Chapter 3_ Data Cleaning
A comprehensive guide on handling missing data, data type constraints, uniformity, cross-field validation, and text data problems.
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Chapter 2_ Feature engineering, Transformation & Accessors
Learn about conditional transformations, binning, mapping, applying logic, vectorization, scaling, encoding, and using accessors.
Chapter 1_ Data manipulation & Essentials
A guide on creating and exporting DataFrames, inspecting data, broadcasting, sorting, subsetting, and computing summary statistics.
Chapter 0_ Introduction to pandas
An introduction to Pandas, NumPy, and Matplotlib, and working with DataFrames and Series.
Introduction to NumPy
A comprehensive guide to NumPy, covering array creation, indexing, slicing, data types, and essential array operations for efficient numerical computing in Python.
Chapter 14_API Requests
Fetching data from APIs using the Requests library and parsing JSON responses.