Third, NumPy Base: array element Query, modify

First, the index

• The order in which the values are taken is from the perimeter to the innermost element position, which is written sequentially.

1.1. Single Value Index

Import NumPy as NPA = Np.arange (+). Reshape (2,2,4) print ("original array: \ n", a) print ("single value index: \ n", a[1][1][2]) >>> original array: [[[0]  1  2  3]  [4  5 6 7] [[8 9] [12 13 14  15]]] Single value index value: 14

1.2. Fancy Index

• You can index more than one element by using an array, indicating the position of the element in the array.

Import NumPy as Npa=np.arange. Reshape (5,5) #定义一个5 two-dimensional array print ("original array: \ n", a) print ("Fancy indexed multi-value (outer row value): \ n", A[[3, 3, 1, 4)] ) #注意这里一定传入的是数组格式print ("Fancy indexed multi-value (two times value, pre-column): \ n", a[[0,2,2,4],[0,2,4,4]]) #注意这里一定传入的是数组格式 >>> original array: [[0  1  2  3  4] [5  6  7 8 9  ] [10 11 12 13 14] [15 16 17 18 19] [20 21 22 23 24]] fancy indexed multi-value (outer row value): [[5] [7] [6] [9] [8] [+] [+ 20] 21 22 23]  ] Fancy indexed multi-value (outer row value): [24 0 12 14]

1.3. Boolean index

• You can generate a Boolean array by setting the filter criteria, and the results that match the criteria are reversed by a Boolean array

Import NumPy as Npa=np.random.random ((bis)) #生成4 a two-dimensional array of x4 b=a>0.5# generate a Boolean array with a>0.5 as filter print (b) print (A[b]) # Take the original value out of a Boolean array and turn it into a one-dimensional array >>>[[true true false] [true True True  false] [true  true True]  True] [True False  True false]][0.72159895 0.85017348 0.88332226 0.7494597  0.8514071  0.91133411 0.89253366 0.80979503 0.61827433 0.94660476 0.67418788]

1.4. Indexer Ix_ ()

• Ix_ () can traverse in axes, rather than single-point traversal in fancy indexes, compared to fancy indexes

Import NumPy as Npa=np.arange. Reshape (4,6) #生成4 x4 Two-dimensional array b=a[[1,3],[3,5]] #花式索引c =a[np.ix_ ([1,3],[3,5]) #索引器d =a[[ 1,3]][:,[3,5]]print ("original array: \ n", a) print ("Fancy index: \ n", b) print ("Indexer index: \ n", c) print ("C equivalent to d:\n", D) >>> original array: [[0  1  2  3  4  5] [6  7  8  9 10 11] [12 13 14 15 16 17] [18 19 20 21 22 23]] Fancy Index: [9 23] Indexer index: [[9] [23]]c equivalent to D: [[9 11] [21 23]]

　　

Second, slicing

• NumPy slicing in the same way as Python by default
• Slice down content share the same memory space as the original array

Import NumPy as NPA = Np.arange (+). Reshape (2,2,4) print ("original array: \ n", a) print ("Take a value: \ n", a[1][1][2]) print ("Slice range value: \ n", a[1 ][1][2:4]) print ("Larger slice traversal:: \ n", A[0][:][0:4]) >>> original array: [[[0  1  2  3]  [4  5  6  7] ] [[8  9]  [12 13 14 15]]] Take a value: 14

[14 15]

A larger range of slice traversal:
[[0 1 2 3] [4 5 6 7]

Third, Element modification

• Direct assignment of variables by index and slice can be modified

Third, NumPy Base: array element Query, modify