How to Convert Text File Data to a Dataframe in Python Without Pandas?

If you don’t want to use the pandas library, you can convert a text file to a DataFrame in Python using the built-in csv module along with the standard list and dict data structures. Here’s an example:

import csv

# Open the text file in read mode
with open('input.txt', 'r') as file:
    # Create a CSV reader
    reader = csv.reader(file, delimiter='\t')

    # Read the file contents into a list of rows
    rows = list(reader)

    # Get the header row
    header = rows[0]

    # Create a list of dictionaries for each row
    data = [dict(zip(header, row)) for row in rows[1:]]

# Print the data as a DataFrame-like representation
for row in data:
    print(row)

In this example, we open the text file 'input.txt' in read mode using open() and create a CSV reader with csv.reader(). We specify the delimiter as \t to match the delimiter used in your text file.

Next, we use a list comprehension to read the file contents into a list of rows. Each row is a list of values.

We then extract the header row from the list of rows and store it in the header variable.

Finally, we create a list of dictionaries data where each dictionary corresponds to a row in the text file. We use zip() to pair the header values with the corresponding row values and dict() to create a dictionary from each pair. We iterate over the rows starting from index 1 to skip the header row.

You can then work with the data list of dictionaries, which represents the data in a tabular format similar to a DataFrame. In the example, we simply print each row for demonstration purposes.

This approach allows you to convert a text file to a DataFrame-like representation without relying on the pandas library.