split_csv_file_into_multiple_small_files.py

import pandas as pd
import random

## Provide file name with path for example: "C:\Users\xxxxx\flights.csv"
split_source_file = input("File Name with absolute Path? : ")

## find number of lines using Pandas
pd_dataframe = pd.read_csv(split_source_file, header=0)
number_of_rows = len(pd_dataframe.index) + 1

## find number of lines using traditional python
# fh = open(split_source_file, 'r')
# for count, line in enumerate(fh):
#     pass
# py_number_of_rows = count

print(f"{number_of_rows}")

## Incase of equal split, provide the same number for min and max
min_rows = int(input("Minimum Number of rows per file? : "))
max_rows = int(input("Maximum Number of rows per file? : "))

file_increment = 1
skip_rows = 1

## first file random numbers
number_of_rows_perfile = random.randint(min_rows, max_rows)

while True:

    if number_of_rows_perfile <= 0:
        break
    ## Read CSV file with number of rows and skip respective number of lines
    df = pd.read_csv(split_source_file, header=None, nrows = number_of_rows_perfile,skiprows = skip_rows)

    ## Target file name
    split_target_file = f"{split_source_file[:-4]}_{file_increment}.csv"

    ## write to csv 
    df.to_csv(split_target_file, index=False, header=False, mode='a', chunksize=number_of_rows_perfile)

    file_increment += 1

    skip_rows += number_of_rows_perfile

    ## Last file handler
    if skip_rows >= number_of_rows:
        number_of_rows_perfile = number_of_rows - skip_rows
    else:
        number_of_rows_perfile = random.randint(min_rows, max_rows)