{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Załaduj bibliotekę `pandas`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Wczytaj dane z pliku *mieszkania.csv* do zmiennej i wyświetl 5 pierwszych wierczy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Unnamed: 0</th>\n",
       "      <th>Id</th>\n",
       "      <th>Expected</th>\n",
       "      <th>Rooms</th>\n",
       "      <th>SqrMeters</th>\n",
       "      <th>Floor</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>269000</td>\n",
       "      <td>3</td>\n",
       "      <td>55.00</td>\n",
       "      <td>1</td>\n",
       "      <td>Poznań Zawady</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>320000</td>\n",
       "      <td>3</td>\n",
       "      <td>79.00</td>\n",
       "      <td>10</td>\n",
       "      <td>Poznań Rataje ul. Orła Bialego</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>146000</td>\n",
       "      <td>1</td>\n",
       "      <td>31.21</td>\n",
       "      <td>1</td>\n",
       "      <td>Poznań Nowe Miasto ul. Kawalerka W Nowym Bloku...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>189000</td>\n",
       "      <td>2</td>\n",
       "      <td>44.00</td>\n",
       "      <td>2</td>\n",
       "      <td>Poznań Grunwald Ogrody Jeżyce Centrum Łazarz u...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>480240</td>\n",
       "      <td>2</td>\n",
       "      <td>65.25</td>\n",
       "      <td>1</td>\n",
       "      <td>Poznań ul. Droga Dębińska 19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Id  Expected  Rooms  SqrMeters  Floor  \\\n",
       "0           0   1    269000      3      55.00      1   \n",
       "1           1   2    320000      3      79.00     10   \n",
       "2           2   3    146000      1      31.21      1   \n",
       "3           3   4    189000      2      44.00      2   \n",
       "4           4   5    480240      2      65.25      1   \n",
       "\n",
       "                                            Location  \n",
       "0                                      Poznań Zawady  \n",
       "1                     Poznań Rataje ul. Orła Bialego  \n",
       "2  Poznań Nowe Miasto ul. Kawalerka W Nowym Bloku...  \n",
       "3  Poznań Grunwald Ogrody Jeżyce Centrum Łazarz u...  \n",
       "4                       Poznań ul. Droga Dębińska 19  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"./mieszkania.csv\")\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Znajdź informacje ilu pokojowe mieszkania są najpopularniejsze i ile ich jest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     2208\n",
       "3     1553\n",
       "1      620\n",
       "4      523\n",
       "5       81\n",
       "6       13\n",
       "10       1\n",
       "7        1\n",
       "Name: Rooms, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Rooms'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Znajdź 10 najtańszych mieszkań."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. Napisz funkcje ``find_borough(desc)``, która przyjmuje 1 argument typu *string* i zwróci jedną z dzielnic zdefiniowaną w liście ``dzielnice``. Funkcja ma zwrócić pierwszą (wzgledem kolejności) nazwę dzielnicy, która jest zawarta w ``desc``. Jeżeli żadna nazwa nie została odnaleziona, zwróć napis *Inne*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_borough(desc):\n",
    "    dzielnice = ['Stare Miasto',\n",
    "                 'Wilda',\n",
    "                 'Jeżyce',\n",
    "                 'Rataje',\n",
    "                 'Piątkowo',\n",
    "                 'Winogrady',\n",
    "                 'Miłostowo',\n",
    "                 'Dębiec']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6. Dodaj kolumnę ``Borough``, która będzie zawierać informacje o dzielnicach i powstanie z kolumny ``Localization``. Wykorzystaj do tego funkcję ``find_borough``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7. Wyświetl histogram przedstawiający liczbę ogłoszeń mieszkań z podziałem na dzielnice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8. Znajdź średnią cenę mieszkania n-pokojowego."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "303861.86277173914"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Rooms'] == 2]['Expected'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "9. Znajdź dzielnice, które zawierają oferty mieszkań na 13 piętrze."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2195    Poznań Winogrady ul. Os. Wichrowe Wzgórze - Zm...\n",
       "2773     Poznań Stare Miasto Winogrady ul. Os. Zwycięstwa\n",
       "Name: Location, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('Floor == 13')['Location']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "10. Znajdź wszystkie ogłoszenia mieszkań, które znajdują się na Winogradach, mają 3 pokoje i są położone na 1 piętrze."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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