How to Calculate Z-Score in Python (2 Examples)

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How to Calculate Z-Score in Python

In this post, we will discuss how to calculate Z-score in python. Z-scores are a statistical measurement that shows the number of standard deviations away from the mean a data point is. It is commonly used in statistics to standardize a distribution and make it easier to compare different data points. In this blog post, we will learn how to calculate Z-scores in Python.

Step 1: Import the necessary libraries:

First, let’s import the necessary libraries. We will use the NumPy library for its statistical functions.

import numpy as np

Step 2: Create a sample data set to work with:

We will create a sample data set to work with. We will use the NumPy random function to generate 100 random numbers.

data = np.random.normal(0, 1, 100)

we are generating 100 random numbers with a mean of 0 and a standard deviation.

Step 3: Calculate the Z-score

z_scores = (data - np.mean(data)) / np.std(data)

In this formula, we are subtracting the mean of the data set from each data point and then dividing it by the standard deviation. This will give us the Z-score for each data point in the data set.

Step 4: Use Scipy.stats library to calculate Z-score:

from scipy import stats

z_scores = stats.zscore(data)

The stats.zscore() function calculates the Z-score for each data point in the data set.

Step 5: Print out the Z-score:

print(z_scores)

This will output an array of Z-scores for each data point in the data set.

Z-scores can be a useful tool for analyzing and comparing data. By standardizing a distribution, we can compare data points that have different units or scales. Python makes it easy to calculate Z-scores using libraries like NumPy and scipy.stats.

For further reading, you can also read this post.

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November 4, 2023 at 2:56 pm

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