Common Statistical Operations with Python

Statistics with Python is the practice of using Python programming to perform statistical analysis. Python offers a variety of libraries and functions that make it easy to calculate and visualize statistics, conduct hypothesis tests, and analyze large datasets.


Key Libraries for Statistics in Python:

  • NumPy: Fundamental library for numerical computing in Python. It provides support for large, multi-dimensional arrays and matrices along with mathematical functions to operate on them.
  • Pandas: Essential for data manipulation and analysis. It provides data structures like Series (1D) and DataFrame (2D) for managing data efficiently.
  • SciPy: Builds on NumPy to provide advanced mathematical functions and algorithms for optimization, integration, and statistics.
  • Matplotlib and Seaborn: Visualization libraries for creating static, animated, and interactive plots. They help in understanding data distribution, relationships, and patterns.
  • Statsmodels: Provides classes and functions for the estimation of many different statistical models, conducting statistical tests, and data exploration.
  • Scikit-learn: Primarily for machine learning, but also includes tools for statistical modeling, regression, clustering, and more.


Common Statistical Operations with Python:

  1. Descriptive Statistics:

Mean, median, mode, variance, and standard deviation.

Example with NumPy:


2. Data Visualization:

Using Matplotlib and Seaborn to create histograms, box plots, scatter plots, etc.

Example with Matplotlib:


3. Hypothesis Testing:

Using SciPy to perform t-tests, chi-square tests, and more.

Example with SciPy


4. Linear Regression:

Using Statsmodels or Scikit-learn for regression analysis.

Example with Statsmodels