Why data preparation consumes most ML time in ML Python - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load data from a CSV file using pandas.

ML Python

import pandas as pd
data = pd.[1]('data.csv')

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Aimport_csv

Bload_csv

Copen_csv

Dread_csv

Attempts:

3 left

2fill in blank

medium

Complete the code to remove rows with missing values from the DataFrame.

ML Python

clean_data = data.[1]()

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Afillna

Bremove_na

Cdropna

Dclean_na

Attempts:

3 left

3fill in blank

hard

Fix the error in the code to convert a categorical column to numeric using one-hot encoding.

ML Python

encoded_data = pd.get_dummies(data, columns=[[1]])

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Acategory_col

B'category_col'

C'category_cols'

Dcategory_cols

Attempts:

3 left

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

ML Python

{word: [1] for word in words if [2]

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Alen(word)

Blen(word) > 3

Cword

Dword > 3

Attempts:

3 left

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that includes only items with value greater than 0 and keys in uppercase.

ML Python

{ [1]: [2] for [3], v in data.items() if v > 0 }

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Ak.upper()

Bv

Ck

Dv.upper()

Attempts:

3 left