue / Green / Blue / Red)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that if we have groups of people with different income like `$0 < $10 000`, `$10 000 < $20 000`, `$20 000 < $30 000` this will be an example of **ordinal categorical data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32561 entries, 0 to 32560\n",
      "Data columns (total 15 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Age              32561 non-null  int64 \n",
      " 1   Workclass        32561 non-null  object\n",
      " 2   fnlgwt           32561 non-null  int64 \n",
      " 3   Education        32561 non-null  object\n",
      " 4   Education Num    32561 non-null  int64 \n",
      " 5   Marital Status   32561 non-null  object\n",
      " 6   Occupation       32561 non-null  object\n",
      " 7   Relationship     32561 non-null  object\n",
      " 8   Race             32561 non-null  object\n",
      " 9   Sex              32561 non-null  object\n",
      " 10  Capital Gain     32561 non-null  int64 \n",
      " 11  Capital Loss     32561 non-null  int64 \n",
      " 12  Hours/Week       32561 non-null  int64 \n",
      " 13  Country          32561 non-null  object\n",
      " 14  Above/Below 50k  32561 non-null  object\n",
      "dtypes: int64(6), object(9)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "adults = pd.read_csv(\"databases/adult.csv\")\n",
    "adults.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exploring \"Marital Status\" column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                   32561\n",
       "unique                      7\n",
       "top        Married-civ-spouse\n",
       "freq                    14976\n",
       "Name: Marital Status, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adults[\"Marital Status\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Marital Status\n",
       "Married-civ-spouse       14976\n",
       "Never-married            10683\n",
       "Divorced                  4443\n",
       "Separated                 1025\n",
       "Widowed                    993\n",
       "Married-spouse-absent      418\n",
       "Married-AF-spouse           23\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adults[\"Marital Status\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Marital Status\n",
       "Married-civ-spouse       0.459937\n",
       "Never-married            0.328092\n",
       "Divorced                 0.136452\n",
       "Separated                0.031479\n",
       "Widowed                  0.030497\n",
       "Married-spouse-absent    0.012837\n",
       "Married-AF-spouse        0.000706\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adults[\"Marital Status\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical data in pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                 int64\n",
       "Workclass          object\n",
       "fnlgwt              int64\n",
       "Education          object\n",
       "Education Num       int64\n",
       "Marital Status     object\n",
       "Occupation         object\n",
       "Relationship       object\n",
       "Race               object\n",
       "Sex                object\n",
       "Capital Gain        int64\n",
       "Capital Loss        int64\n",
       "Hours/Week          int64\n",
       "Country            object\n",
       "Above/Below 50k    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adults.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adults[\"Marital Status\"].dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pandas uses the capital O to represent the object dtype\n",
    "\n",
    "To convert it into categorical dtype:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CategoricalDtype(categories=[' Divorced', ' Married-AF-spouse', ' Married-civ-spouse',\n",
       "                  ' Married-spouse-absent', ' Never-married', ' Separated',\n",
       "                  ' Widowed'],\n",
       ", ordered=False, categories_dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adults[\"Marital Status\"] = adults[\"Marital Status\"].astype(\"category\")\n",
    "adults[\"Marital Status\"].dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Creating a categorical Series**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    A\n",
      "1    A\n",
      "2    C\n",
      "3    B\n",
      "4    C\n",
      "5    A\n",
      "dtype: category\n",
      "Categories (3, object): ['A', 'B', 'C']\n"
     ]
    }
   ],
   "source": [
    "my_data = [\"A\", \"A\", \"C\", \"B\", \"C\", \"A\"]\n",
    "\n",
    "my_series1 = pd.Series(my_data, dtype=\"category\")\n",
    "print(my_series1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way which allows us to set order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A', 'A', 'C', 'B', 'C', 'A']\n",
       "Categories (3, object): ['C' < 'B' < 'A']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_data = [\"A\", \"A\", \"C\", \"B\", \"C\", \"A\"]\n",
    "my_series2 = pd.Categorical(my_data, categories=[\"C\", \"B\", \"A\"], ordered=True)\n",
    "my_series2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Why do we use categorical: memory**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Object: 260488\n",
      "Category: 32617\n"
     ]
    }
   ],
   "source": [
    "adults[\"Marital Status\"] = adults[\"Marital Status\"].astype(\"object\")\n",
    "print(f\"Object: {adults[\"Marital Status\"].nbytes}\")\n",
    "adults[\"Marital Status\"] = adults[\"Marital Status\"].astype(\"category\")\n",
    "print(f\"Category: {adults[\"Marital Status\"].nbytes}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Specify dtypes when reading data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CategoricalDtype(categories=[' Divorced', ' Married-AF-spouse', ' Married-civ-spouse',\n",
       "                  ' Married-spouse-absent', ' Never-married', ' Separated',\n",
       "                  ' Widowed'],\n",
       ", ordered=False, categories_dtype=object)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adults_dtypes = {\"Marital Status\": \"category\"}\n",
    "\n",
    "adults = pd.read_csv(\"databases/adult.csv\", dtype=adults_dtypes)\n",
    "\n",
    "adults[\"Marital Status\"].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age                   int64\n",
       "Workclass            object\n",
       "fnlgwt                int64\n",
       "Education            object\n",
       "Education Num         int64\n",
       "Marital Status     category\n",
       "Occupation           object\n",
       "Relationship         object\n",
       "Race                 object\n",
       "Sex                  object\n",
       "Capital Gain          int64\n",
       "Capital Loss          int64\n",
       "Hours/Week            int64\n",
       "Country              object\n",
       "Above/Below 50k      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adults.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouping data by category in pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>Age</th>\n",
       "      <th>fnlgwt</th>\n",
       "      <th>Education Num</th>\n",
       "      <th>Capital Gain</th>\n",
       "      <th>Capital Loss</th>\n",
       "      <th>Hours/Week</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Above/Below 50k</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>36.783738</td>\n",
       "      <td>190340.86517</td>\n",
       "      <td>9.595065</td>\n",
       "      <td>148.752468</td>\n",
       "      <td>53.142921</td>\n",
       "      <td>38.840210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>44.249841</td>\n",
       "      <td>188005.00000</td>\n",
       "      <td>11.611657</td>\n",
       "      <td>4006.142456</td>\n",
       "      <td>195.001530</td>\n",
       "      <td>45.473026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Age        fnlgwt  Education Num  Capital Gain  \\\n",
       "Above/Below 50k                                                         \n",
       "<=50K            36.783738  190340.86517       9.595065    148.752468   \n",
       ">50K             44.249841  188005.00000      11.611657   4006.142456   \n",
       "\n",
       "                 Capital Loss  Hours/Week  \n",
       "Above/Below 50k                            \n",
       "<=50K               53.142921   38.840210  \n",
       ">50K               195.001530   45.473026  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "groupby_object = adults.groupby(by=[\"Above/Below 50k\"], observed=False)\n",
    "groupby_object.mean(numeric_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>Age</th>\n",
       "      <th>Education Num</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Above/Below 50k</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>&lt;=50K</th>\n",
       "      <td>909294</td>\n",
       "      <td>237190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>&gt;50K</th>\n",
       "      <td>346963</td>\n",
       "      <td>91047</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Age  Education Num\n",
       "Above/Below 50k                       \n",
       "<=50K            909294         237190\n",
       ">50K             346963          91047"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adults.groupby(by=[\"Above/Below 50k\"], observed=False)[[\"Age\", \"Education Num\"]].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Above/Below 50k  Marital Status       \n",
       "<=50K            Divorced                  3980\n",
       "                 Married-AF-spouse           13\n",
       "                 Married-civ-spouse        8284\n",
       "                 Married-spouse-absent      384\n",
       "                 Never-married            10192\n",
       "                 Separated                  959\n",
       "                 Widowed                    908\n",
       ">50K             Divorced                   463\n",
       "                 Married-AF-spouse           10\n",
       "                 Married-civ-spouse        6692\n",
       "                 Married-spouse-absent       34\n",
       "                 Never-married              491\n",
       "                 Separated                   66\n",
       "                 Widowed                     85\n",
       "dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adults.groupby(by=[\"Above/Below 50k\", \"Marital Status\"], observed=False).size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting category variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2937 entries, 0 to 2936\n",
      "Data columns (total 19 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   ID                 2937 non-null   int64  \n",
      " 1   name               2845 non-null   object \n",
      " 2   age                2937 non-null   float64\n",
      " 3   sex                2937 non-null   object \n",
      " 4   breed              2937 non-null   object \n",
      " 5   date_found         2937 non-null   object \n",
      " 6   adoptable_from     2937 non-null   object \n",
      " 7   posted             2937 non-null   object \n",
      " 8   color              2937 non-null   object \n",
      " 9   coat               2937 non-null   object \n",
      " 10  size               2937 non-null   object \n",
      " 11  neutered           1852 non-null   object \n",
      " 12  housebroken        460 non-null    object \n",
      " 13  likes_people       1999 non-null   object \n",
      " 14  likes_children     1219 non-null   object \n",
      " 15  get_along_males    1633 non-null   object \n",
      " 16  get_along_females  1673 non-null   object \n",
      " 17  get_along_cats     431 non-null    object \n",
      " 18  keep_in            1916 non-null   object \n",
      "dtypes: float64(1), int64(1), object(17)\n",
      "memory usage: 436.1+ KB\n"
     ]
    }
   ],
   "source": [
    "dogs = pd.read_csv(\"databases/ShelterDogs.csv\")\n",
    "dogs.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "coat\n",
       "short         1972\n",
       "medium         565\n",
       "wirehaired     220\n",
       "long           180\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs[\"coat\"] = dogs[\"coat\"].astype(\"category\")\n",
    "dogs[\"coat\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The `.cat` accessor object**\n",
    "\n",
    "`Series.cat.method_name`\n",
    "\n",
    "Common parameters:\n",
    "- `new_categories`: a list of categories\n",
    "- `inplace`: Boolean - whether or not the update should overwrite the Series\n",
    "- `ordered`: Boolean - whether or not categorical is treated as an ordered caregorical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using `new_categories`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "dogs[\"coat\"] = dogs[\"coat\"].cat.set_categories(\n",
    "    new_categories = [\"short\", \"medium\", \"long\"]\n",
    ")\n",
    "#values not listed in a new_categories list will be dropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "coat\n",
       "short     1972\n",
       "medium     565\n",
       "NaN        220\n",
       "long       180\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs[\"coat\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    short\n",
       "1    short\n",
       "2    short\n",
       "Name: coat, dtype: category\n",
       "Categories (3, object): ['short' < 'medium' < 'long']"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs[\"coat\"] = dogs[\"coat\"].cat.set_categories(\n",
    "    new_categories = [\"short\", \"medium\", \"long\"],\n",
    "    ordered=True\n",
    ")\n",
    "dogs[\"coat\"].head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Adding categories** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "likes_people\n",
       "yes    1991\n",
       "NaN     938\n",
       "no        8\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs[\"likes_people\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Do not run cause the given df already has these categories\n",
    "dogs[\"likes_people\"] = dogs[\"likes_people\"].astype(\"category\")\n",
    "dogs[\"likes_people\"] = dogs[\"likes_people\"].cat.add_categories(\n",
    "    new_categories=[\"did not check\", \"could not tell\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['no', 'yes', 'did not check', 'could not tell'], dtype='object')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To see existing categories\n",
    "dogs[\"likes_people\"].cat.categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "likes_people\n",
       "yes               1991\n",
       "NaN                938\n",
       "no                   8\n",
       "did not check        0\n",
       "could not tell       0\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs[\"likes_people\"].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Removing categories**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Again do not run\n",
    "dogs[\"coat\"] = dogs[\"coat\"].astype(\"category\")\n",
    "dogs[\"coat\"] = dogs[\"coat\"].cat.remove_categories(\n",
    "    removals=[\"wirehaired\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['short', 'medium', 'long'], dtype='object')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs[\"coat\"].cat.categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Methods recap:**\n",
    "- `cat.set_categories()`\n",
    "- `cat.add_categories()`\n",
    "- `cat.remove_categories()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Updating Categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "breed\n",
       "Unknown Mix                                 1524\n",
       "German Shepherd Dog Mix                      190\n",
       "Dachshund Mix                                147\n",
       "Labrador Retriever Mix                        83\n",
       "Staffordshire Terrier Mix                     62\n",
       "                                            ... \n",
       "English Cocker Spaniel, Vizsla Mix             1\n",
       "English Greyhound Mix                          1\n",
       "English Greyhound, Spanish Greyhound Mix       1\n",
       "Fox Terrier, German Shepherd Dog Mix           1\n",
       "Yorkshire Terrier                              1\n",
       "Name: count, Length: 277, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs[\"breed\"] = dogs[\"breed\"].astype(\"category\")\n",
    "dogs[\"breed\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to rename Unknown mix to be Unknown"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Renaming categories**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Series.cat.rename_categories(new_categories=dict)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "breed\n",
       "Unknown                                     1524\n",
       "German Shepherd Dog Mix                      190\n",
       "Dachshund Mix                                147\n",
       "Labrador Retriever Mix                        83\n",
       "Staffordshire Terrier Mix                     62\n",
       "                                            ... \n",
       "English Cocker Spaniel, Vizsla Mix             1\n",
       "English Greyhound Mix                          1\n",
       "English Greyhound, Spanish Greyhound Mix       1\n",
       "Fox Terrier, German Shepherd Dog Mix           1\n",
       "Yorkshire Terrier                              1\n",
       "Name: count, Length: 277, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_changes = {\"Unknown Mix\": \"Unknown\"}\n",
    "\n",
    "dogs[\"breed\"] = dogs[\"breed\"].cat.rename_categories(my_changes)\n",
    "dogs[\"breed\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Renaming categories with a function**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Female', 'Male'], dtype='object')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs[\"sex\"] = dogs[\"sex\"].astype(\"category\")\n",
    "dogs[\"sex\"] = dogs[\"sex\"].cat.rename_categories(lambda c: c.title())\n",
    "\n",
    "dogs[\"sex\"].cat.categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Collapsing categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need this to give multiple categories the same name and collapse them together. Notice that here we do not use `.cat` in any way, we simply work with strings and then convert an object into a `category`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coat_collapsed\n",
      "short     1972\n",
      "medium     565\n",
      "long       180\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Create the update_coats dictionary\n",
    "update_coats = {\"wirehaired\": \"medium\",\n",
    "                \"medium-long\": \"medium\"}\n",
    "\n",
    "# Create a new column, coat_collapsed\n",
    "dogs[\"coat_collapsed\"] = dogs[\"coat\"].replace(update_coats)\n",
    "\n",
    "# Convert the column to categorical\n",
    "dogs[\"coat_collapsed\"] = dogs[\"coat_collapsed\"].astype(\"category\")\n",
    "\n",
    "# Print the frequency table\n",
    "print(dogs[\"coat_collapsed\"].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reordering categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dogs[\"coat\"] = dogs[\"coat\"].astype(\"category\")\n",
    "\n",
    "dogs[\"coat\"] = dogs[\"coat\"].cat.reorder_categories(\n",
    "    new_categories = [\"short\", \"medium\", \"wirehaired\", \"long\"],\n",
    "    ordered=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The order below will be the same as we have specified it to be"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "coat\n",
       "short     8.364746\n",
       "medium    9.027982\n",
       "long      9.552056\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.groupby(by=[\"coat\"], observed=False)[\"age\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grouping when ordered is False means we do not want the column to be ordinal, but it will have the order we have specified it to be"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dogs[\"coat\"] = dogs[\"coat\"].cat.reorder_categories(\n",
    "    new_categories = [\"short\", \"medium\", \"long\", \"wirehaired\"],\n",
    "    ordered=False\n",
    ")\n",
    "\n",
    "dogs.groupby(by=[\"coat\"], observed=False)[\"age\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example from tasks "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "size    sex   \n",
      "small   Male       260\n",
      "        Female     214\n",
      "medium  Male      1090\n",
      "        Female     854\n",
      "large   Male       331\n",
      "        Female     188\n",
      "Name: count, dtype: int64\n",
      "size    keep_in             \n",
      "small   both flat and garden    238\n",
      "        flat                     80\n",
      "        garden                   21\n",
      "medium  both flat and garden    795\n",
      "        garden                  317\n",
      "        flat                     97\n",
      "large   both flat and garden    191\n",
      "        garden                  172\n",
      "        flat                      5\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "dogs[\"size\"] = dogs[\"size\"].astype(\"category\")\n",
    "\n",
    "dogs[\"size\"] = dogs[\"size\"].cat.reorder_categories(\n",
    "  new_categories=[\"small\", \"medium\", \"large\"],\n",
    "  ordered=True,\n",
    ")\n",
    "\n",
    "# How many Male/Female dogs are available of each size?\n",
    "print(dogs.groupby(\"size\", observed=False)[\"sex\"].value_counts())\n",
    "\n",
    "# Do larger dogs need more room to roam?\n",
    "print(dogs.groupby(\"size\", observed=False)[\"keep_in\"].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning and accessing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Identifying issues:**\n",
    "- `Series.cat.categories()`\n",
    "- `Series.value_counts()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "get_along_cats\n",
       "yes    275\n",
       "no     156\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs[\"get_along_cats\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Imagine that we get:\n",
    "- No 2503\n",
    "- yes 156\n",
    "- no 156\n",
    "- Noo 2\n",
    "-   NO 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fixing issues with whitespace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "dogs[\"get_along_cats\"] = dogs[\"get_along_cats\"].str.strip()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fixing issues with capitalization (`title()`, `upper()`, `lower()`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "dogs[\"get_along_cats\"] = dogs[\"get_along_cats\"].str.title()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fixing issues: misspelled words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "replace_map = {\"Noo\": \"No\"}\n",
    "dogs[\"get_along_cats\"].replace(replace_map, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember to convert the Series back to category"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Searching for a string**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "2932    False\n",
       "2933    False\n",
       "2934    False\n",
       "2935    False\n",
       "2936     True\n",
       "Name: breed, Length: 2937, dtype: bool"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs[\"breed\"].str.contains(\"Shepherd\", regex=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Accessing data with loc**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>ID</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>breed</th>\n",
       "      <th>date_found</th>\n",
       "      <th>adoptable_from</th>\n",
       "      <th>posted</th>\n",
       "      <th>color</th>\n",
       "      <th>coat</th>\n",
       "      <th>size</th>\n",
       "      <th>neutered</th>\n",
       "      <th>housebroken</th>\n",
       "      <th>likes_people</th>\n",
       "      <th>likes_children</th>\n",
       "      <th>get_along_males</th>\n",
       "      <th>get_along_females</th>\n",
       "      <th>get_along_cats</th>\n",
       "      <th>keep_in</th>\n",
       "      <th>coat_collapsed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23807</td>\n",
       "      <td>Gida</td>\n",
       "      <td>0.25</td>\n",
       "      <td>Female</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>12/10/19</td>\n",
       "      <td>12/11/19</td>\n",
       "      <td>12/11/19</td>\n",
       "      <td>red</td>\n",
       "      <td>short</td>\n",
       "      <td>small</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>533</td>\n",
       "      <td>Frida És Ricsi</td>\n",
       "      <td>0.17</td>\n",
       "      <td>Female</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>12/1/19</td>\n",
       "      <td>12/1/19</td>\n",
       "      <td>12/9/19</td>\n",
       "      <td>black and white</td>\n",
       "      <td>short</td>\n",
       "      <td>small</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23793</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>12/8/19</td>\n",
       "      <td>12/23/19</td>\n",
       "      <td>12/8/19</td>\n",
       "      <td>saddle back</td>\n",
       "      <td>short</td>\n",
       "      <td>medium</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23795</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>12/8/19</td>\n",
       "      <td>12/23/19</td>\n",
       "      <td>12/8/19</td>\n",
       "      <td>yellow-brown</td>\n",
       "      <td>medium</td>\n",
       "      <td>medium</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23806</td>\n",
       "      <td>Amy</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>French Bulldog Mix</td>\n",
       "      <td>12/10/19</td>\n",
       "      <td>12/11/19</td>\n",
       "      <td>12/11/19</td>\n",
       "      <td>black</td>\n",
       "      <td>short</td>\n",
       "      <td>small</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>short</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID            name   age     sex               breed date_found  \\\n",
       "0  23807            Gida  0.25  Female             Unknown   12/10/19   \n",
       "1    533  Frida És Ricsi  0.17  Female             Unknown    12/1/19   \n",
       "2  23793             NaN  4.00    Male             Unknown    12/8/19   \n",
       "3  23795             NaN  1.00    Male             Unknown    12/8/19   \n",
       "4  23806             Amy  2.00  Female  French Bulldog Mix   12/10/19   \n",
       "\n",
       "  adoptable_from    posted            color    coat    size neutered  \\\n",
       "0       12/11/19  12/11/19              red   short   small       no   \n",
       "1        12/1/19   12/9/19  black and white   short   small       no   \n",
       "2       12/23/19   12/8/19      saddle back   short  medium       no   \n",
       "3       12/23/19   12/8/19     yellow-brown  medium  medium       no   \n",
       "4       12/11/19  12/11/19            black   short   small       no   \n",
       "\n",
       "  housebroken likes_people likes_children get_along_males get_along_females  \\\n",
       "0         NaN          NaN            NaN             NaN               NaN   \n",
       "1         NaN          yes            yes             yes               yes   \n",
       "2         NaN          NaN            NaN             NaN               NaN   \n",
       "3         NaN          NaN            NaN             NaN               NaN   \n",
       "4         NaN          NaN            NaN             NaN               NaN   \n",
       "\n",
       "  get_along_cats keep_in coat_collapsed  \n",
       "0            NaN     NaN          short  \n",
       "1            Yes     NaN          short  \n",
       "2            NaN     NaN          short  \n",
       "3            NaN     NaN         medium  \n",
       "4            NaN     NaN          short  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1        small\n",
       "6        small\n",
       "9        small\n",
       "13       small\n",
       "16       small\n",
       "         ...  \n",
       "2722    medium\n",
       "2746    medium\n",
       "2763    medium\n",
       "2809     large\n",
       "2815    medium\n",
       "Name: size, Length: 275, dtype: category\n",
       "Categories (3, object): ['small' < 'medium' < 'large']"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dogs.loc[dogs[\"get_along_cats\"] == \"Yes\", \"size\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercise example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sex\n",
      "male      1681\n",
      "female    1256\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Fix the misspelled word\n",
    "replace_map = {\"Malez\": \"male\"}\n",
    "\n",
    "# Update the sex column using the created map\n",
    "dogs[\"sex\"] = dogs[\"sex\"].replace(replace_map)\n",
    "\n",
    "# Strip away leading whitespace\n",
    "dogs[\"sex\"] = dogs[\"sex\"].str.strip()\n",
    "\n",
    "# Make all responses lowercase\n",
    "dogs[\"sex\"] = dogs[\"sex\"].str.lower()\n",
    "\n",
    "# Convert to a categorical Series\n",
    "dogs[\"sex\"] = dogs[\"sex\"].astype(\"category\")\n",
    "\n",
    "print(dogs[\"sex\"].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series([], Name: coat, dtype: category\n",
      "Categories (3, object): ['short' < 'medium' < 'long'])\n"
     ]
    }
   ],
   "source": [
    "# Print the category of the coat for ID 23807\n",
    "print(dogs.loc[dogs.index == 23807, \"coat\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sex\n",
      "male      124\n",
      "female     56\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Find the count of male and female dogs who have a \"long\" coat\n",
    "print(dogs.loc[dogs[\"coat\"] == \"long\", \"sex\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.186153846153847\n"
     ]
    }
   ],
   "source": [
    "# Print the mean age of dogs with a breed of \"English Cocker Spaniel\"\n",
    "print(dogs.loc[dogs[\"breed\"] == \"English Cocker Spaniel\", \"age\"].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35\n"
     ]
    }
   ],
   "source": [
    "print(dogs[dogs[\"breed\"].str.contains(\"English\", regex=False)].shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction to categorical plots using Seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(504, 20)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews = pd.read_csv(\"databases/lasvegas_tripadvisor.csv\")\n",
    "reviews.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 504 entries, 0 to 503\n",
      "Data columns (total 20 columns):\n",
      " #   Column             Non-Null Count  Dtype \n",
      "---  ------             --------------  ----- \n",
      " 0   User country       504 non-null    object\n",
      " 1   Nr. reviews        504 non-null    int64 \n",
      " 2   Nr. hotel reviews  504 non-null    int64 \n",
      " 3   Helpful votes      504 non-null    int64 \n",
      " 4   Score              504 non-null    int64 \n",
      " 5   Period of stay     504 non-null    object\n",
      " 6   Traveler type      504 non-null    object\n",
      " 7   Pool               504 non-null    object\n",
      " 8   Gym                504 non-null    object\n",
      " 9   Tennis court       504 non-null    object\n",
      " 10  Spa                504 non-null    object\n",
      " 11  Casino             504 non-null    object\n",
      " 12  Free internet      504 non-null    object\n",
      " 13  Hotel name         504 non-null    object\n",
      " 14  Hotel stars        504 non-null    int64 \n",
      " 15  Nr. rooms          504 non-null    int64 \n",
      " 16  User continent     504 non-null    object\n",
      " 17  Member years       504 non-null    int64 \n",
      " 18  Review month       504 non-null    object\n",
      " 19  Review weekday     504 non-null    object\n",
      "dtypes: int64(7), object(13)\n",
      "memory usage: 78.9+ KB\n"
     ]
    }
   ],
   "source": [
    "reviews.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will mainly focus on `sns.catplot()` - categorical plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parameters:\n",
    "- `x`: name of variable of data\n",
    "- `y`: name of variable of data\n",
    "- `data`: a DataFrame\n",
    "- `kind`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Box plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Score\n",
       "5    227\n",
       "4    164\n",
       "3     72\n",
       "2     30\n",
       "1     11\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews[\"Score\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Pool\",\n",
    "            y=\"Score\",\n",
    "            data=reviews,\n",
    "            kind=\"box\",\n",
    "            hue=\"Pool\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's improve the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setting font size and plot background\n",
    "sns.set(font_scale=1.4)\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "\n",
    "sns.catplot(x=\"Pool\",\n",
    "            y=\"Score\",\n",
    "            data=reviews,\n",
    "            kind=\"box\",\n",
    "            hue=\"Pool\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.3)\n",
    "sns.set_style(\"darkgrid\")\n",
    "sns.catplot(x=\"Traveler type\",\n",
    "            y=\"Score\",\n",
    "            data=reviews,\n",
    "            kind=\"bar\",\n",
    "            hue=\"Traveler type\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ordering your categories**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Business', 'Couples', 'Families', 'Friends', 'Solo'], dtype='object')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews[\"Traveler type\"] = reviews[\"Traveler type\"].astype(\"category\")\n",
    "reviews[\"Traveler type\"].cat.categories  #automatically alphabetically ordered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Traveler type\",\n",
    "            y=\"Score\",\n",
    "            data=reviews,\n",
    "            kind=\"bar\",\n",
    "            hue=\"Traveler type\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`catplot()` has an `order` parameter, however it is best practice to manage order outside of the plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`hue()`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 632.389x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"darkgrid\")\n",
    "sns.catplot(x=\"Traveler type\",\n",
    "            y=\"Score\",\n",
    "            data=reviews,\n",
    "            kind=\"bar\",\n",
    "            hue=\"Tennis court\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**From exercises about reordering**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HOnXq4OHDh/Dy8tJZt2jRIgQFBaF9+/awsDC63CcioizCKBNo2rRp6N69OyZOnIijR4+iZMmSuHHjBm7cuAEHBweMHTvW0CUSEVEWZnQ9TwAoU6YMdu/ejbZt2+LatWvYvHkz3rx5g2HDhsHd3R02NjaGLpGIiLIwo+x5AkDx4sXx22+/GboMIiKiZIyy50lERGTM0qTn6efnh8uXL+Ply5coV64c2rZti4sXL8LR0ZG7WImIKNP5pPAMCgrClClTcP78eWVZ27Zt0bZtW8yfPx+PHz/G/Pnz0aBBg08ulIiIyFikerft27dv0atXL5w7dw729vYYMGAARERZX6pUKURERGD48OHw8fFJk2KJiIiMQarDc9myZQgMDMTIkSPh6emJcePG6ayfN28eZs2ahfj4eKxcufKTCyUiIjIWqQ7P48ePo2zZshg+fPhHt+ncuTPs7e3h7e2d2ochIiIyOqkOz1evXqF8+fL/ul2JEiXw+vXr1D4MERGR0Ul1eObNmxdPnjz51+0ePXrEeWiJiChTSXV41q1bFw8fPsQff/zx0W28vLzg5+eHOnXqpPZhiIiIjE6qT1UZPnw4jh07hgkTJuD8+fOoVasWAODNmzc4cuQIzpw5gz179iBHjhwYMmRImhVMRERkaKkOz+LFi2Pt2rX47rvv4OnpqVwB5dy5czh37hxEBPnz58f8+fNRunTpNCqXiIjI8D5pkoTKlSvj0KFDOHz4MK5cuYKgoCAkJCSgUKFCqFatGtq0aYOcOXOmVa1ERERGIdXhuXTpUpQvXx4tWrRQZhUiIiLKClIdnhs3bkSRIkXQokWLtKyHiIjI6KV6tG18fDyKFy+elrUQERGZhFSHZ6dOnXD27FlcvXo1LeshIiIyeqnebVuhQgUULlwYffr0Qfny5WFnZ4c8efLAzCzlPJ46dWqqiyQiIjImqQ7PGTNmKP9++PAhHj58+NFtVSoVw5OIiDKNVIfnL7/8kpZ1EBERmYxUh2fHjh3Tsg4iIiKT8UmTJGj5+/vj2rVrePXqFaysrJA/f35Ur14dhQsXTou7JyIiMiqfFJ5v377F9OnTcezYMQCAiEClUinrGzVqhJkzZyJ//vyfViUREZERSXV4vn//Hn369IGvry8KFCiAxo0bo1ixYtBoNAgICMDp06dx4sQJBAQEYOfOnciePXta1k1ERGQwqQ7PtWvXwtfXF1988QVmz56NbNmy6ayPjY3FlClTsH//fmzatAmDBw/+5GKJiIiMQaonSfjzzz9RqFAh/Pzzz8mCEwCsrKwwe/ZsFCxY8B+v+UlERGRqUh2egYGBqFq1KqysrD66jZWVFapWrYqAgIDUPgwREZHRSXV45siRA2/fvv3X7d6+ffuPAUtERGRqUh2eVapUwfXr13Hr1q2PbnPr1i1cu3YNlStXTu3DEBERGZ1Uh2f//v2RkJCAQYMGYcuWLXj16pWyLjg4GFu2bMGgQYOg0WjQv3//NCmWiIjIGKR6tG3t2rUxYcIE/Prrr5g1axZmzZoFC4vEu4uPjweQOKft+PHj4erqmjbVEhERGYFPmiShX79+cHFxgbu7uzLDkIigWLFicHFxQa9evbjLloiIMp1Pnp7PyckJv/76a1rUQkREZBJSfcwTAOLi4rBp0yYsWLBAZ/np06fRtWtXbNq06ZOKIyIiMkap7nlGRkZi4MCBuHHjBkqWLInRo0cr64KDg3Hr1i3cvn0bp0+fxvLly3m6ChERZRqp7nmuW7cO169fR926dbF48WKddV26dMGxY8fQoEEDnD9/HqtXr/7kQomIiIxFqsPz0KFDKFq0KJYvXw47O7tk64sXL45FixahUKFC2Ldv3ycVSUREZExSHZ4BAQFwdHSEpaXlR7exsrJC5cqV8fz589Q+DBERkdFJdXjmyZMHL168+NftXr9+DRsbm9Q+DBERkdFJdXjWqlULd+7cweHDhz+6zenTp3Hjxg3UqFEjtQ9DRERkdFI92nbQoEE4evQoxowZg3bt2uHzzz9HkSJFAAAvX77E2bNn4enpCSsrKwwbNizNCiYiIjK0VIdnhQoVsHDhQkyePBmenp7w8vLSWS8iyJ07N+bMmYOKFSt+ap1ERERG45NmGGrQoAEOHz6MY8eO4erVq3j16hUSEhJQoEABVK1aFa1bt0auXLnSqlYiIiKj8MnT89nY2KBDhw7o0KEDAODNmzcIDQ1F8eLFOTECERFlSnqHp5+fH3bt2oVy5cqhc+fOyvKgoCBMmDABly9fBgBkz54d3bt3x9ixY5WrrRAREWUGeo229fDwQLt27bBhwwZcuXJFWR4ZGYmePXvi8uXLMDc3h4ODA3LmzIkNGzbg22+/TfOiiYiIDOk/h6evry9++OEHqFQq9OvXD1999ZWybtWqVXj+/Dmsra2xa9cu7Nq1CydPnkSLFi1w8uTJfzydhYiIyNT85/B0d3eHRqPBggULMGHCBDg4OCjrvLy8oFKp0KdPH9jb2wNInF3oxx9/hJWVVbKRuEREmYWZygyACgCggur//6bM7j+/yhcvXkSpUqXQrFkzneUPHz5EUFAQAKBly5Y66/LkyYPKlSvjzp07aVAqEZHxMTe3RInCjlBBheKFHWFu/vEpSynz+M8jeYKDg1GrVq1kyy9dugQAyJs3L9RqdbL1BQoUwM2bN1NfIRGRkbMrXQ92pesZugzKQP+55ykiUKlUyZZrR9d+bAq+sLAwWFtbp7I8IiIi4/Ofw7NIkSLw9/fXWRYfH48LFy5ApVKhTp06yW4TFxcHb29vZdo+IiKizOA/h2edOnXg6+ur7KYFgP379yM8PBzm5uZo2rRpstts2bIFERERqF27dtpUS0REZAT+8zHPfv36wcPDA0OHDkXXrl0hIti5cydUKhW6du2K/PnzK9tGRUVh+/btmD9/PqysrNC9e/d0KZ6IiMgQ/nN4Fi9eHG5ubhg3bhw2btyoLK9duza+//575e+rV6+iX79+SEhIAABMmTIFpUqVSsOSiYiIDEuvefOaNGmCU6dO4fjx43jz5g3s7e1Rr57uCDMbGxuoVCpUr14dQ4YMSbaeiIjI1Ok96WyePHnQqVOnj65Xq9W4detWiiNziYiIMoM0n7GdoUlERJkd55EiIiLSE8OTiIhITwxPIiIiPTE8iYiI9MTwJCIi0lOaj7ZNKy9evMCiRYvw119/ITQ0FPny5UPDhg0xatQondmMiIiIMppRhufz58/RpUsXhISEoGHDhihTpgzu3buHHTt24Ny5c/Dw8EC+fPkMXSYREWVRRhmebm5ueP36NX799Vd06NBBWb58+XIsXLgQK1aswOTJkw1XIBERZWlGd8xTo9Hg+PHjKFWqlE5wAsCgQYOQLVs2nDlzxjDFERERwQh7nvHx8Rg1ahRsbGySrTM3N4e5uTmioqIMUBkREVEiowtPKysr9OvXL8V1Fy5cQGRkJKpVq/ZJj2FhodvhNjc3ug74J/mv7WG7Mwe2O222MxVst3EwuvD8mOjoaPzyyy8AgG7duqX6fszMVMib1zqtyjJKuXPnMHQJBsF2Zy1sd9ZibO02ifCMi4vDd999hwcPHqBp06Zo3rx5qu9LoxGEhUXqLDM3NzO6F+ZThIVFISFB86/bsd2ZA9v9z9juzCGldhuyI2T04RkdHY3vvvsOJ0+ehIODA3799ddPvs/4+H9/45myhARNpm9jStjurIXtzlqMrd1GHZ7v3r3DkCFDcOPGDTg6OmLt2rUpDiQiIiLKSMZ1BDaJV69eoVevXrhx4wZq1qyJjRs3wtbW1tBlERERGWd4RkREYMCAAXj48CGaNGnCHicRERkVo9xt+8svv+D+/fuoX78+Fi1aBAsLoyyTiIiyKKNLJX9/f+zZswcAUKJECSxfvjzZNtmzZ8egQYMyujQiIiIARhie169fh0aTOKJq69atKW5ja2vL8CQiIoMxuvBs37492rdvb+gyiIiIPsooBwwREREZM4YnERGRnhieREREemJ4EhER6YnhSUREpCeGJxERkZ4YnkRERHpieBIREemJ4UlERKQnhicREZGeGJ5ERER6YngSERHpieFJRESkJ4YnERGRnhieREREemJ4EhER6YnhSUREpCeGJxERkZ4YnkRERHpieBIREemJ4UlERKQnhicREZGeGJ5ERER6YngSERHpieFJRESkJ4YnERGRnhieREREemJ4EhER6YnhSUREpCeGJxERkZ4YnkRERHpieBIREemJ4UlERKQnhicREZGeGJ5ERER6YngSERHpieFJRESkJ4YnERGRnhieREREemJ4EhER6YnhSUREpCeGJxERkZ4YnkRERHpieBIREemJ4UlERKQnhicREZGeGJ5ERER6YngSERHpieFJRESkJ4YnERGRnhieREREemJ4EhER6YnhSUREpCeGJxERkZ4YnkRERHpieBIREemJ4UlERKQnhicREZGeGJ5ERER6YngSERHpieFJRESkJ5MIz5cvX8LFxQXu7u6GLoWIiMj4w/P9+/cYOXIkIiIiDF0KERERACMPz8DAQPTp0wfe3t6GLoWIiEhhtOG5YcMGtG3bFj4+Pqhdu7ahyyEiIlIYbXhu2rQJxYoVg7u7O9q3b2/ocoiIiBQWhi7gY3788UfUqVMH5ubmePLkiaHLISIiUhhteNavXz/d7tvCQrfDbW5utB3wVPmv7WG7Mwe2O222MxVst3Ew2vBML2ZmKuTNa23oMtJV7tw5DF2CQbDdWQvbnbUYW7uzXHhqNIKwsEidZebmZkb3wnyKsLAoJCRo/nU7tjtzYLv/GdudOaTUbkN2hLJceAJAfPy/v/FMWUKCJtO3MSVsd9bCdmctxtZu49qJTEREZAIYnkRERHpieBIREemJ4UlERKQnhicREZGeTGK0badOndCpUydDl0FERASAPU8iIiK9MTyJiIj0xPAkIiLSE8OTiIhITwxPIiIiPTE8iYiI9MTwJCIi0hPDk4iISE8MTyIiIj0xPImIiPTE8CQiItITw5OIiEhPDE8iIiI9MTyJiIj0xPAkIiLSE8OTiIhITwxPIiIiPTE8iYiI9MTwJCIi0hPDk4iISE8MTyIiIj0xPImIiPTE8CQiItITw5OIiEhPDE8iIiI9MTyJiIj0xPAkIiLSE8OTiIhITwxPIiIiPTE8iYiI9MTwJCIi0hPDk4iISE8MTyIiIj0xPImIiPTE8CQiItITw5OIiEhPDE8iIiI9MTyJiIj0xPAkIiLSE8OTiIhITwxPIiIiPTE8iYiI9MTwJCIi0hPDk4iISE8MTyIiIj0xPImIiPTE8CQiItITw5OIiEhPDE8iIiI9MTyJiIj0xPAkIiLSE8OTiIhITwxPIiIiPTE8iYiI9MTwJCIi0hPDk4iISE8MTyIiIj0xPImIiPTE8CQiItITw5OIiEhPDE8iIiI9MTyJiIj0ZLThGR8fjw0bNqBVq1aoXLkymjZtiqVLlyIuLs7QpRERURZntOH5008/4ZdffkH+/Pnx1VdfoVChQli0aBHGjh1r6NKIiCiLszB0ASm5fv06duzYgTZt2sDNzQ0AICKYOHEivLy8cPr0aTRo0MDAVRIRUVZllD3PLVu2AACGDx+uLFOpVBgzZgxUKhV2795tqNKIiIiMMzyvXr2KggULoly5cjrLP/vsM5QuXRqXL182UGVERERGGJ6xsbEICgpCiRIlUlxfrFgxvH37Fu/evcvgyoiIiBKpREQMXURSwcHBqF+/Pho2bIiVK1cmW//dd9/hzz//xPHjx1G8eHG9719EoNHoNlmlAszMzPAuIhoJCZpU125o5uZmyGOTHRqNBv/lVdW2O+59GESTkP4FphOVmTksrXPr3e6wqHDEm3C7LczMkTtHLr3bHRMWBok33XarLMyRLbf+r/f7TPD5tk7F5zsyIgwJCab7epubmyOnTcqvt7m54fp/RjdgKD4+HgBgZWWV4nrt8piYmFTdv0qlgrm5KsV1eWyyp+o+jY2ZmX5vKEvr3OlUScbSt925c+RKp0oylr7tzpY7a77e1ln0853TJmu+3unNuKoBkD174hv8Y+dzxsbGAgBy5syZYTURERElZXThaWNjAzMzM4SHh6e4Xrs8V67M0WsgIiLTY3ThaWVlhaJFiyIgICDF9QEBAShYsCBsbGwyuDIiIqJERheeAODi4oKgoCD4+/vrLH/58iWePHkCZ2dnwxRGREQEIw3PDh06AADc3NygHQwsIspsQ127djVUaURERMY32hYA6tSpg9atW+PgwYN4/vw5atasiWvXruHatWto06YNPv/8c0OXSEREWZjRneepFRsbi1WrVsHT0xPBwcEoWrQoOnTogAEDBnz0NBYiIqKMYLThSUREZKyM8pgnERGRMWN4EhER6YnhSUREpCeGJxERkZ4YnkRERHpieBIREemJ4UlERKQnhicREZGeGJ5ZVEpzY2g0GgNUQpR18HOXeTA8syCNRgOVSgUACAkJQUBAAOLj443uSu1aWXkSrMzQdoZDoqSfu+DgYPj5+SEmJsZoP3emKqPeb0Y5MTylH41Go3xYd+zYgR07duDx48coXbo0xo8fj+rVqxvN3MHaWrVfOCmty8yStjEsLEx5LqytrQ1c2X+XtA2nT5/GkydP8Pz5c/Tp0wdFixbN9K+hVtLnYcuWLdi+fTsePnyI8uXLY+DAgWjatCmvUZwGkj7P169fx5MnT5A9e3ao1WqUK1cuTR+Lc9tmISKiBNHixYuxdOlS5M+fH4UKFcLDhw+RP39+TJ06FfXr10f27NkNWqv2Q/D06VOcOXMG3t7eKFiwIEqUKIGePXsatLaMkPRLYPfu3Th48CBevHgBR0dH9OjRA1WrVjVwhf8uaRtWrVqFpUuXIi4uDhqNBqVLl8akSZPg6upqND/W0ktKn7uiRYuiSJEiePr0KRISEjBy5Ei0adMGefLkMXC1puvD99uKFSsQGRkJAHB2dsY333yTtlfkEspydu7cKQ4ODjJt2jS5f/++iIhs3bpV1Gq1NGnSRA4dOiTR0dEGqy8hIUFERLy9vaV+/fri7OwsNWvWFGdnZ1Gr1fLVV1/JvXv3lO0yG41Go/x78eLFolarxdnZWRo2bChqtVpatWolFy9eNGCF+lmxYoWo1Wrp27ev7N69WxYsWCB16tSR5s2by/HjxyUmJsbQJWYIDw8PqVy5skybNk0ePnwoIiKHDx+WSpUqSaNGjcTd3V3evXtn4CpN39KlS5XviWXLlsn8+fNFrVZLp06d5Pjx42n2OAzPLOb169fSp08f6datm/IBFhFxc3OT+vXri4uLi9SvX18OHTokUVFRBqvz8ePHUq9ePenYsaPs27dPwsPDJSAgQL755htRq9XSsmVLCQkJMVh9aS0+Pj7Zss2bN0ulSpVk4sSJcu3aNRERmTdvnlEGaGxsbIrLz58/L66urjJx4kTl/RYRESHdunUTtVotjRs3lmPHjmX6AA0LC5MBAwZIx44d5cGDB8ryRYsWScOGDaVOnTpSq1YtcXd3l9DQUANWatpOnjwpNWvWlGnTpsmjR49EJPHHeLt27cTe3l7at28vp06dSpPHyhoHHEgRHR2Ne/fuoWrVqihfvjwA4MKFCzhz5gxcXV3x7bffIiYmBnPnzsXx48cRERGRofXJ/x9FOHz4MN6/f4+vv/4abdu2hY2NDczNzZXdX8OGDUO+fPkQHR2tcztT8+LFCwCAubm5zvKXL1/Cw8MDdevWxeDBg1GtWjUAie20tbXFo0ePMH36dFy5ciXDa/7QjRs3MH/+fLx58ybZutu3byMmJgZ9+vRB+fLlkZCQgGPHjiEiIgLNmzdHdHQ0Zs+ejTNnziAqKsoA1WeMyMhI3L59G1WqVEGFChUAAJcvX8aJEydQrVo1TJo0Cbly5VKuYax9Lk31fW0oN27cAAB07twZZcqUAQDs2rULGo0GDRo0gI+PD+bNm4cTJ04ot0ntc8zwzGLCw8MRHh6O4OBgREZGIiQkBGvWrMGrV68wefJkfPXVV6hXrx4CAwOxePFizJs3D35+fhlWnzYcb9y4gTx58qBFixYAAB8fH8ydOxfHjh3DDz/8gHbt2mH27Nn47bffdG5nSm7duoVGjRph27ZtydaFhITg/v37aNCgAcqUKQMRwZkzZ/DXX3+hd+/e+Prrr/H06VNMnToVFy5cMED1iSIjI7F+/Xpcv34d7969S7b+6dOniImJQd68eQEAvr6+cHd3R+7cuTFhwgT0798fz58/x4IFC3Dy5Enlx0RmIIl79gAAMTExeP/+PZ4/f46IiAiEhoZizZo1CA4OxpQpU/DFF1/g888/x8uXL7Ft2zZMnToVDx48MMn3taFoNBrcu3cPlpaWqFy5MgDg7t272LFjB/LkyYNff/0Vo0ePxsOHD7Fo0SIcPnwYQOq/OxiemdTHhmvb29tjxIgRaNKkCXLmzIktW7bgypUrmDdvnjJYoX79+gASe6keHh7Ili1bhtWtlTNnTqUNAQEBWLVqFQ4ePIgZM2age/fuePfuHS5evIhjx44hJiYmw+tLCzY2Nqhbty6KFy+ebJ124IO2Z/3y5UusWbMGb9++Rc+ePTFhwgQ0bNgQT58+xeTJk7FlyxbExsZmaP1A4us0atQo/PTTT8ov/ffv3yvrixQpAltbW4SGhgIAVq9ejcePH2PmzJkoVqwYWrVqhQIFCsDPzw9jxozBvHnzDNKOtJL0c6dSqZQv5pIlS2L06NFo3749bGxssGXLFpw/fx5z585Fvnz5AACurq4AgPj4eJw4ccIgnztTof1R8mGvsXDhwjAzM4OPjw8AYO3atXj27Bl+/vln5MmTRzmbwNfXF6NGjcKCBQtSXQNPVcmEko46O3HiBLy9vREcHIxcuXJhzJgxGDx4MADg9evX2LVrF5o2bQoXFxfl9ufOnUOpUqWwe/duxMbGKh/u9CJJRiPGx8fDwsICZcqUwcGDBzFr1iyEhYXh0KFDmDZtGnr06AERQZ48eVCgQAG8fv0a0dHRJvdFk5CQgLJly2LJkiXIkSMHAODixYuoXbs2ACB79uxo06aN8txv3LgR3t7e2Lx5s7Lss88+AwCEhoZi3rx5aNmyJfLnz59hbdC+bklPAVi2bBkCAwMxYsQIFClSBEOHDkWdOnVQsWJFHD16FPv378fcuXOV2+TOnRsqlQodO3aEtbU1unTpYrKjb5N+7o4cOYKrV6/C398f+fLlw+jRo9GvXz8kJCQgLCwMnp6eaNy4MWrUqKHc/tSpUyhRogSOHj2Kt2/fKr110pX0eQ4NDUVMTAxy5MiBPHnyYMiQIWjVqhXs7e3h5eWFgwcPYu7cuShZsiSAxM6DlZUVXFxcEBUVhdatW6e6DoZnJiMiyhtr2bJlWLFiBVQqlfKGc3R0ROvWrWFhYYF3794hODgY1tbWsLS0BAAcO3YM3t7ecHFxgZWVVbqee6atKSEhARYWiW9Fbe09e/bE7t27sXPnTgDAlClT0KtXLyQkJMDc3Bx3797FkydP4OLiYlLnPWppj3Fqg3PdunWYO3cuBg4ciHHjximncuTKlQuPHj2Ch4cHvvzyS1SsWFG5j5cvX6JWrVoYNmwYSpYsmaHBCSTf3RUcHIyLFy/i8uXLsLGxQf/+/fHZZ58px2tv3LgBKysrnbDdt28fYmJi0Lx5czRq1ChD609rST93y5YtQ44cOZT3d9WqVdGuXTtYWVkhODgYz58/h4ODg/I+OHbsGK5duwZHR0fExMTA1tbWgC0xXkmDc/v27fDy8sL9+/dhaWmJfv36YcCAAShatCgA4NKlSyhatCiqV6+u3Hb79u2Ij4/HxIkTUbJkyWRjDfTB8MxktF9oe/fuxbJly9ChQwf07t0bRYoUwb1792Bvb68E1WeffYYqVarg1KlT2LRpE16/fo0TJ04gPj4eQ4cOTdcegPZD8PDhQ6xduxY+Pj4wMzODs7Mz2rdvjypVqmDmzJmYMmUKQkNDYWlpidjYWFhZWeHOnTvYsGED3r59q/wQMHWff/45li9fjjVr1gAAxo0bhwIFCgBIPK4YHR2NvHnzKj9yjh49ir///hutW7dWequGVqhQIYwZMwZr167Fxo0bkZCQgEGDBik9ZBFBbGws3r17h4iICFy4cAG7du1CyZIl4ejoaODq08aBAwewfPlytG/fHl9//TWKFCmCR48eoWTJksrnqXjx4nB1dcWlS5fw+++/IyYmBqdPn0Z0dDRGjRplcntRMpI2OFeuXInff/8djo6OaNGiBSIiIhATE4O4uDhYWVkhISEBQUFBeP36NR4/foyiRYvi+PHjOHjwIBwcHJAnT55PCk6AkySYtGfPniEuLi7ZzBkajQZjxozBtWvXsGrVKqW3knT3KJA4COfSpUtwd3eHv78/AKBUqVJYsmSJMiIwPd29exf9+/dHfHw8ChcujISEBDx58gTZsmXD7Nmz0apVK5w6dQrTp09HSEgIypQpA1tbWzx79gzv3r3D6NGjMWDAgBTbZsy0tWr/r/0h8fjxY/To0QOhoaEYPHgwxowZAyBxppRevXqhaNGiGDduHF68eIE9e/YgLi4Oa9asQYkSJTK8Df80w5O3tzdWrVqF48ePo3fv3hg4cCAKFy6Mc+fOYdy4cdBoNLC1tVX2emzcuDHNZ39JT35+fnj//r0yKCWpCRMm4OzZs1i9ejUqVaoEIPl789q1a3j27Bm2bt2K27dvAwDKlSuH33//XRkBT//z4XvtypUrGDFiBGrVqoWxY8eiVKlSEBFERUUhZ86cynbHjh3DiBEjkCdPHpQpUwZ+fn6wsrLCpk2b0ub9liYnvFCGe/LkiTg6OsqyZcskMjJSZ927d++kXr160r9/fxFJPOk+6Yn3IoknEjdu3FjCw8Pl2bNn4unpKSdPnpSgoKB0rTvpxAZDhgyR1q1by4kTJyQ+Pl5iYmJk/fr1UqdOHbG3t5e9e/eKiEhAQIB8//330q1bN2nQoIGMGzdODh8+nOJ9Gruktb5//17Cw8N11vv6+kqtWrVErVbLvHnzlOXayRK0/zVt2lTnfMGMlLQNp06dksWLF8vPP/8sa9asUZbfunVLOSd35syZEhISIvHx8bJ7924ZMGCAtGzZUr799lvlXDxT4e/vL5UqVZI5c+Yke+0iIiKkUaNG0qNHDxFJfJ4+/NytWLFCatWqJc+ePZO3b9/KgQMH5OLFixIcHJxhbTAFV65ckTlz5qS4bseOHeLg4CAnT5786O3XrFkjFy5cEE9PT3F1dZXmzZvLgAED0vT9Zvr7u7KohIQEDBkyBFWqVFGOm2mZmZkhV65cCAgIwIsXL1CkSBGd9drdn4GBgTh37hxatGiRYb0X7Ui4oKAgAIm7K7XHuszNzdGvXz8ULlwY8+bNw5QpU1CyZEk4Oztj9uzZsLCwQFRUlE57TWmO26S17ty5EwcPHsSrV6/g4OCAESNGoHDhwihXrhy2bNmCXr16YfXq1dBoNBg/fjxGjBiBsmXL4vHjx8idOzeaNm2a7HXNCPLBMfXVq1cjNjYWCQkJqFy5Mr7++muYmZnByckJQ4YMAQC4u7tDo9Fg1KhR6NSpEzp27IiIiAhky5bN5AYH5cyZE+PHj0eFChWSjQfInj07ihQpgoCAAAQEBCQbRS0iKFy4MEJDQ3H+/Hl069btkwasZEYigri4OEyYMAE5c+ZEt27dULp0aQBQxjvcunVL2VsF/G+QodaFCxfw22+/YejQofjuu+9Qq1Yt5MqVCwDSdgxHmsUwZbikPc7jx48rU+2JiEybNk3UarVs3bpVZzvtTDa+vr6iVqtl586dGVewiISGhkrz5s3F3t5eKlWqJCtWrFDqStqj2bZtm6jVapkyZYrExMQoM9ik1Is2NUuWLBG1Wi01atQQV1dXUavV0rFjR7l06ZIyLWLSHujcuXMNXHFymzdvFgcHB5kyZYrcvHlTHj16JC9fvhQR0Zkt6M6dOzJs2DBRq9Uya9YsCQwMNFTJaSbpbEr79++Xq1evKn///PPPolarZdGiRTpT7Wk/d0+fPhV7e3udXjol9+rVK7ly5UqK6zw9PUWtVsv69euVZdrvBO3/27dvL927dxeNRpNue6ZM4yc7JSMiSg/s1KlT+Oabb7B27Vo8fPgQADBgwABUqFABK1aswNmzZ5UJkrUHyS9cuACVSpXup6F8KE+ePBg4cCDKlSunHOPU1qU9DggA3bt3h5OTE+7cuQMrKytloEzSc+dMRdJz/x4+fIht27aha9eu2LZtGw4ePIghQ4bg2bNnmDVrFm7evImYmBilB2pra4u1a9di5syZBmyBrufPn2PXrl1wdnZG3759UaVKFZQpUwZ58+bFvn37MHfuXMyePRsPHz6Eg4MDvvnmGzRv3hybN2/Gtm3bkJCQYOgmpJqIKO/FixcvYuzYsVi+fDlu3rwJAPj222/h6OiILVu24MCBA8rEEUk/d2ZmZspgMOGQk2Q0Gg0KFCigjJJdsGABpk2bpqwvU6YM8uTJg1WrVuH06dMAEr8XYmNjle8GEVG+U9JrzxTD0wQlvS6gj48P8uTJgzZt2uDAgQNYt24dHj16hFKlSmHw4MEQEfz000/Ytm0bnjx5Ao1GAy8vL+zYsQMlSpSAk5NThtWt/dLs0qULBg4ciM8++wyenp74888/AUAZPKPdLleuXHjz5o0S/KZK++G9desWrl69ivj4eHTt2hXlypWDra0t+vXrhyFDhuD58+eYPXs2vL29lQDdunUrAMDDwyPF6e8MITo6Gr6+vqhdu7YysMzLywujRo3C999/D3d3d2zevBkTJkyAv78/HB0d0bdvX7Rt2xbt27f/5FGOhpL0c3fz5k3kzZsXX331Fc6dO4cVK1bg5s2bsLa2xtixY2Fra4uFCxdi+fLlePjwId6/f489e/bA3d0dhQsXVkZIm9oPwYwWHR2NzZs3w8PDA3PmzIGIoEqVKhg9ejTevHkDNzc3nDp1CgCUQwBHjx5FUFCQ8gM93X6gpEt/ljKEm5ubNG3aVB49eiRPnjyR77//XtRqtUycOFH8/f0lPj5e9u/fL+3btxe1Wi0uLi5St25dcXBwkPr16+vs5k0P2t0lEREREh4eLo8ePdIZGLF//36pW7euVK9ePdnVDh48eCCNGzeWYcOGSVxcnEkNCkrJgQMHRK1WS7du3aRXr14ikvj8xMXFiUji7uzVq1eLi4uLtG3bVmcX7uPHjzN0YE1gYKA8efJEZ1nSiesDAgKkUaNG8vnnn8u2bdtk8ODBolarpWrVqjJ58mTZt2+fMsDJzc1NuZ2pTf6edPds0kMFbm5u0qhRI7l8+bKEhITIrFmzRK1Wy+DBg+Xu3bsiInLhwgVl8vsqVapIzZo1xcHBQRo2bJjunztTlvR53rlzp1y7dk1evXoljRo1UgafabdZtmyZ8vy6ubnJoUOHxM3NTZo1aya1a9dO9h5OawxPE5I0QC5evCguLi7St29f5QPr6+urBOiECRPE399fRESCg4PFzc1Nhg8fLkOHDpWlS5fKs2fPMqTWe/fuyZAhQ+Tzzz8XtVotjRo1kqlTpyrHgw4cOCCff/65chzo3LlzcvjwYeU42b59+9K1zowSHh4uQ4YMEbVaLQ4ODsprlvSYTNIA7dixo5w9ezbDA+fu3btSq1YtqVmzpmzcuFG8vb1T3G7OnDk6o387d+4s3t7eyuvq7+8vzs7O8sMPP2Rk+WnG29tbFi5cKOHh4Tqfu1u3bknNmjVlwIABcu/ePRGRjwZoTEyMLF++XCZNmiQjR46UtWvXSkBAgEHaYwqSBueiRYtErVbL7NmzRSTxB5v2O2TWrFnKdjt37pQGDRqIg4ODcum+D69ck14Ynibo6NGjMmfOHKlbt26yX7FJA3TixIni6+urrNP2ctKb9kNw+/ZtqVatmri6usqQIUNk8uTJyi/Ibt26yd9//y0i/+uBqtVqsbe3l0aNGknLli1THBBgCj5Wa1hYmIwdO1bUarX0799fHj9+rGyfNEDXrl0rarVaevTokaGXhUtISJBJkyYp13W1t7cXZ2dnmTZtmty6dUvCwsJ0tj9+/Ljs3r1bLl68mOy0DQ8PD6lataq4u7uLiGm9fn///bdUqVJF2rVrp7xGIiJ79uyRH374QerUqSN37tzRuU1ISIjMnj1b1Gq1DBo0SK5fv57BVZu2pO+PN2/eyJAhQ2TkyJE632+BgYE6Aaq9zf379+XKlSuyZcsWuXbtmrx+/TpDamZ4mpgHDx6IWq0WR0dH+eKLL5TlSXsovr6+MmHCBCVADXE+YHBwsHTo0EFatGghJ06cUJY/f/5cZs6cKVWqVJFu3brJ27dvRSRxBF2zZs2UkYpJP0ymtMs2aa0BAQHy7Nkzef78ubIsPDxcRo4cKWq1WkaOHKnsWkoaoG/fvpWNGzeKn59fxhYvie+datWqydixY+XixYvSpk0bUavVUrNmTenTp49cunQpxd6Tr6+vco7wgQMHpF27dtK0aVOdtpuCuLg4GTdunFStWlWOHj2qLA8KClL2GrRu3Vr5IZH0c5c0QAcPHiw3b97M8PpN3erVq6Vly5ZSo0YN8fDwUJZrd6EnDdCku3ANgeFpgg4cOCDOzs5K0Ggl7VlqA9TR0VFGjhyZ4Seje3t7i6Ojo8yfP19Zpv2ief36tbi5uYlarZbx48cr6/fu3Sv16tUTtVqtfHBSOtHcWCUNzq1bt0qbNm2kVq1aUrduXTl27JiyLjw8XEaMGPGPAWqIHwwJCQkSGxsrEydOFLVaLbdv35bQ0FDZt2+f9O7dW9RqtVSuXFm6dOkimzdvViY+ePXqlfTt21fq1q0rrVu3lmrVqkmjRo1M8thebGys9O/fX9RqtTIRh6enp7x69UouX74slSpVErVaLb/++qtym6SfO22AVqpUSXr27Cm3b9/O8DaYqri4OBkzZozUqFFDKleuLKtXr9b5TGif56QB+rGJFDICw9OI/VNoHD58WBwdHcXZ2VnnXM2kH2Q/Pz8ZMWKE1KxZUzkHL718+KW/fft2UavVylXbPzx2FxQUJO3atZMqVaoox45EEn8YaHfh7tmzJ11rTktJXyvtYJk6deoooePo6Ch79+5VtksaoN9++608ffo02f0YyokTJ5RgT3qO8M6dO2X69OnKcc5u3brJrFmzlGN+gwYNkq5du8rMmTPT/Zh6etq7d6+o1Wr54osvlNdo27ZtIpI4EEjb/g0bNii3+TBAp06dKi4uLibX8zYU7YC0yMhI+eGHH5TxEQ8fPhQRSTFAtYeAkg5Ky0gMTyOVtOcRGhoqgYGByXqPBw4cEAcHB6lXr57s3r1bWZ70g/z48eMMm3Lv9u3byqQHZ8+eTXaCvzYYtPUtX75c1Gp1spOhDxw4IA0aNDDJAUO7du0SJycnmT59uvKjYM+ePaJWq8XJyUk8PT2VbZMGaP/+/Y0qcMaMGSPOzs7K4BetpUuXir29vTRt2lRq1KgharVaWrduLT/99JMcOHBANBqNzshcU6N9L2tfs4oVK0r//v11jqOdP39e1Gq1VKpUSTZt2qQsT/q5e/Pmjbx69SrjCjcx/7RnJTIyUmbMmCFqtVpatGihfH99GKD+/v7yxRdfGGwPB8PTCCV9Y7m7u0v37t2latWqUqVKFendu7fs2LFDGdW4f//+fw3QjODj4yPOzs7i6uoqPj4+EhgYKLVr15ZGjRrJ+fPnU6xr/vz5Ym9vr/y6TNrr8vT0lBYtWpjMrj+NRiOhoaHSv39/6dq1q87xyiVLlkjDhg2lVq1a4ujoKJ6enkrARERESN++fcXJySndf+To448//lBCXTtoaf369aJWq2XIkCFy5coV8fPzk++++07pATg5OSUbVGSqVq5cqfQwmzVrJmfPntVZf+7cuX8NUEpZ0u+3q1evyt69e8XNzU2OHz+ufBdERkbKjz/+KGq1Wlq2bCkvXrzQua32eTbk883wNGJLly5Vpm6bPXu2TJgwQRo1aiTVq1eXESNGyJs3b0REtweatGeT3rRv5KioKFm8eLE0bNhQZ3CQp6enODo6ytdff53sy+fBgwfy5ZdfSocOHSQoKEgJzqQfrA9HcBq7oKAgqVmzpkyZMkVZdvHiRenQoYNMnDhRtm3bJi4uLlK5cmXZs2ePEkqRkZHKl4Mx6dGjh9SoUUMCAgKU4Bw4cKDOcbyYmBjx9fWVpUuX6ux+NwUpnc8aGxsr4eHhMm3aNJk3b54sXLhQ1Gq1tGrVSue4tYhugGpHFdM/S/oDefny5VK7dm2pVKmS2Nvbi1qtls8//1z+/PNPEUn8Xvnpp5+UANX+uEy6Z4MDhiiZ8+fPi7Ozs3z//ffK6SbR0dHi7u6u7CoLCwtT3jzaQUSOjo4ZuqvzwYMH0rJlS+ndu7cMHz5cWZ6QkCAhISGycOFCcXR0lMaNG8uiRYvk7t278scffyjnPKY0t64xHPdLjRcvXoiDg4OMGjVKoqKiJCQkRAYMGCCurq4SEhIiIiIzZ84UtVot1apVk4kTJ8q1a9cMXHVy2i+nQ4cOib29vbRr1045BePWrVvKdqbcy/q381lDQ0OVH6cbNmxQAvTDyTzOnTunnGO4Y8eODKvf1Gmf09GjR8uxY8fk8ePHsmjRIuW59PLyEpHEANX2QL/44gujmhuZ4WmktL/0tccDNRqNHD16VFq1aiV16tSRV69eiZ+fn/z111/Kl5iXl5e4urrqnJuWXrQ9xPPnz0vLli2VX40f/pJ/9eqVbNmyRVxcXHROqq9Zs6bOgAtTCsyP1arRaMTd3V358bJkyRJxdHTU2W3t5eUlFStWlC+++EJcXFyM6jjnh/z9/ZXXtnfv3jrHP03p9frQP53PmnSih6T+KUBPnTolNWrU0Dmnmj4uKChIWrVqJV999ZXO4Y2LFy9KrVq1pHbt2vL69WudPTPaH52dO3c2mmPqDE8jNXPmTHF0dFTeQEeOHJEWLVpInTp1lJmDRowYIT179tQZEZneuzqTHqsTSdxt99dff0mfPn1ErVbLb7/9luJJyo8fPxZ3d3eZO3eueHp66pxEbqrnccbExMibN2+UHopIYqhERUXJ69evpUmTJjJ48GCd12fy5MnSpEkTefbsWYpf0sZGewWLyZMni4ju6TSm7L+cz/rhrnTtD9qUAvTDa+rSx927d0/nqigJCQly6NAhadmypdSuXVuCgoLEx8dHJk6cqExGERUVJXPnzjXYNWxTwut5GqlcuXIhLi4ON27cQFRUFObPn4+wsDDs3LkTxYsXR3R0NMLDw3Hjxg0EBASgfPnyUKlUaXu9uhSYm5vj7t276N27NxYtWoT69eujZs2aAICoqChs2bIF+fLlQ8eOHZE3b14AiRPCly5dWrkuX1Kmej1ODw8PHD16FHfu3IGlpSWqV6+Ozp07o1q1asiePTtev36N8PBw2NjYKFe/OX78OC5fvoxq1aqhYMGCyJ49uyGb85+4uLjAzs4OBw8eRL9+/VChQgWTn8xco9GgZMmSaN68OTw9PdGvXz9s2bIFZ86cwc6dO3H58mUMGjQIarUa7dq1Q5s2bZA7d27069cPIoJff/0VCxYsQGxsLFq2bAkAJvFaGgvtVYa0n4sTJ05gwYIFCA8Px86dO/HZZ5/h6NGj8PT0RJ06deDg4IDs2bNj/Pjxhiw7GdP41sqkkl6q6kOtWrVCnjx5MGvWLPz888+IiIjAjh07lAvsZs+eHSqVCmXLlkXhwoUz9AtNG+jDhw/HhQsXYGVlhZo1a2LUqFEoU6YMli5dCk9PT7x9+xYA/vEqGqYSnMD/al2yZAlmzJgBf39/1K1bF6VKlcLp06fxww8/YPHixYiIiIC1tTXKly+PQ4cOYeXKlfjll18wb948JCQk4JtvvjGZL9sSJUqgSZMmiIqKgru7O2JiYgxd0iczMzODpaUlmjdvDgBYtWoVrKys0LZtW2zevBkzZ85Ehw4dcOvWLcyaNQtDhw7F7Nmz8fLlS/Tq1Qtz5szBw4cPsW7dOrx//x4Ar46SEknhaiYigmzZsiFHjhzYt28ftm7dinnz5iEsLAzbt29HiRIlAAClSpUCALx+/TpDa9aLgXu+WVbSXV/79u2TX375RSZPnqyMWAwPD5cZM2aIk5OTVKpUSZkHVuvo0aNSs2ZNGTNmjEF2GW3cuFHs7e2lYsWKyjE97S7cjh07SrVq1WTt2rU6uzQzgxMnToijo6NMmjRJOY0mJCREmTGpW7duyuuxb98+adGihXKct2XLlka12+nfaI9rhoaGSseOHaV+/fo6V8XJDPQ5n7VRo0Yyffp0OXXqlOzatUs5rYKSS/r99vr162SnYWkn0q9ataq4urome18tWrRInJyc5Ny5cxlSb2owPA1Me1kdR0dHZRSm9nSTFy9eyDfffCNqtVq+/vpr2bJli9y6dUsWL16sHP9Mr8FB2mObHw4MSXqZJu0xoI8FaM2aNWXVqlXKSFNTpn0e3NzcpEqVKsqoU41GI8ePH5cWLVpI3bp15dWrV/Ls2TNl8Mj9+/flxIkTcubMGZM9aT42NlbGjBkjarU6082Yo8/5rE2aNFGmKDSF49WGkvQ7Y/369dKmTRupXLmyjB8/Xjnd59WrVzJ06FBRq9UyadIkCQ0NVW5z5MgRadmypXTu3NmoPzMMTwO6fv261KlTRyZMmCCXL1+W9evXS7NmzcTZ2VkZ9h4UFCSzZs1SpqzThlX79u3TpRczatQoZUCS9kPw9OlTnatIfBig2h7oxYsXRSQxQM+dOydt27YVtVqd7AoUpmz48OFSr1495e+jR49KixYtxNXVVXneBg0apHPaTmbg7++vTCGY2ehzPuuSJUtM7nxWQ9E+l61bt5ZOnTqJk5OTtGjRQv744w8REblz544MGDBAmYhi2rRpMmjQIHF1dZXatWsbfc+e4ZmBPhyleObMGWnatKn4+Pgoyw4ePCitW7fWCdDo6Gh5/vy57NixQ7Zu3Srnz59Pl95cr169RK1WK29ukcTwrlq1qvTp00fnyyRpgGovoVWxYkW5dOmSiCR+2Zw8eVLnvjKDiRMnSpUqVcTf319OnTolzZs31xkBHRISIi1bthRXV1edyR/I+GSF81kNQaPRSHh4uPTp00fGjx8vz549k/DwcNm5c6c4OTlJo0aN5MCBAyKSeJm++fPnS4MGDcTe3l4aNmwo3333XYacbvepGJ4ZJGlwXrhwQc6ePSujR4+WL7/8Mtm2SQN0586dEh0dne71de/eXapVqybbtm2T9+/fK8ufP38u06dPFycnJxk6dKjOl0rSANUew3B0dJQzZ86IiG6bTen0hqS1ak890bZVO2l69+7dlV21H57b2rFjR+nSpUuGX8iaUiezns+akT78fIeGhkqbNm2UkNTy8vJSAjTpZC6vX7+WFy9eSGRkZIZ836UFnqqSQbQjNVesWIElS5ZApVJBRFCoUCHcv38farVaORWiVatWABJHdf78888wMzND69atlaHdaa1Hjx548OABxo4di/bt2yNHjhxKLUWKFMHQoUORI0cObNq0CQDwzTffwMnJCZaWloiJiUG2bNnQtm1bHD9+HPHx8Rg0aBBOnDiBwoULJ2u/sUt6OoqXlxfOnDmD+/fvo1ChQujXrx+qV6+OunXr4ty5c8iePTuOHj2KggULKrc/fPgwnjx5gi+++MJQTSA9FS9eHEOGDMHEiRNRsmRJVKpUCZLYsTCZ960hJf3M7N69G1evXoW/vz9CQ0OVz4Z2m/bt2wMApk2bhoULFyIhIQEdOnRA/vz5Teq0NQBgeKazD7+Mly1bhurVq6NKlSrYv38/AgMDsWrVKsycORM5c+ZEQkICzM3NlQBdvnw5pkyZAgsLC+WNl5aSBmfHjh11ghMAYmNjUaRIEQwYMAAAlAAdNmwYKleujGzZsgEAHj9+jKioKAwbNgy2trYoWrRomteaEbTtXr58OVauXAlbW1sULFgQjx49woMHD9CgQQNMnDgRY8aMwcOHD/Hbb7/hiy++QOnSpXH06FHs2bMHNjY2GDBgAKysrAzcGvqvMuP5rBlF+5lZtmwZFi1aBHNzc5iZmSEuLg7bt29HyZIl8dlnnynbJw3Q5cuXIy4uDl26dDGp4ATAU1UySmxsrGzcuFG6d++uXFrs3r17yhyv33//vbK7Iun0U15eXtK1a9d0mfqre/fu4uLiIu7u7srpFUl3vwQGBsr69euVEXIBAQEyZ84cqVixogwZMkQZIOTr6ysjR46U/v376+zKNaVdtUkdP35cHB0dZeLEicrpKL6+vjrHmX19fWXIkCHKXJzaY76tWrUyqdNR6H+0k8BPnz7dZHYdGlLSz/e9e/ekSZMmMnXqVDl//rzs2bNHunXrJg4ODrJw4cIUR83u27dP1Gq1tG/f3uQuAiHC3bYZYu3atdiyZQssLS1Rv359lClTBgBgb2+PsWPHAgD27t0LAPjpp5+QLVs2nd0cTZo0SfOZg7p37447d+5g6tSp+PLLL2FpaanT4wwKCsLmzZuxfv16TJgwAQBQrFgx9OnTB0BiD/TOnTtwcnLC06dP8ejRI0ybNg2WlpbKY5jaL0kRgUqlwvXr1wEkPkd2dnYAgHLlyulsGxYWBjc3N/j4+ODs2bMAgPLly6N69eooVKhQxhZOn0T7uvfr1w+nT5/GyZMnMWLECJ3d8ZSc9vN948YNZUatLl26oHLlyoiPj0fZsmXxyy+/YN26dTAzM0OPHj1QoEAB5fZt27aFubk51Gp1us+Mlh4YnmlI+yH80OvXrxEbG4s3b94gNDQUsbGxMDMzg4WFBSpUqPCvAZrWbyxPT0/cvHkTxYoVQ4ECBXQCD0gMzo0bN2L9+vXo27cvvv76a2Vd0aJF0a9fPxQrVgyLFi3ChQsXUKBAAUyfPh09e/b8x+fBGCStLaV/azQa+Pr6Infu3HBwcEi2HQBs27YNP/74IxYtWoTmzZujWrVqGd8QSjPa1zZnzpwoU6YM/v77b8THxxu4KtNw/vx59O/fH8WLF0exYsVQuXJlAICFhQWcnJwwefJk/Pzzz1izZg0AJAvQ1q1bG6TuNGG4Tm/mkXRU5cd2VS5evFiqVq0qVatWVSYUSLp79sGDB8ou3OHDh6frbqPQ0FBxc3MTR0dH6dWrl5w6dUpZFxgYKHPmzBG1Wi0///yzsjw+Pj5Z24KCgsTPz0/nyiCmsqv25cuX8vTpU4mNjU02unb06NGiVquVy1R9OOLy/PnzolarZcWKFTrrOTLT9GXm81k/xcc+1wEBATJ37lypVq2aqNVq2bt3b7LbeXt7S7du3aRy5cqyZMkSefnyZUaUnO4Ynp/I29tbJkyYIOvXr9c53qf9Ik16jtjSpUulYsWKUr16deWUj/j4eGXbhw8fSq9evcTR0THZdFZpLSwsTH777TdxcHCQ7t27y6VLlyQoKEgJzl9++UXZ9sNLAD19+lQuX76cLOCNPTyePHkiixcvlo4dOyrHKps3by7Dhg2Te/fuydu3b0Uk8RJTDg4OMmHCBJ3ba1/L+Ph4cXZ21rnoNVFm5O/vr3wutD4M0oCAAFmwYIE4ODhI7969k12jVhugPXv2FLVaLStXrjSay4p9CoZnKmk0GgkJCVEGi1SvXl1at24ta9asSXaCb9KeqXY6vho1aig9m6RvJF9f3wybAi0sLEzmzZsnDg4O0rFjRxk5cqSo1Wr59ddflW0+fJM/efJExowZI5UrV1YG05gCb29vad68uVSpUkW6du0qEydOlJEjR0rz5s1FrVZLnTp1ZP78+eLv7y/h4eHKczFz5sxk9+Xl5SUODg6yefNmA7SEKGPcu3dPKleuLI0aNZLt27frTJIiotsxCAwMlN9++00qVaokgwcPTnZx8YSEBLl69ar0798/01z3VCWSwtT39J+NHz8eR44cQc+ePXHz5k3cuHEDNjY26NOnD1xcXFCvXr1kt9EO6c6dOzfWrl0LJycnxMfHw8Ii4w9Bh4eHY9WqVdiwYQPi4uLQvHlzLFq0CAAQFxenczz0yZMnWL16NXbv3o0RI0ZgxIgRGV5vavj4+OCrr75CqVKl0L17d3z55ZfKuvfv32Pt2rU4cOAAAgMD0bJlS0yZMgWhoaGYNGkSbt68iTZt2qB169ZwdHTE4cOH4eHhgXfv3mHr1q3KVW6IMpO4uDjMnDkTO3fuRL58+fDmzRvkyJEDffr0Qf369VG9evVk4xpevHgBd3d3bNiwAfXq1cPw4cOVY6BA4tiB2NhY5fQ2k2fg8DZZ2l2U+/btk+rVq8uMGTMkJCRElixZolxYV3sKysmTJ5MNxV6yZImo1WpxdXXVuTC0IYSFhcmCBQvE0dFR+vTpoxyTFflfOx8/fiyTJ09WdrtoGfsxznfv3snAgQOlSZMmOsd24+Pjld3sMTExcvToUenWrZtUqlRJ5s2bJyIid+/elTFjxkiVKlWU11OtVkvTpk1NqtdNlBpXr16VihUrys8//yz79u2T5s2bi729vTg7O8uwYcPk2rVryQ4v+fv7y9y5cz/aA81MGJ6fKDo6Wr788kupW7euMpGxr6+v7Ny5Uxo1aiQODg7i7Ows7dq1k4MHD+p86a5evVq51JGhzytLegy0W7ducvr0aWWdn5+fSQaniMijR4+kcuXKOrtfkx6b1f47Pj5eTp48Ka1atZLatWsrk3+/evVKvL29xc3NTdzc3MTT0zPdj0cTGZr2c/H999+Lg4OD+Pv7y5s3b+TAgQPKBR+qVq0q7du3lwMHDugMGtQOOnRycpIePXok292bWTA8P4E2PE6ePCkVK1aUn376SWd9TEyMDBgwQBmc4ujoKI0bN5aVK1cqX8A7d+40mqsHJD0G2r17d7l48aI8ffpUpk6dapLBKSLi7u4uarVaLl++LCIp1639ooiJiZHFixeLWq2WoUOHZopBDUSf4sCBA6JWq2XEiBESFhYmIomj9T08PGT8+PHK3pjOnTvL/PnzJSQkROLi4iQ6OlpmzJghtWrVynSXsdPiMc80EBAQgCFDhiAgIAArVqyAq6srAMDd3R2zZs1Cw4YN4eTkhMePH2P//v0AEs+D6tOnjzIBgbHQHgNdv3491Go1bG1tce7cOYwZMwaDBw8GAJOag3L9+vX49ddfsXv3bjg4OHz0HFTt8sjISHTq1Akigv379yc7B/ZjtyfKrAYPHgxvb29s2rQJarVaWb5u3TrMnz8fRYsWRWBgIDQaDcqWLYuqVauiW7duyJs3L6ytrZEvXz4DVp9+TOMb0MgVL14c/fv3R0xMDK5evQog8Y01a9Ys1KtXD6NHj8bw4cMxb948LFu2DO3bt0exYsXSZa7aT5UrVy4MHjwYAwYMwN27d3Hu3Dl8//33Jhec2t+E2vllr1y5AgAfDT6VSoW4uDjkzJkTarUaT58+RVBQUIrbEWUFGo0GANCpUye8e/cOS5cuVdatW7cOc+fORb169eDm5gYPDw+0bNkS8fHx2L17N0aNGgVbW9tMG5wAZxj6ZNqeSL169VCxYkXs2LED0dHRWLt2LerXr49Ro0bp/Fpr3LgxateuDRGBtbW1ASv/uFy5cmHAgAGIiYlBiRIl0KtXLwCmE5zA/0LOxcUFKpUKly5dQrt27ZAvX76P9h61bcuRIwcsLCw4sTtladrPQ+3atWFvb4+//voLfn5++OuvvzB37lzUr18fI0eOhKOjIwBg5syZePPmDdzd3fHll18iV65chiw/3XG3bRpasGABVq5cCQsLC9StWxcjRoyAk5MTANPc3RcbG6sEiCkFZ1IxMTEYMWIE/vrrL0yZMkWZm/fD1yPp3507d4aFhQW2b99ukJqJjIX2c3/kyBGMGjUKFStWxN9//4369evj22+/Vb7fTPX74VNkrdamE+3vjwEDBqBixYqwsrLCwIED4eTkpFwX0NSCE4BOz8tUPxjZsmXD999/DwCYPXs2Dhw4AAA6c9pqNBrlbw8PD/j5+aFRo0bKeqKsSvu5r1ixIsqXL4+///4bNWvWxLhx43Q6Bqb6/fApsl6L04H2wtbZs2dH7dq1ERkZqVxpQ6VSmWRwZiYVKlTA1q1bAQBjx47Fli1b8O7dOwCJr4/2g3/s2DFs27YNRYsWRbt27ZT1RFldiRIl0LFjRwCJV1dSq9XQaDQm2zFIC9xt+w8+tivin3ZR+Pn5oXPnzihSpAgWL16McuXKZek3mDG5ceMGevToAQBo0KABXFxcUK1aNcTExODQoUO4cOECYmNjsXbtWlSoUMHA1RJlnH/6jtJ+3wUHB2Po0KEICQmBh4cHChUqlKW/29jzTCI2Nlb5d3x8vBKQN2/exIkTJ3D06FFERETAzMwMCQkJyW6v0WhQrlw59OrVC48fP8bFixcBsPdiLKpWrYr9+/ejefPmOHfuHNzc3NC7d28MGDAAR44cQeXKlbF582YGJ2Va2u8t7UjaD5enRPs9mD9/ftSqVQsvX77EsmXLEBcXl6W/29jz/H937tzBgQMH0L17d5QqVUpZvnLlSqxcuRKRkZEAgMqVK2PZsmUoUKDAR3ugFy5cwNdff41SpUph3759sLKyytJvMmMTFxeHx48fw8fHB7GxsciePTtq1KiBXLlyIWfOnIYujyjNBQcHKxdpTzoQcO/evbh69Sp8fX3RtWtX1K9fX7neZtJepfbfb9++Rffu3REfH48NGzagRIkShmmQEeCpKgCio6OxZs0aHDp0CCKCXr16oUSJEti+fTsWLFiAWrVqoV69erh48SLOnTuHbt26Yf369ShZsmSKAerq6op+/fqhQ4cOmWcS5EzE0tISdnZ2sLOzM3QpROnu77//RufOndG/f3+MGzdOCc6lS5di8eLFyJEjB6KionDjxg107doVffr0QYUKFZSxHNpxGxqNBra2tnB2dsbBgwez/Klc7Hn+v9u3b2P16tU4duwYevbsiR49euD3339HfHw8Jk6ciJIlSyIuLg7z5s3Dxo0bUaxYMeWXV9IAzcrHAEwNXyvKCu7fv69MyKK9GtKNGzfQv39/tGjRAj179sTbt2+xZ88eHDlyBK1bt8bgwYOV89M//JzcuXMHuXPnRsmSJQ3SHmORpXueMTExiIqKgq2tLZycnDB06FBoNBps374dcXFxuHLlCsaMGYOSJUsquzrGjRsHMzMzrF+/Hv369UsWoPwyNh18rSgrUKvV8PLyQq9evbBkyRJYWlqiYMGCKFasGAYNGoRy5coBAEqWLIk8efLAw8MDAJQA/fBzop0UIavLkgOGtJ3t69ev49SpU8ppC5UqVcKYMWNQp04d7NixA2/fvlVuY2Vlhfj4eFhaWmLs2LH4+uuvERgYiH79+sHf3x9mZmbJDsITERlCVFSUMiUlANjb22PLli2wtrbGggUL4ObmhoIFC6JcuXLKQMkyZcqgf//+6Ny5Mw4ePIhVq1bh/v37hmqC0cuS4an9JXXy5ElMnDgRR44cAQB4eXmhTJkymDBhAtq2bQsAOHToEPz8/AAkTuauvWh10gDt2LEjAgICsuSJwkRkfOLi4rBixQrs2rVLWWZvb48dO3bA2toar1+/RkxMDID/dQwAoHTp0hg4cKASoOvWrcO9e/cM0gZjl6V329aqVQsXL17E3Llzcfz4cZw6dQrv379Hr1698NVXXyEmJgZHjx7Ftm3b0LdvX5QoUSJZgL5//x5//PHHPw71JiLKSE+fPsW5c+fw6NEjFChQAPfu3UNkZCTGjh2LrVu3onfv3rh+/Tp+++03jB8/Xud7rVSpUhg4cCDMzc2xfft2WFlZYdq0aVl+gNCHsvyAoQsXLmD06NEIDw9H9erVMWHCBFSqVAlA4ii1pUuX4uTJk0qgaodma99o8fHxePfuHfLnz2/IZhAR6Th48CC+//57WFtb4927d/j6668xfPhw2NjY4P79++jVqxciIiKUQUTA/77XAODRo0fYtm0bunfvrhwXpf/JkvsZkx6bfPz4MWJiYpAjRw7cunULPj4+CA8PB5B4DHT48OFo1KgRtmzZgk2bNsHf3x+A7i5cBicRGZvWrVvD1dUV7969Q65cuVCwYEHY2NgASBxE5O7uDmtrayxZsgRLliwB8L/vNQAoW7Ysvv/+ewbnR2TJ8NQem1y7di22bduGUaNG4fvvv0fJkiUxa9YsHDhwABEREQCSB+iWLVvw9OlTAFB+oRERGYOkh4+ePXuGkJAQuLi44P3799i8eTP27t2rrE86iOhjAfrhxeApCclCEhISlH9v2rRJ1Gq1jBkzRp49eyYiIocPH5YvvvhCqlatKtu3b5fw8HBl+7t378qIESNErVbLvHnzJC4uLsPrJyL6L7y9vUVE5MmTJ/Lq1Ss5dOiQVKpUSRo0aCBeXl462967d0+qVasmarVa5s6da4hyTVKW6nlqe5zPnz/Hixcv4OLigpEjRyrHMZs1a4Zvv/0WxYoVw6+//oo//vhDGcat7YG2bdsWHTp0YK+TiIzSunXr0LVrV6xevRqlSpVCgQIF0KJFC/zyyy949eoVFixYkKwHum3bNgCAu7s73rx5Y6jSTUqWS4Bdu3Zh6tSpyJcvH1q0aIHSpUsD+N+B8mbNmgEAFi9ejPnz50Oj0SAuLg5+fn74+uuvMXv2bI46IyKjVb58eWTPnh3z58+HiGDw4MEAgHbt2kGlUmHixIlYsGABLCws0KZNG/j5+aFUqVI4cuQI4uPjkS9fPgO3wDRkudG2J06cwPr163HlyhWo1WrMmzdPuYpG0mn2jh07hmXLluHvv/8GkHhu6JEjR7L0RMhEZNzk/6fSu3jxIkaMGIGIiAiMGTNGCVAA+OOPPzBp0iTY2trC1dUVfn5+cHJywvTp02Fubm7A6k1LlgtPADh16pQSoEOGDMFXX32FvHnzAtAN0PPnz+Ovv/5CYGAgRo8ejTJlyhiybCIiRUpzams0GmUi9wsXLmDkyJEpBuiff/6J0aNHAwBy5cqFrVu38lJ8esq04fnh1U60s2lor3Jy/vx5/P7777h79y6+++47dOnSBXny5Enxtkkv4UNEZEw8PDxQvHhx1KxZE+bm5skCdMSIEXj//j3Gjh2LQYMGKbd78OABnj59CicnJxQuXNiALTBNmTI8k4bfwYMHcfHiRdy8eRM5c+ZE3bp10b59e5QsWRJXrlzBb7/9hr///hujRo1C165dlQCVFK5lR0RkTG7cuIEePXqgQoUKmDFjBqpWrZosQM+dO4cBAwYAQLIeKKVepgvPpEG3ZMkSrFixAtmzZ0eRIkUQEBCAqKgoODo6YsiQIWjWrBkuX76M+fPn4+7duxg1ahS6deuG3LlzG7gVRET/7s2bN9i5cyfWr1+PIkWKYOrUqSkG6Nq1a/Hbb78hV65c6Nq1K8aPH2/o0k1epjhVJemMQdrg9PLywpIlS9C2bVusW7cOf/zxB7Zs2YJhw4bhyZMnmDdvHs6ePYuaNWvi22+/haOjI5YtW4aNGzciLCzMUE0hIkpRSv2cfPnyoWvXrhgwYACePXuGWbNm4caNG0hISICZmZky2UGZMmWQK1cu5MuXD3v37uXpKGnApMMzODgY8fHxOpcD02g0iI2NxaFDh1CwYEH0798flStXBpB4ruaAAQMwbdo0vH79GmvWrEFERATq1q2LESNGoGjRoti+fTsneScio6LtRQJAWFgY/P394e/vj/DwcOTLlw89e/bEsGHD8OzZM8ycORPXrl1DbGysMkPQ3bt3UalSJSxYsAA7d+7k6ShpwGTD08fHB23atMGiRYt0AtTMzAxRUVG4c+cOypUrhwoVKkCj0SjhamNjgyZNmqBHjx64ePEi/vjjDwBAvXr1MG3aNOzYsUMZeUtEZGhJx3Bs2bIF/fv3R7NmzdCsWTN88cUX2Lx5M6KiotC3b18MGzYM/v7+mDVrFo4ePYq3b9/iyJEjOHLkCGxsbGBvb4+iRYsauEWZg8lOkpCQkICEhARs3rwZ2bJlw+DBg5VfWdmzZ4e1tTWioqIAJM4slHSXh42NDRo2bIg1a9bA19dXOU5au3Ztg7SFiOhjtMG5dOlSLF68GHZ2dujbty9evXqFmzdvYs6cObh16xZGjRqFPn36wMzMDOvWrcOECROQL18+vH37FtbW1li4cCGvOZyGTDY8HRwcsHnzZowYMQIrVqwAACVA4+LiYGdnh6NHj8LLywsdOnRQzoESEZibm6NcuXLKBMgcSUtExuz06dNYtWoV2rRpg2+++Ua50snVq1exbds2/PHHH8iZMyfGjx+Pbt26oVy5cti8eTNCQkJQvXp1jBw5kueppzGTDU8gMUAXLVqEb7/9VidAbWxs0K1bNxw9ehRLly6FtbU1mjVrpvOr6+DBgwCAqlWrGqR2IiIt7d4v7R6yD3/Q37p1CyKCnj17oly5csqu3OrVq6NYsWLQaDTYs2cPateujVatWqFhw4Zo2LAh4uLiAPDqKOnBJPrw/3Q2jZOTExYtWoQCBQpgxYoVWLlyJWJjY1GvXj1Mnz4d/v7+mDp1KjZu3IjQ0FDExMRgz5492LZtG4oXLw5XV9cMbAkRUXLasEw6MAiAMlr2+vXrsLKyQsmSJZNtU6RIEXTv3h1WVlZYvXo1YmNjlUGPlpaWDM50YhLneX4440/SZdpfbHfu3MHIkSPx+vVrDBkyBEOGDIGlpSU8PDwwY8YMaDQaFChQACqVCm/evEH+/PmxZs0a2NnZGahVRESJV3k6d+4cLl26hPDwcNSsWRPVqlXT2Ss2d+5cbNiwAdu3b0flypWTTdwiIujVqxeCgoKwb98+5aLXlH6MfrftnTt3sHr1ajRq1AgFCxZEjRo1oFKplF9T2jeQo6MjFi1alOwYaJcuXVCmTBkcO3YMt2/fRs6cOVGpUiV06dIFxYsXN1i7iIhu376NyZMnw8/PD9myZUN0dDROnz6N/v37o1KlSsp0onZ2dtBoNPjtt9/w66+/omjRojpjOFQqFWJiYpAnTx5kz57dwK3KGoy65xkSEoI+ffrg0aNHyrKyZcuiYMGCqFu3LsqVK4eyZcuiZMmSytUAbt++jdGjR+PFixcYMmQIhg4dynlpicjo3LlzB3379kWpUqXw5ZdfokePHnjw4AHu37+PEiVKoFq1ajp73b799lscOXIEbdq0wXfffadzhaeDBw9ixowZaN++PSZPnsxRtRnAqMMzIiICW7Zswf79+/Hw4UMULVoUlpaWePr0qbKNjY0NypQpgzJlyqBu3booXbo0VCoVBg8eDAsLC3Tp0gXffPMNLC0tlWt2cq5aIjKk4OBgfPPNN4iLi8P48eNRr149AP+7rnBKh6piYmIwdOhQXLhwAeXKlcPQoUNRoEAB3Lx5E15eXnj//j22bt2KkiVLGqJJWY5RhycAhIeHY/fu3di0aRNUKhV++OEH5M+fH48ePcLdu3dx4cIFvHnzBsHBwcptKlasiJCQEAQHB6NUqVJo3Lgxxo4dCwsLo99LTUSZmPaH+5kzZzBq1Ch899136Nu3LwDdqzfFxsbi5s2buH37NlQqFYoVK4YWLVogLi4OU6ZMwb59+5T7tLS0ROnSpeHm5sbLimUgow9P4H8BumTJEhQpUgSTJk1CnTp1AADR0dF4//49rl+/juDgYJw+fRovXrzA06dPERsbCwAoUaIEduzYwSmpiMgoLFiwAOvWrcOpU6eQP39+nZ7mgQMHcObMGezdu1fnNr1798bUqVMBQPmee/HiBRwdHeHs7IyCBQtmeDuyMpPoiuXKlQudO3cGACxfvhw//vgjZsyYARcXF2TPnh3Zs2dHs2bNAACdO3eGhYUF7t69i8ePH+P27dvo2bMng5OIjIZ2MpeHDx8if/78UKlUePHiBVatWgUPDw9lOtE2bdrA1tYWly5dgru7OwoVKoTBgwejQYMGBm4BmUTPUysiIgK7du3C8uXLkS9fPkybNg01a9bUOY754SksCQkJymAiIiJD0n4veXt7Y+DAgShVqhSaNGmCwMBA+Pj44M6dOzAzM0OnTp3QsGFDNG3aFEDihatHjhwJGxsbbN26FRYWFvxeMzCTCk/gnwOUiMgUREREYPny5di9ezdCQ0MBADly5EChQoUwefJkVK1aVbmusPZY6PTp03H48GEcP34c1tbWHPRoYCYXngADlIhMX1hYGLy9vXH16lXEx8ejZs2asLOzQ5EiRZRtku4569OnD96/f489e/YYqmRKwiTDE0geoNOnT0f16tU5FRURmbyEhASoVCplENHevXvxyy+/oGvXrhg9ejSA5PPfUsYy2TNpbWxs0LlzZwwbNgzh4eEYN24cbty4YeiyiIj+M23fRTuHrfYyiubm5kpwHjt2DBs2bECuXLnQtWtXqFQqBqcRMOn9nNoAjYmJgYeHBwoXLmzokoiI/jNtCFpYWODNmzcYMWIEKlSogC5dukCj0WDfvn04ceIEYmJisH79ek4pakRMdrdtUhEREYiPj4etra2hSyEiSpUHDx6gV69eCA8PV5ZZWFigZs2amDp1KsqWLWvA6uhDmSI8iYgyg8DAQBw+fBgvXryAlZUVateuDScnJ3YMjBDDk4iISE8mO2CIiCgzStqfYd/GeLHnSUREpCf2PImIiPTE8CQiItITw5OIiEhPDE8iIiI9MTyJiIj0xPAkIiLSE8OTiP6VKZ/RZsq1k/FieJLJu3TpEtRqNfr06fOP2wUEBECtVqNx48YZVJnpe/fuHWbMmIFjx47pLFer1ahataqBqvpv/Pz80LdvX7x9+9bQpVAmxPAkoo+aM2cOtm/fjoSEBEOXorfBgwfj4sWLhi6DMimTviQZEaWvj+3yPHjwoHK9SWPF3bWUnhieRKS3cuXKGboEIoMy7p+ORBkgKioKv//+O9q1a4eqVauievXq6NmzJ3bt2pVi78Xf3x+TJk1C/fr14ejoiMaNG2PWrFl48+aNznbaY6zjxo2Dh4cH6tWrB2dnZwwYMOA/1bRixQq0bdsWzs7OaNCgAb799ls8ePAg2bavX7/GrFmz0LhxYzg6OqJOnToYNWoUfHx8km2rVqvRo0cPhISEYOrUqahXrx6cnJzQrl07bN++Pdm2np6eAIBRo0ZBrVbj0qVLyrqkxzy1x50XLFiAv//+G4MGDUL16tVRtWpVfPXVV8rtPuTj44NRo0bB1dUVjo6OaNGiBX7//XdERkbqbKfP/Wu3DQwMBAC4urpCrVb/63NOpA/2PClLExEMHToUFy9eRNGiRVGnTh3ExMTg0qVLuHbtGvz9/TF69Ghl+1u3bmHAgAEICwuDnZ0dnJ2d8fDhQ2zevBknTpyAu7s7ihYtqvMY3t7eOHDgAKpXrw6VSoXSpUv/Y01hYWHo27cv/v77bxQsWBD169fHq1evcPjwYZw+fRqbN29G5cqVAQCPHj1Cnz598Pr1a5QqVQqNGzfG8+fPcejQIRw/fhwLFixAs2bNdO4/PDwc3bt3x9u3b1G1alVER0fjypUrmDFjBsLDwzFo0CAAQNu2bXHz5k34+/ujevXqKFKkCAoUKPCPtd+5cwfr169Hvnz5UKtWLTx79kx5Ljdv3oxq1aop2548eRLffvstYmNj4ejoiGLFiuH27dtYtmwZTp06hU2bNiFXrlx633+BAgXQtm1bHD9+HJGRkWjRogWsrKz+sW4ivQmRibt48aLY2dlJ7969/3E7f39/sbOzk0aNGinLrly5InZ2dtKnTx+Jj49Xlj98+FCcnZ3F0dFRIiMjRUQkJiZGGjVqJGq1Wjw9PZVtNRqNLF68WOzs7KRfv37JHs/Ozk4WLVqkLE9ISPjHOqdPny52dnYycuRIiY6OVpbv3r1b7OzspF27dsrjtmvXTuzs7MTNzU3nfg8dOiSVKlUSZ2dnCQoKUpZr6+nWrZu8fv1aWb53716xs7OTOnXq6NQyYcIEsbOzkz///FNnuZ2dnTg7Oyt/a18DOzs7mTFjhsTExCjrpkyZInZ2djJq1Chl2atXr8TFxUWcnJzk7NmzyvLY2FiZOnWq2NnZyZQpU1J9/yIijRo1Ejs7OwkJCUn5iSb6BNxtS1lacHAwAKBAgQIwNzdXlpcvXx6zZ8/GnDlzlF23hw8fRmBgINq3b48OHToo26pUKgwfPhwVK1bE+fPn8fDhw2SP06tXL+Xf/zTQJiYmBnv37kXOnDnx888/I1u2bMq6Tp06oU6dOsiZMydCQ0Nx6dIl+Pj4wMHBAd99953O/bZo0QI9evRAZGRkst2xADB+/Hjkz59f+btt27bInTs3Xr9+/UmndlhbW2PSpEk6PT1t2/38/JRlu3btQnh4OAYOHIi6desqyy0tLTF16lQUKFAAXl5eePfuXarunyi9MTwpS3N2doaFhQUOHDiAoUOHwsvLCyEhIQCA1q1bo02bNsiZMycA4PLlywCAWrVqJbsflUqlhIB2O62CBQsiX758/6me27dvIyoqCi4uLrCxsUm2fv369di2bRtsbW1x9epVAEDz5s2hUqmSbdu6dWsAULZLWquTk1OyZdowjYqK+k+1pkStVusEPgBlV2/S45j/9Fxmy5YNNWrUQFxcHG7evJmq+ydKbzzmSSZP22OUfzk1QaPRAEjs3WgVLVoUs2fPxo8//oiTJ0/i5MmTSri0bt0a3bp1U8IzKCgIADBp0iRMmjTpo4+j7c1q5c6d+z+35dWrVwCAIkWK/OdtPzzGqlWsWDGd7bSyZ8+e4jFAC4vErwPt85QaHx6jBFJ+fbTP5VdfffWP9/fy5ctU3T9RemN4ksmztrYG8O89j/fv3wNIHmYdOnRAw4YNcfToUZw+fRqXLl3CrVu3cOvWLWzbtg07duxA3rx5lVCpV68e8ubN+9HHKVu2rM7f+pwPqU9w/VtYaNd/GJQp9VLTyn+9b+2kC82bN0/Wk0zqwx8R6Vk7kT4YnmTytD2v58+f/+N2T548AZByr87W1hZdunRBly5dkJCQgCtXrmDmzJnw9fXFzp07MWTIEBQsWBAA0KVLF7Rs2TJtG/H/tLtOP+xxaV2+fBkvX76Eq6srChUqBADKKRkfCggI0LlPY1KoUCE8efIEgwcPTrYLmcgU8Jgnmbw8efKgbNmyePv2bbLjjUmdPHkSAFCjRg1l2datW9GoUSPs27dPWWZubo7atWujX79+AIAXL14AAFxcXAAAZ86cSfH+x40bh86dO+PChQupboujoyMsLS1x7dq1FHvSixcvxrhx4xAaGqrUc/To0RR7oYcOHQIAVK9ePdX1pFdPT3vKSkrPpYjgq6++Qvfu3VM8r5XIGDA8KVMYOHAggMTjkR9ODiAi2L17N/744w/kz59fZ6Rs6dKl8fz5c6xYsUJnkoPY2FgcOXIEAJSeUevWrZE/f37s2bNHmTxAa9euXfjjjz/g5+cHR0fHVLfDxsYGbdu2RUREBH788UfExcUp6/bt24fLly9DrVajfPnyqF27Nuzs7HD37l0sXLhQZ5fvsWPHsG3bNuTMmVOnvfrS7vINDw9P9X2kpGvXrsiWLRtWrVqlE6AigiVLluDSpUsIDg7+pJmMtLuDIyIiPrleog9xty1lCp06dYK3tzd27NiB9u3bw9HREcWLF0dsbCzu3buHFy9ewNbWFosWLdIZdFKnTh20adMGBw4cQNOmTVGtWjVky5YNt2/fxsuXL1GtWjW0bdsWQOKx1fnz52PYsGGYOHEiVq9ejbJly8Lf3x8+Pj6wsLCAm5tbioNa9DFhwgTcunULXl5euHTpEipXrowXL17g1q1byJkzJ+bPnw8gsVfo5uaGvn37YsWKFfjzzz9RsWJFvHjxAt7e3siWLRt+/vlnZeBQamgndJg/fz7++usvfP3112lyNZVixYph1qxZmDhxIgYNGoRKlSqhePHiePDgAZ48eYKcOXNi4cKFOqcP6atUqVJ49OgRhgwZgvLly+PXX39VBn8RfSr2PClTUKlU+Omnn7Bq1Sq0aNECb9++xalTp3Dt2jXkzp0bQ4cOxb59+1LchTlnzhyMHTsWxYsXx+XLl3H27FnkyZMH3333HTZs2KAz4MbV1RV79uxBhw4dEB4ejlOnTiEsLAwtWrSAh4cHGjVq9MltsbW1xY4dOzB06FBYWVnhxIkTePbsGVq2bIldu3ahQoUKyrYVKlSAp6cnevbsibi4OBw/fhwvXrxA+/bt4eHhgVatWn1SLd27d8cXX3yB2NhY/PXXX2m6G1U7JWCLFi0QFBSEkydPIiEhAZ06dYKXl5cyi1JqTZw4EdWqVUNgYCAuX74Mf3//NKqcCFAJx3cTERHphT1PIiIiPTE8iYiI9MTwJCIi0hPDk4iISE8MTyIiIj0xPImIiPTE8CQiItITw5OIiEhPDE8iIiI9MTyJiIj0xPAkIiLSE8OTiIhIT/8HyybcuMaEx2MAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert \"User continent\" to a categorical variable\n",
    "reviews[\"User continent\"] = reviews[\"User continent\"].astype(\"category\")\n",
    "\n",
    "# Here we sort regions by popularity (most visitors from here)\n",
    "continent_categories = list(reviews[\"User continent\"].value_counts().index)\n",
    "reviews[\"User continent\"] = reviews[\"User continent\"].cat.reorder_categories(new_categories = continent_categories)\n",
    "\n",
    "sns.catplot(x=\"User continent\",\n",
    "            y=\"Score\",\n",
    "            data=reviews,\n",
    "            kind=\"bar\", \n",
    "            hue=\"User continent\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Point and count plots**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Point plot helps to feel the difference between categories with a help of a connecting line (especially if there is more than two possible (outcomes) categorical variables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 515.889x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Pool\",\n",
    "            y=\"Score\",\n",
    "            data=reviews,\n",
    "            kind=\"point\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 632.389x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Spa\",\n",
    "            y=\"Score\",\n",
    "            data=reviews,\n",
    "            kind=\"point\",\n",
    "            hue=\"Tennis court\",\n",
    "            dodge=True) #to avoid overlapping\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tennis courts make a big deal for hotels with no Spa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To dismiss joining dots with a line - `linestyle='none'`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 515.889x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Score\",\n",
    "            y=\"Review weekday\",\n",
    "            data=reviews,\n",
    "            kind=\"point\",\n",
    "            linestyle='none') #to avoid overlapping\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**count** (basically a barplot, which has number of occurence on the y-axis instead of a numerical variable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2422c1da690>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 611.375x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Tennis court\", data=reviews, kind=\"count\", hue=\"Spa\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Additional catplots options"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Difficulties with showing multiple plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Маша\\AppData\\Local\\Temp\\ipykernel_9820\\580216195.py:1: UserWarning: The palette list has more values (9) than needed (5), which may not be intended.\n",
      "  sns.catplot(x=\"Traveler type\",\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"Traveler type\",\n",
    "            kind=\"count\",\n",
    "            col=\"User continent\",\n",
    "            col_wrap=3,\n",
    "            data=reviews,\n",
    "            hue = \"Traveler type\",\n",
    "            palette = sns.color_palette(\"Set1\"))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Updating plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Маша\\AppData\\Local\\Temp\\ipykernel_9820\\1880904458.py:1: UserWarning: The palette list has more values (9) than needed (5), which may not be intended.\n",
      "  ax = sns.catplot(x=\"Traveler type\",\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.catplot(x=\"Traveler type\",\n",
    "            kind=\"count\",\n",
    "            col=\"User continent\",\n",
    "            col_wrap=3,\n",
    "            data=reviews,\n",
    "            hue = \"Traveler type\",\n",
    "            palette = sns.color_palette(\"Set1\"))\n",
    "\n",
    "ax.fig.suptitle(\"Hotel Score by Travel Type & User Continent\")\n",
    "ax.set_axis_labels(\"Traveler Type\", \"Number of Reviews\")\n",
    "plt.subplots_adjust(top=.9)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical pitfalls (downsides)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 38531 entries, 0 to 38530\n",
      "Data columns (total 30 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   manufacturer_name  38531 non-null  object \n",
      " 1   model_name         38531 non-null  object \n",
      " 2   transmission       38531 non-null  object \n",
      " 3   color              38531 non-null  object \n",
      " 4   odometer_value     38531 non-null  int64  \n",
      " 5   year_produced      38531 non-null  int64  \n",
      " 6   engine_fuel        38531 non-null  object \n",
      " 7   engine_has_gas     38531 non-null  bool   \n",
      " 8   engine_type        38531 non-null  object \n",
      " 9   engine_capacity    38521 non-null  float64\n",
      " 10  body_type          38531 non-null  object \n",
      " 11  has_warranty       38531 non-null  bool   \n",
      " 12  state              38531 non-null  object \n",
      " 13  drivetrain         38531 non-null  object \n",
      " 14  price_usd          38531 non-null  float64\n",
      " 15  is_exchangeable    38531 non-null  bool   \n",
      " 16  location_region    38531 non-null  object \n",
      " 17  number_of_photos   38531 non-null  int64  \n",
      " 18  up_counter         38531 non-null  int64  \n",
      " 19  feature_0          38531 non-null  bool   \n",
      " 20  feature_1          38531 non-null  bool   \n",
      " 21  feature_2          38531 non-null  bool   \n",
      " 22  feature_3          38531 non-null  bool   \n",
      " 23  feature_4          38531 non-null  bool   \n",
      " 24  feature_5          38531 non-null  bool   \n",
      " 25  feature_6          38531 non-null  bool   \n",
      " 26  feature_7          38531 non-null  bool   \n",
      " 27  feature_8          38531 non-null  bool   \n",
      " 28  feature_9          38531 non-null  bool   \n",
      " 29  duration_listed    38531 non-null  int64  \n",
      "dtypes: bool(13), float64(2), int64(5), object(10)\n",
      "memory usage: 5.5+ MB\n"
     ]
    }
   ],
   "source": [
    "cars = pd.read_csv(\"databases/cars.csv\")\n",
    "cars.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**If we have a lot of unique values in a categorical series, then we are unlikely to save much memory, however if there is only a couple of unique values, then we will save a significant amount of memory**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Huge memory savings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count          38531\n",
      "unique            55\n",
      "top       Volkswagen\n",
      "freq            4243\n",
      "Name: manufacturer_name, dtype: object\n",
      "\n",
      "As object: 308248\n",
      "As category: 38971\n"
     ]
    }
   ],
   "source": [
    "print(cars[\"manufacturer_name\"].describe())\n",
    "print()\n",
    "print(f\"As object: {cars[\"manufacturer_name\"].nbytes}\")\n",
    "print(f\"As category: {cars[\"manufacturer_name\"].astype(\"category\").nbytes}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Little memory savings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count      38531.000000\n",
      "mean      248864.638447\n",
      "std       136072.376530\n",
      "min            0.000000\n",
      "25%       158000.000000\n",
      "50%       250000.000000\n",
      "75%       325000.000000\n",
      "max      1000000.000000\n",
      "Name: odometer_value, dtype: float64\n",
      "\n",
      "As object: 308248\n",
      "As category: 125566\n"
     ]
    }
   ],
   "source": [
    "print(cars[\"odometer_value\"].describe())\n",
    "print()\n",
    "print(f\"As object: {cars[\"odometer_value\"].nbytes}\")\n",
    "print(f\"As category: {cars[\"odometer_value\"].astype(\"category\").nbytes}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using categories can be frustrating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- using the `.str` accessor object to manipulate data converts the Series to an object\n",
    "- the `apply()` method outputs a new Series as an object\n",
    "- NumPy functions generally do not work with categorical Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From exercises (don't print out, just look)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Print the frequency table of body_type and include NaN values\n",
    "print(cars[\"body_type\"].value_counts(dropna=False))\n",
    "\n",
    "# Update NaN values\n",
    "cars.loc[cars[\"body_type\"].isna(), \"body_type\"] = \"other\"\n",
    "print(cars[\"body_type\"].value_counts(dropna=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Print the frequency table of Sale Rating\n",
    "print(cars[\"Sale Rating\"].value_counts())\n",
    "\n",
    "# Find the average score\n",
    "average_score = cars[\"Sale Rating\"].astype(int).mean()     #Key idea is to convert to int!!!\n",
    "\n",
    "# Print the average\n",
    "print(average_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Label encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "cars[\"manufacturer_name\"] = cars[\"manufacturer_name\"].astype(\"category\")\n",
    "\n",
    "cars[\"manufacturer_code\"] = cars[\"manufacturer_name\"].cat.codes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a code book"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{45: 'Subaru', 24: 'LADA', 12: 'Dodge', 54: 'УАЗ', 23: 'Kia', 35: 'Opel', 53: 'Москвич', 1: 'Alfa Romeo', 0: 'Acura', 10: 'Dacia', 27: 'Lexus', 33: 'Mitsubishi', 25: 'Lancia', 9: 'Citroen', 32: 'Mini', 21: 'Jaguar', 38: 'Porsche', 44: 'SsangYong', 11: 'Daewoo', 15: 'Geely', 50: 'ВАЗ', 13: 'Fiat', 14: 'Ford', 39: 'Renault', 42: 'Seat', 40: 'Rover', 48: 'Volkswagen', 28: 'Lifan', 22: 'Jeep', 5: 'Cadillac', 2: 'Audi', 52: 'ЗАЗ', 47: 'Toyota', 51: 'ГАЗ', 49: 'Volvo', 7: 'Chevrolet', 16: 'Great Wall', 4: 'Buick', 37: 'Pontiac', 29: 'Lincoln', 18: 'Hyundai', 34: 'Nissan', 46: 'Suzuki', 3: 'BMW', 30: 'Mazda', 26: 'Land Rover', 20: 'Iveco', 43: 'Skoda', 41: 'Saab', 19: 'Infiniti', 6: 'Chery', 17: 'Honda', 31: 'Mercedes-Benz', 36: 'Peugeot', 8: 'Chrysler'}\n"
     ]
    }
   ],
   "source": [
    "codes = cars[\"manufacturer_name\"].cat.codes\n",
    "categories = cars[\"manufacturer_name\"]\n",
    "\n",
    "name_map = dict(zip(codes, categories))\n",
    "print(name_map)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a book to convert back"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          Subaru\n",
       "1          Subaru\n",
       "2          Subaru\n",
       "3          Subaru\n",
       "4          Subaru\n",
       "           ...   \n",
       "38526    Chrysler\n",
       "38527    Chrysler\n",
       "38528    Chrysler\n",
       "38529    Chrysler\n",
       "38530    Chrysler\n",
       "Name: manufacturer_code, Length: 38531, dtype: object"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars[\"manufacturer_code\"].map(name_map) #.map is similar to .replace"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Boolean coding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find all body types that have \"van\" in them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Code from previous lesson\n",
    "cars[\"body_type\"].str.contains(\"van\", regex=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "van_code\n",
       "0    34115\n",
       "1     4416\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#New one\n",
    "cars[\"van_code\"] = np.where(\n",
    "    cars[\"body_type\"].str.contains(\"van\", regex=False), 1, 0\n",
    ")\n",
    "cars[\"van_code\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One-hot encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`pd.get_dummies()`\n",
    "- data: a pandas DataFrame\n",
    "- columns: a list-like object of column names\n",
    "- prefix: a string to add to the beginning of each category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "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>odometer_value</th>\n",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>190000</td>\n",
       "      <td>silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>290000</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>402000</td>\n",
       "      <td>red</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000</td>\n",
       "      <td>blue</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>280000</td>\n",
       "      <td>black</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   odometer_value   color\n",
       "0          190000  silver\n",
       "1          290000    blue\n",
       "2          402000     red\n",
       "3           10000    blue\n",
       "4          280000   black"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars[[\"odometer_value\", \"color\"]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "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>odometer_value</th>\n",
       "      <th>color_black</th>\n",
       "      <th>color_blue</th>\n",
       "      <th>color_brown</th>\n",
       "      <th>color_green</th>\n",
       "      <th>color_grey</th>\n",
       "      <th>color_orange</th>\n",
       "      <th>color_other</th>\n",
       "      <th>color_red</th>\n",
       "      <th>color_silver</th>\n",
       "      <th>color_violet</th>\n",
       "      <th>color_white</th>\n",
       "      <th>color_yellow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>190000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>290000</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>402000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>280000</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   odometer_value  color_black  color_blue  color_brown  color_green  \\\n",
       "0          190000        False       False        False        False   \n",
       "1          290000        False        True        False        False   \n",
       "2          402000        False       False        False        False   \n",
       "3           10000        False        True        False        False   \n",
       "4          280000         True       False        False        False   \n",
       "\n",
       "   color_grey  color_orange  color_other  color_red  color_silver  \\\n",
       "0       False         False        False      False          True   \n",
       "1       False         False        False      False         False   \n",
       "2       False         False        False       True         False   \n",
       "3       False         False        False      False         False   \n",
       "4       False         False        False      False         False   \n",
       "\n",
       "   color_violet  color_white  color_yellow  \n",
       "0         False        False         False  \n",
       "1         False        False         False  \n",
       "2         False        False         False  \n",
       "3         False        False         False  \n",
       "4         False        False         False  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars_onehot = pd.get_dummies(cars[[\"odometer_value\", \"color\"]])\n",
    "cars_onehot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "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>manufacturer_name</th>\n",
       "      <th>model_name</th>\n",
       "      <th>transmission</th>\n",
       "      <th>odometer_value</th>\n",
       "      <th>year_produced</th>\n",
       "      <th>engine_fuel</th>\n",
       "      <th>engine_has_gas</th>\n",
       "      <th>engine_type</th>\n",
       "      <th>engine_capacity</th>\n",
       "      <th>body_type</th>\n",
       "      <th>...</th>\n",
       "      <th>_brown</th>\n",
       "      <th>_green</th>\n",
       "      <th>_grey</th>\n",
       "      <th>_orange</th>\n",
       "      <th>_other</th>\n",
       "      <th>_red</th>\n",
       "      <th>_silver</th>\n",
       "      <th>_violet</th>\n",
       "      <th>_white</th>\n",
       "      <th>_yellow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Subaru</td>\n",
       "      <td>Outback</td>\n",
       "      <td>automatic</td>\n",
       "      <td>190000</td>\n",
       "      <td>2010</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>False</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>2.5</td>\n",
       "      <td>universal</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Subaru</td>\n",
       "      <td>Outback</td>\n",
       "      <td>automatic</td>\n",
       "      <td>290000</td>\n",
       "      <td>2002</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>False</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>3.0</td>\n",
       "      <td>universal</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Subaru</td>\n",
       "      <td>Forester</td>\n",
       "      <td>automatic</td>\n",
       "      <td>402000</td>\n",
       "      <td>2001</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>False</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>2.5</td>\n",
       "      <td>suv</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Subaru</td>\n",
       "      <td>Impreza</td>\n",
       "      <td>mechanical</td>\n",
       "      <td>10000</td>\n",
       "      <td>1999</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>False</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>3.0</td>\n",
       "      <td>sedan</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Subaru</td>\n",
       "      <td>Legacy</td>\n",
       "      <td>automatic</td>\n",
       "      <td>280000</td>\n",
       "      <td>2001</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>False</td>\n",
       "      <td>gasoline</td>\n",
       "      <td>2.5</td>\n",
       "      <td>universal</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  manufacturer_name model_name transmission  odometer_value  year_produced  \\\n",
       "0            Subaru    Outback    automatic          190000           2010   \n",
       "1            Subaru    Outback    automatic          290000           2002   \n",
       "2            Subaru   Forester    automatic          402000           2001   \n",
       "3            Subaru    Impreza   mechanical           10000           1999   \n",
       "4            Subaru     Legacy    automatic          280000           2001   \n",
       "\n",
       "  engine_fuel  engine_has_gas engine_type  engine_capacity  body_type  ...  \\\n",
       "0    gasoline           False    gasoline              2.5  universal  ...   \n",
       "1    gasoline           False    gasoline              3.0  universal  ...   \n",
       "2    gasoline           False    gasoline              2.5        suv  ...   \n",
       "3    gasoline           False    gasoline              3.0      sedan  ...   \n",
       "4    gasoline           False    gasoline              2.5  universal  ...   \n",
       "\n",
       "   _brown _green  _grey  _orange  _other   _red  _silver  _violet  _white  \\\n",
       "0   False  False  False    False   False  False     True    False   False   \n",
       "1   False  False  False    False   False  False    False    False   False   \n",
       "2   False  False  False    False   False   True    False    False   False   \n",
       "3   False  False  False    False   False  False    False    False   False   \n",
       "4   False  False  False    False   False  False    False    False   False   \n",
       "\n",
       "   _yellow  \n",
       "0    False  \n",
       "1    False  \n",
       "2    False  \n",
       "3    False  \n",
       "4    False  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cars_onehot = pd.get_dummies(cars, columns=[\"color\"], prefix=\"\")\n",
    "cars_onehot.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  }
 ],
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