import pandas as pdКейс про банк
Рассмотрим пример данных Bank Marketing Data Set. Данные связаны с кампаниями прямого маркетинга (телефонными звонками) португальского банковского учреждения.
url = "https://raw.githubusercontent.com/dm-fedorov/pandas_basic/master/data/bank-full.csv"bank = pd.read_csv(url, delimiter=';')
bank.head()| age | job | marital | education | default | balance | housing | loan | contact | day | month | duration | campaign | pdays | previous | poutcome | y | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 58 | management | married | tertiary | no | 2143 | yes | no | unknown | 5 | may | 261 | 1 | -1 | 0 | unknown | no |
| 1 | 44 | technician | single | secondary | no | 29 | yes | no | unknown | 5 | may | 151 | 1 | -1 | 0 | unknown | no |
| 2 | 33 | entrepreneur | married | secondary | no | 2 | yes | yes | unknown | 5 | may | 76 | 1 | -1 | 0 | unknown | no |
| 3 | 47 | blue-collar | married | unknown | no | 1506 | yes | no | unknown | 5 | may | 92 | 1 | -1 | 0 | unknown | no |
| 4 | 33 | unknown | single | unknown | no | 1 | no | no | unknown | 5 | may | 198 | 1 | -1 | 0 | unknown | no |
Определите средний возраст клиентов банка
Определите профессии клиентов банка, которые имеют семейный статус ‘single’