Identifying missing values (isnull, isna) in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What do the functions isnull() and isna() do in pandas?

Both isnull() and isna() check for missing values in a DataFrame or Series. They return a new object of the same shape with True where the data is missing and False where it is present.

beginner

How can you use isnull() to find how many missing values are in each column of a DataFrame?

You can call df.isnull().sum(). This returns a Series with the count of missing values for each column in the DataFrame df.

beginner

Are isnull() and isna() different in pandas?

No, they are exactly the same. isna() is just an alias for isnull(). You can use either one to detect missing values.

beginner

What type of values do isnull() and isna() detect as missing?

They detect NaN (Not a Number), None, and other null-like values as missing in pandas objects.

intermediate

How would you filter rows in a DataFrame to keep only those with missing values in a specific column?

Use df[df['column_name'].isnull()]. This returns rows where the specified column has missing values.

Which pandas function can you use to detect missing values in a DataFrame?

A dropna()

B fillna()

C replace()

D isnull()

isnull() returns a boolean mask showing missing values. fillna() fills missing values, dropna() removes them, and replace() replaces values.

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

A Count of missing values per column

B Total number of missing values in the DataFrame

C Rows with missing values

D DataFrame without missing values

It returns a Series with the count of missing values for each column.

Are isnull() and isna() different functions?

A No, they are aliases and behave the same

B No, but one is deprecated

C Yes, one works only on Series

D Yes, they detect different types of missing values

isna() is an alias for isnull(). Both behave identically.

Which of these values is detected as missing by isnull()?

A "" (empty string)

B NaN

C 0

D False

NaN is recognized as missing. Zero, empty string, and False are not missing.

How to select rows where column 'Age' has missing values?

A df[df['Age'].notnull()]

B df['Age'].fillna()

C df[df['Age'].isnull()]

D df.dropna(subset=['Age'])

Using df[df['Age'].isnull()] filters rows where 'Age' is missing.

Explain how to identify missing values in a pandas DataFrame using isnull() or isna().

Think about how to get a True/False map and then count Trues.

Describe how to filter rows in a DataFrame to keep only those with missing data in a specific column.

Focus on selecting rows where a column is missing.