Pandas | DataScienceBase

Introduction to pandas

A beginner-friendly introduction to pandas, the powerful data manipulation library for Python, and its role in data science.

Learn how to create and manipulate Series in pandas, including accessing data, performing operations, and applying functions.

Learn the basics of pandas DataFrames, including how to create, explore, and manipulate them for data analysis tasks.

Learn how to select and filter data in pandas DataFrames using labels, positions, and conditions.

Learn how to perform basic operations on pandas DataFrames, including adding, modifying, and deleting columns and rows, and handling missing data.

Learn how to identify, fill, and drop missing data in pandas DataFrames to ensure clean and usable datasets.

Learn essential data manipulation techniques in pandas, including sorting, grouping, merging, and reshaping DataFrames.

Learn how to handle and manipulate date and time data in pandas for effective time series analysis.

Learn how to read data from various file formats and export DataFrames to different formats using pandas.

Learn how to aggregate and group data in pandas to summarize and analyze your datasets effectively.

Learn how to work with categorical data in pandas, including converting columns to categorical types and performing one-hot encoding.

Learn how to set, reset, and work with custom indexes, including hierarchical MultiIndexing, in pandas.

Learn advanced data manipulation techniques in pandas, including pivot tables, multi-indexing, and efficient data operations.

Learn the basics of handling and analyzing time series data in pandas, including datetime indexing, resampling, and rolling windows.

Learn how to optimize your data processing workflows in pandas using vectorized operations, apply functions, and memory management techniques.

Learn how to create basic visualizations using pandas to effectively analyze and present your data.