SoFunction
Updated on 2025-03-03

The usage of NumPy in detail

1. NumPy library and introduction

NumPy, full nameNumerical Python, is one of the important extension libraries used for numerical calculations in Python. It provides multidimensional array objects, a series of derived objects, and functions for quickly manipulating arrays. NumPy plays a crucial role in the fields of data processing, scientific computing, machine learning, etc.

is a function in the NumPy library forCreate a new array of given shape and type, but not initialize the array entry. This means that the memory space of the new array contains arbitrary data, depending on the memory state. So unless you are sure that the previous contents of the memory can be used safely,Best to usezerosoronesSo that function is used to initialize the array instead of usingempty。

2. Basic usage

The basic usage of the simplified method is very simple, you only need to specify the shape and data type of the array. Here is a basic example:

import numpy as np

# Create an uninitialized array of shapes (3, 2)empty_array = ((3, 2))
print(empty_array)

Run this code and you will get a 2D array of shape (3, 2), but the element values ​​in the array are undefined, they depend on the current content in memory.

It should be noted thatThe array is not assigned a specific initial value, so you should be clear about this before using it to avoid problems caused by uninitialized values.

3. Detailed explanation of parameters

The function accepts several parameters, which are used to specify the shape, data type and other properties of the array.

  • shape: Defines an integer or tuple of integers in the shape of a new array. For example,shape=(2, 3)A two-dimensional array of 2 rows and 3 columns will be created.
  • dtype: The expected data type. If not given, the data type is inferred from other input parameters.
  • order: Specify the storage method of arrays in memory, including 'C' (by row) and 'F' (by column). The default is 'C'.

Here is a more complex example that demonstrates how to use these parameters:

Examples are as follows:

import numpy as np

# Create an uninitialized array with shape (2, 3) and data type float64float_array = ((2, 3), dtype=np.float64)
print(float_array)

In this example, we create a two-dimensional array of shape (2, 3) and specify the data type asnp.float64. Likewise, the element values ​​in the array are undefined.

4. Performance optimization

AlthoughThere is no initialization when creating an array, which can improve performance in some cases, but it also poses risks. An uninitialized array may contain arbitrary data, which may result in unpredictable results in subsequent calculations.

therefore,In most cases, it is recommended to useoretc functions to create and initialize the array to ensure that the elements in the array have the expected value. These functions initialize all elements of the array to 0 or 1 while creating the array, thus avoiding the possible problems with using uninitialized arrays.

However, in certain specific scenarios, such asYou already know that the array will be overwritten immediately, or you are working on a lot of data and want to save time and memory required for initialization steps, then useProbably suitable。

5. Combination with other NumPy functions

Functions are often used with other NumPy functions to perform subsequent operations on created uninitialized arrays. For example, you can useorWait for functions to fillThe created array.

Examples are as follows:

import numpy as np

# Create an uninitialized array of shapes (2, 3)empty_array = ((2, 3))

# Fill array with random numbersempty_array[:] = (*empty_array.shape)
print(empty_array)

In this example, we first useCreate an uninitialized array and useRandom numbers with the same shape as the array are generated and these random numbers are assigned to the array, thus realizing the initialization of the array.

Summarize

It is a powerful but potentially dangerous tool in the NumPy library. It allows you to create uninitialized arrays, thereby improving performance in some cases. However, this also brings the risk of using uninitialized arrays, so in useExtra caution must be taken. By understandingThe basic usage, parameters, and underlying mechanisms of this function can be used safer and more efficiently.

Remember, in most cases, use、orEasily initializing functions to create arrays is a safer and more predictable option. However, in specific performance-critical scenarios, if you are sure you can safely use uninitialized arrays,Possibly a useful tool.

This is the end of this article about the detailed usage of NumPy. For more related NumPy content, please search for my previous articles or continue browsing the related articles below. I hope everyone will support me in the future!