How to Combine Pandas DataFrames Generated in a For Loop: A Comprehensive Solution

Combining Pandas DataFrames Generated in a For Loop: A Comprehensive Solution

When it comes to data manipulation, Pandas offers a powerful set of tools for working with structured data. One common task is to combine data from multiple sources. One way to achieve this is by generating dataframes in a for loop and then appending them to create a unified dataframe.

To append dataframes generated in a for loop, you'll need to utilize a slightly different approach from the one you tried. The code you provided:

appended_data = pandas.DataFrame.append(data) # requires at least two arguments

requires at least two dataframes as arguments, which is not suitable for appending multiple dataframes one by one. Instead, we can employ pd.concat to merge a list of dataframes into a single, larger dataframe.

Here's an improved solution:

<code class="python">appended_data = []
for infile in glob.glob("*.xlsx"):
    data = pandas.read_excel(infile)
    # Store each dataframe in a list
    appended_data.append(data)
# Concatenate the list of dataframes into a single dataframe
appended_data = pd.concat(appended_data)
# Write the resulting dataframe to a new Excel file
appended_data.to_excel('appended.xlsx')</code>

In this revised code:

1. We create an empty list appended_data to store individual dataframes.
2. Within the loop, we read each Excel file into a dataframe and append it to this list.
3. Using pd.concat, we merge all the dataframes in the list into a single dataframe named appended_data.
4. Finally, we write the appended dataframe to a new Excel file named "appended.xlsx".

This approach ensures that all dataframes generated in the loop are combined into a single dataframe, providing you with a unified dataset.

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