.histogram()

The .histogram() function in NumPy is used to compute the frequency distribution of data by dividing values into bins and counting how many fall into each bin. It’s commonly used in data analysis, statistical modeling, and visualization to understand the distribution of numerical data.

Syntax

numpy.histogram(a, bins=10, range=None, density=None, weights=None)

Parameters:

• a (array_like): Input data. The histogram is computed over the flattened array.
• bins (int, sequence of scalars, or str, optional):
  • If an int, it defines the number of equal-width bins (default is 10).
  • If a sequence, it specifies the bin edges.
  • If a str, it defines the method used to calculate the optimal bin width.
• range (tuple, optional): Lower and upper range of the bins. Defaults to (a.min(), a.max()).
• density (bool, optional):
  • If False, returns the count of samples in each bin.
  • If True, returns the probability density function.
• weights (array_like, optional): Weights for each value in a. If density=True, weights are normalized.

Return value:

The .histogram() function in NumPy returns a tuple of two arrays:

(hist, bin_edges)

• hist: A NumPy array of counts or probability densities (depending on the density parameter). It tells you how many elements from the input fell into each bin.
• bin_edges: A NumPy array of bin edge values. It has one more element than hist, representing the edges of the bins, including the rightmost edge.

Example

This example demonstrates the use of .histogram() in NumPy:

import numpy as np
# Creating a NumPy histogram from array and set bins
a = np.histogram([1, 2, 1, 3, 2, 4, 3, 0, 0, 5], bins=[0, 1, 2, 3, 4, 5, 6])

# Print the histogram
print(str(1)  + ':' + str(a))

# Creating a NumPy histogram with numpy.arange() and setting density as True
b = np.histogram(np.arange(4), bins=np.arange(5), density=True)

# Print the histogram
print(str(2) + ':' + str(b))

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The output for the example will be:

1:(array([2, 2, 2, 2, 1, 1]), array([0, 1, 2, 3, 4, 5, 6]))
2:(array([0.25, 0.25, 0.25, 0.25]), array([0, 1, 2, 3, 4]))

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Codebyte Example

This codebyte example shows the use of NumPy .histogram() to display an array in a histogram display:

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