Description

The HR boss wants to delve into the metrics of the two employee groups: those who left the company and those who still work for us. You decided to present the information in a table.

The HR boss asks for the following metrics:

• the median number of projects the employees in a group worked on, and how many employees worked on more than five projects;

• the mean and median time spent in the company;

• the share of employees who've had work accidents;

• the mean and standard deviation of the last evaluation score.

Remember that the guy from HR asks for those metrics for two different groups!

Objectives

Continue using your code from Stage 2 which successfully sorts the dataset by index. On top of that, implement the following features:

1. Write the count_bigger_5 function that counts the number of employees who worked on more than five projects. You will then use it in the agg() method to calculate the metric from the first point in the Description section;

2. Use groupby() with left column and agg() to generate a table with metrics according to the boss's needs (refer to example for the metrics);

3. Round all the numbers to two decimals. To round numbers to the second decimal place, you can apply the round(2) method to a DataFrame;

4. Print the resulting table as a Python dictionary. To do so, use the to_dict() method.

Example

Input: a merged DataFrame

Output (the answer is for reference only; the actual values may differ):

DataFrame:

    number_project           time_spend_company   Work_accident   last_evaluation
     median count_bigger_5        mean   median        mean          mean   std
left
0.0      3.0            120       2.40    3.0          0.24          0.64  0.25
1.0      5.0            85        4.85    4.0          0.11          0.77  0.13

Resulting dictionary:

{('number_project', 'median'): {0: 3, 1: 5},
 ('number_project', 'count_bigger_5'): {0: 120, 1: 85},
 ('time_spend_company', 'mean'): {0: 2.40, 1: 4.85},
 ('time_spend_company', 'median'): {0: 3.0, 1: 4.0},
 ('Work_accident', 'mean'): {0: 0.24, 1: 0.11},
 ('last_evaluation', 'mean'): {0: 0.64, 1: 0.77},
 ('last_evaluation', 'std'): {0: 0.25, 1: 0.13}}