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# What is Fancy Indexing in Pandas?

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*schedule*Jul 1, 2022

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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:

```
```

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:

```
```

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]])
```