Lecture 7 – Data 100, Spring 2023¶

by Josh Hug and Narges Norouzi

Adapted from material by Ani Adhikari, Suraj Rampure, and Fernando Pérez.

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

%matplotlib inline
In [2]:
births = pd.read_csv('data/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

This would have been the Data 8 code to do something similar:

from datascience import Table
t = Table.from_df(births['Maternal Smoker'].value_counts().reset_index())
t.barh("index", "Maternal Smoker")
In [6]:
births['Maternal Smoker'].value_counts().plot(kind = 'bar');
In [7]:
ms = births['Maternal Smoker'].value_counts();
plt.bar(ms.index.astype('string'), ms);

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

In [8]:
sns.countplot(data = births, x = 'Maternal Smoker');
In [9]:
import plotly.express as px
px.histogram(births, x = 'Maternal Smoker', color = 'Maternal Smoker')
In [10]:
sns.countplot(data = births, x = 'Maternal Pregnancy Weight');
In [11]:
sns.histplot(data = births, x = 'Maternal Pregnancy Weight', bins = 20);
In [12]:
px.histogram(births, x = 'Maternal Pregnancy Weight')
In [13]:
sns.histplot(data = births, x = 'Maternal Pregnancy Weight');
sns.rugplot(data = births, x = 'Maternal Pregnancy Weight', color = 'red');
In [14]:
median = births['Maternal Pregnancy Weight'].median()
mean = births['Maternal Pregnancy Weight'].mean()

print("Median", median)
print("Mean", mean)

Median 125.0
Mean 128.4787052810903
In [15]:
sns.histplot(data = births, x = 'Maternal Pregnancy Weight', kde = True);
sns.rugplot(data = births, x = 'Maternal Pregnancy Weight', color = 'red');
In [16]:
q1, median, q3 = np.percentile(births['Birth Weight'], [25, 50, 75])
iqr = q3 - q1

births['category'] = None
births.loc[(births['Birth Weight'] < q1) | (births['Birth Weight'] > q3), 'category'] = 'Outside of the middle 50%'
births.loc[(births['Birth Weight'] > q1) & (births['Birth Weight'] < q3), 'category'] = 'In the middle 50%'

sns.histplot(births, x = 'Birth Weight', hue = 'category', bins = 30);

births.drop(columns = ['category'], inplace = True)
In [17]:
sns.displot(data = births, x = 'Birth Weight', stat = 'density', hue = 'Maternal Smoker');
In [18]:
sns.displot(data = births, x = 'Birth Weight', kde = True, stat = 'density', hue = 'Maternal Smoker');
In [19]:
sns.displot(data = births, x = 'Birth Weight', kind = 'kde', hue = 'Maternal Smoker');

Box Plots¶

In [20]:
plt.figure(figsize = (3, 6))
sns.boxplot(data = births, y = 'Birth Weight');
In [21]:
bweights = births['Birth Weight']
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[21]:
(73.5, 108.0, 120.0, 131.0, 165.5)

Violin Plots¶

In [22]:
plt.figure(figsize = (3, 6))
sns.violinplot(y = births['Birth Weight']);

Side by side box plots and violin plots¶

In [23]:
plt.figure(figsize=(5, 8))
sns.boxplot(data = births, x = 'Maternal Smoker', y = 'Birth Weight');
In [24]:
plt.figure(figsize=(5, 8))
sns.violinplot(data = births, x = 'Maternal Smoker', y = 'Birth Weight');

Scatter plots¶

In [25]:
births.head()
Out[25]:
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 [26]:
plt.scatter(births['Maternal Height'], births['Birth Weight']);
plt.xlabel('Maternal Height')
plt.ylabel('Birth Weight');

Most matplotlib functions also accept a data= keyword, and when using this mode, you can then refer to x and y as names of columns in the data DataFrame, instead of passing the series explicitly:

In [27]:
plt.scatter(data = births, x = 'Maternal Height', y = 'Birth Weight');
plt.xlabel('Maternal Height')
plt.ylabel('Birth Weight');
In [28]:
sns.scatterplot(data = births, x = 'Maternal Height', y = 'Birth Weight', hue = 'Maternal Smoker');
In [29]:
births['Maternal Height (jittered)'] = births['Maternal Height'] + np.random.uniform(-0.2, 0.2, len(births))
fig = sns.scatterplot(data = births, x = 'Maternal Height (jittered)', y = 'Birth Weight', hue = 'Maternal Smoker');
In [30]:
sns.lmplot(data = births, x = 'Maternal Height', y = 'Birth Weight', 
           ci = False, hue = 'Maternal Smoker');
In [31]:
sns.jointplot(data = births, x = 'Maternal Height', y = 'Birth Weight');
In [32]:
sns.jointplot(data = births, x = 'Maternal Height', y = 'Birth Weight', hue = 'Maternal Smoker');

Hex plots and contour plots¶

In [33]:
sns.jointplot(data = births, x = 'Maternal Height', y = 'Birth Weight', kind = 'hex');
In [34]:
sns.jointplot(data = births, x = 'Maternal Height', y = 'Birth Weight', kind = 'kde', fill = True);
In [35]:
sns.jointplot(data = births, x = 'Maternal Height', y = 'Birth Weight', kind = 'kde', hue = 'Maternal Smoker');