Why handling missing data matters in Pandas - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

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

Pandas

missing = df.[1]()

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

Aisnull

Bdropna

Cfillna

Dhead

Attempts:

3 left

💡 Hint

Common Mistakes

Using dropna() removes rows instead of showing missing values.

Using fillna() replaces missing values but does not identify them.

The isnull() function returns a DataFrame showing where values are missing (NaN).

2fill in blank

medium

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

Pandas

clean_df = df.[1]()

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

Aisnull

Bdropna

Cinfo

Dfillna

Attempts:

3 left

💡 Hint

Common Mistakes

Using isnull() only identifies missing values but does not remove them.

Using fillna() replaces missing values but does not remove rows.

The dropna() function removes rows that contain any missing values.

3fill in blank

hard

Fix the error in the code to fill missing values with zero.

Pandas

df_filled = df.[1](0)

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

Adropna

Bisnull

Cfillna

Dreplace

Attempts:

3 left

💡 Hint

Common Mistakes

Using dropna() removes rows instead of filling missing values.

Using isnull() only checks for missing values but does not fill them.

The fillna(0) function replaces all missing values with zero.

4fill in blank

hard

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

Pandas

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 '<=' includes words with length 3 or less, which is not desired.

Using 'word' instead of 'len(word)' does not give the length.

The dictionary comprehension maps each word to its length only if the word length is greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase keys and values greater than zero.

Pandas

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

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'k' instead of 'k.upper()' does not change key case.

Using '<' or '<=' includes values less than or equal to zero.

Using 'k' or 'v' incorrectly in the dictionary comprehension.

This dictionary comprehension creates keys as uppercase versions of original keys and includes only items where the value is greater than zero.

Practice

1. Why is it important to handle missing data in a pandas DataFrame?

easy

A. Because missing data can cause errors or lead to wrong conclusions.

B. Because pandas does not allow any missing data by default.

C. Because missing data always improves model accuracy.

D. Because missing data automatically fills with zeros.

Solution

1. Step 1: Understand the impact of missing data

   Missing data can cause errors in calculations or models and lead to incorrect results.

2. Step 2: Recognize the need for handling missing data

   Handling missing data ensures the analysis is accurate and reliable.

3. Final Answer:

   Because missing data can cause errors or lead to wrong conclusions. -> Option A

4. Quick Check:

   Missing data impact = errors/wrong conclusions [OK]

Hint: Missing data can break analysis; always check first [OK]

Common Mistakes:

• Assuming pandas never allows missing data
• Thinking missing data improves accuracy
• Believing missing data fills automatically

2. Which pandas function is used to find missing values in a DataFrame?

easy

A. replace()

B. fillna()

C. dropna()

D. isnull()

Solution

1. Step 1: Identify function to detect missing data

   The isnull() function returns True for missing values in a DataFrame.

2. Step 2: Differentiate from other functions

   fillna() fills missing data, dropna() removes rows/columns with missing data, and replace() replaces specified values.

3. Final Answer:

   isnull() -> Option D

4. Quick Check:

   Find missing = isnull() [OK]

Hint: Use isnull() to spot missing data quickly [OK]

Common Mistakes:

• Confusing fillna() with isnull()
• Using dropna() to find missing data
• Thinking replace() detects missing values

3. What will be the output of this code?

import pandas as pd
df = pd.DataFrame({'A': [1, None, 3], 'B': [4, 5, None]})
print(df.isnull().sum())

medium

A. A 2
B 2
dtype: int64

B. A 0
B 0
dtype: int64

C. A 1
B 1
dtype: int64

D. A 1
B 2
dtype: int64

Solution

1. Step 1: Understand the DataFrame content

   Column 'A' has one None (missing), column 'B' has one None (missing).

2. Step 2: Apply isnull() and sum()

   isnull() marks missing as True, sum() counts True values per column, so both columns have 1 missing value.

3. Final Answer:

   A 1 B 1 dtype: int64 -> Option C

4. Quick Check:

   Count missing per column = 1 each [OK]

Hint: sum() on isnull() counts missing per column [OK]

Common Mistakes:

• Counting total missing instead of per column
• Confusing None with zero
• Expecting different counts for columns

4. Identify the error in this code snippet for handling missing data:

import pandas as pd
df = pd.DataFrame({'X': [1, None, 3]})
df.fillna(df.mean())

medium

A. fillna() returns a new DataFrame; df is unchanged without assignment.

B. df.mean() returns a Series, which cannot be used in fillna().

C. fillna() modifies df in place by default, no error here.

D. None values cannot be replaced using fillna().

Solution

1. Step 1: Understand fillna() behavior

   fillna() returns a new DataFrame by default and does not change df unless inplace=True or reassigned.

2. Step 2: Check code effect

   Since df.fillna(df.mean()) is not assigned back, df remains unchanged, so missing values are not filled.

3. Final Answer:

   fillna() returns a new DataFrame; df is unchanged without assignment. -> Option A

4. Quick Check:

   fillna() needs assignment or inplace=True [OK]

Hint: Assign fillna() result or use inplace=True to update df [OK]

Common Mistakes:

• Assuming fillna() changes df without assignment
• Thinking df.mean() is invalid input
• Believing None cannot be replaced

5. You have a DataFrame with many missing values scattered across columns. Which approach best preserves data while handling missing values for analysis?

hard

A. Use dropna() to remove all rows with any missing data.

B. Use fillna() with column means to replace missing values.

C. Ignore missing data and proceed with analysis.

D. Replace missing values with zeros regardless of context.

Solution

1. Step 1: Evaluate dropna() impact

   Dropping all rows with missing data can remove too much data, losing valuable information.

2. Step 2: Consider fillna() with column means

   Replacing missing values with column means keeps data size and reduces bias from missingness.

3. Step 3: Assess ignoring or zero replacement

   Ignoring missing data risks errors; zero replacement may distort data if zeros are not meaningful.

4. Final Answer:

   Use fillna() with column means to replace missing values. -> Option B

5. Quick Check:

   fillna() with means preserves data best [OK]

Hint: Fill missing with means to keep data and reduce bias [OK]

Common Mistakes:

• Dropping too many rows with dropna()
• Ignoring missing data effects
• Replacing missing with zeros blindly