3.7. Advanced Indexing¶
3.7.1. Indexing¶
two types of indexes: int, bool
Also known as Fancy indexing
import numpy as np
a = np.array([[1, 2, 3],
[4, 5, 6]])
a > 2
# array([[False, False, True],
# [ True, True, True]])
a[a > 2]
# array([3, 4, 5, 6])
a[a > a.mean()]
# array([4, 5, 6])
a[a >= a.mean()+1]
# array([5, 6])
import numpy as np
a = np.array([[1, 2, 3],
[4, 5, 6]])
a[a % 2 == 0]
# array([2, 4, 6])
even = (a % 2 == 0)
a[even]
# array([2, 4, 6])
import numpy as np
a = np.array([[1, 2, 3],
[4, 5, 6]])
a[ (a>2) & (a<=5) & (a%2==1) ]
# array([3, 5])
query1 = (a > 2)
query2 = (a <= 5)
query3 = (a % 2 == 1)
a[query1 & query2 & query3]
# array([3, 5])
large = (a > 2)
small = (a <= 5)
odd = (a % 2 == 1)
a[large & small & odd]
# array([3, 5])
import numpy as np
a = np.array([1, 2, 3])
at_index = np.array([0, 1, 0])
a[at_index]
# array([1, 2, 1])
at_index = np.array([0, 2])
a[at_index]
# array([1, 3])
import numpy as np
a = np.array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
a[[0,2]]
# array([[1, 2, 3],
# [7, 8, 9]])
a[[0,2], [1,2]]
# array([2, 9])
a[:2, [1,2]]
# array([[2, 3],
# [5, 6]])
rows,cols creates coordinate system for selecting values (like zip()). For example: (0,0); (0,1); (1,0); (1,1); (0,1), as in this example:
import numpy as np
a = np.array([[1, 4], [9, 16]], float)
rows = np.array([0, 0, 1, 1, 0], int)
cols = np.array([0, 1, 0, 1, 1], int)
a[rows]
# array([[ 1., 4.],
# [ 1., 4.],
# [ 9., 16.],
# [ 9., 16.],
# [ 1., 4.]])
a[rows,cols]
# array([ 1., 4., 9., 16., 4.])
3.7.2. Use Cases¶
import numpy as np
# '2000-01-01' -> [1, 2, 3]
# '2000-01-02' -> [4, 5, 6]
# '2000-01-03' -> [7, 8, 9]
date = np.array([
'2000-01-01',
'2000-01-02',
'2000-01-03'])
values = np.array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
date == '2000-01-02'
# array([False, True, False])
values[date == '2000-01-02']
# array([[4, 5, 6]])
values[date != '2000-01-02']
# array([[1, 2, 3],
# [7, 8, 9]])
values[ (date=='2000-01-01') | (date=='2000-01-03') ]
# array([[1, 2, 3],
# [7, 8, 9]])
import numpy as np
index = np.array([
'2000-01-01',
'2000-01-02',
'2000-01-03'])
data = np.array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
jan01 = (index == '2000-01-01')
jan03 = (index == '2000-01-03')
data[jan01|jan03]
# array([[1, 2, 3],
# [7, 8, 9]])
data[jan01|jan03, 0]
# array([1, 7])
data[jan01|jan03, :2]
# array([[1, 2],
# [7, 8]])
data[jan01|jan03, :2] = 0
data
# array([[0, 0, 3],
# [4, 5, 6],
# [0, 0, 9]])
import numpy as np
# Morning Noon Evening
# 1999-12-30 1.76405235, 0.40015721, 0.97873798,
# 1999-12-31 2.2408932 , 1.86755799, -0.97727788,
# 2000-01-01 0.95008842, -0.15135721, -0.10321885,
# 2000-01-02 0.4105985 , 0.14404357, 1.45427351,
index = np.array([
'1999-12-30',
'1999-12-31',
'2000-01-01',
'2000-01-02'])
columns = np.array(['Morning', 'Noon', 'Evening'])
data = np.array([[ 1.76405235, 0.40015721, 0.97873798],
[ 2.2408932 , 1.86755799, -0.97727788],
[ 0.95008842, -0.15135721, -0.10321885],
[ 0.4105985 , 0.14404357, 1.45427351]])
dec31 = (index == '1999-12-31') # array([False, True, False, False])
jan01 = (index == '2000-01-01') # array([False, False, True, False])
morning = (columns == 'Morning') # array([ True, False, False])
data[dec31|jan01]
# array([[ 2.2408932 , 1.86755799, -0.97727788],
# [ 0.95008842, -0.15135721, -0.10321885]])
data[dec31|jan01, (columns == 'Morning')]
# array([2.2408932 , 0.95008842])
data[dec31|jan01, morning]
# array([2.2408932 , 0.95008842])
3.7.3. Diagonal problem¶
Warning
Without the
np.ix_call, only the diagonal elements would be selected. This difference is the most important thing to remember about indexing with multiple advanced indexes.
import numpy as np
# Morning Noon Evening
# 1999-12-30 1.76405235, 0.40015721, 0.97873798,
# 1999-12-31 2.2408932 , 1.86755799, -0.97727788,
# 2000-01-01 0.95008842, -0.15135721, -0.10321885,
# 2000-01-02 0.4105985 , 0.14404357, 1.45427351,
index = np.array([
'1999-12-30',
'1999-12-31',
'2000-01-01',
'2000-01-02'])
columns = np.array(['Morning', 'Noon', 'Evening'])
data = np.array([[ 1.76405235, 0.40015721, 0.97873798],
[ 2.2408932 , 1.86755799, -0.97727788],
[ 0.95008842, -0.15135721, -0.10321885],
[ 0.4105985 , 0.14404357, 1.45427351]])
dec31 = (index == '1999-12-31') # array([False, True, False, False])
jan01 = (index == '2000-01-01') # array([False, False, True, False])
morning = (columns == 'Morning') # array([ True, False, False])
evening = (columns == 'Evening') # array([False, False, True])
data[dec31|jan01]
# array([[ 2.2408932 , 1.86755799, -0.97727788],
# [ 0.95008842, -0.15135721, -0.10321885]])
data[(dec31|jan01), (morning|evening)]
# array([ 2.2408932 , -0.10321885])
data[np.ix_((dec31|jan01), (morning|evening))]
# array([[ 2.2408932 , -0.97727788],
# [ 0.95008842, -0.10321885]])