How to Efficiently Append Multiple DataFrames within a For Loop in Python?

Appending Multiple DataFrames within a For Loop in Python

When working with large datasets stored in multiple Excel files, it is often necessary to consolidate all the data into a single DataFrame for further analysis or processing. This can be effortlessly achieved using Python's pandas library within a for loop.

To append dataframes, it is important to note that the DataFrame.append() method requires at least two arguments. In the provided code, only one argument, data, is passed. The correct approach is to store all the DataFrames in a list within the loop, and then use pd.concat to merge the list into a single DataFrame.

Here's a detailed explanation:

<code class="python">import pandas as pd
import glob

# Initialize an empty list to store DataFrames
appended_data = []

# Iterate over Excel files in a specified directory
for infile in glob.glob("*.xlsx"):
    print("Processing file:", infile)
    
    # Read data from Excel file into a DataFrame
    data = pd.read_excel(infile)
    
    # Append DataFrame to the list
    appended_data.append(data)

# Concatenate DataFrames to create a consolidated DataFrame
final_df = pd.concat(appended_data, ignore_index=True)

# Save consolidated data to a new Excel file
final_df.to_excel('appended.xlsx', index=False)</code>

By following this approach, you can seamlessly append multiple DataFrames generated within a for loop and save the consolidated data to a new Excel file. This allows you to work with large and disjointed datasets efficiently and effectively.

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