import numpy as np
(1) Create a matrix:
a=np.array([
[1,2,3],
[2,3,4],
[4,5,6]],dtype=np.int64)
| function |
illustrate |
| np.ones((3,4)) |
A matrix of all 1s |
| np.zeros((3,4)) |
A matrix of all 0s |
| np.empty((3,4)) |
A matrix with all elements almost close to 0 |
| np.random.random((3,4)) |
3*4 random number matrix (values between 0~1) |
| np.arange(0,12,1).reshape((3,4)) |
Left closed and right open, the step size is 2 to generate a list, reshape() changes the row and column shape to 3*4 |
| e.g. linspace (1,10,5) |
Left closed and right open, 5 segments increase to get the list |
| a.dtype |
Element type, int32, int64, float32, float64, etc. |
(2) Matrix shape
| function |
illustrate |
| a.ndim |
The return is a few-dimensional array |
| a.size |
returns the number of elements |
| a.shape |
Return the number of rows and columns (m,n) |
(3) Matrix operation
| operation |
function |
| plus minus power |
ce, c + e, c ** 2 |
| Trigonometric functions |
np.sin (a) |
| Transpose |
np.transpose(A)或A.T |
| Multiply one by one |
c=a*b |
| matrix multiplication |
c_dot=np.dot(a,b) |
| sum |
np.sum(a) |
| Find the most value |
np.min(g), np.max(g, axis=1), where axis=0: internal summation of each column, 1: internal summation of each row |
| Extract elements |
A[2,:] 、A[2,1] 、A[1,1:3]) |
| output index |
e.g., argmin (A) 、 e.g., argmax (A) |
| mean median |
np.mean(A)、np.median(A) |
| cumulative difference |
np.cumsum(A)、np.diff(A) |
| Sort row by row in ascending order |
np.sort(A) |
| print(c<18) |
Determine which element is less than 18 and greater than or equal to 18 |
| print(np.clip(A,5,9)) |
The number >9 becomes 9, the number <5 becomes 5, and the middle number remains unchanged |
| A.flatten() |
Flatten the matrix A into only one row, A.flat means that the iterator produced by A.flatten() is used for for loops, etc. |
A.flatten()
for item in A.flat:
print(item)
(4) Matrix merging and division
The following functions can realize simultaneous multi-matrix merging, assuming that the A matrix is 3*4
| function |
illustrate |
| np.array([1,1,1]) |
The output shape is (3,) that there is only one dimension |
| np.array ([1,1,1]) [:, np.newaxis] |
Add a dimension in the column direction, the shape is (3,1) or directly use reshape((3,1)) |
| np.vstack((A,B)) |
Merge vertically |
| np.hstack((A,B)) |
merge horizontally |
| np.concatenate((A,B),axis=0) |
axis=0 is arranged and merged vertically, axis=1 is merged horizontally |
| function |
illustrate |
| np.split(A,3,axis=0) or np.vsplit(A,3) |
Split vertically |
| np.split(A,2,axis=1) or np.hsplit(A,2) |
split horizontally |
| np.array_split(A,3,axis=1) |
The 3*4 matrix is unequally divided into 2 1 1 in the horizontal direction |
