Since pandas is the go-to library for data analysis and manipulation, one of its key features is its ability to read and write various file types, including CSV files.

A CSV (Comma-Separated Values) file is a plain text file used to store tabular data, where each row represents a record, and columns are separated by commas.

A CSV file can contain the following data:

• Numbers: integer or decimal values (e.g., 42, 3.14);
• Text: strings or categorical data (e.g., John, Active);
• Dates/Times: timestamps (e.g., 2023-12-30);
• Booleans: logical values (True, False).

Each row must have the same number of columns, and the first row often contains column headers.

Functions like read_csv() and to_csv() come in handy for dealing with CSV data.

The basic syntax of read_csv() and key parameters are as follows:

• filepath_or_buffer: path to the CSV file (string or URL);
• sep: delimiter (default is a comma ,);
• header: row number to use as the column headers (default is the first row);
• names: List of column names to use;
• usecols: olumns to read (subset of columns).

Note

Make sure that the dataset link is wrapped in quotation marks.

The basic syntax of to_csv() and key parameters are as follows:

• path_or_buf: file path or object where the CSV should be written;
• sep: delimiter for separating values (default is a comma ,);
• columns: subset of columns to write (default is all columns);
• header: whether to include column names as the header (default is True);
• index: whether to write row indices to the file (default is True).