Notes | numpy-08 Advanced index

#%%
# NumPy  More than average  Python  Sequences provide more indexing methods . In addition to the indexes with integers and slices seen before , Arrays can be indexed by integer arrays 、 Boolean index and fancy index 
#%% md
##  Integer array index 
#%%
import numpy as np

x = np.array([
    [1, 2],
    [3, 4],
    [5, 6]
])
y = x[[0, 1, 2], [0, 1, 0]] #  Take... Separately  x Of  x[0,0] = 1 x[1,1] = 4 x[2,0] = 5
y
#%%
#  The following example obtained  4X3  Elements of four corners in an array .  The row index is  [0,0]  and  [3,3], And the column index is  [0,2]  and  [0,2]
x = np.array([
    [0, 1, 2],
    [3, 4, 5],
    [6, 7, 8],
    [9, 10, 11]
])

x
#%%
rows = np.array([[0, 0], [3, 3]]) # r  Row index  0=r0, 2=r0, 9=r3, 11=r3
cols = np.array([[0, 2], [0, 2]]) # r  Row index  0=c0, 2=c2, 9=c0, 11=c2
y = x[rows, cols]
y
#%%
#  You can slice  :  or  …  Combined with index array 
import numpy as np

a = np.array([
    [1, 2, 3],
    [4, 5, 6],
    [7, 8, 9]
])
a
#%%
a[1:3, 1:3] #  Row index 1,2  Column index 1,2 [[5,6],[8,9]]
#%%
a[1:3, [1,2]] #  Row index 1,2  Column index 1,2  ditto 
#%%
a[...,1:] #  All right ,  Column index 1 Start to finish  [[2,3], [5,6], [8,9]]
#%% md
##  Boolean index 
 We can index the target array through a Boolean array 
 The Boolean index uses Boolean operations  ( Such as ： Comparison operator )  To get an array of elements that meet the specified criteria 
#%%
#  Get greater than  5  The elements of 
x = np.array([
    [0, 1, 2],
    [3, 4, 5],
    [6, 7, 8],
    [9, 10, 11]
])
x
#%%
b = x > 5 # nr1c
b
#%%
x[b]
#%%
#  Used  ~ ( Complement operator )  To filter  NaN
import numpy as np

a = np.array([np.nan, 1, 2, np.nan, 3, 4, 5])
a
#%%
b = ~np.isnan(a)
b
#%%
a[b]
#%%
#  Filtering out non complex elements from an array  |  Keep the plural 
a = np.array([1, 2+6j, 5, 3.5+5j])
a
#%%
b = np.iscomplex(a)
b
#%%
a[b]
#%% md
##  Fancy index 
 Fancy index refers to using an array of integers to index 
 The fancy index takes the value of the index array as the index of a certain axis of the target array .
 For using a one-dimensional integer array as an index , If the target is a one-dimensional array , Then the result of the index is the row corresponding to the subscript , If the target is a two-dimensional array , Then it is the element corresponding to the position 
 Fancy indexing is not the same as slicing , It always copies the data into a new array 
#%%
#  Pass in the ordinal index array 
import numpy as np

x = np.arange(32) # 0~32( Not included 32)
x = x.reshape((8, 4)) # 8r4c
x
#%%
x[[4,2,1,7]] #  Take... Separately   Index row 4,2,1,7  Follow the order given by the index 
#%%
#  Pass in the inverted index array 
x[[-4,-2,-1,-7]] #  From bottom to top  -1 It's the last line  |  length + Indexes = Real index  | 4, 6, 7, 1
#%%
b = np.ix_([1, 5, 7, 2], [0, 3, 1, 2])
b[0]
#%%
b[1]
#%%
#  Pass in multiple index arrays  ( To use np.ix_)
"""  Index row  1 Of   Index columns  0,3,1,2  Index row  5 Of   Index columns  0,3,1,2  Index row  7 Of   Index columns  0,3,1,2  Index row  2 Of   Index columns  0,3,1,2 """
x[b]
#%%
x[[1, 5, 7, 2], [0, 3, 1, 2]] #  Don't use  np.ix_  Will get 4 Elements   successively  x[1,0], x[5,3], x[7,1] x[2,2]
#%%