Select one of the following commands which can be used to find the total number of NaN values in a data frame.

(e.g., returns one number corresponding to the total number of NaN values)

Question

Select one of the following commands which can be used to find the total number of NaN values in a data frame.

(e.g., returns one number corresponding to the total number of NaN values)

BlackTom AI · Accepted Answer

To determine which command returns the total number of NaN values in a DataFrame, let's evaluate each option individually.
Option 1: df.isna().any().sum() 
- This sequence checks for any NaN values along each column with isna().any(), producing a boolean Series indicating whether each column contains any NaNs. Then .sum() adds up the True values, effectively counting how many columns contain at least one NaN, not the total number of NaNs. This does not meet the requirement of counting every NaN across the entire DataFrame.
Option 2: df.isnull().sum() 
- isnull() is an alias for isna(), so this behaves similarly to isna().sum(): it computes the sum of NaNs for each column, yielding a Series where each element is the count of NaNs in that column. While informative, it reports per-column counts rather than the total across the DataFrame. Therefore, it fails to give the single total number requested.
Option 3: df.isnull.any() 
- This expression uses isnull() (alias of isna()) to get a boolean DataFrame, then .any() reduces along the index to a boolean Series indicating whether each column contains any NaNs. It results in a per-column boolean, not a numeric total of NaNs. It does not provide the required total count.
Option 4: df.isna().sum().sum() 
- Here, isna() marks NaN locations in the DataFrame. The first .sum() aggregates counts by column, producing a Series of NaN counts per column. The second .sum() then adds those per-column totals together, yielding the total number of NaN values in the entire DataFrame. This is precisely the total NaN count across all data.
In summary, Options 1, 2, and 3 each report partial information (whether any NaNs exist per column or per-column counts), but only Option 4 provides the single aggregated total of all NaN values in the DataFrame. Thus, Option 4 is the correct choice.

Select one of the following commands which can be used to find the total number of NaN values in a data frame. (e.g., returns one number corresponding to the total number of NaN values)Single choice

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