df = pd.read_csv("sample.csv")

df["new_column"] = "abc"

df.to_csv("sample.csv", index=False)

import pandas as pd

csv_input = pd.read_csv('input.csv')

csv_input['Berries'] = csv_input['Name']

csv_input.to_csv('output.csv', index=False)

​

import csv

​

# Define the function abc() - replace this with your own function implementation

def abc(value):

    return value * 2  # For example, doubling the input value

​

def add_new_column(input_file, output_file):

    # Read the CSV file and store its data in a list of dictionaries

    with open(input_file, 'r') as csvfile:

        reader = csv.DictReader(csvfile)

        data = list(reader)

​

    # Add a new column to the data and fill it with the result of abc() function

    for row in data:

        new_column_value = abc(int(row['SomeColumn']))  # Replace 'SomeColumn' with the name of the column you want to use as input

        row['NewColumn'] = new_column_value

​

    # Write the modified data back to the CSV file with the new column

    fieldnames = reader.fieldnames + ['NewColumn']

    with open(output_file, 'w', newline='') as csvfile:

        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)

        writer.writeheader()

        writer.writerows(data)

​

if __name__ == "__main__":

    input_csv_file = "input.csv"  # Replace with the name of your input CSV file

    output_csv_file = "output.csv"  # Replace with the desired name of your output CSV file

    add_new_column(input_csv_file, output_csv_file)

    print("New column added and CSV file saved successfully!")