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# Getting rows except some in Pandas DataFrame

schedule Aug 12, 2023
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local_offer
PythonPandas
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Consider the following DataFrame:

``` df = pd.DataFrame({"A":[2,3,4],"B":[5,6,7]}, index=["a","b","c"])df A Ba 2 5b 3 6c 4 7 ```

# Getting all rows except some using integer index

To get all rows except rows at integer index `0` and `2`:

``` df.drop(df.iloc[[0,2]].index) A Bb 3 6 ```

Here, we are first extracting the rows at integer index `0` and `2` as a DataFrame using `iloc`:

``` df.iloc[[0,2]] A Ba 2 5c 4 7 ```

We then extract the index of this DataFrame using the `index` property:

``` df.iloc[[0,2]].index Index(['a', 'c'], dtype='object') ```

Note that this step is needed because the `drop(~)` method can only remove rows using row labels. We then pass this `Index` object directly into `drop(~)` to remove the corresponding rows:

``` df.drop(df.iloc[[0,1]].index) A Bb 3 6 ```

`drop(~)` returns a new DataFrame, and so the original `df` is kept intact.

# Getting rows except some using row labels

To get rows except those with row labels `a` and `c`:

``` df.drop(index=["a","c"]) A Bb 3 6 ```
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