Follow the money

In this exercise, you're working with another version of the banking DataFrame that contains missing values for both the cust_id column and the acct_amount column.

You want to produce analysis on how many unique customers the bank has, the average amount held by customers and more. You know that rows with missing cust_id don't really help you, and that on average acct_amount is usually 5 times the amount of inv_amount.

In this exercise, you will drop rows of banking with missing cust_ids, and impute missing values of acct_amount with some domain knowledge.

This exercise is part of the course

Cleaning Data in Python

Exercise instructions

Use .dropna() to drop missing values of the cust_id column in banking and store the results in banking_fullid.
Use inv_amount to compute the estimated account amounts for banking_fullid by setting the amounts equal to inv_amount * 5, and assign the results to acct_imp.
Impute the missing values of acct_amount in banking_fullid with the newly created acct_imp using .fillna().

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# 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())

Edit and Run Code

This exercise is part of the course

Cleaning Data in Python

IntermediateSkill Level

4.8+

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