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# Counting number of rows with no missing values in Pandas DataFrame

schedule Aug 12, 2023
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Consider the following DataFrame with some missing values:

``` df = pd.DataFrame({"A":[pd.np.NaN,4,3],"B":[pd.np.NaN,5,6], "C":[pd.np.NaN,pd.np.NaN,9]})df A B C0 NaN NaN NaN1 4.0 5.0 NaN2 3.0 6.0 9.0 ```

# Solution

To count the number of rows with no missing values:

``` df.notna().all(axis=1).sum() 1 ```

# Explanation

We first get a boolean mask where `True` indicates the presence of a non-missing value using `notna()`:

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

We then use `all(axis=1)` to check for the rows with all `True`s:

``` df.notna().all(axis=1) 0 False1 False2 Truedtype: bool ```

Here, `True` indicates that the corresponding row does not have any missing value.

Finally, since `True` is internally represented as a `1` while a `False` as a `0` in Pandas, taking the sum of this Series will tell us the number of rows with no missing values:

``` df.notna().all(axis=1).sum() 1 ```
Published by Isshin Inada
Edited by 0 others
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