A guide to load (almost) anything into a DataFrame

Pandas is a powerful Python library for data manipulation and analysis, offering numerous options to read data into DataFrames. This guide explores some of the most useful methods for loading data from various sources such as CSV, JSON, Parquet files, SQL databases, HTML documents, and more. It also covers reading data from remote storage solutions like S3, Google Cloud, SFTP, or GitHub using FSSPEC library. Pandas can automatically detect compression algorithms and decompress the data before reading it. The guide provides examples of reading different file formats and working with remote files, highlighting the flexibility and ease of use of Pandas for data processing and visualization.