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Can someone help me understand why I am having issues with using "groupby and sum" in Pandas. I have data where col_1 is a string and col_2 is a column of ones I am using to create additional variables by group. I have the following for calculating a cumulative sum within groups that appears to work as expected:

df['var'] = df.groupby(['col_1'])['col_2'].cumsum()

(this is working fine to my knowledge)

However, when I attempt to calculate a sum or max by group, the resulting column is all nan and I am struggling to understand how or why this is happening to me.

df['var'] = df.groupby(['col_1'])['col_2'].cumsum()

(this is creating a column of nan)

Appreciate the help here - thanks!

I was trying to calcuate a sum by group (which should be 1 or 2) but instead am receiving just nan values.

Stuart
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1 Answers1

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Based on your description, it seems that you're trying to calculate a sum or max by group using the groupby function in Pandas. However, you mentioned that the resulting column is all "nan".

The reason you're getting "nan" values is likely because you're assigning the result of the groupby operation to the same column 'var' that you're trying to calculate. This overwrites the original values with the intermediate result of the groupby operation, which contains missing values for all rows except the last row of each group.

To fix this, you should assign the result of the groupby operation to a new column instead of overwriting the existing one. Here's the corrected code:

df['var'] = df.groupby('col_1')['col_2'].transform('sum')

In this code, the transform method is used instead of cumsum. The 'sum' argument specifies that you want to calculate the sum within each group. This will assign the sum value to each row within the respective group, without modifying the original values.

Similarly, you can calculate the maximum value within each group using the max function:

df['var'] = df.groupby('col_1')['col_2'].transform('max')

Now, the 'var' column should contain the sum or maximum value by group instead of "nan" values.

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