Why handling missing data matters in Pandas - Challenge Your Understanding

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this code with missing data?

Consider the following pandas DataFrame with missing values. What will be the output of the code below?

Pandas

import pandas as pd
import numpy as np
df = pd.DataFrame({'A': [1, 2, np.nan, 4], 'B': [5, np.nan, np.nan, 8]})
result = df['A'].mean()
print(round(result, 2))

A 2.25

B 2.33

C nan

D 3.0

💡 Hint

Remember that pandas mean() skips missing values by default.

The mean() function in pandas ignores NaN values by default. So it calculates mean of 1, 2, and 4 which is (1+2+4)/3 = 2.33.

❓ data_output

intermediate

How many rows remain after dropping missing data?

Given this DataFrame, how many rows remain after dropping all rows with any missing values?

Pandas

import pandas as pd
import numpy as np
df = pd.DataFrame({'X': [1, np.nan, 3, 4], 'Y': [np.nan, 2, 3, 4]})
df_clean = df.dropna()
print(len(df_clean))

A 1

B 2

C 3

D 4

💡 Hint

dropna() removes rows with any NaN values.

Only the last row has no missing values, so after dropna(), only 1 row remains.

🔧 Debug

advanced

What error does this code raise when filling missing data?

What error will this code produce when trying to fill missing values?

Pandas

import pandas as pd
df = pd.DataFrame({'A': [1, 2, None], 'B': ['x', 'y', None]})
df['B'] = df['B'].fillna(0)
print(df)

A TypeError

B KeyError

C ValueError

D No error, fills missing with 0

💡 Hint

fillna can fill missing values with any scalar, even if column is string type.

fillna replaces None with 0 without error, even in string columns.

🧠 Conceptual

advanced

Why is handling missing data important before modeling?

Which of the following best explains why handling missing data is important before building a machine learning model?

A Models ignore missing data automatically, so no need to handle it.

B Missing data always improves model accuracy.

C Missing data can cause models to crash or produce biased results.

D Handling missing data slows down model training without benefits.

💡 Hint

Think about how missing values affect calculations and predictions.

Missing data can cause errors or bias in models, so it must be handled properly.

🚀 Application

expert

Which option produces the correct imputed DataFrame?

Given this DataFrame with missing values, which code snippet correctly fills missing numeric values with the column mean and missing categorical values with the mode?

Pandas

import pandas as pd
import numpy as np
df = pd.DataFrame({'num': [1, 2, np.nan, 4], 'cat': ['a', np.nan, 'a', 'b']})

A

df['num'].fillna(df['num'].mean(), inplace=True)
df['cat'].fillna(df['cat'].mode()[0], inplace=True)
print(df)

B

df.fillna(df.mean(), inplace=True)
df.fillna(df.mode().iloc[0], inplace=True)
print(df)

C

df['num'] = df['num'].fillna(df['num'].median())
df['cat'] = df['cat'].fillna(df['cat'].mode()[0])
print(df)

D

df.fillna(method='ffill', inplace=True)
print(df)

💡 Hint

Use mean for numeric and mode for categorical separately.

Option A correctly fills numeric NaNs with mean and categorical NaNs with mode. Others either misuse fillna or use wrong methods.

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