Recalculating Values in a Pandas DataFrame Based on Conditions

In this article, we’ll explore how to recalculate values in a pandas DataFrame based on specific conditions using Python and the popular data analysis library, pandas.

Introduction

The original example provided is a simple way to calculate the percentage of OT hours for each employee and then subtract that percentage from their TRVL hours. We will build upon this example by using a more general approach that allows us to update values in a DataFrame based on specific conditions.

Using GroupBy and apply

One common technique used in data analysis is grouping data by certain criteria and applying a function to each group. In our case, we can use the groupby function from pandas to group the DataFrame by ‘Empl_Id’ and then apply an operation to each group that calculates the percentage of OT hours for that employee.

Grouping by Category

Another approach is to separate the categories that we don’t want to update (OT, MILE, REST) and calculate the factor for those categories. Then we can use this factor to adjust the values in the other categories.

Using boolean indexing

We’ll explore both approaches using Python code examples.

Approach 1: Calculating Percentage of OT Hours and Subtracting from Other Categories

The following approach uses grouping by ‘Empl_Id’ and calculating the percentage of OT hours for each employee. Then, it subtracts that percentage from their TRVL hours.

Code

import pandas as pd

# create a sample DataFrame
df = pd.DataFrame({
    'Empl_Id': [1, 2, 3],
    'Category': ['MILE', 'REST', 'TRVL'],
    'Value': [43, 10, 20]
})

def calculate_percentage(group):
    ot_hours = group[group['Category'] == 'OT']['Value'].sum()
    total_hours = (group.loc[~group['Category'].isin(['OT']), 'Value']).sum()

    if total_hours > 0:
        factor = ot_hours / total_hours
    else:
        return 1

    # subtract percentage of OT from other categories
    for category in ['MILE', 'REST']:
        group.loc[group['Category'] == category, 'Value'] -= factor * group.loc[group['Category'] == category, 'Value']

    return group

# apply the function to each group
grouped_df = df.groupby('Empl_Id').apply(calculate_percentage).reset_index(drop=True)

print(grouped_df)

Approach 2: Calculating Factor for Categories and Adjusting Values

The following approach separates the categories that we don’t want to update (OT, MILE, REST) and calculates the factor for those categories. Then it uses this factor to adjust the values in the other categories.

Code

import pandas as pd

# create a sample DataFrame
df = pd.DataFrame({
    'Empl_Id': [1, 2, 3],
    'Category': ['MILE', 'REST', 'TRVL'],
    'Value': [43, 10, 20]
})

keep = ['OT', 'MILE', 'REST']
factor = (df.groupby('Empl_Id')
          .apply(lambda g: g.loc[g['Category'].eq(keep), 'Value'].sum()
                  /g.loc[~g['Category'].isin(keep), 'Value'].sum()
                )
         )

# update values in categories that are not OT, MILE or REST
df.loc[~df['Category'].isin(keep), 'Value'] *= df['Empl_Id'].map(factor)

print(df)

Conclusion

In this article, we have explored how to recalculate values in a pandas DataFrame based on specific conditions. We have used two different approaches: calculating the percentage of OT hours and subtracting from other categories, and calculating a factor for certain categories and adjusting the values accordingly.

We can use these approaches to solve a variety of data analysis problems where you need to update values in a DataFrame based on certain conditions.

Last modified on 2024-05-10