Lecture 9 – Data 100, Summer 2021

by Suraj Rampure, updates by Fernando Pérez.

adapted from Ani Adhikari

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

births = pd.read_csv('baby.csv')

births.head()

	Birth Weight	Gestational Days	Maternal Age	Maternal Height	Maternal Pregnancy Weight	Maternal Smoker
0	120	284	27	62	100	False
1	113	282	33	64	135	False
2	128	279	28	64	115	True
3	108	282	23	67	125	True
4	136	286	25	62	93	False

births.shape

(1174, 6)

Bar Plots

We often use bar plots to display distributions of a categorical variable:

births['Maternal Smoker'].value_counts()

False    715
True     459
Name: Maternal Smoker, dtype: int64

births['Maternal Smoker'].value_counts().plot(kind = 'bar');

Note: putting a semicolon after a plot call hides all of the unnecessary text that comes after it (the <matplotlib.axes_....>).

sns.countplot(x=births['Maternal Smoker']);

But we can also use them to display a numerical variable that has been measured on individuals in different categories.

# These are made up!
majors = ['Data Science', 'History', 'Biology', 'Business']
gpas = [3.35, 3.20, 2.98, 3.51]

# What if we change bar to barh?
plt.bar(majors, gpas);

sns.barplot(x=majors, y=gpas);

Rug plots

Used for visualizing a single quantitative variable. Rug plots show us each and every value.

bweights = births["Birth Weight"]

bweights

0       120
1       113
2       128
3       108
4       136
       ... 
1169    113
1170    128
1171    130
1172    125
1173    117
Name: Birth Weight, Length: 1174, dtype: int64

sns.rugplot(bweights);

Histograms

Our old friend!

# By default, you get some arbitrary bins. We often like to pick our own.
plt.hist(bweights);

min(bweights), max(bweights)

(55, 176)

bw_bins = range(50, 200, 5)

plt.hist(bweights, bins=bw_bins, ec='w');

The above plot shows counts, if we want to see a distribution we can use the density keyword:

plt.hist(bweights, density=True, bins=bw_bins, ec='w');

# alternative way of getting this plot
bweights.plot(kind = 'hist', density=True, bins=bw_bins, ec='w');

Increasing bin width loses granularity, but this may be fine for our purposes.

plt.hist(bweights, bins = np.arange(50, 200, 20), density=True, ec='w');

The bin widths don't all need to be the same!

plt.hist(bweights, bins = [50, 100, 120, 140, 200], density=True, ec='w');

Density Curves

sns.kdeplot(bweights);

Seaborn has several related functions for plotting distributions: kdeplot, histplot, rugplot and displot. The latter is more generic but uses the others under the hood:

sns.histplot(bweights, kde=True);

Can even show a rugplot with it!

sns.displot(bweights, kde=True, rug=True);

displot is quite flexible, so instead of a histogram we can ask it, for example, to show the density curve and rugplot only:

sns.displot(bweights, kind='kde', rug=True);

Box Plots

plt.figure(figsize = (3, 6))
sns.boxplot(y=bweights);

q1 = np.percentile(bweights, 25)
q2 = np.percentile(bweights, 50)
q3 = np.percentile(bweights, 75)
iqr = q3 - q1
whisk1 = q1 - 1.5*iqr
whisk2 = q3 + 1.5*iqr

whisk1, q1, q2, q3, whisk2

(73.5, 108.0, 120.0, 131.0, 165.5)

Violin Plots

plt.figure(figsize = (3, 6))
sns.violinplot(y=bweights);

Overlaid Histograms and Density Curves

births.head()

	Birth Weight	Gestational Days	Maternal Age	Maternal Height	Maternal Pregnancy Weight	Maternal Smoker
0	120	284	27	62	100	False
1	113	282	33	64	135	False
2	128	279	28	64	115	True
3	108	282	23	67	125	True
4	136	286	25	62	93	False

sm_bweights = births[births['Maternal Smoker'] == True]['Birth Weight']
nsm_bweights = births[births['Maternal Smoker'] == False]['Birth Weight']

sns.histplot(nsm_bweights, bins=bw_bins, kde=True, stat='density', label='non smoker', ec='w');
sns.histplot(sm_bweights, bins=bw_bins, kde=True, stat='density', label='smoker', color='orange', ec='w');
plt.legend();

Side by side box plots and violin plots

plt.figure(figsize=(5, 8))
sns.boxplot(data=births, x = 'Maternal Smoker', y = 'Birth Weight');

plt.figure(figsize=(5, 8))
sns.violinplot(data=births, x = 'Maternal Smoker', y = 'Birth Weight');

A less fancy version of the above two plots:

two_distributions = [nsm_bweights.values, sm_bweights.values]
groups = ['non-smokers', 'smokers']

plt.boxplot(two_distributions, labels=groups);

plt.violinplot(two_distributions);

Scatter plots

births.head()

	Birth Weight	Gestational Days	Maternal Age	Maternal Height	Maternal Pregnancy Weight	Maternal Smoker
0	120	284	27	62	100	False
1	113	282	33	64	135	False
2	128	279	28	64	115	True
3	108	282	23	67	125	True
4	136	286	25	62	93	False

plt.scatter(births['Maternal Height'], births['Birth Weight']);
plt.xlabel('Maternal Height')
plt.ylabel('Birth Weight');

plt.scatter(data=births, x='Maternal Height', y='Birth Weight');
plt.xlabel('Maternal Height')
plt.ylabel('Birth Weight');

sns.scatterplot(data = births, x = 'Maternal Height', y = 'Birth Weight', hue = 'Maternal Smoker');

sns.lmplot(data = births, x = 'Maternal Height', y = 'Birth Weight', ci=False, hue='Maternal Smoker');

sns.jointplot(data = births, x = 'Maternal Height', y = 'Birth Weight');

sns.jointplot(data = births, x = 'Maternal Height', y = 'Birth Weight', hue='Maternal Smoker');

Hex plots and contour plots

sns.jointplot(data = births, x = 'Maternal Height', y = 'Birth Weight', kind='hex');

sns.jointplot(data = births, x = 'Maternal Height', y = 'Birth Weight', kind='kde', fill=True);

sns.jointplot(data = births, x = 'Maternal Height', y = 'Birth Weight', kind='kde', hue='Maternal Smoker');

Bonus

Calling .plot() results in weird things!

births.plot();