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.
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.
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
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()
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
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
alpha- The alpha/transparency of the lines
align- To which side of the bins are the bars alligned, default is
orientation- Horizontal/Vertical orientation, default is
log- Whether the plot should be put on a logarithmic scale or not
This now results in:
Since we've put the
right, we can see that the bar is offset a bit, to the vertical right of the 2020 bin.
In this tutorial, we've gone over several ways to plot a histogram plot using Matplotlib and Python.
If you're interested in Data Visualization and don't know where to start, make sure to check out our book on Data Visualization in Python.
Data Visualization in Python, a book for beginner to intermediate Python developers, will guide you through simple data manipulation with Pandas, cover core plotting libraries like Matplotlib and Seaborn, and show you how to take advantage of declarative and experimental libraries like Altair.