Introduction to scipy.stats

The scipy.stats module is one of the most powerful and versatile parts of SciPy.

It provides a full suite of tools for statistical analysis — including probability distributions, hypothesis testing, and summary statistics.

First, import the required modules:

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import numpy as np
from scipy import stats

You can use scipy.stats to calculate key descriptive statistics such as the mean, median, and mode — all in just a few lines of code.

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data = [5, 7, 8, 7, 2, 17, 2, 9, 4, 11]

mean = np.mean(data)
median = np.median(data)
mode = stats.mode(data, keepdims=True)

print("Mean:", mean)
print("Median:", median)
print("Mode:", mode.mode[0], "Frequency:", mode.count[0])

In this example, we calculate the mean, median, and mode of the dataset.

You can also use scipy.stats to perform hypothesis testing, such as a one-sample t-test to compare a sample mean against a known value.

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# Test if the mean of data is significantly different from 5
t_stat, p_value = stats.ttest_1samp(data, 5)

print("t-statistic:", t_stat)
print("p-value:", p_value)

If the p-value is below 0.05, we reject the null hypothesis — meaning the sample mean is statistically different from 5.

You can also use scipy.stats to work with probability distributions, such as generating the probability density function (PDF) for a normal curve.

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x = np.linspace(-3, 3, 100)
pdf = stats.norm.pdf(x, loc=0, scale=1)

print("First 5 PDF values:", pdf[:5])

In this example, we generate the PDF of a normal distribution with a mean of 0 and a standard deviation of 1.

scipy.stats is your go-to toolkit for statistical analysis in Python. It provides powerful methods for:

• Summary statistics – quick descriptive insights
• Hypothesis testing – comparing groups or means
• Probability distributions – modeling and simulation