Creating a New Column in Pandas Using Logical Slicing and Group By by Different Columns

Introduction

In this article, we will explore how to create a new column in a pandas DataFrame using logical slicing and the groupby function. We will also discuss an alternative approach using SQL.

Problem Statement

Given a DataFrame df with columns 'a', 'b', 'c', and 'd', we want to add a new column 'sum' that contains the sum of column 'c' only for rows where conditionals are met, such as when column 'a' == 'a' and column 'b' == 1. We also want to avoid using the merge function.

Solution

Method 1: Using Logical Slicing and Group By

To solve this problem, we will use logical slicing to create a boolean mask that identifies rows meeting our conditionals. Then, we will group the DataFrame by column 'd' and apply the transform method with a lambda function that calculates the sum of column 'c' only for rows where our conditionals are met.

import pandas as pd

# Create sample DataFrame
df = pd.DataFrame({'a': ['a', 'a', 'b', 'a', 'b', 'a', 'a', 'a'], 
                   'b': [1, 0, 0, 1, 0, 1, 1, 1], 
                   'c': [1, 2, 3, 4, 5, 6, 7, 8],
                   'd': ['1', '2', '1', '2', '1', '2', '1', '2']})

# Create boolean mask
mask = np.logical_and(df['a'] == 'a' , df['b'] == 1)

# Apply groupby and transform with lambda function
df['sum'] = df.groupby('d')['c'].transform(lambda x: x[mask].sum())

print(df)

Output:

Method 2: Using SQL (Alternative Approach)

For completeness, we will also explore an alternative approach using SQL. While this method can be useful for certain use cases, it may not be the most efficient or flexible solution for all scenarios.

# Alternative approach using SQL
import pandas as pd

# Create sample DataFrame
df = pd.DataFrame({'a': ['a', 'a', 'b', 'a', 'b', 'a', 'a', 'a'], 
                   'b': [1, 0, 0, 1, 0, 1, 1, 1], 
                   'c': [1, 2, 3, 4, 5, 6, 7, 8],
                   'd': ['1', '2', '1', '2', '1', '2', '1', '2']})

# Create SQL query
query = """
    SELECT d, SUM(c) as sum_val
    FROM df
    WHERE (a == 'a' AND b == 1)
    GROUP BY d
"""

# Execute SQL query and store results in new DataFrame
new_df = pd.read_sql(query, df)

# Reorder columns to match desired output
new_df.columns = ['d', 'sum']
print(new_df)

Output:

Conclusion

In this article, we explored how to create a new column in a pandas DataFrame using logical slicing and the groupby function. We also discussed an alternative approach using SQL. While both methods have their use cases, the first method is generally more efficient and flexible for most scenarios.

Last modified on 2024-05-09