Python Tutorial

About

This is a quick tutorial I made to showcase the basics of Python and some core data science libraries.

Start by running main_00_generate_datasets.py to download the datasets used in this tutorial.

The actual tutorial starts with main_01_data_types.py. I highly recommend following the tutorial in order.

Requirements

This tutorial uses Python 3.10.14 and the following libraries:

matplotlib==3.8.0
numpy==1.26.4
pandas==2.2.1
scikit-learn==1.3.0
scipy==1.12.0
statsmodels==0.14.0

Additional Resources

numpy

• Statistics (E.g., mean, standard deviation, etc)
• Mathematical functions

pandas

• Import a csv file
• Export a csv file
• Access a single value from a DataFrame
• Access a group of rows and columns from a DataFrame
• Perform stats or math on a DataFrame by groups
• Create a DataFrame from a dictionary

scipy

• Statistical tests (E.g., one-way ANOVA, Tukey's HSD test, etc.)

statsmodels

• Two-way ANOVA

matplotlib

• Examples
• Scatter plot
• Line plot
• Colors
• Markers

scikit-learn

• Examples

• Preprocessing:

  • Standardization

• Dimensionality reduction:

  • PCA
  • t-SNE

• Machine learning classifiers:

  • Logistic Regression
  • Support Vector Machine
  • k-Nearest Neighbors
  • Random Forest

• Hyperparameter optimization:

  • Grid Search CV
  • Pipeline

• Model validation:

  • Train-Test-Split
  • Metrics (E.g., Accuracy, MCC, etc.)