Comma Separated Files

This lesson focuses on CSV type files. It gives a complete explanation about how to read data from CSV files using the Pandas library of Python.

We'll cover the following

Introduction to CSV file #

Comma-separated files (CSV) are common in machine learning. These files have a row of data per line of the file and each line is a comma-separated list in which each element is a column. Pandas makes it easy to read this data.

Reading CSV file with Pandas #

The documentation can be found here. Before reading a CSV file, there are three parameters that should be known:

  • sep - this defaults to a comma, but we can specify anything we want. For example, CSV format is poor if some of your columns contain commas. A better option might be a |.
  • header - which row (if any) have the column names.
  • names - column names to use.

If your CSV is well-formatted where the first row is the column names, then the default parameters should work well.

It is important to note that while it might sound simple to read in a CSV file without Pandas, CSV files are often very messy and reading them appropriately can often consist of handling many edge cases. Pandas module handles many of those edge cases right out of the box and has many parameters that you can change to handle messier CSV files.

Let’s see an example with code.