Alles aan elkaar koppelen!

In de vorige les heb je het grootste deel van het werk voltooid om restaurants en restaurants_new te koppelen. Je hebt verschillende paren met mogelijk overeenkomende rijen gegenereerd, exact gezocht naar overeenkomsten in de kolommen cuisine_type en city, en vergelijkbare strings vergeleken in de kolom rest_name. De DataFrame met de scores heb je opgeslagen in potential_matches.

Nu is het eindelijk tijd om beide DataFrames te koppelen. Dat doe je door eerst alle rij-indexen van restaurants_new te extraheren die overeenkomen op de bovenstaande kolommen uit potential_matches. Vervolgens subset je restaurants_new op deze indexen en voeg je tot slot de niet-duplicaatwaarden samen met restaurants. Alle DataFrames staan in je omgeving, en pandas is geïmporteerd als pd.

Deze oefening maakt deel uit van de cursus

Data opschonen in Python

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Oefeninstructies

Isoleer rijen in potential_matches waarvoor de rijsom groter dan of gelijk aan 3 is met de methode .sum().
Extraheer de tweede kolomindex uit matches, die de rij-indexen van overeenkomende records uit restaurants_new voorstelt, met de methode .get_level_values().
Subselecteer restaurants_new voor rijen die niet in matching_indices staan.
Concateneer restaurants en non_dup.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Isolate potential matches with row sum >=3
matches = ____[____.___(____) >= ____]

# Get values of second column index of matches
matching_indices = matches.____.____(____)

# Subset restaurants_new based on non-duplicate values
non_dup = ____[~restaurants_new.index.____(____)]

# Concatenate restaurants and non_dup
full_restaurants = pd.____([____, ____])
print(full_restaurants)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Data opschonen in Python

SkillTag.level.intermediateSkillTag.label

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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: 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: 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: Strings vergelijken Exercise 2: Minimale bewerkingsafstand Exercise 3: Het afkappunt Exercise 4: Categorieën opnieuw toewijzen II Exercise 5: Paren genereren Exercise 6: Koppelen of niet koppelen?Exercise 7: Paren van restaurants Exercise 8: Vergelijkbare restaurants Exercise 9: DataFrames koppelen Exercise 10: De juiste index ophalen Exercise 11: Alles aan elkaar koppelen!

Huidige oefening

Exercise 12: Gefeliciteerd!