A scatter plot is a graphical representation of data points in a dataset, where individual data points are plotted on a two-dimensional coordinate system. This type of plot allows us to visualize the relationship between two variables by showing how they are distributed across the plot.
Pandas, a powerful data manipulation library in Python, allows us to create scatter plots with ease. In this post, we will explore how to leverage Pandas to create simple scatter 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 scatter plots need quantitative variables, we will get the Gap Minder dataset using the read_csv() function. The data can be accessed using the url below.

url = 'https://raw.githubusercontent.com/holtzy/The-Python-Graph-Gallery/master/static/data/gapminderData.csv'
df = pd.read_csv(url)

Scatter plot with the scatter() function

Once we've opened our dataset, we'll now create the graph. The following displays the relation between the life expectancy and the gdp/capita using the scatter() function. This is probably one of the shortest ways to display a scatter plot in Python.

df.plot.scatter('lifeExp', # x-axis
                'gdpPercap', # y-axis
                grid=True, # Add a grid in the background

Scatter plot with the plot() function

We'll now look at how to create a scatter plot 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 'scatter'). For example, we could have put 'line' for a line chart.

df.plot('lifeExp', # x-axis
        'gdpPercap', # y-axis
        kind='scatter', # type of chart
        grid=True, # Add a grid in the background

Going further

This post explains how to create a simple scatter plot 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 scatter plots with Matplotlib and Seaborn.

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