Creating a Column Based on Conditional Logic in Python

When working with Pandas DataFrames, we often encounter scenarios where we need to create a new column based on a conditional check between existing columns. This can be achieved using the np.where function with nested conditions.

To illustrate, consider the following DataFrame:

import pandas as pd

df = pd.DataFrame({
    "A": [2, 3, 1],
    "B": [2, 1, 3]
})

We want to create a new column C based on the following criteria:

• If A equals B, C should be 0.
• If A is greater than B, C should be 1.
• If A is less than B, C should be -1.

Using a Custom Function

One approach is to create a custom function that implements the conditional logic and apply it to the DataFrame:

def f(row):
    if row['A'] == row['B']:
        return 0
    elif row['A'] > row['B']:
        return 1
    else:
        return -1

df['C'] = df.apply(f, axis=1)

Using np.where

Alternatively, we can use the np.where function to directly assign values to the new column:

df['C'] = np.where(df['A'] == df['B'], 0, np.where(df['A'] > df['B'], 1, -1))

This approach is vectorized and more efficient for large datasets.

Result:

Both approaches produce the following result:

print(df)

   A  B  C
0  2  2  0
1  3  1  1
2  1  3 -1