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# Getting integer index of rows based on column values in Pandas DataFrame

schedule Aug 10, 2023
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To get the integer indexes of rows based on column values in Pandas DataFrame, use NumPy's `where(~)` method.

As an example, consider the following:

``` df = pd.DataFrame({"A":[3,4,3],"B":[5,6,7]}, index= ["a","b","c"])df A Ba 3 5b 4 6c 3 7 ```

# Solution

To get the integer indexes of rows where value for column `A` is `3`:

``` np.where(df["A"] == 3)[0] array([0, 2]) ```

# Explanation

We first obtain a boolean mask (`Series`) where `True` corresponds to rows where the condition is met:

``` df["A"] == 3 a Trueb Falsec TrueName: A, dtype: bool ```

We then use NumPy's `where(~)` method to get the integer indexes of entries with `True`. We also include the `[0]` at the end because `where(~)` returns a tuple, where the first item is the integer indexes we are after.

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