Counting missing values in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Counting missing values in a DataFrame column

What is the output of this code that counts missing values in the 'Age' column?

Pandas

import pandas as pd

data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'],
        'Age': [25, None, 30, None]}
df = pd.DataFrame(data)

missing_count = df['Age'].isna().sum()
print(missing_count)

A 2

B 0

C 4

D None

💡 Hint

Use isna() to find missing values and sum() to count them.

The 'Age' column has two missing values (None). isna() marks them as True, sum() counts True as 1 each, so the result is 2.

❓ data_output

intermediate

Count missing values per column in a DataFrame

What is the output of this code that counts missing values for each column?

Pandas

import pandas as pd

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

missing_per_column = df.isna().sum()
print(missing_per_column)

A

A    2
B    1
C    0
dtype: int64

B

A    0
B    0
C    0
dtype: int64

C

A    1
B    2
C    0
dtype: int64

D

A    1
B    1
C    1
dtype: int64

💡 Hint

Use isna() on the whole DataFrame and sum() to count per column.

Column 'A' has 1 missing, 'B' has 2 missing, 'C' has none. So the output shows counts per column.

❓ visualization

advanced

Visualizing missing values count per column

Which option produces a bar chart showing the count of missing values per column?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

data = {'X': [1, None, 3, None], 'Y': [None, 2, None, 4], 'Z': [5, 6, 7, 8]}
df = pd.DataFrame(data)

missing_counts = df.isna().sum()

# Which code below plots the bar chart correctly?

A

df.plot(kind='bar')
plt.show()

B

missing_counts.plot(kind='bar')
plt.show()

C

missing_counts.plot.bar()
plt.show()

D

df.isna().plot(kind='bar')
plt.show()

💡 Hint

Plot the Series of missing counts, not the whole DataFrame.

missing_counts is a Series with counts per column. Using plot(kind='bar') on it creates the correct bar chart.

🧠 Conceptual

advanced

Understanding the difference between isna() and notna()

If df is a DataFrame, what does df.notna().sum() return?

A Boolean DataFrame showing missing values

B Total number of rows in the DataFrame

C Count of missing values per column

D Count of non-missing values per column

💡 Hint

notna() is the opposite of isna().

notna() returns True for non-missing values. sum() counts True as 1, so it counts non-missing values per column.

🔧 Debug

expert

Why does this code give an error when counting missing values?

What error does this code raise and why? import pandas as pd data = {'A': [1, 2, None], 'B': [None, 4, 5]} df = pd.DataFrame(data) missing = df.isna.sum() print(missing)

A AttributeError: 'method' object has no attribute 'sum' because isna is missing parentheses

B TypeError: unsupported operand type(s) for +: 'method' and 'method' because sum is called incorrectly

C KeyError because 'isna' is not a column

D No error, prints the count of missing values

💡 Hint

Check if isna is called as a method.

isna is a method and must be called with parentheses: isna(). Without parentheses, isna is a method object, which has no sum attribute.