Segui il denaro

In questo esercizio lavori con un'altra versione del DataFrame banking che contiene valori mancanti sia per la colonna cust_id sia per la colonna acct_amount.

Vuoi analizzare quanti clienti unici ha la banca, l'importo medio detenuto dai clienti e altro ancora. Sai che le righe con cust_id mancanti non sono davvero utili e che in media acct_amount è di solito 5 volte l'importo di inv_amount.

In questo esercizio eliminerai da banking le righe con cust_id mancanti e imputerai i valori mancanti di acct_amount usando alcune conoscenze del dominio.

Questo esercizio fa parte del corso

Pulizia dei dati in Python

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Istruzioni dell'esercizio

Usa .dropna() per eliminare i valori mancanti della colonna cust_id in banking e salva il risultato in banking_fullid.
Usa inv_amount per calcolare gli importi di conto stimati per banking_fullid impostando gli importi uguali a inv_amount * 5, e assegna il risultato a acct_imp.
Imputa i valori mancanti di acct_amount in banking_fullid con il nuovo acct_imp usando .fillna().

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Drop missing values of cust_id
banking_fullid = banking.____(subset = ['____'])

# Compute estimated acct_amount
acct_imp = ____

# Impute missing acct_amount with corresponding acct_imp
banking_imputed = banking_fullid.____({'____':____})

# Print number of missing values
print(banking_imputed.isna().sum())

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Questo esercizio fa parte del corso

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

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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: Uniformità Exercise 2: Date ambigue Exercise 3: Valute uniformi Exercise 4: Date uniformi Exercise 5: Convalida tra campi Exercise 6: Validazione incrociata o no?Exercise 7: Com'è messa l'integrità dei nostri dati?Exercise 8: Completezza Exercise 9: Questi valori sono missing at random?Exercise 10: Investitori mancanti Exercise 11: Segui il denaro

Esercizio in corso

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!