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Asked: 2024-09-22T08:07:37+05:30 In:

How can I add a new column to a pandas DataFrame that assigns values based on specific conditions evaluated from other columns? I am looking for a method to implement this effectively using pandas in Python.

Hey everyone!

I’m currently working with a pandas DataFrame in Python and I’m trying to add a new column that assigns values based on specific conditions from existing columns. Here’s the scenario:

I have a DataFrame that consists of employee data with the following columns: `Name`, `Age`, `Department`, and `Salary`. What I want to do is create a new column called `BonusEligibility` that assigns values based on the following conditions:

1. If the employee’s `Salary` is greater than 70,000, they should get a bonus eligibility status of “Eligible”.
2. If the `Department` is “Sales” and `Salary` is less than or equal to 70,000, they should receive “Consider Review”.
3. All other employees should receive “Not Eligible”.

Here’s a little illustration of what my DataFrame looks like:

| Name | Age | Department | Salary |
|——–|—–|————|——–|
| Alice | 30 | Sales | 65,000 |
| Bob | 45 | HR | 72,000 |
| Charlie| 29 | Sales | 60,000 |
| David | 50 | IT | 85,000 |

How can I add this new column effectively? I’m looking for a way that’s efficient and concise using pandas. Any tips or code snippets would be greatly appreciated! Thanks!

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    2 Answers

    1. “`html





      Pandas DataFrame – Bonus Eligibility

      How to Add a BonusEligibility Column in Pandas DataFrame

      To add a new column called BonusEligibility based on the conditions you specified, you can use the apply method along with a custom function. Here’s a simple code snippet that demonstrates how to do this:

      import pandas as pd

# Sample DataFrame
data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'David'],
    'Age': [30, 45, 29, 50],
    'Department': ['Sales', 'HR', 'Sales', 'IT'],
    'Salary': [65000, 72000, 60000, 85000]
}

df = pd.DataFrame(data)

# Function to determine Bonus Eligibility
def bonus_eligibility(row):
    if row['Salary'] > 70000:
        return 'Eligible'
    elif row['Department'] == 'Sales' and row['Salary'] <= 70000:
        return 'Consider Review'
    else:
        return 'Not Eligible'

# Applying the function to create the new column
df['BonusEligibility'] = df.apply(bonus_eligibility, axis=1)

# Display the updated DataFrame
print(df)

      This code defines a function bonus_eligibility that checks the salary and department of each employee and assigns the appropriate eligibility status. By applying this function across the rows of the DataFrame using apply method, we can efficiently create the new BonusEligibility column.

      Hope this helps!



      ```


    2. To add the `BonusEligibility` column to your DataFrame based on the conditions you’ve outlined, you can use the `apply` method in pandas along with a custom function. In this case, you’ll define a function that checks the conditions for each row and returns the appropriate eligibility status. Here’s a concise code snippet that demonstrates this approach:

      import pandas as pd

# Sample DataFrame
data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'David'],
    'Age': [30, 45, 29, 50],
    'Department': ['Sales', 'HR', 'Sales', 'IT'],
    'Salary': [65000, 72000, 60000, 85000]
}

df = pd.DataFrame(data)

# Function to determine bonus eligibility
def determine_bonus(row):
    if row['Salary'] > 70000:
        return 'Eligible'
    elif row['Department'] == 'Sales' and row['Salary'] <= 70000:
        return 'Consider Review'
    else:
        return 'Not Eligible'

# Applying the function to create a new column
df['BonusEligibility'] = df.apply(determine_bonus, axis=1)

# Display the updated DataFrame
print(df)

      This piece of code initializes a DataFrame with your sample data, defines a function to assign the bonus eligibility status, and then applies that function across the DataFrame to create the new column. The `apply` function effectively processes each row, allowing for concise conditional logic to be implemented. The resulting DataFrame will have the desired `BonusEligibility` column added!


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