NumPy Array Slicing

Slicing Basics: start:stop:step

Slicing lets you pull out a range of elements instead of one at a time, using the syntax arr[start:stop:step]. start is the first index included, stop is the index where the slice ends (excluded), and step controls how many elements are skipped between selections. Any of the three can be omitted: a missing start defaults to the beginning, a missing stop defaults to the end, and a missing step defaults to 1.

import numpy as np

arr = np.array([10, 20, 30, 40, 50, 60, 70])

print(arr[1:5])    # [20 30 40 50]
print(arr[:4])     # [10 20 30 40]
print(arr[3:])     # [40 50 60 70]
print(arr[::2])    # [10 30 50 70]

Negative Slicing and Reversal

Negative numbers work in slices the same way they do in indexing - they count from the end of the array. A negative step reverses the direction of the slice entirely, which is how arr[::-1] flips an array.

import numpy as np

arr = np.array([10, 20, 30, 40, 50, 60, 70])

print(arr[-3:])     # [50 60 70]
print(arr[:-2])     # [10 20 30 40 50]
print(arr[::-1])    # [70 60 50 40 30 20 10]

Slicing 2-D Arrays

In a 2-D array, you slice each axis separately, separated by a comma: arr[row_slice, col_slice]. This lets you cut out a sub-block of rows and columns in a single expression.

import numpy as np

grid = np.array([[1, 2, 3, 4],
                 [5, 6, 7, 8],
                 [9, 10, 11, 12]])

print(grid[0:2, 1:3])
# [[2 3]
#  [6 7]]

print(grid[:, 2])   # every row, column 2 -> [ 3  7 11]
print(grid[1, :])   # row 1, every column -> [5 6 7 8]