How to Read Specific Columns from a Headerless CSV File Using Pandas?

How to Read a Table Without Headers and Selecting Specific Columns Using Pandas

In Python's Pandas library, reading data from a CSV file without headers can be performed using the pd.read_csv function with the header=None parameter. However, accessing specific columns within such a table requires a different approach than using usecols.

To read only the 4th and 7th columns from a CSV file with no headers, you can utilize the usecols parameter as follows:

df = pd.read_csv(file_path, header=None, usecols=[3,6])

Here, file_path represents the path to the CSV file, header=None specifies that the table doesn't have a header row, and usecols=[3,6] indicates that you want to read data from the 4th and 7th columns.

The numerical values passed to usecols refer to the positions of the desired columns. For example, the numbers 0, 1, 2, and so on, represent the first, second, third, and subsequent columns in the table.

By using this method, you can read only the specific columns you need, even from a table that doesn't have headers. Refer to the Pandas documentation for more information on the pd.read_csv function and its parameters.

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