## Lecture 9 – Data 100, Fall 2020¶

In [1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

In [2]:
births = pd.read_csv('baby.csv')

In [3]:
births.head()

Out[3]:
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
In [4]:
births.shape

Out[4]:
(1174, 6)

## Bar Plots¶

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

In [5]:
births['Maternal Smoker'].value_counts()

Out[5]:
False    715
True     459
Name: Maternal Smoker, dtype: int64
In [6]:
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_....>).

In [7]:
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.

In [8]:
# These are made up!
majors = ['Data Science', 'History', 'Biology', 'Business']
gpas = [3.35, 3.20, 2.98, 3.51]

In [9]:
# What if we change bar to barh?
plt.bar(majors, gpas);

In [10]:
sns.barplot(x=majors, y=gpas);


## Rug plots¶

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

In [11]:
bweights = births["Birth Weight"]

In [12]:
bweights

Out[12]:
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
In [13]:
sns.rugplot(bweights);


## Histograms¶

Our old friend!

In [14]:
# By default, you get some arbitrary bins. We often like to pick our own.
plt.hist(bweights);

In [15]:
min(bweights), max(bweights)

Out[15]:
(55, 176)
In [16]:
bw_bins = range(50, 200, 5)

In [17]:
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:

In [18]:
plt.hist(bweights, density=True, bins=bw_bins, ec='w');

In [19]:
# 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.

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


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

In [21]:
plt.hist(bweights, bins = [50, 100, 120, 140, 200], density=True, ec='w');


## Density Curves¶

In [22]:
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:

In [23]:
sns.histplot(bweights, kde=True);


Can even show a rugplot with it!

In [24]:
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:

In [25]:
sns.displot(bweights, kind='kde', rug=True);


## Box Plots¶

In [26]:
plt.figure(figsize = (3, 6))
sns.boxplot(y=bweights);

In [27]:
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

Out[27]:
(73.5, 108.0, 120.0, 131.0, 165.5)

## Violin Plots¶

In [28]:
plt.figure(figsize = (3, 6))
sns.violinplot(y=bweights);


## Overlaid Histograms and Density Curves¶

In [29]:
births.head()

Out[29]:
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
In [30]:
sm_bweights = births[births['Maternal Smoker'] == True]['Birth Weight']
nsm_bweights = births[births['Maternal Smoker'] == False]['Birth Weight']

In [31]:
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¶

In [32]:
plt.figure(figsize=(5, 8))
sns.boxplot(data=births, x = 'Maternal Smoker', y = 'Birth Weight');

In [33]:
plt.figure(figsize=(5, 8))
sns.violinplot(data=births, x = 'Maternal Smoker', y = 'Birth Weight');


A less fancy version of the above two plots:

In [34]:
two_distributions = [nsm_bweights.values, sm_bweights.values]
groups = ['non-smokers', 'smokers']

In [35]:
plt.boxplot(two_distributions, labels=groups);

In [36]:
plt.violinplot(two_distributions);


## Scatter plots¶

In [37]:
births.head()

Out[37]:
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
In [38]:
plt.scatter(births['Maternal Height'], births['Birth Weight']);
plt.xlabel('Maternal Height')
plt.ylabel('Birth Weight');

In [39]:
plt.scatter(data=births, x='Maternal Height', y='Birth Weight');
plt.xlabel('Maternal Height')
plt.ylabel('Birth Weight');

In [40]:
sns.scatterplot(data = births, x = 'Maternal Height', y = 'Birth Weight', hue = 'Maternal Smoker');

In [41]:
sns.lmplot(data = births, x = 'Maternal Height', y = 'Birth Weight', ci=False, hue='Maternal Smoker');

In [42]:
sns.jointplot(data = births, x = 'Maternal Height', y = 'Birth Weight');

In [43]:
sns.jointplot(data = births, x = 'Maternal Height', y = 'Birth Weight', hue='Maternal Smoker');


## Hex plots and contour plots¶

In [44]:
sns.jointplot(data = births, x = 'Maternal Height', y = 'Birth Weight', kind='hex');

In [45]:
sns.jointplot(data = births, x = 'Maternal Height', y = 'Birth Weight', kind='kde', fill=True);

In [46]:
sns.jointplot(data = births, x = 'Maternal Height', y = 'Birth Weight', kind='kde', hue='Maternal Smoker');


## Bonus¶

Calling .plot() results in weird things!

In [47]:
births.plot();