Lecture 7 – Data 100, Spring 2023

by Josh Hug and Narges Norouzi

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

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

%matplotlib inline

births = pd.read_csv('data/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

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")

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

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_....>).

sns.countplot(data = births, x = 'Maternal Smoker');

import plotly.express as px
px.histogram(births, x = 'Maternal Smoker', color = 'Maternal Smoker')