A barplot is a graphical representation of data points in a dataset, where individual data points are represented by rectangular bars on a two-dimensional coordinate system. This type of plot allows us to visualize the distribution of categorical data by showing the frequency or count of each category along the plot.
Pandas, a powerful data manipulation library in Python, allows us to create bar plots with ease. In this post, we will explore how to leverage Pandas to create simple bar plots.


Pandas is a popular open-source Python library used for data manipulation and analysis. It provides data structures and functions that make working with structured data, such as tabular data (like Excel spreadsheets or SQL tables), easy and intuitive.

To install Pandas, you can use the following command in your command-line interface (such as Terminal or Command Prompt):

pip install pandas

Matplotlib functionalities have been integrated into the pandas library, facilitating their use with dataframes and series. For this reason, you might also need to import the matplotlib library when building charts with Pandas.

This also means that they use the same functions, and if you already know Matplotlib, you'll have no trouble learning plots with Pandas.

import pandas as pd
import matplotlib.pyplot as plt


In order to create graphics with Pandas, we need to use pandas objects: Dataframes and Series. A dataframe can be seen as an Excel table, and a series as a column in that table. This means that we must systematically convert our data into a format used by pandas.

Since barplots need qualitative variables, we will create a dataset with a categorical variable.

# Create a list with 3 different categories
category = ['Group1']*30 + ['Group2']*50 + ['Group3']*20

# Store the data into a dataframe
df = pd.DataFrame({'category': category})

Barplot with the bar() function

Once we've opened our dataset, we'll now create the graph. An important feature of barplots in Python is that you can't simply give them a column name as an argument.

Instead, we have to calculate the counts for each category ourselves and give them as input to the function. To do this, we simply use the pandas value_counts() function.

# Get the repartition of each category
values = df['category'].value_counts()

# Create the plot
values.plot.bar(grid=True) # Add a grid in the background

Barplot with the plot() function

We'll now look at how to create a barplot using the plot() function. This function is very general and therefore requires more arguments to be specified when it is called.

The main argument is kind. This specifies the type of chart we want (in our case it's 'bar'). For example, we could have put 'line' for a line chart.

# Get the repartition of each category
values = df['category'].value_counts()

# Create the plot
values.plot(kind='bar', # type of chart
            grid=True # Add a grid in the background

Going further

This post explains how to create a simple barplot with pandas in 2 different ways.

For more examples of how to create or customize your plots with Pandas, see the pandas section. You may also be interested in how to customize your barplots with Matplotlib and Seaborn.

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