{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Databehandling i Jupyter (forberedelse)\n",
    "\n",
    "Når vi skal jobbe med data som passer på tabellformat kan livet vårt bli svært mye enklere om vi brukere et bibliotek som heter *pandas*. Det skal da lite kode til for å lese inn filer og for å gjøre enkle plots. Dessuten passer dataformatet i pandas sammen med flere kjente maskinlæringsbiblioteker som *tensorflow* og *scikit-learn*. Dermed slipper vi å bruke tid på å manipulere data til å passe inn i spesifikke formater for hvert bibliotek vi skal bruke. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Når man bruker kraftige biblioteker for databehnadling, vil det alltid være mange funksjoner man ikke kan navnet på, eller ikke vet hva gjør. Det er helt greit, og helt vanglig. Det er lov til å si at man \"kan pandas\" uten å vite hva alle funksjonene gjør, på samme måte som det er lov å si man kan engelsk selv om man ike vet betydningen av *floccinaucinihilipilification*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{admonition} Søk i dokumentasjonen\n",
    ":class: note \n",
    "\n",
    "Når vi jobber med *pandas* kommer vi til å bruke mange forskjellige funksjoner med mange forskjellige navn. Den eneste måten å huske alle disse på, er å bruke dem jevnlig. Det er det ikke så mange av oss som gjør, og dermed kan vi like godt gi opp å huske alle funksjonene. Prøv heller å gjøre det til en vane å søke i [pandas-dokumenasjonen](https://pandas.pydata.org/docs/user_guide/10min.html) for å finne en funksjon som gjør det du ønsker å få til.\n",
    "\n",
    "``` "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Når vi jobber med datavitenskap er det ofte hensiktsmessig å bruke *Jupyter notebook* i *JupyterLab*. Vi kan kjøre JupyterLab både lokalt på maskinen vår og på forskjellige skytjenester. Vi anbefaler å begynne på egen maskin. Etterhvert kan det hende vi vil bruke Kaggle eller Google Colab som skytjenester, fordi de gir tilgang på en fornuftig mengde regnekraft. I del 1 av kurset skal vi jobbe med prosjekter som ikke krever noe særlig regnekraft, og da fungerer det fint å kjøre lokalt. \n",
    "\n",
    "I denne økta skal vi bruke Jupyter notebook til å trene på å lese inn og visualisere data med *Pandas* og *Matplotlib*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lese inn data\n",
    "Når vi skal analysere datasett, er det første vi må gjøre å lese inn dataene. Data kan finnes i mange forskjellige formater: Tabeller, bilder, lister osv. I første omgang skal vi forholde oss til tabeller. Dette svarer til excel-ark og csv-filer. \n",
    "\n",
    "La oss lese inn en tabell med data fra en csv-fil (comma separated values-fil). Vi begynner med en fil som inneholder lite nok data til at vi kan sjekke manuelt at det gikk bra. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Rangering</th>\n",
       "      <th>Navn</th>\n",
       "      <th>År</th>\n",
       "      <th>IMDb Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>1994</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>The Godfather</td>\n",
       "      <td>1972</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>The Godfather: Part II</td>\n",
       "      <td>1974</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>2008</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>12 Angry Men</td>\n",
       "      <td>1957</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Schindler's List</td>\n",
       "      <td>1993</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>The Lord of the Rings: The Return of the King</td>\n",
       "      <td>2003</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>1994</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>The Good, the Bad and the Ugly</td>\n",
       "      <td>1966</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>The Lord of the Rings: The Fellowship of the Ring</td>\n",
       "      <td>2001</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rangering                                               Navn    År  \\\n",
       "0          1                           The Shawshank Redemption  1994   \n",
       "1          2                                      The Godfather  1972   \n",
       "2          3                             The Godfather: Part II  1974   \n",
       "3          4                                    The Dark Knight  2008   \n",
       "4          5                                      12 Angry Men   1957   \n",
       "5          6                                   Schindler's List  1993   \n",
       "6          7      The Lord of the Rings: The Return of the King  2003   \n",
       "7          8                                       Pulp Fiction  1994   \n",
       "8          9                     The Good, the Bad and the Ugly  1966   \n",
       "9         10  The Lord of the Rings: The Fellowship of the Ring  2001   \n",
       "\n",
       "   IMDb Rating  \n",
       "0          9.2  \n",
       "1          9.1  \n",
       "2          9.0  \n",
       "3          9.0  \n",
       "4          9.0  \n",
       "5          8.9  \n",
       "6          8.9  \n",
       "7          8.8  \n",
       "8          8.8  \n",
       "9          8.8  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv(\"../../data/filmer.csv\")\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{admonition} Csv-filer\n",
    ":class: warning \n",
    "\n",
    "Siden komma er standard desimalskilletegn i Norge, mens punktum brukes i USA, oppstår det i blant trøbbel når vi skal lese inn norske CSV-filer. De bruker nemlig ofte semikolon i stedet for komma til å separere verdiene. Dette løser vi ved å bruke `delimiter=\";\"` som argument til `read_csv`. \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kaste ut data vi ikke skal bruke"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ofte har en csv fil kollonner eller data vi ikke skal bruke, og da blir det ryddigere å ikke ha de med. Hvis vi f.eks vil undersøke hvilket år som hadde de beste filmene trenger vi ikke navnene eller rangeringene."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = df.drop([\"Rangering\", \"Navn\"], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Man kan også fjerne rader med data, enten ved å fjerne spesifikke rader eller ved å filtrere på ulike måter. Hvis man f.eks tenker at superheltfilmer ikke er ekte filmer, kan man fjerne The Dark Knight fra datasettet med indeksen dens. Og hvis man mener det ikke ble laget ekte filmer før 70-tallet kan man filtrere de ut slik:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>År</th>\n",
       "      <th>IMDb Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1994</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1972</td>\n",
       "      <td>9.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1974</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1993</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2003</td>\n",
       "      <td>8.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1994</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2001</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     År  IMDb Rating\n",
       "0  1994          9.2\n",
       "1  1972          9.1\n",
       "2  1974          9.0\n",
       "5  1993          8.9\n",
       "6  2003          8.9\n",
       "7  1994          8.8\n",
       "9  2001          8.8"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_filtered = data.drop([3])\n",
    "data_filtered = data_filtered[data_filtered[\"År\"] >= 1970]\n",
    "display(data_filtered)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lage en dataframe manuelt\n",
    "\n",
    "I blant trenger vi å skrive inn data manuelt. Da er det fint å vite hvordan vi kan lage en *Pandas* *DataFrame* fra grunnen. Dette kan også være nyttig om vi skal sette inn data fra eksempelvis *numpy*-arrayer i en *DataFrame*. En enkel og ryddig måte å lage en *DataFrame* er å først legge dataene i en *dictionary* og deretter be *Pandas* lage en *DataFrame* av denne dictionary'en."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\"innsats\" : [2, 4, 3, 8, 4, 1, 6, 6, 10, 3, 7], \n",
    "        \"talent\" : [4, 5, 3, 4, 6, 2, 3, 5, 2, 4, 5], \n",
    "        \"resultat\" : [24, 73, 25, 185, 93, 4, 55, 110, 198, 42, 155]}\n",
    "grit_data = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vi er interresert i å se på sammenhengen mellom de ulike kolonnene i datasettet. En scatter matrise plotter fordelingen av de ulike verdiene for hver av kolonnene med søylediagrammer, og hvordan disse verdieme sammenfaller med scatter plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hfig = pd.plotting.scatter_matrix(grit_data, figsize=(8,6), alpha=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{admonition} Ta imot returvariabelen fra plottingen\n",
    ":class: note\n",
    "\n",
    "Noen ganger vil plottekommandoer returnere et objekt som holder på figuren vi har laget. Dette objektet kan vi senere bruke om vi vil endre på farger, fontstørrelser og så videre. Det er ofte også slik at om vi ikke eksplisitt tar imot returvariabelen fra en plottekommando, får vi en del unyttig tekst i output-vinduet (med mindre man bruker plt.show()).\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Et større datasett: Presidenvalget i USA, 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>state</th>\n",
       "      <th>county</th>\n",
       "      <th>candidate</th>\n",
       "      <th>party</th>\n",
       "      <th>votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>Kent County</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>DEM</td>\n",
       "      <td>44552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>Kent County</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>REP</td>\n",
       "      <td>41009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>Kent County</td>\n",
       "      <td>Jo Jorgensen</td>\n",
       "      <td>LIB</td>\n",
       "      <td>1044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>Kent County</td>\n",
       "      <td>Howie Hawkins</td>\n",
       "      <td>GRN</td>\n",
       "      <td>420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>New Castle County</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>DEM</td>\n",
       "      <td>195034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31107</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>Maricopa County</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>REP</td>\n",
       "      <td>993691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31108</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>Maricopa County</td>\n",
       "      <td>Jo Jorgensen</td>\n",
       "      <td>LIB</td>\n",
       "      <td>31608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31109</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>Mohave County</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>REP</td>\n",
       "      <td>78534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31110</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>Mohave County</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>DEM</td>\n",
       "      <td>24831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31111</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>Mohave County</td>\n",
       "      <td>Jo Jorgensen</td>\n",
       "      <td>LIB</td>\n",
       "      <td>1302</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>31112 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          state             county      candidate party   votes\n",
       "0      Delaware        Kent County      Joe Biden   DEM   44552\n",
       "1      Delaware        Kent County   Donald Trump   REP   41009\n",
       "2      Delaware        Kent County   Jo Jorgensen   LIB    1044\n",
       "3      Delaware        Kent County  Howie Hawkins   GRN     420\n",
       "4      Delaware  New Castle County      Joe Biden   DEM  195034\n",
       "...         ...                ...            ...   ...     ...\n",
       "31107   Arizona    Maricopa County   Donald Trump   REP  993691\n",
       "31108   Arizona    Maricopa County   Jo Jorgensen   LIB   31608\n",
       "31109   Arizona      Mohave County   Donald Trump   REP   78534\n",
       "31110   Arizona      Mohave County      Joe Biden   DEM   24831\n",
       "31111   Arizona      Mohave County   Jo Jorgensen   LIB    1302\n",
       "\n",
       "[31112 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = pd.read_csv(\"../../data/president_county_candidate.csv\")\n",
    "display(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Når vi leser inn slik i en jupyter notebook, får vi opp en visning av deler av tabellen. Om det er en veldig liten tabell ser vi hele. I dette tilfellet har vi lastet inn en tabell med valgresultatene for presidentvalget i USA i 2020. Det er en liste med mye innhold. La oss i første omgang bare lese ut litt forskjellige data fra tabellen, for å demonstrere hvordan vi *aksesserer* deler av dataene fra tabellen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>state</th>\n",
       "      <th>county</th>\n",
       "      <th>candidate</th>\n",
       "      <th>party</th>\n",
       "      <th>votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>Kent County</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>DEM</td>\n",
       "      <td>44552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>Kent County</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>REP</td>\n",
       "      <td>41009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>Kent County</td>\n",
       "      <td>Jo Jorgensen</td>\n",
       "      <td>LIB</td>\n",
       "      <td>1044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>Kent County</td>\n",
       "      <td>Howie Hawkins</td>\n",
       "      <td>GRN</td>\n",
       "      <td>420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>New Castle County</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>DEM</td>\n",
       "      <td>195034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>New Castle County</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>REP</td>\n",
       "      <td>88364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>New Castle County</td>\n",
       "      <td>Jo Jorgensen</td>\n",
       "      <td>LIB</td>\n",
       "      <td>2953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>New Castle County</td>\n",
       "      <td>Howie Hawkins</td>\n",
       "      <td>GRN</td>\n",
       "      <td>1282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>Sussex County</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>REP</td>\n",
       "      <td>71230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>Sussex County</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>DEM</td>\n",
       "      <td>56682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>Sussex County</td>\n",
       "      <td>Jo Jorgensen</td>\n",
       "      <td>LIB</td>\n",
       "      <td>1003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>Sussex County</td>\n",
       "      <td>Howie Hawkins</td>\n",
       "      <td>GRN</td>\n",
       "      <td>437</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       state             county      candidate party   votes\n",
       "0   Delaware        Kent County      Joe Biden   DEM   44552\n",
       "1   Delaware        Kent County   Donald Trump   REP   41009\n",
       "2   Delaware        Kent County   Jo Jorgensen   LIB    1044\n",
       "3   Delaware        Kent County  Howie Hawkins   GRN     420\n",
       "4   Delaware  New Castle County      Joe Biden   DEM  195034\n",
       "5   Delaware  New Castle County   Donald Trump   REP   88364\n",
       "6   Delaware  New Castle County   Jo Jorgensen   LIB    2953\n",
       "7   Delaware  New Castle County  Howie Hawkins   GRN    1282\n",
       "8   Delaware      Sussex County   Donald Trump   REP   71230\n",
       "9   Delaware      Sussex County      Joe Biden   DEM   56682\n",
       "10  Delaware      Sussex County   Jo Jorgensen   LIB    1003\n",
       "11  Delaware      Sussex County  Howie Hawkins   GRN     437"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>county</th>\n",
       "      <th>candidate</th>\n",
       "      <th>party</th>\n",
       "      <th>votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>Kent County</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>DEM</td>\n",
       "      <td>44552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>New Castle County</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>DEM</td>\n",
       "      <td>195034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>Sussex County</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>DEM</td>\n",
       "      <td>56682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>DEM</td>\n",
       "      <td>35485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>Ward 2</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>DEM</td>\n",
       "      <td>26086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31098</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>Graham County</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>DEM</td>\n",
       "      <td>4034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31101</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>Greenlee County</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>DEM</td>\n",
       "      <td>1182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31104</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>La Paz County</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>DEM</td>\n",
       "      <td>2236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31106</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>Maricopa County</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>DEM</td>\n",
       "      <td>1038364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31110</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>Mohave County</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>DEM</td>\n",
       "      <td>24831</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4633 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      state                county  candidate party    votes\n",
       "0                  Delaware           Kent County  Joe Biden   DEM    44552\n",
       "4                  Delaware     New Castle County  Joe Biden   DEM   195034\n",
       "9                  Delaware         Sussex County  Joe Biden   DEM    56682\n",
       "12     District of Columbia  District of Columbia  Joe Biden   DEM    35485\n",
       "19     District of Columbia                Ward 2  Joe Biden   DEM    26086\n",
       "...                     ...                   ...        ...   ...      ...\n",
       "31098               Arizona         Graham County  Joe Biden   DEM     4034\n",
       "31101               Arizona       Greenlee County  Joe Biden   DEM     1182\n",
       "31104               Arizona         La Paz County  Joe Biden   DEM     2236\n",
       "31106               Arizona       Maricopa County  Joe Biden   DEM  1038364\n",
       "31110               Arizona         Mohave County  Joe Biden   DEM    24831\n",
       "\n",
       "[4633 rows x 5 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(data[data[\"state\"] == \"Delaware\"])\n",
    "display(data[data[\"candidate\"] == \"Joe Biden\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Det finnes mange flere måter å hente ut data på. Vi kommer til å introdusere dem i det tempoet vi trenger dem. Det er for kjedelig å liste opp alle mulige måter, og for dem som trenger å lese om alle måtene for å få sove godt om natta, finnes denne informasjonen [andre steder](https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html). \n",
    "\n",
    "La oss så begynne å regne litt. Kanskje er vi interessert i å finne ut hvor mange stemmer Donald trump fikk totalt i staten Delaware. Da kan vi summere opp stemmene for de radene i tabellen som svarer til Donald Trump og Delaware. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Donald Trump fikk 200603 stemmer i Delaware\n"
     ]
    }
   ],
   "source": [
    "subset = data[data[\"state\"] == \"Delaware\"]\n",
    "subset = subset[subset[\"candidate\"] == \"Donald Trump\"]\n",
    "\n",
    "trump_votes_delaware = 0\n",
    "for number in subset[\"votes\"]: \n",
    "    trump_votes_delaware += number\n",
    "    \n",
    "print(f\"Donald Trump fikk {trump_votes_delaware} stemmer i Delaware\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200603"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data.state == \"Delaware\") & (data.candidate == \"Donald Trump\")][\"votes\"].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Over skjer det litt av hvert. For det første bruker vi et sammensatt logisk uttrykk for å hente ut de linjene i tabellen som inneholder *både* staten \"Delaware\" *og* kandidaten \"Donald Trump\". Når vi har hentet ut linjene som oppfyller disse kriteriene, velger vi kolonnen \"votes\", siden vi er interessert i antallet stemmer. Til slutt bruker vi `sum`-funksjonen på DataFrame-objektet. DataFrame-objektet er pandas sin vanlige måte å holde på data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gruppere data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_15813/1083400130.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  by_candidate_sum = data.groupby(\"candidate\", as_index=False).sum()\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>candidate</th>\n",
       "      <th>votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Zachary Scalf</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Keith McCormic</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Gary Swing</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Jordan Scott</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Joseph Kishore</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         candidate  votes\n",
       "37   Zachary Scalf     29\n",
       "26  Keith McCormic    127\n",
       "13      Gary Swing    143\n",
       "23    Jordan Scott    173\n",
       "24  Joseph Kishore    192"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>candidate</th>\n",
       "      <th>votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Write-ins</td>\n",
       "      <td>155165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Howie Hawkins</td>\n",
       "      <td>376714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Jo Jorgensen</td>\n",
       "      <td>1808321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>72691128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>78220235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        candidate     votes\n",
       "1       Write-ins    155165\n",
       "15  Howie Hawkins    376714\n",
       "19   Jo Jorgensen   1808321\n",
       "12   Donald Trump  72691128\n",
       "20      Joe Biden  78220235"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "by_candidate_sum = data.groupby(\"candidate\", as_index=False).sum()\n",
    "by_candidate_sum = by_candidate_sum.sort_values(by=\"votes\")\n",
    "\n",
    "display(by_candidate_sum.head()) #viser kun de fem første radene\n",
    "\n",
    "display(by_candidate_sum.tail()) #viser kun de fem siste radene"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Her innser vi for det første at det ar veldig mange kandidater. La oss så plotte resultatene for alle disse kandidatene:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.bar(by_candidate_sum[\"candidate\"], by_candidate_sum[\"votes\"])\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Henting av subsets og kollaps av subsets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hvis vi vil hente ut en del av dataene til en ny dataframe, som f.eks alle radene med Biden stemmer gjør vi det slik:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "biden = data[data[\"candidate\"] == \"Joe Biden\"]\n",
    "trump = data[data[\"candidate\"] == \"Donald Trump\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hvis vi så vil slå sammen alle delene av dataene som har samme verdi for \"state\" gjør vi det slik. Her slår vi de sammen ved å summere verdiene, men det går også ann å ta gjennomsnitt eller bare telle antall rader."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_15813/350905879.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  biden_states = biden.groupby(\"state\").sum() #.mean() og .size() er også nyttige ofte!\n",
      "/tmp/ipykernel_15813/350905879.py:2: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  trump_states = trump.groupby(\"state\").sum()\n"
     ]
    }
   ],
   "source": [
    "biden_states = biden.groupby(\"state\").sum() #.mean() og .size() er også nyttige ofte!\n",
    "trump_states = trump.groupby(\"state\").sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nå har vi to dataframes som holder antall stemmer hver kandidat har i hver stat, så vi kan f.eks regne ut forskjellen og plotte den:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diff_states = biden_states - trump_states\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.bar(diff_states.index, diff_states[\"votes\"])\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Masker for å effektivt hente subsets av subsets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I forrige eksempel brukte vi en maske(rekke med `True` og `False` verdier) for å plukke ut verdiene vi ville ha med i dataframen `biden`.\n",
    "\n",
    "```\n",
    "biden = data[data[\"candidate\"] == \"Joe Biden\"]\n",
    "```\n",
    "\n",
    "Masken er det inni hakeparantesen, og kan bli lagret i en egen variabel:\n",
    "\n",
    "```\n",
    "biden_mask = data[\"candidate\"] == \"Joe Biden\"\n",
    "```\n",
    "Dette blir utrolig nyttig om vi skal ha tak i mange ulike subsets av subsets av dataen. Dette eksempelet illustrerer ikke dette veldig godt, siden vi har få kollonner å jobbe med, men i datasett med egenskaper som kjønn, alder og rase blir dette helt nødvendig for å effektivt hente alle mulige kombinasjoner som hvite menn, asiatiske kvinner etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "biden_mask = data[\"candidate\"] == \"Joe Biden\"\n",
    "trump_mask = data[\"candidate\"] == \"Donald Trump\"\n",
    "other = np.logical_not(np.logical_or(biden_mask, trump_mask)) #evt. ~(biden_mask | trump_mask)\n",
    "\n",
    "even = data[\"votes\"] % 2 == 0\n",
    "more10k = data[\"votes\"] >= 10000\n",
    "more100k = data[\"votes\"] >= 100000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vi kombinerer masker med numpy sine logiske funksjoner. Man kan også bruke python sine innebygde [bitwise operatorer](https://realpython.com/python-bitwise-operators/#overview-of-pythons-bitwise-operators).\n",
    "\n",
    "\n",
    "Hvis vi bare vil telle antall `True`, kan vi ta summen av masken. Hugs at masker også kan brukes til å hente ut en subset av dataen."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trump fikk et partall antall stemmer i flere fylker:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2354\n",
      "2381\n"
     ]
    }
   ],
   "source": [
    "print(np.logical_and(biden_mask, even).sum()) #evt. (biden_mask & even).sum()\n",
    "print(np.logical_and(trump_mask, even).sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Trump fikk mer enn 10000 stemmer i flere fylker enn Biden:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "932\n",
      "1347\n"
     ]
    }
   ],
   "source": [
    "print(np.logical_and(biden_mask, more10k).sum()) #evt. (biden_mask & more10k).sum()\n",
    "print(np.logical_and(trump_mask, more10k).sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Men Biden fikk med enn 100000 stemmer i flere fylker enn Trump!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "168\n",
      "136\n"
     ]
    }
   ],
   "source": [
    "print(np.logical_and(biden_mask, more100k).sum()) #evt. (biden_mask & more10k).sum()\n",
    "print(np.logical_and(trump_mask, more100k).sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vi kan lage en funksjon som sammenligner hvor mange fylker en maske gjelder for for hver kandidat. Denne kan vi bruke til å finne når Biden tok igjen Trump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trumpVbiden(subset_mask=True):\n",
    "    return np.logical_and(biden_mask, subset_mask).sum() < np.logical_and(trump_mask, subset_mask).sum()\n",
    "    #evt. (biden_mask & subset_mask).sum() < (trump_mask & subset_mask).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Biden tar igjen Trump i antall fylker når man ser på fylker med 67114 stemmer eller flere."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "67114\n"
     ]
    }
   ],
   "source": [
    "votes = 10000\n",
    "while trumpVbiden(data[\"votes\"] >= votes):\n",
    "    votes += 1\n",
    "    if votes > 100000:\n",
    "        break\n",
    "print(votes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For å finne hvilket fylke som var vippepunktet er det bare å lage en maske for 67113 stemmer. Trump mistet ledelsen i antall fylker når man ser på fylker med mer enn 67113 stemmer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>state</th>\n",
       "      <th>county</th>\n",
       "      <th>candidate</th>\n",
       "      <th>party</th>\n",
       "      <th>votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18799</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Bell County</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>REP</td>\n",
       "      <td>67113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       state       county     candidate party  votes\n",
       "18799  Texas  Bell County  Donald Trump   REP  67113"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data[\"votes\"] == votes-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "227\n",
      "227\n"
     ]
    }
   ],
   "source": [
    "print(np.logical_and(biden_mask, data[\"votes\"] >= votes).sum()) #evt. (biden_mask & (data[\"votes\"] >= votes)).sum()\n",
    "print(np.logical_and(trump_mask, data[\"votes\"] >= votes).sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kombinere DataFrames (Bonus)\n",
    " \n",
    "Noen ganger ønsker vi å kombinere tabeller. \n",
    "Vi er nå klare til å gjøre litt mer omfattende oppgaver. For eksempel om vi skal legge data fra presidentvalget inn i et kart. Da må vi kombinere kartdata og stemmedata. Vi kan bruke modulen `geopandas` til å lage figurer som viser fordelingen av stemmer rundt omkring i USA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "states = gp.read_file(\"https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_state_500k.zip\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Først fjerner vi alle små kandidater fra dataen. Vi må så summere stemmene hver kandidat har per stat. Så må vi vri om dataframen slik at statene blir kolonnene, hver kandidat har en rad og antall stemmer er verdiene. Så gjør vi antall stemmer om til andel av alle stemmen i den staten. Til slutt slår vi sammen kartet og dataframen med alle stemmene slik at de kan dele data om hver stat. Da kan vi plotte og få andel av stemmer inn i kartet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trumpOrBiden = data[(data[\"candidate\"] == \"Donald Trump\") | (data[\"candidate\"] == \"Joe Biden\")] #used bitwise or\n",
    "votes_by_state = trumpOrBiden.groupby([\"state\", \"candidate\"], as_index=False)[\"votes\"].sum()\n",
    "votes_pivot = votes_by_state.pivot(index=\"state\", columns=\"candidate\", values=\"votes\")\n",
    "votes_share = votes_pivot.div(votes_pivot.sum(axis=1), axis=0)\n",
    "\n",
    "plotting_df = pd.merge(states, votes_share, left_on=\"NAME\", right_on=\"state\")\n",
    "\n",
    "plotting_df.plot(figsize=(16, 6), column=\"Donald Trump\", cmap=\"bwr\", legend=True, vmin=0.4, vmax=0.6)\n",
    "plt.xlim(-180, -63)\n",
    "plt.title(\"Donald Trump\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Større bredde av verdier lar oss se forskjellene mellom de røde statene og mellom de blå statene bedre, men gjør vippestatene vanskeligere å avklare."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotting_df.plot(figsize=(16, 6), column=\"Donald Trump\", cmap=\"bwr\", legend=True, vmin=0.2, vmax=0.8)\n",
    "plt.xlim(-180, -63)\n",
    "plt.title(\"Donald Trump\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.11.3"
  },
  "vscode": {
   "interpreter": {
    "hash": "15c7244d04e63f29aefef262ac4bf2a01bfbb9f74a1bfa1d4e8c8c1ae7ab8e48"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}