Python数据分析必备库—NumPy：ndarray的基本操作

强大的多维度数组与矩阵计算库

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ndarray的基本操作

1. 索引

一维与列表完全一致, 多维度同理

# 列表l = [1,2,3,4,5,6]l[3]# 输出#  4# numpy数组(ndarray类型)n = np.array(l)n[3]# 输出#  4# 二维数组n = np.random.randint(0,10, size=(4,5))n# array([[1, 2, 5, 1, 5],#       [5, 5, 6, 9, 8],#       [3, 4, 2, 2, 0],#       [4, 4, 8, 4, 3]])# 找到3n[3][4]n[-1][-1]# 简写n[3,4]n[-1,-1]# 三维数组n = np.random.randint(0, 100, size=(4,5,6))# 3个维度n[2,2,3]n[-2,-3,3]

根据索引修改数据

# 定位到指定元素,直接修改n[2,2,3] = 6666# 修改一个数组n[0,0] = [1, 2, 3]n = np.zeros((6,6), dtype=int)n# 输出# array([[0, 0, 0, 0, 0, 0],#       [0, 0, 0, 0, 0, 0],#       [0, 0, 0, 0, 0, 0],#       [0, 0, 0, 0, 0, 0],#       [0, 0, 0, 0, 0, 0],#       [0, 0, 0, 0, 0, 0]])# 修改1行n[0] = 1n# 输出# array([[1, 1, 1, 1, 1, 1],#       [0, 0, 0, 0, 0, 0],#       [0, 0, 0, 0, 0, 0],#       [0, 0, 0, 0, 0, 0],#       [0, 0, 0, 0, 0, 0],#       [0, 0, 0, 0, 0, 0]])# 修改多行n[[0,3,-1]] = 2n# 输出# array([[2, 2, 2, 2, 2, 2],#       [0, 0, 0, 0, 0, 0],#       [0, 0, 0, 0, 0, 0],#       [2, 2, 2, 2, 2, 2],#       [0, 0, 0, 0, 0, 0],#       [2, 2, 2, 2, 2, 2]])

2. 切片

一维与列表完全一致 多维度同理

# 列表切片l = [1,2,3,4,5,6]l[::-1]# 输出# [6, 5, 4, 3, 2, 1]# 一维数组n = np.arange(10)n# 输出# array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])n[::2]# 输出# array([0, 2, 4, 6, 8])n[::-1]# 输出# array([9, 8, 7, 6, 5, 4, 3, 2, 1, 0])# 多维数组n = np.random.randint(0,100, size=(5,6))n# 输出# array([[94, 45, 60, 71, 70, 88],#       [43, 16, 39, 70, 14,  4],#       [59, 12, 84, 38, 96, 88],#       [80,  9, 72, 95, 69, 91],#       [44, 84,  5, 47, 92, 31]])#  行 翻转n[::-1]# 列 翻转 (第二个维度)n[:, ::-1]

实例: 把猫图片翻转

# cat.jpgcat = plt.imread('cat.jpg')#[[[231 186 131]#  [232 187 132]#  [233 188 133]#  ...#  [100  54  54]#  [ 92  48  47]#  [ 85  43  44]]#  ...#  ]cat.shape# 图片: 3维#  (456, 730, 3)# 显示图片plt.imshow(cat)

# 上下翻转plt.imshow(cat[::-1])  

# 左右翻转plt.imshow(cat[:,::-1])  

# 第三个维度翻转,颜色翻转,模糊处理plt.imshow(cat[::10,::10,::-1])  

3. 变形

使用reshape函数, 注意参数是一个tuple

n = np.arange(1, 21)n# array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20])n.shape# (20,)# 变成2维n2 = np.reshape(n, (4,5))n2# array([[ 1,  2,  3,  4,  5],#       [ 6,  7,  8,  9, 10],#       [11, 12, 13, 14, 15],#       [16, 17, 18, 19, 20]])n2.shape# (4, 5)n2.reshape((20,))  # 变成一维n2.reshape((-1,))  # 变成一维# 改变cat的形状cat.shape# (456, 730, 3)#  -1 表示行会自动分配,列数是6cat2 = cat.reshape((-1, 6))cat2.shape# (166440, 6)#  -1 表示列会自动分配,行数是6cat2 = cat.reshape((6,-1))cat2.shape# (6, 166440)

4. 级联

concatenate

n1 = np.random.randint(0,100, size=(4,5))n2 = np.random.randint(0,100, size=(4,5))display(n1, n2)# array([[48, 89, 82, 88, 55],#       [63, 80, 77, 27, 51],#       [11, 77, 90, 23, 71],#       [ 4, 11, 19, 84, 57]])# #array([[86, 26, 71, 62, 46],#       [75, 43, 84, 87, 99],#       [34, 33, 58, 56, 29],#       [56, 32, 53, 43,  5]])# 级联,合并# 上下合并:垂直级联np.concatenate((n1, n2))np.concatenate((n1, n2), axis=0)  # axis=0表示行，第一个维度# array([[48, 89, 82, 88, 55],#       [63, 80, 77, 27, 51],#       [11, 77, 90, 23, 71],#       [ 4, 11, 19, 84, 57],#       [86, 26, 71, 62, 46],#       [75, 43, 84, 87, 99],#       [34, 33, 58, 56, 29],#       [56, 32, 53, 43,  5]])# 左右合并:水平级联np.concatenate((n1, n2), axis=1)  # axis=1表示列，第二个维度# array([[48, 89, 82, 88, 55, 86, 26, 71, 62, 46],#       [63, 80, 77, 27, 51, 75, 43, 84, 87, 99],#       [11, 77, 90, 23, 71, 34, 33, 58, 56, 29],#       [ 4, 11, 19, 84, 57, 56, 32, 53, 43,  5]])

np.hstack与np.vstack

# 左右合并:水平级联np.hstack((n1, n2)) # array([[48, 89, 82, 88, 55, 86, 26, 71, 62, 46],#       [63, 80, 77, 27, 51, 75, 43, 84, 87, 99],#       [11, 77, 90, 23, 71, 34, 33, 58, 56, 29],#       [ 4, 11, 19, 84, 57, 56, 32, 53, 43,  5]])# 上下合并:垂直级联np.vstack((n1, n2)) # array([[48, 89, 82, 88, 55],#       [63, 80, 77, 27, 51],#       [11, 77, 90, 23, 71],#       [ 4, 11, 19, 84, 57],#       [86, 26, 71, 62, 46],#       [75, 43, 84, 87, 99],#       [34, 33, 58, 56, 29],#       [56, 32, 53, 43,  5]])

5. 拆分

np.split / np.vsplit / np.hsplit

n = np.random.randint(0, 100, size=(6,4))n# array([[ 3, 90, 62, 89],#       [75,  7, 10, 76],#       [77, 94, 88, 59],#       [78, 66, 81, 83],#       [18, 88, 40, 81],#       [ 2, 38, 26, 21]])# 垂直方向，平均切成3份np.vsplit(n, 3)# [array([[ 3, 90, 62, 89],#        [75,  7, 10, 76]]),# array([[77, 94, 88, 59],#        [78, 66, 81, 83]]),# array([[18, 88, 40, 81],#        [ 2, 38, 26, 21]])]# 如果是数组np.vsplit(n, (1,2,4))# [array([[ 3, 90, 62, 89]]),#  array([[75,  7, 10, 76]]),#  array([[77, 94, 88, 59],#        [78, 66, 81, 83]]),#  array([[18, 88, 40, 81],#        [ 2, 38, 26, 21]])]# 水平方向np.hsplit(n, 2)# [array([[97, 86],#        [16, 70],#        [26, 95],#        [ 6, 83],#        [97, 43],#        [96, 57]]),# array([[88, 69],#        [60,  7],#        [32, 82],#        [24, 86],#        [62, 23],#        [43, 19]])]# 通过axis来按照指定维度拆分np.split(n, 2, axis=1)# [array([[97, 86],#        [16, 70],#        [26, 95],#        [ 6, 83],#        [97, 43],#        [96, 57]]),# array([[88, 69],#        [60,  7],#        [32, 82],#        [24, 86],#        [62, 23],#        [43, 19]])]

示例: 把猫拆分

cat.shape# (456, 730, 3)# 拆分cat2 = np.split(cat, 2, axis=0)cat2[0]plt.imshow(cat2[0])

# 拆分成5分cat2 = np.split(cat, 5, axis=1)plt.imshow(cat2[2])

6. 拷贝/复制/副本

copy

# 赋值: 不使用copyn1 = np.arange(10)n2 = n1n1[0] = 100display(n1, n2)# array([100,   1,   2,   3,   4,   5,   6,   7,   8,   9])# array([100,   1,   2,   3,   4,   5,   6,   7,   8,   9])# 拷贝: copyn1 = np.arange(10)n2 = n1.copy()n1[0] = 100display(n1, n2)# array([100,   1,   2,   3,   4,   5,   6,   7,   8,   9])# array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

7. 转置

# 转置n = np.random.randint(0, 10, size=(3, 4)) n.T  # transpose改变数组维度n = np.random.randint(0, 10, size=(3, 4, 5)) # shape(3, 4, 5)np.transpose(n, axes=(2,0,1))