In this tutorial, we want to create a Barplot. In order to do this, we use the barplot() function of Seaborn.

Import Libraries

First, we import the following python modules:

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

Load Data

We would like to use a seaborn sample dataset. Let's load the "tips" dataset into python.

tips_df = sns.load_dataset("tips")



In a Barplot, a categorical variable is used on one axis and a numerical variable on the other axis. The numerical values are aggregated for the specific categories. By default, the mean is taken as the aggregation function.

Example 1: Simple Barplot

We want to visualize the average tips per day with a Barplot. In order to do this, we set the x parameter to "day" and the y parameter to "tip". We use the color palette "crest".

sns.barplot(data=tips_df, x="day", y="tip", 

Example 2: Horizontal Barplot

Now, let's align the bars horiziontal instead of vertical. To do this, we swap the x and y axes.

sns.barplot(data=tips_df, x="tip", y="day", 

Example 3: Barplot with Grouping

Now, let's analyze the average tips per day for lunch and dinner. In order to do this, we use the hue parameter and set it to "time".

sns.barplot(data=tips_df, x="day", y="tip", hue="time", 

Example 4: Change Aggregation Function

Now, let's visualize the total tips per day. In order to do this, we use the estimator parameter and set it to np.sum. Here the sum() function from NumPy is used.

sns.barplot(data=tips_df, x="day", y="tip", estimator=np.sum,


Congratulations! Now you are one step closer to become an AI Expert. You have seen that it is very easy to create a Barplot. We can simply use the barplot() function of Seaborn. Try it yourself!


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