5  Slicing

5.1 Basic Slicing with Numeric Indices

Figure 5.1: Accessing individual axes for one-, two-, and three-dimensional arrays including examples

To select data within an array, use the following formats:

1. [a] returns the single value at position a
2. [a:b] returns all values from position a up to (but not including) b
3. [a:b:c] returns values from position a to b-1 in steps of c

list = np.array([1, 2, 3, 4, 5, 6])

# Selecting the first element
list[0]

np.int64(1)

# Selecting the last element
list[-1]

np.int64(6)

# Selecting a range of elements
list[1:4]

array([2, 3, 4])

For two-dimensional arrays, you use a comma to separate the selection along the first and second dimensions.

matrix = np.array([[1, 2, 3], [4, 5, 6]])

# Selecting a single element
matrix[1,1]

np.int64(5)

For three-dimensional arrays, an additional index is used, again separated by commas. The order remains: first, second, then third dimension.

matrix_3d = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])
print(matrix_3d)

[[[ 1  2  3]
  [ 4  5  6]]

 [[ 7  8  9]
  [10 11 12]]]

# Selecting a specific element
matrix_3d[1,0,2]

np.int64(9)

5.2 Slicing with Boolean Values (Boolean Masking)

Boolean slicing uses a boolean mask to select specific elements from an array. The mask is an array of the same length as the original, consisting of True or False values.

# Create a sample array
a = np.array([1, 2, 3, 4, 5, 6])

# Create the mask
maske = a > 3

print(maske)

[False False False  True  True  True]

This results in an array of boolean values. Applying this mask to the original array returns all elements where the mask is True.

# Applying the mask
print(a[maske])

[4 5 6]

Warning

Using boolean arrays is only possible with the NumPy module. This approach cannot be applied to native Python lists. In such cases, you must iterate over the list manually.

a = [1, 2, 3, 4, 5, 6]
result = [x for x in a if x > 3]
print(result) 

[4, 5, 6]

Mini Exercise: Array Slicing

Select the colored sections of the array matrix using the slicing techniques you just learned.

matrix = np.array([
    [2, 11, 18, 47, 33, 48, 9, 31, 8, 41],
    [55, 1, 8, 3, 91, 56, 17, 54, 23, 12],
    [19, 99, 56, 72, 6, 13, 34, 16, 77, 56],
    [37, 75, 67, 5, 46, 98, 57, 19, 14, 7],
    [4, 57, 32, 78, 56, 12, 43, 61, 3, 88],
    [96, 16, 92, 18, 50, 90, 35, 15, 36, 97],
    [75, 4, 38, 53, 1, 79, 56, 73, 45, 56],
    [15, 76, 11, 93, 87, 8, 2, 58, 86, 94],
    [51, 14, 60, 57, 74, 42, 59, 71, 88, 52],
    [49, 6, 43, 39, 17, 18, 95, 6, 44, 75]
])

Solution

• Red: matrix[1,3]
• Green: matrix[4:6,2:6]
• Pink: matrix[:,7]
• Orange: matrix[7,:5]
• Blue: matrix[-1,-1]