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# Removing rows from a DataFrame based on column values in Pandas

schedule Aug 10, 2023
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To remove rows from a DataFrame based on column values, use the DataFrame's `query(~)` method.

NOTE

The `query(~)` method returns a new copy of the DataFrame, so modifying the returned DataFrame will not mutate the original DataFrame.

Consider the following DataFrame:

``` df = pd.DataFrame({"A":[3,4,5], "B":[6,7,8]}, index=["a","b","c"])df A Ba 3 6b 4 7c 5 8 ```

# Removing rows baed on a column value

To remove all rows where `A == 3`:

``` df = df.query("A != 3")df A Bb 4 7c 5 8 ```

Notice how we are selecting rows where `A != 3`, which is equivalent to removing all rows where `A == 3`.

Just as another example, to remove all rows where `A > 3`:

``` df = df.query("A <= 3")df A Ba 3 6 ```

You could also use the `not` syntax like so:

``` df = df.query("not A > 3")df A Ba 3 6 ```

# Removing rows based on multiple column values

Consider the same DataFrame as above:

``` df A Ba 3 6b 4 7c 5 8 ```

## Individual column values

To drop rows where `A` is greater than `3` and `B` is less than `8`:

``` df.query("not (A > 3 and B < 8)") A Ba 3 6c 5 8 ```

## Column value aggregates

To drop rows where the sum of `A` and `B` is less than `10`:

``` df.query("not (A + B < 10)") A Bb 4 7c 5 8 ```
Published by Isshin Inada
Edited by 0 others
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