np.histogram() computes the frequency distribution of a dataset by counting how many values fall into each of a set of bins, without plotting anything itself.

Syntax

np.histogram(arr, bins=10, range=None)

Deep Dive Course

np.histogram() divides the range of the data into a number of equal-width bins, 10 by default, or a specific number/explicit bin edges you provide, counts how many values fall into each bin, and returns two arrays: the counts per bin, and the bin edges, one more element than the counts, since each bin has a start and end edge. Despite the name, it purely computes numbers — actually visualizing the result as a bar chart requires a separate plotting call, such as with Matplotlib's own hist() function or by feeding this function's output into a bar plot.

1Understanding np.histogram()

💡

np.histogram() only computes the numeric bin counts and edges — it doesn't draw anything. For a quick visual histogram in a script, matplotlib's plt.hist() is usually more convenient, since it computes and plots in one call; use np.histogram() directly when you need the raw numbers for further processing.

editor.html

import numpy as np

data = np.array([1, 2, 2, 3, 3, 3, 4, 4, 5])
counts, edges = np.histogram(data, bins=5)
print(counts)
print(edges)

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2Practical Example

Here is a real-world application of np.histogram() showing how it is used in production NumPy code.

editor.html

import numpy as np

data = np.random.default_rng(0).normal(0, 1, 1000)
counts, edges = np.histogram(data, bins=[-3, -1, 1, 3])
print(counts)

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3Best Practices

Follow these guidelines when working with np.histogram():

1. Use np.histogram() when you need the raw bin counts and edges for further numeric processing, not just a visualization

2. Choose an appropriate number of bins deliberately for your dataset's size and range, rather than always relying on the default of 10

3. Pass explicit bin edges as an array when you need consistent, comparable bins across multiple different histograms

⚠️

Tip: np.histogram() only computes the numeric bin counts and edges — it doesn't draw anything. For a quick visual histogram in a script, matplotlib's plt.hist() is usually more convenient, since it computes and plots in one call; use np.histogram() directly when you need the raw numbers for further processing.

editor.html

import numpy as np

data = np.array([1, 2, 2, 3, 3, 3, 4, 4, 5])
counts, edges = np.histogram(data, bins=5)
print(counts)
print(edges)

localhost:3000

Examples

Example 01Basic Usage

import numpy as np

data = np.array([1, 2, 2, 3, 3, 3, 4, 4, 5])
counts, edges = np.histogram(data, bins=5)
print(counts)
print(edges)

Example 02Advanced Example

import numpy as np

data = np.random.default_rng(0).normal(0, 1, 1000)
counts, edges = np.histogram(data, bins=[-3, -1, 1, 3])
print(counts)

Best Practices

Use np.histogram() when you need the raw bin counts and edges for further numeric processing, not just a visualization
Choose an appropriate number of bins deliberately for your dataset's size and range, rather than always relying on the default of 10
Pass explicit bin edges as an array when you need consistent, comparable bins across multiple different histograms

Interview Question

Why does np.histogram() return a bin-edges array that's one element longer than the counts array?▼

Hint: Think about how many numbers you need to describe the boundaries of N bins.

Each bin is defined by two boundaries, a start edge and an end edge, and adjacent bins share their boundary — one bin's end edge is the next bin's start edge, and so on. Describing N consecutive bins this way only requires N+1 total edge values, since each internal boundary is shared between two neighboring bins rather than being listed twice. That's why the edges array returned by np.histogram() always has exactly one more element than the counts array, which has one count per bin.

Exercises

MediumPractice using np.histogram() in a real scenario.

View Solution

import numpy as np

data = np.array([1, 2, 2, 3, 3, 3, 4, 4, 5])
counts, edges = np.histogram(data, bins=5)
print(counts)
print(edges)

Frequently Asked Questions

Why does np.histogram() return a bin-edges array that's one element longer than the counts array?▼

Related Functions

np-percentilenp-uniquenp-random-normal