Working with Conditional Logic in Pandas

When working with data in pandas, it’s common to encounter scenarios where you want to apply a function or operation to each row of a DataFrame based on certain conditions. In this post, we’ll explore how to achieve this using conditional logic and the pandas library.

Understanding the Problem

The problem statement presents a scenario where we have a DataFrame df with columns col1, col2, and col3. We want to apply a function to each row of df only if the value in column col1 is equal to 12. For rows that don’t meet this condition, we want to return 0 on column col3.

Analyzing the Given Code

The provided code snippet attempts to solve this problem using the following approach:

df['col3'] = df['col2'].str.lower().str.contains('apple', na=0)

This code uses the str method to apply a function to each value in column col2. The lower() method is used to convert all values to lowercase, and then str.contains('apple') is applied to check if each value contains the string ‘apple’. Finally, na=0 ensures that NaN values are treated as 0.

However, this approach has some limitations. It doesn’t account for cases where the condition in column col1 is not met, and it treats all values in column col2 as strings.

The Correct Approach

To achieve our desired outcome, we need to use a different approach that combines the conditions in columns col1 and col2. One way to do this is by using the bitwise AND operator (&) to combine two conditions.

df['col3'] = (df['col1'] == 12) & (df['col2'].str.contains('apple'))

This code checks if the value in column col1 is equal to 12, and then applies the same function to each value in column col2. The bitwise AND operator ensures that only rows where both conditions are met have a true value assigned to column col3.

Understanding the Code

Let’s break down this code into smaller sections:

• df['col1'] == 12: This checks if the value in column col1 is equal to 12. The result is a boolean Series where each element represents whether the corresponding value in col1 meets the condition.
• df['col2'].str.contains('apple'): This applies the same function as before, but this time only to values that meet the condition in column col1. It checks if each value in column col2 contains the string ‘apple’.
• ( ) & ( ): The bitwise AND operator (&) is used to combine the two conditions. Only rows where both conditions are met have a true value assigned to column col3.

Example Walkthrough

Suppose we have the following DataFrame:

   col1  col2  col3
0    12  apple     ?
1    13  apple     ?
2    12  grape     ?

We want to assign a value to col3 based on the condition that col1 is equal to 12 and col2 contains the string ‘apple’.

1. First, we check if the value in column col1 is equal to 12:

   • For row 0: True
   • For row 1: False
   • For row 2: True

2. Then, we apply the function to each value in column col2 for rows where col1 meets the condition:

   • For row 0 (col1 is 12): apple contains ‘apple’, so this becomes True
   • For row 1 (col1 is 13): Does not meet the condition, so this remains False.
   • For row 2 (col1 is 12): grape does not contain ‘apple’, so this becomes False.

3. Finally, we combine these two conditions using the bitwise AND operator:

   • Row 0: Both conditions are met, so True & True = True.
   • Row 1: One condition is false, so False & False = False.
   • Row 2: Both conditions are not met, so False & False = False.

Therefore, the resulting DataFrame with assigned values for column col3 would be:

   col1  col2  col3
0    12  apple     True
1    13  apple     False
2    12  grape     False

This approach ensures that only rows where both conditions are met have a true value assigned to column col3.

Conclusion

In this post, we explored how to apply a function to each row of a DataFrame based on certain conditions using pandas. We discussed the limitations of the provided code snippet and introduced an alternative approach using bitwise logic. By combining multiple conditions with the bitwise AND operator, we can achieve our desired outcome and make informed decisions about data processing in pandas.

Last modified on 2023-09-16