Drawing Charts

This notebook presents various options for drawing charts of data, to complement the Week 3 chart types video.

This tutorial uses concepts from both the Selection and Reshaping notebooks.

This notebook uses the “MovieLens + IMDB/RottenTomatoes” data from the HETREC data. It also uses data sets built in to Seaborn.

Setup

First we will import our modules:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

Then import the HETREC MovieLens data. A few notes:

• Tab-separated data

• Not UTF-8 - latin-1 encoding seems to work

• Missing data encoded as \N (there’s a good chance that what we have is a PostgreSQL data dump!)

Movies

movies = pd.read_csv('hetrec2011-ml/movies.dat', delimiter='\t', encoding='latin1', na_values=['\\N'])
movies.head()

	id	title	imdbID	spanishTitle	imdbPictureURL	year	rtID	rtAllCriticsRating	rtAllCriticsNumReviews	rtAllCriticsNumFresh	...	rtAllCriticsScore	rtTopCriticsRating	rtTopCriticsNumReviews	rtTopCriticsNumFresh	rtTopCriticsNumRotten	rtTopCriticsScore	rtAudienceRating	rtAudienceNumRatings	rtAudienceScore	rtPictureURL
0	1	Toy story	114709	Toy story (juguetes)	http://ia.media-imdb.com/images/M/MV5BMTMwNDU0...	1995	toy_story	9.0	73.0	73.0	...	100.0	8.5	17.0	17.0	0.0	100.0	3.7	102338.0	81.0	http://content7.flixster.com/movie/10/93/63/10...
1	2	Jumanji	113497	Jumanji	http://ia.media-imdb.com/images/M/MV5BMzM5NjE1...	1995	1068044-jumanji	5.6	28.0	13.0	...	46.0	5.8	5.0	2.0	3.0	40.0	3.2	44587.0	61.0	http://content8.flixster.com/movie/56/79/73/56...
2	3	Grumpy Old Men	107050	Dos viejos gruñones	http://ia.media-imdb.com/images/M/MV5BMTI5MTgy...	1993	grumpy_old_men	5.9	36.0	24.0	...	66.0	7.0	6.0	5.0	1.0	83.0	3.2	10489.0	66.0	http://content6.flixster.com/movie/25/60/25602...
3	4	Waiting to Exhale	114885	Esperando un respiro	http://ia.media-imdb.com/images/M/MV5BMTczMTMy...	1995	waiting_to_exhale	5.6	25.0	14.0	...	56.0	5.5	11.0	5.0	6.0	45.0	3.3	5666.0	79.0	http://content9.flixster.com/movie/10/94/17/10...
4	5	Father of the Bride Part II	113041	Vuelve el padre de la novia (Ahora también abu...	http://ia.media-imdb.com/images/M/MV5BMTg1NDc2...	1995	father_of_the_bride_part_ii	5.3	19.0	9.0	...	47.0	5.4	5.0	1.0	4.0	20.0	3.0	13761.0	64.0	http://content8.flixster.com/movie/25/54/25542...

5 rows × 21 columns

movies.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10197 entries, 0 to 10196
Data columns (total 21 columns):
 #   Column                  Non-Null Count  Dtype  
---  ------                  --------------  -----  
 0   id                      10197 non-null  int64  
 1   title                   10197 non-null  object 
 2   imdbID                  10197 non-null  int64  
 3   spanishTitle            10197 non-null  object 
 4   imdbPictureURL          10016 non-null  object 
 5   year                    10197 non-null  int64  
 6   rtID                    9886 non-null   object 
 7   rtAllCriticsRating      9967 non-null   float64
 8   rtAllCriticsNumReviews  9967 non-null   float64
 9   rtAllCriticsNumFresh    9967 non-null   float64
 10  rtAllCriticsNumRotten   9967 non-null   float64
 11  rtAllCriticsScore       9967 non-null   float64
 12  rtTopCriticsRating      9967 non-null   float64
 13  rtTopCriticsNumReviews  9967 non-null   float64
 14  rtTopCriticsNumFresh    9967 non-null   float64
 15  rtTopCriticsNumRotten   9967 non-null   float64
 16  rtTopCriticsScore       9967 non-null   float64
 17  rtAudienceRating        9967 non-null   float64
 18  rtAudienceNumRatings    9967 non-null   float64
 19  rtAudienceScore         9967 non-null   float64
 20  rtPictureURL            9967 non-null   object 
dtypes: float64(13), int64(3), object(5)
memory usage: 1.6+ MB

It’s useful to index movies by ID, so let’s just do that now.

movies = movies.set_index('id')

And extract scores:

movie_scores = movies[['rtAllCriticsRating', 'rtTopCriticsRating', 'rtAudienceRating']].rename(columns={
    'rtAllCriticsRating': 'All Critics',
    'rtTopCriticsRating': 'Top Critics',
    'rtAudienceRating': 'Audience'
})
movie_scores

	All Critics	Top Critics	Audience
id
1	9.0	8.5	3.7
2	5.6	5.8	3.2
3	5.9	7.0	3.2
4	5.6	5.5	3.3
5	5.3	5.4	3.0
...	...	...	...
65088	4.4	4.7	3.5
65091	7.0	0.0	3.7
65126	5.6	4.9	3.3
65130	6.7	6.9	3.5
65133	0.0	0.0	0.0

10197 rows × 3 columns

Movie Info

movie_genres = pd.read_csv('hetrec2011-ml/movie_genres.dat', delimiter='\t', encoding='latin1')
movie_genres.head()

	movieID	genre
0	1	Adventure
1	1	Animation
2	1	Children
3	1	Comedy
4	1	Fantasy

movie_tags = pd.read_csv('hetrec2011-ml/movie_tags.dat', delimiter='\t', encoding='latin1')
movie_tags.head()

	movieID	tagID	tagWeight
0	1	7	1
1	1	13	3
2	1	25	3
3	1	55	3
4	1	60	1

tags = pd.read_csv('hetrec2011-ml/tags.dat', delimiter='\t', encoding='latin1')
tags.head()

	id	value
0	1	earth
1	2	police
2	3	boxing
3	4	painter
4	5	whale

Ratings

ratings = pd.read_csv('hetrec2011-ml/user_ratedmovies-timestamps.dat', delimiter='\t', encoding='latin1')
ratings.head()

	userID	movieID	rating	timestamp
0	75	3	1.0	1162160236000
1	75	32	4.5	1162160624000
2	75	110	4.0	1162161008000
3	75	160	2.0	1162160212000
4	75	163	4.0	1162160970000

We’re going to compute movie statistics too:

movie_stats = ratings.groupby('movieID')['rating'].agg(['count', 'mean']).rename(columns={
    'mean': 'MeanRating',
    'count': 'RatingCount'
})
movie_stats.head()

	RatingCount	MeanRating
movieID
1	1263	3.735154
2	765	2.976471
3	252	2.873016
4	45	2.577778
5	225	2.753333

Titanic data

We’ll also use the Titanic data set from Seaborn:

titanic = sns.load_dataset('titanic')
titanic

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	0	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.0	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.0	0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
886	0	2	male	27.0	0	0	13.0000	S	Second	man	True	NaN	Southampton	no	True
887	1	1	female	19.0	0	0	30.0000	S	First	woman	False	B	Southampton	yes	True
888	0	3	female	NaN	1	2	23.4500	S	Third	woman	False	NaN	Southampton	no	False
889	1	1	male	26.0	0	0	30.0000	C	First	man	True	C	Cherbourg	yes	True
890	0	3	male	32.0	0	0	7.7500	Q	Third	man	True	NaN	Queenstown	no	True

891 rows × 15 columns

Initial Example

This is the chart for the initial example - same as from the Charting notebook:

sns.catplot('class', 'survived', data=titanic, kind='bar', color='firebrick', height=5, aspect=1)

<seaborn.axisgrid.FacetGrid at 0x228d53fb0a0>

Pseudo-3D

Psuedo-3D charts plot two explanatory variables on the x and y axes, use another means to indicate the response variable.

For this, we are going to use the RottenTomatoes all-critics and audience scores, and we want to see the joint distribution: how frequently do different combinations of critic and audience scores appear?

We’ll start with the scatter plot, just to show its clutter:

sns.scatterplot('All Critics', 'Audience', data=movie_scores)

<matplotlib.axes._subplots.AxesSubplot at 0x228d5a5d550>

Zeros look odd here - since there are no missing values, and there’s a huge gap between zero and the first actual score, it looks more likely that zeros are missing data. Let’s treat them as such:

movie_scores[movie_scores == 0] = np.nan
movie_scores.describe()

	All Critics	Top Critics	Audience
count	8441.000000	4662.000000	7345.000000
mean	6.068404	5.930330	3.389258
std	1.526898	1.534093	0.454034
min	1.200000	1.600000	1.500000
25%	5.000000	4.800000	3.100000
50%	6.200000	6.100000	3.400000
75%	7.200000	7.100000	3.700000
max	9.600000	10.000000	5.000000

Now we have missing values! The assignment did a couple of things:

1. Create a data frame with all logical columns, that is True everywhere a score is 0 (vectorization works in more than one dimension!)

2. Use it as a mask, to set all values where it’s True to Not a Number (Pandas’ missing-data signal)

Then we look at the description, and we see counts that indicate a lot of missing data.

We’re going to focus on all critics and audience, so let’s drop top critics, and drop all rows without values for both all critics and audience, since they will be unplottable:

ms_trimmed = movie_scores[['All Critics', 'Audience']].rename(columns={
    'All Critics': 'Critics'
}).dropna()

sns.scatterplot('Critics', 'Audience', data=ms_trimmed)

<matplotlib.axes._subplots.AxesSubplot at 0x228d5b2a6d0>

This has less funny business going on, but also is super cluttered - we see a mass, but how does the relative distribution of dots in different parts of the mass actually differ? It’s just a blob.

Contour Plot

A contour plot shows how frequent different points in a 2D space are, using contour lines (like topographic maps).

Seaborn does this with the two-parameter kdeplot. Unlike many other methods, kdeplot doesn’t know how to extract columns from data frames.

sns.kdeplot(ms_trimmed['Critics'], ms_trimmed['Audience'])

<matplotlib.axes._subplots.AxesSubplot at 0x228d5b6fa30>

The peak is at about \((7,3.5)\).

Heat Map

Now let’s do a heat map. Seaborn heat maps require us to pre-compute the different values, and can display arbitrary statistics. The heat map actually wants a 2D data structure. So we’re going to:

• Bin values - critics to whole stars, audience to half.

  • Critics: round

  • Audience: multiply by 2, round, divide by 2

• Use pivot_table to count values in each combination of rounded audience ratings. The pivot_table method requires a value column to aggregate, so we’ll make a column filled with 1s.

ms_bins = pd.DataFrame({
    'Critics': ms_trimmed['Critics'].round(),
    'Audience': (ms_trimmed['Audience'] * 2).round() / 2
})
ms_bins

	Critics	Audience
id
1	9.0	3.5
2	6.0	3.0
3	6.0	3.0
4	6.0	3.5
5	5.0	3.0
...	...	...
65037	6.0	4.0
65088	4.0	3.5
65091	7.0	3.5
65126	6.0	3.5
65130	7.0	3.5

7212 rows × 2 columns

Now we pivot, and the droplevel method gets rid of the extra level of the column index (try without it and see what happens!)

msb_counts = ms_bins.assign(v=1).pivot_table(index='Audience', columns='Critics', aggfunc='count', fill_value=0)
msb_counts = msb_counts.droplevel(0, axis=1)
msb_counts

Critics	1.0	2.0	3.0	4.0	5.0	6.0	7.0	8.0	9.0	10.0
Audience
1.5	0	1	0	0	0	1	0	0	0	0
2.0	0	12	17	6	1	0	0	0	0	0
2.5	1	50	118	205	110	39	7	1	0	0
3.0	0	21	157	477	616	672	257	41	0	0
3.5	0	4	29	135	361	882	883	400	26	0
4.0	0	0	1	18	50	215	472	625	156	1
4.5	0	0	0	0	1	4	20	58	59	1
5.0	0	0	0	0	0	0	1	0	0	0

sns.heatmap(msb_counts)

<matplotlib.axes._subplots.AxesSubplot at 0x228d5be1610>

That audience axis is upside down. Let’s reverse it:

sns.heatmap(msb_counts.iloc[::-1, :])

<matplotlib.axes._subplots.AxesSubplot at 0x228d5cd06d0>

The ::-1 slice means ‘all elements in reverse order’.

Secondary Aesthetics

We’re going to use the Titanic data again to show the survival rate by both class and sex:

sns.barplot('class', 'survived', data=titanic, hue='sex')

<matplotlib.axes._subplots.AxesSubplot at 0x228d5d71fa0>

Now let’s do the same thing with a point plot:

sns.pointplot('class', 'survived', data=titanic, hue='sex', join=False)

<matplotlib.axes._subplots.AxesSubplot at 0x228d5db1dc0>

Breaking Down by More Things

We’re going to show survival rate by:

• age

• sex

• class

The chart look really noisy with raw age, so we’re going to bin into 10-year bands:

titanic['agebin'] = titanic['age'].round(-1)

Then we compute the rate for each point:

rate = titanic.groupby(['class', 'sex', 'agebin'], observed=True)['survived'].mean().reset_index(name='Survival')
rate

	class	sex	agebin	Survival
0	Third	female	0.0	0.733333
1	Third	female	10.0	0.166667
2	Third	female	20.0	0.526316
3	Third	female	30.0	0.476190
4	Third	female	40.0	0.230769
5	Third	female	50.0	0.000000
6	Third	female	60.0	1.000000
7	Third	male	0.0	0.384615
8	Third	male	10.0	0.285714
9	Third	male	20.0	0.107843
10	Third	male	30.0	0.211268
11	Third	male	40.0	0.078947
12	Third	male	50.0	0.000000
13	Third	male	60.0	0.000000
14	Third	male	70.0	0.000000
15	First	female	0.0	0.000000
16	First	female	10.0	1.000000
17	First	female	20.0	0.961538
18	First	female	30.0	1.000000
19	First	female	40.0	1.000000
20	First	female	50.0	0.923077
21	First	female	60.0	1.000000
22	First	male	0.0	1.000000
23	First	male	10.0	1.000000
24	First	male	20.0	0.363636
25	First	male	30.0	0.500000
26	First	male	40.0	0.481481
27	First	male	50.0	0.363636
28	First	male	60.0	0.125000
29	First	male	70.0	0.000000
30	First	male	80.0	1.000000
31	Second	female	0.0	1.000000
32	Second	female	10.0	1.000000
33	Second	female	20.0	0.947368
34	Second	female	30.0	0.916667
35	Second	female	40.0	0.857143
36	Second	female	50.0	1.000000
37	Second	female	60.0	0.500000
38	Second	male	0.0	1.000000
39	Second	male	10.0	1.000000
40	Second	male	20.0	0.037037
41	Second	male	30.0	0.096774
42	Second	male	40.0	0.062500
43	Second	male	50.0	0.000000
44	Second	male	60.0	0.250000
45	Second	male	70.0	0.000000

And plot:

sns.relplot('agebin', 'Survival', hue='sex', col='class', data=rate, kind='line')

<seaborn.axisgrid.FacetGrid at 0x228d5e4b4f0>