Contents of ndarray object can be accessed and modified by indexing or slicing, just like Python's in-built container objects.

As mentioned earlier, items in ndarray object follows zero-based index. Three types of indexing methods are available − field access, basic slicing and advanced indexing.

Basic slicing is an extension of Python's basic concept of slicing to n dimensions. A Python slice object is constructed by giving start, stop, and step parameters to the built-in slice function. This slice object is passed to the array to extract a part of array.

In [2]:

```
import numpy as np
a = np.arange(10)
s = slice(2,7,2)
print (a[s])
```

In [3]:

```
import numpy as np
a = np.arange(10)
b = a[2:7:2]
print (b)
```

In [5]:

```
# slice single item
import numpy as np
a = np.arange(10)
b = a[5]
print (b)
```

In [8]:

```
# slice items starting from index
import numpy as np
a = np.arange(10)
print (a[2:])
```

In [9]:

```
# slice items between indexes
import numpy as np
a = np.arange(10)
print (a[2:5])
```

In [12]:

```
import numpy as np
a = np.array([[1,2,3],[3,4,5],[4,5,6]])
print (a)
# slice items starting from index
print ('Now we will slice the array from the index a[1:]')
print (a[1:])
```

In [14]:

```
# array to begin with
import numpy as np
a = np.array([[1,2,3],[3,4,5],[4,5,6]])
print ('Our array is:')
print (a)
print ('\n')
# this returns array of items in the second column
print ('The items in the second column are:')
print (a[...,1])
print ('\n')
# Now we will slice all items from the second row
print ('The items in the second row are:')
print (a[1,...])
print ('\n')
# Now we will slice all items from column 1 onwards
print ('The items column 1 onwards are:')
print (a[...,1:])
```

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