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Practice

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.

In this lesson, we’ll:

  • Explore summary statistics
  • Perform a basic statistical test
  • Work with probability distributions

Setting Up​

Importing the required modules:

Import NumPy and SciPy Stats
import numpy as np
from scipy import stats

Example 1: Summary Statistics​

Summary Statistics
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])

Example 2: Hypothesis Testing​

One-Sample t-Test
# 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.

Example 3: Probability Distributions​

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

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

Probability density functions (PDF) are useful for understanding the likelihood of different outcomes.

Key Takeaways​

  • scipy.stats is your go-to module for statistical work in Python.

  • It provides:

    • Summary statistics
    • Hypothesis tests
    • Probability distributions

In the next lessons, we’ll dig deeper into descriptive and inferential statistics with SciPy.

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