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# Counting number of missing values (NaN) in each column of a Pandas DataFrame

schedule Aug 12, 2023
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local_offer
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# Example

Consider the following DataFrame with some `NaN` values:

``` df = pd.DataFrame({"A":[np.nan,3,np.nan], "B":[4,np.nan,5], "C":[6,7,8]})df    A    B    C0  NaN  4.0  61  3.0  NaN  72  NaN  5.0  8 ```

## In each column

To count the number of `NaN`s of each column of `df`:

``` df.isna().sum() A 2B 1C 0dtype: int64 ```

### Explanation

Here, the `df.isna()` returns a DataFrame of booleans where `True` indicates entries that are `NaN`:

``` df.isna()    A      B      C0  True   False  False1  False  True   False2  True   False  False ```

Internally, `True` is represented by `1` while a `False` is represented by `0`. Therefore, summing up the booleans for each column is equivalent to counting the number of `True` (`NaN` values) per column:

``` df.isna().sum() A 2B 1C 0dtype: int64 ```

## In a particular column

To count the number of `NaN`s in just column `A`:

``` df["A"].isna().sum() 2 ```

## In multiple columns

To count the number of NaNs in columns `A` and `B`:

``` df[["A","B"]].isna().sum() A 1B 1dtype: int64 ```
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