# Introduction to boxplots with matplotlib

A boxplot is a graphical representation used to display the distribution of a dataset, showing key statistics such as the median, quartiles, and potential outliers. It provides a concise summary of the data's central tendency and spread.
Creating boxplots with Matplotlib allows us to effectively visualize the distribution of data points. In this post, we will explore how to use Matplotlib to customize boxplots, creating visually informative representations of data distribution while exploring available customization options.

## Libraries

First, you need to install the following librairies:

• matplotlib is used for creating the plot
• `pandas` for data manipulation
``````import matplotlib.pyplot as plt
import pandas as pd``````

## Dataset

We will use a dataset about temperature variation in Trentino (Italy), that you can easily access using the `url` below.

``````url = 'https://raw.githubusercontent.com/holtzy/The-Python-Graph-Gallery/master/static/data/trentino_temperature.csv'

# Drop rows (5 in total) with NaN values
df = df.dropna()``````

## Basic boxplot

Once we've opened our dataset, we'll now create the graph. The following displays the distribution of the temperature variation using the `boxplot()` function.

``````# Create a figure and axis
fig, ax = plt.subplots()

# Create a boxplot for the desired column
ax.boxplot(df['temp'])

# Show the plot
plt.show()``````

To clarify things for the reader, it's a good idea to add a title and a name to the axes. And to do this with matplotlib, nothing could be simpler: we simply use the `set_xlabel()` (or `set_ylabel()`) and `set_title()` functions

``````# Create a figure and axis
fig, ax = plt.subplots(figsize=(8,6))

# Create a boxplot for the desired column with custom colors
boxplot = ax.boxplot(df['temp'])

# Set labels and title
ax.set_xlabel('Column')
ax.set_ylabel('Values')
ax.set_title('Boxplot')

# Show the plot
plt.show()``````

## Color customization features

With matplotlib, you can change the color of each element in our box plot.

We just have to define what color we want for each element and then add it to our plot using `setp()` function. Here's an example of how:

``````# Create a figure and axis
fig, ax = plt.subplots(figsize=(8,6))

# Create a boxplot for the desired column with custom colors
boxplot = ax.boxplot(df['temp'], patch_artist=True)

# Set custom colors
box_color = 'lightblue'
whisker_color = 'blue'
cap_color = 'gold'
flier_color = 'red'
median_color = 'red'

# Add the right color for each part of the box
plt.setp(boxplot['boxes'], color=box_color)
plt.setp(boxplot['whiskers'], color=whisker_color)
plt.setp(boxplot['caps'], color=cap_color)
plt.setp(boxplot['fliers'], markerfacecolor=flier_color)
plt.setp(boxplot['medians'], color=median_color)

# Set labels and title
ax.set_xlabel('Column')
ax.set_ylabel('Values')
ax.set_title('Boxplot')

# Show the plot
plt.show()``````

## Going further

This post explains how to create a simple boxplot with matplotlib.

For more examples of how to create or customize your boxplots, see the boxplot section. You may also be interested in how to created an boxplot with multiple groups.

## Contact & Edit

👋 This document is a work by Yan Holtz. You can contribute on github, send me a feedback on twitter or subscribe to the newsletter to know when new examples are published! 🔥