Analysis of common parameters and usage of numpy functions

Analysis of common parameters and usage of numpy functions

Numpy is a commonly used numerical calculation library in Python. It provides a wealth of numerical operation functions and data structures, which can be convenient and fast. Perform array operations and numerical calculations efficiently. This article will analyze the common parameters and usage of numpy functions and provide specific code examples.

1. Common parameters of numpy function

1. array_like: This is the most common parameter in numpy function, indicating that it accepts various iterable objects (such as list, tuple, array, etc.) as input. It can be a multi-dimensional array or a one-dimensional array.

Example:

import numpy as np

a = np.array([1, 2, 3, 4])  # 定义一维数组
b = np.array([[1, 2], [3, 4]])  # 定义二维数组

print(a)  # 输出：[1 2 3 4]
print(b)  # 输出：[[1 2]
          #       [3 4]]

2. dtype: This is the parameter that specifies the data type of the array elements. Numpy supports multiple data types, such as int, float, bool, etc.

Example:

import numpy as np

a = np.array([1, 2, 3], dtype=np.float)  # 指定数组元素为浮点型
b = np.array([1, 2, 3], dtype=np.int)  # 指定数组元素为整型

print(a)  # 输出：[1. 2. 3.]
print(b)  # 输出：[1 2 3]

3. shape: This is the parameter that specifies the dimensions of the array. Can be a number or a tuple (or list).

Example:

import numpy as np

a = np.array([1, 2, 3, 4])  # 一维数组
b = np.array([[1, 2], [3, 4]])  # 二维数组

print(a.shape)  # 输出：(4,)
print(b.shape)  # 输出：(2, 2)

4. axis: This is a parameter that specifies the operation on an axis. The axis represents the dimension of the array, starting from 0 and increasing one by one.

Example:

import numpy as np

a = np.array([[1, 2], [3, 4]])

print(np.sum(a, axis=0))  # 按列求和，输出：[4 6]
print(np.sum(a, axis=1))  # 按行求和，输出：[3 7]

5. out: This is a parameter that specifies the location where the output results are stored. It can be an existing array or a new array.

Example:

import numpy as np

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
c = np.zeros(3)

np.add(a, b, out=c)  # 将a和b相加，结果放在c中

print(c)  # 输出：[5. 7. 9.]

2. Common usage of numpy functions

1. Creating arrays: You can use various functions provided by numpy Create functions to create arrays, such as np.array(), np.zeros(), np.ones(), np.arange( )wait.

Example:

import numpy as np

a = np.array([1, 2, 3])  # 创建一维数组
b = np.zeros((2, 2))  # 创建全0的二维数组
c = np.ones((3, 3))  # 创建全1的二维数组
d = np.arange(0, 10, 2)  # 创建一个等差数列

print(a)  # 输出：[1 2 3]
print(b)  # 输出：[[0. 0.]
          #       [0. 0.]]
print(c)  # 输出：[[1. 1. 1.]
          #       [1. 1. 1.]
          #       [1. 1. 1.]]
print(d)  # 输出：[0 2 4 6 8]

2. Array operation: numpy provides a wealth of array operation functions, such as addition, subtraction, multiplication, division, and summation , average, etc.

Example:

import numpy as np

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])

print(np.add(a, b))  # 数组相加，输出：[5 7 9]
print(np.subtract(a, b))  # 数组相减，输出：[-3 -3 -3]
print(np.multiply(a, b))  # 数组相乘，输出：[4 10 18]
print(np.divide(a, b))  # 数组相除，输出：[0.25 0.4 0.5]
print(np.sum(a))  # 数组求和，输出：6
print(np.mean(a))  # 数组平均值，输出：2

3. Array transformation: Numpy provides various array transformation functions, such as transpose, reshape, merge, etc.

Example:

import numpy as np

a = np.array([[1, 2], [3, 4]])
b = np.transpose(a)  # 转置数组
c = np.reshape(a, (1, 4))  # 将数组重塑为1行4列的数组
d = np.concatenate((a, b), axis=1)  # 按列合并数组

print(b)  # 输出：[[1 3]
          #       [2 4]]
print(c)  # 输出：[[1 2 3 4]]
print(d)  # 输出：[[1 2 1 3]
          #       [3 4 2 4]]

This article introduces the common parameters and usage of numpy functions, and provides specific code examples. Mastering the usage of these functions can perform array operations and numerical calculations more efficiently and improve programming efficiency.

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