Plotting Histogram in Python using Matplotlib (original) (raw)

Last Updated : 14 Oct, 2025

Histograms are one of the most fundamental tools in data visualization. They provide a graphical representation of data distribution, showing how frequently each value or range of values occurs. Histograms are especially useful for analyzing continuous numerical data, such as measurements, sensor readings, or experimental results.

A histogram is a type of bar plot where:

• The X-axis represents intervals (called bins) of the data.
• The Y-axis represents the frequency of values within each bin.

Unlike regular bar plots, histograms group data into bins to summarize data distribution effectively.

Creating a Matplotlib Histogram

1. Divide the data range into consecutive, non-overlapping intervals called bins.
2. Count how many values fall into each bin.
3. Use the matplotlib.pyplot.hist() function to plot the histogram.

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

Plotting Histogram in Python using Matplotlib

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

1. Basic Histogram
2. Customized Histogram with Density Plot
3. Customized Histogram with Watermark
4. Multiple Histograms with Subplots
5. Stacked Histogram
6. 2D Histogram (Hexbin Plot)

1. Basic Histogram

Python `

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

**Explanation:

• Generates 1000 random numbers from a standard normal distribution.
• Plots a histogram with 30 bins, sky-blue bars, and black edges.
• Adds X and Y axis labels and a title.
• Displays the histogram plot.

This is the simplest way to visualize data distribution.

2. Customized Histogram with Density Plot

Python `

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

**Explanation:

• Generates 1000 random numbers.
• Plots a histogram with 30 bins and a smooth density curve (KDE) using Seaborn.
• Colors bars light green with red edges.
• Adds axis labels and a title.
• Displays the plot showing data distribution and density.

3. Customized Histogram with Watermark

Python `

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 = 0.8 ** x + np.random.randn(N_points) + 25 legend = ['distribution']

Creating figure and axes

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 (updated syntax)

axs.grid(visible=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 gradient

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 with Watermark')

Show plot

plt.show()

`

**Output

**Explanation:

• Sets the random seed for reproducibility.
• Generates 10,000 random numbers (x) from a standard normal distribution.
• Plots a histogram with 20 bins and applies a color gradient to the bars.
• Removes axis spines and ticks, adds gridlines, and includes a watermark.
• Adds axis labels, a legend, and a title.
• Displays a visually enhanced, customized histogram.

4. Multiple Histograms with Subplots

Python `

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

**Explanation:

• Generates two datasets of 1,000 random numbers each.
• Creates side-by-side histograms using subplots.
• Plots data1 in yellow and data2 in pink with 30 bins.
• Adds axis labels and titles, adjusts layout, and displays the figure.

5. Stacked Histogram

Python `

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

**Explanation:

• Generates two datasets of 1,000 random numbers each.
• Creates a stacked histogram showing both datasets combined.
• Uses cyan and purple bars with black edges.
• Adds axis labels, a title, and a legend.
• Displays the histogram showing combined data distribution.

6. 2D Histogram (Hexbin Plot)

Python `

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(label='Counts')

plt.show()

`

**Output

Output

**Explanation:

• Generates two sets of 1,000 random numbers (x and y).
• Creates a 2D histogram (hexbin plot) showing data density with hexagons.
• Uses a blue color map to represent density intensity.
• Adds axis labels, a title, and a colorbar to interpret density.
• Displays the 2D histogram showing the relationship between x and y.

Related Articles

• Introduction to Matplotlib
• Data Visualization with Seaborn
• Numpy

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