 Matplotlib Histogram Plot - Tutorial and Examples - Stack Abuse

# Matplotlib Histogram Plot - Tutorial and Examples ### Introduction

Matplotlib is one of the most widely used data visualization libraries in Python. From simple to complex visualizations, it's the go-to library for most.

In this tutorial, we'll take a look at how to plot a histogram plot in Matplotlib. Histogram plots are a great way to visualize distributions of data - In a histogram, each bar groups numbers into ranges. Taller bars show that more data falls in that range.

A histogram displays the shape and spread of continuous sample data.

### Import Data

We'll be using the Netflix Shows dataset and visualizing the distributions from there.

Let's import Pandas and load in the dataset:

import pandas as pd

df = pd.read_csv('netflix_titles.csv')


### Plot a Histogram Plot in Matplotlib

Now, with the dataset loaded in, let's import Matplotlib's PyPlot module and visualize the distribution of release_years of the shows that are live on Netflix:

import matplotlib.pyplot as plt
import pandas as pd

df = pd.read_csv('netflix_titles.csv')
plt.hist(df['release_year'])

plt.show()


Here, we've got a minimum-setup scenario. We load in the data into a DataFrame (df), then, we use the PyPlot instance and call the hist() function to plot a histogram for the release_year feature. By default, this'll count the number of occurences of these years, populate bars in ranges and plot the histogram.

Running this code results in: Here, the movie bins (ranges) are set to 10 years. Each bar here includes all shows/movies in batches of 10 years. For example, we can see that around ~750 shows were released between 2000. and 2010. At the same time, ~5000 were released between 2010. and 2020.

These are pretty big ranges for the movie industry, it makes more sense to visualize this for ranges smaller than 10 years.

### Change Histogram Bin Size in Matplotlib

Say, let's visualize a histogram (distribution) plot in batches of 1 year, since this is a much more realistic time-frame for movie and show releases.

We'll import numpy, as it'll help us calculate the size of the bins:

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

df = pd.read_csv('netflix_titles.csv')
data = df['release_year']

plt.hist(data, bins = np.arange(min(data), max(data) + 1, 1))

plt.show()


This time around, we've extracted the DataFrame column into a data variable, just to make it a bit easier to work with.

We've passed the data to the hist() function, and set the bins argument. It accepts a list, which you can set manually, if you'd like, especially if you want a non-uniform bin distribution.

Since we'd like to pool these entries each in the same time-span (1 year), we'll create a Numpy array, that starts with the lowest value (min(data)), ends at the highest value (max(data)) and goes in increments of 1.

This time around, running this code results in: Instead of a list, you can give a single bins value. This will be the total number of bins in the plot. Using 1 will result in 1 bar for the entire plot.

Say, we want to have 20 bins, we'd use:

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

df = pd.read_csv('netflix_titles.csv')
data = df['release_year']

plt.hist(data, bins = 20)

plt.show()


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This results in 20 equal bins, with data within those bins pooled and visualized in their respective bars: This results in 5-year intervals, considering we've got ~100 years worth of data. Splitting it up in 20 bins means that each will include 5 years worth of data.

### Plot Histogram with Density

Sometimes, instead of the count of the features, we'd want to check what the density of each bar/bin is. That is, how common it is to see a range within a given dataset. Since we're working with 1-year intervals, this'll result in the probablity that a movie/show was released in that year.

To do this, we can simply set the density argument to True:

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

df = pd.read_csv('netflix_titles.csv')
data = df['release_year']
bins = np.arange(min(data), max(data) + 1, 1)

plt.hist(data, bins = bins, density = True)
plt.ylabel('Density')
plt.xlabel('Year')

plt.show()


Now, instead of the count we've seen before, we'll be presented with the density of entries: We can see that ~18% of the entries were released in 2018, followed by ~14% in 2019.

### Customizing Histogram Plots in Matplotlib

Other than these settings, there's a plethora of various arguments you can set to customize and change the way your plot looks like. Let's change a few of the common options people like to fiddle around with to change plots to their tastes:

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

df = pd.read_csv('netflix_titles.csv')
data = df['release_year']
bins = np.arange(min(data), max(data) + 1, 1)

plt.hist(data, bins = bins, density = True, histtype = 'step', alpha = 0.5, align = 'right', orientation = 'horizontal', log = True)

plt.show()


Here, we've set various arguments:

• bins - Number of bins in the plot
• density - Whether PyPlot uses count or density to populate the plot
• histtype - The type of histogram plot (default is bar, though other values such as step or stepfilled are available)
• alpha - The alpha/transparency of the lines
• align - To which side of the bins are the bars alligned, default is mid
• orientation - Horizontal/Vertical orientation, default is vertical
• log - Whether the plot should be put on a logarithmic scale or not

This now results in: Since we've put the align to right, we can see that the bar is offset a bit, to the vertical right of the 2020 bin.

### Conclusion

In this tutorial, we've gone over several ways to plot a histogram plot using Matplotlib and Python.

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Last Updated: April 12th, 2021

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