How's our data integrity?

New data has been merged into the banking DataFrame that contains details on how investments in the inv_amount column are allocated across four different funds A, B, C and D.

Furthermore, the age and birthdays of customers are now stored in the age and birth_date columns respectively.

You want to understand how customers of different age groups invest. However, you want to first make sure the data you're analyzing is correct. You will do so by cross field checking values of inv_amount and age against the amount invested in different funds and customers' birthdays. Both pandas and datetime have been imported as pd and dt respectively.

1
- Find the rows where the sum of all rows of the fund_columns in banking are equal to the inv_amount column.
- Store the values of banking with consistent inv_amount in consistent_inv, and those with inconsistent ones in inconsistent_inv.
2
- Store today's date into today, and manually calculate customers' ages and store them in ages_manual.
- Find all rows of banking where the age column is equal to ages_manual and then filter banking into consistent_ages and inconsistent_ages.

Exercise

How's our data integrity?

Instructions 1/2