The numpy of the Python operation learning------The slice index of the array and the loop traversal, condition and Boolean array,

The slice index of the array:

　　The tile index of the array is very similar to the list, and the following code is a simple explanation

1A = Np.random.rand (+). Reshape (4, 4)2 Print("Array a:\n", a)3 Print(A[-1][1:4])4 out[1]: 5 Array A:6[0.04175379 0.43013992 0.5398909 0.40638248]7[0.3305902 0.11958799 0.48680358 0.30755734]8[0.00893887 0.3848716 0.21018253 0.88170218]9[0.80198391 0.4922656 0.67535542 0.64647139]]Ten[0.4922656 0.67535542 0.64647139]

Because it is similar to a list, and to symbolize the characteristics of a multidimensional array, it is not much elaborated here. There is a need to refer to the list of relevant knowledge.

Loop traversal of the array:

1A = Np.random.rand (9). Reshape (3, 3)2 Print("Array a:\n", a)3 forRowinchA:4 Print(ROW)#output of a row of rows5 forIteminchA.flat:6 Print(item)7 #General-purpose cyclic functions8For_test = Np.apply_along_axis (Np.mean, axis=0, Arr=a)#Apply_along_axis (func1d, axis, arr, *args, **kwargs)9 Print("Debug of Np.apply_along_axis: \ n", for_test)#axis=0 to column, Axis=1 to rowTenout[2]: One Array A: A[[0.97420758 0.20766438 0.52942127] -[0.82673775 0.44288163 0.41729451] -[0.1373707 0.68103565 0.92256133]] the[0.97420758 0.20766438 0.52942127] -[0.82673775 0.44288163 0.41729451] -[0.1373707 0.68103565 0.92256133] -0.9742075804081937 +0.20766438289931244 -0.5294212665874829 +0.8267377457345865 A0.44288163199889663 at0.4172945079908593 -0.13737070280419617 -0.6810356459375222 -0.922561331228303 - Np.apply_along_axis Debug: -[0.64610534 0.44386055 0.62309237]

The Np.apply_along_axis () method is best used when we are doing column-by-row operations on the matrix. Note that the first parameter is a method, and the method can be a function method that operates on each element of the matrix itself.

Conditions and Boolean arrays:

1 a = Np.random.rand (9). Reshape (3, 3) 2 print (A < 0.5) # output boolean array 3 print (A[a < 0.5]) # output true for element 4 out[3": 5 [[true False true] 6 [false True] 7 [false True]] 8 [0.19353844 0.03944841 0.38137674 0.3069755]

Array shape changes:

1A = Np.random.rand (9). Reshape (3, 3)2A = A.ravel ()#A is a new array at this point.3 Print(a)#to tile an array into a one-dimensional array4A.shape = (3, 3)#You can also use reshape5 Print(a)6 out[4]: 7[0.83017305 0.11660585 0.83060752 0.221212 0.35489551 0.7492569680.61087204 0.85969402 0.90966368]9[[0.83017305 0.11660585 0.83060752]Ten[0.221212 0.35489551 0.74925696] One[0.61087204 0.85969402 0.90966368]]

Note the transpose with A.T, or with A.transpose ().

The numpy of the Python operation learning------The slice index of the array and the loop traversal, condition and Boolean array,