Coppie di ristoranti

Nella lezione precedente, hai ripulito il dataset restaurants per prepararlo alla creazione di un motore di raccomandazione di ristoranti. Hai un nuovo DataFrame chiamato restaurants_new con nuovi ristoranti su cui addestrare il tuo modello, estratti da una nuova fonte di dati.

Hai già ripulito le colonne cuisine_type e city usando le tecniche viste nel corso. Tuttavia, hai notato duplicati con refusi nei nomi dei ristoranti che richiedono il record linkage invece di join con restaurants.

In questo esercizio, eseguirai il primo passo del record linkage e genererai possibili coppie di righe tra restaurants e restaurants_new. Entrambi i DataFrame, pandas e recordlinkage sono presenti nel tuo ambiente.

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Pulizia dei dati in Python

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Prova a risolvere questo esercizio completando il codice di esempio.

# Create an indexer and object and find possible pairs
indexer = ____

# Block pairing on cuisine_type
indexer.____(____)

# Generate pairs
pairs = indexer.____(____, ____)

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Pulizia dei dati 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: 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: Confrontare le stringhe Exercise 2: Distanza di edit minima Exercise 3: Il punto di cutoff Exercise 4: Rimappare le categorie II Exercise 5: Generare coppie Exercise 6: Collegare o non collegare?Exercise 7: Coppie di ristoranti

Esercizio in corso

Exercise 8: Ristoranti simili Exercise 9: Collegare i DataFrame Exercise 10: Ottenere l'indice giusto Exercise 11: Collegali insieme!Exercise 12: Congratulazioni!