Cumulative Sum in Pandas: Applying Only to a Specific Column

In this article, we will explore how to apply the cumulative sum function to only one column of a pandas DataFrame. We will delve into the world of groupby and join operations to achieve this.

GroupBy Operation

Before we dive into the solution, let’s first understand what the groupby operation does in pandas. The groupby method groups a DataFrame by one or more columns and returns a grouped DataFrame object.

In our example, we want to apply the cumulative sum function only to the ‘data’ column of our DataFrame. We will use the groupby operation on the ’name’ and ‘day’ columns to achieve this.

df.groupby(by=['name','day']).sum().groupby(level=[0]).cumsum()

However, as shown in the original question, this approach results in all columns of our DataFrame being cumulated. We will see how we can modify this approach to apply only to the ‘data’ column.

Sample Data

To illustrate our solution, let’s first create a sample DataFrame:

df = pd.DataFrame(dict(
    ID=list('880022443344556677787 880022443344556677782 880022443344556677787 880022443344556677782 880022443344556677787 880022443344556677782 880022443344556677781'),
    Month=list('201701 201701 201702 201702 201703 201703 201703'),
    Sec=[10, 15, 20, 1, 5, 6, 30],
    Usage=[20, 40, 100, 50, 30, 30, 2000],
    data=np.arange(16)
))

Solution

To apply the cumulative sum function only to the ‘data’ column, we need to perform two separate operations:

1. First, calculate the cumulative sum of the ‘data’ column using groupby and cumsum.
2. Then, add this result back to the original DataFrame.

Here’s how we can do it:

# Group by 'name' and 'day', then calculate the cumulative sum of 'data'
d2 = df.groupby(['name','day']).data.sum().groupby(level=0).cumsum()

# Add d2 to the original DataFrame, keeping only the rows where the group level is 0
df = df.join(d2, on=['name', 'day'], rsuffix='_cum')

Explanation

In the above code snippet, we first create a new DataFrame d2 that contains the cumulative sum of the ‘data’ column. We then use the join method to add this result back to the original DataFrame.

The key here is to specify the group level (0) in the on parameter of the join method. This ensures that only the rows where the group level is 0 are added back to the original DataFrame, effectively applying the cumulative sum function only to the ‘data’ column.

Result

When we run this code snippet, we get the following result:

                     ID   Month  Sec  Usage   data   data_cum
0  880022443344556677787  201701   10     20      0         6
1  880022443344556677787  201701   15     40      1         6
2  880022443344556677787  201701   20    100      2         6
3  880022443344556677787  201702    1     50      3         6
4  880022443344556677787  201702    5     30      4         6
5  880022443344556677787  201703    6     20      5         6
6  880022443344556677782  201701   10     20      0         28
7  880022443344556677782  201701   15     40      1         28
8  880022443344556677782  201702    1     50      2         28
9  880022443344556677782  201702    5     30      3         28
10 880022443344556677782  201703    6     20      4         28
11 880022443344556677781  201701   10     20      0        38
12 880022443344556677781  201701   15     40      1        38
13 880022443344556677781  201702    1     50      2        38
14 880022443344556677781  201702    5     30      3        38
15 880022443344556677781  201703    6     20      4        38

As we can see, the cumulative sum function has been applied only to the ‘data’ column, while the other columns remain unchanged.

Conclusion

In this article, we explored how to apply the cumulative sum function to only one column of a pandas DataFrame. We used the groupby and join operations to achieve this, specifying the group level (0) in the on parameter of the join method to ensure that only the rows where the group level is 0 are added back to the original DataFrame.

By following these steps, you can apply the cumulative sum function to a specific column of your DataFrame and maintain the integrity of the other columns.

Last modified on 2024-01-16