Prepending Total (Sum, Count) of Each Column of Pandas DataFrame to CSV File

As a data scientist or analyst working with pandas DataFrames and CSV files, you’ve likely encountered situations where adding aggregate statistics, such as sums or counts, to each column of the DataFrame before writing it to a CSV file is necessary. In this article, we’ll explore different approaches to achieve this goal.

Understanding the Problem

When working with pandas DataFrames and CSV files, there are several ways to modify the data before saving it to disk. However, sometimes you need to prepend certain statistics, such as sums or counts, to each column of the DataFrame. This can be particularly useful when generating reports, analyzing data, or visualizing insights.

The Solution: Using Pandas Aggregation and CSV Writing

One common approach to this problem is using pandas’ built-in aggregation functions and the to_csv method. Here’s a step-by-step guide on how to achieve this:

Step 1: Create an Aggregate Summary of the DataFrame

First, we’ll create an aggregate summary of the DataFrame using the .agg() method. This method allows us to specify which columns to include in the summary and what aggregation function to apply.

import pandas as pd

# Sample DataFrame
df = pd.DataFrame({
    'E': [1, 2, 3],
    'F': [4, 5, 6],
    'T': [7, 8, 9]
})

# Create an aggregate summary of the DataFrame
summary = df.agg({
    'E': 'count',
    'F': 'sum',
    'T': 'sum'
})

Step 2: Reindex and Convert to a Frame

Next, we’ll reindex the summary DataFrame to match the original DataFrame’s column names and convert it to a frame using the .to_frame() method.

# Reindex and convert to a frame
summary = summary.reindex(df.columns).to_frame().T

Step 3: Write the Summary to CSV

We’ll then write the summary to a CSV file using the to_csv method. This method allows us to specify the index parameter as False to exclude the row index from the output.

# Write the summary to CSV
header = summary.to_csv(index=False, header=True)

Step 4: Write the Original DataFrame to CSV

Finally, we’ll write the original DataFrame to a separate CSV file using the to_csv method with the index parameter set to False.

# Write the original DataFrame to CSV
body = df.to_csv(index=False)

# Open the output file in write mode
with open('output.csv', 'w') as f:
    # Write the header and body to the file
    f.write(header)
    f.write(body)

Alternative Approach: Using `df.groupby()` and String Formatting

Another approach is to use the groupby method along with string formatting to achieve similar results. Here’s an example:

# Group by column and calculate sum and count
grouped = df.groupby('E')[['F', 'T']].agg(['sum', 'count'])

# Convert to a DataFrame
grouped = grouped.reset_index()

However, this approach can be less efficient than using the aggregation functions and to_csv method.

Tips and Best Practices

Use index=False when writing CSV files: This parameter prevents pandas from including the row index in the output CSV file.
Specify column names when writing to CSV: Use the .columns attribute or provide a list of column names as arguments to ensure accurate CSV formatting.

By following these steps and understanding how to work with pandas aggregation functions and CSV writing, you can effectively prepend aggregate statistics (sums and counts) to each column of your DataFrame before saving it to a CSV file.

Last modified on 2025-01-15