Handling Data with pandas DataFrame

The pandas library is an extremely resourceful open source toolkit for handling, manipulating, and analyzing structured data. Data tables can be stored in the DataFrame object available in pandas, and data in multiple formats (for example, .csv, .tsv, .xlsx, and .json) can be read directly into a DataFrame. Utilizing built-in functions, DataFrames can be efficiently manipulated (for example, converting tables between different views, such as, long/wide; grouping by a specific column/feature; summarizing data; and more).

Reading Data from Files

Most small-to medium-sized datasets are usually available or shared as delimited files such as comma-separated values (CSV), tab-separated values (TSV), Excel (.xslx), and JSON files. Pandas provides built-in I/O functions to read files in several formats, such as, read_csv, read_excel, and read_json, and so on into a DataFrame. In this section, we will use the diamonds dataset (hosted in book GitHub...