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.