Question 13
Consider the given Table 1: Banking data for the given subquestions stored as pandas dataframe in variable df
>>> import pandas as pd>>> df = pd.read_csv('dataset.csv')>>> print(df)| Age | Job | Marital | Education | Balance | Housing | Contact |
|---|---|---|---|---|---|---|
| 21 | unemployed | married | secondary | 77387 | no | telephone |
| 49 | management | married | tertiary | 2037 | no | nan |
| 72 | self-employed | married | tertiary | 132 | no | cellular |
| 31 | blue-collar | married | secondary | 298 | yes | nan |
| 28 | admin | single | secondary | 2831 | yes | nan |
| 39 | technician | single | secondary | 15 | yes | cellular |
| 32 | blue-collar | married | primary | 131 | yes | nan |
| 59 | management | married | tertiary | 5314 | no | cellular |
| 27 | technician | single | secondary | 155 | yes | cellular |
| 47 | blue-collar | married | primary | 259 | no | cellular |
Table 1: Banking Data
Based on the above data, answer the given subquestions.
In Contact column which of the following statistical measures can be used to replace nan values?
Mean
Median
Mode
Variance