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])

Drag options to blanks, or click blank then click option'

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()

Drag options to blanks, or click blank then click option'

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')

Drag options to blanks, or click blank then click option'

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}

Drag options to blanks, or click blank then click option'

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}

Drag options to blanks, or click blank then click option'

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).

Practice

1. Why does data cleaning usually take the most time in data analysis?

easy

A. Because data cleaning is only about deleting data

B. Because data cleaning requires writing complex machine learning models

C. Because real-world data often has missing or incorrect values that need fixing

D. Because data cleaning is about creating visualizations

Solution

1. Step 1: Understand real-world data quality

   Real-world data is often messy with missing or wrong values.

2. Step 2: Recognize cleaning tasks

   Cleaning fixes these issues to prepare data for analysis.

3. Final Answer:

   Because real-world data often has missing or incorrect values that need fixing -> Option C

4. Quick Check:

   Messy data = cleaning time [OK]

Hint: Messy data means more cleaning time [OK]

Common Mistakes:

• Thinking cleaning is about building models
• Confusing cleaning with visualization
• Assuming cleaning just deletes data

2. Which Python code snippet correctly replaces missing values in a DataFrame column 'age' with the mean age?

easy

A. df['age'].dropna().mean()

B. df['age'].replace('NaN', df['age'].mean())

C. df.fillna('age', df.mean())

D. df['age'].fillna(df['age'].mean(), inplace=True)

Solution

1. Step 1: Identify correct method to fill missing values

   The fillna() method replaces missing values; inplace=True updates the DataFrame directly.

2. Step 2: Check correct usage of mean for replacement

   df['age'].mean() calculates the mean of the 'age' column correctly.

3. Final Answer:

   df['age'].fillna(df['age'].mean(), inplace=True) -> Option D

4. Quick Check:

   fillna with mean = correct syntax [OK]

Hint: Use fillna with mean() and inplace=True [OK]

Common Mistakes:

• Using replace with string 'NaN' instead of np.nan
• Wrong method fillna arguments
• Dropping missing values instead of filling

3. Given this code snippet cleaning a DataFrame:

import pandas as pd

df = pd.DataFrame({'score': [10, None, 20, None, 30]})
df['score'] = df['score'].fillna(int(df['score'].median()))
print(df['score'].tolist())

What is the output list?

medium

A. [10, 20, 20, 20, 30]

B. [10, 15, 20, 15, 30]

C. [10, null, 20, null, 30]

D. [10, 20, 20, null, 30]

Solution

1. Step 1: Calculate median of non-missing scores

   Non-missing values are [10, 20, 30]. Median is 20.

2. Step 2: Replace null with median

   Missing values replaced by 20, so list becomes [10, 20, 20, 20, 30].

3. Final Answer:

   [10, 20, 20, 20, 30] -> Option A

4. Quick Check:

   Median fill replaces null with 20 [OK]

Hint: Median of [10,20,30] is 20; fill null with 20 [OK]

Common Mistakes:

• Using mean instead of median
• Not replacing null values
• Confusing null with string 'null'

4. This code tries to remove duplicate rows from a DataFrame but does not work as expected:

df.drop_duplicates()
print(len(df))

What is the main issue?

medium

A. drop_duplicates() does not remove duplicates without inplace=True or reassignment

B. drop_duplicates() cannot remove duplicates in pandas

C. len(df) counts duplicates, so it is always wrong

D. drop_duplicates() requires a column name argument

Solution

1. Step 1: Understand drop_duplicates behavior

   By default, drop_duplicates() returns a new DataFrame and does not change original unless inplace=True or reassigned.

2. Step 2: Check code effect on original DataFrame

   Since result is not saved or inplace=True used, original df remains unchanged, so length stays same.

3. Final Answer:

   drop_duplicates() does not remove duplicates without inplace=True or reassignment -> Option A

4. Quick Check:

   Use inplace=True or assign result [OK]

Hint: Use inplace=True or assign result to remove duplicates [OK]

Common Mistakes:

• Assuming drop_duplicates changes df without inplace
• Thinking len(df) excludes duplicates automatically
• Believing drop_duplicates needs column argument always

5. You have a dataset with columns ['name', 'age', 'income']. Some age values are missing, and some income values are negative due to data entry errors. Which sequence of cleaning steps is best to prepare data for analysis?

hard

A. Delete all rows with missing 'age', then fill negative 'income' with mean

B. Fill missing 'age' with median, then replace negative 'income' with zero

C. Fill missing 'age' with zero, then delete rows with negative 'income'

D. Replace negative 'income' with median, then fill missing 'age' with mean

Solution

1. Step 1: Handle missing 'age' values

   Filling missing 'age' with median preserves data without bias from zeros or deletion.

2. Step 2: Fix negative 'income' errors

   Replacing negative 'income' with zero corrects errors without deleting data.

3. Final Answer:

   Fill missing 'age' with median, then replace negative 'income' with zero -> Option B

4. Quick Check:

   Median fill + zero fix = best cleaning [OK]

Hint: Fill missing with median; fix errors with zero [OK]

Common Mistakes:

• Deleting rows unnecessarily
• Filling missing with zero causing bias
• Replacing errors with mean without fixing negatives