# Array Indexing

## Array Indexing

Elements in NumPy arrays can be accessed by *indexing*. Indexing is an operation that pulls out a select set of values from an array. The *index* of a value in an array is that value's *location* within the array. There is a difference between *the value* and *where the value is stored* in an array.

An array with 3 values is created in the code section below.

```
import numpy as np
```a = np.array([2,4,6])

`2`

, `4`

and `6`

. Each of these values has a different index.
**Remember counting in Python starts at 0 and ends at n-1.**

The value `2`

has an index of 0. `2`

is in the 0 location of the array. The value `4`

has an index of `1`

and the value `6`

has an index of `2`

. The table below shows the index of each value in the array.

Index (or location) | Value |
---|---|

0 | `2` |

1 | `4` |

2 | `6` |

Individual values stored in an array can be accessed with indexing.

The general form to index a NumPy array is:

```
<value> = <array>[index]
```

Where `<value>`

is the value stored in the array, `<array>`

is the array object name and `[index]`

specifies the index or location of that value.

In the array above, the value 6 is stored at index 2.

```
import numpy as np
```a = np.array([2,4,6])
value = a[2]
print(value)

### Multi-dimensional Array Indexing

Multi-dimensional arrays can be indexed as well. A simple 2-D array is defined by a list of lists.

```
import numpy as np
```a = np.array([[2,3,4],[6,7,8]])
print(a)

```
<value> = <array>[row,col]
```

Where `<value>`

is the value pulled out of the 2-D array and `[row,col]`

specifies the row and column index of the value. Remember Python counting starts at `0`

, so the first row is row zero and the first column is column zero.

We can access the value `8`

in the array above by calling the row and column index `[1,2]`

for the 2nd row (remember row 0 is the first row) and the 3rd column (remember column 0 is the first column).

```
import numpy as np
```a = np.array([[2,3,4],[6,7,8]])
value = a[1,2]
print(value)

### Assigning Values with Indexing

Array indexing is used to *access* values in an array. And array indexing can also be used for *assigning* values of an array.

The general form used to assign a value to a particular index or location in an array is below:

```
<array>[index] = <value>
```

Where `<value>`

is the new value going into the array and `[index]`

is the location the new value will occupy.

The code below puts the value `10`

into the second index or location of the array `a`

.

```
import numpy as np
a = np.array([2,4,6])
a[2] = 10
print(a)
```

```
<array>[row,col] = <value>
```

The code example below shows the value `20`

assigned to the 2nd column and 3rd row of the array.

```
import numpy as np
```a = np.array([[2,3,4],[6,7,8]])
print(a)

a[1,2]=20
print(a)