Reading Multiple CSV Files and Concatenating Them into a Single DataFrame

Overview

In this article, we will explore how to read multiple CSV files from a directory, extract specific file names based on certain criteria, and concatenate them into a single DataFrame. We will also discuss the importance of handling different data types and providing explanations for each step.

Introduction

As a developer working with data, it’s common to encounter large datasets that need to be processed or analyzed. One such task is reading multiple CSV files from a directory and concatenating them into a single DataFrame. In this article, we will explore how to achieve this using Python and the pandas library.

Requirements

• Python 3.x
• pandas library
• glob library (for finding files in a directory)

Step 1: Setting Up the Directory Path and File Mask

The first step is to set up the directory path where the CSV files are stored. We will also define a file mask that specifies which files we want to include based on certain criteria.

path = 'C:/Users/csvfiles'
fmask = os.path.join(path, '*nba*.csv')

In this example, fmask is set to find all CSV files with the string “nba” in their names. This can be adjusted as needed to match specific file criteria.

Step 2: Defining a Function to Read and Concatenate CSV Files

Next, we will define a function called get_merged_csv() that takes in the directory path and file mask as parameters. Within this function, we will use the pandas library’s read_csv() function to read each CSV file and concatenate them into a single DataFrame.

import os
import glob
import pandas as pd

def get_merged_csv(flist, **kwargs):
    return pd.concat([pd.read_csv(f, **kwargs) for f in flist], ignore_index=True)

This function uses list comprehension to read each file and apply the provided arguments to read_csv(). The results are then concatenated using pd.concat().

Step 3: Using a List Comprehension or Dictionary

To get the desired output, we can use either a list comprehension or a dictionary to store the resulting DataFrames. Here’s how both approaches work:

List Comprehension

df_list = [get_merged_csv(path, fmask) for fmask in ['*nba.csv', '*basketball.csv', '*soccer.csv']]

This approach creates a list of DataFrames where each DataFrame corresponds to the specified file mask.

Dictionary

df_dict = {}
df_dict['nba'] = get_merged_csv(path, '*nba.csv')
df_dict['basketball'] = get_merged_csv(path, '*basketball.csv')
df_dict['soccer'] = get_merged_csv(path, '*soccer.csv')

This approach stores the resulting DataFrames in a dictionary where the keys match the file masks used.

Step 4: Handling Different File Types

When working with different data types (e.g., CSV files), it’s essential to handle them individually and provide explanations for each step. For instance, when reading CSV files, we might need to specify certain parameters like index_col=None or header=0.

df = get_merged_csv(glob.glob(fmask), index_col=None, usecols=['rank', 'name'])

In this case, the usecols parameter is used to select specific columns from the CSV files.

Example Usage

Here’s an example of how you can use these functions:

import os
import glob
import pandas as pd

def get_merged_csv(flist, **kwargs):
    return pd.concat([pd.read_csv(f, **kwargs) for f in flist], ignore_index=True)

path = 'C:/Users/csvfiles'
fmask = os.path.join(path, '*nba*.csv')

df_list = [get_merged_csv(path, fmask) for fmask in ['*nba.csv', '*basketball.csv', '*soccer.csv']]

print(df_list)

When run, this code will print out a list of DataFrames corresponding to the specified file masks.

Conclusion

In this article, we explored how to read multiple CSV files from a directory and concatenate them into a single DataFrame. We discussed different approaches like using a function with parameters or dictionary storage for storing resulting DataFrames. By following these steps and providing explanations for each step, you should be able to effectively handle your data when working with Python.

Last modified on 2023-07-29