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Atlassian application homework

Project description

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

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