Conditional DataFrame Operations using Pandas

In this article, we will explore how to perform conditional operations on a pandas DataFrame. We will use the groupby method and apply a custom function to each group to calculate the desired output.

Introduction

Pandas is a powerful library for data manipulation and analysis in Python. One of its key features is the ability to perform grouping and aggregation operations on DataFrames. In this article, we will focus on conditional DataFrame operations using pandas.

Problem Statement

Given a DataFrame A with columns key1, key2, C1, and C2, we want to add a new column RESULT. The calculation for the RESULT column depends on the value of key2. We will perform the following operation:

• For each group having the same key1 values:
  • If key2 = X, then RESULT = 0
  • else, RESULT = (C1 | key2= Y) + (C2| key2= Y) + (C2| key2= X)
• We will use the groupby method and apply a custom function to each group to calculate the desired output.

Solution

To solve this problem, we will define a custom function f that takes a DataFrame as input and performs the desired calculation. The function will be applied to each group using the apply method.

Step 1: Define the Custom Function

def f(df):
    df['RESULT'] = df['C2'].sum() + df['C1'].loc[df['key2'] == 'Y'].sum()
    df['RESULT'].loc[df['key2'] == 'X'] = 0
    return df

In this function, we first calculate the sum of C2 for each row using the sum() method. We then select the values of C1 where key2 is equal to 'Y', sum them up, and add it to the previous result. Finally, we set the value of RESULT to 0 when key2 is equal to 'X'.

Step 2: Apply the Custom Function to Each Group

df = A.groupby('key1', sort=False).apply(f)

In this line, we use the groupby method to group the DataFrame by the key1 column. The sort=False parameter is used to ignore the sorting of the groups. We then apply the custom function f to each group using the apply() method.

Step 3: Print the Result

print(df)

Finally, we print the resulting DataFrame to verify that the calculation was performed correctly.

Example Use Case

Here is an example use case for this solution:

Suppose we have a DataFrame A with the following values:

   key1  key2  C1  C2
0    A    X   5   2
1    A    Y   3   2
2    B    X   6   1
3    B    Y   1   3
4    C    Y   1   4
5    D    X   2   3
6    D    Y   1   3

We can apply the custom function f to this DataFrame as follows:

df = A.groupby('key1', sort=False).apply(f)
print(df)

The resulting DataFrame will have the following values:

   key1  key2  C1  C2  RESULT
0    A    X   5   2       0
1    A    Y   3   2       7
2    B    X   6   1       0
3    B    Y   1   3       5
4    C    Y   1   4       5
5    D    X   2   3       0
6    D    Y   1   3       7

As we can see, the custom function f has successfully applied the desired calculation to each group of the DataFrame.

Conclusion

In this article, we have demonstrated how to perform conditional operations on a pandas DataFrame using the groupby method and a custom function. We have also provided an example use case to illustrate the solution. This technique can be useful in various data analysis tasks where grouping and aggregation are involved.

Last modified on 2024-09-04