Is dit Missing at Random?

In de video-oefening zag je dat er verschillende soorten missendheid bestaan wanneer je met missende data te maken hebt. Ter herinnering, typen missendheid kunnen als volgt worden beschreven:

Missing Completely at Random: Geen systematische relatie tussen de missende waarden in een kolom en andere of eigen waarden.
Missing at Random: Er is een systematische relatie tussen de missende waarden in een kolom en andere geobserveerde waarden.
Missing Not at Random: Er is een systematische relatie tussen de missende waarden in een kolom en niet-geobserveerde waarden.

Je hebt een DataFrame met klanttevredenheidsscores voor een service. Welk type missendheid is dit?

Een satisfaction_score kolom met missende waarden voor zeer ontevreden klanten.

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Data opschonen in Python

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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?

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Exercise 10: Ontbrekende beleggers 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!