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Counting non-missing values in Pandas

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Handling Missing Values
schedule Jul 1, 2022
Last updated
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To count non-missing values in rows or columns of a Pandas DataFrame use the `count(~)` method.

Examples

Consider the following DataFrame:

``` df = pd.DataFrame({"A":[pd.np.nan,pd.np.nan], "B":[3,4]})df A B0 NaN 31 NaN 4 ```

Column-wise

To count the number of non-missing values for each column:

``` df.count() # axis=0 A 0B 2dtype: int64 ```

Here, we have `0` non-`NaN` values in column `A`, and 2 non-`NaN` values in `B`.

Row-wise

To count the number of non-missing values for each row, set `axis=1`:

``` df.count(axis=1) 0 11 1dtype: int64 ```

Here, we have 1 non-missing value in both row `0` and row `1`.

Numeric and boolean columns/rows only

Consider the following DataFrame:

``` df = pd.DataFrame({"A":["a","b"], "B":[3,4]})df A B0 a 31 b 4 ```

To count only numeric and boolean columns, set `numeric_only=True`:

``` df.count(numeric_only=True) B 2dtype: int64 ```

Notice how column `A` is ignored since it is a non-numeric type.

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