Part II — Python Basics

The following chapters give an overview of the basic principles needed to understand the recipes in the “Data Visualizations” part. The focus is on practical examples of how data can be loaded, transformed, and analyzed using Python and pandas.

Chapter overview

• Pandas Introduction — Series, DataFrames, indexing, and data types

• Loading Data — Import data from CSV, Excel, JSON, and URLs

• Datetime Handling — Parse timestamps, handle time zones, use the dt accessor

• Data Wrangling — Reshape, filter, merge, and transform data

• Time Series — Regular time grids, resampling, rolling windows, and gap filling

• Exploratory Data Analysis — Descriptive statistics and visual exploration

• Data Quality — Detect and handle missing values, gaps, and duplicates

• Meteo Data — Find Swiss weather stations and download weather data

It is not the goal of this book to provide a complete introduction to Python. There are many excellent resources for learning the fundamentals:

• Python Tutorial — Official Python documentation

• Real Python — Practical tutorials for all levels

• Python Data Science Handbook — Free online book by Jake VanderPlas

• pandas documentation — Getting started tutorials