Ontbrekende beleggers

Omgaan met missende data is een van de meest voorkomende taken in data science. Er zijn verschillende soorten ontbrekende waarden, en ook verschillende manieren om ermee om te gaan.

Je hebt zojuist een nieuwe versie van de banking-DataFrame gekregen met gegevens over het bedrag op rekeningen en belegd vermogen voor nieuwe en bestaande klanten. Er zijn echter rijen met missende waarden in inv_amount.

Je weet zeker dat de meeste klanten onder de 25 nog geen beleggingsrekening hebben, en vermoedt dat dit de missendheid kan verklaren. De pakketten pandas, missingno en matplotlib.pyplot zijn respectievelijk als pd, msno en plt geïmporteerd. De DataFrame banking staat in je omgeving.

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Data opschonen in Python

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# Print number of missing values in banking
print(____)

# Visualize missingness matrix
____
____

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Deze oefening maakt deel uit van de cursus

Data opschonen in Python

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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

Categorical and text data can often be some of the messiest parts of a dataset due to their unstructured nature. In this chapter, you’ll learn how to fix whitespace and capitalization inconsistencies in category labels, collapse multiple categories into one, and reformat strings for consistency.

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

In this chapter, you'll dive into more advanced data cleaning problems, such as ensuring that weights are all written in kilograms instead of pounds. You'll also gain invaluable skills that will help you verify that values have been added correctly, and that missing values don't negatively impact your analyses.

Exercise 1: Uniformiteit Exercise 2: Ambigue datums Exercise 3: Eenduidige valuta Exercise 4: Eenduidige datums Exercise 5: Validatie over velden heen Exercise 6: Cross field of geen cross field?Exercise 7: Hoe staat het met onze dataintegriteit?Exercise 8: Volledigheid Exercise 9: Is dit Missing at Random?Exercise 10: Ontbrekende beleggers

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Exercise 11: Volg het geld

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!