[numpy] array index and slice

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If the array is one-dimensional , Then index and slice are and Python The list of is the same

import numpy as np
#  Index and slice of one-dimensional array 
arr1 = np.arange(10) # array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
##  Index operation 
arr1[5] # 5
arr1[-1] # 9
##  Slice 
arr1[4:6] # array([4, 5])
arr1[::2] # array([0, 2, 4, 6, 8])

If the array is multidimensional ( Here's a two-dimensional array ), Then in brackets [] in , Give two values , The two values are passed comma , separated , Before the comma is the line , Column after comma ; If brackets [] Only one value in , Then the representative bank

arr2

Be careful :arr2 Is random

import numpy as np
#  Index and slice of multidimensional array 
arr2 = np.random.randint(0,10,size=(4,6))

#  For the first 0 Row data 
arr2[0] # array([8, 8, 0, 0, 9, 2])

#  For the first 1,2 Row data 
'''
array([[9, 2, 9, 7, 5, 0],
       [6, 0, 8, 3, 4, 7]])
'''
arr2[1:3] 

#  Get multiple rows of data   example 0,2,3 Row data 
'''
array([[8, 8, 0, 0, 9, 2],
       [6, 0, 8, 3, 4, 7],
       [2, 7, 6, 5, 0, 9]])
'''
arr2[[0,2,3]] 

#  For the first 2 Xing di 1 Column data 
arr2[2,1] # 0

#  Get multiple data   Example No 1 Xing di 4 Column 、 The first 2 Xing di 5 Column data 
arr2[[1,2],[4,5]] # array([5, 7])

#  Get multiple data   Example No 1、2 OK, No 4、5 Columns of data 
'''
array([[5, 0],
       [4, 7]])
'''
arr2[1:3,4:6]

#  Get a column of data   Example No 1 All data of the column 
arr2[:,1] # array([8, 2, 0, 7])

#  Get multiple columns of data   Example No 1、3 All data of the column 
'''
array([[8, 0],
       [2, 7],
       [0, 3],
       [7, 5]])
'''
arr2[:,[1,3]] 

Expand and supplement knowledge : Boolean index

The Boolean index uses Boolean operations ( Such as : Comparison operator ) To get an array of elements that meet the specified criteria

import numpy as np
#  Generate 1-24 Of 4 That's ok 6 Two dimensional array of columns 
arr = np.arange(24).reshape((4,6))
# array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
arr[arr < 10]
# array([ 0,  1,  2,  3,  4, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,23])
arr[(arr < 5)|(arr > 10)]

example 1: Get greater than 5 The elements of  

import numpy as np
arr = np.array([[0,1,2],[3,4,5],[6,7,8],[9,10,11]])
#  Get greater than 5 The elements of 
# array([ 6,  7,  8,  9, 10, 11])
arr[arr >  5]

example 2: Filter NaN 

import numpy as np 
arr = np.array([np.nan,1,2,np.nan,3,4,5])
# array([1., 2., 3., 4., 5.])
arr[~np.isnan(arr)]

example 3: Use & Connect two conditions , Screening is greater than 5 And less than 10 The elements of   

import numpy as np
arr = np.array([[0,1,2],[3,4,5],[6,7,8],[9,10,11]])
#  Screening is greater than 5 And less than 10 The elements of 
# array([6, 7, 8, 9])
arr[(arr > 5) & (arr < 10)]

Tips

1. Boolean index is through the same data True and False To extract

2. The extraction conditions can be one or more , Use when there are multiple extraction conditions & Represents and , Use | For or on behalf of

3. When there are multiple extraction conditions , Each condition should be enclosed in parentheses