Welcome to the Data Department! I'm excited to have you join our team. Given your strong performance in your previous role, I'm confident you'll excel here as well. To help you get up to speed with our data analysis tools and processes, I'm assigning you a project that will introduce you to Pandas, a crucial library for data manipulation in Python.
Your task is to analyze our company's employee performance dataset using Pandas. This project will help you familiarize yourself with essential data analysis techniques while providing valuable insights for our HR department.
If you need a reference for setting up your workspace and virtual environment please refer to the Python Workspace Setup
Before you begin to analyze the data, take a look at the employee_performance.csv as well as the data-dictionary.md to understand our company's employee performance data.
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Implement your solution in the
employee_performance_challenge.pyfile. -
Write your functions above the
mainfunction. The following functions need to be implemented:load_employee_data()explore_employee_dataframe(df)select_and_filter_employee_data(df)employee_data_operations(df)top_3_employees_with_highest_salary(df)avg_performance_score_by_department(df)plot_salary_vs_years_of_experience(df)
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The
mainfunction is already provided and contains calls to your functions. -
To test your implementation:
- Uncomment the print statements in the
mainfunction. - Run the script in the terminal to see the output using
python3 employee_performance_challenge.py.
- Uncomment the print statements in the
-
Do not modify the
mainfunction itself; only implement the required functions above it.
Ensure your functions match the expected names and signatures as they are already being called in the main function.
The employee performance dataset is stored in a CSV file named employee_performance.csv in the data directory.
Note: If you can't remember how to complete any of the steps, check out the Pandas Basics Tutorial for a refresher. Try not to use AI for this, use the lesson, documentation for Pandas or do the research online. If you do use AI, create prompts that check for understanding and avoid simply giving you the answer.
Write a function load_employee_data() to load the dataset into a Pandas DataFrame.
Use the df.columns = df.columns.str.lower() to convert all column names to lowercase.
This will make it easier to work with the data in the rest of the challenge.
Write a function explore_employee_dataframe(df) to:
- Display the first 10 rows.
- Show the DataFrame info.
- Provide summary statistics.
Write a function select_and_filter_employee_data(df) to:
- Select the 'employee_id', 'first_name', 'last_name', and 'performance_score' columns.
- Filter employees with a
performance_scoregreater than 80. - Use
locto select specific rows and columns.
Write a function employee_data_operations(df) to:
- Add a new column
salary_per_yearthat displays the salary based on the number of years the employee has worked at the company. - Sort the DataFrame by
performance_scorein descending order.
Write a function top_3_employees_with_highest_salary(df) to:
- Sort the DataFrame by
salaryin descending order. - Select the top 3 employees.
- Display their
first_name,last_nameandemail, and `salary. Salary should be rounded to 2 decimal places.
Write a function avg_performance_score_by_department(df) to calculate the average performance score by department.
- Display the average performance score for each department.
Write a function plot_salary_vs_years_of_experience(df) to plot a scatter plot of salary vs years_at_company.
- use plt.scatter to plot the data
- use plt.show to display the plot
You can see the output snapshots in the output-snapshots.md file.