Understanding the Problem and the Current Code

The problem at hand involves cleaning and transforming a dataset that contains information about employees, their statuses, and manager IDs. The goal is to create a new table with only active employees who are not managers of other employees.

We have been provided with an example code snippet that attempts to solve this problem but seems to be stuck. In this article, we’ll break down the current code, understand what’s going wrong, and then provide a step-by-step guide on how to achieve our desired output.

Problem Statement

Given a table of employee information, including Emp_ID, Status, Manager_ID, and Employee_count, we want to filter out employees who are terminated (Termed) and not managers of other employees. We also want to count the number of non-terminated employees for each manager ID.

Current Code Analysis

The current code snippet attempts to solve the problem by:

1. Grouping the data by Emp_ID and taking the last row’s Status value.
2. Mapping this new Status value back to all rows in the original dataset.
3. Counting the unique Emp_IDs for each Manager_ID.
4. Merging these counts with the mapped Status values.

However, there are a few issues with the current code:

• The first step assumes that the last row of each Emp_ID has the correct status, which might not always be true.
• The second step maps the wrong value to all rows in the original dataset (it should map to the Employee_count column).
• The third and fourth steps are incorrect and do not solve the problem.

Step-by-Step Solution

Let’s break down the solution into smaller, manageable pieces:

1. Clean and Transform the Data

First, we’ll create a new column that indicates whether an employee is active or terminated.

import pandas as pd

# Create a sample dataset (in reality, you'd use your actual data)
data = {
    'Emp_ID': [1, 2, 3, 4, 5],
    'Status': ['Active', 'Active', 'Termed', 'Termed', 'Termed'],
    'Manager_ID': [3, 3, 5, 3, -],
    'Employee_count': [0, 0, 3, 0, 1]
}

df = pd.DataFrame(data)

# Add a new column that indicates whether an employee is active or terminated
df['Active'] = df['Status'] == 'Active'

2. Filter Out Terminated Employees

Next, we’ll filter out the rows where Emp_ID is terminated.

# Filter out employees who are terminated
active_df = df[~df['Emp_ID'].isin(df[df['Status'] == 'Termed']['Emp_ID'])]

3. Create a Lookup Series for Manager IDs

We’ll create a lookup series that maps each Manager_ID to its corresponding count of non-terminated employees.

# Group by Manager_ID and count the number of non-terminated employees
s_mng_cnt = active_df.groupby('Manager_ID')['Emp_ID'].nunique()

# Merge this new column with our original dataset
active_df['Employee_count'] = s_mng_cnt.map(lambda x: x)

4. Final Result

Finally, we’ll select only the columns that we’re interested in and display the result.

# Select only the desired columns and display the result
final_result = active_df[['Emp_ID', 'Status', 'Manager_ID', 'Employee_count']]

print(final_result)

Putting it All Together

Here is the complete code snippet:

import pandas as pd

data = {
    'Emp_ID': [1, 2, 3, 4, 5],
    'Status': ['Active', 'Active', 'Termed', 'Termed', 'Termin'],
    'Manager_ID': [3, 3, 5, 3, -],
    'Employee_count': [0, 0, 3, 0, 1]
}

df = pd.DataFrame(data)

active_df = df[~df['Emp_ID'].isin(df[df['Status'] == 'Termin']['Emp_ID'])]

s_mng_cnt = active_df.groupby('Manager_ID')['Emp_ID'].nunique()

active_df['Employee_count'] = s_mng_cnt.map(lambda x: x)

final_result = active_df[['Emp_ID', 'Status', 'Manager_ID', 'Employee_count']]

print(final_result)

This code snippet should give you the desired output. Remember to replace the sample data with your actual dataset.

Last modified on 2023-09-30