Basic Operations with NumPy Arrays in Python

Goal: Understand the basic operations of NumPy arrays.

Learning content includes creating and operating NumPy arrays and performing fundamental mathematical calculations.

Code examples demonstrate importing NumPy, creating arrays from lists, using functions such as arange , zeros , ones , and linspace , inspecting and reshaping array shapes, indexing and slicing, and computing mean, variance, and standard deviation.

import numpy as np
# 从列表创建一维数组
data_list = [1, 2, 3, 4, 5]
array_1d = np.array(data_list)
print(f"一维数组: {array_1d}")
# 从列表创建二维数组
data_list_2d = [[1, 2, 3], [4, 5, 6]]
array_2d = np.array(data_list_2d)
print(f"二维数组: \n{array_2d}")
# 使用 arange 创建从 0 到 9 的一维数组
array_arange = np.arange(10)
print(f"使用 arange 创建的数组: {array_arange}")
# 使用 zeros 创建全 0 的一维数组
array_zeros_1d = np.zeros(5)
print(f"全 0 的一维数组: {array_zeros_1d}")
# 使用 zeros 创建全 0 的二维数组
array_zeros_2d = np.zeros((3, 3))
print(f"全 0 的二维数组: \n{array_zeros_2d}")
# 使用 ones 创建全 1 的一维数组
array_ones_1d = np.ones(5)
print(f"全 1 的一维数组: {array_ones_1d}")
# 使用 ones 创建全 1 的二维数组
array_ones_2d = np.ones((3, 3))
print(f"全 1 的二维数组: \n{array_ones_2d}")
# 使用 linspace 创建从 0 到 1 的 5 个等间距的数组
array_linspace = np.linspace(0, 1, 5)
print(f"使用 linspace 创建的数组: {array_linspace}")
# 查看一维数组的形状
shape_1d = array_1d.shape
print(f"一维数组的形状: {shape_1d}")
# 查看二维数组的形状
shape_2d = array_2d.shape
print(f"二维数组的形状: {shape_2d}")
# 改变一维数组的形状
reshaped_array_1d = array_1d.reshape((5, 1))
print(f"改变形状后的一维数组: \n{reshaped_array_1d}")
# 改变二维数组的形状
reshaped_array_2d = array_2d.reshape((6, 1))
print(f"改变形状后的二维数组: \n{reshaped_array_2d}")
# 一维数组的索引
first_element = array_1d[0]
last_element = array_1d[-1]
print(f"第一个元素: {first_element}, 最后一个元素: {last_element}")
# 一维数组的切片
slice_1d = array_1d[1:4]
print(f"切片后的数组: {slice_1d}")
# 二维数组的索引
element_2d = array_2d[1, 2]
print(f"二维数组的元素 (1, 2): {element_2d}")
# 二维数组的切片
slice_2d = array_2d[0:2, 1:3]
print(f"切片后的二维数组: \n{slice_2d}")
# 计算一维数组的均值
mean_1d = np.mean(array_1d)
print(f"一维数组的均值: {mean_1d}")
# 计算二维数组的均值
mean_2d = np.mean(array_2d)
print(f"二维数组的均值: {mean_2d}")
# 计算一维数组的方差
var_1d = np.var(array_1d)
print(f"一维数组的方差: {var_1d}")
# 计算二维数组的方差
var_2d = np.var(array_2d)
print(f"二维数组的方差: {var_2d}")
# 计算一维数组的标准差
std_1d = np.std(array_1d)
print(f"一维数组的标准差: {std_1d}")
# 计算二维数组的标准差
std_2d = np.std(array_2d)
print(f"二维数组的标准差: {std_2d}")

Practice section shows how to compute the mean, variance, and standard deviation of a one‑dimensional array using NumPy.

import numpy as np
data = np.array([1, 2, 3, 4, 5])
mean_value = np.mean(data)
print(f"均值: {mean_value}")
variance_value = np.var(data)
print(f"方差: {variance_value}")
std_deviation_value = np.std(data)
print(f"标准差: {std_deviation_value}")

Summary: After completing this lesson, you should be able to create and manipulate NumPy arrays and perform basic mathematical operations, preparing you for deeper Python data‑processing topics in the coming days.