Counting missing values in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to count missing values in the 'Age' column of the DataFrame df.

Pandas

missing_count = df['Age'].[1]().sum()

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

Aisna

Bsum

Ccount

Disnull

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'count' which counts non-missing values.

Using 'sum' directly without first identifying missing values.

The isna() method returns a boolean Series indicating missing values. To count them, you need to sum these booleans.

2fill in blank

medium

Complete the code to count the total number of missing values in the entire DataFrame df.

Pandas

total_missing = df.[1]().sum().sum()

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

Anotnull

Bcount

Cisnull

Ddropna

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'count' which counts non-missing values.

Using 'dropna' which removes missing values instead of counting.

The isnull() method returns a DataFrame of booleans showing missing values. Summing twice counts all missing values.

3fill in blank

hard

Fix the error in the code to correctly count missing values in the 'Salary' column.

Pandas

missing_salary = df['Salary'].[1]().sum()

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

Aisnull

Bnotnull

Ccount

Ddropna

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'count' which counts non-missing values.

Using 'notnull' which returns True for non-missing values.

To count missing values, use isnull() to get True for missing entries, then sum them.

4fill in blank

hard

Fill both blanks to create a dictionary with words as keys and their lengths as values, but only for words longer than 3 letters.

Pandas

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

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

Alen(word)

Bword > 3

Clen(word) > 3

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'word > 3' which compares a string to a number.

Using 'word' as value instead of length.

The dictionary comprehension uses len(word) for values and filters words with length greater than 3 using len(word) > 3.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase words as keys and their lengths as values, only for words longer than 4 letters.

Pandas

result = [1]: [2] for w in words if [3]

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

Aw.upper()

Blen(w)

Clen(w) > 4

Dw.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using w.lower() instead of uppercase for keys.

Filtering with incorrect length condition.

The dictionary comprehension uses w.upper() as keys, len(w) as values, and filters words with length greater than 4.

Practice

1. What does the pandas method df.isna().sum() do when applied to a DataFrame df?

easy

A. Counts the total number of rows in the DataFrame

B. Counts the number of missing values in each column of the DataFrame

C. Replaces missing values with zero in the DataFrame

D. Returns the data types of each column in the DataFrame

Solution

1. Step 1: Understand df.isna()

   This method returns a DataFrame of the same shape as df with True where values are missing and False otherwise.

2. Step 2: Apply .sum() on the boolean DataFrame

   Summing True values (which count as 1) per column gives the count of missing values in each column.

3. Final Answer:

   Counts the number of missing values in each column of the DataFrame -> Option B

4. Quick Check:

   df.isna().sum() counts missing per column [OK]

Hint: Use isna() then sum() to count missing per column [OK]

Common Mistakes:

• Thinking sum() counts rows instead of missing values
• Confusing isna() with fillna()
• Assuming it replaces missing values

2. Which of the following is the correct syntax to count all missing values in a pandas DataFrame df?

easy

A. df.isna().count()

B. df.count().sum()

C. df.isna().sum().sum()

D. df.isnull().count()

Solution

1. Step 1: Use df.isna() or df.isnull() to find missing values

   Both return a boolean DataFrame marking missing values as True.

2. Step 2: Use .sum().sum() to count all missing values

   The first sum() sums missing per column, the second sums those counts to get total missing in the DataFrame.

3. Final Answer:

   df.isna().sum().sum() -> Option C

4. Quick Check:

   Total missing = df.isna().sum().sum() [OK]

Hint: Double sum() after isna() counts all missing values [OK]

Common Mistakes:

• Using count() which counts non-missing values
• Using isna().count() which counts all rows, not missing
• Confusing isna() with isnull().count()

3. Given the DataFrame df below:

import pandas as pd
data = {'A': [1, None, 3], 'B': [None, None, 6], 'C': [7, 8, 9]}
df = pd.DataFrame(data)

What is the output of df.isna().sum()?

medium

A. A 1
B 1
C 1
dtype: int64

B. A 0
B 0
C 0
dtype: int64

C. A 2
B 1
C 0
dtype: int64

D. A 1
B 2
C 0
dtype: int64

Solution

1. Step 1: Identify missing values in each column

   Column A has one None (missing), column B has two None values, column C has none.

2. Step 2: Apply df.isna().sum()

   This counts missing per column: A=1, B=2, C=0.

3. Final Answer:

   A 1 B 2 C 0 dtype: int64 -> Option D

4. Quick Check:

   Count missing per column matches output [OK]

Hint: Count None or NaN per column to verify sums [OK]

Common Mistakes:

• Mixing counts of missing and non-missing
• Miscounting missing in column B
• Assuming None counts as zero

4. You run the code below but get an error:

import pandas as pd
data = {'X': [1, 2, None], 'Y': [None, 5, 6]}
df = pd.DataFrame(data)
missing = df.isna.sum()

What is the error and how to fix it?

medium

A. Missing parentheses after isna; fix with df.isna().sum()

B. DataFrame missing columns; add columns before counting

C. sum() is not a pandas method; use count() instead

D. None values cause error; replace with zeros first

Solution

1. Step 1: Identify the error in method call

   The code uses df.isna.sum() missing parentheses after isna, which is a method.

2. Step 2: Correct the syntax

   Use df.isna().sum() to call the method and then sum missing values.

3. Final Answer:

   Missing parentheses after isna; fix with df.isna().sum() -> Option A

4. Quick Check:

   Method calls need parentheses [OK]

Hint: Remember to add () after isna to call the method [OK]

Common Mistakes:

• Forgetting parentheses on isna()
• Using sum() instead of count() incorrectly
• Thinking None causes errors in counting

5. You have a DataFrame df with columns ['age', 'income', 'score']. You want to find the percentage of missing values in each column. Which code snippet correctly calculates this?

hard

A. df.isna().sum() / len(df) * 100

B. df.isna().count() / len(df) * 100

C. df.count() / df.isna().sum() * 100

D. df.isna().sum() * 100

Solution

1. Step 1: Count missing values per column

   Use df.isna().sum() to get missing counts per column.

2. Step 2: Calculate percentage of missing values

   Divide missing counts by total rows len(df) and multiply by 100 to get percentage.

3. Final Answer:

   df.isna().sum() / len(df) * 100 -> Option A

4. Quick Check:

   Missing % = (missing count / total rows) * 100 [OK]

Hint: Divide missing count by total rows, multiply by 100 [OK]

Common Mistakes:

• Using count() which counts non-missing values
• Multiplying missing count by 100 without dividing
• Dividing count by missing count incorrectly