二维列表
一、概念
二、创建二维列表
1、追加一维列标来生成二维列标
- 生成一个4行3列的二维列表
row1 = [3, 4, 5] row2 = [1, 5, 9] row3 = [2, 5, 8] row4 = [7, 8, 9] matrix = [] matrix.append(row1) matrix.append(row2) matrix.append(row3) matrix.append(row4) print(matrix) 输出结果：
[[3, 4, 5], [1, 5, 9], [2, 5, 8], [7, 8, 9]] 2、直接赋值生成二维列表
- 定义一个3行4列的二维列表
matrix = [[], [], []] matrix[0] = [3, 4, 5, 6] matrix[1] = [8, 7, 9, 5] matrix[2] = [0, 2, 5, 8] print(matrix) 输出结果：
[[3, 4, 5, 6], [8, 7, 9, 5], [0, 2, 5, 8]] 三、一维列标与二维列表的转换
1、一维列表转换成二维列表
- 将1到24的全部数字按顺序放到一个4行6列的二维列表里
# 将1到24的全部数字按顺序放到一个4行6列的二维列表里 nums = [] for i in range(1, 25): nums.append(i) martix = [] for k in range(4): row = [] for j in range(1, 7): row.append(j + 6 * k) martix.append(row) for arr in martix: print(arr) 输出结果：
[1, 2, 3, 4, 5, 6] [7, 8, 9, 10, 11, 12] [13, 14, 15, 16, 17, 18] [19, 20, 21, 22, 23, 24] 2、二维列表转换成一维列表
- 将一个3行5列的二维列表扁平化一维列表
# 将一个3行5列的二维列表扁平化一维列表 nums = [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15]] arr = [] for i in nums: for j in i: arr.append(j) print(arr) 输出结果：
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15] 3、利用NumPy实现数组的变维操作
- 利用NumPy数组提供的
reshape(m, n)实现数组的变维
（1）一维数组变成二维数组
In [31]:import numpy as np In [32]:arr1 = np.arange(1,25) # arange() 创建一个等差数组 In [33]:arr2 = arr1.reshape(4, 6) # reshape（）一维转二维 In [34]:arr2 Out[34]: array([[ 1, 2, 3, 4, 5, 6], [ 7, 8, 9, 10, 11, 12], [13, 14, 15, 16, 17, 18], [19, 20, 21, 22, 23, 24]]) In [35]:arr2 = arr1.reshape(3, 8) In [36]:arr2 Out[36]: array([[ 1, 2, 3, 4, 5, 6, 7, 8], [ 9, 10, 11, 12, 13, 14, 15, 16], [17, 18, 19, 20, 21, 22, 23, 24]]) （2）二维数组转换成一维数组
In [36]:arr2 Out[36]: array([[ 1, 2, 3, 4, 5, 6, 7, 8], [ 9, 10, 11, 12, 13, 14, 15, 16], [17, 18, 19, 20, 21, 22, 23, 24]]) In [37]:arr1 = arr2.reshape(1, 24)[0] In [38]:arr1 Out[38]: array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]) 四、访问二维列表
- 通过行标与列标来访问二维列表（可以通过切片运算访问行）
1、访问行
In [36]:arr2 Out[36]: array([[ 1, 2, 3, 4, 5, 6, 7, 8], [ 9, 10, 11, 12, 13, 14, 15, 16], [17, 18, 19, 20, 21, 22, 23, 24]]) In [39]:arr2[1] Out[39]: array([ 9, 10, 11, 12, 13, 14, 15, 16]) 2、访问元素
In [40]:arr2 Out[40]: array([[ 1, 2, 3, 4, 5, 6, 7, 8], [ 9, 10, 11, 12, 13, 14, 15, 16], [17, 18, 19, 20, 21, 22, 23, 24]]) In [41]:arr2[1][2] # 第2行第3列 Out[41]: 11 3、NumPy二维数组的访问
In [42]:import numpy as np In [43]:arr2 Out[43]: array([[ 1, 2, 3, 4, 5, 6, 7, 8], [ 9, 10, 11, 12, 13, 14, 15, 16], [17, 18, 19, 20, 21, 22, 23, 24]]) In [44]:arr2[1] # 访问行 Out[44]: array([ 9, 10, 11, 12, 13, 14, 15, 16]) In [45]:arr2[:, 0] # 访问列 Out[45]: array([ 1, 9, 17]) In [46]:arr2[2, 3] # 访问元素 Out[46]: 20 版权声明：本文内容由互联网用户自发贡献，该文观点仅代表作者本人。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。如发现本站有涉嫌侵权/违法违规的内容， 请联系我们举报，一经查实，本站将立刻删除。
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