Titels verwijderen en namen overhouden

Bij het verzamelen van metadata van enquêterespondenten in de airlines-DataFrame is de volledige naam van respondenten opgeslagen in de kolom full_name. Maar bij nader inzien bleek dat veel namen voorafgegaan worden door aanspreektitels zoals "Dr.", "Mr.", "Ms." en "Miss".

Je uiteindelijke doel is om twee nieuwe kolommen te maken, first_name en last_name, met respectievelijk de voor- en achternaam van respondenten. Voordat je dat doet, moet je echter eerst de aanspreektitels verwijderen.

De DataFrame airlines staat in je omgeving, samen met pandas als pd.

Deze oefening maakt deel uit van de cursus

Data opschonen in Python

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Oefeninstructies

Verwijder "Dr.", "Mr.", "Miss" en "Ms." uit full_name door ze in die volgorde te vervangen door een lege string "".
Voer de assert-instructie uit met .str.contains() die test of full_name nog een van de aanspreektitels bevat.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Replace "Dr." with empty string ""
airlines['full_name'] = airlines['full_name'].____.____("____","")

# Replace "Mr." with empty string ""
airlines['full_name'] = ____

# Replace "Miss" with empty string ""
____

# Replace "Ms." with empty string ""
____

# Assert that full_name has no honorifics
assert airlines['full_name'].str.contains('Ms.|Mr.|Miss|Dr.').any() == False

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Data opschonen in Python

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Begin de cursus gratis

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: Lidmaatschapsbeperkingen Exercise 2: Alleen voor leden Exercise 3: Consistentie vinden Exercise 4: Categorische variabelen Exercise 5: Categorieën van fouten Exercise 6: Inconsistente categorieën Exercise 7: Categorieën opnieuw toewijzen Exercise 8: Tekstgegevens opschonen Exercise 9: Titels verwijderen en namen overhouden

Huidige oefening

Exercise 10: Hou het beschrijvend

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: 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?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!