Filtering arrays

Introduction to NumPy

Izzy Weber

Core Curriculum Manager, DataCamp

Two ways to filter

Masks and fancy indexing
np.where()

Boolean masks

one_to_five = np.arange(1, 6)
one_to_five

array([1, 2, 3, 4, 5])

mask = one_to_five % 2 == 0
mask

array([False, True, False, True, False])

Filtering with fancy indexing

one_to_five = np.arange(1, 6)
mask = one_to_five % 2 == 0
one_to_five[mask]

array([2, 4])

2D fancy indexing

classroom_ids_and_sizes = np.array([[1, 22], [2, 21], [3, 27], [4, 26]])
classroom_ids_and_sizes

array([[ 1, 22],
       [ 2, 21],
       [ 3, 27],
       [ 4, 26]])

classroom_ids_and_sizes[:, 1] % 2 == 0

2D fancy indexing

classroom_ids_and_sizes = np.array([[1, 22], [2, 21], [3, 27], [4, 26]])
classroom_ids_and_sizes

array([[ 1, 22],
       [ 2, 21],
       [ 3, 27],
       [ 4, 26]])

classroom_ids_and_sizes[:, 0][classroom_ids_and_sizes[:, 1] % 2 == 0]

array([1, 4])

Fancy indexing vs. np.where()

Fancy indexing

Returns array of elements

np.where()

Returns array of indices
Can create an array based on whether elements do or don't meet condition

Filtering with np.where()

classroom_ids_and_sizes

array([[ 1, 22],
       [ 2, 21],
       [ 3, 27],
       [ 4, 26]])

np.where(classroom_ids_and_sizes[:, 1] % 2 == 0)

(array([0, 3]),)

np.where() element retrieval

sudoku_game

array([[0, 0, 4, 3, 0, 0, 2, 0, 9],
       [0, 0, 5, 0, 0, 9, 0, 0, 1],
       [0, 7, 0, 0, 6, 0, 0, 4, 3],
       [0, 0, 6, 0, 0, 2, 0, 8, 7],
       [1, 9, 0, 0, 0, 7, 4, 0, 0],
       [0, 5, 0, 0, 8, 3, 0, 0, 0],
       [6, 0, 0, 0, 0, 0, 1, 0, 5],
       [0, 0, 3, 5, 0, 8, 6, 9, 0],
       [0, 4, 2, 9, 1, 0, 3, 0, 0]])

A tuple of indices

row_ind, column_ind = np.where(sudoku_game == 0)
row_ind, column_ind

(array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4,
        4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8,
        8, 8]),
 array([0, 1, 4, 5, 7, 0, 1, 3, 4, 6, 7, 0, 2, 3, 5, 6, 0, 1, 3, 4, 6, 2,
        3, 4, 7, 8, 0, 2, 3, 6, 7, 8, 1, 2, 3, 4, 5, 7, 0, 1, 4, 8, 0, 5,
        7, 8]))

Find and replace

np.where(sudoku_game == 0, "", sudoku_game)

array([['', '', '4', '3', '', '', '2', '', '9'],
       ['', '', '5', '', '', '9', '', '', '1'],
       ['', '7', '', '', '6', '', '', '4', '3'],
       ['', '', '6', '', '', '2', '', '8', '7'],
       ['1', '9', '', '', '', '7', '4', '', ''],
       ['', '5', '', '', '8', '3', '', '', ''],
       ['6', '', '', '', '', '', '1', '', '5'],
       ['', '', '3', '5', '', '8', '6', '9', ''],
       ['', '4', '2', '9', '1', '', '3', '', '']])

Let's practice!

Introduction to NumPy