NumPy: Compute the histogram of a set of data

Last update on August 29 2025 12:45:48 (UTC/GMT +8 hours)

Compute Histogram of Data

Write a NumPy program to compute the histogram of a set of data.

Sample Solution:

Python Code:

# Importing the NumPy library and aliasing it as 'np'
import numpy as np

# Importing the matplotlib.pyplot module and aliasing it as 'plt'
import matplotlib.pyplot as plt

# Creating a histogram using plt.hist()
# The histogram displays the frequency of occurrences of values in the input list [1, 2, 1]
# Bins are specified using the 'bins' parameter as [0, 1, 2, 3, 5]
plt.hist([1, 2, 1], bins=[0, 1, 2, 3, 5])

# Displaying the histogram using plt.show()
plt.show()

Sample Output:

Explanation:

The above code creates a histogram using Matplotlib's plt.hist() function and displays it using plt.show().

plt.hist([1, 2, 1], bins=[0, 1, 2, 3, 5]): The plt.hist() function is called with the input data [1, 2, 1] and the specified bin edges [0, 1, 2, 3, 5]. This will create a histogram with four bins:

Bin 2: 1 <= x < 2
Bin 3: 2 <= x < 3
Bin 4: 3 <= x < 5

The input data contains two occurrences of the value ‘1’ and one occurrence of the value ‘2’, so the histogram will have the following counts for each bin:

Bin 1: 0
Bin 2: 2
Bin 3: 1
Bin 4: 0

plt.show(): This line displays the histogram plot created by the plt.hist() function. The resulting plot will show the four bins on the x-axis and their corresponding counts on the y-axis.

For more Practice: Solve these Related Problems:

Write a NumPy program to compute and plot the histogram of a random dataset using np.histogram.
Create a function that returns both the histogram counts and bin edges for a given 1D array.
Test the histogram computation on an array with a known distribution to verify the bin counts.
Implement a solution that normalizes the histogram and compares it with a probability density function.

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Python-Numpy Code Editor:

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