Summing Until Condition in Pandas: A Comprehensive Guide

Introduction

When working with data, it’s often necessary to perform calculations that involve summing up values based on certain conditions. In this article, we’ll explore how to achieve this using pandas, a popular library for data manipulation and analysis.

The Problem Statement

Given a pandas DataFrame df containing ‘Date’, ‘Holiday’, and ‘Value’ columns, we want to:

1. Sum the ‘Value’ column for non-holiday days (i.e., ‘Holiday’ == 0).
2. Drop the holidays from the original DataFrame.
3. Display the results in a specific format.

The desired output should include the sum of values for each group of consecutive days ending with a holiday, along with the corresponding dates and totals.

Understanding Pandas and DataFrames

Before we dive into the solution, let’s briefly review how pandas and DataFrames work:

• DataFrames: A 2-dimensional labeled data structure with columns of potentially different types. Each column represents a variable, and each row represents an observation.
• Series: A 1-dimensional labeled array of values.

Key Pandas Functions Used in This Article

We’ll be using the following pandas functions:

• groupby(): Groups rows by one or more columns and performs aggregation operations on them.
• agg(): Applies a specified function to each group in the DataFrame.
• cumsum(): Calculates the cumulative sum of values along a specified axis.

Step 1: Importing Libraries and Loading Data

First, we need to import the necessary libraries and load our sample data:

# Import pandas library
import pandas as pd

# Define the sample data
data = {
    'Date': ['01.01.2022', '02.01.2022', '03.01.2022', '04.01.2022', '05.01.2022', '06.01.2022', '07.01.2022', '08.01.2022', '09.01.2022'],
    'Holiday': [0, 1, 1, 0, 0, 0, 0, 1, 0],
    'Value': [4, 0.5, 0.1, 0.2, 0.2, 0.4, 5, 0.3, 5]
}

# Create the DataFrame
df = pd.DataFrame(data)

Step 2: Filtering Non-Holiday Days

Next, we want to filter out non-holiday days from our original DataFrame:

# Filter non-holiday days
non_holiday_days = df[df['Holiday'] == 0]

Step 3: Grouping and Summing Values

Now, let’s group the remaining rows by consecutive days (i.e., Date column) ending with a holiday. We’ll calculate the cumulative sum of values within each group:

# Calculate cumulative sum for non-holiday days
group = non_holiday_days['Holiday'].rsub(1)[::-1].cumsum()

# Group and sum values
out = (non_holiday_days.groupby(group, sort=False, as_index=False)
        .agg({'Date': 'last', 'Holiday': 'last', 'Value': 'sum'})
       )

Step 4: Displaying the Results

Finally, we’ll display our desired output:

# Print the results
print(out)

Combining All Steps into a Single Function

Here’s the complete function combining all steps:

def sum_until_condition():
    # Import pandas library
    import pandas as pd

    # Define the sample data
    data = {
        'Date': ['01.01.2022', '02.01.2022', '03.01.2022', '04.01.2022', '05.01.2022', '06.01.2022', '07.01.2022', '08.01.2022', '09.01.2022'],
        'Holiday': [0, 1, 1, 0, 0, 0, 0, 1, 0],
        'Value': [4, 0.5, 0.1, 0.2, 0.2, 0.4, 5, 0.3, 5]
    }

    # Create the DataFrame
    df = pd.DataFrame(data)

    # Filter non-holiday days
    non_holiday_days = df[df['Holiday'] == 0]

    # Calculate cumulative sum for non-holiday days
    group = non_holiday_days['Holiday'].rsub(1)[::-1].cumsum()

    # Group and sum values
    out = (non_holiday_days.groupby(group, sort=False, as_index=False)
            .agg({'Date': 'last', 'Holiday': 'last', 'Value': 'sum'})
           )

    # Return the results
    return out

# Call the function
result = sum_until_condition()
print(result)

Conclusion

In this article, we’ve covered how to use pandas to achieve a specific task: summing values for non-holiday days and displaying the results in a particular format. We hope this comprehensive guide has provided you with a deeper understanding of working with DataFrames in pandas.

Last modified on 2023-08-03