Plotting Matplotlib Histogram of one pandas DataFrame Column with Average of Another Represented by a Dot

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In this article, we will explore how to plot a histogram of one column in a pandas DataFrame while overlaying the average value of another column. We will go through the steps required to achieve this using Python and its various libraries, including Matplotlib, Seaborn, and Pandas.

Introduction

Data visualization is an essential part of data analysis and science. It allows us to gain insights into our data by presenting it in a graphical format that can be easily understood by both humans and machines. In this article, we will focus on creating a histogram of one column in a pandas DataFrame while overlaying the average value of another column.

Prerequisites

Before we dive into the code, let’s make sure you have the necessary libraries installed:

pandas: For data manipulation and analysis.
matplotlib: For creating static, animated, and interactive visualizations in python.
seaborn: A visualization library built on top of Matplotlib.

Creating the DataFrame

First, we need to create a pandas DataFrame that contains the data we want to visualize. The data should be in a tabular format with rows representing individual observations and columns representing different variables.

import pandas as pd

# Create a dictionary containing sample data
data = {'Percentage': [8, 20, 24, 27, 58], 
        'Assets': [10, 12, 53, 32, 11]}

# Convert the dictionary into DataFrame
df = pd.DataFrame(data)

print(df)

Calculating Bins

To plot a histogram, we need to determine which bin each data point falls under. We can do this by using Pandas’ cut() function, which bins the values in a specified column according to the given bins.

# Define the bins
bins = [0, 25, 50, 75, 100]

# Calculate the bins for the 'Percentage' column
df['bins'] = pd.cut(df['Percentage'], bins=bins)

print(df.head())

Creating the Bar Plot

We will create a bar plot that displays the frequency of each bin. To calculate this, we can use Seaborn’s barplot() function.

# Import necessary libraries
import matplotlib.pyplot as plt
import seaborn as sns

# Calculate the count for each bin
df['count'] = 1

# Create a figure and axis object
fig, ax1 = plt.subplots()

# Plot the bar plot
sns.barplot(data=df, x='bins', y='count', estimator=np.sum, ax=ax1)

# Add a second y-axis with average Assets for each bin
ax2 = ax1.twinx()
sns.pointplot(data=df, x='bins', y='Assets', color='m', join=False, ci=None, ax=ax2)

plt.show()

Explanation of the Code

Let’s break down what each part of this code does:

We first import the necessary libraries: pandas, matplotlib.pyplot, and seaborn.
Then we create a dictionary containing sample data that will be used to create our DataFrame.
Next, we define the bins for the ‘Percentage’ column using Pandas’ cut() function.
We calculate the count for each bin by setting it to 1. This can be adjusted as needed.
After that, we create a figure and axis object using plt.subplots().
Then we plot the bar plot using Seaborn’s barplot() function with the ‘bins’ column on the x-axis and the count on the y-axis.
We add a second y-axis to the plot for the average Assets using ax1.twinx() followed by Seaborn’s pointplot() function.

Conclusion

In this article, we learned how to create a histogram of one column in a pandas DataFrame while overlaying the average value of another column. We used Pandas’ cut() function to determine which bin each data point falls under and then created a bar plot using Seaborn’s barplot() function. This technique can be applied to various types of data visualization tasks, providing insights into the distribution of values within specific bins.

Additional Tips

You can customize the bins according to your needs by changing the parameters in Pandas’ cut() function.
To change the color scheme or styles for the plot, you can use Seaborn’s various options and parameters.
Consider exploring other types of plots like histograms, scatter plots, and box plots depending on the nature of your data.

Last modified on 2025-04-21