PYTHON — Histograms With Numpy In Python

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PYTHON — Thonny A Python Overview

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# Histograms with NumPy in Python

In this tutorial, you will learn how to create histograms using NumPy in Python. A histogram represents the distribution of a continuous variable by dividing the range of values into intervals and then counting how many observations fall into each interval. NumPy’s histogram() function provides the basis for creating histograms using other Python libraries such as Matplotlib and Pandas.

Creating a Histogram with NumPy

Let’s start by generating a set of random data using NumPy. In this example, we will work with a sample of floats drawn from the Laplace distribution, which has fatter tails than a normal distribution and is characterized by two parameters: location and scale.

import numpy as np

# Set the seed for reproducibility
np.random.seed(444)

# Generate data from the Laplace distribution
data = np.random.laplace(loc=15, scale=3, size=500)
print(data[:5])

When working with a continuous distribution like the Laplace distribution, it is more practical to bin or bucket the data and count the observations that fall into each bin. The resulting count of values within each bin forms the histogram.

To create the histogram using NumPy, you need to define bins to collect the data points. The histogram() function in NumPy accomplishes this by returning the frequency counts for each bin and the bin edges.

# Create the histogram
hist, bin_edges = np.histogram(data, bins=10)
print(hist)
print(bin_edges)

The hist array contains the frequency counts for each bin, while the bin_edges array provides the boundaries of each bin. The number of bin edges is one more than the number of bins, and the values in bin_edges indicate where the bin cuts occur.

Understanding Bin Edges

It’s important to note that the bin edges are inclusive on the left and exclusive on the right. This means that a value falls into a bin if it is greater than or equal to the left bin edge and less than the right bin edge.

NumPy determines the bin edges by looking for the smallest and largest values in the dataset and then dividing that range into equally spaced bins. In the example above, we specified 10 bins, and NumPy calculated the bin edges accordingly.

Conclusion

In this tutorial, you learned how to use NumPy to create histograms by binning data and counting the observations within each bin. The histogram serves as a visual representation of the distribution of a continuous variable. In the next part of this tutorial, you will explore how to plot histograms using Matplotlib and Pandas.

By understanding the basics of creating histograms with NumPy, you are now ready to proceed to the next step of visualizing and analyzing data in Python.

I hope this tutorial has been helpful in understanding the fundamentals of creating histograms with NumPy!

PYTHON — Thonny A Python Overview