04.Data_Wrangling_2z3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Warsztaty Python w Data Science"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Wrangling z Biblioteką Pandas \n",
    "### Część 2 z 3 - Zmiana kształtu ramki danych"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# https://github.com/MichalKorzycki/PythonDataScience"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Wrangling 2\n",
    "\n",
    "- Zastosowanie formuł\n",
    "- Długa w szeroką, Szeroka w długą\n",
    "- Tabele przestawne\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = {'year': [2010, 2011, 2012, 2011, 2012, 2010, 2011, 2012],\n",
    "        'team': ['Bears', 'Bears', 'Bears', 'Packers', 'Packers', 'Lions',\n",
    "                 'Lions', 'Lions'],\n",
    "        'wins': [11, 8, 10, 15, 11, 6, 10, 4],\n",
    "        'losses': [5, 8, 6, 1, 5, 10, 6, 12]}\n",
    "\n",
    "\n",
    "football = pd.DataFrame(data)\n",
    "football"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Zadanie 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podsumować dane dla zespołu `Packers`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "football[football.team == \"Packers\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "football[football.team == \"Packers\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Zadanie 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dodać do DataFrame `football` kolumnę `games_played`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "football['games_played'] = football.wins + football.losses\n",
    "football"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Zadanie 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dodać do DataFrame `football` kolumnę `percentage_games_won`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "football['percentage_games_won'] = 100.0 * football.wins / football.games_played\n",
    "football"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Zadanie 4\n",
    "\n",
    "Wyświetlić dane dla zespołu `Packers` z lat parzystych"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "football[(football.year % 2 == 0) & (football.team == \"Packers\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tidy Data\n",
    "\n",
    "- __Each variable you measure should be in one column.__\n",
    "- __Each different observation of that variable should be in a different row.__\n",
    "- There should be one table for each \"kind\" of variable.\n",
    "- If you have multiple tables, they should include a column in the table that allows them to be linked."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas\n",
    "## Zastosowanie formuł"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randint(1,100, 80).reshape(-1, 10))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def highlight_max(s):\n",
    "    is_max = s == s.max()\n",
    "    return ['background-color: blue' if v else '' for v in is_max]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.style.apply(highlight_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.style.apply(highlight_max, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.apply(max,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.style.apply(highlight_max, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.apply(max,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"max\"] = df.apply(max,axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.append(df.apply(max,axis=0), ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Zmiana kształtu DataFrame\n",
    "### Szeroki w długi\n",
    "\n",
    "- __Each variable you measure should be in one column.__ Każda obserwacja w jednej kolumnie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'Student': {0: 'Kowalski J.', 1: 'Nowak A.', 2: 'Korzycki M.'},\n",
    "                   'WuEf': {0: 5, 1: 4, 2: 2},\n",
    "                   'Polski': {0: 4, 1: 4, 2: 2},\n",
    "                   'Matma': {0: 5, 1: 3, 2: 2}})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.melt(df, id_vars=['Student'], value_vars=['WuEf', 'Matma', 'Polski'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.melt(df, id_vars=['Student'], value_vars=['WuEf', 'Matma', 'Polski'],\n",
    "       var_name='Przedmiot', value_name='Ocena')\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.sort_values('Student')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.sort_values([ 'Przedmiot', 'Student'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.sort_values(['Student', 'Przedmiot']).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.sort_values(['Student', 'Przedmiot'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.sort_values(['Student', 'Przedmiot']).sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Długi w szeroki"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({ 'foo': ['one', 'one', 'one', 'two', 'two', 'two'],\n",
    "                    'bar': ['A', 'B', 'C', 'A', 'B', 'C'],\n",
    "                    'baz': [1, 2, 3, 4, 5, 6], \n",
    "                    'zoo': ['x', 'y', 'z', 'q', 'w', 't']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe thead th {\n",
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       "   foo bar  baz zoo\n",
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   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stack i Unstack\n",
    "\n",
    "![](img/stack.png)\n",
    "\n",
    "Źródło: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "   bar      C\n",
       "   baz      6\n",
       "   zoo      t\n",
       "dtype: object"
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <td>B</td>\n",
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       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>baz</td>\n",
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       "    level_0 level_1    0\n",
       "0         0     foo  one\n",
       "1         0     bar    A\n",
       "2         0     baz    1\n",
       "3         0     zoo    x\n",
       "4         1     foo  one\n",
       "5         1     bar    B\n",
       "6         1     baz    2\n",
       "7         1     zoo    y\n",
       "8         2     foo  one\n",
       "9         2     bar    C\n",
       "10        2     baz    3\n",
       "11        2     zoo    z\n",
       "12        3     foo  two\n",
       "13        3     bar    A\n",
       "14        3     baz    4\n",
       "15        3     zoo    q\n",
       "16        4     foo  two\n",
       "17        4     bar    B\n",
       "18        4     baz    5\n",
       "19        4     zoo    w\n",
       "20        5     foo  two\n",
       "21        5     bar    C\n",
       "22        5     baz    6\n",
       "23        5     zoo    t"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.stack().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['foo', 'bar', 'baz', 'zoo'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.inde"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](img/unstack.png)\n",
    "\n",
    "Źródło: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.stack().unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Długi w szeroki\n",
    "\n",
    "![title](img/pivot.png)\n",
    "\n",
    "Źródło: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.pivot(index='foo', columns='bar', values='baz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.pivot(index='foo', columns='bar', values=['baz', 'zoo'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_e = pd.DataFrame({\"foo\": ['one', 'one', 'two', 'two'],\n",
    "                   \"bar\": ['A', 'A', 'B', 'C'],\n",
    "                   \"baz\": [1, 2, 3, 4]})\n",
    "df_e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_e.pivot(index='foo', columns='bar', values='baz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\"foo\": ['one', 'two', 'three', 'four'],\n",
    "                   \"bar\": ['A', 'B', 'C', 'D'],\n",
    "                   \"baz\": [1, 2, 3, 4]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.pivot(index='bar', columns='foo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.pivot(index='foo', columns='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.groupby(['foo', 'bar'])['baz'].aggregate('mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.groupby(['foo', 'bar'])['baz'].aggregate('mean').unstack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Zadanie 1\n",
    "\n",
    "Napraw `df_e` biorąc maksimum wartości `baz` dla pary `foo`, `bar` (odrzuć konflikty)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Zadanie 2\n",
    "\n",
    "Do poniższego DataFrame:\n",
    "    \n",
    "`df = pd.DataFrame(np.random.randint(1,100, 80).reshape(8, 10))`\n",
    "\n",
    "Dołożyć:\n",
    "    \n",
    "- kolumnę z sumą po wierszach\n",
    "- wiersz z sumą po kolumnach"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Zadanie 3\n",
    "\n",
    "Przerobic DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'weekday': [\"Monday\", \"Tuesday\", \"Wednesday\", \n",
    "         \"Thursday\", \"Friday\", \"Saturday\", \"Sunday\"],\n",
    "        'Person 1': [12, 6, 5, 8, 11, 6, 4],\n",
    "        'Person 2': [10, 6, 11, 5, 8, 9, 12],\n",
    "        'Person 3': [8, 5, 7, 3, 7, 11, 15]}\n",
    "df = pd.DataFrame(data, columns=['weekday',\n",
    "        'Person 1', 'Person 2', 'Person 3'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Na postać \"tidy\" (1 wiersz na ocenę)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Zadanie 4\n",
    "\n",
    "Biorąc następujący DataFrame:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data = {\n",
    "    'Day' : ['MON', 'TUE', 'WED', 'THU', 'FRI'], \n",
    "    'Google' : [1129,1132,1134,1152,1152], \n",
    "    'Apple' : [191,192,190,190,188] \n",
    "})\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reshaped_df = df.melt(id_vars=['Day'], var_name='Company', value_name='Closing Price')\n",
    "reshaped_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stworzyć korzystając z `reshaped_df` przy uzyciu `pivot` DataFrame identyczny z `df`"
   ]
  }
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