Analiza podatkov.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Knjige\n",
    "## Projektna naloga\n",
    "\n",
    "#### Analizirala bom najbolj priljubljene knjige zadnjih 100 let (med leti 1920 in 2020) iz strani Goodreads z uporabo Pandas.\n",
    "\n",
    "#### Za vsako knjigo je zajeto:\n",
    "- id knjige\n",
    "- naslov\n",
    "- zbirka (če je v zbirki)\n",
    "- avtor in njegov id\n",
    "- ocena\n",
    "- število ocen\n",
    "- število glasov in\n",
    "- leto izida\n",
    "\n",
    "#### Hipoteze:\n",
    "- Katere zbirke se največkrat pojavijo?\n",
    "- Kateri avtorji se največkrat pojavijo?\n",
    "- Ali večje število ocen pomeni boljšo oceno?\n",
    "- Katero leto je bilo najbolje ocenjeno?\n",
    "- Katero leto je imelo največ ocen?\n",
    "- Ali imajo boljšo oceno zbirke ali samostojne knjige?\n",
    "- Kateri so najbolje ocenjeni avtorji?\n",
    "- Kateri so največkrat ocenjeni avtorji?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.options.display.max_rows = 10\n",
    "%matplotlib inline\n",
    "import os.path\n",
    "csv_datoteka = os.path.join(\"obdelani-podatki\", \"knjige.csv\")\n",
    "knjige = pd.read_csv(csv_datoteka)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Razpredelnica s podatki"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
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       "    }\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>naslov</th>\n",
       "      <th>zbirka</th>\n",
       "      <th>id_avtorja</th>\n",
       "      <th>avtor</th>\n",
       "      <th>ocena</th>\n",
       "      <th>stevilo_ocen</th>\n",
       "      <th>leto</th>\n",
       "      <th>glasovi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16343</td>\n",
       "      <td>The Mysterious Affair at Styles</td>\n",
       "      <td>Hercule Poirot</td>\n",
       "      <td>123715</td>\n",
       "      <td>Agatha Christie</td>\n",
       "      <td>3.99</td>\n",
       "      <td>256238</td>\n",
       "      <td>1920</td>\n",
       "      <td>465296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53835</td>\n",
       "      <td>The Age of Innocence</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>Edith Wharton</td>\n",
       "      <td>3.95</td>\n",
       "      <td>139727</td>\n",
       "      <td>1920</td>\n",
       "      <td>315835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46165</td>\n",
       "      <td>This Side of Paradise</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3190</td>\n",
       "      <td>F. Scott Fitzgerald</td>\n",
       "      <td>3.66</td>\n",
       "      <td>62463</td>\n",
       "      <td>1920</td>\n",
       "      <td>159803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>325421</td>\n",
       "      <td>The Story of Doctor Dolittle</td>\n",
       "      <td>Doctor Dolittle</td>\n",
       "      <td>70762</td>\n",
       "      <td>Hugh Lofting</td>\n",
       "      <td>3.97</td>\n",
       "      <td>45609</td>\n",
       "      <td>1920</td>\n",
       "      <td>62518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9784</td>\n",
       "      <td>Women in Love</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17623</td>\n",
       "      <td>D.H. Lawrence</td>\n",
       "      <td>3.67</td>\n",
       "      <td>29337</td>\n",
       "      <td>1920</td>\n",
       "      <td>59323</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20195</th>\n",
       "      <td>49217470</td>\n",
       "      <td>Brave Girl, Quiet Girl</td>\n",
       "      <td>NaN</td>\n",
       "      <td>126447</td>\n",
       "      <td>Catherine Ryan Hyde</td>\n",
       "      <td>4.23</td>\n",
       "      <td>13258</td>\n",
       "      <td>2020</td>\n",
       "      <td>38464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20196</th>\n",
       "      <td>51941088</td>\n",
       "      <td>The Comeback</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19475743</td>\n",
       "      <td>Ella Berman</td>\n",
       "      <td>3.82</td>\n",
       "      <td>4931</td>\n",
       "      <td>2020</td>\n",
       "      <td>37904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20197</th>\n",
       "      <td>49381341</td>\n",
       "      <td>Peace Talks</td>\n",
       "      <td>The Dresden Files</td>\n",
       "      <td>10746</td>\n",
       "      <td>Jim Butcher</td>\n",
       "      <td>4.21</td>\n",
       "      <td>22215</td>\n",
       "      <td>2020</td>\n",
       "      <td>71539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20198</th>\n",
       "      <td>52287403</td>\n",
       "      <td>Devoted</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9355</td>\n",
       "      <td>Dean Koontz</td>\n",
       "      <td>4.14</td>\n",
       "      <td>18047</td>\n",
       "      <td>2020</td>\n",
       "      <td>58070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20199</th>\n",
       "      <td>49021976</td>\n",
       "      <td>Rhythm of War</td>\n",
       "      <td>The Stormlight Archive</td>\n",
       "      <td>38550</td>\n",
       "      <td>Brandon Sanderson</td>\n",
       "      <td>4.55</td>\n",
       "      <td>544</td>\n",
       "      <td>2020</td>\n",
       "      <td>49280</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20200 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id                           naslov                  zbirka  \\\n",
       "0         16343  The Mysterious Affair at Styles          Hercule Poirot   \n",
       "1         53835             The Age of Innocence                     NaN   \n",
       "2         46165            This Side of Paradise                     NaN   \n",
       "3        325421     The Story of Doctor Dolittle         Doctor Dolittle   \n",
       "4          9784                    Women in Love                     NaN   \n",
       "...         ...                              ...                     ...   \n",
       "20195  49217470           Brave Girl, Quiet Girl                     NaN   \n",
       "20196  51941088                     The Comeback                     NaN   \n",
       "20197  49381341                      Peace Talks       The Dresden Files   \n",
       "20198  52287403                          Devoted                     NaN   \n",
       "20199  49021976                    Rhythm of War  The Stormlight Archive   \n",
       "\n",
       "       id_avtorja                avtor  ocena  stevilo_ocen  leto  glasovi  \n",
       "0          123715      Agatha Christie   3.99        256238  1920   465296  \n",
       "1              16        Edith Wharton   3.95        139727  1920   315835  \n",
       "2            3190  F. Scott Fitzgerald   3.66         62463  1920   159803  \n",
       "3           70762         Hugh Lofting   3.97         45609  1920    62518  \n",
       "4           17623        D.H. Lawrence   3.67         29337  1920    59323  \n",
       "...           ...                  ...    ...           ...   ...      ...  \n",
       "20195      126447  Catherine Ryan Hyde   4.23         13258  2020    38464  \n",
       "20196    19475743          Ella Berman   3.82          4931  2020    37904  \n",
       "20197       10746          Jim Butcher   4.21         22215  2020    71539  \n",
       "20198        9355          Dean Koontz   4.14         18047  2020    58070  \n",
       "20199       38550    Brandon Sanderson   4.55           544  2020    49280  \n",
       "\n",
       "[20200 rows x 9 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knjige"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hipoteze"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Katere zbirke se največkrat pojavijo?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kolikokrat se vsaka zbirka pojavi:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "zbirka\n",
       "1-800-Where-R-You                1\n",
       "87th Precinct                    7\n",
       "A Bridge of Leaves               1\n",
       "A Course in Religion             1\n",
       "A Court of Thorns and Roses      4\n",
       "                                ..\n",
       "Zones of Thought                 2\n",
       "xxxHOLiC                         1\n",
       "À la recherche du temps perdu    1\n",
       "İnce Memed                       1\n",
       "御手洗潔                             1\n",
       "Length: 2234, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knjige.groupby('zbirka').size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Razporejene po številu pojavitev:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "zbirka\n",
       "1-800-Where-R-You                                       1\n",
       "Pern Publication Order                                  1\n",
       "Percy Jackson and the Olympians: The Graphic Novels     1\n",
       "Percy Jackson & Kane Chronicles Crossover               1\n",
       "Penn Cage                                               1\n",
       "                                                       ..\n",
       "Discworld                                              39\n",
       "Perry Mason                                            40\n",
       "Nero Wolfe                                             43\n",
       "Hercule Poirot                                         44\n",
       "Nancy Drew Mystery Stories                             56\n",
       "Length: 2234, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knjige.groupby('zbirka').size().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "10 zbirk z največ pojavitvami urejene v graf:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='zbirka'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "knjige.groupby('zbirka').size().sort_values().tail(10).plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Največkrat se pojavi zbirka Nancy Drew Mystery Stories s 56 pojavitvami, nato Hercule Poirot s 44 in Nero Wolfe s 43. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Kateri avtorji se največkrat pojavijo?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kolikokrat se vsak avtor(oz. njegov id) pojavi:"
   ]
  },
  {
   "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>pojavitve</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id_avtorja</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100439</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228526</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227840</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227470</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227159</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9494</th>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3389</th>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10657</th>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123715</th>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7660 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            pojavitve\n",
       "id_avtorja           \n",
       "100439              1\n",
       "228526              1\n",
       "227840              1\n",
       "227470              1\n",
       "227159              1\n",
       "...               ...\n",
       "9494               87\n",
       "3389               87\n",
       "625                89\n",
       "10657             107\n",
       "123715            169\n",
       "\n",
       "[7660 rows x 1 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "najveckrat_avtorji = knjige.groupby('id_avtorja').size().sort_values().to_frame('pojavitve')\n",
    "najveckrat_avtorji"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zdaj imamo samo kolikokrat se pojavi id avtroja. Da dobimo zraven imena ustarimo novo razpredelnico z imeni avtorjev in njihovimi id-ji:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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_avtorja</th>\n",
       "      <th>avtor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>123715</td>\n",
       "      <td>Agatha Christie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>Edith Wharton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3190</td>\n",
       "      <td>F. Scott Fitzgerald</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70762</td>\n",
       "      <td>Hugh Lofting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17623</td>\n",
       "      <td>D.H. Lawrence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20195</th>\n",
       "      <td>126447</td>\n",
       "      <td>Catherine Ryan Hyde</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20196</th>\n",
       "      <td>19475743</td>\n",
       "      <td>Ella Berman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20197</th>\n",
       "      <td>10746</td>\n",
       "      <td>Jim Butcher</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20198</th>\n",
       "      <td>9355</td>\n",
       "      <td>Dean Koontz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20199</th>\n",
       "      <td>38550</td>\n",
       "      <td>Brandon Sanderson</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20200 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id_avtorja                avtor\n",
       "0          123715      Agatha Christie\n",
       "1              16        Edith Wharton\n",
       "2            3190  F. Scott Fitzgerald\n",
       "3           70762         Hugh Lofting\n",
       "4           17623        D.H. Lawrence\n",
       "...           ...                  ...\n",
       "20195      126447  Catherine Ryan Hyde\n",
       "20196    19475743          Ella Berman\n",
       "20197       10746          Jim Butcher\n",
       "20198        9355          Dean Koontz\n",
       "20199       38550    Brandon Sanderson\n",
       "\n",
       "[20200 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imena_avtorjev = knjige[['id_avtorja', 'avtor']]\n",
    "imena_avtorjev"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sedaj ju še združimo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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_avtorja</th>\n",
       "      <th>pojavitve</th>\n",
       "      <th>avtor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100439</td>\n",
       "      <td>1</td>\n",
       "      <td>Jane Bowles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>228526</td>\n",
       "      <td>1</td>\n",
       "      <td>Donald R. Morris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>227840</td>\n",
       "      <td>1</td>\n",
       "      <td>Jean Estoril</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>227470</td>\n",
       "      <td>1</td>\n",
       "      <td>Win Blevins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>227159</td>\n",
       "      <td>1</td>\n",
       "      <td>Hiroyuki Takei</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7655</th>\n",
       "      <td>9494</td>\n",
       "      <td>87</td>\n",
       "      <td>H.P. Lovecraft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7656</th>\n",
       "      <td>3389</td>\n",
       "      <td>87</td>\n",
       "      <td>Stephen King</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7657</th>\n",
       "      <td>625</td>\n",
       "      <td>89</td>\n",
       "      <td>Nora Roberts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7658</th>\n",
       "      <td>10657</td>\n",
       "      <td>107</td>\n",
       "      <td>Enid Blyton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7659</th>\n",
       "      <td>123715</td>\n",
       "      <td>169</td>\n",
       "      <td>Agatha Christie</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7660 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id_avtorja  pojavitve             avtor\n",
       "0         100439          1       Jane Bowles\n",
       "1         228526          1  Donald R. Morris\n",
       "2         227840          1      Jean Estoril\n",
       "3         227470          1       Win Blevins\n",
       "4         227159          1    Hiroyuki Takei\n",
       "...          ...        ...               ...\n",
       "7655        9494         87    H.P. Lovecraft\n",
       "7656        3389         87      Stephen King\n",
       "7657         625         89      Nora Roberts\n",
       "7658       10657        107       Enid Blyton\n",
       "7659      123715        169   Agatha Christie\n",
       "\n",
       "[7660 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imena_in_pojavitve = pd.merge(najveckrat_avtorji, imena_avtorjev, left_on='id_avtorja', right_on='id_avtorja').drop_duplicates().reset_index(drop=True)\n",
    "imena_in_pojavitve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Največkrat se pojavi Agathe Christie s kar 169 knjigami. Naslednja je Enid Blyton z približno tretjino manj (107). To sta edina avtorja ki se pojavita več kot stokrat, sledi jima Nora Roberts z 89 knjigami.\n",
    "Poleg tega izvemo še, da čeprav je vseh knjig 20200, je različnih avtorjev samo 7660, torej je v povprečju vsak avtor napisal več kot 2 knjigi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.637075718015666"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "20200/ 7660"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Kateri so najbolje ocenjeni avtorji?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Najprej izračunamo povprečje za vsakega avtorja in iz razpredelnice izberemo samo id avtorja in njegovo povprečno oceno:"
   ]
  },
  {
   "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>id_avtorja</th>\n",
       "      <th>ocena</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>277404</td>\n",
       "      <td>2.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1124248</td>\n",
       "      <td>2.710000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>682865</td>\n",
       "      <td>2.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4285032</td>\n",
       "      <td>2.810000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>264856</td>\n",
       "      <td>2.830000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7655</th>\n",
       "      <td>13778</td>\n",
       "      <td>4.709412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7656</th>\n",
       "      <td>19527332</td>\n",
       "      <td>4.710000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7657</th>\n",
       "      <td>4094635</td>\n",
       "      <td>4.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7658</th>\n",
       "      <td>18851256</td>\n",
       "      <td>4.770000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7659</th>\n",
       "      <td>2793859</td>\n",
       "      <td>4.790000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7660 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id_avtorja     ocena\n",
       "0         277404  2.070000\n",
       "1        1124248  2.710000\n",
       "2         682865  2.800000\n",
       "3        4285032  2.810000\n",
       "4         264856  2.830000\n",
       "...          ...       ...\n",
       "7655       13778  4.709412\n",
       "7656    19527332  4.710000\n",
       "7657     4094635  4.720000\n",
       "7658    18851256  4.770000\n",
       "7659     2793859  4.790000\n",
       "\n",
       "[7660 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "povprecna_ocena_avtorja = knjige.groupby('id_avtorja').mean().sort_values('ocena').reset_index()[[\"id_avtorja\", \"ocena\"]]\n",
    "povprecna_ocena_avtorja"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podobno kot prej to združimo z razpredelnico imena_avtorjev, da dobimo njihova imena:"
   ]
  },
  {
   "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>id_avtorja</th>\n",
       "      <th>ocena</th>\n",
       "      <th>avtor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>277404</td>\n",
       "      <td>2.070000</td>\n",
       "      <td>Anne Carroll Moore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1124248</td>\n",
       "      <td>2.710000</td>\n",
       "      <td>Grace Moon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>682865</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>Ernest Vincent Wright</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4285032</td>\n",
       "      <td>2.810000</td>\n",
       "      <td>Grace Taber Hallock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>264856</td>\n",
       "      <td>2.830000</td>\n",
       "      <td>Margaret Sanger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7655</th>\n",
       "      <td>13778</td>\n",
       "      <td>4.709412</td>\n",
       "      <td>Bill Watterson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7656</th>\n",
       "      <td>19527332</td>\n",
       "      <td>4.710000</td>\n",
       "      <td>Chanel Miller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7657</th>\n",
       "      <td>4094635</td>\n",
       "      <td>4.720000</td>\n",
       "      <td>John Andrew Simpson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7658</th>\n",
       "      <td>18851256</td>\n",
       "      <td>4.770000</td>\n",
       "      <td>Mary Loyola</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7659</th>\n",
       "      <td>2793859</td>\n",
       "      <td>4.790000</td>\n",
       "      <td>Mustafa Kemal Atatürk</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7660 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id_avtorja     ocena                  avtor\n",
       "0         277404  2.070000     Anne Carroll Moore\n",
       "1        1124248  2.710000             Grace Moon\n",
       "2         682865  2.800000  Ernest Vincent Wright\n",
       "3        4285032  2.810000    Grace Taber Hallock\n",
       "4         264856  2.830000        Margaret Sanger\n",
       "...          ...       ...                    ...\n",
       "7655       13778  4.709412         Bill Watterson\n",
       "7656    19527332  4.710000          Chanel Miller\n",
       "7657     4094635  4.720000    John Andrew Simpson\n",
       "7658    18851256  4.770000            Mary Loyola\n",
       "7659     2793859  4.790000  Mustafa Kemal Atatürk\n",
       "\n",
       "[7660 rows x 3 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ime_avtorja_in_pov_ocena = pd.merge(povprecna_ocena_avtorja, imena_avtorjev, left_on='id_avtorja', right_on='id_avtorja').drop_duplicates().reset_index(drop=True)\n",
    "ime_avtorja_in_pov_ocena"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Najboljšo povprečno oceno ima Mustafa Kemal Atatürk - 4.79, najmanjšo pa Anne Carroll Moore - 2.07. Ampak ali je ta povprečna ocena odvisno od tega koliko knjig je avtor napisal?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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_avtorja</th>\n",
       "      <th>pojavitve</th>\n",
       "      <th>ocena</th>\n",
       "      <th>avtor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>277404</td>\n",
       "      <td>1</td>\n",
       "      <td>2.070000</td>\n",
       "      <td>Anne Carroll Moore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2610</th>\n",
       "      <td>1124248</td>\n",
       "      <td>1</td>\n",
       "      <td>2.710000</td>\n",
       "      <td>Grace Moon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2844</th>\n",
       "      <td>682865</td>\n",
       "      <td>1</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>Ernest Vincent Wright</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3012</th>\n",
       "      <td>4285032</td>\n",
       "      <td>1</td>\n",
       "      <td>2.810000</td>\n",
       "      <td>Grace Taber Hallock</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5625</th>\n",
       "      <td>264856</td>\n",
       "      <td>2</td>\n",
       "      <td>2.830000</td>\n",
       "      <td>Margaret Sanger</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>7516</th>\n",
       "      <td>13778</td>\n",
       "      <td>17</td>\n",
       "      <td>4.709412</td>\n",
       "      <td>Bill Watterson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2187</th>\n",
       "      <td>19527332</td>\n",
       "      <td>1</td>\n",
       "      <td>4.710000</td>\n",
       "      <td>Chanel Miller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3019</th>\n",
       "      <td>4094635</td>\n",
       "      <td>1</td>\n",
       "      <td>4.720000</td>\n",
       "      <td>John Andrew Simpson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2161</th>\n",
       "      <td>18851256</td>\n",
       "      <td>1</td>\n",
       "      <td>4.770000</td>\n",
       "      <td>Mary Loyola</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5222</th>\n",
       "      <td>2793859</td>\n",
       "      <td>2</td>\n",
       "      <td>4.790000</td>\n",
       "      <td>Mustafa Kemal Atatürk</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7660 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id_avtorja  pojavitve     ocena                  avtor\n",
       "674       277404          1  2.070000     Anne Carroll Moore\n",
       "2610     1124248          1  2.710000             Grace Moon\n",
       "2844      682865          1  2.800000  Ernest Vincent Wright\n",
       "3012     4285032          1  2.810000    Grace Taber Hallock\n",
       "5625      264856          2  2.830000        Margaret Sanger\n",
       "...          ...        ...       ...                    ...\n",
       "7516       13778         17  4.709412         Bill Watterson\n",
       "2187    19527332          1  4.710000          Chanel Miller\n",
       "3019     4094635          1  4.720000    John Andrew Simpson\n",
       "2161    18851256          1  4.770000            Mary Loyola\n",
       "5222     2793859          2  4.790000  Mustafa Kemal Atatürk\n",
       "\n",
       "[7660 rows x 4 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "povp_ocena_in_pojavitve = pd.merge(imena_in_pojavitve[['id_avtorja', 'pojavitve']], ime_avtorja_in_pov_ocena, left_on='id_avtorja', right_on='id_avtorja').sort_values('ocena')\n",
    "povp_ocena_in_pojavitve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='pojavitve', ylabel='ocena'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "povp_ocena_in_pojavitve.plot.scatter(x='pojavitve', y='ocena')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vidimo, da imajo avtorji z samo 1 knjigo zelo razporejene ocene, avtorji z več kot 25 knjigami pa imajo povprečno oceno okoli 4."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Kateri so največkrat ocenjeni avtorji?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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_avtorja</th>\n",
       "      <th>stevilo_ocen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1830746</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>580393</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3341792</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6643050</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1010053</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7655</th>\n",
       "      <td>15872</td>\n",
       "      <td>8344782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7656</th>\n",
       "      <td>941441</td>\n",
       "      <td>10797229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7657</th>\n",
       "      <td>153394</td>\n",
       "      <td>11862467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7658</th>\n",
       "      <td>3389</td>\n",
       "      <td>12672412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7659</th>\n",
       "      <td>1077326</td>\n",
       "      <td>24454837</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7660 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id_avtorja  stevilo_ocen\n",
       "0        1830746             7\n",
       "1         580393             7\n",
       "2        3341792             8\n",
       "3        6643050             8\n",
       "4        1010053            12\n",
       "...          ...           ...\n",
       "7655       15872       8344782\n",
       "7656      941441      10797229\n",
       "7657      153394      11862467\n",
       "7658        3389      12672412\n",
       "7659     1077326      24454837\n",
       "\n",
       "[7660 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avtorji_po_stevilu_ocen = knjige.groupby('id_avtorja').sum().sort_values('stevilo_ocen').reset_index()[[\"id_avtorja\", \"stevilo_ocen\"]]\n",
    "avtorji_po_stevilu_ocen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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_avtorja</th>\n",
       "      <th>stevilo_ocen</th>\n",
       "      <th>avtor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1830746</td>\n",
       "      <td>7</td>\n",
       "      <td>Heruy Wolde Selassie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>580393</td>\n",
       "      <td>7</td>\n",
       "      <td>James Truslow Adams</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3341792</td>\n",
       "      <td>8</td>\n",
       "      <td>Elizabeth M. Ricker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6643050</td>\n",
       "      <td>8</td>\n",
       "      <td>Roy C McHenry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1010053</td>\n",
       "      <td>12</td>\n",
       "      <td>Wolfgang Köhler</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7655</th>\n",
       "      <td>15872</td>\n",
       "      <td>8344782</td>\n",
       "      <td>Rick Riordan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7656</th>\n",
       "      <td>941441</td>\n",
       "      <td>10797229</td>\n",
       "      <td>Stephenie Meyer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7657</th>\n",
       "      <td>153394</td>\n",
       "      <td>11862467</td>\n",
       "      <td>Suzanne Collins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7658</th>\n",
       "      <td>3389</td>\n",
       "      <td>12672412</td>\n",
       "      <td>Stephen King</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7659</th>\n",
       "      <td>1077326</td>\n",
       "      <td>24454837</td>\n",
       "      <td>J.K. Rowling</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7660 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id_avtorja  stevilo_ocen                 avtor\n",
       "0        1830746             7  Heruy Wolde Selassie\n",
       "1         580393             7   James Truslow Adams\n",
       "2        3341792             8   Elizabeth M. Ricker\n",
       "3        6643050             8         Roy C McHenry\n",
       "4        1010053            12       Wolfgang Köhler\n",
       "...          ...           ...                   ...\n",
       "7655       15872       8344782          Rick Riordan\n",
       "7656      941441      10797229       Stephenie Meyer\n",
       "7657      153394      11862467       Suzanne Collins\n",
       "7658        3389      12672412          Stephen King\n",
       "7659     1077326      24454837          J.K. Rowling\n",
       "\n",
       "[7660 rows x 3 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avtor_ocene = pd.merge(avtorji_po_stevilu_ocen, imena_avtorjev, left_on='id_avtorja', right_on='id_avtorja').drop_duplicates().reset_index(drop=True)\n",
    "avtor_ocene"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Največkrat je ocenjena J.K.Rowling, ki ima več kot 24 milijonov ocen. Naslednji za njo je Stephen King s pol manj ocen, 12 milijonov, sledijo mu Suzanne Collins, Stephanie Meyer in Rick Riordan."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. Katero je bilo najboljše ocenjeno leto?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>povprecna_letna_ocena</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>leto</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1920</th>\n",
       "      <td>3.89090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1921</th>\n",
       "      <td>3.83375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1922</th>\n",
       "      <td>3.89240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1923</th>\n",
       "      <td>3.85975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924</th>\n",
       "      <td>3.83690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>3.99445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>4.02805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>3.99300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>4.03310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>4.01465</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      povprecna_letna_ocena\n",
       "leto                       \n",
       "1920                3.89090\n",
       "1921                3.83375\n",
       "1922                3.89240\n",
       "1923                3.85975\n",
       "1924                3.83690\n",
       "...                     ...\n",
       "2016                3.99445\n",
       "2017                4.02805\n",
       "2018                3.99300\n",
       "2019                4.03310\n",
       "2020                4.01465\n",
       "\n",
       "[101 rows x 1 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "letne_ocene = knjige.groupby('leto').mean().ocena.to_frame('povprecna_letna_ocena')\n",
    "letne_ocene"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>povprecna_letna_ocena</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>leto</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1921</th>\n",
       "      <td>3.83375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924</th>\n",
       "      <td>3.83690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1923</th>\n",
       "      <td>3.85975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1928</th>\n",
       "      <td>3.86045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1925</th>\n",
       "      <td>3.88280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>4.04010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>4.04575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>4.04930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>4.05445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>4.06005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      povprecna_letna_ocena\n",
       "leto                       \n",
       "1921                3.83375\n",
       "1924                3.83690\n",
       "1923                3.85975\n",
       "1928                3.86045\n",
       "1925                3.88280\n",
       "...                     ...\n",
       "1988                4.04010\n",
       "1982                4.04575\n",
       "2012                4.04930\n",
       "1986                4.05445\n",
       "1996                4.06005\n",
       "\n",
       "[101 rows x 1 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "letne_ocene.sort_values('povprecna_letna_ocena')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Najboljše je bilo ocenjeno leto 1996, najslabše pa 1921, vendar je razlika med njima razmeroma majhna. Poglejmo še graf:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='leto'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "knjige.groupby('leto').mean().ocena.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Povprečna ocena je skozi leta naraščala, po letu 1996 začela upadati, po 2005 pa je ponovno začela naraščati. Čeprav zgleda razlika med najbolje in naslabše ocenjenim letom na grafu velika, je dejanska razlika samo približno 0.20, kar ni tako veliko."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6. Katero leto je imelo največ ocen?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>stevilo_ocen_na_leto</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>leto</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1920</th>\n",
       "      <td>771613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1921</th>\n",
       "      <td>281047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1922</th>\n",
       "      <td>1505918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1923</th>\n",
       "      <td>614118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924</th>\n",
       "      <td>516467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>17527915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>15566998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>15738021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>10563580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>4604548</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      stevilo_ocen_na_leto\n",
       "leto                      \n",
       "1920                771613\n",
       "1921                281047\n",
       "1922               1505918\n",
       "1923                614118\n",
       "1924                516467\n",
       "...                    ...\n",
       "2016              17527915\n",
       "2017              15566998\n",
       "2018              15738021\n",
       "2019              10563580\n",
       "2020               4604548\n",
       "\n",
       "[101 rows x 1 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "letno_stevilo_ocen = knjige.groupby('leto').sum().stevilo_ocen.to_frame('stevilo_ocen_na_leto')\n",
    "letno_stevilo_ocen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>stevilo_ocen_na_leto</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>leto</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1921</th>\n",
       "      <td>281047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1924</th>\n",
       "      <td>516467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933</th>\n",
       "      <td>539282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1944</th>\n",
       "      <td>604113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1923</th>\n",
       "      <td>614118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>26291189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>28918915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>32037492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>32405268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>34806830</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      stevilo_ocen_na_leto\n",
       "leto                      \n",
       "1921                281047\n",
       "1924                516467\n",
       "1933                539282\n",
       "1944                604113\n",
       "1923                614118\n",
       "...                    ...\n",
       "2009              26291189\n",
       "2008              28918915\n",
       "2011              32037492\n",
       "2005              32405268\n",
       "2012              34806830\n",
       "\n",
       "[101 rows x 1 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "letno_stevilo_ocen.sort_values('stevilo_ocen_na_leto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='leto'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "knjige.groupby('leto').sum().stevilo_ocen.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Največ ocen ima leto 2012, najmanj pa 1921. Število ocen skozi leta narašča. Glede na to da ima povprečno vsako leto več ocen in da je ocena boljša (po prejšnji hipotezi) ali obstaja povezava med njima?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7. Ali večje število ocen pomeni boljšo oceno?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='stevilo_ocen', ylabel='ocena'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "knjige.sort_values('ocena').plot.scatter(x='stevilo_ocen', y='ocena')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Iz grafa se vidi, imajo knjige z večjim številom ocen navadno tudi višjo oceno. Vidimo, da imajo knjige z več kot tremi milijoni ocenami povprečno višjo oceno, medtem ko imajo knjige z manj kot milijonom ocen zelo razporejene ocene."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.1414285714285715"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "povprecna_ocena_knjig_z_vec_kot_3M_ocen = knjige[knjige.stevilo_ocen >= 3000000].mean().ocena\n",
    "povprecna_ocena_knjig_z_vec_kot_3M_ocen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.989712837837838"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "povprecna_ocena_knjig_z_manj_kot_1M_ocen = knjige[knjige.stevilo_ocen < 1000000].mean().ocena\n",
    "povprecna_ocena_knjig_z_manj_kot_1M_ocen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 8. Ali imajo boljšo oceno zbirke ali samostojne knjige?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Najprej poglejmo koliko knjig je v zbirkah, koliko pa ne:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5501"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stevilo_knjig_v_zbirkah = knjige.count().zbirka\n",
    "stevilo_knjig_v_zbirkah"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14699"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stevilo_knjig_brez_zbirke = 20200 - 5501\n",
    "stevilo_knjig_brez_zbirke"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Izmed vseh knjig jih je približno četrtina v zbirki in tri četrine samostojnih."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\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>naslov</th>\n",
       "      <th>zbirka</th>\n",
       "      <th>id_avtorja</th>\n",
       "      <th>avtor</th>\n",
       "      <th>ocena</th>\n",
       "      <th>stevilo_ocen</th>\n",
       "      <th>leto</th>\n",
       "      <th>glasovi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>53835</td>\n",
       "      <td>The Age of Innocence</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>Edith Wharton</td>\n",
       "      <td>3.95</td>\n",
       "      <td>139727</td>\n",
       "      <td>1920</td>\n",
       "      <td>315835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46165</td>\n",
       "      <td>This Side of Paradise</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3190</td>\n",
       "      <td>F. Scott Fitzgerald</td>\n",
       "      <td>3.66</td>\n",
       "      <td>62463</td>\n",
       "      <td>1920</td>\n",
       "      <td>159803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9784</td>\n",
       "      <td>Women in Love</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17623</td>\n",
       "      <td>D.H. Lawrence</td>\n",
       "      <td>3.67</td>\n",
       "      <td>29337</td>\n",
       "      <td>1920</td>\n",
       "      <td>59323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11376</td>\n",
       "      <td>Main Street</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7330</td>\n",
       "      <td>Sinclair Lewis</td>\n",
       "      <td>3.77</td>\n",
       "      <td>23864</td>\n",
       "      <td>1920</td>\n",
       "      <td>50987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4662</td>\n",
       "      <td>The Short Stories</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3190</td>\n",
       "      <td>F. Scott Fitzgerald</td>\n",
       "      <td>4.23</td>\n",
       "      <td>13917</td>\n",
       "      <td>1920</td>\n",
       "      <td>33157</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14694</th>\n",
       "      <td>51373979</td>\n",
       "      <td>He Started It</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17310869</td>\n",
       "      <td>Samantha  Downing</td>\n",
       "      <td>3.59</td>\n",
       "      <td>7065</td>\n",
       "      <td>2020</td>\n",
       "      <td>39449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14695</th>\n",
       "      <td>48814109</td>\n",
       "      <td>The Lions of Fifth Avenue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14903705</td>\n",
       "      <td>Fiona  Davis</td>\n",
       "      <td>3.97</td>\n",
       "      <td>10035</td>\n",
       "      <td>2020</td>\n",
       "      <td>47784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14696</th>\n",
       "      <td>49217470</td>\n",
       "      <td>Brave Girl, Quiet Girl</td>\n",
       "      <td>NaN</td>\n",
       "      <td>126447</td>\n",
       "      <td>Catherine Ryan Hyde</td>\n",
       "      <td>4.23</td>\n",
       "      <td>13258</td>\n",
       "      <td>2020</td>\n",
       "      <td>38464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14697</th>\n",
       "      <td>51941088</td>\n",
       "      <td>The Comeback</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19475743</td>\n",
       "      <td>Ella Berman</td>\n",
       "      <td>3.82</td>\n",
       "      <td>4931</td>\n",
       "      <td>2020</td>\n",
       "      <td>37904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14698</th>\n",
       "      <td>52287403</td>\n",
       "      <td>Devoted</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9355</td>\n",
       "      <td>Dean Koontz</td>\n",
       "      <td>4.14</td>\n",
       "      <td>18047</td>\n",
       "      <td>2020</td>\n",
       "      <td>58070</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14699 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id                     naslov zbirka  id_avtorja  \\\n",
       "0         53835       The Age of Innocence    NaN          16   \n",
       "1         46165      This Side of Paradise    NaN        3190   \n",
       "2          9784              Women in Love    NaN       17623   \n",
       "3         11376                Main Street    NaN        7330   \n",
       "4          4662          The Short Stories    NaN        3190   \n",
       "...         ...                        ...    ...         ...   \n",
       "14694  51373979              He Started It    NaN    17310869   \n",
       "14695  48814109  The Lions of Fifth Avenue    NaN    14903705   \n",
       "14696  49217470     Brave Girl, Quiet Girl    NaN      126447   \n",
       "14697  51941088               The Comeback    NaN    19475743   \n",
       "14698  52287403                    Devoted    NaN        9355   \n",
       "\n",
       "                     avtor  ocena  stevilo_ocen  leto  glasovi  \n",
       "0            Edith Wharton   3.95        139727  1920   315835  \n",
       "1      F. Scott Fitzgerald   3.66         62463  1920   159803  \n",
       "2            D.H. Lawrence   3.67         29337  1920    59323  \n",
       "3           Sinclair Lewis   3.77         23864  1920    50987  \n",
       "4      F. Scott Fitzgerald   4.23         13917  1920    33157  \n",
       "...                    ...    ...           ...   ...      ...  \n",
       "14694    Samantha  Downing   3.59          7065  2020    39449  \n",
       "14695         Fiona  Davis   3.97         10035  2020    47784  \n",
       "14696  Catherine Ryan Hyde   4.23         13258  2020    38464  \n",
       "14697          Ella Berman   3.82          4931  2020    37904  \n",
       "14698          Dean Koontz   4.14         18047  2020    58070  \n",
       "\n",
       "[14699 rows x 9 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knjige_brez_zbirke = knjige[pd.isnull(knjige['zbirka'])].reset_index(drop=True)\n",
    "knjige_brez_zbirke"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id              4.702102e+06\n",
       "zbirka                   NaN\n",
       "id_avtorja      1.101726e+06\n",
       "ocena           3.979865e+00\n",
       "stevilo_ocen    2.819723e+04\n",
       "leto            1.964951e+03\n",
       "glasovi         5.492229e+04\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "povprecje_knjige_brez_zbirke = knjige_brez_zbirke.mean()\n",
    "povprecje_knjige_brez_zbirke"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>naslov</th>\n",
       "      <th>zbirka</th>\n",
       "      <th>id_avtorja</th>\n",
       "      <th>avtor</th>\n",
       "      <th>ocena</th>\n",
       "      <th>stevilo_ocen</th>\n",
       "      <th>leto</th>\n",
       "      <th>glasovi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16343</td>\n",
       "      <td>The Mysterious Affair at Styles</td>\n",
       "      <td>Hercule Poirot</td>\n",
       "      <td>123715</td>\n",
       "      <td>Agatha Christie</td>\n",
       "      <td>3.99</td>\n",
       "      <td>256238</td>\n",
       "      <td>1920</td>\n",
       "      <td>465296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>325421</td>\n",
       "      <td>The Story of Doctor Dolittle</td>\n",
       "      <td>Doctor Dolittle</td>\n",
       "      <td>70762</td>\n",
       "      <td>Hugh Lofting</td>\n",
       "      <td>3.97</td>\n",
       "      <td>45609</td>\n",
       "      <td>1920</td>\n",
       "      <td>62518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6217</td>\n",
       "      <td>Kristin Lavransdatter</td>\n",
       "      <td>Kristin Lavransdatter</td>\n",
       "      <td>4203</td>\n",
       "      <td>Sigrid Undset</td>\n",
       "      <td>4.29</td>\n",
       "      <td>9017</td>\n",
       "      <td>1920</td>\n",
       "      <td>31771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>765300</td>\n",
       "      <td>Glinda of Oz</td>\n",
       "      <td>Oz</td>\n",
       "      <td>3242</td>\n",
       "      <td>L. Frank Baum</td>\n",
       "      <td>3.93</td>\n",
       "      <td>5829</td>\n",
       "      <td>1920</td>\n",
       "      <td>14443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6220</td>\n",
       "      <td>The Wreath</td>\n",
       "      <td>Kristin Lavransdatter</td>\n",
       "      <td>4203</td>\n",
       "      <td>Sigrid Undset</td>\n",
       "      <td>4.02</td>\n",
       "      <td>5301</td>\n",
       "      <td>1920</td>\n",
       "      <td>13910</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5496</th>\n",
       "      <td>49884715</td>\n",
       "      <td>Find Me</td>\n",
       "      <td>Inland Empire</td>\n",
       "      <td>4327517</td>\n",
       "      <td>Anne Frasier</td>\n",
       "      <td>4.15</td>\n",
       "      <td>18672</td>\n",
       "      <td>2020</td>\n",
       "      <td>51844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5497</th>\n",
       "      <td>51518972</td>\n",
       "      <td>Majesty</td>\n",
       "      <td>American Royals</td>\n",
       "      <td>13499819</td>\n",
       "      <td>Katharine McGee</td>\n",
       "      <td>3.88</td>\n",
       "      <td>13125</td>\n",
       "      <td>2020</td>\n",
       "      <td>45806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5498</th>\n",
       "      <td>43565384</td>\n",
       "      <td>The Night Country</td>\n",
       "      <td>The Hazel Wood</td>\n",
       "      <td>8384949</td>\n",
       "      <td>Melissa Albert</td>\n",
       "      <td>3.74</td>\n",
       "      <td>7094</td>\n",
       "      <td>2020</td>\n",
       "      <td>39014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5499</th>\n",
       "      <td>49381341</td>\n",
       "      <td>Peace Talks</td>\n",
       "      <td>The Dresden Files</td>\n",
       "      <td>10746</td>\n",
       "      <td>Jim Butcher</td>\n",
       "      <td>4.21</td>\n",
       "      <td>22215</td>\n",
       "      <td>2020</td>\n",
       "      <td>71539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5500</th>\n",
       "      <td>49021976</td>\n",
       "      <td>Rhythm of War</td>\n",
       "      <td>The Stormlight Archive</td>\n",
       "      <td>38550</td>\n",
       "      <td>Brandon Sanderson</td>\n",
       "      <td>4.55</td>\n",
       "      <td>544</td>\n",
       "      <td>2020</td>\n",
       "      <td>49280</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5501 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id                           naslov                  zbirka  \\\n",
       "0        16343  The Mysterious Affair at Styles          Hercule Poirot   \n",
       "1       325421     The Story of Doctor Dolittle         Doctor Dolittle   \n",
       "2         6217            Kristin Lavransdatter   Kristin Lavransdatter   \n",
       "3       765300                     Glinda of Oz                      Oz   \n",
       "4         6220                       The Wreath   Kristin Lavransdatter   \n",
       "...        ...                              ...                     ...   \n",
       "5496  49884715                          Find Me           Inland Empire   \n",
       "5497  51518972                          Majesty         American Royals   \n",
       "5498  43565384                The Night Country          The Hazel Wood   \n",
       "5499  49381341                      Peace Talks       The Dresden Files   \n",
       "5500  49021976                    Rhythm of War  The Stormlight Archive   \n",
       "\n",
       "      id_avtorja              avtor  ocena  stevilo_ocen  leto  glasovi  \n",
       "0         123715    Agatha Christie   3.99        256238  1920   465296  \n",
       "1          70762       Hugh Lofting   3.97         45609  1920    62518  \n",
       "2           4203      Sigrid Undset   4.29          9017  1920    31771  \n",
       "3           3242      L. Frank Baum   3.93          5829  1920    14443  \n",
       "4           4203      Sigrid Undset   4.02          5301  1920    13910  \n",
       "...          ...                ...    ...           ...   ...      ...  \n",
       "5496     4327517       Anne Frasier   4.15         18672  2020    51844  \n",
       "5497    13499819    Katharine McGee   3.88         13125  2020    45806  \n",
       "5498     8384949     Melissa Albert   3.74          7094  2020    39014  \n",
       "5499       10746        Jim Butcher   4.21         22215  2020    71539  \n",
       "5500       38550  Brandon Sanderson   4.55           544  2020    49280  \n",
       "\n",
       "[5501 rows x 9 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knjige_v_zbirki = knjige[pd.notnull(knjige['zbirka'])].reset_index(drop=True)\n",
    "knjige_v_zbirki"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id              5.030567e+06\n",
       "id_avtorja      1.023958e+06\n",
       "ocena           4.017673e+00\n",
       "stevilo_ocen    6.283883e+04\n",
       "leto            1.983490e+03\n",
       "glasovi         1.045242e+05\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "povprecje_knjige_v_zbirki = knjige_v_zbirki.mean()\n",
    "povprecje_knjige_v_zbirki"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Boljšo oceno imajo knjige v zbirki (vendar je razlika zelo majhna), vendar imajo povprečno veliko večje število ocen knjige v zbirki."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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