I have looked up for the answer but wasn't satisfied. I don't want to store all the csv files in a dictionary or append in a single dataframe.

I have a folder consisting of 10 csv files. I want a separate dataframe for each csv file instead.

I was able to create a dictionary consisting of key representing file name and value representing its data but I want to break that further into individual dataframes.

import pandas as pd
import numpy as np
import os 
import glob

path = r'C:\Users\.....\files'
csv_files = glob.glob(os.path.join(path_new, '*.csv'))
files_name = os.listdir(path_new)

d = {os.path.splitext(os.path.basename(f))[0] : pd.read_csv(f) for f in glob.glob(os.path.join(path, '*.csv'))} 

This creates a dictionary and I can access separate dataframes by calling the key of dictionary.

I tried 2nd approach by defining a function to read csv:

def read_files(name, path_final):
    name = pd.read_csv(path_final)
    return name

for csvs in files_name:
    name = csvs.split('.')[0]
    path_final = os.path.join(path, csvs)
    read_files(name, path_final)

I tried 3rd approach but it doesn't help either:

for each_file in files_name:
    with open(os.path.join(path_new, each_file)) as f:
        each_file = pd.read_csv(f)
        print(each_file)

Is there a way to create separate dataframe dynamically?

I have referred to below links so far:

• How to read each file from a folder and create seperate data frames for each file?
• Extracting dataframe from dictionary of multiple dataframes
• Get separate dataframes from a dictionary of dataframes Python

CodePudding user response：

globals() returns a dictionary of current module's variables. Instead of creating a dictionary in your first approach, you can create a new variable dynamically by creating a key on globals():

for f in glob.glob(os.path.join(path, '*.csv'):
    variable_name = os.path.splitext(os.path.basename(f))[0]
    globals()[variable_name] = pd.read_csv(f)