Pandas_class3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "967181c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c6d0c80a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "titanic = pd.read_csv(\"Titanic_data.csv\")\n",
    "titanic.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "09914a35",
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2002fe68",
   "metadata": {},
   "source": [
    "# Aggregate Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e4b417ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29.69911764705882"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[\"Age\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "251885eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age     28.0000\n",
       "Fare    14.4542\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[[\"Age\", \"Fare\"]].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1b788863",
   "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>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29.699118</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>14.526497</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20.125000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age        Fare\n",
       "count  714.000000  891.000000\n",
       "mean    29.699118   32.204208\n",
       "std     14.526497   49.693429\n",
       "min      0.420000    0.000000\n",
       "25%     20.125000    7.910400\n",
       "50%     28.000000   14.454200\n",
       "75%     38.000000   31.000000\n",
       "max     80.000000  512.329200"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[[\"Age\", \"Fare\"]].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5a5fe2db",
   "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>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skew</th>\n",
       "      <td>0.389108</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age        Fare\n",
       "min      0.420000    0.000000\n",
       "max     80.000000  512.329200\n",
       "median  28.000000   14.454200\n",
       "skew     0.389108         NaN\n",
       "mean          NaN   32.204208"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.agg(\n",
    "    {\n",
    "        \"Age\": [\"min\", \"max\", \"median\", \"skew\",\"mod\"],\n",
    "        \"Fare\": [\"min\", \"max\", \"median\", \"mean\",\"mod\"],\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "40071ffa",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.420000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29.699118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum</th>\n",
       "      <td>21205.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Age\n",
       "min        0.420000\n",
       "max       80.000000\n",
       "mean      29.699118\n",
       "sum    21205.170000\n",
       "count    714.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.agg(\n",
    "    {\n",
    "        \"Age\": [\"min\", \"max\", \"mean\", \"sum\",\"count\"],\n",
    "        \n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a9596e11",
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Age\n",
       "Sex         \n",
       "female  27.0\n",
       "male    29.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[[\"Sex\", \"Age\"]].groupby(\"Sex\").median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f485bfdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "19afcb4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex     Survived\n",
       "female  0            81\n",
       "        1           233\n",
       "male    0           468\n",
       "        1           109\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[[\"Sex\", \"Survived\"]].groupby([\"Sex\",\"Survived\"])[\"Survived\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "adb15330",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex     Survived\n",
       "female  0            64\n",
       "        1           197\n",
       "male    0           360\n",
       "        1            93\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[[\"Sex\", \"Survived\",\"Age\"]].groupby([\"Sex\",\"Survived\"])[\"Age\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5908f2e2",
   "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></th>\n",
       "      <th>count</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">female</th>\n",
       "      <th>0</th>\n",
       "      <td>64</td>\n",
       "      <td>2.00</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>197</td>\n",
       "      <td>0.75</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">male</th>\n",
       "      <th>0</th>\n",
       "      <td>360</td>\n",
       "      <td>1.00</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>93</td>\n",
       "      <td>0.42</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count   min   max\n",
       "Sex    Survived                   \n",
       "female 0            64  2.00  57.0\n",
       "       1           197  0.75  63.0\n",
       "male   0           360  1.00  74.0\n",
       "       1            93  0.42  80.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[[\"Sex\", \"Survived\",\"Age\"]].groupby([\"Sex\",\"Survived\"])[\"Age\"].agg(['count','min','max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7bcac421",
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></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>female</th>\n",
       "      <td>431.028662</td>\n",
       "      <td>0.742038</td>\n",
       "      <td>2.159236</td>\n",
       "      <td>27.915709</td>\n",
       "      <td>0.694268</td>\n",
       "      <td>0.649682</td>\n",
       "      <td>44.479818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>454.147314</td>\n",
       "      <td>0.188908</td>\n",
       "      <td>2.389948</td>\n",
       "      <td>30.726645</td>\n",
       "      <td>0.429809</td>\n",
       "      <td>0.235702</td>\n",
       "      <td>25.523893</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PassengerId  Survived    Pclass        Age     SibSp     Parch  \\\n",
       "Sex                                                                      \n",
       "female   431.028662  0.742038  2.159236  27.915709  0.694268  0.649682   \n",
       "male     454.147314  0.188908  2.389948  30.726645  0.429809  0.235702   \n",
       "\n",
       "             Fare  \n",
       "Sex                \n",
       "female  44.479818  \n",
       "male    25.523893  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby(\"Sex\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f2225d75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    27.915709\n",
       "male      30.726645\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby(\"Sex\")[\"Age\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e97b9a24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex     Pclass\n",
       "female  1         106.125798\n",
       "        2          21.970121\n",
       "        3          16.118810\n",
       "male    1          67.226127\n",
       "        2          19.741782\n",
       "        3          12.661633\n",
       "Name: Fare, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby([\"Sex\", \"Pclass\"])[\"Fare\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8100d343",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    491\n",
       "1    216\n",
       "2    184\n",
       "Name: Pclass, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic[\"Pclass\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "39e04371",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass\n",
       "1    216\n",
       "2    184\n",
       "3    491\n",
       "Name: Pclass, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.groupby(\"Pclass\")[\"Pclass\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "04b69bbe",
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5e0a40c5",
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>520</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Pavlovic, Mr. Stefo</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349242</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>522</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Vovk, Mr. Janko</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349252</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>523</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Lahoud, Mr. Sarkis</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2624</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>525</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Kassem, Mr. Fared</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2700</td>\n",
       "      <td>7.2292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>547</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Beane, Mrs. Edward (Ethel Clarke)</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2908</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>216</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Newell, Miss. Madeleine</td>\n",
       "      <td>female</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35273</td>\n",
       "      <td>113.2750</td>\n",
       "      <td>D36</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>217</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Honkanen, Miss. Eliina</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101283</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>219</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bazzani, Miss. Albina</td>\n",
       "      <td>female</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11813</td>\n",
       "      <td>76.2917</td>\n",
       "      <td>D15</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>446</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Dodge, Master. Washington</td>\n",
       "      <td>male</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>33638</td>\n",
       "      <td>81.8583</td>\n",
       "      <td>A34</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                               Name     Sex  \\\n",
       "0              1         0       3            Braund, Mr. Owen Harris    male   \n",
       "519          520         0       3                Pavlovic, Mr. Stefo    male   \n",
       "521          522         0       3                    Vovk, Mr. Janko    male   \n",
       "522          523         0       3                 Lahoud, Mr. Sarkis    male   \n",
       "524          525         0       3                  Kassem, Mr. Fared    male   \n",
       "..           ...       ...     ...                                ...     ...   \n",
       "546          547         1       2  Beane, Mrs. Edward (Ethel Clarke)  female   \n",
       "215          216         1       1            Newell, Miss. Madeleine  female   \n",
       "216          217         1       3             Honkanen, Miss. Eliina  female   \n",
       "218          219         1       1              Bazzani, Miss. Albina  female   \n",
       "445          446         1       1          Dodge, Master. Washington    male   \n",
       "\n",
       "      Age  SibSp  Parch            Ticket      Fare Cabin Embarked  \n",
       "0    22.0      1      0         A/5 21171    7.2500   NaN        S  \n",
       "519  32.0      0      0            349242    7.8958   NaN        S  \n",
       "521  22.0      0      0            349252    7.8958   NaN        S  \n",
       "522   NaN      0      0              2624    7.2250   NaN        C  \n",
       "524   NaN      0      0              2700    7.2292   NaN        C  \n",
       "..    ...    ...    ...               ...       ...   ...      ...  \n",
       "546  19.0      1      0              2908   26.0000   NaN        S  \n",
       "215  31.0      1      0             35273  113.2750   D36        C  \n",
       "216  27.0      0      0  STON/O2. 3101283    7.9250   NaN        S  \n",
       "218  32.0      0      0             11813   76.2917   D15        C  \n",
       "445   4.0      0      2             33638   81.8583   A34        S  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.sort_values([\"Survived\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "15262d41",
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Fortune, Mr. Charles Alexander</td>\n",
       "      <td>male</td>\n",
       "      <td>19.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>19950</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Uruchurtu, Don. Manuel E</td>\n",
       "      <td>male</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17601</td>\n",
       "      <td>27.7208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Meyer, Mr. Edgar Joseph</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17604</td>\n",
       "      <td>82.1708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Holverson, Mr. Alexander Oskar</td>\n",
       "      <td>male</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113789</td>\n",
       "      <td>52.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>838</th>\n",
       "      <td>839</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Chip, Mr. Chang</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1601</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>855</th>\n",
       "      <td>856</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Aks, Mrs. Sam (Leah Rosen)</td>\n",
       "      <td>female</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>392091</td>\n",
       "      <td>9.3500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>858</th>\n",
       "      <td>859</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Baclini, Mrs. Solomon (Latifa Qurban)</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2666</td>\n",
       "      <td>19.2583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>870</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Master. Harold Theodor</td>\n",
       "      <td>male</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>876</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Najib, Miss. Adele Kiamie \"Jane\"</td>\n",
       "      <td>female</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2667</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                   Name  \\\n",
       "6              7         0       1                McCarthy, Mr. Timothy J   \n",
       "27            28         0       1         Fortune, Mr. Charles Alexander   \n",
       "30            31         0       1               Uruchurtu, Don. Manuel E   \n",
       "34            35         0       1                Meyer, Mr. Edgar Joseph   \n",
       "35            36         0       1         Holverson, Mr. Alexander Oskar   \n",
       "..           ...       ...     ...                                    ...   \n",
       "838          839         1       3                        Chip, Mr. Chang   \n",
       "855          856         1       3             Aks, Mrs. Sam (Leah Rosen)   \n",
       "858          859         1       3  Baclini, Mrs. Solomon (Latifa Qurban)   \n",
       "869          870         1       3        Johnson, Master. Harold Theodor   \n",
       "875          876         1       3       Najib, Miss. Adele Kiamie \"Jane\"   \n",
       "\n",
       "        Sex   Age  SibSp  Parch    Ticket      Fare        Cabin Embarked  \n",
       "6      male  54.0      0      0     17463   51.8625          E46        S  \n",
       "27     male  19.0      3      2     19950  263.0000  C23 C25 C27        S  \n",
       "30     male  40.0      0      0  PC 17601   27.7208          NaN        C  \n",
       "34     male  28.0      1      0  PC 17604   82.1708          NaN        C  \n",
       "35     male  42.0      1      0    113789   52.0000          NaN        S  \n",
       "..      ...   ...    ...    ...       ...       ...          ...      ...  \n",
       "838    male  32.0      0      0      1601   56.4958          NaN        S  \n",
       "855  female  18.0      0      1    392091    9.3500          NaN        S  \n",
       "858  female  24.0      0      3      2666   19.2583          NaN        C  \n",
       "869    male   4.0      1      1    347742   11.1333          NaN        S  \n",
       "875  female  15.0      0      0      2667    7.2250          NaN        C  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.sort_values([\"Survived\",\"Pclass\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "61ce17d4",
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sloper, Mr. William Thompson</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113788</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>A6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Spencer, Mrs. William Augustus (Marie Eugenie)</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17569</td>\n",
       "      <td>146.5208</td>\n",
       "      <td>B78</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>883</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dahlberg, Miss. Gerda Ulrika</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7552</td>\n",
       "      <td>10.5167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>885</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sutehall, Mr. Henry Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 392076</td>\n",
       "      <td>7.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>886</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Mrs. William (Margaret Norton)</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "1              2         1       1   \n",
       "3              4         1       1   \n",
       "11            12         1       1   \n",
       "23            24         1       1   \n",
       "31            32         1       1   \n",
       "..           ...       ...     ...   \n",
       "882          883         0       3   \n",
       "884          885         0       3   \n",
       "885          886         0       3   \n",
       "888          889         0       3   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "11                            Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "23                        Sloper, Mr. William Thompson    male  28.0      0   \n",
       "31      Spencer, Mrs. William Augustus (Marie Eugenie)  female   NaN      1   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "882                       Dahlberg, Miss. Gerda Ulrika  female  22.0      0   \n",
       "884                             Sutehall, Mr. Henry Jr    male  25.0      0   \n",
       "885               Rice, Mrs. William (Margaret Norton)  female  39.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch           Ticket      Fare Cabin Embarked  \n",
       "1        0         PC 17599   71.2833   C85        C  \n",
       "3        0           113803   53.1000  C123        S  \n",
       "11       0           113783   26.5500  C103        S  \n",
       "23       0           113788   35.5000    A6        S  \n",
       "31       0         PC 17569  146.5208   B78        C  \n",
       "..     ...              ...       ...   ...      ...  \n",
       "882      0             7552   10.5167   NaN        S  \n",
       "884      0  SOTON/OQ 392076    7.0500   NaN        S  \n",
       "885      5           382652   29.1250   NaN        Q  \n",
       "888      2       W./C. 6607   23.4500   NaN        S  \n",
       "890      0           370376    7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.sort_values(by=[\"Survived\",\"Pclass\"], ascending=[False,True])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "deba8e00",
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic.sort_values(by=[\"Survived\",\"Pclass\"], ascending=[False,True],\n",
    "                   inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "554d5605",
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Bonnell, Miss. Elizabeth</td>\n",
       "      <td>female</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113783</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>C103</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sloper, Mr. William Thompson</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>113788</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>A6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Spencer, Mrs. William Augustus (Marie Eugenie)</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17569</td>\n",
       "      <td>146.5208</td>\n",
       "      <td>B78</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>883</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dahlberg, Miss. Gerda Ulrika</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7552</td>\n",
       "      <td>10.5167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>885</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sutehall, Mr. Henry Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 392076</td>\n",
       "      <td>7.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>886</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Mrs. William (Margaret Norton)</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "1              2         1       1   \n",
       "3              4         1       1   \n",
       "11            12         1       1   \n",
       "23            24         1       1   \n",
       "31            32         1       1   \n",
       "..           ...       ...     ...   \n",
       "882          883         0       3   \n",
       "884          885         0       3   \n",
       "885          886         0       3   \n",
       "888          889         0       3   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "11                            Bonnell, Miss. Elizabeth  female  58.0      0   \n",
       "23                        Sloper, Mr. William Thompson    male  28.0      0   \n",
       "31      Spencer, Mrs. William Augustus (Marie Eugenie)  female   NaN      1   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "882                       Dahlberg, Miss. Gerda Ulrika  female  22.0      0   \n",
       "884                             Sutehall, Mr. Henry Jr    male  25.0      0   \n",
       "885               Rice, Mrs. William (Margaret Norton)  female  39.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch           Ticket      Fare Cabin Embarked  \n",
       "1        0         PC 17599   71.2833   C85        C  \n",
       "3        0           113803   53.1000  C123        S  \n",
       "11       0           113783   26.5500  C103        S  \n",
       "23       0           113788   35.5000    A6        S  \n",
       "31       0         PC 17569  146.5208   B78        C  \n",
       "..     ...              ...       ...   ...      ...  \n",
       "882      0             7552   10.5167   NaN        S  \n",
       "884      0  SOTON/OQ 392076    7.0500   NaN        S  \n",
       "885      5           382652   29.1250   NaN        Q  \n",
       "888      2       W./C. 6607   23.4500   NaN        S  \n",
       "890      0           370376    7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2296717b",
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic.sort_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "95f2883f",
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cad8113b",
   "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>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PC 17599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>113803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>0</td>\n",
       "      <td>887</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>19.0</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>0</td>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>112053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>2</td>\n",
       "      <td>889</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>W./C. 6607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>26.0</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>0</td>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>111369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>0</td>\n",
       "      <td>891</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age Cabin Embarked     Fare  \\\n",
       "0    22.0   NaN        S   7.2500   \n",
       "1    38.0   C85        C  71.2833   \n",
       "2    26.0   NaN        S   7.9250   \n",
       "3    35.0  C123        S  53.1000   \n",
       "4    35.0   NaN        S   8.0500   \n",
       "..    ...   ...      ...      ...   \n",
       "886  27.0   NaN        S  13.0000   \n",
       "887  19.0   B42        S  30.0000   \n",
       "888   NaN   NaN        S  23.4500   \n",
       "889  26.0  C148        C  30.0000   \n",
       "890  32.0   NaN        Q   7.7500   \n",
       "\n",
       "                                                  Name  Parch  PassengerId  \\\n",
       "0                              Braund, Mr. Owen Harris      0            1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...      0            2   \n",
       "2                               Heikkinen, Miss. Laina      0            3   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)      0            4   \n",
       "4                             Allen, Mr. William Henry      0            5   \n",
       "..                                                 ...    ...          ...   \n",
       "886                              Montvila, Rev. Juozas      0          887   \n",
       "887                       Graham, Miss. Margaret Edith      0          888   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"      2          889   \n",
       "889                              Behr, Mr. Karl Howell      0          890   \n",
       "890                                Dooley, Mr. Patrick      0          891   \n",
       "\n",
       "     Pclass     Sex  SibSp  Survived            Ticket  \n",
       "0         3    male      1         0         A/5 21171  \n",
       "1         1  female      1         1          PC 17599  \n",
       "2         3  female      0         1  STON/O2. 3101282  \n",
       "3         1  female      1         1            113803  \n",
       "4         3    male      0         0            373450  \n",
       "..      ...     ...    ...       ...               ...  \n",
       "886       2    male      0         0            211536  \n",
       "887       1  female      0         1            112053  \n",
       "888       3  female      1         0        W./C. 6607  \n",
       "889       1    male      0         1            111369  \n",
       "890       3    male      0         0            370376  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.sort_index(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "64879946",
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>851</th>\n",
       "      <td>852</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Svensson, Mr. Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>347060</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>117</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Connors, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>70.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370369</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>281</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Duane, Mr. Frank</td>\n",
       "      <td>male</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>336439</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>484</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Turkula, Mrs. (Hedwig)</td>\n",
       "      <td>female</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4134</td>\n",
       "      <td>9.5875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>327</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Nysveen, Mr. Johan Hansen</td>\n",
       "      <td>male</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>345364</td>\n",
       "      <td>6.2375</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                       Name     Sex   Age  \\\n",
       "851          852         0       3        Svensson, Mr. Johan    male  74.0   \n",
       "116          117         0       3       Connors, Mr. Patrick    male  70.5   \n",
       "280          281         0       3           Duane, Mr. Frank    male  65.0   \n",
       "483          484         1       3     Turkula, Mrs. (Hedwig)  female  63.0   \n",
       "326          327         0       3  Nysveen, Mr. Johan Hansen    male  61.0   \n",
       "\n",
       "     SibSp  Parch  Ticket    Fare Cabin Embarked  \n",
       "851      0      0  347060  7.7750   NaN        S  \n",
       "116      0      0  370369  7.7500   NaN        Q  \n",
       "280      0      0  336439  7.7500   NaN        Q  \n",
       "483      0      0    4134  9.5875   NaN        S  \n",
       "326      0      0  345364  6.2375   NaN        S  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic.sort_values(by=['Pclass', 'Age'], ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "bb239df3",
   "metadata": {},
   "outputs": [],
   "source": [
    "air_quality = pd.read_csv(\n",
    "    \"Air_quality_data.csv\",\n",
    "    index_col=\"date.utc\", \n",
    "    parse_dates=True\n",
    ")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b0f9cc45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 5272 entries, 2019-06-18 06:00:00+00:00 to 2019-04-09 02:00:00+00:00\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   city       5272 non-null   object \n",
      " 1   country    5272 non-null   object \n",
      " 2   location   5272 non-null   object \n",
      " 3   parameter  5272 non-null   object \n",
      " 4   value      5272 non-null   float64\n",
      " 5   unit       5272 non-null   object \n",
      "dtypes: float64(1), object(5)\n",
      "memory usage: 288.3+ KB\n"
     ]
    }
   ],
   "source": [
    "air_quality.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f8c2796d",
   "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>city</th>\n",
       "      <th>country</th>\n",
       "      <th>location</th>\n",
       "      <th>parameter</th>\n",
       "      <th>value</th>\n",
       "      <th>unit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date.utc</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>2019-06-18 06:00:00+00:00</th>\n",
       "      <td>Antwerpen</td>\n",
       "      <td>BE</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>18.0</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-17 08:00:00+00:00</th>\n",
       "      <td>Antwerpen</td>\n",
       "      <td>BE</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>6.5</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-17 07:00:00+00:00</th>\n",
       "      <td>Antwerpen</td>\n",
       "      <td>BE</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>18.5</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-17 06:00:00+00:00</th>\n",
       "      <td>Antwerpen</td>\n",
       "      <td>BE</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>16.0</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-17 05:00:00+00:00</th>\n",
       "      <td>Antwerpen</td>\n",
       "      <td>BE</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>7.5</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                city country location parameter  value   unit\n",
       "date.utc                                                                     \n",
       "2019-06-18 06:00:00+00:00  Antwerpen      BE  BETR801      pm25   18.0  µg/m³\n",
       "2019-06-17 08:00:00+00:00  Antwerpen      BE  BETR801      pm25    6.5  µg/m³\n",
       "2019-06-17 07:00:00+00:00  Antwerpen      BE  BETR801      pm25   18.5  µg/m³\n",
       "2019-06-17 06:00:00+00:00  Antwerpen      BE  BETR801      pm25   16.0  µg/m³\n",
       "2019-06-17 05:00:00+00:00  Antwerpen      BE  BETR801      pm25    7.5  µg/m³"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "16af2aa5",
   "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>city</th>\n",
       "      <th>country</th>\n",
       "      <th>location</th>\n",
       "      <th>parameter</th>\n",
       "      <th>value</th>\n",
       "      <th>unit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date.utc</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>2019-06-21 00:00:00+00:00</th>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>20.0</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-20 23:00:00+00:00</th>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>21.8</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-20 22:00:00+00:00</th>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>26.5</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-20 21:00:00+00:00</th>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>24.9</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-20 20:00:00+00:00</th>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>21.4</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-09 06:00:00+00:00</th>\n",
       "      <td>London</td>\n",
       "      <td>GB</td>\n",
       "      <td>London Westminster</td>\n",
       "      <td>no2</td>\n",
       "      <td>41.0</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-09 05:00:00+00:00</th>\n",
       "      <td>London</td>\n",
       "      <td>GB</td>\n",
       "      <td>London Westminster</td>\n",
       "      <td>no2</td>\n",
       "      <td>41.0</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-09 04:00:00+00:00</th>\n",
       "      <td>London</td>\n",
       "      <td>GB</td>\n",
       "      <td>London Westminster</td>\n",
       "      <td>no2</td>\n",
       "      <td>41.0</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-09 03:00:00+00:00</th>\n",
       "      <td>London</td>\n",
       "      <td>GB</td>\n",
       "      <td>London Westminster</td>\n",
       "      <td>no2</td>\n",
       "      <td>67.0</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-09 02:00:00+00:00</th>\n",
       "      <td>London</td>\n",
       "      <td>GB</td>\n",
       "      <td>London Westminster</td>\n",
       "      <td>no2</td>\n",
       "      <td>67.0</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3447 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             city country            location parameter  \\\n",
       "date.utc                                                                  \n",
       "2019-06-21 00:00:00+00:00   Paris      FR             FR04014       no2   \n",
       "2019-06-20 23:00:00+00:00   Paris      FR             FR04014       no2   \n",
       "2019-06-20 22:00:00+00:00   Paris      FR             FR04014       no2   \n",
       "2019-06-20 21:00:00+00:00   Paris      FR             FR04014       no2   \n",
       "2019-06-20 20:00:00+00:00   Paris      FR             FR04014       no2   \n",
       "...                           ...     ...                 ...       ...   \n",
       "2019-04-09 06:00:00+00:00  London      GB  London Westminster       no2   \n",
       "2019-04-09 05:00:00+00:00  London      GB  London Westminster       no2   \n",
       "2019-04-09 04:00:00+00:00  London      GB  London Westminster       no2   \n",
       "2019-04-09 03:00:00+00:00  London      GB  London Westminster       no2   \n",
       "2019-04-09 02:00:00+00:00  London      GB  London Westminster       no2   \n",
       "\n",
       "                           value   unit  \n",
       "date.utc                                 \n",
       "2019-06-21 00:00:00+00:00   20.0  µg/m³  \n",
       "2019-06-20 23:00:00+00:00   21.8  µg/m³  \n",
       "2019-06-20 22:00:00+00:00   26.5  µg/m³  \n",
       "2019-06-20 21:00:00+00:00   24.9  µg/m³  \n",
       "2019-06-20 20:00:00+00:00   21.4  µg/m³  \n",
       "...                          ...    ...  \n",
       "2019-04-09 06:00:00+00:00   41.0  µg/m³  \n",
       "2019-04-09 05:00:00+00:00   41.0  µg/m³  \n",
       "2019-04-09 04:00:00+00:00   41.0  µg/m³  \n",
       "2019-04-09 03:00:00+00:00   67.0  µg/m³  \n",
       "2019-04-09 02:00:00+00:00   67.0  µg/m³  \n",
       "\n",
       "[3447 rows x 6 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2 = air_quality[air_quality[\"parameter\"] == \"no2\"]\n",
    "no2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1a17b2df",
   "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>city</th>\n",
       "      <th>country</th>\n",
       "      <th>location</th>\n",
       "      <th>parameter</th>\n",
       "      <th>value</th>\n",
       "      <th>unit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date.utc</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>2019-04-09 01:00:00+00:00</th>\n",
       "      <td>Antwerpen</td>\n",
       "      <td>BE</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>no2</td>\n",
       "      <td>22.5</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-09 01:00:00+00:00</th>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>24.4</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-09 02:00:00+00:00</th>\n",
       "      <td>London</td>\n",
       "      <td>GB</td>\n",
       "      <td>London Westminster</td>\n",
       "      <td>no2</td>\n",
       "      <td>67.0</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-09 02:00:00+00:00</th>\n",
       "      <td>Antwerpen</td>\n",
       "      <td>BE</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>no2</td>\n",
       "      <td>53.5</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-09 02:00:00+00:00</th>\n",
       "      <td>Paris</td>\n",
       "      <td>FR</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>27.4</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-09 03:00:00+00:00</th>\n",
       "      <td>London</td>\n",
       "      <td>GB</td>\n",
       "      <td>London Westminster</td>\n",
       "      <td>no2</td>\n",
       "      <td>67.0</td>\n",
       "      <td>µg/m³</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                city country            location parameter  \\\n",
       "date.utc                                                                     \n",
       "2019-04-09 01:00:00+00:00  Antwerpen      BE             BETR801       no2   \n",
       "2019-04-09 01:00:00+00:00      Paris      FR             FR04014       no2   \n",
       "2019-04-09 02:00:00+00:00     London      GB  London Westminster       no2   \n",
       "2019-04-09 02:00:00+00:00  Antwerpen      BE             BETR801       no2   \n",
       "2019-04-09 02:00:00+00:00      Paris      FR             FR04014       no2   \n",
       "2019-04-09 03:00:00+00:00     London      GB  London Westminster       no2   \n",
       "\n",
       "                           value   unit  \n",
       "date.utc                                 \n",
       "2019-04-09 01:00:00+00:00   22.5  µg/m³  \n",
       "2019-04-09 01:00:00+00:00   24.4  µg/m³  \n",
       "2019-04-09 02:00:00+00:00   67.0  µg/m³  \n",
       "2019-04-09 02:00:00+00:00   53.5  µg/m³  \n",
       "2019-04-09 02:00:00+00:00   27.4  µg/m³  \n",
       "2019-04-09 03:00:00+00:00   67.0  µg/m³  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2_subset = no2.sort_index().groupby([\"location\"]).head(2)\n",
    "no2_subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bfe36287",
   "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>location</th>\n",
       "      <th>BETR801</th>\n",
       "      <th>FR04014</th>\n",
       "      <th>London Westminster</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date.utc</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-04-09 01:00:00+00:00</th>\n",
       "      <td>22.5</td>\n",
       "      <td>24.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-09 02:00:00+00:00</th>\n",
       "      <td>53.5</td>\n",
       "      <td>27.4</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-09 03:00:00+00:00</th>\n",
       "      <td>54.5</td>\n",
       "      <td>34.2</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-09 04:00:00+00:00</th>\n",
       "      <td>34.5</td>\n",
       "      <td>48.5</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-04-09 05:00:00+00:00</th>\n",
       "      <td>46.5</td>\n",
       "      <td>59.5</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-20 20:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-20 21:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>24.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-20 22:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>26.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-20 23:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-06-21 00:00:00+00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1705 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "location                   BETR801  FR04014  London Westminster\n",
       "date.utc                                                       \n",
       "2019-04-09 01:00:00+00:00     22.5     24.4                 NaN\n",
       "2019-04-09 02:00:00+00:00     53.5     27.4                67.0\n",
       "2019-04-09 03:00:00+00:00     54.5     34.2                67.0\n",
       "2019-04-09 04:00:00+00:00     34.5     48.5                41.0\n",
       "2019-04-09 05:00:00+00:00     46.5     59.5                41.0\n",
       "...                            ...      ...                 ...\n",
       "2019-06-20 20:00:00+00:00      NaN     21.4                 NaN\n",
       "2019-06-20 21:00:00+00:00      NaN     24.9                 NaN\n",
       "2019-06-20 22:00:00+00:00      NaN     26.5                 NaN\n",
       "2019-06-20 23:00:00+00:00      NaN     21.8                 NaN\n",
       "2019-06-21 00:00:00+00:00      NaN     20.0                 NaN\n",
       "\n",
       "[1705 rows x 3 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2.pivot(columns=\"location\", values=\"value\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0b8e6121",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date.utc'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAENCAYAAAD0eSVZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAACPWUlEQVR4nO2dd7wdRfn/37On3J7eSUISQmjpBAiEQOiISJMqCIiKoAj+FARUFLCAgAgq4heUoiBFlCIC0g0QICQQEnoCCem93H5P2fn9sTN7ZvfsOfec28t+eIVzt8/Mzn7mmaeNkFISIkSIECF6FqzOLkCIECFChGh7hOQeIkSIED0QIbmHCBEiRA9ESO4hQoQI0QMRknuIECFC9ECE5B4iRIgQPRDRzi4AwKBBg+SYMWM6uxghQoQI0a2wcOHCzVLKwUHHmiV3IcRdwLHARinlRLXvRuBLQAL4FPialHK7OnYl8HUgDVwspfxvc88YM2YMCxYsKKw2IUKECBECACHE57mOFaKWuQc42rfvOWCilHIy8AlwpXrQnsDpwF7qmj8KISItKHOIECFChGgFmiV3KeVcYKtv37NSypTafAMYqf4+HnhQStkkpVwOLAP2bcPyhggRIkSIAtAWBtXzgKfV3zsBq4xjq9W+ECFChAjRgWiVQVUI8WMgBdyvdwWcFpi8RghxPnA+wOjRo1tTjBAhujWSySSrV6+msbGxs4sSoouitLSUkSNHEovFCr6mxeQuhDgHx9B6mMxkH1sNjDJOGwmsDbpeSnkHcAfAjBkzwuxlIXotVq9eTVVVFWPGjEGIIPkoRG+GlJItW7awevVqxo4dW/B1LVLLCCGOBi4HjpNS1huHngBOF0KUCCHGArsC81vyjBAhegsaGxsZOHBgSOwhAiGEYODAgUXP7JoldyHEA8DrwG5CiNVCiK8DfwCqgOeEEIuEEH8CkFK+DzwMfAA8A3xHSpkurioh2PghvHZrZ5ciRAciJPYQ+dCS/tGsWkZKeUbA7r/kOf+XwC+LLkmIDO48DJJ1sM83IV7e2aUJEaIgVFZWUltb22b3e+yxx5gwYQJ77rknAD/96U856KCDOPzww9vsGT0ZYfqBrohknfP77I87txwhQnQiHnvsMT744AN3+9prrw2JvQiE5N6VsW1FZ5cgRIiiIaXksssuY+LEiUyaNImHHnrIPXbDDTcwadIkpkyZwhVXXAHAnXfeyT777MOUKVP48pe/TH19PfPmzeOJJ57gsssuY+rUqXz66aece+65PPLIIwC88MILTJs2jUmTJnHeeefR1NQEONHuP/vZz5g+fTqTJk3io48+6vgG6CIIyb0rQ9qdXYIQIYrGv/71LxYtWsS7777L888/z2WXXca6det4+umneeyxx3jzzTd59913+eEPfwjASSedxFtvvcW7777LHnvswV/+8hcOOOAAjjvuOG688UYWLVrELrvs4t6/sbGRc889l4ceeoglS5aQSqW4/fbb3eODBg3i7bff5sILL+Smm27q8Pp3FYTk3pURknuIbohXX32VM844g0gkwtChQzn44IN56623eP755/na175GebljRxowYAAA7733HrNnz2bSpEncf//9vP/++3nv//HHHzN27FgmTJgAwDnnnMPcuXPd4yeddBIAe++9NytWrGiHGnYPdImskCFyIFy8PEQ3hMzRb6WUgV4f5557Lo899hhTpkzhnnvu4eWXX27R/TVKSkoAiEQipFKpvOf2ZISSe1dGKLmH6IY46KCDeOihh0in02zatIm5c+ey7777cuSRR3LXXXdRX++Exmzd6qSsqqmpYfjw4SSTSe6//373PlVVVdTU1GTdf/fdd2fFihUsW7YMgL/97W8cfPDBHVCz7oWQ3Lsy7DBEIET3w4knnsjkyZOZMmUKhx56KDfccAPDhg3j6KOP5rjjjmPGjBlMnTrV1Yf//Oc/Z7/99uOII45g9913d+9z+umnc+ONNzJt2jQ+/fRTd39paSl33303p5xyCpMmTcKyLC644IIOr2dXh2huitMRmDFjhgzzuRu4uq/zO3Jf+MZznVuWEO2ODz/8kD322KOzixGiiyOonwghFkopZwSdH0ruXRmhWiZEiBAtREjuXRqdP6sKESJE90RI7l0ZoeQeIkSIFiIk966MkNxDhAjRQoTk3pURknuIECFaiJDcuzK6gCdTiBAhuidCcu/KCCX3EB2ESCTC1KlTmTJlCtOnT2fevHkArFixgrKyMqZOner+++tf/8p+++3H1KlTGT16NIMHD3aPrVixgjFjxjBp0iQmT57MwQcfzOeff+4+57e//S177bUXEydO5IwzznAXoNi6dStHHHEEu+66K0cccQTbtm0DYMuWLRxyyCFUVlZy0UUXdXzDdGOE5N6VEZJ7iA5CWVmZm+zruuuu48orr3SP7bLLLixatMj9d/bZZ/Pmm2+yaNEirr32Wk477TT32JgxYwB46aWXWLx4MXPmzOEXv/gFAGvWrOF3v/sdCxYs4L333iOdTvPggw8CcP3113PYYYexdOlSDjvsMK6//nrACVj6+c9/3qsTgLUUIbl3ZYTkHqITUF1dTf/+/dvkXvvvvz9r1qxxt1OpFA0NDaRSKerr6xkxYgQAjz/+OOeccw7gJAJ77LHHAKioqODAAw+ktLS0TcrTmxAmDuuK6DsKdqyCnfbu7JKE6CVoaGhg6tSpNDY2sm7dOl588UX32KeffsrUqVPd7d///vfMnj27oPs+88wznHDCCQDstNNOXHrppYwePZqysjKOPPJIjjzySAA2bNjA8OHDARg+fDgbN25sm4r1YoTk3hUx/nBYeDf027mzSxKig3HNv9/ng7XVbXrPPUf04Wdf2ivvOVotA/D6669z9tln89577wEZtUwxOOSQQ9iwYQNDhgxx1TLbtm3j8ccfZ/ny5fTr149TTjmF++67j7POOqvoOoVoHqFapktCecmEapkQnYD999+fzZs3s2nTphbf46WXXuLzzz9nr7324qc//SkAzz//PGPHjmXw4MHEYjFOOukk13A7dOhQ1q1bB8C6desYMmRI6yvSyxFK7l0RMiT33ormJOyOwEcffUQ6nWbgwIFuet6WoKysjFtuuYVJkybxk5/8hNGjR/PGG29QX19PWVkZL7zwAjNmODmvjjvuOO69916uuOIK7r33Xo4//vi2qk6vRbPkLoS4CzgW2CilnKj2DQAeAsYAK4BTpZTb1LErga8DaeBiKeV/26XkPRohuYfoWGidOziLYdx7771EIhEgW+d+3nnncfHFFxd03+HDh3PGGWdw2223cdVVV3HyySczffp0otEo06ZN4/zzzwfgiiuu4NRTT+Uvf/kLo0eP5h//+Id7jzFjxlBdXU0ikeCxxx7j2WefZc8992ybivdgNJvyVwhxEFAL/NUg9xuArVLK64UQVwD9pZSXCyH2BB4A9gVGAM8DE6SUeROThyl/fXj8InjnbzDrEjji2s4uTYh2RpjyN0QhaPOUv1LKucBW3+7jgXvV3/cCJxj7H5RSNkkplwPLcIg+RDEI1TIhQoRoJVpqUB0qpVwHoH619WMnYJVx3mq1L0RR0OQeph8IESJEy9DW3jLZq9/mSEouhDhfCLFACLGgNVb5HolQcg8RIkQr0VJy3yCEGA6gfnXEwWpglHHeSGBt0A2klHdIKWdIKWcMHjy4hcXoqQjJPUSIEK1DS8n9CeAc9fc5wOPG/tOFECVCiLHArsD81hWxFyKU3EOECNFKFOIK+QAwBxgkhFgN/Ay4HnhYCPF1YCVwCoCU8n0hxMPAB0AK+E5znjIhAqBJPST3ECFCtBCFeMucIaUcLqWMSSlHSin/IqXcIqU8TEq5q/rdapz/SynlLlLK3aSUT7dv8bshpIT7T4Glz+c7Sf2E5J4Pv3zjlxz9z6M7uxjti/otsPYdsNu3L+iUv2bq3pdffpm+ffsybdo0dt99dy699FL3fCklF198MePHj2fy5Mm8/fbbnvul02mmTZvGscce6+5rTVrf4447jokTJ7ZDzXsuwvQDHY10EpY+Cw+clvscrZaxw0lPPjz48YOsqV3T/IndGTXrnV872a6P0bll/Kl7Z8+ezTvvvMM777zDk08+yWuvvQbA008/zdKlS1m6dCl33HEHF154oed+t956a5ZPdkvT+v7rX/+isrKyjWvc8xGSe0dDKIciO5XnpFByD9G1oBfs0Ol7H3/8cc4++2yEEMycOZPt27e7uWFWr17Nf/7zH77xjW947tGStL61tbXcfPPN/OQnP2nH2vVMhOTe0SjEd93VuYd+7r0ebh8I8jJuO+j0A1OnTuXEE0/MOr5t2zaWLl3KQQcdBDgLb4walXGMGzlypEv83/ve97jhhhuwLC+9tCSt71VXXcUPfvADysvLW1y33oowcViHoxByDyX3Xounr4D1SzLbiTrAhlgFiBbKYsMmwReuz3uKmfLXxCuvvMLkyZP5+OOPueKKKxg2bBjg6Nz9EELw5JNPMmTIEPbee29efvnllpVXYdGiRSxbtozf/va3rFixolX36o0IJfeORkHSeEjuIXxoX8E9J2bPns3ixYtZsmQJt99+uzsAjBw5klWrMsHoq1evZsSIEbz22ms88cQTjBkzhtNPP50XX3zRzddebFrf119/nYULFzJmzBgOPPBAPvnkE+bMmdMu9eyJCCX3DkcouYfIA7+EvX6JY58ZuhdE4p1TJmDChAlceeWV/PrXv+aBBx7guOOO4w9/+AOnn346b775Jn379mX48OFcd911XHfddQC8/PLL3HTTTdx3331A8Wl9L7zwQtdQu2LFCo499thWzwZ6E0Jy72iEknuIlqALmF8uuOACbrrpJpYvX84xxxzDU089xfjx4ykvL+fuu+9u9vowrW/HIiT3DkcouYfoeqitrc3aN2fOHI8apKyszLPY9W233Zb3nv7rBw4cyAsvvBB4bnM69TFjxrjL/oUoDKHOvaNRkLdMSO4hFNz+0gVE9xDdCiG5dziKUcuEQUwhQoRoGUK1TEehYbvj1lZS1fy5ruQeSmshQoRoGULJvaNw+yz47Z4UJ7mHapkQGuFAH6I4hOTeUahe7fwWE6GaqGu/8oToXgi5PUSRCMm9w1GEQXXHqvznhegFCFk9RMsQkntHo6DUreqDTjW1a1FChNBoj6yLc+bMYcGCBa26h5SSQYMGuemB161bhxCCV1991T1n8ODBbNmypaj73nLLLdTX1xd1zYIFC7j44ouLuqY1z2stQnLvaCQLeMFuyl9f5si6zVBXXCcO0c0hs/7oVRBCsN9++/H6668DMG/ePKZNm8a8efMA+Pjjjxk0aBADBw4s6r4tIdsZM2bwu9/9rqhrWvO8dLp13nIhuXc08qb6VdA6d/+5N+4CN45r+zKF6MLoPFJftGgRM2fOZPLkyZx44omu9Dxnzhwuv/xy9t13XyZMmMArr7wCOJklTz/9dCZPnsxpp51GQ0ODe68HHniASZMmMXHiRC6//HJ3f2VlJT/+8Y+ZMmUKM2fOZMOGDVnlmDVrlkvm8+bN4/vf/76H7A844AAAbrzxRvbZZx8mT57Mz372MwDq6ur44he/yJQpU5g4cSIPPfQQv/vd71i7di2HHHIIhxxyiFuOyy+/nL333pvDDz+c+fPnM2fOHMaNG8cTTzwBOOkU9OIjV199Needd557jib9Qp/37LPPsv/++zN9+nROOeUUN4hszJgxXHvttRx44IGeCN6WICT3DkcR3jKFDAQhegk6nuTPPvtsfv3rX7N48WImTZrENddc48wqpU0qlWL+/Pnccsstzn7g9ttvp7y8nMWLF/PjH/+YhQsXArB27Vouv/xyXnzxRRYtWsRbb73l5nKvq6tj5syZvPvuuxx00EHceeedWeU44IADXHKfP38+J5xwgpu0bN68ecyaNYtnn32WpUuXMn/+fBYtWsTChQuZO3cuzzzzDCNGjODdd9/lvffe4+ijj+biiy9mxIgRvPTSS7z00ktuOebMmcPChQupqqriJz/5Cc899xyPPvooP/3pTwPb56OPPuK///0v8+fP55prriGZTBb0vM2bN/OLX/yC559/nrfffpsZM2Zw8803u/ctLS3l1Vdf5fTTT2/V+wv93FuJpnQTNYkaBpUNKuyCYiJUjZWY1tWuYxidlhwwRAfh1/N/zUdbP8rsSKi0ALHyFqf83X3A7ly+7+XNn2hgx44dbN++nYMPPhhwFtc45ZRToH4rJOs56YtHAbD33nu7qQPmzp3r6qQnT57M5MmTAXjrrbeYM2cOgwcPBuDMM89k7ty5nHDCCcTjcVca3nvvvXnuueeyyrLvvvvyzjvvUFdXRzKZpLKyknHjxrFs2TLmzZvHD37wA/785z/z7LPPMm3aNMBJp7B06VJmz57NpZdeyuWXX86xxx7L7NmzA+sbj8c5+mhnycZJkyZRUlJCLBZj0qRJOVMjfPGLX6SkpISSkhKGDBnChg0bmDRpUrPPe+ONN/jggw+YNWsWAIlEgv333989ftppeVZpKwIhubcS33/5+8xdPZcl5yxp/mQo0HfdK7kv27aME584kUv7VHFOdU3LChoiRFtA2YxKYo6YEYlESKUyM0whssWPoNzvGrFYzL3Gfy+N8vJyxo8fz1133cX06dMBmDlzJk899RQbN25kt912Q0rJlVdeybe+9a2s6xcuXMhTTz3FlVdeyZFHHhkoiZvlsCyLkpIS9++gMgHuOWbZJ0yY0OzzpJQcccQRPPDAA4H3raioCNxfLEJybyXmrp4LQNpOE7EizV9QyLqoPoPqypqVACwoLQnJvYcjS8Je+47zO2gCxNvmoy8Effv2pX///rzyyivMnj2bv/3tb0qK18JJNokfdNBB3H///RxyyCG89957LF68GID99tuPSy65hM2bN9O/f38eeOABvvvd7xZVnlmzZnHLLbdw9dVXA7D//vtz1llnMXPmTIQQHHXUUVx11VWceeaZVFZWsmbNGmKxGKlUigEDBnDWWWdRWVnJPffcA0BVVRU1NTUMGlTgjLtArF27ttnnzZw5k+985zssW7aM8ePHU19fz+rVq5kwYUKbliUk9zZCU7qJcquApcAKkdxNcpfSlXxClUyI9kJ9fT0jR450t7///e9z7733csEFF1BfX8+4ceOctL5yh3NCQGe88MIL+drXvsbkyZOZOnUq++67L4Cb5/2QQw5BSskxxxzTbC53P2bNmsWtt97qqi+mT5/O6tWr3XVajzzySD788EP3eGVlJffddx/Lli3jsssuw7IsYrEYt99+OwDnn38+X/jCFxg+fLird28LLFmypKDn3XPPPZxxxhk0NTnuzr/4xS/anNxFvilTsxcL8f+Ab+DoEZYAXwPKgYeAMcAK4FQp5bZ895kxY4ZsrT9sZ2HSvZMAePnUlxlYlscd6+q+zu8Fr8GfZql9O4LPvfsY+NxZZZ6fbuW5VS/y/Ze/z2F19dyycXPu63oZdNsXrBLrovjwww/ZY489gg92kuSeE1uXQ+N26LczlA/o7NL0KgT1EyHEQinljKDzW+wtI4TYCbgYmCGlnAhEgNOBK4AXpJS7Ai+o7R6PtbVrCzuxGMkdwE6FknuILgTVNwN06yG6FlrrChkFyoQQURyJfS1wPHCvOn4vcEIrn9GlURopBWB9/frCLijGoApgp7CVnjP8nHoxukyG0LAXdhe0mNyllGuAm4CVwDpgh5TyWWColHKdOmcdkH8V3G6OYRXOavDpQnOvt0By11xvdZkPPESIEF0dLTaoCiH640jpY4HtwD+EEGcVcf35wPkAo0ePbmkxOge2TcNLv+TOihiN6Ua1q8D0vAWRu3GOncaWoeTe0yGlDHQj7LIIBY0ORUtso61RyxwOLJdSbpJSJoF/AQcAG4QQwwHU78agi6WUd0gpZ0gpZ+jghm6Dz17iniV3cOfH97O+zlHHtKnk7lHLpJGEOveejNLSUrZs2dKiD7jj0UN7oW1DzYbCXJU7GFJKtmzZQmlpaVHXtcYVciUwUwhRDjQAhwELgDrgHOB69ft4K57RNdFUTdInZdmFLqzRArWMvnevyRWxfgmseBVmXtjZJekQjBw5ktWrV7Np06bsg9uVbLQFiBb3cbcL6rc46wxsSkM8Ow9Mt0XjDudf+QCIt32GzNaitLTU46paCFpM7lLKN4UQjwBvAyngHeAOoBJ4WAjxdZwB4JSWPqPLItmA8AlZxZD7fyrKEcAxuU8ybpxyJXerOwh2bYE/Hej89hJyj8VijB07NvuAlHDNTOfvc/8DY6Z1bMGC8K/zYfFDcPwfYY8zO7s0bYf//ADe+jMccxNM+WZnl6ZN0KogJinlz4Cf+XY34UjxPRfpBP5Y1GLUMlcMcaLicpK7R+eeMnTuvYXdQwCw4K7M311GZaNmrD1t8fZI3PntQWso9JqZfptCWFg+om0/tUw69HPvTZh/J6xd5Py94X3jQBchd62O7GkZSyMx5zed6NxytCFCcm8JRIRIi9UyRaT8BbBTJO0kAEvjscKe0VNQoAdS9zBE5oGdhtdvg2QjPHUp3OFkYnQJp0tBk3sPldzTyc4tRxsizC3TEliRLCm6cLVMEYnDAOwUKSUl9bqRWKYppNYSiejO85p3H4T//sgxVpqwjM+zqwxgOu1wjyX3UHLv3RCRrElym6plfJK7HjiiXeT77jAU2KbdXnJP1Dm/jdXe/VYXlL30GNpTde7pUOfeu2H5Ne5g71jtLGTQHApdrMNSU3I77Uruke5OYsWiQOlQdhV9dFvDo5bpInV0JfeeqnNvhVrmld/AL4a1TXnaACG5twRSIn1agNQbt8ENAe5sWdcWaFDVnc1Qy7S3HHflv5Zwzb/fb/7EjkKB0mHPIXdfPaxQ595xUPVqjVrmhWsh1dD8eR2EkNxbAplNJ7cM6E9BSoRC1TJWNrlnzxfaFh+vr2bphtp2fUZRKJRAuju350o7YC7+0lVmbT1V5647UVvo3LvIuwrJvUUIlhUL6u4FS+5KTje8ZfyBU82ifitsXlbw6ZYQXUsKLlTn3pXK3Br4SaFLqmV6qJ+7bvtUG5B7Fxn4QnJvCQo1ngahoBcvMwYeO0VKOpK7XaxDyO0HwB/2Lvh0IQr2PuwY9DZy96NYg2qyEbZ93j5l8aOn6dzbUnLvIm3TM8i9bkvH+qdKiW243pVHy5zdAOlmXmyhWSEDDKqyWHe/mnVFnS66muReqEG1i0yD2xymzr2QOj52Adw6uX2/Bf1Ouoh02mbQ32VbtJ3dNXzluz+52zbcOA4ev6gDHypJGzxbajlSti0EpBo9Zzalm3hx5YvGpcWrZVIdJAkIwO4MnkzUwdbPsvf3Fsnd1bnLHPsLxNLnnN9kOxr19DvpItJpm0G2QHJfeC/cMSd7fxdpm+5P7g3K/fD9RzvumdImZUjRJUrCkpD1Ym+YfwOXvHQJi0vi7rUFPCDQoNreFCZEBzwkCPd9GX4XkBSrUG+Zbi+5az22rx6e7QLqqA2wPgGjTaHL1BrVZJeEJvci/Nz/fXFmjVsTzc3eOwjdn9zrVJrUjlw8WHol9xKlG5XG/zVW1qwEoFZ7GRTq5x7gCtneFNZpBtWVrwfv765Tfymd9LHFX+jbLJJAtUAQSu7Fw5XcW6FSEWpw7SJt063JPWWneHDpP1kdjWQk+PbApk8cybJus7MtbdKG5B5TL/WNslIafB9WJulXgMTz9t+CnyftjDFN2m6Eqg38r6yUdbXF6dILhRCdpJbJhe6qlnn9Nrh+NGxfVdj5IpfkbtS/kCrqPtOukrsacPc8of2e0Rlwde4tMKhqLwQ9cwp17q1HWqb55bKH+FO/vu37oNdudXTCHz+ldkhShuQeV1L5JUMHc/WCGzyXZq2iZH6wT1wE1UFE7ZXctStkrWVx0bAh/HDuD1tXnxywhOhcFUc+csuDglM/dBQ+etL53b6yyAuN+kvpU0t1EbWMnYKB42Hn/dvvGZ2CVrhC6vfUxaJ3uzW5x604VZHSrFWR2hyWL3BDSq/OXWSCTT6tXuG51F1ow93hIyKdV8RzkVfn/uqaVwGot5xnLtq0qNgaFIxOldz95F6gWqbLkXtLk5j5Uj0XrZbR57dne9iprpnzprXQTd8S/31N5q5apmuoE7s1uQshGBApa/9JuX5pxsdjSu4xoxn9KoKsxa39H16yPuCBhrdMOkmZcrVsaudBzNG5dyL8H1aBH9qHWz9sh8K0BQptzQC1jJ30kkQhMypX+GhPck9nvoeehLaIXdEzpy6SNrhbkzs4n4WE9u1wrjFUdwBJ2iDaEpPcfR9h1kIbWeQeYPySGNKRpC7pSPea3KOifSQnITrZ88TfNgV+cN98tosti9biQdibDbR4yT0zs2w32GlvWoQeA9Vmm5cGz6bzwZXcQ7VMm0NCu3a47U3OR9bQpPRxUqJf337D92N6WSYTnF9yd3Xu7rTP98EGGXCk7Q5W9uZl2eRe7LS4wI/d0bkXd+s2hb9tusj0tt0RZFBNJ33tUYiXVaiWaTF029tJeOirRV4bGlTbBR0huS/43HFrW7GpxtkhM5L70PKhlOXRsa6vWw8YqQP8H16glCfdjpJ88zZ3b6PS/RdP7oV97E4QUyeyu5/Me1r+kmbRWp17B/ig91RyN9v+s5eKuzTUubcPBCCFaFfJvSHlvPiKeCaSUEvuaZlGmCO1jxzX1TneMO7nVmiEquoo6YCBI1ZsKtgCO5voapJ7lzOUFol17+Y/nk7Bfy6FHaud7dbq3N1zQ3IvGh630yI/Ar9apifo3IUQ/YQQjwghPhJCfCiE2F8IMUAI8ZwQYqn67d9WhQ0sg/tH+5F7WjWTJTMGq5SSuG3bxjKybfnVMtoY6pJ0FoEFdSTpeuikAgT7bU3beHPdm4VXoFDJXXSy5O6X1IvIYtYlo1T/+6P8x1e9AW/dCXNvdLbN92SnYK7pVivhtd/Bxo9y30/k6GNtiZ6qc29N//EbVHuIzv1W4Bkp5e7AFOBD4ArgBSnlrsALarvdIHRudav9JiFSNZNLIFK66X3TMo3HxySHQXVLNFce7IBOZQQxpXKofC56oYhcOgWqN6zOXoY0y8+98OmtzpzZNVBgQwpfnzVJ2S/9SQnPXQV3Htr8c9uT3GUPJffW+IllqWW6Rl9sMSMKIfoABwF/AZBSJqSU24HjgXvVafcCJ7SuiM2Ug/bXubvk7hKzzEju0vbGnvg6SZPKVfGjwYOcHf4XHyQxmGqZHDxRVFRmwTp30cmSe8vVMqk3boenL2/jAjX30CaYf2fL8yT71Rt5yV31vUDXWYUOkdx7qlqmDSR33f7dndyBccAm4G4hxDtCiD8LISqAoVLKdQDqd0gblDMnBEqf3Y7ShO1ffcbwc0/JlGeFpM9qV3P9/Ovd7SwS9icmCvwQMwbVIJ07OIPG+c+ez46mAnKYFKhzt6w29qJr3FHcDf3lLMIwlXrup/Dmnwp/Vlvgf7+Gpy6F9/7p3V+oK2SWQOLTuRu4ffXzPFteli3te2+obtPO5B76ufuu7Xl+7lFgOnC7lHIaUEcRKhghxPlCiAVCiAWbNm1qcSG05L5NUBjRtQA6j7o0/Ig16drSxh/6c/+H97t/79p/VwDGJ5TLoxHeHJRoTN/fVcvk4YnX173O3NVzC6hAJ0ju2z538qu8+X+FX5MluRdO7u0epRyEGscTKivcv1DJza9K9LtCGvjj2hf5wdDBhQ0c7aqWsZsZYIpA445Mvqb2xnM/hWfy2UCKzMBpwn3fHTC4FoHWvKXVwGoppbbsPYJD9huEEMMB1O/GoIullHdIKWdIKWcMHjy4xYXQXf2gQSUc+OCBLb5PPthKUpEBkntapgOXv9O69oi6tlzH9RuS+6potMWSu8aPXv0Rz33+XP4KFEjYQrRK8+iFzs/+8X8Kv6ZIP3fTa+jjeCcsJq1JPVri3V8oufslYL9BNfCaPJ9srgRkbQlJ4TOT5nDTbnDjLm1zryAsvAf+p4zSr90Kb9yW+9y2UMtkbtbye7UhWkzuUsr1wCohxG5q12HAB8ATwDlq3znA460qYTMQSMcVsh1h62YyIlQThrdMUNC+NvDpdL3aR73RkPLqLJFD5243q3M38dHWPB4UULAE3D6ukEW8m6z0A/kLE9dLEYJrA+lQpNRA7Sf3QiW3LFWiKbnnSmCVr54dITnKtpPcU+2Ymhjg35fAS78sMBlYGxhU3Vt1f8kd4LvA/UKIxcBU4FfA9cARQoilwBFqu/0gi38tSTvJ5obCp4NS+AyqMkPujuSeXYK0OndD3QYAdks6HeDl+tVm0V0CW1+3ntpErXt/N4ipANKymvvYCuxsVpumH2jBfYpUy+w5cE/37+ZmOO0CTcCRkvzn5UKWt0xutUzmmjz17AiDahchrqJQSFBSm0juGW+6roBWmb2llIuAGQGHDmvNfYuBAArXzDq4et7VPPHpE7z91bcLCghyJXdDLeMh9yDJ3U5Rn6ynJulEtf5quyOx95WZj1Mi3I/liEeOYJ9h+3DXUXfhSEcOub8Xj6t65l5IQzRHbIUGMdEOWSGLkaiLNKhawqIsWkZDqqGgGU6bQ0vuWV4vBTZivpWXcg5snSy5S9l2apmOQrS0+XNaQ8hZ76prkHuPiFDdHimuGk98+gRQeKpYV3I31TKmQTXgXSbsBL9885eZHSrLY8SYwtnqXlp189b6t/Tt3Q9I12yPAbuRC6K5j63gIKZOXiC7BfncdSqGTjGoasm9xR+3/zzTWyZH3QtRibS3WqYzZkl+fPw0bPq4sHNjZQWc1JZqmZDc2wQWsCnSMtesdIESrcyS3A21jB0sub+w8gV3EDlgxAFufvaEYVC1BSBt1xfefKL+iLXhNp5nhmE19xoL1rm3oF+mk3DdaFj8sO+Z+kbF6NyL9JaRGaNqp2Tz0PaTrJw4hUruefz6c9W909Uybahzbw0eOB1u27ewcw3bTE4EzaIathV2/5a+/3ZGF3hLrUMqLdkYbZl2qWApVX9P6iVK26ZRhXOmZHAM6bWvX+v+feTOR7orKyWNUd7Ruds0+A1L0nYfqnXJ+dL8tpXOXdACg2r9VmjaERBqr25UjERdpEEVMu2S7hTJXenFszxbWqqWsYP/NpGohdtm5jASdpDOvbupZYrJqKnxym/g12NyrJTmwybl0NDFFg/v9uS+o74Fy2IppAuVaPUf6vztdqOrBnDUMvk7T9SKugbShJ0pr40jKq+tXeu9wNBral1yNE+QVrNqmQIjKFtkUHWXGGuDwJYWRKhGXJfRToD7MbdUcvOdZ0qA+WaVmz6EmrXZ+ztCcu8qapliUFB7+N6FXioxqJ39ePqHsGp+7nt1Ero1uadt2apuViiRWVo7rkiy2lCjpGxvhGoQHHJXkrvh4mYLxwPnk22f+EsGQJOApCu55yH35lqhqMRhBZ2agT9pkvvMIu8DTjlrzYC2/DeRSDeOINkRPt5+uOHmLdS5Z/n1m9lFWzJcdYSfewsNqgvvgTvmBB975/7g/W2FQoSbfMbtQrB1uXFtKLm3GolU6xqxUMldk7ceSszrcrlCmoi++7ATjUe2WuaxLW9zzevXADCmzxh1wNFrzhgzmt8MdJJq5iX3Zg2qhSYOExxgL4BN/sEmDzQh5Sxfkd4yN43PbBeillEGVbeG7fVh1W6CZ38C6xYbO3OQe8GCu+9EU9XSkpzgHSW5F6tzb9zh+JyvfSf4nT7+7bYpWi4UlGY71zkF9l8rgvviF9wNjdWFXdeO6NbkLpGBxkw/3t7wNs8sfyZrf6HeMvoZul+m1XVRKR2jbDMk9MnnL3F1mc2miEXSkM5sYO72jMW/Ilbh/BGg12zIs6J9pDmVSBGS+82pX8Ft+xR0PpAhpKysnG3h556/3BLpkvuHJcpo1l5Sa/0WmPd72LI0oCBt5C1jBi7peux+bKElNK5tbz/3TN/8vPpzbnjrBk9wXhauH535uzPyrrRELVNs/zVnrqvnO+kOOhndm9xlYePqOc+cw2VzL8vaXyy52+pXk3uJlKRlulm1zKfxGP/sU8kbZaUkfK6QZYYXTIkbDJOt11yweVFBZQ1EgVLg5Ufvnr3z+avh6Twpg9LN+HoXZVAtJNe9F1XxKqcYue7RZtD1MT4Z19DejCuclLD0uea9Kjzkrs4t6xfcuwLbpuP93H//zu/52wd/491N7xZ2fbLI9UmbvZ93UJFSZn/Xhcxcc6llCu2/aV8f0AGJnYjuTe60rgKFkrtL3q7k7nSWuJLcm1PLzGpwvGHSCJJGR5PCm6jL9d4J0GtOGzgp5/2btR0UWM9+5QEuY6/+Ft68PfdFWnJvC7VMoZJ7OgXPX4NMJ7GExU6VO7kqs3YjNn3fIJVEczr3j56E+0+G1325TbLUMoZLrHtPQWCNguop8hzLh/qthfuM+wSPbY2Ou2Ch3xKJPCmLW4IarzfLrW/fypS/TvHMkFsmuRcJ/wBS7Gpp7YBuTe7QOrt90ZK7Ty0Tk5K0TFHTzEIRw1LOi08DCaMT2b4yZP72fkCWsLhuxg/5v3WBOdiwgz//DNpzLVJlS6CksvX3ynofOT64j5+CV2+GrcsRCCxh5V6jtq0QSO56QGmmfXX2yG0r/Df1bprxDq4XklU4ubd0gPu/gwv3GZcUpnNf8Rp8+mL2/qyYjtbC24b3fXgfAElT/VPIzDVn3yuQYbIWvm/rehaPbk3uUsqs6NCCJQiKcYXUD/GqZbTkPsWqynv9TimH/NNCkDTJ3Lin85cpuWdejSUshpUN5oDGRn62eUvW/Zutc3t6T+hAj5I+/ocWf68stUWOeqlEXVKpMSyTADuS3HN5y6j2rraEG32c954a5tTeWADCDuKXIMJqqUF1x8rCz/XZg4Qa3LNiRu45Bv52YsD1bdAXTe8XXzsEfguFrlschLxBY8ZsNd8srJPQvckdsgyqqe2rYPMySDSv2yvcFVJL7oKznz6bs7c4OdS1zn0XKzt3xZJzljCk3FmnpE/a6Vw/HzSAJVGTzL1rlmpXS6RXFk/ZKbeDnlyTXa9m69Geq7HrlYGyMiO2hc494KN87NuZgCk7hRCO5N7uOvci1TIPV1Uya+dRnP/c+Z793tP82+nsv4XlSYrmnlHMoNGm8NmD1CLgBQtVbdEXzXbKQe6e8rRELVMINUQM1Yu0ve8zn4G5g9C9yT3AoJp85FzHn3b+Hc1eHyi5N+5wdJAGtFyYIs07G99x9x9dV88lky/I2Xke+OID3Dfha5ja6Hkxb+KwpFLpTG5s8qhl0lmdLfdH0WykbUdkCTTWl+WFn8NmrcNtY4PqovthyzLnsNoVEZFM2ue2qms66SzbV7vRe98gcg8IYloZcwzMa2rW5Bng8gUx6Xp4de7u4i2B/aGVbVCIsJPDiyFdqBdMW6gI8+S919+059suSHLPdaCFaplQcm8lAvpZqnEHJGoKWuElcMp8wy5ww1jPLi25zxXeFaP2a2jklHHHgrS5YNsOJjcaL3T7KoakbaZUjCSSo+fYAhrtJJMbm+hv2x5pI+sTMI6dtaOaEUnD66ZZtUw7Su4eOwHO4PjKTS1zBcsiyeY/SqH+a3PJfemzzrJ9T12q7hvgLeP6uWenH2hUhJ5v4E37rzO3TZ270cmT5FAFYeT+b6nqI9dsINnoDNiJesjh557Ot7ar5xlt0BftgBmO/xTPANAanXuh14RqmTaF6edeqSQd1xulbjNc3Rfe/lvO60964iSe/eRRb1SkHSSBOM+o81FuBBwJS9p8Z/sO7l+3IXPwlonwm90AQSRHP5FAo52iREqElM4yflK7XfpPzuy5fOt2/rw+Y1ztVLWMK7lnSP6SIYP44sjhzmZbq2W8JwAQ8ejcC/goq9dBU03+czTRGWmeHRj10XXzCxIyQ+4ekkk2wv8dBCvf4O8f/p0Zr3yXbWZ8gB0gbQrLSVOhb5FDz//amtfYt0+ad0riLSf3XAuELPiLM2DP+x2un3s66XlO1kCVC20iuTefpsGrlinkmS3QuWepfkK1TJtBygwJ9lV67ZRucDV1z3I/8+G/z13qjYoMhM39fSpZL7wJviJSqo6T/2PK1cg2kJBJ4tJx5Pus+nMe+ugh1kUi/GHHEt/JXqIzbQ3Ne8u0s9+z+WvbvFhRzspYC1zBssLEmyepjLdMESqJm3eHP8327qvfCs/9LGPU9NsM/Nt/OtCJuAR4/Q9Z5X63xLFB2DKNOyBs/czRUT96Afd9eB8pabPFzGgaRFpCeEQKTe5LdizjP59lljB8fuXzJAR8HI+3/H3nIvek6vepRqcdErXw80GOm6y+tGByb4O+WEAOHjuP6iYQLUk/kE+v3wUk91Yt1tHZkEDKcl5Cla3JXb1s1SFlM4JjoCeCD5usOn7bbwDg9VQZmk47Hcd4sUcOnMLmSASWKw8EIZxBwEBUSlLC0aU2ppMMVccTdoJfzP8lx/fvy+M1vqXz/rC3Z9McMDwdOdXkWPEjxqvtCLWM/vWlV2g2703QvdztQg3eLVDLbFsOWz6FgWoNz2d/4ujzR0yFvU4kM7D4Bg2tkljvG3x95W6wdKoKw7tEG+C2LYdRIwDfDC1I3SAsTx9Oqr+/tei31KQbOGbsMQgh3MyiMSlbTqDNLkcnAAk71Gpiix9GG5TSudyB1y7ybheYxC4vCpDKPTr3guwBLZjteBZXyePW2kno5pK7JKV8ISsMcp80djR3W7XuOfmQBmqFYNYDs3jwowcDz9kmgl/UoLTtfJBGZ/vNhK9y7xfu9Zznb+Qy5TD/2wH9WZbYSlxKzzmPVzXvMx719Ctj4xdD4L6TvCcXqpbJ11Y1G3Ic0JK7zclPnMwv3rnVPZKEdlXL6NK22M/999MzZKMlLU0EWZJ7HoNqQMlc51kzXD+gbB7hwySkD/+t/hAebxm9jkBN2iHz+lQ9VK8los5ptEQrDKoFRnJqT7SSjAtwoWsjtLdB1T3F7Ms6FgPgvz/mvP+ex6R7J/HZ9s+C7+nsyLHffQDmgLC93scRXUBy797kDiSFV3JPqJdxc5nWXecnd1sINkciVCequXNxsIdNSng7UB8R5bItyr978UNeySCAIP2xm2WqjGuUR4XWuReEMieR2OB0mqs2O2XIMtot/593u+APLw8pNG5v5hrJx9s+5qHl/3YP6Zz3BePZn/juXUCbCK1zz6+W2dywmVfXvJp9QKsiNGn718MUlmOTmXuj97x8UDMz8EmQQUZQc8MkqjUL1fO83jJbfAvT1Gz9FG7eg8j69wAcXX9LyT2on0jpUb8gbYPcK92et7JmddalAMTKm3+Gfk5Lymn8bRK6p921URzg9T+4K555+kOu5zezv0E49VuzzWdQDnXurYOUuJJ7pWrspI/oZDMdPU3zS7SZ9+hf0p/XBh7K2dXKIPfiz+ETIylZwPP8dx+e8nbwEp/knhcHX+He89S6BuJWvO28ZfLeR8AzV8JLvwq8JqidG4UFO1YV9myArZ8WUR5DcWIS4MJ74MEzs84977/nceHzF2a3lZsbJ+J9prmS1OPfziyyLKwCpvkSTdMeCdJ4D1pdVUjSalN1uMWXoK1u++dO8audvOONwmqF5B5w3Yb3MnlShFLL7HyAs73T3u6MeV39+uB7+r+tXH3RThUhhJjknhkQTUIvJIalzLP8Xg6de07J3dn/YvnRzr2iwnuLUHJvHSTS1UFWKYNqk+9lZJHOyjc9mzYZPWYu/bApGVfGK3O88BySY8DAMTidpsTQPcZkEXnpo0b+FyuGJay285bJQwrvb/2Qaev/zfpXbwq8JhVQhgYhHN20L26gcDRTr0QdQkIEkXEDnHtDZqEFA8t3LAcCYhv8uXH0cVMtY3rWFELuUroCQ1qm8+R9p/mFvaUd7C2joBdg1/ubWiO5BxFvkKFRLzgdibsE8t/Vvtmie36BqrY1b8O1A5wEa83BDib3nJJ7DpirpeWeOeTf35AZxb2He4LkLoSICCHeEUI8qbYHCCGeE0IsVb/9W1/MHJCQsLTkrsjd5zliSmryo6fgriM9x9NCuB9GJMeU21TtVMYqgzuCm/LTH5jiPP8XmzLG2LiUWaqagsndzL4YiTtSa5a06UOhknueQeDeD/5KSgjeKvVHoioX1ACPHZeImlqY27ogyV2q9AOFtWBuyd3yPdOQ3M12FVZ2e1YN92x+Ykka1P2ac8lrloalHegto1Gnsizq4aahVTr3fP3aPSnTB+206/IZ82cFdU/3lSVXH9Mzo0LI3bxndWalJJPQi0lDknVPz/5c35Rz/sY6lVqk0NQZHYi2kNwvAT40tq8AXpBS7gq8oLbbBZJMxF4/Jbn7g/NNyd3enL0IhSO5OzfxfDamLs/4BHNK7lrySzZ6/Z7VlPb42kzJmoTIaviCX4SH3GOK2LSnSq4pb4EdLU+HrFPLA1ZkSXJKcl/1RtY1SbdB8/kKG/fz56cpMIgpQo7kWgHI+gj19Fn4yT1HOYTIbufSfp7Nc41UQ7ZpUA14P80OStL2qGVSvtNrFblrHX9ji1Y514UJylfjX2FLBzE5g0izwVpZpJejj2rVT7SQxayNe9RnhKbgJHzZqFKilbt+gnPTHM/KT+4p9eWm0+3okdZCtIrchRAjgS8CfzZ2Hw9od5F7gRNa84x8kBL6qDYdk3RklzVZfdHwBw8IUEqLjNTjWdHI8Pk171FZvd7JSuiHJt1/fQNu3CXwPhoH1zdkJTwrGCa5R0uwsJB1m+Gm3XIngCpC5/5IVQWnjRjKsm3LmPXALDZoA572Rsq6Rts6sknKXTo8byCI0RC5csJr+LIqSnWOJSwW+2cUOZA1Xc9lUDXVMh5yD9Bp+zw2asy4JPNYELkXq5bxtXNCDU56IG2VQbXZ69TAIYQ7g3EXitd1+/hpeOXm3PfMJWhoI22kgPdo3sNQkXkivPP0+ZQi8vJoOTzzI1j1VgCJF6ZztzW52zZZA0R7JuwrAK2V3G8Bfoh3djlUSrkOQP0OaeUzckIiuWBDjG9t28F0Ffq/zfexmGuC2gG60jSOtwzgupM5BwxyF4bkvuEjqPOmIQCyiUnDMKwMUdkhI+BJSSBwjKoFwXyOFQUB9sYPoHZ9wFqUuSXGQEibawYN5IOSEv76wV+pTlTzYnmZOuSUvcG/4pIm9wACz+zLx2BGvXWGSaM8JuxEHfPKSj2fkBDQTy3Ysd0T7Rn8UWZJdHaOoCXP4hzGEz99Mbs9c7jjRaV0M4g6tzQMqq7E6+CTWIz1Eb8KBLDTWWoZsxcnlVpJ70u0uc7d2KcNqghHXWOoZVLaz/2B0+GFa4zrvWX5rH4ta2rXZD9nuzK8x8uzj+Urk/5OazeSTmRsI/Wp3OkQtJo1ZkXhjducDJY5YyzyS+5p6fS5/mUBQXudbFRtMbkLIY4FNkopF7bw+vOFEAuEEAs2bQogywIgJezZCBdt30GFlESkZIefe4xxJx0gRdvAChVN2Tdm+Jd7JILMCy7P9eFkLTOn75N55v/buh2AsYmkp+EFsHuTc97OFSOC7+M+x6tzt4SF1Aauel8YvGvIKyBCr24zbP2UqKrr9ianrCnfPeqzvB+Uzj2Av4P2ZUG3Z6SEbMnH29YPr3mJbw0bwjMVBgFIyYFDZgDwepmRnTMwjUSAROe2jUnm5rN9ao7Xb8smwRxSYkxKpDBiiT1BSqo8avPLI4dzxOidsm8ibV8Qk6DO6GsJRe5aXZNsa1fIoFWmhHDUNTLteOfgBGsFGvZ9ZTn+3d9w9D+PzkS9alQrwq8c2nw5zXvq7+umXbHv+7K7e+m2gOUQ9SWq8V3342h2VtfAZ3n2S3UvZ0Ae0TdAnVS7oYhFUNoerZHcZwHHCSFWAA8Chwoh7gM2CCGGA6jfwBUmpJR3SClnSClnDB48uEUFcGQI9aKAclvyQoV35PcYVAPIPS0yfuhDSwcYB5xzP9r6Ef/slzHa5MoTk87VlMYzj62r580Vq5iUSGCZNjpUZCFQuj2Hv7BGgFrGraH/g3GJKg2fvw513ghbD367F9x5qDuDSKhyv6vXJtXk7vddd8k9n+SeZ1aiP7DAiD7vdauqHelugyHhCmD20OkA/HDIoMzJOTxabJVe4OWyMk4ZMYxq7Qljuj6a236ybNweILkHk7supWui9RCljsNoTufuldzfLCthQzRT/19+/gSQkdxTgrZVy3jqptpCKMndyKEDORLx5ZqR/mY333NS3t98MMtkCmEbM6a/fOsKu7U0yT2nWqYZyR1DGPB/A6/eDH8+vG2icluAFpO7lPJKKeVIKeUY4HTgRSnlWcATwDnqtHOAx1tdytxl8JjS+tpptvmmtqahZ27DWt6Pe0dYG+FKPVo/agNv3/dF2PgRTy9/2nN+rvVSN9fnUH34BpRy1VnM+wgJelJXYnTWa0cfx683bubBsr2MAngNqkKITLpbP7mbapm7j4a7vxBcRnBdt/QgU66CT16oKGezZakcKfCZmuXY0mbhhoUZcg8gqVxJrrwogPj15uIHzVq5OveqSBlZMCT3JmPgSKn2/UP/vnxUEmddg5Y9gj/uLXaSN/0RylnZHDP108vOAVi+JHAyHUDuzercpWcAKLcldQFeXUn3t43VMnYOtYywHLWMMdgng2ZLucpiRo1Cpk0LSRXgUctk3o3Zlvl07pk5mfYys+Hj/+Q4uzCDKpLsgaB2k+MpVp9HqGpHtIef+/XAEUKIpcARartdIKVXdz2pKUDtYnSuH+5YyOk7DfMcbzISM+mc1Pf1qeKcihSvPf3drPu5Q0e/0Z79OSX3HPk6zLP3b2hkh5pqa0I8q/9kThy8N8fU1bMXxoBk5oyxYt5ViHJK7uqMzc1PEeOqg26qd1RlKSH4Y/++NKiPb3UsClf35eEld3HuM+fycv1q9zw/GgpJ5qWPxQNSLvhjFnLcwr9gC+BZ1eiRTx5x/9ZGdd3OKT345kgcdWXjJ3yjpI4d5owlSLVTuwmWz+W4x45zd+s3lVQ9zOyLOqYisGVM1YSd9vjCp4XI8phx6qN/29gV0qxrSR+PQVXaKRqFoNRN/REkuWfK8nk0TyorPYMqxD6UQ3I30zQU4grpqmX86kzItEUzahnblNz97acXA68OsDF0ANqE3KWUL0spj1V/b5FSHial3FX9tjSCpSCYwdn7N2QHDjQX4LM5EiGtP3T14S9XaQFWk8py8XLVKdXehXnTWZ7r+kAwuWtPkklpiwMaG6lV5HFgQwMLVqzksiGzM+RsDhDmdNOKOGoZV3L3GZH09YUupADEVP0WbVrk7vtHnyrelk5HjUvJq2Wl/FLlkFnS6Eglr5Zn6y3Xa/VBXnJXDzzwe7mP6U3VZpZvbyAp2UlYcDfphu1cPz8jX6TVO9YlynhQ6Y/Zm37g9dR2AFe3rC7yPSvlGOXu/ZJrq4BMDqFGf/pgcF10JT5DMMCgCUb1bM/AmSYzkO5UuRODo84MS3uAJFujlsmjc79q0AD+Wf0RpkE1JdOkhaBS1XP9R09kX28MDk9V5jGWaieFHLYSDwz1C+mEW26P5F7AICGC2mnkPtBnZGa7meAmj1rGfz/tAeRbxLuj0L0jVKVXvRFEr7IZD+iaSGaJNq2W0fdJBUiE7kzB1wntnOQebDHfEnGafqRt+CfjEEKJdJJhZcjZIHdzObt17xKRdiY61EfuCWHxSSxW2AejEGtmMLQRXDgs4wD17AZHKvlD/35Z575ZWuq0bSGSeyTAICVtx62uZj2fbv/U1fdbSLZalpObJ1cWxE9fgie/x/Znfugtv3/lHjdRmHuC2vbqkz3GYZ/kXo3k1bqVWQusaON7vWp/EaBPTgvBUz47kactpO3xjkmLjDtqn3gfV2Wjz1kZi7WLWuaxqkqu3vA/595WBITFZ0knOG2YmiU9+sk/WRP1fQeGYPHHgD6S/bzsNlpft96dSQLw2cvG/RPu92HWupAUxIEascG740njnU4GC0c+V0hHyPCTu/oeu7Pk3lmQSCLGKw1KvmU3Q1YA9Zb2VXVeos64mEZmpSSwctzOzpVQKofUrPXkh6rD2pVT/7pGK8hN7sl6SqrXkfClOda4vn8FXx45nPWGNNkc4s2Su3cAWFGZO/p0XnkZt/TvV6DOPeBT2/QRvHANyd/sxgmPn8CjRrbMg3ceydaIcl4NIjOl52xs8Oo70yoYS3ugpPzeMm60r81PB2UM7B6Dsa8+v6uKc+GwIbxW5p296LZs0IOrRy2TeWqWWGCu/yvT7rNjUpJGuJJ7SaSEdCQGpf3cnEq1lpXxOS8WgQZVv7eM7QgdIsLJ1fMBmJBQ6sy6ZRw9yufxYwgWWn3Tv6R/7jIEkPIRjxzBof84NLMjbgQfpZOuy6HtUcsUILkH7bQi3na4/8vwz69nn2eQu+3aOXKpZdbSGeje5C69+lb/R/Kb/v14wWre11RPi/WHrnWaMURWOHQuG3xOtUwzvq5l6kM8uKGRVz9fxd5NxvmuWsbY54mqgxiShP4gfM9aEnfUS1uTtXnL4L+fxo0H3Zh1vNqysjxjLh88MOf9Xi0vzS9JaiLKCnPP7KvzqS08A2zDVrDTnLPDGWQkUCME569+kgWlJfgVdVqiywjqvhS/BimYZL24xBhUfcShYyvu61vl2a+foSV37zJ6ztEP4gEzFg+52257l9rSUcuoQyXREkf9YEU8CfMShXicBCEwK6Tf/1uRu/G+qvJ5g6h71gpBo3qPo/uMzn1+uoCyl5guywlXgPLYJtQ7ahJw0dDBfFrivMtFJZn2XtG0NSsRmzYUB9XBA9UulaVRhA5syym5h2qZoiHB4y3jr8w9/frwK0P6ygVNHluStWyxMmqaCMLJimcgl7fMOHK4MOZa3UahxAhb7mtGXHnUMgZpR7zBEnEpSWhDlvmsf5xLXN07UcTCAaarZ98Psz0IPinJJqOnKiuy9mk0G1SjjwW5rqn61/jcL80tsfFDuH3/zEpcwGfxGK83ruO2fn1p9Ae1pb3knjJ07p/GothGyt9648P/yeCBbIpYjiDg+9g1ub1e5vXauWC7M+DUp50hxtTx9o86bptSBEiQiRo+jsUcI7udkdxLpFQGVUX2kVLHiCkiJI17J/yGzc/+5/xrDoFqmQCyNfsm2eS+1SRMRbzPGPp2W9qObjsI5vPuOxlu3iv7nEmnOr+l/RyBRvVv88vU73FxSQn/Ky/jlwOd2YKpUoRMfnxP3aTt9X0PTEnhPO1Hx+ylyD1ALZMM1TIthpTNq2UKQZN6wSsS25izs2FMCdADu1KjLw9KLtJvjtzLcxk7daAIeFU7vjLFpSSpP2YzE937j7o+603NlMGEKZVHFz9U8HV5kW+KrD/mIMld1bveJ10FTae1CiQhhPvBpoSgTp181h5nObdUZJ6R3J3nf5Sq4YSRI7hn6yJVZpnlAXTo6JEcOWpEVn38aaYBjq6t49PSw5zyv6GWejSu216X0RO/40ud0NhUy8kjh/ODIYOccqj9pSqJmEctY6fBipJEUqZItslPyH89zvmn8ffT4P5TssocqEIMzDfjI/e0l9QuGmrEraj2jhlNlG7Y5iw5GATTPrTsOaheTdyKc97E8zL7h0+Gq3c4v+mk+42Z3jJactfCSsqKwI+yJegsLy+h1DKm+jPfjEZYmVgIP//ofDmhWqZliAibRulIs7nDFvKjyf+CFdLl2VK/22CH/6ywmzdDrEOSuYydIhMUYapbssgd3mxYl30eGcJrKkJyNw2HzRlXjxl9WmE31R/C3Jvg7mO8x/KpZbSazEfnZmZKHVEbM8hdu2BGpWTD6nkADK9wMjemXZfOmHfbdgbGh6s/5GfzfuZK2340WBaf1xokYUUDA7gswLKdZ+ioXku9hzohWJ5yPD5sBHXG9aujEXb0HwXAm2WlHp17qZQeg2pptNQhMaWWqVAzvyzJ3Y9PnoGlz2bvD0pT6xsovjdkEPMbN3jVMj6JdYk5WKl1ZqNGX7K3fubYRAaOhzGzvVJyAJEm7AR3vXcXH2750HsgEne+r3nOGrami2jadY7QXi0C4uVZgkFWFHWg5N4cuasUFblmqNVr83jdtB+6NblLIEaaRuUH3lw8SC40+D5OvZVGUuOTGl1vGavABaCbWZdycMpH7hVK6tHJmcA7QESi8IUbcjzLS+JutKlduORuEmk0T3/8UWw0ZZHc6hgP9LT9xZ/D5695j7lrhWbIwgY+jsXcXEB+v+7/lWfUH9pzOq7eS5MQ7FCeSBFgqyKhIeXOdDzt8xxKKTWNXsFrTaqWfy39F2/W5l5k5Kcf3pXZiJa56StMCGBQ0vECbrQEK6JRXi8rp0YI/mro5tMCTx9bXFJC3UE/cLcb7JSX3MnMTEojDrlLIUghqVTt3NhSnXuQfchHbC9UlHPdlvme91Vp5+korzkusya5u7rxgePh3Ceh3Igs9pXdvPOOhC/wKVLilHnBX5ziB0juug/rPnRIg+/9+1nDCpLc8/jvu0nUAiR3jWRdy9NetwLdm9wlRAxyb2ll/CH1LrlL21lN3oCrlsmVKMyPHFLz2ESSmJRE/B3H/WgMbxm/QXW/b8FuXwRgP+Xbn4Qsl8eMWqZwV0hTCo3kUjUBR4tKRKHrqxdpUH2isoKTRw7nufR2wDvdBlzDHOAuPq7rmhSCd5Txs9y2qbYshJT0iTtqNNsnGdrSaZtGH4k15RkQzUjMlyvK+TDADiGBgxvmAo4R+kujRnD+sEFcPHSwxyUwjbOewBgVo9woBDXGO7jB3uBKl6W2I7m7M81lzwPwWdRRTpapNliifPOLRhC5B0jSW9ONHrXMwALS3Zo9xe0N+h7Vhr3K11fNnpP09+N4hcf91xQCbCUQCVNyBwZKx1Hi1kNuzbrGuUA4dY4a9pNA7yNp1EHgX1M1S9TsBNVMtyZ3kERJ06TJvYVTn/qY1xD2cB9HsrKlpEw665seXO+4GboUVDC5B5PEXes28NSqtdl6Tn3fnH7uiki0X7yqs18vDYYeugDJfVPE4geDB7rrukJ+yb08nS6c3PP52QdI7jqIbJV0yCbfakX6Kq3T3RKx+Id6f/1tm+qIRZVtE1Xt6pfctyZqueTFS7gu8blnf+1K3wzDvCaZyT74fgCxa2wRjlvgLQMyrn8LfO6SaeEQjM4x/mxFOd+cf417/C1Z70qXH8fjLIvH+TTuDAT7rnMijrUBc7LytCrU9rRo4yK++ew3M5GjgWqZADUJac9gvHMyI6Ds1dTEgACyN1V87mAd5D7sj0Mw/s5KbxCv8HgWmfpzTe468O3jqGPIlUIQQbj9IafO3fye8hhU3e/Ub1D1cUpnGFW7Nbk7krtNQjiSWosl9xw6yjROStP9GxoZpDqsO4AE6YiDkEMtM8i2GZZOZ+vzdKfyqGUCyFF1ynI1FfeoltR1ruTe3DTdtnm+vJxnfV4vI1PB11lSEk8n6VNSoFrGn0fEwIYdSvIy2lO/Rx1Cnk8u1JK7boe5hsrm5fIyNkYi9LFtJyiMbHL/X82nvLjqRRp9wW61eT7GSRWj3TKtCkrTiyPDnV3//Twld7ApEiEhBFXCIZvXystoMPT9sylzZ1N6tbHXykqJSEmVqrueyZQr9Ugy30zJKN+SzUt4Y90bmcHTJ7k3pBqoCUidKwFpEHMfw1tmcmMiO+c/3vgJN8tlkK3LT+5mIFkQuSfrHb09kKzIGHKblERvCgaralaRFhaWSe5+CTtIxdKsQTXAFVKTuzs76Xh3yO5N7jg69zrbeVEtrYydQ/2QVvmqS6SkRH04bqh/KyX3nDDVMq63TMA9VKcpdyV3k9yd6/QH1dhchKpMZ7uEkZkV+FFhS4RMc8i4KZ79QRKbUzhfBgqDDB6ev0KVOfP2XLWY+tDTQWVT93hCBTb1VdsbDbLdGonwfkmcPrZNRA0etjpPr2H7Su3ywCJXR7y9aTcjb9EzW97h9n592WJZ/Ceem0hXy2E5j2m8UFHO0nicChHcn6KRuKuWuWfdBsCZpcVVimvIkHuZm8yqeXL/W58qbnjLsd24OnOfoHHi4ydywPu3ZF1rGYFUF9U0eugxjvQamNUqVbonjYhUZAbrIiX3rKyTsTJHcu+/s3OuQe4NKrbDbImtjVuxhSBCZiaRrZaxnHYw+34+gyo4HdZP7lqtU6n6QKiWKQ5a567VMoWsbnTedkeKvG39Rm6sz1/9PzR8RoMlKJXSdXV0X1+kQINqseTuqmUMyT2InDW5K5KqFxarolFu7t/PjZbNipDMBTvtMVKeWFPLi+nhOU8fnE5DOkHc8qokymzJXxQB9UmnmdrYxMpYjJrG7QCsiEa5tX9f7Gv7w8J7ASjVnGZI7tp9TaeOCBoy/IuGaOnxcSOKFRwdfJDkXtqM6qJWnf+t8vG81NQ/60N5o6zUk3rXj4IXX1GIWRF3MRcT6dEHkBSCiJQuIdVaFqUysw6vTpOg65QySSYrU6iDF433rQ2xfmINXFQDRe7qfWlD6dzPV/PSoCOcuAuT3JUErYWimBCZSFLdv2ddkjlfl0Et3pFXco/EAenObM1BrV5L7uYqVumkI7kLQfTBrzrX+AUHbVA12zBg5nvnsn/yRGWFz1vGeOdaco+WOE4SoVqmOEhpExU2jdIhmXwGQI19VHh/H9tm//rmVyhfGYs5enf9TH0gV7oBP4pdjcWdEQjfMwRc8Jp3m4x0vSIW5fLBA7m7Xx+WK3bUZW5ozjXOTvGWoQselE5ne/GQCR+3kJBOUOJbEq1M2sxobOKo2jpu29rANNXWHzU6hP//hg7iz/36OvlHXvwF1G/lm0vOdIogTLWMGkh1tsF8y/Qp9E0HS6tbIxFiEqKKCvVsoLk86rVq8NgvPohB0eyEV5W2zXZjQLplg3fBmXz2CoAKX+BPjYDRSe97KouWYVsRUghieAPMSqR0iVWr5HSiMk3u1YlqaNieucggH3Nm4s7QCsw7HhEWKdU3dT372zaDrDhxHWilT04nIF7l0m4UkVGV6P5tzoK1ClIlEsurc9cClrIV6LQLMStGvdpnqmVSMuVK7hWN1ezWlKDU9PSxot41EDQC1DK/++QBfjx4YB61TGmmjn1GdErysO5N7sqNbVD/vkDuypxUkwm/168pKqFvooGf1TdPHCW2zOiB1enLN/uX4s6BotUyhlRjSqc7HwDDJhrnOcc0qV05ZBDLlKFtq1LRaAJr1jXOdzyFCCy3NlrOrm+EdJKYb/bizHDgpk1bmHrJBxxS70hP2stBu/y9Wlbm1NOYqiZtAV99DDB17s7bCpLcoz7JOF8I/PpoBGubo35xyb2Z167dKS0EROIkfOfHpGS7Oufx1Ws5rN4rITf3Yd223jsY2MLKmk3EI3HS0iYhBFFDUgcnb7sme51TvcyQ3D/Z9gmzHpjFkyuN9BmG7Wap8gIbXDYoM8zlCDZb5UvVKzySu9nu0iVLM+ka5QOQZzi5+KNmHtcgctdEqvqWSc5Z3jLaHVkJUFpy7xPv48YpmDWypU1aCCwEE5JJHlm7nulmuo9I3Bs4OGKa45WWx4axPd0IOrdMkFpGWFA1IlTLFA31oZaWOYY98/u7fMs2HkwP5qYNmzh3R8bHVEuBUSQk6zhelnPH/j8PnBJrlErpukDq17d4jc9IOGxS8MVBHgj54Pef1fDPFNT2rkYQlFZV1FgWn8ai3N3Pcf9rqNuQ/5k+yaTGEoHTea2fLJXOVNgvufcdYKSqFRF3MNAh/ruoBFO2IDOVVWhKC9jlENjzBGOlHKWWCZDch6a8ZY6RLQ1rRCRYTzi5+W2ZWZAlH9YplUtE6VPH+aTqlyvK3WCqoFlDeR7f7+s2bmZak3dGZ1mxLHKPiAg2UuVMz+jYwSF0PVPVWm9th0jJNP/5zEkdceVCIz9Q0ALxngW8g7+Bl8u9nh+WkQLBH+im3SI/ML2IInFXLROVxvt0yd0QElQZ7l/5HP+uKHcSzwEHjDiAA3c60FswHdCn+mpKkXxVvIoG5YJsG30nbaexsXKTXiTmeqM9nN7CYivtCFi+78OcQaxq2prxljE1B6ZBtc+IUC1TLETKeamxciW5G207NZlmr0gFR9U3MNb4MN28MRKwU8SiZew/dB9u3LiFmSkra3oNULbrUYzZNpo9GlMcX+NI7GvKds+ccPbjcNa/ggtZrOTudiQfufsNuKrTCmBSo5cotkYsfjkwE13b2IxaI+3zxW8SVtagNL2x0c1YWSptSCfoX5px8dtz4J78cNfTPeXVSciSiVrYmjFcOqs2CXjup1RbjiSnU6fuSNSyTkmK0jUQZuPyrdsA+MWmTNZH02tjWKovA0QmuC3S5MzeNJmZqh5/+DzAMhWYFJWAnWZkMrsUWnXTXz33p5u3cERdPdNj/flKtTdZ25hEhhDm1DdkfXhRYWXVMyIiJGWKGktQKr1JpZuEKbk7v3G9jc1d72UCrdzPIoC865P1GU8rH4mNrHRScWzyBWklZNpQy5iGR8kY1U6fGy61ROJuUFGsblOA5G7UTBHn9R/ezY+GDHIN5seMPSY74ZiS7pMpx9cpVbsegD4lfdim3FU9BlmZIm2027pIhCbz04jEIVaOBH5ubefM2Ha2CJk1o2kyVK1bUnVG+oEgbxnhkHvDtmzHgtqNTu6cjb7I2zZC9yZ3NWKn445fs5nfxRow3rvogYKWGlz9fPlAiMSZ3tTEnTV21vQanDDvPukIf1m7jUG2zaXp77AjaqQm2GmG6xWQF4VEtbp+34a3DGSTu3Gv6w2CA7i3Tx+PDt1vfPSjyZcHfmQqlZUb/t51GzPrvNqO5B4zyvDAFx9gXL9xRvks9/zkR/+G30112z4pBAhB7WcvMWvnUdwwoD/9+vWnLlnHYemlbmpfveBCkOQ+PpFkyfKVHF+bUY/pwf2kmlpO37IP9/XbD4BJTU2uJ8n8rR8AXsm9f4BOVadktnA+7tEBNohGIdg9PsD9iE6pqePmjZu5t88MhqoBo0+TM6u8adNm9lLSelBa5V3j/dnbN0hbwuLR5U/xbGWFY0A1rksJ4Q6eDUoq1Nuvp73RkHfriNhHvp6lHmi0E5w6Qnl0+EhMuwsmdtrbs39HupEGVWmvekwyIhWQMiIScxfNiaooWyCHWiZ49lAWLcveqch9enQdVw4e6HrpfLL1E9Y1bWNRSdzTd2xpY4sM6Z00cjgXDzHy4ETiECtjm/G9/DKxKqtMjYaraq2dyOjcAXY5FE673yu576aWt3znb97yf/yUE8D1+m2BdW4tuje5a6NJwIuPHP4z+OJv3O2nVq3h2ZVrmNTUxC83bWGwntYP3i2zdF0yWIVSEilBYhFTspUQEVJpo1MrsmoW+VZZ17BNcjcld59nRjQz7R2dSrnGTsgmj+Yk90YfkZ+7o9pti2dXruHpVQ55lKvbzmxszJqRWMLKynuj1TLalU+XUH+Em6c7ybz+3qcSonGqm6ppQjJaqZpSeXTuQXlv9OC+SyJNRbqaUZFy/rZ2PZdv2cZO6n1XKFWS+dH300Qc4MoZkRLsFMdFBvL7oYcwb0UmLcHC0lJKg96pnXT7gy1st7y3btjM3es2uIsmarI+a0c1X5/8zSwTr7nI8w7LysqdpCXQt5V6qNK2GZBO058I4/uNd89bo3Xmy56De78EeAOdVihbDa/dCtdlpOOkesdNAY4KeviImNKqlI66E58RfN0iN2YhiszYO4LcinOk/A1sZ6O/PVVZwa9VsJgm37dKSz15o9K2s9h4RBtgcVZic2FFIVrKIiM3zpRon2y1jKH7r0v7yH3UTNjjWK/Ofeheji/+/Du99fv3JZlz2gHdnNwdKbu2r7OS+ihj6mz5pORRqTTD02lGptIcV1vnBoBQ0scwzDgv/bQJp3quLYuWsseIPkTVh4oVIeWZyvs9W3IgGs99TOuv3Y+lGbWMr7Pva0h9O3w+2v7cOX40pLzG4XIpQbWtbjOAodJiz6YmR/9ctxEePJML620OT6rnZZG7ipBVtJVwJXfATlGnfFelEDBsiqvLvCBVRh9bkpI2myIWn8WzZzxB5K49NwSlVNnbAZjalHDJtCpeRVM6kaVv1wFqc+obGGN5BYXhIg52ivjgPZgzdD+qpHTtMzURi5IgibIpo5KZumE3dmtKMDKZYmg6zQzjPY1Rg9ihx95B+bhDPLf4xqRvuO6bAJui0ayPdbAq93pF3n3TNmMTSVJINjVk1IueReO3LAOyBQB3qyljS0oock8EkLv2Q/BL7q6dRR1vFFrtpshdmrMmdVLEq3OvTmTnYckluZtDQUIZli/f53LAMbKbgk1KpkgBMUXWsxoavYZ1pZbRBuQqKTg1PiLLoGrq3C2dfkBL9/qdmWoZcFKG7FgFnzydXY9CAyKLRLcmd1tEWGyPJVk6EPqOZuDA3ThSTdMDP7oglPbJdC4lwf5k5k+8p0TLGNm/gqiSIYUVIek3mBVE7nkk9y/f6ah3TMndfOn+DuAjUtOYuMHn3dBg5Sf30+dmElUNKOmX87zvN8BDaw3j7EdP8u0Nq/ltY0lgmUrPehSAeUpF1GSqZdIJXm8wVASRqBukEjvqOqIIaoTk0NEjubdvH/e08SmbJctXMjDAeHqK8oqqkhaWbSyVphCzYjyw5iVmmmmdyejMxyZT/LtsIret3+ge6yOijougFXXfwQur17vGyzKfURmA9//lPnlwQ38eWbueoGE9qtor6usXZ+1xFpdMv8RVi2j432K5lPQTUbYq8u5j20SBOtLsaNrBxdMuZmzFiCyDKDgzl29s38H52xwy/8LIEZmDqg/qbKLB5K4NpIZUGyt3yV6rZY4cNYIrBg903Vo9ahndp42+/Qi1zHpgVtbz/MZ757pYYPDdF0Y7qZbrLOEhd1va1IuMsdvC5xI7fArESt17vizGUBaJ5TSo3rhxM6cMm4VngQ99O3+E6oQvQN/R8Ob/ZdcjlNyz0dh/N45L/JLqwXvD9xbDhfP48ZZt/HbDJkYpYxCHXpX/JiVVGcnddhY+QAh+XZ/pcKWR0iwViUdyL1gtE9BB3XtYGcOMs8N7T/9iFj4iNd09/cinlkmmk2xXxqdv73Q4dx/2x9xlzOVSqdvGNzPpN/ZgIBNFq8l9cUkc0il2qNlB1Ioyd/Vc3t74NgC/fuvXNArYEvGW+/b1G7lrR24/lzOra/jj+o0c2CiwAtz6tI1A2yCu3bSFe9KD3ECwmJSQavLOCqTt1NuKZN7B2IOpUKeUWjneqamz9yGpbBMl6pClyM3/loTx3s7YUUMQ4sYnXGXbRKWkWtW9ItHA4Hg/1030vXicuWWlSBy1SVRm3CfNnELatTCpchK907DePVTmBs0518XMQTZa4mYbSglnFaRtkQhPV1a4knsMww1VD17GIPapCHZACCT3SDwwXXdVYy1RKXmzrJSH+mSC2tJ2mgak2x8j0rfC8nG/h1i5qzaMWjGnbH5XYbX9r6oK7lrzgvpuNbnrb0EP2Hp2EoV9vwErXoEN7+de9L4N0a3JXX82Qqj/WRYDbJvD6xsQWho/6FK47NPcNynp47g7Ca8UMUJmmmZc37EeohWWT+fufpbNEHxenbvIhD7rSuVTy/i2ZzY2MacuOw9IREoajPs0JlPsaNrhSh8fKAMjwFdGH8m4/rvlLmL/nWH6ObDnCb6iB6tlwPES0YOLloh0bu31aae8AsF3XvgO17x+DQCbGzbTgBN1a2JqYxP9c4Tpg9OZZzc0etvRgF8SnpBIsnfS5qi6ekYnk+zf0AipRpd0AcfFTZO7NrSlk0ba3fxG8pjPByYlLbae8QwA36i3GZ1MslPlTqod1CNVz6420sR+tdr5W2cBvWyL4y20UWZIIoYzQNWrupe8fD271G13bRZn7DSM7wwb4hJaBOkamr2FdJ7huhMa3iEDlFCjFXlRgN2PdTbstJPHXjopCGqM9+c1qCoEkHsshwupabyncYdjE4pEA8k9Wr2GlBDUWBbLjKyuaZmmXmQGKAvDjz5SAvFyiJWREE45rajye5dpbGmTuv0AePYn7rfzelkZz2x51/lWc6llTKl/2lcdXfyb/+dNAdzVJHchxCghxEtCiA+FEO8LIS5R+wcIIZ4TQixVv/2bu1dL0bcsxpF7DmVwZQBpWj5DSS6Uqim//kjVuTHVNBEp2XnAbp4XkKWW8fvt5kI+yT0S803vrGbI3ajf5NMgWpblVXLnug2cUV3D5miESWNHI4FH31nJgQ8eyD3v3QNAnVrE969r19O3bFB2WoXvf2RsCDjudzDG52+snxtA7qVSuuTuknwkAn1GsMHWEmK2J4oU2baCCHgDu3LAtqJqSTvv9TUJr/Tbx05DqokpTQn+s3odE5LOYsvxQMk9mhEA7CSHNykCdVMwZMndgJP7yEQai6iSkg/Zspr/rF7HoNKBnjtoIpwzag4AB9c3MCqVhn6j+fP6jSxZvpKzq4Ml+QhQq55ZIiWxmg2khOBBIy2DGcg31eehA0CqifV1GWldGrMP7Vn0jqxX95AwYqpDjkYO9RSCGsP2Y+ZXl0Il4nUFqkyfk8Zg8+C0K9y/PQFzN+8FL1ybU3K31r/HrPoGN4WExry186hHurOViDTUMpqco06itpiUakCPgJ3m8rmXc1xsK8z7vdtfy2zbMfoLy4juVfdzyd0Y3MsHwORTYfHDUJNp3y5H7jjG5h9IKfcAZgLfEULsCVwBvCCl3BV4QW23C8YOquCOs2cwaWTf7IMm+ZkdY86P4FxjbdBR+6lz1EeqSDSqOk2JlGoA8EvuARJPc6qZfJK7iHjJ3UwcBtmkpsn+gO/CSXfA9LOzDIVxCQw/zN2uFcL1JCjZsQbWLqJBqUbKpIRR+zh10G3RZyfoY+aYkcH1aIbc3y4tQQJbVGBQY6wc0kkam0mL4F+vNSpl9hTWdEE95R64aCEICyGzde5+ch+ZSmcHmSUbGJ5KsWsiwXUbNyv/5bRH5046QaUOHIrrzJjeZ+meEPWRu41F1D+A5ug32kNEJzkLal8/ov3HuLOKEpWPJolwo5cho5aKIIMX2kg1unll+qbTJOOZ9At6MNDDcVTivJN0E3zwuHvehyUxNu1znruddHP6GPmCAnTuprm7Xzxja4n5A52sCETiWSrHu9ZtgEStM8ipen5JxaaUREpoENLQuUvWxKKsj0QyMz2lc49L6Qw6KgXwMyueYVUsRpPIeMuUScfo751x+yV3Xx/f71uOs8Jbd2b2FRsLUyBaTO5SynVSyrfV3zXAh8BOwPHAveq0e4ETWlnGlsGUdP1S7yBD9aBfgm+KqCX3UikdMjNG128fujtXfmEP44ZtILlrKUG/6ObUMpog9GAQjZMWUGp8q6ckrkJU7Oxub49YbjrZ0vl3wh0HU9/kEJ4nolKTyF4nOr9HXef8aom2zD8Zy03ujUJQb1msNIy89UhkOlFQaloTjuRutMNp98OFRr6dYZNh0HgQEaIBWeRMCfTmDZucUvtz/6SaGGjb/GvNeo6tq1eSe1oNvpnQ9IkpQcyKscegyc6+qhHe+yjS+Y/tCA8L7V0BR1KMlflz1TjnzmhsZEgqxeE7Hw4oWw8Z3XxQ+365zin/EUolFzXUVk7+GZukyBBdiW27OejLbMkQw/3TpZhUozuj28W2SBoEpSODG7T3CzJDzttWOPexBEkEW41BQXve6ChcCWBFHBdJ450m1PGRlSMZVJqJJYkbdbftFElhkRaWx6A6sanJyR2VqMWS0l1YfVZDA7tZFdQkaqgHylW/08167vChmQaNlZNEOF4/kZgruWtsiEQNyV0qd11TLaPdiDS5+2al2i1y4T2Zfang5G6tRZvMB4QQY4BpwJvAUCnlOnAGAGBInkvbDx6p1xj1JxwZ7JKoyVJJSzHlMlYipTehEDBqUB9GDzQ+ULeDNSO5BxmF3DJGnXLqFx2Je8ndL7G6KoKUe28bQV/fKy2LZYh4uxXhjc3/ADK+5w0qY2O5SbT6Q9KDkVZd6XN2OwbGHpRZJFz7/QaQz4ENDaSE4C8qFcJQEWcjSSZXNbJMFLccnAVuxksAJhwNfQ3PF72kXt9yDtk1QwypiPOhab/yR1av4wgdrOb/sLLSRTgBW87gq0goneALKYu3v/o2x0w6G875Nxz/+8Ayfyp3Ykzj31lij3UuxSLqn4WpOu2eSPLCqrXsM2wfIGNE3KrVG7qPjtwHpnwFgKs3bmDJ8pXcvHEzHPJjhNE+WnKXQvBMhdNfmyyLP/RzZrqzGxook5IrNzuRk18cpQaol35FrVrcub+IetRmfd0o2IxR0i/UzFB2gYRh9/h0u2P3cpObCcE2mWb/v+/P3zfOd89LYjO8YjhPf/lpSowZYuzj/wKwo2kHU3YezvS1/+KhNf+jyfAEe0B7ciXqiKq6gmPQL7Wi/G/1/2hAumXQKh2PMTlaSlKQkdytiEf6Xh+NZMhd2hnJXX8Duo/oxGFBueD3u8C7nSO+prVoNbkLISqBfwLfk1JmO6jmvu58IcQCIcSCTZuyQ/5bDY/kblQzWhY8vdUDgCI0TX4ltlT6cIO4s4xoBUruJZW5j1kR731NNYDe9p8Pbsf7XCaYX1bquO4ppBvGMrHfQeyrPraPSmK8s+NJwJFAAOoTAZK7fpYejHR7aXK3LIfQDvmxepAjPcoAf92aKkcq0lGnu0bytEEBkKZB1d/e+h0qI5gub1KRu5Ya+5oeHgGSu/eBtlO/WJlhUE14B9uxB0GJXzXoHej1UpA2FrGIgC//xTg1WCiY0N+JsHaDkCqVhDliumP78GPyaQw0XIAdcs8YDzWWlJbQP512A7v0AtfaX54Pn3DVd1VYHnLvr0KpGg2/db/gEcHxiEkYKqnVNc5SegMU2dVagk12gvpUPdd9/rjrW57AzqhgjP4Uf8whxBXbl7v7pCVcgr5qsxHa/+6DnmjectumT6QEgcAWmX5g6ut36EEi5ujc41I6Hi7CUNkA80tLXW+ZMls6mSiFlZlx6284poS/oIV2dMQqwNCJsNcJ2ee0AVpF7kKIGA6x3y+l1MlVNgghhqvjw4GNQddKKe+QUs6QUs4YPHhw0CmtQ67AgFgOco94yV1PPy/cviNbis5loG2O3MsH+i/w3FOa923umbq8qvOstB0JdFrEkZDPH/dNQJCOlnPTxs0AfBqLu3lojlTT+IbtKwB/fnOtW9d2CD2Q+KQQLZ0oQkwH6G8POfBHnu19YgOyzikGEdOH30+KEaO8dtpdAaou7izA/MMt2xD4FhXJ0rn7PI6k7dQvWmKoZVIBA4t3W1gWZbFMH2xyyV04Lo5xc5BT9fjqo/ClDGnPGDaDqngVX9XG0yF7OvaiI38R3NfsFOPjmZlaiZSuZ0hSCMYZ+W2OMjyrgvTuTesWO8d8FNFPkbuOWo2ZahkFC7WQt0GKOxI7sKRkkPqutlkRTxTr/WppxKSUGRWMcV8dHLW1IUMnaTJ+7JPMRGzJek+py23JbvGBrlrOXcTGkPof6dPPfWZCCEfd9PHTYEVIGv1+TSxq2A8kaWk7/VALBZZXAxC4FoMVgZPvdv4++S7Y40vZ57QBWuMtI4C/AB9KKW82Dj0BnKP+Pgd43H9thyCXcdMfHKThI/cKKVmyfCXH1NUrYs0jubveMs2oZbLIPYMT//Qma2sMNYXW92n4p/L9VJi4khBm9xnPkuUruSo+iiXnLOHIXb8GQFOkiv57nsTOacmmiMXgdJpdEwkmJhxJo37Th5TZNlaQysjvq5t1XEmJqmMH2eZm7XqCh9DHLX89+F4KZ+1xlmf7mEYnaGnJ8pWqKEabZJF7NHOOTLuDUXXM0QyeVFvH4kFHEreiMPFkKO2bmY18Zz7stHc22dvK6BotNQyqTdl9yEe2Avjg2qPc7Qa15oDrPWPoo91BaZdDYe9z3N19S/oy74x5nOrGMEjHUynqG/i/eDMccS0MGOfq6cEh99mNmT411SDAUsNG0Occw8FAIbHgz855PqlczwwbPJK7t+6WVJK7Qe7VTdXEpXQDxrZHLHd2DLj68YSQGcndzPFf7gzQZqoMWwjXllDpC2oz8+mXSUnMEOhiAZJ7fUVmUHQl99oNICJcNHRQ5hgYahlJUjYjueeKDZl4Ely9w0l/0k5ojeQ+C/gqcKgQYpH6dwxwPXCEEGIpcITa7nx85WHY9ahsw5dGQnWade9mH8sybrZQLVOWW2pN2IKUmT0kEoOYsUapX3Ifdwic8Cc45Epn22dH0GratJRw8l8YVjWKZysreLu0xKOC2efzhXxtR7XjaaKhpXj9QeSql5bcVce2pSQhI66k7Fwr2G38Me7mrIYGDqvJvcqTEIK4FedYu5SD6xv4WdJnfMwXqm1O520b9v8O7H4sS/vslzkn1eioN07+S2aAhIzNI+ELBtMSmUdy96llIHtbS+gKWi0T137vnndb4GfYZyfj9gYzVg51VjMSglKDxOJSslP9NvqpmYqZmjg+JLM2wGRtFDagiW+0L7a2yooTtaI0GrlisCJw0GXuORFQknvmeatrVxMVlpvH542yUo/k/q7K55LEMJ6aDgjanTaZSZWRRlCrBoUqn2Rh9c84EgxKp91oYMisktU4MJN/p3TKme7fCaFmCiICkSjzjAjfhBAZbxnbpiZV56wJq4UC/Rz32yjOrtSWaI23zKtSSiGlnCylnKr+PSWl3CKlPExKuav63dr83ToAE46CMx/OSHcn/RkuWpA5Xr3ae/7EL3u3TfIsVHIvVXrYymFO4v9yH7kbRJUmQlKa5B53yqql56CUv1PPyHiu+CRvSyeuUp0+pshneyTiCVw5qKGRC7dXZzojkFHLlATXS0NLJ+4SdpLDEjfx8H7/9Jx25h6ZDycGHFg7ip9tdjJZfqFyPFXxKmYMnUHcijNrxCxKo6VUEeEPGzZRrtvQrViemAWPWiYFo/aF0+9nyh5GeuZUIsvt1bk2Frx0opYUPZJ7slnJ3Q9N7m5QkzZSa6N0IZh+TvB+Q0IeGKty/66wJcJOs5tSx1SMP8I9FjfqGovE+FJNHTsZuZmahMCSkpHCS+6VVpSYFaPR8FtHRBzdsYKlIj/9i8RES/owYueDAHi7pMRD7joTY0Labl/1qK5Un20wDOC2yAS6VQw2vdcgqu4RxbGxxIzvI676d8IYPNKGPSyJktwrBmX6ODA+kXDI3adqWSVkJuLUNaiWe8rdGejWEaqtwuRTYNCuuY/79WAmeeYiGC089NvZIfQK5Sg0+VQ4/+XsD9mYAaSxEKa+Vx/TftTNhSj73CwjSqLRevBD+2SklMCFJMxcPPUqhbCucy7i8g08tpSskkNJx731HFk10lWtPN7/XBIywsk1dSxavpIbTvwnr5z2CncffTfzz5zPrJ0ccnf1of42y0eiEWM6bxDesOGjMuekm4LJ3Yp6t7/5kiMRa4nMlNyT9bDhPV+5RN5trZaxtItmf8d7hv2+lbs+GhNPhuln+wZgA4ZOeLe+49y/+9k2IN089wfO/H/uMTcpmRIOIkjPqkcJ4SwMH9V9QaE0VoEtberQ5C5dn3ONCE76gTvXvexUtUQ9w4pReeY/mZGOkBaCpPpgdq8a4yr+kkIQTyjpPG7MbtQ+Uy1Tk2pwXSGjMe8Mb7MqXwpnXh0zPW8kcNKfGVqRcYE0Bw03iOm0+yFWzmF19ewaH0Af2/aQ+yzlqFAnRLbkrp+Xz/25ndF7yd0PTabfftP59ZOK+ZL8Bln9IeuOt98FcOnHxiiuiLOkynudIT2VkKCkyfiQtMQaYFwKLr86rqacruSutquMdUDLZRC5Z3fCj7YkePitVeTUuet6qXpotacIkvSV6151fCiNaoYSKekLlkVElV3/njzhZGYK9WH72yyf5G4GU5nuZSP3yXia1G81DMUmuce82yOmOdfoFakiJfnVJ0P3grP+6bhnGiiNOdc0+lOHRaLw060Zj6N8OPkvTt4TP/Y6yfk1pMN4RcY5Qb+FqzZv5ao9zmXPAXsyWRnUt6TqnbQclziGUxvlLaO8fpqU3jm6w7uCkKga7knjEEW5QhrfhAXUG22lo0ujVhSEoCzqhPjrpfpKo44bL2X9nQCipFKFmYSdaoR0ikaDhFNIksLRoQtzIAA+SnvVaya5l0gJA8dx5b5XcuTORwJw13t38ejSR0mmk1RblqPDHzDOzTUTlTZxKfk4HnO9ZfSg2VTe38mSChkdu26jqtwqyPZGzyN3n17bzrPcmQdfexqmnJGR5kdM8x43ozJzGUb1dE0Ts37Rucjd+EhkpIR3Bx2bOaZcCF1XqpghWQdCfcra9c3yknuFYWhrFCJ78Aq4/+MbBnPjsx8XJbkDRHKMBQBRC5q0+ikHWV445UKOiWh1k48U/d4sQRi0K2xZmtF3RqJw+DXO3ztWUZOK8J3736Y2kekbFz/2GdVmoKC2s5hqmeZmT+MPz7SJ0t2fuZ+j+20KygtpRZo3wueDO6AbU/+y/oxJJDnYWHSmv21z6qgjEEJwhcpJM71qjKN2UOohveLRh0r3nbCU5O5PfDZgHFMGT3E3XbVMJNOXI1K6aZ4hE12qB4USBAkhSKn2LCnpix2JwblPkYxEM4nbLIu9GxrZo0m9mIatNCbrKbGdpAFlsYpMNKkvC+ypFeM82zHjuPZhH9N3DL+Zk1nz4afzfsr89fPZHrEcw29JFcTLnQEknaTGsqiOZPzctRG3wVQd6oj3qqFw1K/gzH/QWehR5J62JSvPfpNt313m7rvskcWc+ec3mr941D5w4p8yH4zWjw+d5PxqyTZemT8vO0BZP+dXk300B7kbH85frziHI6btkn2veseNsdlVnKJe4+awPqW8fdURHD/VMcLt22cc16oVm/qmbW/iIvN6cPWntaUjnDQLuQhIt4lqs7SeNQSlGNbTZyFJ2KqN86lYXF97X1uPm5P7Go19vg5ff857f/1O6jaTJMbHG2pIqe6fjlXwxHubqU8HBBdpyT1a0vzsCbL09t+cPY59xvSnQbbD9FzXz0fu/1qzjlv9y0Wqsk9KJFiwYiVHDsw2ogI0qfZ2JXeD2y/euh0GT/CkAohKFQdivCcBnshRP7nHFLknddRstJR0JAZD9yTRf2fixkLwf16/kfvXqjwsN+1K4+IHKZGSiLCwkRkViu+bPH/AdM/2McNm8ts5vwUUuefoe/9c+k+2RyKOVB518ru/XlbGYrvOHTAbPn0ByJD7IlvNEvrslBHKwDHo9xtFZ6FHkXtjMs1Bt77FQ+9liOutFVupKilgebsgXLIYvvaU87c7OhcgaelzteSodaUe32Y8AQ4DK0uIl+YJ8Fn1Zt5HrqxVX6Eio4glGFARp1T5WpdESzmqrp7ztu/g/B07sm/Qx/AiOv9/8JNNRCPCyX6Zi4S1rld9vCVRi6/sN5oJQ/2DGOh2i1hWRnLPR+6N+h2qesWr4MfrndmVH995Cy54NbM9YBwMn+ydGeiZSaKWAX378Pz3D6ZfhbNPqvw0KenXm1sZ+4Nfcj/i2uBy+4yIw/qW8o8LDqCRFvbBfBDBknsMslZtMtu6JCDwyL1cvcsmkS25C4CyAV5yB0c37tO5a2+bE8efmFHLCC25w8pYzF2HtSRS4iYWS6QTWfc3W67RTjgL1osIaZkmWTnUIWv/bNo3Ey0t6evm6slH7s99/hwA6X2+AcBHhqpU26pqVr9JDIsJylD9TGKjMxvuhEWw86FHkXt5PELEEtQq314pJdccvxdfnz22ZTfsv3PGq0GnuR1QwL1ctYxWqSjdYcVAJ3hBRycWoBpxMWrfvI/831ZHjdGw+wnBJwiLcin5f9t2MCKVhhmZpE4c9SufTSEK0TixiEXStt0PoS6R5KK/v50JVho4HvY8Hr7s+ERXlcb41YmTmDkuQG015woYdwgfDzyUhkIk988VWX/4pOPVdPE7KgAtgCQHT4Bhk3LfC7z6Wz2DUpKkJnc982DP41X5RMZ/OV7uldx9BjwXjdWBuxtoB8l9oFI9GHr2nGv5+sncNwvR7pJCr5sqLOLSt9LSmNmwx5dc8rWkdAgkVp6Z+cUrsaR0V0WaNmSae762qdQqIt+adgSRkkiJuwxf0k4StwwpfNwc57kKjUJQpiV32yZpWY6BNFbu+I1rASpezri+45hsqxlgWX83v42f3M3YAI3JOzkLhpyy4OeZ81Rb1FgWMTvl1uuzdC2PVlZk3aOz0aPIXQhBZUmUmsaku33IbkPYZ0zroiIBh5i//hx85aHmz9Uffq3KdWEGxkw8yXGzPPrX8AUVAqAllXzkriSJXOg/dDS7NP6Nz3c+JfgEvyHSHFh2PiDwkqilJHcldafTkicXr2PpRhUxGYnCqX91Ur42h36j4OzHuPCoaXzvyD2dfYUuUjBoV6gcHFwPYGN1I9WNAZGAJsy2dcndeb5QHiNNQp2j9abm4NN/jLe8uchd6+i1jl9hj90dVcOndhsa2Gb9P/jKPxxdv1uuHB41/oHU1/Y/6+/ks7G15B6JUjJ0IrHznstcMv4wiMQykrjm/Xh5xiVXSs+sIWpFXXWM/p2pBro6JfxoyX3ZtmVsbdzqWYCasx93hQdw0kCX2hnJPSHUQuyuQVWnESjn4S89zD1H3QUHXAyDdiVhm+RuqI1Uff7v8MwqSYftfJinfS7dss31j/8kHnOeaSTQq450PSrteiVqJSpLotQ0tVPgwKh9veoLP478hZNnZKBPd65duzSEgJkXZDqHlppzEQYES6wGJgyt4vhpo4lHcxCmX19s6tyzsjyqYkUsUraTeAqgLB7huCkjEIWopnKgX3mcvlWKXGvX5z8ZsskqgNyv/NcSTvu/Zuwq5UZglR7YNLmXO/Wv1x46Zk59cFRCfUcGq3n80Dp634B501kHcF7iUs5I/CTgohYiEnUS4RVilM1KGe3djuiFVJRhNGFFKSntz9DhGd219oLSEqsmO/qPzdioRu3jiXaORWKZsAmllokon3CtutHqkj8t/hMAb6zzvUvDE6ZJGXotlVEyiXTIWr8PnUguVk5JpITYiGlw5M9BCJ5d8Wym3MZgp2cKO1UZQWI+lEubkWrt3DXRqDNbGDTBPT63rAwG757j6s5BjyP3K76wO6fO6CQjxgHfdRJqaZzzpBMFaapATGjpKVKA5N4MJgyt4ubTpjJucA69vZ8Udz0y83cO75+Y63GjtiMWvztjGrsNC9KpFwE9mBUS4HHAxd7tAKPmmu0NjOibb5UrnLz0A5QaQwesKJuHFYnyyxMnstPgfs5+LX1rAthljto2nh3PMQ3XszTfQB0Rghft6WwkeCBtF5x8V+bvZiJqI2rAthXhNtkJ4pE4QgjXT10P6jovvhu+Xz7A6btffx5O/SvV8Uw/jokY04Y4nmdaco8oj7YmtfarjkjV988SHoxo3oxaRknu2E5+G93eeQQlrU8vtb3kfvKEk4EcS/kplNqSMSrSusGynGeWVLnJ3ZbFYzDz2zmv7wz0OHL/0pQRwTrfzsDY2fC9JbkDGXR+ER3dlk9yby1MAr/gVdjtC2yx1L4sLx4HUTXVTPsNjUVgW12CppQv4Vj/Mc1f+M0XnYjM/b/j3R/PLqslBKMGFNB2Ix3Vg1vfTc4qU6JyGGfutzODRylJbIhWG6nPQ7edSei5dNtH/cqRZH2zt0APovaC9s4y+1NWojN/si8ludc6/tpNwnLJrkT1X026+w3fT2373CRH7QOlfdkvnbl31Iq65O3GM0w7G4BGdV/9HJ1DfljFMF9ZM2VvFJY7Y5i/fr6TaMyU3HW94rn7Q6lPLfPtqd9m/pnzXd37JdMvAWD5juWeayp3z7gqR6WEWCl/+8LfeK50Eo+sWd/MMpodjzwRISHaHYP3cKZyB37f2S4mFL1YmMEUyvj4/0qvZeKgCD/McUlMOaynbBnkpV0QLn7wHaobUzz+HWNF+2GT4NtvOJG8ubDT3s4/PyJROOYmT3TxU5fMzj4vCFplog3e21c5v9qeMe1sx0i8syqrVl3pwcgcIIfuFfyM8YfBJYsKK0974bKlzu/2lZl9WekSfOQuvJJ7omKgS8qa9PxqmWi01Anc8iG22zGw4lH3XL9kbpU4g6Q2cGpynzJ4Cq+seYWr9786Z9UcnbtN2k5THi13JHdtUAXnvaxZmHtmRbZaxhIWZdEyd3CpUikcTEm+VEpKRx8AC1UdJWCnKY+VUz7+KPjwP/lVtp2AHie5dyvESuE7b8KU0zLbh1/jTeLVVqgcmrXrg8QwtvWbGHCyg6hOYSBb3k3W5lKZDNkjr3SVF/t+szB/dz80WQ9XQThnPeJkYtTkbVlO1kUt1alVqlxvFFO3nYc88mHnge04O9MoqXL+mV40zUjuWi2T3uNYOPwaGmMlLrmtqF4BwFPLHbdgrV6Jxyq8xlx9L2NQNiV37e4YUQNLY7oRgXB13im17KKZFsCPxlgppTsfyMwRM2lKN5HCWZDEldyPvt75foZNybp2/+H7A8q1MsBTK61sLXqGMaJyBJMHO/EApVJCnxHsM8SxQcRML6K9z3Ei28cWKGR0EELJvavhwO95t7//Ue60ocUgEoX9L3KySSpYAvqV5zbUHjtlBPuMHUBZ4+IWPzaRthnRr+W2hDbFF2+GDx5zluMDh9h3OTT3+domEOTV04LI0vevOcrN+dMhMCMn/XXw2Xe0WkZGYnDg96j/+0OU+9SES7c5MwJN7gPLgtWflkGcUSvqDhJbGhyfcU3uO5p2UBmrdMm0SWXgjAUF7A3ZE5pqaCrtQ9mgCchkPQ2pBqKa3LWUHa/ILA/pw62H3krdr5R0HUDu18+/3lM+gMqYY58pOcuZiVSo2XUM6TGoMqRrGVMhJPeujz5t6Dp31C89m/N/nC11mRhUWcKgyhL43Ju3phi88sNDC08B0d4YuAvM/kHh57th8AYxXjgPtnzaosdXlHTw52baevxqGR9xuzp3KZFSUp+qp1zlI3ryxCc59tFj+ceXnFB6TX5+8ncfaxjvY1aMvipfzYb6DZ7rdzTtoDJeyU6VOzF7p9lsa9rmXuPH+6fcSVWsksb/nEpppJS0TLOmdg1jKXHULP6I6wCURcvcxUuCyP35lc87h4yB++LpFzOwdCC7DnMkdq2yifXd2ZPmuCsiJPcQBaB10maHGhPbFIrcTSIYuldufXtXhn+mEeDNA2AjaUg1YEubCuWlsnOfnVlyzpLMudqFNEe/MCXfqBVlfD8nI6lOuKUl++pENRWxCuaMmsOcUXM477/nee5v4vJXr6A2UUtDqoHSaKk7EKyzJJOjpY46rShkl/2UCafwj0/+QaMRl7LXwL341exfuduVKkgq1m907piCLoJQ5x6ieTS3CElPhZ5wFJJTpqtDpwfQenifvUMbVC9a8zQfbXW8iCpiwXaFqMgvE5rkLIRwDbI64ZY+vq5uHWVGQq+yaBnDK4JnqmP7jGVLo6PWKY2Wuu6VjXaC0ilfgcohecuUhYA+XaU8sfz52k1oNU2k0AC8TkQv/WpDFIXWZC7s1tCSe9f/kJuFltS/+SIcdV1W4Jo2qKawuWPxHUAecldql0Ik96Z0kxukFHQ8buSkSdkpBpcFr6e8x8DMYhylkVJXZaS3i0YAuZ884WQOG30Yh4/Ora6MNRNM2JUQqmVCNI9eK7kr/WxPkNz1AN1vNOyfHWwTMYh6wQZnhbJcOnVXLZNj0DfJu39Jf1ciPmz0YVnXme6GKTvl0debMAeB0mip57ySliyIEdCnR1WN4pZDbsl7Wb0KcNt3eP5cT10BIbmHKAC9VHJ3yb0bfybfegW2f97saRXCWBVMuSzmktw1eeeU3PUiHJFSRvdx1ql99LhHGV7pqFxShvdXSaSEumQdl/7vUuavn8++w4JJ00wm5id3U4ovGC2cjeoVm6pirYzS7gB0414bosPQW9UysgeoZYZPdv41g0qDLDX5VkSDyV17s6RyuOhqg+meA/d0943vn1nmceKgTGyFzgj56honC2ghknsf3zKOuWYYedHC2eg3J32T7U3bOXaXY5s/uZPRS+fbIYpCbyV3rXPPt7xeD0GlyNYl55LctQ7dk73RPK504HsPDYgwBgaUDnDzucQjcVdtAzBv7bzAa0zSH1YxrA0k95a906EVQ7np4Jtytk1XQrv1WiHE0UKIj4UQy4QQV7TXc0J0AHqtzr0HSO4FIiYs/r1qLQeUj3T35ZKINXk3pZsCj08ZPIV7j76Xb03Jvfi31rUXagzV+WwAhlcM9/jCd6Tk3p3QLmoZIUQEuA04AlgNvCWEeEJK+UF7PC9Ee6O3S+49n9wBxqRS9DF027mk05FVI5kzag7fnPTNwONCCKYPnR54TMN1KSywbXeq3InBZYMpi5ZRFa/yRMGWRVsQAR2Se4uxL7BMSvkZgBDiQeB4ICT37ohe8CEEohdJ7lr1ZhpJc6k7olaU3x/6+1Y9Tmd+rE40H1kKjh7/xVNfdLdNQu9Ig2p3Qnt9tTsBq4zt1WpfiO4I/SH4F6vu6dCDWi+R3AEml2aCgQqVqluCIeXOc+r8C9kUiD0GZPzeQ7VMMNqrhkHDoifBiBDifCHEAiHEgk2bNgWc3g1wyI9hUo5l7XoS+o5y0u8edlVnl6RjcehPnPTEepGPnowpp8OIacyZdDbj+43n2HHt6w0ycdBE9hq4F1/Z4ytAJof6Hw/7Y0HXCyH43SG/Y/KgyYysHNn8BRpfvNlJFtcLBBUhW5AMqtmbCrE/cLWU8ii1fSWAlPK6oPNnzJghFyxY0OblCBEiRIieDCHEQinljKBj7SW5vwXsKoQYK4SIA6cDT7TTs0KECBEihA/tYlCVUqaEEBcB/wUiwF1Syvfb41khQoQIESIb7RahKqV8Cniqve4fIkSIECFyo+ebjEOECBGiFyIk9xAhQoTogQjJPUSIECF6IEJyDxEiRIgeiHbxcy+6EEJsAppPOt15GARs7uxCdBH0prboTXUtBr2pXbp6XXeWUgYuX9UlyL2rQwixIFegQG9Db2qL3lTXYtCb2qU71zVUy4QIESJED0RI7iFChAjRAxGSe2G4o7ML0IXQm9qiN9W1GPSmdum2dQ117iFChAjRAxFK7iFChAjRAxGSuw9C9IIlWkJ4EL7zED2xD4TkjvNihRD/TwgxUvZyPZVa/7ZHdnYT4TvPj97QD3p6H+j15C6EOBt4CZgGVPfkzpwPQohzhRDvAJd0dlnaG+E7z43e0g96Qx/o1QZVIcQs4BVgXynlAt8x0RNH8yAIIXYH/oqTf38S8H0p5WdCCEtKaXdu6doW4TvPjd7SD3pLH+h1kruebgJIKV8D3gT2UMeuEEJ8SQhR2VNecC4IIar031LKj4Czgd8CHwAXqf094oMO33lu9JZ+0Bv7QK8idyHEtcBPhRBmLoYLgHuFEIuAfsB3gRuVFNMjIYS4AnhHCPFrIcS5avfHUsqtwKPALkKIg9S53bqPhO88N3pLP+itfaDbvrBiIIQoUYt0nwNMxtGzASClfBdHQvm6lPIK4ExgOLBzZ5S1vSGEOBQ4BjgCeAa4Tggx2ZBYPsTRRX4LHKnNlHq6C8J3nh+9oR/09j7QK8gdSAJPAnsCbwCHCCHG6oNSyj9KKReqvzcBW4EBnVHQDkAMeEdKuVxK+RJwK3CdcbwOeASoFUL8XAhxAzCm44vZaoTvPD96Qz/o1X2gV5C70hl+IqWsAx4CRgL7CiFKIOPuJYQYIIT4Dc4o/1ZnlbedUQ4MFEKUAkgprweGCyFOUdsSaMQxqF0IbJJSftpZhW0pwnfeLHp8P+jtfaDHkbsQYo+g/VLKJvW7AngVOBjYXe2TakR/CEeiOVhKuaxDCtxOEELM8ekYAZBSPgrsAhxr7L4B+L6xfR3wPjBaSnljuxa0DZCnrr3qnQdBCPFVIcQk//6e1g/y1LP39gEpZY/5B/weWA6MMfZZgKX+jqjfPurcrwBfBb6k9g/s7Dq0QRscDcwF7gT6GPsFUKL+Pl2dM0ZtjwZuA6rUdmln16MN6tpr3nmOtpkCvAs8Bkzpqf2gmXr26j4QLXAM6JII8EkdAGwDDhdC/E1K2SSVG5cQYhyOTm27lLJaCLEUpyNvAS4GkFJu6dgatA3U9FIApwH/h2Mk+od5XLVTk2qHh3D0kD9RAStfAlZIKWsApJSNHV2HQlFEXWVPfucF4BjgNimlJ6thT+kHBvLVs1f3gW6rljGJ3bDivwHcjmP53tU493JgHrC/cLA78D3gRinleCnlUx1a+DaEbgc1iK3FCUJZpo6dIoQYiTPlRAjxYxz/3gOB3wB3A/sCL0gpL+iM8heDFtT1NXrgOw+C1h8b2B1Yr479PyHE0UKIfmr7cmA+3bAftKCePfK7LwTdMkJVCHERcBjwP+BhKeVaIUQcxzf3HODbQBynA38OHAA8KKXcpq4vxZmq1XVG+dsKRju8gkN024CvA18D+gNLgCiwQUp5gTr/ft0O6h4RKWW6wwtfJFpb157yzoPg+x7+IaVcI4S4DvgU+CIO+fUHIsAvgP2Bh7pbP2htPXtyHwhEZ+uFiv0HnIhj0T4ER+L4AzBdHbtG/Z4BVOP46g40ro12dvnbsR1uAyYAI4DrgWnqvIE4U9ApxrUR1MDeHf61sq495p0X0TajcfTpL+JIqfqdv4jSM3e3ftDKevboPpDrX3dUy+wH3C4d39yrcQyo31bHviiEeAW4HHgcR01TB+6UPtXxxW03+NthBfBDKeVa4Gop5Tvg6hP/hSPR6HZIS9XruwlaU9ee9M6D4G+bz4ErpZQP4kiyMSHEUOlI5fNwPGS6Yz9oTT17eh8IRJcld79uzdj+DMfajZTyc+A/QD8hxP44gRjzpJRTpZRfBYah8kd0o07sQRHt8G+gSghxnDQMYUKIq4C9cGYxXbodelNdi0URbfMEMFgIcSBwI04gz5WqbU7GUWl02bbpLfXsCHRZcgevJ4/xkh4B6oUQx6vtdTjTsINwdKyXG5edqKW6boxi2uFlHO8HhBCzhRAv4agvviyl3NAxxW0VelNdi0Wx38MBqu9fB3yEE7R0eDf4HnpLPdsdXY7chRAzhRD3A9cIIXYVmUUD9EvfhmM4vVBNuXYAFTg+ubYQIiJUkiPZPVy5AtHCdqgEytTxFcB3pJRflVKu6+DiF4XeVNdi0YrvoQJASrlVSvknKeWVUsrVnVGHQtBb6tmR6FLkLoSYiBNk8CSwETgfJwUpht6sDCff9DrgDiHECJyEQCl1Xlp28xSlrWyHhDpvlZTygw4uetHoTXUtFm3RNt0BvaWeHY0uRe7ATOAjKeUDOFGH9cCZwglEQAjxc5zReyjwA2AD8HdgO47XRE9Bb2qH3lTXYtFb2qa31LND0ankLoQ4WAixn7HrLWCUEGK8dHxRbZwXeI4QogLHAv5tKeU7ahr2E+AYKeW3ZRf30c2H3tQOvamuxaK3tE1vqWdno1PSDwhn9Zd7gTnAY0KIpdJZIOBTnMCju4QQW1X57gNmAA1Syq+o691lv6SU9Z1QhTZBb2qH3lTXYtFb2qa31LOroFMiVIWTcvObOO5NBwCrpJT/ZxyfDIyVUj4uhJgB/FxK+QV1rMes59ib2qE31bVY9Ja26S317CroMMldOKuNfw68K6XcLoT4M870axBwoBBigpTyEwAp5WJgsbr0UOANZSGX3f0F96Z26E11LRa9pW16Sz27ItpVchdCCJxAor/jvNBPcVyXLpFSblbn7IqTD6ZRSvkL49q9cZIapYHzZTdbKMBEb2qH3lTXYtFb2qa31LOro90MqsJJRCSBKmCNlPIwnDQBW3FStQIgpVwKLARGCCHGCyFM3+WfSSkP684vuDe1Q2+qa7HoLW3TW+rZHdDmahnhBB1cC0SEEE/hJMhPg+OzKoS4GFgrhDhYSqlDhB8VzgpKzwCVQohDpeO3/L+2Ll9HoTe1Q2+qa7HoLW3TW+rZndCmkrsQ4mCc0bg/Tp7tn+PkfDhECLEvuOHE1+Ik/9HXnQL8GGe19cmymwek9KZ26E11LRa9pW16Sz27G9pU5y6EmI2zZNff1PYfcfJsNwDflVLuLZzUAEOA3wGXSymXq+uQUr7SZoXpRPSmduhNdS0WvaVteks9uxvaWue+EHhYZFZGeg1ncd17cKZr31VW75FAWkq5HJyX28NecG9qh95U12LRW9qmt9SzW6FNyV1KWS+ddUt11NgRwCb199eAPYQQTwIPAG+35bO7EnpTO/SmuhaL3tI2vaWe3Q3t4ueuRnCJkwviCbW7BvgRMBFYLqVc0x7P7kroTe3Qm+paLHpL2/SWenYXtJcrpI2zUPFmYLIata8CbCnlq73oBfemduhNdS0WvaVteks9uwXaLYhJCDETZ7mrecDdUsq/tMuDujh6Uzv0proWi97SNr2lnt0B7UnuI4GvAjdLKZva5SHdAL2pHXpTXYtFb2mb3lLP7oBOSRwWIkSIECHaF11tsY4QIUKECNEGCMk9RIgQIXogQnIPESJEiB6IkNxDhAgRogciJPcQIUKE6IEIyT1Ej4YQ4mohxKV5jp8ghNizDZ/3o7a6V4gQrUFI7iF6O04A2ozccULtQ4TodITkHqLHQQjxYyHEx0KI54Hd1L5vCiHeEkK8K4T4pxCiXAhxAHAccKMQYpEQYhf17xkhxEIhxCtCiN0D7n+uEOIPxvaTQog5QojrgTJ1r/vVsbOFEIvVc//WMS0QIkQHLpAdIkRHQDhrcJ4OTMPp32/jpKT9l5TyTnXOL4CvSyl/L4R4AnhSSvmIOvYCcIGUcqkQYj/gjziLNTcLKeUVQoiLpJRT1b32wlmMYpaUcrMQYkBb1jVEiHwIyT1ET8Ns4FEpZT2AIm+AiYrU+wGVwH/9FwohKoEDgH8IIfTuklaU5VDgEakWhZZSbm3FvUKEKAohuYfoiQjKqXEPcIKU8l0hxLnAnIBzLGC7lrzzIIVXpVma4zyRoywhQrQ7Qp17iJ6GucCJQogyIUQV8CW1vwpYJ4SIAWca59eoY0gpq4Hlam1PhIMpAc9YAUwVQlhCiFHAvsaxpHoGwAvAqUKIgep+oVomRIchJPcQPQpSyreBh4BFwD8BvYzbVcCbwHPAR8YlDwKXCSHeEULsgkP8XxdCvAu8DxwPIIQ4TghxrbrmNWA5zjqhN+FdXegOYLEQ4n4p5fvAL4H/qfvd3MbVDREiJ8KskCFChAjRAxFK7iFChAjRAxGSe4gQIUL0QITkHiJEiBA9ECG5hwgRIkQPREjuIUKECNEDEZJ7iBAhQvRAhOQeIkSIED0QIbmHCBEiRA/E/wdGacJ5CMGFrgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "no2.pivot(columns=\"location\", values=\"value\").plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "06b14991",
   "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>parameter</th>\n",
       "      <th>no2</th>\n",
       "      <th>pm25</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BETR801</th>\n",
       "      <td>26.950920</td>\n",
       "      <td>23.169492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FR04014</th>\n",
       "      <td>29.374284</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London Westminster</th>\n",
       "      <td>29.740050</td>\n",
       "      <td>13.443568</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "parameter                 no2       pm25\n",
       "location                                \n",
       "BETR801             26.950920  23.169492\n",
       "FR04014             29.374284        NaN\n",
       "London Westminster  29.740050  13.443568"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality.pivot_table(\n",
    "    values=\"value\", index=\"location\", columns=\"parameter\", aggfunc=\"mean\"\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4ef00ab1",
   "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>parameter</th>\n",
       "      <th>no2</th>\n",
       "      <th>pm25</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BETR801</th>\n",
       "      <td>26.950920</td>\n",
       "      <td>23.169492</td>\n",
       "      <td>24.982353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FR04014</th>\n",
       "      <td>29.374284</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.374284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London Westminster</th>\n",
       "      <td>29.740050</td>\n",
       "      <td>13.443568</td>\n",
       "      <td>21.491708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>29.430316</td>\n",
       "      <td>14.386849</td>\n",
       "      <td>24.222743</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "parameter                 no2       pm25        All\n",
       "location                                           \n",
       "BETR801             26.950920  23.169492  24.982353\n",
       "FR04014             29.374284        NaN  29.374284\n",
       "London Westminster  29.740050  13.443568  21.491708\n",
       "All                 29.430316  14.386849  24.222743"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality.pivot_table(\n",
    "    values=\"value\",\n",
    "    index=\"location\",\n",
    "    columns=\"parameter\",\n",
    "    aggfunc=\"mean\",\n",
    "    margins=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7f2d689",
   "metadata": {},
   "source": [
    "# Groupby"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bddb9248",
   "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></th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parameter</th>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">no2</th>\n",
       "      <th>BETR801</th>\n",
       "      <td>26.950920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FR04014</th>\n",
       "      <td>29.374284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London Westminster</th>\n",
       "      <td>29.740050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">pm25</th>\n",
       "      <th>BETR801</th>\n",
       "      <td>23.169492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>London Westminster</th>\n",
       "      <td>13.443568</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  value\n",
       "parameter location                     \n",
       "no2       BETR801             26.950920\n",
       "          FR04014             29.374284\n",
       "          London Westminster  29.740050\n",
       "pm25      BETR801             23.169492\n",
       "          London Westminster  13.443568"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality.groupby([\"parameter\", \"location\"]).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e0ea7079",
   "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>parameter</th>\n",
       "      <th>location</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>no2</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>26.950920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>no2</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>29.374284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>no2</td>\n",
       "      <td>London Westminster</td>\n",
       "      <td>29.740050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>pm25</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>23.169492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pm25</td>\n",
       "      <td>London Westminster</td>\n",
       "      <td>13.443568</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  parameter            location      value\n",
       "0       no2             BETR801  26.950920\n",
       "1       no2             FR04014  29.374284\n",
       "2       no2  London Westminster  29.740050\n",
       "3      pm25             BETR801  23.169492\n",
       "4      pm25  London Westminster  13.443568"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality.groupby([\"parameter\", \"location\"]).mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "85f794f6",
   "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>location</th>\n",
       "      <th>date.utc</th>\n",
       "      <th>BETR801</th>\n",
       "      <th>FR04014</th>\n",
       "      <th>London Westminster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-04-09 01:00:00+00:00</td>\n",
       "      <td>22.5</td>\n",
       "      <td>24.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-04-09 02:00:00+00:00</td>\n",
       "      <td>53.5</td>\n",
       "      <td>27.4</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-04-09 03:00:00+00:00</td>\n",
       "      <td>54.5</td>\n",
       "      <td>34.2</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-04-09 04:00:00+00:00</td>\n",
       "      <td>34.5</td>\n",
       "      <td>48.5</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-04-09 05:00:00+00:00</td>\n",
       "      <td>46.5</td>\n",
       "      <td>59.5</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>2019-06-20 20:00:00+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>2019-06-20 21:00:00+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>2019-06-20 22:00:00+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>2019-06-20 23:00:00+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.8</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1704</th>\n",
       "      <td>2019-06-21 00:00:00+00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1705 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "location                  date.utc  BETR801  FR04014  London Westminster\n",
       "0        2019-04-09 01:00:00+00:00     22.5     24.4                 NaN\n",
       "1        2019-04-09 02:00:00+00:00     53.5     27.4                67.0\n",
       "2        2019-04-09 03:00:00+00:00     54.5     34.2                67.0\n",
       "3        2019-04-09 04:00:00+00:00     34.5     48.5                41.0\n",
       "4        2019-04-09 05:00:00+00:00     46.5     59.5                41.0\n",
       "...                            ...      ...      ...                 ...\n",
       "1700     2019-06-20 20:00:00+00:00      NaN     21.4                 NaN\n",
       "1701     2019-06-20 21:00:00+00:00      NaN     24.9                 NaN\n",
       "1702     2019-06-20 22:00:00+00:00      NaN     26.5                 NaN\n",
       "1703     2019-06-20 23:00:00+00:00      NaN     21.8                 NaN\n",
       "1704     2019-06-21 00:00:00+00:00      NaN     20.0                 NaN\n",
       "\n",
       "[1705 rows x 4 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no2_pivoted = no2.pivot(columns=\"location\", values=\"value\").reset_index()\n",
    "no2_pivoted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cec26ea0",
   "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>date.utc</th>\n",
       "      <th>location</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-04-09 01:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-04-09 02:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>53.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-04-09 03:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>54.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-04-09 04:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>34.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-04-09 05:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>46.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5110</th>\n",
       "      <td>2019-06-20 20:00:00+00:00</td>\n",
       "      <td>London Westminster</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5111</th>\n",
       "      <td>2019-06-20 21:00:00+00:00</td>\n",
       "      <td>London Westminster</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5112</th>\n",
       "      <td>2019-06-20 22:00:00+00:00</td>\n",
       "      <td>London Westminster</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5113</th>\n",
       "      <td>2019-06-20 23:00:00+00:00</td>\n",
       "      <td>London Westminster</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5114</th>\n",
       "      <td>2019-06-21 00:00:00+00:00</td>\n",
       "      <td>London Westminster</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5115 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      date.utc            location  value\n",
       "0    2019-04-09 01:00:00+00:00             BETR801   22.5\n",
       "1    2019-04-09 02:00:00+00:00             BETR801   53.5\n",
       "2    2019-04-09 03:00:00+00:00             BETR801   54.5\n",
       "3    2019-04-09 04:00:00+00:00             BETR801   34.5\n",
       "4    2019-04-09 05:00:00+00:00             BETR801   46.5\n",
       "...                        ...                 ...    ...\n",
       "5110 2019-06-20 20:00:00+00:00  London Westminster    NaN\n",
       "5111 2019-06-20 21:00:00+00:00  London Westminster    NaN\n",
       "5112 2019-06-20 22:00:00+00:00  London Westminster    NaN\n",
       "5113 2019-06-20 23:00:00+00:00  London Westminster    NaN\n",
       "5114 2019-06-21 00:00:00+00:00  London Westminster    NaN\n",
       "\n",
       "[5115 rows x 3 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_2 = no2_pivoted.melt(id_vars=\"date.utc\")\n",
    "no_2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63c5ab6f",
   "metadata": {},
   "source": [
    "# Combining DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5590ce34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2068\n"
     ]
    },
    {
     "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>date.utc</th>\n",
       "      <th>location</th>\n",
       "      <th>parameter</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-06-21 00:00:00+00:00</td>\n",
       "      <td>FR04014</td>\n",
       "      <td>no2</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    date.utc location parameter  value\n",
       "0  2019-06-21 00:00:00+00:00  FR04014       no2   20.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality_no2 = pd.read_csv(\"Air_quality_NO2_data.csv\",\n",
    "                              parse_dates=True)\n",
    "\n",
    "\n",
    "air_quality_no2 = air_quality_no2[[\"date.utc\", \"location\", \"parameter\", \"value\"]]\n",
    "print(len(air_quality_no2))\n",
    "air_quality_no2.head(1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7814a4a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1110\n"
     ]
    },
    {
     "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>date.utc</th>\n",
       "      <th>location</th>\n",
       "      <th>parameter</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-06-18 06:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    date.utc location parameter  value\n",
       "0  2019-06-18 06:00:00+00:00  BETR801      pm25   18.0"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality_pm25 = pd.read_csv(\"Air_quality_PM25_data.csv\",\n",
    "                               parse_dates=True)\n",
    "\n",
    "\n",
    "air_quality_pm25 = air_quality_pm25[[\"date.utc\", \"location\",\n",
    "                                     \"parameter\", \"value\"]]\n",
    "print(len(air_quality_pm25))\n",
    "air_quality_pm25.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9f68cd9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3178\n"
     ]
    },
    {
     "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>date.utc</th>\n",
       "      <th>location</th>\n",
       "      <th>parameter</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-06-18 06:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    date.utc location parameter  value\n",
       "0  2019-06-18 06:00:00+00:00  BETR801      pm25   18.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality = pd.concat([air_quality_pm25, air_quality_no2], axis=0)\n",
    "print(len(air_quality))\n",
    "air_quality.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e810e2ec",
   "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></th>\n",
       "      <th>date.utc</th>\n",
       "      <th>location</th>\n",
       "      <th>parameter</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">PM25</th>\n",
       "      <th>0</th>\n",
       "      <td>2019-06-18 06:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-06-17 08:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-06-17 07:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-06-17 06:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-06-17 05:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">NO2</th>\n",
       "      <th>2063</th>\n",
       "      <td>2019-05-07 06:00:00+00:00</td>\n",
       "      <td>London Westminster</td>\n",
       "      <td>no2</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2064</th>\n",
       "      <td>2019-05-07 04:00:00+00:00</td>\n",
       "      <td>London Westminster</td>\n",
       "      <td>no2</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2065</th>\n",
       "      <td>2019-05-07 03:00:00+00:00</td>\n",
       "      <td>London Westminster</td>\n",
       "      <td>no2</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2066</th>\n",
       "      <td>2019-05-07 02:00:00+00:00</td>\n",
       "      <td>London Westminster</td>\n",
       "      <td>no2</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2067</th>\n",
       "      <td>2019-05-07 01:00:00+00:00</td>\n",
       "      <td>London Westminster</td>\n",
       "      <td>no2</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3178 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            date.utc            location parameter  value\n",
       "PM25 0     2019-06-18 06:00:00+00:00             BETR801      pm25   18.0\n",
       "     1     2019-06-17 08:00:00+00:00             BETR801      pm25    6.5\n",
       "     2     2019-06-17 07:00:00+00:00             BETR801      pm25   18.5\n",
       "     3     2019-06-17 06:00:00+00:00             BETR801      pm25   16.0\n",
       "     4     2019-06-17 05:00:00+00:00             BETR801      pm25    7.5\n",
       "...                              ...                 ...       ...    ...\n",
       "NO2  2063  2019-05-07 06:00:00+00:00  London Westminster       no2   26.0\n",
       "     2064  2019-05-07 04:00:00+00:00  London Westminster       no2   16.0\n",
       "     2065  2019-05-07 03:00:00+00:00  London Westminster       no2   19.0\n",
       "     2066  2019-05-07 02:00:00+00:00  London Westminster       no2   19.0\n",
       "     2067  2019-05-07 01:00:00+00:00  London Westminster       no2   23.0\n",
       "\n",
       "[3178 rows x 4 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality_ = pd.concat([air_quality_pm25, air_quality_no2], \n",
    "                         keys=[\"PM25\", \"NO2\"])\n",
    "air_quality_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "85e359d9",
   "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>location</th>\n",
       "      <th>coordinates.latitude</th>\n",
       "      <th>coordinates.longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BELAL01</td>\n",
       "      <td>51.23619</td>\n",
       "      <td>4.38522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BELHB23</td>\n",
       "      <td>51.17030</td>\n",
       "      <td>4.34100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BELLD01</td>\n",
       "      <td>51.10998</td>\n",
       "      <td>5.00486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BELLD02</td>\n",
       "      <td>51.12038</td>\n",
       "      <td>5.02155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BELR833</td>\n",
       "      <td>51.32766</td>\n",
       "      <td>4.36226</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  location  coordinates.latitude  coordinates.longitude\n",
       "0  BELAL01              51.23619                4.38522\n",
       "1  BELHB23              51.17030                4.34100\n",
       "2  BELLD01              51.10998                5.00486\n",
       "3  BELLD02              51.12038                5.02155\n",
       "4  BELR833              51.32766                4.36226"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations_coord = pd.read_csv(\"Air_quality_stations_data.csv\")\n",
    "\n",
    "stations_coord.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "440e0587",
   "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>date.utc</th>\n",
       "      <th>location</th>\n",
       "      <th>parameter</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-06-18 06:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-06-17 08:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-06-17 07:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-06-17 06:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-06-17 05:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    date.utc location parameter  value\n",
       "0  2019-06-18 06:00:00+00:00  BETR801      pm25   18.0\n",
       "1  2019-06-17 08:00:00+00:00  BETR801      pm25    6.5\n",
       "2  2019-06-17 07:00:00+00:00  BETR801      pm25   18.5\n",
       "3  2019-06-17 06:00:00+00:00  BETR801      pm25   16.0\n",
       "4  2019-06-17 05:00:00+00:00  BETR801      pm25    7.5"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "33ca003e",
   "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>date.utc</th>\n",
       "      <th>location</th>\n",
       "      <th>parameter</th>\n",
       "      <th>value</th>\n",
       "      <th>coordinates.latitude</th>\n",
       "      <th>coordinates.longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-06-18 06:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>18.0</td>\n",
       "      <td>51.20966</td>\n",
       "      <td>4.43182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-06-17 08:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>6.5</td>\n",
       "      <td>51.20966</td>\n",
       "      <td>4.43182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-06-17 07:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>18.5</td>\n",
       "      <td>51.20966</td>\n",
       "      <td>4.43182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-06-17 06:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>16.0</td>\n",
       "      <td>51.20966</td>\n",
       "      <td>4.43182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-06-17 05:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>7.5</td>\n",
       "      <td>51.20966</td>\n",
       "      <td>4.43182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    date.utc location parameter  value  coordinates.latitude  \\\n",
       "0  2019-06-18 06:00:00+00:00  BETR801      pm25   18.0              51.20966   \n",
       "1  2019-06-17 08:00:00+00:00  BETR801      pm25    6.5              51.20966   \n",
       "2  2019-06-17 07:00:00+00:00  BETR801      pm25   18.5              51.20966   \n",
       "3  2019-06-17 06:00:00+00:00  BETR801      pm25   16.0              51.20966   \n",
       "4  2019-06-17 05:00:00+00:00  BETR801      pm25    7.5              51.20966   \n",
       "\n",
       "   coordinates.longitude  \n",
       "0                4.43182  \n",
       "1                4.43182  \n",
       "2                4.43182  \n",
       "3                4.43182  \n",
       "4                4.43182  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality = pd.merge(air_quality, stations_coord, how=\"left\", on=\"location\")\n",
    "air_quality.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "19fa5aa3",
   "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>description</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bc</td>\n",
       "      <td>Black Carbon</td>\n",
       "      <td>BC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>co</td>\n",
       "      <td>Carbon Monoxide</td>\n",
       "      <td>CO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>no2</td>\n",
       "      <td>Nitrogen Dioxide</td>\n",
       "      <td>NO2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>o3</td>\n",
       "      <td>Ozone</td>\n",
       "      <td>O3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>pm10</td>\n",
       "      <td>Particulate matter less than 10 micrometers in...</td>\n",
       "      <td>PM10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id                                        description  name\n",
       "0    bc                                       Black Carbon    BC\n",
       "1    co                                    Carbon Monoxide    CO\n",
       "2   no2                                   Nitrogen Dioxide   NO2\n",
       "3    o3                                              Ozone    O3\n",
       "4  pm10  Particulate matter less than 10 micrometers in...  PM10"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality_parameters = pd.read_csv(\"Air_quality_parameters_data.csv\")\n",
    "air_quality_parameters.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "204baf26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date.utc                 4182\n",
       "location                 4182\n",
       "parameter                4182\n",
       "value                    4182\n",
       "coordinates.latitude     4182\n",
       "coordinates.longitude    4182\n",
       "id                       4182\n",
       "description              4182\n",
       "name                     4182\n",
       "dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality = pd.merge(air_quality, air_quality_parameters,\n",
    "                       how='left', left_on='parameter', right_on='id')\n",
    "air_quality.notnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4b199d5c",
   "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>date.utc</th>\n",
       "      <th>location</th>\n",
       "      <th>parameter</th>\n",
       "      <th>value</th>\n",
       "      <th>coordinates.latitude</th>\n",
       "      <th>coordinates.longitude</th>\n",
       "      <th>id</th>\n",
       "      <th>description</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-06-18 06:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>18.0</td>\n",
       "      <td>51.20966</td>\n",
       "      <td>4.43182</td>\n",
       "      <td>pm25</td>\n",
       "      <td>Particulate matter less than 2.5 micrometers i...</td>\n",
       "      <td>PM2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-06-17 08:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>6.5</td>\n",
       "      <td>51.20966</td>\n",
       "      <td>4.43182</td>\n",
       "      <td>pm25</td>\n",
       "      <td>Particulate matter less than 2.5 micrometers i...</td>\n",
       "      <td>PM2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-06-17 07:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>18.5</td>\n",
       "      <td>51.20966</td>\n",
       "      <td>4.43182</td>\n",
       "      <td>pm25</td>\n",
       "      <td>Particulate matter less than 2.5 micrometers i...</td>\n",
       "      <td>PM2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-06-17 06:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>16.0</td>\n",
       "      <td>51.20966</td>\n",
       "      <td>4.43182</td>\n",
       "      <td>pm25</td>\n",
       "      <td>Particulate matter less than 2.5 micrometers i...</td>\n",
       "      <td>PM2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-06-17 05:00:00+00:00</td>\n",
       "      <td>BETR801</td>\n",
       "      <td>pm25</td>\n",
       "      <td>7.5</td>\n",
       "      <td>51.20966</td>\n",
       "      <td>4.43182</td>\n",
       "      <td>pm25</td>\n",
       "      <td>Particulate matter less than 2.5 micrometers i...</td>\n",
       "      <td>PM2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    date.utc location parameter  value  coordinates.latitude  \\\n",
       "0  2019-06-18 06:00:00+00:00  BETR801      pm25   18.0              51.20966   \n",
       "1  2019-06-17 08:00:00+00:00  BETR801      pm25    6.5              51.20966   \n",
       "2  2019-06-17 07:00:00+00:00  BETR801      pm25   18.5              51.20966   \n",
       "3  2019-06-17 06:00:00+00:00  BETR801      pm25   16.0              51.20966   \n",
       "4  2019-06-17 05:00:00+00:00  BETR801      pm25    7.5              51.20966   \n",
       "\n",
       "   coordinates.longitude    id  \\\n",
       "0                4.43182  pm25   \n",
       "1                4.43182  pm25   \n",
       "2                4.43182  pm25   \n",
       "3                4.43182  pm25   \n",
       "4                4.43182  pm25   \n",
       "\n",
       "                                         description   name  \n",
       "0  Particulate matter less than 2.5 micrometers i...  PM2.5  \n",
       "1  Particulate matter less than 2.5 micrometers i...  PM2.5  \n",
       "2  Particulate matter less than 2.5 micrometers i...  PM2.5  \n",
       "3  Particulate matter less than 2.5 micrometers i...  PM2.5  \n",
       "4  Particulate matter less than 2.5 micrometers i...  PM2.5  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air_quality.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee6c5e79",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}