Counting missing values in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What function in pandas helps you count missing values in a DataFrame?

The isna() or isnull() function marks missing values, and sum() counts them. For example, df.isna().sum() counts missing values per column.

beginner

How do you count the total number of missing values in the entire DataFrame?

Use df.isna().sum().sum(). The first sum() counts missing values per column, the second sums those counts to get the total missing values.

beginner

What is the difference between isna() and notna() in pandas?

isna() returns True for missing values, while notna() returns True for non-missing values. They help identify missing or present data.

beginner

How can you count missing values only in a specific column named 'Age'?

Use df['Age'].isna().sum(). This counts missing values only in the 'Age' column.

beginner

Why is it important to count missing values before analysis?

Counting missing values helps you understand data quality. It guides decisions on cleaning data, like filling or removing missing entries, to avoid wrong results.

Which pandas function marks missing values as True?

A isna()

B notna()

C fillna()

D dropna()

isna() returns True for missing values, helping to identify them.

How do you count missing values in all columns of a DataFrame df?

A df.fillna()

B df.isna().sum()

C df.count()

D df.dropna()

df.isna().sum() counts missing values per column.

What does df.isna().sum().sum() return?

A Number of rows

B Number of columns

C Total missing values in the DataFrame

D Total non-missing values

The double sum() adds missing counts across all columns for total missing values.

Which method counts non-missing values in pandas?

A count()

B isna()

C notna()

D fillna()

count() counts non-missing values per column.

To count missing values in column 'Score', which code is correct?

A df['Score'].fillna()

B df.isna().sum()

C df['Score'].count()

D df['Score'].isna().sum()

This counts missing values only in the 'Score' column.

Explain how to count missing values in a pandas DataFrame and why it matters.

Think about how missing data affects your results.

Describe the difference between isna() and notna() in pandas.

One finds missing, the other finds present data.

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