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.

This exercise is part of the course

Cleaning Data in Python

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Store fund columns to sum against
fund_columns = ['fund_A', 'fund_B', 'fund_C', 'fund_D']

# Find rows where fund_columns row sum == inv_amount
inv_equ = banking[____].____(____) == ____

# Store consistent and inconsistent data
consistent_inv = ____[____]
inconsistent_inv = ____[____]

# Store consistent and inconsistent data
print("Number of inconsistent investments: ", inconsistent_inv.shape[0])

Edit and Run Code

This exercise is part of the course

Cleaning Data in Python

IntermediateSkill Level

4.8+

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In this chapter, you'll learn how to overcome some of the most common dirty data problems. You'll convert data types, apply range constraints to remove future data points, and remove duplicated data points to avoid double-counting.

Exercise 1: Data type constraints Exercise 2: Common data types Exercise 3: Numeric data or ... ?Exercise 4: Summing strings and concatenating numbers Exercise 5: Data range constraints Exercise 6: Tire size constraints Exercise 7: Back to the future Exercise 8: Uniqueness constraints Exercise 9: How big is your subset?Exercise 10: Finding duplicates Exercise 11: Treating duplicates

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Exercise 1: Membership constraints Exercise 2: Members only Exercise 3: Finding consistency Exercise 4: Categorical variables Exercise 5: Categories of errors Exercise 6: Inconsistent categories Exercise 7: Remapping categories Exercise 8: Cleaning text data Exercise 9: Removing titles and taking names Exercise 10: Keeping it descriptive

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Exercise 1: Uniformity Exercise 2: Ambiguous dates Exercise 3: Uniform currencies Exercise 4: Uniform dates Exercise 5: Cross field validation Exercise 6: Cross field or no cross field?Exercise 7: How's our data integrity?

Current Exercise

Exercise 8: Completeness Exercise 9: Is this missing at random?Exercise 10: Missing investors Exercise 11: Follow the money

Record linkage is a powerful technique used to merge multiple datasets together, used when values have typos or different spellings. In this chapter, you'll learn how to link records by calculating the similarity between strings—you’ll then use your new skills to join two restaurant review datasets into one clean master dataset.

Exercise 1: Comparing strings Exercise 2: Minimum edit distance Exercise 3: The cutoff point Exercise 4: Remapping categories II Exercise 5: Generating pairs Exercise 6: To link or not to link?Exercise 7: Pairs of restaurants Exercise 8: Similar restaurants Exercise 9: Linking DataFrames Exercise 10: Getting the right index Exercise 11: Linking them together!Exercise 12: Congratulations!