split-apply-combine-netflix-data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Groupby, split-apply-combine and pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Netflix recently released some user ratings data. I wanted to ask a straightforward question: _do Netflix subscribers prefer older or newer movies?_\n",
    "\n",
    "Intuitively, you want to split the dataset into groups, one for each year, and then to compute a summary statistic, such as the mean or the median, and then to see whether this statistic increases over the years (after this, you may want to perform a statistical test).\n",
    "\n",
    "The great thing is that there is a conceptual framework for doing and thinking about this, along with implementations in both Python and R. The framework is known as split-apply-combine because we\n",
    "\n",
    "* [Step 1](#step_1): split the data into groups by creating a _groupby_ object from the original DataFrame;\n",
    "* [Step 2](#step_2): apply a function, in this case, an aggregation function that computes a summary statistic (you can also transform or filter your data in this step);\n",
    "* [Step 3](#step_3): combine the results into a new DataFrame.\n",
    "\n",
    "This is the conceptual framework for the analysis at hand. In this post you'll learn how to do this to answer the Netflix ratings question above using the Python package `pandas`. You could do the same in R using, for example, the `dplyr` package. I'll also necessarily delve into _groupby_ objects, wich are not the most intuitive objects. The process of split-apply-combine with _groupby_ objects is a pattern that we all perform intuitively, as we'll see, but it took took Hadley Wickham to formalize the procedure in 2011 with his paper _The Split-Apply-Combine Strategy for Data Analysis_.\n",
    "\n",
    "If you find this technique useful, you can learn more about it (among many other things) and practice it in our [Manipulating DataFrames with pandas course](https://www.datacamp.com/courses/manipulating-dataframes-with-pandas), taught by Dhavide Aruliah of Anaconda."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration with pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import your data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here you'll use `pandas`, _groupby_ objects and the principles of split-apply-combine to check out how Netflix movie ranges vary as a function of the year they were released. I originally came across the data on data.world [here](https://data.world/chasewillden/netflix-shows) and you can also find it at [here](https://theconceptcenter.com/simple-research-study-netflix-shows-analysis/) at The Concept Centre. You can find all the code in this post [here](https://github.com/datacamp/community-groupby) if you would like to reproduce it.\n",
    "\n",
    "You'll first import the necessary packages and the data and check out the first five rows of the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import packages and set visualization style\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>rating</th>\n",
       "      <th>ratinglevel</th>\n",
       "      <th>ratingdescription</th>\n",
       "      <th>release_year</th>\n",
       "      <th>user_rating_score</th>\n",
       "      <th>user_rating_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>White Chicks</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>crude and sexual humor, language and some drug...</td>\n",
       "      <td>80</td>\n",
       "      <td>2004</td>\n",
       "      <td>82.0</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lucky Number Slevin</td>\n",
       "      <td>R</td>\n",
       "      <td>strong violence, sexual content and adult lang...</td>\n",
       "      <td>100</td>\n",
       "      <td>2006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Grey's Anatomy</td>\n",
       "      <td>TV-14</td>\n",
       "      <td>Parents strongly cautioned. May be unsuitable ...</td>\n",
       "      <td>90</td>\n",
       "      <td>2016</td>\n",
       "      <td>98.0</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Prison Break</td>\n",
       "      <td>TV-14</td>\n",
       "      <td>Parents strongly cautioned. May be unsuitable ...</td>\n",
       "      <td>90</td>\n",
       "      <td>2008</td>\n",
       "      <td>98.0</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>How I Met Your Mother</td>\n",
       "      <td>TV-PG</td>\n",
       "      <td>Parental guidance suggested. May not be suitab...</td>\n",
       "      <td>70</td>\n",
       "      <td>2014</td>\n",
       "      <td>94.0</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   title rating  \\\n",
       "0           White Chicks  PG-13   \n",
       "1    Lucky Number Slevin      R   \n",
       "2         Grey's Anatomy  TV-14   \n",
       "3           Prison Break  TV-14   \n",
       "4  How I Met Your Mother  TV-PG   \n",
       "\n",
       "                                         ratinglevel  ratingdescription  \\\n",
       "0  crude and sexual humor, language and some drug...                 80   \n",
       "1  strong violence, sexual content and adult lang...                100   \n",
       "2  Parents strongly cautioned. May be unsuitable ...                 90   \n",
       "3  Parents strongly cautioned. May be unsuitable ...                 90   \n",
       "4  Parental guidance suggested. May not be suitab...                 70   \n",
       "\n",
       "   release_year  user_rating_score  user_rating_size  \n",
       "0          2004               82.0                80  \n",
       "1          2006                NaN                82  \n",
       "2          2016               98.0                80  \n",
       "3          2008               98.0                80  \n",
       "4          2014               94.0                80  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import data and check out head of DataFrame\n",
    "df = pd.read_csv('data/chasewillden-netflix-shows/data/netflix.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks pretty cool to me: you have titles, ratings, release year and user rating score, among several other columns. Before performing our _groupby_ and split-apply-combine procedure, lets look a bit more closely at the data to make sure it's what we think it is and to deal with missing values. Note that there is a missing value NaN in the *user_rating_score* of the second row (row 1)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summarising your data with plots and statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `pandas` DataFrame `.info()` method is invaluable. Applying it below shows that you have 1000 rows and 7 columns of data, but also that the column of interest, *user_rating_score*, has only 605 non-null values. This means that there are 395 missing values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 7 columns):\n",
      "title                1000 non-null object\n",
      "rating               1000 non-null object\n",
      "ratinglevel          941 non-null object\n",
      "ratingdescription    1000 non-null int64\n",
      "release_year         1000 non-null int64\n",
      "user_rating_score    605 non-null float64\n",
      "user_rating_size     1000 non-null int64\n",
      "dtypes: float64(1), int64(3), object(3)\n",
      "memory usage: 54.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# Check out info of DataFrame\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can drop rows that have any missing values, drop any duplicate rows and build a _pairplot_ of the DataFrame using `seaborn` in order to get a visual sense of the data. You'll color the data by the 'rating' column. Check out the plots and see what information you can get from them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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T4AYWU8ok0k3xeOpRVXvBylcKXq+r3CpsC9VqVzHsZMxa3d9W1O/SawuG6Rdnw3jf27wl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\nCzxCdtGYU7QDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b574668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Drop rows with missing values and drop duplicate\n",
    "df.dropna(inplace=True)\n",
    "df.drop_duplicates(inplace=True)\n",
    "\n",
    "# Visualize pairplot of df\n",
    "sns.pairplot(df, hue='rating');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check out, for example, *user_rating_score* as a function of *release_year*. There is *not* a visually identifiable trend but perhaps some data analysis will draw out any trends. If you'd like to check out several summary statistics of the DataFrame, you can also do this using the `.describe()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ratingdescription</th>\n",
       "      <th>release_year</th>\n",
       "      <th>user_rating_score</th>\n",
       "      <th>user_rating_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>246.000000</td>\n",
       "      <td>246.000000</td>\n",
       "      <td>246.000000</td>\n",
       "      <td>246.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>73.556911</td>\n",
       "      <td>2010.272358</td>\n",
       "      <td>81.390244</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>26.616145</td>\n",
       "      <td>8.887219</td>\n",
       "      <td>12.677883</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>1940.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>80.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>83.500000</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>92.750000</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>124.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ratingdescription  release_year  user_rating_score  user_rating_size\n",
       "count         246.000000    246.000000         246.000000             246.0\n",
       "mean           73.556911   2010.272358          81.390244              80.0\n",
       "std            26.616145      8.887219          12.677883               0.0\n",
       "min            10.000000   1940.000000          55.000000              80.0\n",
       "25%            60.000000   2007.000000          71.000000              80.0\n",
       "50%            80.000000   2015.000000          83.500000              80.0\n",
       "75%            90.000000   2016.000000          92.750000              80.0\n",
       "max           124.000000   2017.000000          99.000000              80.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary stats of df\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Groupbys and split-apply-combine to answer the question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='step_1'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1. Split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that you've checked out out data, it's time for the fun part. You'll first use a *groupby* method to split the data into groups, where each group is the set of movies released in a given year. This is the *split* in split-apply-combine: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Group by year\n",
    "df_by_year = df.groupby('release_year')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This creates a *groupby* object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.groupby.DataFrameGroupBy"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check type of GroupBy object\n",
    "type(df_by_year)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='step_2'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Apply"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Such *groupby* objects are very useful. Remember that the `.describe()` method for a DataFrame returns summary statistics for numeric columns? Well, the `.describe()` method for `DataFrameGroupBy` objects returns summary statistics for each numeric column, but computed for each group in the split. In your case, it's for each *release_year*. This is an example of the *apply* in split-apply-combine: you're *applying* the `.describe()` method to each group in the *groupby*. Do this and print the first 5 rows of the result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">ratingdescription</th>\n",
       "      <th colspan=\"5\" halign=\"left\">user_rating_score</th>\n",
       "      <th colspan=\"8\" halign=\"left\">user_rating_size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>...</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1940</th>\n",
       "      <td>1.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>...</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>1.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>...</td>\n",
       "      <td>86.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>1.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>...</td>\n",
       "      <td>68.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>1.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>...</td>\n",
       "      <td>67.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>1.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>...</td>\n",
       "      <td>58.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             ratingdescription                                          \\\n",
       "                         count  mean std   min   25%   50%   75%   max   \n",
       "release_year                                                             \n",
       "1940                       1.0  35.0 NaN  35.0  35.0  35.0  35.0  35.0   \n",
       "1978                       1.0  60.0 NaN  60.0  60.0  60.0  60.0  60.0   \n",
       "1982                       1.0  60.0 NaN  60.0  60.0  60.0  60.0  60.0   \n",
       "1986                       1.0  35.0 NaN  35.0  35.0  35.0  35.0  35.0   \n",
       "1987                       1.0  60.0 NaN  60.0  60.0  60.0  60.0  60.0   \n",
       "\n",
       "             user_rating_score        ...              user_rating_size        \\\n",
       "                         count  mean  ...    75%   max            count  mean   \n",
       "release_year                          ...                                       \n",
       "1940                       1.0  61.0  ...   61.0  61.0              1.0  80.0   \n",
       "1978                       1.0  86.0  ...   86.0  86.0              1.0  80.0   \n",
       "1982                       1.0  68.0  ...   68.0  68.0              1.0  80.0   \n",
       "1986                       1.0  67.0  ...   67.0  67.0              1.0  80.0   \n",
       "1987                       1.0  58.0  ...   58.0  58.0              1.0  80.0   \n",
       "\n",
       "                                                \n",
       "             std   min   25%   50%   75%   max  \n",
       "release_year                                    \n",
       "1940         NaN  80.0  80.0  80.0  80.0  80.0  \n",
       "1978         NaN  80.0  80.0  80.0  80.0  80.0  \n",
       "1982         NaN  80.0  80.0  80.0  80.0  80.0  \n",
       "1986         NaN  80.0  80.0  80.0  80.0  80.0  \n",
       "1987         NaN  80.0  80.0  80.0  80.0  80.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary stats over years\n",
    "df_by_year.describe().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to see what the grouping looks like, you can pass the _groupby_ object to the function `list()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1995,             title rating                                ratinglevel  \\\n",
       " 766         Balto      G  General Audiences. Suitable for all ages.   \n",
       " 967  Heavyweights     PG              some rude language and pranks   \n",
       " \n",
       "      ratingdescription  release_year  user_rating_score  user_rating_size  \n",
       " 766                 35          1995               64.0                80  \n",
       " 967                 60          1995               74.0                80  )"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cast grouping as a list and check out one year\n",
    "list(df_by_year)[10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='step_3'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Combine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say that you wanted the mean or median *user_rating_score* for each year. Then you can apply the `.mean()` or `.median()` method, respectively, to the *groupby* object and 'combine' these into a new DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ratingdescription</th>\n",
       "      <th>user_rating_score</th>\n",
       "      <th>user_rating_size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1940</th>\n",
       "      <td>35.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>60.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>60.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>35.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>60.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ratingdescription  user_rating_score  user_rating_size\n",
       "release_year                                                        \n",
       "1940                       35.0               61.0              80.0\n",
       "1978                       60.0               86.0              80.0\n",
       "1982                       60.0               68.0              80.0\n",
       "1986                       35.0               67.0              80.0\n",
       "1987                       60.0               58.0              80.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get median values by year and print first 5 rows\n",
    "df_med_by_year = df_by_year.median()\n",
    "df_med_by_year.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's an important subtlety concerning the index of the DataFrame `df_med_by_year`. Recall that the index of a DataFrame consists of the row labels. Check out the index of the original DataFrame `df`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Int64Index([  0,   2,   3,   4,   5,   6,   7,   8,   9,  10,\n",
      "            ...\n",
      "            908, 911, 917, 931, 962, 966, 967, 972, 973, 979],\n",
      "           dtype='int64', length=246)\n"
     ]
    }
   ],
   "source": [
    "# Print index of df\n",
    "print(df.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This index consists of  the original row numbers, labelled by integers. '1' is missing as you dropped some rows above. The index of `df_med_by_year` consists of the values in the original column that you grouped by, the years from *release_year*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Int64Index([1940, 1978, 1982, 1986, 1987, 1989, 1990, 1992, 1993, 1994, 1995,\n",
      "            1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,\n",
      "            2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017],\n",
      "           dtype='int64', name='release_year')\n"
     ]
    }
   ],
   "source": [
    "# Print index\n",
    "print(df_med_by_year.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You're interested in the *user_rating_score* column, which contains the median rating for each year. You can slice out the *user_rating_score* column of `df_med_by_year`  and plot it as a function of the year (given by the index of the DataFrame `df_rat_by_year`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f4f0208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Slice out user rating and plot\n",
    "df_rat_by_year = df_med_by_year['user_rating_score']\n",
    "plt.scatter(df_rat_by_year.index, df_rat_by_year)\n",
    "plt.xlabel('year of release')\n",
    "plt.ylabel('median rating');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the figure, the median rating definitely increases over time. You'd need to leverage some more sophisticated statistics to convince me of the trend in general but this an example of Exploratory Data Analysis being a great starting point for further research. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Groupbys and split-apply-combine in daily use"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Groupby* objects are not intuitive. They do, however, correspond to a natural the act of splitting a dataset with respect to one its columns (or more than one, but let's save that for another post about grouping by multiple columns and hierarchical indexes).\n",
    "\n",
    "The split-apply-combine principle is not only elegant and practical, it's something that Data Scientists use daily, as in the above example. To appreciate more of its uses, check out Hadley Wickham's original [paper](https://www.jstatsoft.org/article/view/v040i01) *The Split-Apply-Combine Strategy for Data Analysis*. If you have any thoughts, responses and/or ruminations, feel free to reach out to me on twitter: [@hugobowne](https://twitter.com/hugobowne)."
   ]
  }
 ],
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