A histogram is often use for showing the distribution of one variable or one group. However sometimes it's useful to compare the distribution of several groups or variables at the same time.
Pandas, a powerful data manipulation library in Python, allow us to create easily histograms: check this introduction to histograms with pandas. Once you understood how to build a basic histogram with pandas, we will explore how to leverage Pandas to show the distribution of mutliple groups and variables at the same time.


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 numpy as np
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 histograms need quantitative variables, we will create a dataset with 2 columns. The first column is called "type", which stores the categories "group1" and "group2" repeated a total of 1000 times each.

The second column is named "value". It holds numbers. The first 1000 numbers are random values from a normal distribution with an average of 0 and a standard deviation of 1. The next 1000 numbers are random values from another normal distribution with an average of 4 and a standard deviation of 1. We concatenate them into one single column thanks to the concatenate() function from numpy.

# Create 2 columns: one categorical and one numerical
sample_size = 1000
data = {
    'type': ['group1'] * sample_size + ['group2'] * sample_size,
    'value': np.concatenate([np.random.normal(0, 1, sample_size),
                             np.random.normal(4, 1, sample_size)])
df = pd.DataFrame(data)

Basic histogram with 2 groups

Once we've opened our dataset, we'll now create a simple histogram, representing the distributions of the 'value' variable with the 2 groups. We will iterate over all distinct value in the 'type' variable and use the hist() function.

# Plot the histograms of each group
for group in df['type'].unique():
    # Filter the dataset on the group
    filtered_df = df[df['type']==group]
    # Add the histogram to the graphic
    filtered_df['value'].hist(figsize=(8, 4))

# Display the plot    

Customize histogram with 2 groups

The above histograms can be easily customized with the following features

  • change the bins argument to the value we want
  • change the color argument to the color we want
  • change the edgecolor argument to the color we want
  • add a title and axis label
  • add a legend

Our first step will be to get a list of the labels in the type variable and then define a list of colors of the same length as the first list.

# Get group names and define colors
group_name = df['type'].unique()
colors = ['purple', 'orange']

# Plot the histograms
for i, group in enumerate(group_name):
    ax = df[df['type']==group]['value'].hist(figsize=(8, 4),

# Add a legend

# Add a title and axis label
ax.set_title('Distribution of 2 different groups')

# Show the plot

Histogram with small multiples

Now we will see how to create a chart with small multiple histograms that display the distribution of several variables at the same time. First we need a dataset with more variables with different distributions.

Create the dataset

For this, we will use numpy random functions and generate 9 different numeric variables. Don't worry if this seems complicated to you: it's only useful for generating fake data and making the graphs readable!

# Number of data points
num_data_points = 1000

# Generate data for each distribution
normal_data = np.random.normal(loc=0, scale=1, size=num_data_points)
uniform_data = np.random.uniform(low=-1, high=1, size=num_data_points)
bimodal_data = np.concatenate((np.random.normal(loc=-2, scale=1, size=num_data_points // 2),
                               np.random.normal(loc=2, scale=1, size=num_data_points // 2)))
poisson_data = np.random.poisson(lam=5, size=num_data_points)
exponential_data = np.random.exponential(scale=2, size=num_data_points)
gamma_data = np.random.gamma(shape=2, scale=2, size=num_data_points)
beta_data = np.random.beta(a=2, b=5, size=num_data_points)
lognormal_data = np.random.lognormal(mean=0, sigma=1, size=num_data_points)
triangular_data = np.random.triangular(left=-1, mode=0, right=1, size=num_data_points)

# Create a DataFrame
data = {
    'Normal': normal_data,
    'Uniform': uniform_data,
    'Bimodal': bimodal_data,
    'Poisson': poisson_data,
    'Exponential': exponential_data,
    'Gamma': gamma_data,
    'Beta': beta_data,
    'LogNormal': lognormal_data,
    'Triangular': triangular_data

df = pd.DataFrame(data)

Create the chart

Now we can create a small multiple histograms with pandas and matplotlib:

  • The following code goes through each column of the dataframe and creates a histogram plot
  • For each subplot, the code adds a histogram of a specific column's data from the dataframe
  • It adds a title and axis label
  • The code adjusts the layout (thanks to the tight_layout() function) to make sure they fit well together in the figure
  • Finally, it displays the entire set of subplots as a single plot
# Initialize a 3x3 charts
fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(8, 8))

# Flatten the axes array (makes it easier to iterate over)
axes = axes.flatten()

# Loop through each column and plot a histogram
for i, column in enumerate(df.columns):
    # Add the histogram
    df[column].hist(ax=axes[i], # Define on which ax we're working on
                    edgecolor='white', # Color of the border
                    color='#69b3a2' # Color of the bins
    # Add title and axis label
    axes[i].set_title(f'{column} distribution') 

# Adjust layout

# Show the plot

Going further

This post explains how to show the distribution of multiple groups and variables with pandas.

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 histograms with Matplotlib and Seaborn.

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