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This article mainly introduces how to import Numpy library into Python, which is very detailed and has certain reference value. Friends who are interested must finish reading it!

Import Numpy Library

For ease of writing, the alias np is generally used instead of the Numpy library (if the anaconda,Numpy library is installed, it comes with it)

A = np.array ([[1, 2, 3, 4], [5, 6, 7, 8]) print (a)

Common function

After importing the Numpy library, you can use Numpy to generate arrays. You can use the array () function to generate an array that differs from a list without a comma:

A = np.array ([[1, 2, 3, 4], [5, 6, 7, 8]) print (a)

The code above generates a two-dimensional array, as shown in the figure:

If you want to change the dimension of an array, you can get an array of four rows and two columns, which can be achieved through the reshape () method:

B = a.reshape ((4)) print (b)

The parameter in reshape () (4maxim 1) represents a new array that converts the array to 4cm X, where X equals 1x4 of the number of elements in the array a. Parameter-1 is a "lazy" method, which means that the number of columns is automatically calculated by Python through row parameter 4.

In addition, the default is to change the data dimension by row priority, or you can set the parameter order= "F" to change the data dimension by column priority:

C = a.reshape ((4) 2), order= "F") print (c)

Although the dimensions of the array have not changed, the corresponding positions of the elements have changed.

If you want to extract some elements from the array, you can use slicing to extract them. Such as extracting 5 and 7 in line 2:

D = c [1m:] print (d)

The result is as shown in the figure:

Of course, you can also use slicing to modify the values at the corresponding positions in the array, such as changing the middle two numbers of the second column in the array c to 0. You can do this:

C [1JV 3jue 1] = 0print (c)

As you can see, the left side of the comma represents the line number and the right side represents the column number, so we can arbitrarily cut out the data we need.

Arrays can be not only two-dimensional, but also multi-dimensional. The following generates a 3D array e:

E = np.array ([cmaine 2]) print (e)

The array e is an array of 2-2-4, that is, it is made up of 2-2-4 matrices.

The linspace () function in Numpy can generate a fixed number of equally spaced (step-size) arrays between two specified numbers, such as:

F = np.linspace (start=1,stop=12,num=5) print (f)

The above code generates an array of 5 evenly spaced arrays from 1 to 12.

If you want to generate a vector with a specified step size, you can use the arange () method, such as starting at 1 with a step size of 3, to generate a vector less than or equal to 12:

G = np.arange (1 and 12) print (g)

Results: [1 4 7 10]

Use

The ones () function can generate an all-1 array, such as a 2-3 all-1 array:

H = np.ones ((2B3)) print (h)

Use the zeros () function to generate an all-zero array, such as a 2-3 all-zero array:

I = np.zeros ((2B3)) print (I)

If you want to generate an array of units (diagonal 1, the rest are all zeros), you can use the eye () function, such as an array of 3-3 units:

J = np.eye (3) print (j)

Of course, you can also specify the element value of the diagonal:

K = np.diag (np.arange (1m 13pm 4)) print (k)

Using the diag () function, you can get the diagonal elements of an array, or you can get the value of the diagonals:

L = np.diag (np.arange (1Magna 26je 3). Reshape ((3je 3)) print (l)

Results: [1 13 25]

The Random module in Numpy is a powerful tool for generating random numbers. The random number seeds can be specified by the seek () method to ensure that the generated random numbers are repeatable. To generate a repeatable 3x3 random array, you can use the following methods:

Np.random.seed (2) m = np.random.randn (3,3) print (m)

Of course, some commonly used statistical functions can also be implemented, such as the mean using the mean () method:

N = np.arange (10) print (n.mean ())

You can use the std () method for standard deviation:

N = np.arange (10) print (n.std ())

Array sorting can be done using the sort () method, which is automatically sorted per row by default:

P = np.sort ([2, 5, 5, 3], [10, 6, 8]) print (p)

You can use the percentile () function for the percentile of an array:

Q = np.arange (10) print (np.percentile (Q50))

The median uses the median () function:

Q = np.arange (10) print (np.median (Q))

When the number is even, the median is the sum of the middle two numbers divided by 2.

There are many calculation methods of Numpy, you can go to the Internet to check, here are only a few commonly used.

Numpy mainly operates on arrays, and has obvious advantages in generating and adjusting arrays. However, it is not convenient to observe the array directly, and the problems such as no sequence number and uneven arrangement are not good for us to find the data rules, so we need to continue to learn.

These are all the contents of the article "how to Import Numpy Library in Python". Thank you for reading! Hope to share the content to help you, more related knowledge, welcome to follow the industry information channel!

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