atlassianhw 0.0.3

Atlassian application homework

atlassianhw

A collection of simple functions to help with the homework.

Install

pip install atlassianhw

How to use

Simple way to load in the data using AData():

for name, data in AData().raw_data.items():
    print(f'Data source: {name}')
    display(data.head())

/Users/jasonorawe/Documents/py_wd/git/atlassianhw/atlassianhw/utils.py:22: FutureWarning: Inferring datetime64[ns] from data containing strings is deprecated and will be removed in a future version. To retain the old behavior explicitly pass Series(data, dtype=datetime64[ns])
  'employee_details': pd.read_excel(pkgutil.get_data(__name__, '_data/Employee_details.xlsx')),

Data source: employee_details
Data source: glossary
Data source: manager_details
Data source: manager_mapping
Data source: promo_meta_data

<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </style>

	emp_id	Attrited	kudos_received_so_far	kudos_count_given_so_far	job_family	tenure_in_days_new	level	mnths_in_team	last_promotion_date
0	1	0	5.157031	2	Software Engineering	1065	Junior IC	35.0	2022-10-01
1	2	0	5.466410	9	Software Engineering	1066	Junior IC	35.0	2022-04-01
2	3	0	4.744126	18	Program Management	1079	Manager	19.0	2021-07-16
3	4	1	0.261903	0	Software Engineering	154	Director	6.0	2020-03-30
4	5	0	5.918518	8	Product Manager	1079	Manager	35.0	2022-10-01

<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </style>

	Dataset	Dataset info	Column Name	Description
0	promo_meta_data	historical promo metadata	level	level of employee
1	promo_meta_data	historical promo metadata	months_for_promotion	months for promotion in past
2	Manager_mapping	Manager mapping dataset	mgr_id	manager ID
3	Manager_mapping	Manager mapping dataset	emp_id	employee id
4	Attrition_Manager_info	Manager meta data	mgr_cum_churn_new	total churns under manager

<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </style>

	mgr_id	mgr_cum_churn_new	Tenure_Mgr	total_teams_mgr	exp_as_mgr
0	7582.0	0.000000	6-12 mons	1.0	90.0
1	6132.0	6.136273	3-6 mons	2.0	1489.0
2	7306.0	0.000000	More Than 10 years	2.0	1124.0
3	3712.0	2.899962	1-2 years	8.0	579.0
4	NaN	0.000000	0	1.0	1489.0

<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </style>

	mgr_id	emp_id
0	7582.0	1
1	6132.0	2
2	7306.0	3
3	3712.0	4
4	NaN	5

<style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } </style>

	level	months_for_promotion
0	Junior IC	37.0
1	Junior IC	40.0
2	Manager	30.0
3	Director	37.0
4	Manager	34.0