Numpy notes

1、 establish numpy Variable

a = np.array([1,2,3,4,5,6])
print(a.shape,a.dtype)
b = np.array([1,2,3,4,5,6]).astype(np.float32)
print(b.shape,b.dtype)
c = np.array([[1,2],[3,4],[5,6]])
print(c.shape,c.dtype)

The running results are as follows ：

2、numpy and list convert

a = [1,2,3,4,5,6]
b = np.array(a)
print(b.shape,b.dtype)

a = [[1,2],[3,4],[5,6]]
b = np.array(a)
print(b.shape,b.dtype)

c = np.array([1,2,3,4,5,6])
print(c.tolist()) # Turn into list

d = np.array([[1,2],[3,4],[5,6]])
print(d.tolist()) # Turn into list

The running results are as follows ：

3、 Create whole 0 whole 1 Of numpy object

a = np.zeros([3,4])
print(a)
a = np.ones([3,4])
print(a)

The running results are as follows ：

4、 Statistical applications

1、 Sum up

To a numpy Object for direct summation ：

Specify data type summation ：

If you don't operate on a two-dimensional matrix, you can sum all the elements directly ：

The two-dimensional matrix operates in different dimensions （ The two-dimensional matrix is not rigorous , Because he is just numpy object , But we can treat it as a matrix ）

The concept of dimension is added here , It can be understood as if axis=0 Just look up , And then it's equal to 1 Just look sideways ：

2、 mean value

The effect here is similar to the previous average , The explanation can be seen in the mean part ：

3、 Standard deviation

The effect here is similar to the previous average , The explanation can be seen in the mean part ：

4、 Mean square error

The effect here is similar to the previous average , The explanation can be seen in the mean part ：

In addition to the above part , There's more to it ：

• abs： Find the absolute value
• sqrt： take a square root

5、 Data processing

1、 Find data

Directly find out whether a certain data exists , Return the location of the data ：

Want to find the number of all matching data ：

2、 Data sorting

If it is a direct sort , Just write it directly ：

If it is a two-dimensional sort , We need to deal with , Or according to the previous numerical direction and horizontal direction ：

If you want to get their ranked position ：

3、 Get non repeating elements

Here you can use functions directly ：

4、 Take the maximum and minimum

Simple value , Use functions directly ：

If it's a two-dimensional matrix , Still refer to the above , It is divided into vertical direction and horizontal direction ：

If you want to get the position of the maximum and minimum value ：（ Here I directly use the two-dimensional , One dimensional is the same ）

5、 Interval uniform sampling

This is the same as the ordinary python The operation is basically the same , Is the beginning and end, and then add the step size OK 了 ：

6、 Matrix operation

1、 Dimension extension

Next, a one-dimensional matrix is extended to a two-dimensional matrix , As shown below ：

2、 Matrix splicing

The same direction dimension can be spliced , The effect is as follows ：

Of course, we can also use our previous method of adding dimension parameters to splice ：

3、 Matrix replication

Copy as you want ：

4、 Four operations of matrix

It's the same as ordinary four arithmetic operations ：

6、 The inner product of a matrix

It's the stuff of linear algebra , But it should be expressed by functions ：

7、 random number

There are three common random numbers , As shown below ：

For multi-dimensional Gaussian random numbers , The meaning of each parameter is as follows ：