Merging DataFrames with NaN Values in Results

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In this article, we’ll explore the issue of obtaining NaN values when merging two DataFrames using pd.merge. We’ll go through a step-by-step explanation of why this happens and provide solutions to avoid it.

Introduction

The problem arises because the iteration over rows in the original DataFrame (df) is not designed correctly. This results in incorrect calculations that lead to NaN values in the merged DataFrame.

Understanding the Issue

To understand what’s happening, let’s break down the code:

def calculate_days_and_overtime(df):
    results = []

    for index, row in df.iterrows():
        for index, row in df.iterrows():
            # calculations and operations...

In this code snippet, we have an inner loop that iterates over each row of df. The first iteration is correct, but the second iteration will iterate over every row again. This means that for each row, it’s calculating and appending data to the results list multiple times.

Why the Incorrect Calculation?

The incorrect calculation happens because we’re using row as both an index and a value inside the inner loop. When we use df.iterrows() twice in a row, it returns the same index and values for each iteration of the loop. This leads to duplicated data being appended to our results list.

Solution: Iterating Over Unique Rows

To avoid this issue, we need to ensure that we’re only iterating over unique rows once. We can do this by changing our approach:

def calculate_days_and_overtime(df):
    results = []

    for index, row in df.iterrows():
        # calculations and operations...

In this corrected version, we iterate only over each row of df once.

Solution: Avoiding NaN Values

Another solution to avoid NaN values is to use the groupby method instead of iterating over rows. This will ensure that all data for a given employee ID is calculated together.

def calculate_days_and_overtime(df):
    results = []

    for index, group in df.groupby('Employee_ID'):
        total_hours = group['Hours_Worked_Per_Week'].sum()
        # calculations and operations...

        results.append({
            'Employee_ID': group['Employee_ID'].values[0],
            'Regular Days': regular_days,
            'Overtime Days': overtime_days,
            'Remaining Overtime Hours': remaining_overtime_hours,
            'Total Days Worked': total_days_worked,
            'Work Location': group['Employment_Type'].values[0]
        })

In this solution, we use df.groupby to group the rows by employee ID and perform our calculations. We then append each calculated result to the results list.

Solution: Properly Calculating Days Worked

To correctly calculate days worked and overtime hours, you should be using the formula (total_hours - regular_hours) / regular_hours_per_day + 1 for remaining overtime hours.

def calculate_days_and_overtime(df):
    results = []

    for index, group in df.groupby('Employee_ID'):
        total_hours = group['Hours_Worked_Per_Week'].sum()
        regular_hours_per_day = 8
        days_per_week = 5

        regular_days = (total_hours - (total_hours // regular_hours_per_day) * regular_hours_per_day)
        overtime = max(0, (total_hours % regular_hours_per_day))

        regular_days_worked = min(days_per_week, total_hours // regular_hours_per_day)

        results.append({
            'Employee_ID': group['Employee_ID'].values[0],
            'Regular Days': regular_days_worked,
            'Overtime Days': overtime // regular_hours_per_day,
            'Remaining Overtime Hours': overtime % regular_hours_per_day,
            'Total Days Worked': regular_days + (1 if overtime else 0)
        })

In this corrected version, we’re using the formula to calculate regular_days_worked and total_days_worked. We also ensure that there’s a remaining overtime hour only when total_hours % regular_hours_per_day != 0.

Merging DataFrames Correctly

To merge two DataFrames correctly, use the pd.merge() function with the correct arguments.

merged_df = pd.merge(df, results_df, on='Employee_ID')

In this solution, we’re using the on argument to specify that we want to merge on the employee ID column.

Last modified on 2023-05-17