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This article mainly explains "how to create the array of Numpy". Interested friends may wish to have a look at it. The method introduced in this paper is simple, fast and practical. Now let the editor take you to learn "how to create the array of Numpy"!

Background of the array object

The core data structure of Numpy is called array, which is an array. Array objects can be either one-dimensional or multi-dimensional arrays.

Python's List can also achieve the same function, but the advantages of array over List are better performance, including array metadata information, and a large number of convenient functions.

Numpy has become the "universal underlying language" of the de facto Scipy, Pandas, Scikit-Learn, Tensorflow, PaddlePaddle and other frameworks.

One of the differences between Numpy's array and Python's List is that its elements must all be of the same data type, such as digital int, which is also one of the reasons for the high performance of Numpy.

Properties of array itself

Shape: returns a tuple that represents the dimension of array

Ndim: a number that represents the number of dimensions of the array

Size: a number that represents the number of all data elements in the array

The data type of the element in dtype:array

The method of creating array

Create an array from Python's list List and nested lists

Create using predetermined functions such as arange, ones/ones_like, zeros/zeros_like, empty/empty_like, full/full_like, eye, etc.

Construction of np.random Module for generating Random numbers

A large number of operations and functions supported by array itself

Direct arithmetic operations such as addition, subtraction, multiplication and division of elements

Easier to use multidimensional array index

Find the aggregate function such as sum/mean

Linear algebraic functions, such as solving inverse matrices and solving equations

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