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# What is Fancy Indexing in Pandas?

schedule Aug 12, 2023
Last updated
local_offer
PandasPython
Tags

Fancy indexing is used to access multiple values in an array-like structure. In the context of Pandas, array-like structures include, but are not limited to, Numpy arrays, Series and DataFrames.

# Examples

## Fancy Indexing for Series

Suppose we have the following Series:

``` s = pd.Series([5,8,6,7]) ```

To get the value at indices `0` and `2`:

``` indices = [0,2]s[indices] 0 52 6dtype: int64 ```

The return type here is `Series`, since we are accessing values from a `Series`.

## Fancy Indexing for Numpy Arrays

Consider the following 1D Numpy array:

``` a = np.array([5,8,6,7,9]) ```

Suppose we wanted to create a 2D array using some of the values in `a`.

To do so, we must first create a 2D array of indices:

``` indices = np.array([[1,3],[0,0]])indices array([[1, 3], [0, 0]]) ```

Now, to create the array with the values that correspond to these indices:

``` a[indices] array([[8, 7], [5, 5]]) ```

Notice how the shape of the resulting array is the same as that of the indices. The return type here is a Numpy array since we are accessing values from a Numpy array.

## Fancy indexing in multi-dimensions

Consider the following 2D NumPy array:

``` a = np.array([[5,8,3],[6,7,2]])a array([[5, 8, 3], [6, 7, 2]]) ```

To fetch multiple values in this array:

``` indices_row = [0,1,0]indices_column = [2,0,1]a[indices_row, indices_column] array([3, 6, 8]) ```

Here, we're fetching the values at `(0,2)=3`, `(1,0)=6` and `(0,1)=8`.The return type here a NumPy array since we are accessing values from a NumPy array.

## Slicing using Fancy Indexing

The slicing syntax also works when fancy indexing.

Consider the same 2D Numpy array:

``` a = np.array([[5,8,3],[6,7,2]])a array([[5, 8, 3], [6, 7, 2]]) ```

To get the columns with indices 0 and 2:

``` a[:, [0,2]] array([[5, 3], [6, 2]]) ```

Just to break this down, the rows we are after are denoted by `:`, which just means to fetch all rows. Next, the `[0,2]` means to fetch columns with indices 0 and 2.

## Assigning values using Fancy Indexing

You can assign new values using fancy indexing as well.

Consider the same 2D Numpy array:

``` a = np.array([[5,8,3],[6,7,2]])a array([[5, 8, 3], [6, 7, 2]]) ```

Let's change the values `3` and `7`:

``` indices_row = [0,1]indices_column = [2,1]a[indices_row, indices_column] = 10a array([[ 5, 8, 10], [ 6, 10, 2]]) ```

Here, notice how we assigned a scalar value of `10` instead of `[10,10]`. A scalar value of `10` simply gets broadcasted (i.e. repeated) to match the appropriate size.

Of course, if you wanted to assign individual values instead, you could just supply an array, like so:

``` a[indices_row, indices_column] = [10,20]a array([[ 5, 8, 10], [ 6, 20, 2]]) ```
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