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This article mainly introduces how to install and use NumPy in python. It is very detailed and has certain reference value. Friends who are interested must finish reading it.

What is Numpy?

Quite simply, Numpy is a scientific computing library of Python, which provides the function of matrix operation, which is generally used with Scipy and matplotlib. In fact, list already provides a representation similar to a matrix, but numpy provides us with more functions. If you have come into contact with matlab or scilab, then numpy is easy to get started.

NumPy is a high-performance basic package for scientific computing and data analysis.

Installation of NumPy

The installation of NumPy is relatively simple, and we can install NumPy through the commands in Anaconda or through the "pip install numpy" statement. If you need to verify that NumPy is installed successfully, you can run it after the NumPy installation is completed by typing "import numpy" to see if you output an error prompt.

Create a multidimensional array import numpy as np # create an a=np.array with array ([1Magne2Magne3]) # create an one-dimensional array print (a) b=np.array ([[1mem2mem3], [4mem5] 6]]) # create a high-dimensional array print (b) # use ones in NumPy to create an array c=np.ones ([2jue 3]) # an array of all 1s print (c) c [1J 2] = 3 # elements in a logarithmic array are overwritten print (c) # create an array d=np.zeros ([2p3]) print (d) with all elements 0 and create a dimension specified and meta Common properties of array e=np.empty ([2mam 3]) print (e) multidimensional array with all prime random numbers

Ndim: returns the array dimension of statistics, that is, the number of dimensions

# create an array of dimensions specified and the elements are all random numbers e=np.empty ([2jue 3]) print (e) print (e.ndim)

Results:

[[0. 0. 0.]

[0. 0. 0.]]

two

Shape: returns the dimension value of the array and uses a tuple with an integer data type to represent the returned result. For example, if the result returned by a two-dimensional array is (n) m, then n and m represent the length of the data of the corresponding dimension in the array. If you use shape to output the dimensions of the matrix, then in the output (n ~ m), n represents the rows of the matrix and m represents the columns of the matrix.

# create an array of dimensions specified and the elements are all random numbers e=np.empty ([2jue 3]) print (e) print (e.shape)

Results:

[[0. 0. 0.]

[0. 0. 0.]]

(2, 3)

Size: returns the total number of elements in the array to be counted

# create an array of dimensions specified and the elements are all random numbers e=np.empty ([2jue 3]) print (e) print (e.size)

Results:

[[0. 0. 0.]

[0. 0. 0.]]

six

Dtype: returns the data type of the elements in the array. However, the data types displayed are different from the data type names of the variables we defined earlier, because these data types are defined using NumPy, while the data types are represented in NumPy using names in formats such as numpy.int32, numpy.int16, and numpy.float64.

# create an array of dimensions specified and the elements are all random numbers e=np.empty ([2jue 3]) print (e) print (e.dtype)

Results:

[[0. 0. 0.]

[0. 0. 0.]]

Float64

Basic operations of multidimensional arrays arithmetic operations of multidimensional arrays

Arrays can perform arithmetic operations of addition, subtraction, multiplication and division directly.

Import numpy as npg=np.array ([1memb = ", aplob) # print the result print (" aqb = ", aqb) # print the result print (" alphabet b = ", aqpb) # print the result print (" alphabet = ", aplob) # print the result print (" alphabet = ", aplob) # print the result print (" alphabet = ", aplob) # print the result print (" alphabet = ", aplob) # print the result of Aqumb

Results:

AMub-[- 3-3-3]

Astatb = [5 7 9]

A _ b = [0.25 0.4 0.5]

Astatb = [4 10 18]

Process finished with exit code 0

As can be seen from the above example, although the construction of the array is similar to the matrix, its operation is different from the matrix operation introduced earlier: first, there is no division operation in the matrix, but the array can perform division operation: secondly, the multiplication mechanism of the array is accomplished by multiplying the corresponding elements of the position, which is different from the multiplication mechanism of the matrix. Let's take a look at how to implement matrix multiplication through an array.

A=np.array ([1memb = ", aplob) # print the result print of Amurb (" aqb = ", aqb) # print the result print of aqqb (" Matrix1: Avocb = ", aqpb) # print the result print of Acharb (" Avocb = ", aqab) # print the result of aqurb c = a.dot (b) print (" Matrix1: Avocb = ") C) # print the result of aqimb d = np.dot (aforb) print ("Matrix2: aquib", c) # print the result of aqumb after running Output content

Results:

AMub = [- 3-3-3]

Astatb = [5 7 9]

A _ b = [0.25 0.4 0.5]

Astatb = [4 10 18]

Matrix1: astatb = 32

Matrix2: afigb 32

Process finished with exit code 0

In the above code, two methods are used to realize the multiplication of the matrix, and the result is the same. There is another big difference between the arithmetic operation of array and matrix, that is, the array can perform arithmetic operation directly with scalar, but it is not allowed in matrix operation.

A = np.array ([1jin2jin3]) print ("aqum2 =", aqq2) print ("a12 =", aqin2) print ("aMuth2 =", aMuth2) print ("ahum2 =", aqur2)

Results:

A * 2 = [2 46]

A 12 = [0.5 1.1.5]

A-2 = [- 101]

A + 2 = [3 4 5]

Self-operation of array

(1) min: find the element with the lowest value in all the elements of the array by default. You can find the minimum value in the element by row or column by setting the value of axis.

(2) max: find the element with the highest value of all elements in the array by default. You can find the maximum value in the element by row or column by setting the value of axis.

(3) sum: all elements in the array are summed by default and the result is returned. You can also sum elements by row or column by setting the value of axis.

(4) exp: performs an exponential operation on all elements in the array.

(5) sqrt: performs the square root operation on all elements in the array.

(6) square: squares all elements in the array.

Random array

Generating random numbers is very useful in our usual applications. In NumPy, there are many methods to generate random numbers with different attributes to meet the needs of using random numbers in calculation.

(1) seed: random factor, after the random factor of the random number generator is determined, no matter how many times we run the random program, the final number is the same. The random factor is more like a mechanism that turns a random process into a pseudo-random, but it is conducive to the reproduction of the results.

(2) rand: generate a random sample number that satisfies uniform distribution in the range of [0recover1).

(3) randn: generate a positive distribution random sample number with an average value of 0 and a variance of 1.

(4) randint: generates a random number of samples of type integer within a given range.

(5) binomial: generate the number of random samples specified by one dimension and satisfy the binomial distribution.

(6) beta: generate the number of random samples with a specified dimension and satisfy the beta distribution.

(7) normal: generates a random sample with a specified dimension and satisfies the Gaussian positive etheric distribution.

Indexing, slicing, iteration

There are also indexes, slices, and iterations in arrays, and the operation process is similar to lists, but multi-dimensional arrays are more complex in indexing, slicing, and iterations than one-dimensional arrays.

A = np.arange (10) print (a) # outputs the entire array print (a [: 5]) # the first five elements of the output array for i in a: # all elements of the iterative output array print (I)

Results:

[0 1 2 3 4 5 6 7 8 9]

[0 1 2 3 4]

0

one

two

three

four

five

six

seven

eight

nine

Process finished with exit code 0

The above is all the contents of the article "how to install and use NumPy 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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