Welcome to the lesson on parsing tables from CSV files. In our previous lesson, we focused on parsing text-based tables. Now, we're expanding on that knowledge to work with CSV files, a more structured and widely used format for tabular data.

CSV, which stands for Comma-Separated Values, is a file format that stores tabular data, such as a database or spreadsheet, in plain text. Each line in a CSV file corresponds to a row in the table, and each value is separated by a comma. CSV files are popular because they are simple and easily processed by a variety of programs, including Excel and most data analysis tools.

The CSV file is naturally formatted as a table. Here is an example:

It uses new lines for rows, and some separator (in this case, a comma) for columns.

In Python, the csv module provides a straightforward way to handle CSV files. This module is included with Python’s standard library, so you don't need to install anything separately. It simplifies reading from, and writing to, CSV files, helping avoid common pitfalls that arise from manual parsing.

The csv module contains different methods and classes that allow for easy reading and writing. For our purposes, the csv.reader will be particularly important, as it reads a CSV file and allows us to iterate over it row by row.

Before we can parse the CSV data, we need to open the file properly. Using Python’s built-in open() function, we can open our CSV file for reading. We use the with statement to ensure the file is closed automatically when we're done, freeing up resources efficiently.

Here's how you would open a CSV file named data.csv:

Notice the use of newline=''. This is important because it ensures consistent reading across different platforms by preventing automatic newline translation. The with statement handles the file closing, saving us from having to call close() manually.

Once we've got our file object using with open(), we can start reading the contents using csv.reader. This functionality helps us process the CSV data correctly, converting each line into a series of values.

In this snippet, csv_reader is an object that allows us to loop over each row in the CSV file. The csv.reader takes care of correctly parsing the lines based on commas.

Often, CSV files have a header row. When reading a txt file table, we skipped the header with slicing. For csv.reader, it doesn't work, as it is not subscriptable. Instead, you can skip the header row using the next() function, which moves the reader to the next line:

Skipping the header is useful for data consistency, to make the parsing easier.

With the header out of the way, you can now iterate through the remaining rows and extract data. This time, let's not only read the data but solve some real-work related task. For example, imagine you want to collect all ages from our CSV file into a list to make a statistical analysis:

In this code, the for loop goes through each row in the CSV file, appending the value from the second column to the ages list. Note that we also convert ages to integer. Once complete, ages contains all the age data from the CSV.

Output:

This output confirms that ages have been successfully extracted and stored in a list.

By default, the csv.reader assumes the delimiter is a comma. However, CSV files can use different delimiters, such as semicolons or tabs, depending on how the data was exported. To specify a different delimiter, you need to provide the delimiter parameter when creating the csv.reader object. For instance, if your CSV file uses semicolons as delimiters, you would specify it like this:

This tells the csv.reader to use semicolons instead of commas to separate values, ensuring accurate parsing of the data. Adjust the delimiter parameter to match the character used in your specific CSV files.

In this lesson, you've learned how to parse data from a CSV file using Python’s csv module. You saw how to open files using the with statement, read data using csv.reader, skip headers, and extract specific columns into Python lists.

These skills are essential for working with structured tabular data and will serve as a foundation for more advanced data manipulation tasks. As you move on to the practice exercises, you'll have the opportunity to apply what you've learned, further reinforcing your understanding of CSV parsing.

Keep practicing, and remember, you're well on your way to becoming proficient in handling data from different file formats.