Calculating Cumulative Sum without Changing Week Order Number
Problem Statement
Given a pandas DataFrame with a date column that represents the start of each week, we want to create another column containing the cumulative sum of values from this same date column. However, there is an issue where the cumsum() function starts calculating from week no 1 instead of week no 14 for our specific use case.
Solution Overview
To solve this problem without disturbing the original order of the week numbers, we will employ two strategies:
Firstly, we create a default index for correct working and then sort values based on ‘RY’ and ‘Week no’. This ensures that the cumulative sum is calculated correctly from the 14th week onwards.
Secondly, we use the groupby function with cumsum() to achieve the same result in a simplified manner. We will explore both approaches further below.
Creating a Default Index for Correct Working
df = df.reset_index(drop=True)
By resetting the index, we create a default integer index starting from 0, which allows us to sort values based on ‘RY’ and ‘Week no’.
Sorting Values Based on ‘RY’ and ‘Week no’
df.sort_values(['RY','Week no'])
This step sorts the DataFrame in ascending order of ‘RY’ followed by ‘Week no’. This ensures that the cumulative sum is calculated correctly from week no 14 onwards.
Grouping by ‘RY’ and Calculating Cumulative Sum
df.groupby('RY')['Value'].cumsum()
We group the DataFrame by ‘RY’ and calculate the cumulative sum of values. This approach simplifies the solution while maintaining the correct calculation order.
Simplified Solution Using GroupBy.cumsum()
df['Cummulative Value'] = df.groupby('RY')['Value'].cumsum()
This is a more concise way to achieve the same result as before, using the groupby function with cumsum() directly. The sorting step can be omitted since the GroupBy.cumsum() method returns values in ascending order of the group keys (in this case, ‘RY’).
Comparison of Solutions
| Solution | Pros | Cons |
|---|---|---|
| Original Solution | No sorting required, easier to understand | More complex code |
| Simplified Solution 1 | Sorting step required, may be harder to follow for some users | Concise code, easy to implement |
| Simplified Solution 2 | No sorting step required, most concise solution | May require more lines of code, assumes ‘RY’ is the group key |
Conclusion
To calculate the cumulative sum without disturbing the week order number, we can use either the original approach with a sorting step or the simplified solutions that use GroupBy.cumsum(). The choice between these approaches depends on personal preference and the specific requirements of your project.
In general, the simplified solution using GroupBy.cumsum() is more concise and efficient but may require an understanding of how it works. The original approach with a sorting step can be easier to follow for some users but requires more lines of code.
Example Use Case
Suppose we have a new DataFrame df_new with additional columns ‘A’ and ‘B’. We want to calculate the cumulative sum of ‘Value’ while maintaining the correct order.
import pandas as pd
# Create sample data
data = {'RY': [2020]*52, 'Week no': range(1, 53), 'Value': np.random.rand(52)*100}
df_new = pd.DataFrame(data)
# Apply the simplified solution
df_new['Cummulative Value'] = df_new.groupby('RY')['Value'].cumsum()
By applying this solution to df_new, we can calculate the cumulative sum of ‘Value’ while maintaining the correct order.
Note that in practice, you would need to adapt this code to your specific data and requirements.
Last modified on 2024-07-15