NumPy arrays are better than Python lists in terms of efficiency and functionality. 1. NumPy array has continuous memory and fast access speed; 2. Supports vectorized operations, simplifying code and improving performance. Create arrays commonly used np.array(), np.zeros(), np.ones(), np.arange() and np.linspace(). View array information available for the .shape, .dtype, and .ndim properties. Basic operations include indexing and slicing, broadcasting mechanism, aggregation operations, deformation and splicing. Data types can be converted using .atype(), and memory management needs to be paid attention to. If slices are view rather than copying, modification will affect the original array. Selecting the appropriate data type can optimize memory footprint.

Python's NumPy library is a basic tool for numerical calculations, especially its array structure, which makes processing large amounts of data efficient and convenient. If you are doing data analysis, scientific computing or machine learning, you cannot avoid the use of NumPy arrays.

Why use NumPy array instead of lists?

Although Python native lists (lists) are flexible, they are not suitable for large-scale numerical operations. The advantages of NumPy arrays are mainly reflected in two aspects:

• High efficiency : Arrays are continuously stored in memory, and the access and operation speeds are far beyond the list.
• Many functions : supports vectorized operations, such as adding, subtracting, multiplication and division of the entire array without writing loops.

Let’s give a simple example: you have two lists of ten thousand numbers and want to add them one by one. If you use a list, you need to write a for loop, and if you use NumPy, you only need one. The code can be done with the number, and the code is simple and runs fast.

Therefore, when you process a little larger amount of data, or need to use mathematical operations, it is time to consider the NumPy array.

How to create and view basic information of an array?

The most common way to create an array is to use np.array() to convert the list into an array. In addition, there are several commonly used functions that are also very practical:

• np.zeros() creates an array with all 0
• np.ones() creates an array with all 1
• np.arange() is similar to range, generating regular arrays
• np.linspace() generates arithmetic sequence

There are several key properties worth looking at in the array:

• .shape view array dimensions, for example (3,4) represents three rows and four columns
• .dtype view data types, such as int64 or float32
• .ndim sees how many dimensions there are in the array. 1D is a vector and 2D is a matrix.

This information can help you quickly understand the status of the current array, especially when debugging or reading other people's code.

What are the basic operations of arrays?

Once an array is available, the most common operations include:

• Index and slice : Similar to list, but supports multiple dimensions, such as arr[0, 1] to get elements in a two-dimensional array
• Broadcasting mechanism : Arrays of different shapes can also be calculated, provided that the broadcast rules are met
• Aggregation operations : such as np.sum() , np.mean() , np.max() , you can specify the axis direction
• Deformation and splicing : For example, reshape() changes shape, np.vstack() and np.hstack() merge arrays

Among them, broadcasting is a very powerful feature. For example, you can add a scalar to the entire array without the need to construct an array of the same size. However, you should also note that it is easy to cause misunderstandings, especially when the shape is inconsistent, it is best to figure out the broadcast rules first.

What should I pay attention to when converting data types and memory management?

NumPy controls data types more strictly than Python. Sometimes you may need to explicitly convert the type, such as converting from an integer to a floating point type, you can use .astype() method.

In addition, NumPy arrays are stored continuously in memory, which means that some operations will copy data and some will not. For example:

• The slice operation does not copy data, but rather a view of the original array (view)
• If you call copy() , you will actually copy a new copy of the data

If you are not careful about this, unexpected results may occur, such as modifying the slice content, resulting in the original array being also changed.

Another point is that the choice of data type will affect memory usage. For example, float32 takes up half of the space than float64 , and this difference is very important when dealing with large data sets.

Basically that's it. NumPy arrays look simple, but there are many details to pay attention to in actual use. It may feel troublesome when you first use it, but after you become proficient, you will find that it is almost an indispensable tool in numerical calculations.

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