Plotting Histogram in Python using Matplotlib

Last Updated : 28 Apr, 2025

Histograms are a fundamental tool in data visualization, providing a graphical representation of the distribution of data. They are particularly useful for exploring continuous data, such as numerical measurements or sensor readings. This article will guide you through the process of Plot Histogram in Python using Matplotlib, covering the essential steps from data preparation to generating the histogram plot.

What is Matplotlib Histograms?

A Histogram represents data provided in the form of some groups. It is an accurate method for the graphical representation of numerical data distribution. It is a type of bar plot where the X-axis represents the bin ranges while the Y-axis gives information about frequency.

Creating a Matplotlib Histogram

To create a Matplotlib histogram the first step is to create a bin of the ranges, then distribute the whole range of the values into a series of intervals, and count the values that fall into each of the intervals. Bins are identified as consecutive, non-overlapping intervals of variables.The matplotlib.pyplot.hist() function is used to compute and create a histogram of x.

The following table shows the parameters accepted by matplotlib.pyplot.hist() function :

Attribute	Parameter
x	array or sequence of array
bins	optional parameter contains integer or sequence or strings
density	Optional parameter contains boolean values
range	Optional parameter represents upper and lower range of bins
histtype	optional parameter used to create type of histogram [bar, barstacked, step, stepfilled], default is "bar"
align	optional parameter controls the plotting of histogram [left, right, mid]
weights	optional parameter contains array of weights having same dimensions as x
bottom	location of the baseline of each bin
rwidth	optional parameter which is relative width of the bars with respect to bin width
color	optional parameter used to set color or sequence of color specs
label	optional parameter string or sequence of string to match with multiple datasets
log	optional parameter used to set histogram axis on log scale

Plotting Histogram in Python using Matplotlib

Here we will see different methods of Plotting Histogram in Matplotlib in Python:

Basic Histogram
Customized Histogram with Density Plot
Customized Histogram with Watermark
Multiple Histograms with Subplots
Stacked Histogram
2D Histogram (Hexbin Plot)

Create a Basic Histogram in Matplotlib

Let's create a basic histogram in Matplotlib using Python of some random values.

Python3

import matplotlib.pyplot as plt import numpy as np # Generate random data for the histogram data = np.random.randn(1000) # Plotting a basic histogram plt.hist(data, bins=30, color='skyblue', edgecolor='black') # Adding labels and title plt.xlabel('Values') plt.ylabel('Frequency') plt.title('Basic Histogram') # Display the plot plt.show()

Output:

Histogram in Python using Matplotlib

Customized Histogram in Matplotlib with Density Plot

Let's create a customized histogram with a density plot using Matplotlib and Seaborn in Python. The resulting plot visualizes the distribution of random data with a smooth density estimate.

Python3

import matplotlib.pyplot as plt import seaborn as sns import numpy as np # Generate random data for the histogram data = np.random.randn(1000) # Creating a customized histogram with a density plot sns.histplot(data, bins=30, kde=True, color='lightgreen', edgecolor='red') # Adding labels and title plt.xlabel('Values') plt.ylabel('Density') plt.title('Customized Histogram with Density Plot') # Display the plot plt.show()

Output:

Histogram Matplotlib

Customized Histogram with Watermark

Create a customized histogram using Matplotlib in Python with specific features. It includes additional styling elements, such as removing axis ticks, adding padding, and setting a color gradient for better visualization.

Python3

import matplotlib.pyplot as plt import numpy as np from matplotlib import colors from matplotlib.ticker import PercentFormatter # Creating dataset np.random.seed(23685752) N_points = 10000 n_bins = 20 # Creating distribution x = np.random.randn(N_points) y = .8 ** x + np.random.randn(10000) + 25 legend = ['distribution'] # Creating histogram fig, axs = plt.subplots(1, 1, figsize =(10, 7), tight_layout = True) # Remove axes splines  for s in ['top', 'bottom', 'left', 'right']: axs.spines[s].set_visible(False) # Remove x, y ticks axs.xaxis.set_ticks_position('none') axs.yaxis.set_ticks_position('none') # Add padding between axes and labels  axs.xaxis.set_tick_params(pad = 5) axs.yaxis.set_tick_params(pad = 10) # Add x, y gridlines  axs.grid(b = True, color ='grey', linestyle ='-.', linewidth = 0.5, alpha = 0.6) # Add Text watermark  fig.text(0.9, 0.15, 'Jeeteshgavande30', fontsize = 12, color ='red', ha ='right', va ='bottom', alpha = 0.7) # Creating histogram N, bins, patches = axs.hist(x, bins = n_bins) # Setting color fracs = ((N**(1 / 5)) / N.max()) norm = colors.Normalize(fracs.min(), fracs.max()) for thisfrac, thispatch in zip(fracs, patches): color = plt.cm.viridis(norm(thisfrac)) thispatch.set_facecolor(color) # Adding extra features  plt.xlabel("X-axis") plt.ylabel("y-axis") plt.legend(legend) plt.title('Customized histogram') # Show plot plt.show()

Output :

Histogram using Matplotlib

Multiple Histograms with Subplots

Let's generates two histograms side by side using Matplotlib in Python, each with its own set of random data and provides a visual comparison of the distributions of data1 and data2 using histograms.

Python3

import matplotlib.pyplot as plt import numpy as np # Generate random data for multiple histograms data1 = np.random.randn(1000) data2 = np.random.normal(loc=3, scale=1, size=1000) # Creating subplots with multiple histograms fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 4)) axes[0].hist(data1, bins=30, color='Yellow', edgecolor='black') axes[0].set_title('Histogram 1') axes[1].hist(data2, bins=30, color='Pink', edgecolor='black') axes[1].set_title('Histogram 2') # Adding labels and title for ax in axes: ax.set_xlabel('Values') ax.set_ylabel('Frequency') # Adjusting layout for better spacing plt.tight_layout() # Display the figure plt.show()

Output:

Screenshot-2023-12-05-222526

Stacked Histogram using Matplotlib

Let's generates a stacked histogram using Matplotlib in Python, representing two datasets with different random data distributions. The stacked histogram provides insights into the combined frequency distribution of the two datasets.

Python3

import matplotlib.pyplot as plt import numpy as np # Generate random data for stacked histograms data1 = np.random.randn(1000) data2 = np.random.normal(loc=3, scale=1, size=1000) # Creating a stacked histogram plt.hist([data1, data2], bins=30, stacked=True, color=['cyan', 'Purple'], edgecolor='black') # Adding labels and title plt.xlabel('Values') plt.ylabel('Frequency') plt.title('Stacked Histogram') # Adding legend plt.legend(['Dataset 1', 'Dataset 2']) # Display the plot plt.show()

Output:

Screenshot-2023-12-05-222933

Plot 2D Histogram (Hexbin Plot) using Matplotlib

Let's generates a 2D hexbin plot using Matplotlib in Python, provides a visual representation of the 2D data distribution, where hexagons convey the density of data points. The colorbar helps interpret the density of points in different regions of the plot.

Python3

import matplotlib.pyplot as plt import numpy as np # Generate random 2D data for hexbin plot x = np.random.randn(1000) y = 2 * x + np.random.normal(size=1000) # Creating a 2D histogram (hexbin plot) plt.hexbin(x, y, gridsize=30, cmap='Blues') # Adding labels and title plt.xlabel('X values') plt.ylabel('Y values') plt.title('2D Histogram (Hexbin Plot)') # Adding colorbar plt.colorbar() # Display the plot plt.show()

Output:

Screenshot-2023-12-05-222826

Conclusion

Plotting Matplotlib histograms is a simple and straightforward process. By using the hist() function, we can easily create histograms with different bin widths and bin edges. We can also customize the appearance of histograms to meet our needs