Question

Pandas provide a function __________ to check whether any value is missing or not in the DataFrame.

• A. isnull()
• B. isnan()
• C. isna()
• D. ismissing()

Hint:

Both "isnull()" and "isna()" are correct in Python, but always look for "isnull()" first in textbook-aligned exams.
You can chain it with ".sum()" to get the count of nulls easily.

Answer 1

The Correct Option is A

•

Step 1: Understanding the Question:
The question asks for the built-in function provided by the Pandas library in Python to check for missing (NaN/Null) values within a DataFrame.
Identifying missing data is a crucial step in data preprocessing and cleaning.

•

Step 2: Key Formula or Approach:
Pandas provides two primary, identical functions to detect missing values: isnull() and isna().
These functions return a boolean DataFrame of the same shape as the original, where each cell is True if the value is missing, and False otherwise.
In many high school curricula, such as NCERT Informatics Practices, isnull() is the standard function emphasized for this task.

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Step 3: Detailed Explanation:
Missing data in Pandas is typically represented by NaN (Not a Number) or None.
To detect these missing values, we can apply the df.isnull() method.
This method scans the entire DataFrame and flags every missing value.
For example, if we have a DataFrame df with some missing values, calling df.isnull() returns True for missing entries and False for valid data.
To get a count of missing values per column, we can chain it with the sum() function: df.isnull().sum().
While isna() is also a valid alias in modern Pandas, isnull() remains the historically preferred and widely taught function in academic syllabus guidelines.
The option isnan() is a NumPy function (numpy.isnan()) and not directly a Pandas DataFrame method, and ismissing() does not exist in Pandas.

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Step 4: Final Answer:
Thus, isnull() is the appropriate function to check for missing values in a Pandas DataFrame.