5 Best Ways to Save a Histogram Plot in Python

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💡 Problem Formulation: When conducting data analysis in Python, you may create a histogram plot to visualize the distribution of a numeric dataset. The challenge arises when you need to save your visualized histogram for reports, further analysis, or sharing. This article explains how to save a histogram plot from data input to an image file such as PNG or PDF for later use, using several different methods in Python’s popular libraries.

Method 1: Using matplotlib’s savefig

Saving a histogram using matplotlib’s savefig() function is straightforward and customizable. The savefig() function offers various parameters to adjust the output format, resolution (DPI), and size of the saved figure. It can output a wide range of file formats including PNG, PDF, SVG, and more.

Here’s an example:

import matplotlib.pyplot as plt

# Generating some data
data = [1, 2, 2, 3, 4, 5, 5, 5, 6]

# Creating histogram
plt.hist(data, bins=6, edgecolor='black')

# Saving the histogram
plt.savefig('histogram.png')

The output of this code snippet would be a saved file named “histogram.png” in the current working directory.

In this code example, we import matplotlib.pyplot, generate some dummy data, and create a histogram with specified bins and edge color. The savefig() is then used to save the histogram as a PNG file.

Method 2: Using seaborn’s savefig

Seaborn, a statistical data visualization library that builds on matplotlib, simplifies the process of creating visually appealing histograms with its histplot(). After plotting, you can use matplotlib’s savefig() to save the plot as you do with a regular matplotlib figure.

Here’s an example:

import seaborn as sns
import matplotlib.pyplot as plt

# Generate some data
data = sns.load_dataset('tips')['total_bill']

# Create a histogram with seaborn
sns.histplot(data)

# Save the histogram
plt.savefig('seaborn_histogram.png')

The output is a file named “seaborn_histogram.png” that contains the histogram plot.

This code snippet first imports seaborn, alongside matplotlib, to create a more sophisticated histogram. After using Seaborn’s histplot() to plot the histogram, we simply call savefig() to save the plot to an image file.

Method 3: Using Pandas plot with savefig

Pandas DataFrame objects have built-in plotting capabilities that allow creating histograms directly from DataFrame columns. After plotting, the figure handle can be used to save the plot with savefig() as well.

Here’s an example:

import pandas as pd

# Create a DataFrame with some data
df = pd.DataFrame({'data': [1, 2, 2, 3, 4, 5, 5, 5, 6]})

# Plot histogram and get the figure handle
ax = df['data'].plot.hist()

# Save the figure
fig = ax.get_figure()
fig.savefig('pandas_histogram.png')

The output is a “pandas_histogram.png” file, which is a histogram of the DataFrame’s ‘data’ column.

This snippet shows how you can plot a histogram using a Pandas DataFrame and use the handle of the plot to access the figure object, which is then saved as a .png file with the savefig() method.

Method 4: Using Plotly

Plotly is an interactive graphing library that allows users to create elaborate plots, including histograms, that can be saved as a static image file. To save a Plotly figure, the write_image() function from Plotly’s io module is used.

Here’s an example:

import plotly.express as px

# Create some data
data = [1, 2, 2, 3, 4, 5, 5, 5, 6]

# Create interactive histogram
fig = px.histogram(data, nbins=6)

# Save the histogram as an image
fig.write_image('plotly_histogram.png')

The output is an interactive plot saved as “plotly_histogram.png”.

This example utilizes Plotly’s express module to create an interactive histogram plot. The write_image() method is utilized to save the plot as a static image file.

Bonus One-Liner Method 5: Using plt.savefig with Compression

You can also save a matplotlib histogram with compression, which is particularly useful when dealing with large plots that could benefit from reduced file sizes. The savefig() function allows specifying compression levels for certain formats, such as PNG.

Here’s an example:

plt.hist([1, 2, 2, 3, 4, 5, 5, 5, 6], bins=6, edgecolor='black')
plt.savefig('compressed_histogram.png', dpi=300, format='png', bbox_inches='tight', pad_inches=0.1, optimize=True, compression='9')

This saves a high-resolution compressed histogram as “compressed_histogram.png”.

In this one-liner, we create and save a compressed PNG histogram all in one go. The additional parameters control resolution, padding, and compression level, making the overall file size smaller without significant quality loss.

Summary/Discussion

  • Method 1: Matplotlib’s savefig. Most common and versatile method. Works across varieties of formats. Full control on resolution and size.
  • Method 2: Seaborn’s histplot with savefig. Convenient for statistical graphics. Relies on matplotlib’s savefig for saving. Produces aesthetically pleasing plots.
  • Method 3: Pandas plot with savefig. Quick and efficient when working with DataFrame data. Saves in one step if you have the figure handle.
  • Method 4: Plotly’s write_image. Ideal for interactive plots. It can require additional setup for non-interactive image exports.
  • Bonus Method 5: Compressed savefig. Reduces file size, useful for large plots. Requires understanding of compression settings.