PAD_3_Praca_Domowa.ipynb

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    "Programowanie w Analityce Danych\n",
    "Praca domowa 4\n",
    "Zadanie 1\n",
    "Zbiór danych bank.csv opisuje kontakty telefoniczne jakich dokonano z klientami pewnego banku, w celu przekonania ich do skorzystania z nowej oferty tegoż banku.\n",
    "W oparciu o zawarte w zbiorze dane odpowiedz na następujące pytania:\n",
    "a)\tW którym miesiącu dokonano najwięcej połączeń, a w którym najmniej?\n",
    "b)\tW którym miesiącu średni czas rozmowy był największy?\n",
    "c)\tCzy wykształcenie przekłada się na posiadane oszczędności? (Kolumna balance)\n",
    "d)\tIle lat miała najstarsza osoba będąca w związku małżeńskim, a ile najstarszy singiel?\n"
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     "data": {
      "text/plain": "   age          job  marital  education default  balance housing loan  \\\n0   30   unemployed  married    primary      no     1787      no   no   \n1   33     services  married  secondary      no     4789     yes  yes   \n2   35   management   single   tertiary      no     1350     yes   no   \n3   30   management  married   tertiary      no     1476     yes  yes   \n4   59  blue-collar  married  secondary      no        0     yes   no   \n\n    contact  day month  duration  \n0  cellular   19   oct        79  \n1  cellular   11   may       220  \n2  cellular   16   apr       185  \n3   unknown    3   jun       199  \n4   unknown    5   may       226  ",
      "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>job</th>\n      <th>marital</th>\n      <th>education</th>\n      <th>default</th>\n      <th>balance</th>\n      <th>housing</th>\n      <th>loan</th>\n      <th>contact</th>\n      <th>day</th>\n      <th>month</th>\n      <th>duration</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>30</td>\n      <td>unemployed</td>\n      <td>married</td>\n      <td>primary</td>\n      <td>no</td>\n      <td>1787</td>\n      <td>no</td>\n      <td>no</td>\n      <td>cellular</td>\n      <td>19</td>\n      <td>oct</td>\n      <td>79</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>33</td>\n      <td>services</td>\n      <td>married</td>\n      <td>secondary</td>\n      <td>no</td>\n      <td>4789</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>cellular</td>\n      <td>11</td>\n      <td>may</td>\n      <td>220</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>35</td>\n      <td>management</td>\n      <td>single</td>\n      <td>tertiary</td>\n      <td>no</td>\n      <td>1350</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>cellular</td>\n      <td>16</td>\n      <td>apr</td>\n      <td>185</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>30</td>\n      <td>management</td>\n      <td>married</td>\n      <td>tertiary</td>\n      <td>no</td>\n      <td>1476</td>\n      <td>yes</td>\n      <td>yes</td>\n      <td>unknown</td>\n      <td>3</td>\n      <td>jun</td>\n      <td>199</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>59</td>\n      <td>blue-collar</td>\n      <td>married</td>\n      <td>secondary</td>\n      <td>no</td>\n      <td>0</td>\n      <td>yes</td>\n      <td>no</td>\n      <td>unknown</td>\n      <td>5</td>\n      <td>may</td>\n      <td>226</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
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   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df = pd.read_csv(\"bank.csv\",delimiter = \";\")\n",
    "df.head(5)"
   ],
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    "a)"
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   "cell_type": "code",
   "execution_count": 65,
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    {
     "data": {
      "text/plain": "may    1398\njul     706\naug     633\njun     531\nnov     389\napr     293\nfeb     222\njan     148\noct      80\nsep      52\nmar      49\ndec      20\nName: month, dtype: int64"
     },
     "execution_count": 65,
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   ],
   "source": [
    "df['month'].value_counts() #najwiecej polaczen w may, najmniej w dec"
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    "b)"
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   "cell_type": "code",
   "execution_count": 64,
   "outputs": [
    {
     "data": {
      "text/plain": "month\napr    294.354949\naug    243.742496\ndec    416.900000\nfeb    255.698198\njan    269.540541\njul    271.205382\njun    252.425612\nmar    198.897959\nmay    267.316166\nnov    272.066838\noct    272.800000\nsep    215.730769\nName: duration, dtype: float64"
     },
     "execution_count": 64,
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   ],
   "source": [
    "df.groupby(['month']).duration.mean() #najwiecej dec, najmniej mar"
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    "c)"
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   "execution_count": 66,
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    {
     "data": {
      "text/plain": "education\nprimary      1411.544248\nsecondary    1196.814397\ntertiary     1775.423704\nunknown      1701.245989\nName: balance, dtype: float64"
     },
     "execution_count": 66,
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   "source": [
    "df.groupby(['education']).balance.mean() #nie mozna okreslic jednoznacznie, osoby z wyksztalceniem primary maja wieksze oszczednosci niz secondary, ale tertiary maja najwieksze."
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    "d)"
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   "execution_count": 69,
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     "data": {
      "text/plain": "          min  max\nmarital           \ndivorced   26   84\nmarried    23   87\nsingle     19   69",
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     "execution_count": 69,
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   "source": [
    "df.groupby(['marital']).age.agg([min,max])"
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