Articles → Numpy → Slice Notation In Numpy

# Slice Notation In Numpy

## Syntax

```Array_object[lower:upper]
Or
Array_object[:upper]
Or
Array_object[lower:]
Or
Array_object[:]```

1. The maximum value of upper is equal to the size of an array.
2. If you don't specify lower, then by default the value is zero.
3. If you don't specify upper, then by default the value is equal to the length of the array.

## Example

```import numpy as np

arr = np.arange(0,10)

# The value is displayed from position 1 to 5
print ("Value from position 1 to 5 is: ", arr[1:5])

# The value is displayed from position 0 to 5. Here we don't specify the lower.
print ("Value from position 0 to 5 is: ", arr[:5])

# The value is displayed from position 5 to 10
print ("Value from position 5 to 10 is: ", arr[5:])

# Display all the elements
print ("Display all the elements", arr[:])```

Click to Enlarge

## Slice Notation In Matrices

```import numpy as np

my_matrix = np.array([[1,2,3],[4,5,6],[7,8,9]])

# single bracket approach
print(my_matrix[0,1:])
# double bracket approach
print(my_matrix[0][1:])```

Click to Enlarge

 Posted By  - Karan Gupta Posted On  - Wednesday, March 27, 2019 Updated On  - Tuesday, June 4, 2019

Query/Feedback

 Your Email Id Subject Query/Feedback Characters remaining 250