Netflix provides for its users a complete list of their viewing history, through the settings page. Although the viewing history is only given with a day resolution it’s still an interesting data set to explore.

Using the data one can learn about his own Netflix viewing patterns, the shows they’ve watched and when.

Below is the Jupyter notebook I’ve created to explore this data set.

import pandas as pd
import seaborn as sns
import numpy as np
import datetime
import matplotlib.pyplot as plt
import calendar
%matplotlib inline

netflix_df = pd.read_csv('NetflixViewingHistory.csv')

netflix_df.tail(10)
Title Date
208 22 July 28/10/2018
209 Chef's Table: Volume 5: Cristina Martínez 28/10/2018
210 Louis Theroux: Collection: Louis and the Nazis 23/10/2018
211 You Laugh But It's True 23/10/2018
212 Peaky Blinders: Season 1: Episode 1 23/10/2018
213 Unacknowledged 23/10/2018
214 Louis Theroux: Collection: Louis and the Brothel 23/10/2018
215 Ozark: Season 1: My Dripping Sleep 20/10/2018
216 Ozark: Season 1: Blue Cat 20/10/2018
217 Ozark: Season 1: Sugarwood 18/10/2018 
netflix_df.head()
Title Date
0 Black Mirror: Season 5: Smithereens 08/06/2019
1 Black Mirror: Season 5: Striking Vipers 08/06/2019
2 Money Heist: Part 1: Episode 1 05/06/2019
3 The Great Gatsby 30/05/2019
4 WHAT / IF: Part I: Pilot 27/05/2019 
netflix_df['Date']=pd.to_datetime(netflix_df['Date'])

netflix_df['week_day'] = netflix_df['Date'].apply(lambda x: x.weekday())
netflix_df['week_day_name'] = netflix_df['Date'].apply(lambda x: x.weekday_name)

netflix_df['week_day_name'] = pd.Categorical(netflix_df['week_day_name'], categories=
    ['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday', 'Sunday'],
    ordered=True)

weekday_df=netflix_df.groupby('week_day_name').Title.agg(['count'])

weekday_df
count
week_day_name
Monday 22
Tuesday 21
Wednesday 27
Thursday 17
Friday 10
Saturday 46
Sunday 75 
fig, ax = plt.subplots(figsize=(14,5))
weekday_df.plot.bar(ax=ax, legend=False);
plt.title('Netflix views by day of the week', fontsize=24);
plt.xticks(size=16);
plt.yticks(size=16);
plt.ylabel('Views',fontsize=22);
plt.xlabel('Week day',fontsize=22);

png

netflix_df['month'] = netflix_df['Date'].apply(lambda x: x.month)
netflix_df['month_name'] = netflix_df['month'].apply(lambda x: calendar.month_abbr[x])
netflix_df['month_name'] = pd.Categorical(netflix_df['month_name'],categories=
    ['Jan','Feb','Mar','Apr','May','Jun', 'Jul','Aug','Sep','Oct','Nov','Dec'],
    ordered=True)

month_day_views = netflix_df.groupby(['week_day_name','month_name']).Title.agg('count').unstack().fillna(0)

month_day_views
month_name Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
week_day_name
Monday 0.0 4.0 4.0 1.0 2.0 5.0 0.0 0.0 1.0 0.0 2.0 3.0
Tuesday 2.0 2.0 0.0 0.0 0.0 0.0 1.0 2.0 7.0 6.0 1.0 0.0
Wednesday 3.0 1.0 1.0 8.0 1.0 4.0 2.0 0.0 2.0 5.0 0.0 0.0
Thursday 2.0 3.0 1.0 0.0 1.0 0.0 2.0 2.0 0.0 5.0 0.0 1.0
Friday 1.0 2.0 3.0 2.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
Saturday 10.0 7.0 9.0 3.0 1.0 3.0 0.0 3.0 0.0 3.0 7.0 0.0
Sunday 4.0 15.0 26.0 2.0 3.0 0.0 0.0 3.0 3.0 2.0 6.0 11.0 
plt.figure(figsize=(14,6))
sns.heatmap(month_day_views)
plt.xticks(fontsize=16);
plt.yticks(fontsize=16);
plt.xlabel('Month',fontsize=18)
plt.ylabel('Week Day', fontsize=18)
plt.title('Views by week day and month', fontsize=22);

png

netflix_df['show'] = netflix_df['Title'].apply(lambda x: x.split(':')[0])
netflix_df['is_movie'] = netflix_df['Title'].apply(lambda x: ':' not in x)

show_views = netflix_df.groupby(['show','Date']).Title.agg('count').unstack()

# import matplotlib.dates as mdates

fig, ax = plt.subplots(figsize=(18,15));
sns.heatmap(show_views, yticklabels=True,xticklabels=True, annot=True,cmap='Blues');#YlGnBu');
ax.set_xticklabels(show_views.columns.strftime('%d-%m-%Y'));
plt.xticks(rotation=-90);
plt.ylabel('');

png

series_counts = netflix_df[~netflix_df.is_movie].groupby('show').Title.agg(['count']).sort_values(by='count',ascending=False)

fig, ax = plt.subplots(figsize=(14,5));
series_counts.plot.bar(ax=ax, legend=False);
plt.xticks(fontsize=14);
plt.yticks(fontsize=16);
plt.ylabel("Times watched");
plt.xlabel("")
plt.title("Netflix series watched", fontsize=22);

png

movies = netflix_df[netflix_df.is_movie]

movies
Title Date week_day week_day_name month month_name show is_movie
0 The Great Gatsby 2019-05-30 3 Thursday 5 May The Great Gatsby True
17 Split 2019-04-20 5 Saturday 4 Apr Split True
23 The Boy in the Striped Pyjamas 2019-02-04 0 Monday 2 Feb The Boy in the Striped Pyjamas True
46 Abducted in Plain Sight 2019-03-17 6 Sunday 3 Mar Abducted in Plain Sight True
121 Lion 2019-02-14 3 Thursday 2 Feb Lion True
122 High Flying Bird 2019-10-02 2 Wednesday 10 Oct High Flying Bird True
166 Bird Box 2019-06-01 5 Saturday 6 Jun Bird Box True
180 The Art of Loving 2018-12-14 4 Friday 12 Dec The Art of Loving True
187 How It Ends 2018-02-12 0 Monday 2 Feb How It Ends True
190 Before We Go 2018-11-25 6 Sunday 11 Nov Before We Go True
193 Inside Russias Toughest Prison 2018-11-24 5 Saturday 11 Nov Inside Russias Toughest Prison True
194 The Hangover 2018-11-24 5 Saturday 11 Nov The Hangover True
195 Passengers 2018-11-24 5 Saturday 11 Nov Passengers True
196 The Girl on the Train 2018-11-24 5 Saturday 11 Nov The Girl on the Train True
197 Brain on Fire 2018-11-24 5 Saturday 11 Nov Brain on Fire True
200 The Notebook 2018-11-19 0 Monday 11 Nov The Notebook True
201 Operation Finale 2018-10-11 3 Thursday 10 Oct Operation Finale True
203 What the Health 2018-05-11 4 Friday 5 May What the Health True
205 Set It Up 2018-03-11 6 Sunday 3 Mar Set It Up True
206 22 July 2018-10-28 6 Sunday 10 Oct 22 July True
209 You Laugh But It's True 2018-10-23 1 Tuesday 10 Oct You Laugh But It's True True
211 Unacknowledged 2018-10-23 1 Tuesday 10 Oct Unacknowledged True