Introduction to scipy.stats

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

It provides tools for statistical analysis, including probability distributions, statistical tests, 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 summary statistics like the mean, median, and mode.

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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 use scipy.stats to perform a one-sample t-test.

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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 less than 0.05, we reject the null hypothesis and conclude that the mean is significantly different from 5.

You can use scipy.stats to generate the probability density function (PDF) of a normal distribution.

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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 the go-to module for statistical analysis in Python. It provides tools for:

• Summary statistics
• Hypothesis testing
• Probability distributions