A quick start guide to learn more about NumPy functions

Quickly get started with NumPy functions: Detailed introduction, specific code examples are required

Introduction: NumPy is one of the commonly used numerical calculation libraries in Python. It provides efficient multi-dimensional Array (ndarray) objects and powerful function libraries allow us to perform numerical calculations and data processing quickly and efficiently. This article will introduce in detail some commonly used functions in NumPy, and use specific code examples to help readers get started quickly.

1. Create ndarray objects

1. numpy.array function: used to create ndarray objects, and data elements can be specified through list, tuple, etc.

Sample code:

import numpy as np

# 创建一维数组
a = np.array([1, 2, 3, 4, 5])
print(a)  # 输出：[1 2 3 4 5]

# 创建二维数组
b = np.array([[1, 2, 3], [4, 5, 6]])
print(b)  # 输出：
# [[1 2 3]
#  [4 5 6]]

2. numpy.zeros function: used to create an ndarray object of the specified shape (shape) and initialize the elements to 0.

Sample code:

import numpy as np

# 创建一维数组
a = np.zeros(5)
print(a)  # 输出：[0. 0. 0. 0. 0.]

# 创建二维数组
b = np.zeros((2, 3))
print(b)  # 输出：
# [[0. 0. 0.]
#  [0. 0. 0.]]

3. numpy.ones function: used to create an ndarray object of the specified shape and initialize the elements to 1.

Sample code:

import numpy as np

# 创建一维数组
a = np.ones(5)
print(a)  # 输出：[1. 1. 1. 1. 1.]

# 创建二维数组
b = np.ones((2, 3))
print(b)  # 输出：
# [[1. 1. 1.]
#  [1. 1. 1.]]

2. Array operations

1. Array shape: The shape of the array can be obtained through the shape attribute of the ndarray object.

Sample code:

import numpy as np

a = np.array([[1, 2, 3], [4, 5, 6]])
print(a.shape)  # 输出：(2, 3)

2. Transpose of the array: The transpose of the array can be obtained through the T attribute of the ndarray object.

Sample code:

import numpy as np

a = np.array([[1, 2, 3], [4, 5, 6]])
b = a.T  # 转置
print(b)  # 输出：
# [[1 4]
#  [2 5]
#  [3 6]]

3. Splicing of arrays: Splicing of arrays can be done through the numpy.concatenate function.

Sample code:

import numpy as np

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
c = np.concatenate((a, b))  # 拼接
print(c)  # 输出：[1 2 3 4 5 6]

3. Array operations

1. Array addition: Array addition can be performed through the operator of the ndarray object.

Sample code:

import numpy as np

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
c = a + b
print(c)  # 输出：[5 7 9]

2. Array multiplication: Array multiplication can be performed through the * operator of the ndarray object.

Sample code:

import numpy as np

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
c = a * b
print(c)  # 输出：[4 10 18]

4. Array statistics

1. The maximum and minimum values ​​of the array: You can use the max and min methods of the ndarray object Get the maximum and minimum value of an array.

Sample code:

import numpy as np

a = np.array([1, 2, 3, 4, 5])
max_value = a.max()
min_value = a.min()
print(max_value)  # 输出：5
print(min_value)  # 输出：1

2. Sum of the array: You can get the sum of the array through the sum method of the ndarray object.

Sample code:

import numpy as np

a = np.array([1, 2, 3, 4, 5])
sum_value = a.sum()
print(sum_value)  # 输出：15

Summary: This article introduces some commonly used functions in NumPy, including creating ndarray objects, array operations, array operations and array statistics. Through specific code examples, readers can quickly get started with NumPy functions and improve the efficiency of numerical calculations and data processing. I hope this article can be helpful to readers and further master the skills of using NumPy.

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