Bar Plot using Pandas¶

Date published: 2018-09-13

Category: Data Visualizations

Subcategory: Pandas Plot

Tags: bar plot

Import Modules¶

In [1]:

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
% matplotlib inline

import matplotlib.pyplot as plt import pandas as pd import seaborn as sns % matplotlib inline

Bar charts are great at visualizing counts of categorical data. Let's try them out in Pandas Plot.

Read in Tips Dataset from Seaborn Library¶

This data contains logs of tips received by waiters and waittresses at a restaurant.

In [2]:

df_tips = sns.load_dataset('tips')

df_tips = sns.load_dataset('tips')

Preview the Data¶

Preview the top 5 rows.

In [3]:

df_tips.head()

df_tips.head()

Out[3]:

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4

With this tips dataset, we have several categorical fields including sex, day, time and size (# of people seated at a table).

Examine Count of Tips Recorded by Gender¶

First, we can call the value_counts method on the sex (aka gender) field to see the count of unique values for each gender type.

In [4]:

df_tips['sex'].value_counts()

df_tips['sex'].value_counts()

Out[4]:

Male      157
Female     87
Name: sex, dtype: int64

The output above is a Pandas Series in which the index items are Male and Female and the values are the count of each index item in our original tips sex field.

Visualize Count of Tips Recorded by Gender¶

We access the sex field, call the value_counts method to get a count of unique values, then call the plot method and pass in bar (for bar chart) to the kind argument.

Please see the Pandas Series official documentation page for more information.

In [5]:

df_tips['sex'].value_counts().plot(kind='bar');

df_tips['sex'].value_counts().plot(kind='bar');

In our tips dataset, we have more records of tips for male waiters than female waitresses.

Style Plot¶

Below, I apply several styles to our plot from above so it's easier to interpret.

In [6]:

sns.set(font_scale=1.4)
df_tips['sex'].value_counts().plot(kind='bar', figsize=(7, 6), rot=0)
plt.xlabel("Gender", labelpad=14)
plt.ylabel("Count of People", labelpad=14)
plt.title("Count of People Who Received Tips by Gender", y=1.02);

sns.set(font_scale=1.4) df_tips['sex'].value_counts().plot(kind='bar', figsize=(7, 6), rot=0) plt.xlabel("Gender", labelpad=14) plt.ylabel("Count of People", labelpad=14) plt.title("Count of People Who Received Tips by Gender", y=1.02);

Visualize Count of Days for Recorded Tips¶

We access the day field, call the value_counts method to get a count of unique values, then call the plot method and pass in bar (for bar chart) to the kind argument.

In [7]:

df_tips['day'].value_counts().plot(kind='bar');

df_tips['day'].value_counts().plot(kind='bar');

Most of our tip records were on Saturday followed by Sunday. Only 4 days have recorded tips.

Style Plot¶

Below, I apply several styles to our plot from above so it's easier to interpret. I turn our vertical bar plot into a horizontal bar plot.

In [8]:

df_tips['day'].value_counts().plot(kind='barh', figsize=(8, 6))
plt.xlabel("Count of Tips Received", labelpad=14)
plt.ylabel("Day of Week", labelpad=14)
plt.title("Count of Tips by Day of Week", y=1.02);

df_tips['day'].value_counts().plot(kind='barh', figsize=(8, 6)) plt.xlabel("Count of Tips Received", labelpad=14) plt.ylabel("Day of Week", labelpad=14) plt.title("Count of Tips by Day of Week", y=1.02);