# Numpy get ith column and specific column and row data from an array

We want to select specific column and rows in a numpy array

In this post we will see the slicing and indexing of numpy array for the following scenarios:

- get column by index in a numpy array
- get multiple columns by index
- get specific rows and their columns
- get last column

Beside this, we will also see how to use transpose and ellipsis to get the specific row and column values from an ndarray

Ndarrays can be indexed using the standard python syntax, There are different kinds of indexing available such as basic indexing, advanced indexing and field access.

## get specific column by index

Let’s create a numpy array

```
a=np.random.randint(1, 105, size=(5, 4))
a
```

```
array([[76, 43, 27, 50],
[10, 34, 40, 3],
[11, 33, 46, 68],
[27, 14, 30, 85],
[32, 92, 73, 39]])
```

To get all the rows of 2nd column of the array

```
a[:,2]
```

```
array([27, 40, 46, 30, 73])
```

We could also use Ellipsis to get the same result

`Ellipsis`

expands to the number of`:`

objects needed for the selection tuple to index all dimensions.

```
a[...,2]
```

```
array([27, 40, 46, 30, 73])
```

## get multiple columns by index

To get multiple columns in a numpy array, we will pass that as a list, shown below

We want the 2nd and 3rd column of the array a.

```
a[:,[2,3]]
```

```
array([[27, 50],
[40, 3],
[46, 68],
[30, 85],
[73, 39]])
```

## get rows and columns by index

In this case we want to get all the values upto 3rd row for the 2nd and 3rd column only

```
a[:3,[2,3]]
```

```
array([[27, 50],
[40, 3],
[46, 68]])
```

## get last column

We want the last column of the array, we could use negative indices for indexing from the end of the array

```
a[:,-1]
```

```
array([50, 3, 68, 85, 39])
```

The output array is 1D of shape 5. We could also change the shape of the output array by using newaxis.

Each newaxis object in the selection tuple serves to expand the dimensions of the resulting selection by one unit-length dimension.

The added dimension is the position of the newaxis object in the selection tuple. newaxis is an alias for `None`

, and `None`

can be used in place of this with the same result.

We want a 2D array as output instead of an 1D

```
a[np.newaxis,:,-1]
# or
a[None,:,-1]
```

Out:

```
array([[50, 3, 68, 85, 39]])
```

Let’s find out the shape of this new output

```
a[np.newaxis,:,-1].shape
```

Out:

```
(1,5)
```

## get first n rows of 2nd column

In this case, we want all the values of 2nd column upto 3rd row

```
a[:3,2]
```

```
array([27, 40, 46])
```

Alternatively,we could also use Transpose

```
a.T[2][:3]
```

```
array([27, 40, 46])
```