We can pass various parameters to barplot like hue, confidence interval (ci), capsize, estimator (mean, median etc.), order, palette, color, saturation etc.
Lets explore Bar Plot using Tips dataset.
Step 1: Import required libraries
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
Step 2: Load Tips dataset
tips=sns.load_dataset(‘tips')
tips.head()
Step 3: Explore data using Bar Plot
sns.barplot(x='day', y='total_bill', data=tips)
Horizontal Bar Plot
sns.barplot(x='total_bill', y='day', data=tips)
Set color and saturation level
sns.barplot(x='day', y='total_bill', data=tips, color='green')
sns.barplot(x='day', y='total_bill', data=tips, color='green', saturation=0.3)
By default, estimator is mean, you can also set it to median or anything else
sns.barplot(x='day', y='total_bill', data=tips, estimator=np.median)
Add hue parameter
sns.barplot(x='day', y='total_bill', data=tips, hue='sex')
sns.barplot(x='day', y='total_bill', data=tips, hue='sex', palette='autumn')
sns.barplot(x='day', y='total_bill', data=tips, hue='sex', color='green')
sns.barplot(x='day', y='total_bill', data=tips, hue='sex', palette='spring', order=[‘Sat', ‘Sun', ‘Thur', ‘Fri'])
sns.barplot(x='sex', y='total_bill', data=tips, hue='sex', palette='spring', order=[‘Male', ‘Female'])
Add confidence interval and capsize parameter
Black lines in bar plot represent error parts. We can set the capsize and confidence interval (ci) of the error parts. A confidence interval is a range of values, derived from sample statistics, that is likely to contain the value of an unknown population parameter.
sns.barplot(x='day', y='total_bill', data=tips, ci=99)
sns.barplot(x='day', y='total_bill', data=tips, ci=34)
sns.barplot(x='day', y='total_bill', data=tips, capsize=0.3)
You can download my Jupyter notebook from here. I recommend to also try above code with Iris dataset.
