Comma Separated Variables (CSV)

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We now know how to create our own DataFrame. However, most of the time, we'll be working with datasets that already exist. One of the most common formats for big datasets is the CSV.

CSV (comma separated values) is a text-only spreadsheet format. You can find CSVs in lots of places:

• Online datasets
• Export from Excel or Google Sheets
• Export from SQL

The first row of a CSV contains column headings. All subsequent rows contain values. Each column heading and each variable is separated by a comma:

column1,column2,column3
value1,value2,value3

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Loading and Saving CSVs

When you have data in a CSV, you can load it into a DataFrame in Pandas using .read_csv():

pd.read_csv('my-csv-file.csv')

In the example above, the .read_csv() method is called. The CSV file called my-csv-file is passed in as an argument.

We can also save data to a CSV, using .to_csv().

df.to_csv('new-csv-file.csv')

In the example above, the .to_csv() method is called on df (which represents a DataFrame object). The name of the CSV file is passed in as an argument (new-csv-file.csv). By default, this method will save the CSV file in your current directory.