Why data cleaning consumes most analysis time in Data Analysis Python - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load data from a CSV file.

Data Analysis Python

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

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A'data.csv'

Bcsv.read

Cread.csv

Ddata.csv

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting quotes around the file name

Using wrong function names

The file path must be a string, so it needs quotes around it.

2fill in blank

medium

Complete the code to check for missing values in the data.

Data Analysis Python

missing = data.[1]().sum()

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Adropna

Bisnull

Cnotnull

Disna

Attempts:

3 left

💡 Hint

Common Mistakes

Using dropna() which removes missing values instead of detecting them

Using notnull() which detects non-missing values

The isna() function returns True for missing values, which we sum to count them.

3fill in blank

hard

Fix the error in the code to remove duplicate rows.

Data Analysis Python

clean_data = data.[1](keep='first')

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Adrop_duplicate

Bdrop_duplicates

Cremove_duplicates

Ddropna

Attempts:

3 left

💡 Hint

Common Mistakes

Using dropna which removes missing values, not duplicates

Misspelling the function name

The correct pandas function to remove duplicate rows is drop_duplicates().

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 letters.

Data Analysis Python

lengths = {word: [1] for word in words if len(word) [2] 3}

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Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length

Using < instead of > in the condition

We want the length of each word as the value, and only words longer than 3 letters, so use len(word) and >.

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase words and their counts if count is positive.

Data Analysis Python

result = [1]: [2] for k, v in data.items() if v [3] 0}

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Ak.upper()

Bv

C>

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using k.lower() instead of k.upper()

Using < instead of > in the condition

Swapping keys and values

We want keys as uppercase words (k.upper()), values as counts (v), and filter counts greater than zero (v > 0).