利用Python进行数据分析（2）—— Numpy Basic（2）

Boolean indexing

names = np.array(['Bob', 'Joe', 'Will', 'Bob', 'Will', 'Joe', 'Joe'])data = np.random.randn(7, 4)print(names)print(data)# 对 names 和 字符串 'Bob' 的比较运算符会产生一个布尔型数组print(names == 'Bob')# 这个布尔型数组可以用于索引print(data[names == 'Bob'])# data 数组的第一个下标对应 行数，第二个下标 对应 列数print(data[names == 'Bob', 2:])print(data[names == 'Bob', 3])# 不等于print(names != 'Bob')# 非print(data[~(names == 'Bob')])# 或mask = (names == 'Bob') | (names == 'Will')print(mask)print(data[mask])# 令 data 数组中，小于 0 的数据，变为 0data[data < 0] = 0print(data)#data[names != 'Joe'] = 7print(data)names = np.array([0, 0, 0, 0, 1, 0, 1])# 打印一个由 data 数组的 0,0,0,0,1,0,1 行 组成的数组print(data[names])# -----------------------------------------------------------

['Bob' 'Joe' 'Will' 'Bob' 'Will' 'Joe' 'Joe'][[-0.2305  1.2412  0.2335 -0.8078] [-1.1944  0.5652 -1.1813 -0.223 ] [-1.2092  0.0819  0.0676 -1.2792] [ 1.3819  1.6011  1.5403 -0.3922] [ 1.5291 -1.2984 -0.8702 -0.4224] [-0.8074  1.5405 -0.2572  0.0881] [-0.3954 -0.9237 -0.7061 -0.0459]][ True False False  True False False False][[-0.2305  1.2412  0.2335 -0.8078] [ 1.3819  1.6011  1.5403 -0.3922]][[ 0.2335 -0.8078] [ 1.5403 -0.3922]][-0.8078 -0.3922][False  True  True False  True  True  True][[-1.1944  0.5652 -1.1813 -0.223 ] [-1.2092  0.0819  0.0676 -1.2792] [ 1.5291 -1.2984 -0.8702 -0.4224] [-0.8074  1.5405 -0.2572  0.0881] [-0.3954 -0.9237 -0.7061 -0.0459]][ True False  True  True  True False False][[-0.2305  1.2412  0.2335 -0.8078] [-1.2092  0.0819  0.0676 -1.2792] [ 1.3819  1.6011  1.5403 -0.3922] [ 1.5291 -1.2984 -0.8702 -0.4224]][[ 0.      1.2412  0.2335  0.    ] [ 0.      0.5652  0.      0.    ] [ 0.      0.0819  0.0676  0.    ] [ 1.3819  1.6011  1.5403  0.    ] [ 1.5291  0.      0.      0.    ] [ 0.      1.5405  0.      0.0881] [ 0.      0.      0.      0.    ]][[ 7.      7.      7.      7.    ] [ 0.      0.5652  0.      0.    ] [ 7.      7.      7.      7.    ] [ 7.      7.      7.      7.    ] [ 7.      7.      7.      7.    ] [ 0.      1.5405  0.      0.0881] [ 0.      0.      0.      0.    ]][[ 7.      7.      7.      7.    ] [ 7.      7.      7.      7.    ] [ 7.      7.      7.      7.    ] [ 7.      7.      7.      7.    ] [ 0.      0.5652  0.      0.    ] [ 7.      7.      7.      7.    ] [ 0.      0.5652  0.      0.    ]]Process finished with exit code 0

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Fancy indexing

# # fancy indexing 总是将数据复制到新数组中，与切片不同arr = np.empty((8, 4))for i in range(8):    arr[i] = iprint(arr)# fancy indexing ，利用整数数组进行索引print(arr[[4, 3, 0, 6]])print(arr[[-3, -5, -7]])# more on reshape in Chapter 12arr = np.arange(32).reshape((8, 4))print(arr)# 选出的元素为 (1,0) (5,3) (7,1) (2,2)print(arr[[1, 5, 7, 2], [0, 3, 1, 2]])# 选出的矩阵由 1,5,7,2 行构成，每行中，列的顺序为 0,3,1,2print(arr[[1, 5, 7, 2]][:, [0, 3, 1, 2]])# np.ix_ 函数可以将 两个一维数组转换 为 一个用于选择方形区域的 索引器print(arr[np.ix_([1, 5, 7, 2], [0, 3, 1, 2])])# -----------------------------------------------------

[[ 0.  0.  0.  0.] [ 1.  1.  1.  1.] [ 2.  2.  2.  2.] [ 3.  3.  3.  3.] [ 4.  4.  4.  4.] [ 5.  5.  5.  5.] [ 6.  6.  6.  6.] [ 7.  7.  7.  7.]][[ 4.  4.  4.  4.] [ 3.  3.  3.  3.] [ 0.  0.  0.  0.] [ 6.  6.  6.  6.]][[ 5.  5.  5.  5.] [ 3.  3.  3.  3.] [ 1.  1.  1.  1.]][[ 0  1  2  3] [ 4  5  6  7] [ 8  9 10 11] [12 13 14 15] [16 17 18 19] [20 21 22 23] [24 25 26 27] [28 29 30 31]][ 4 23 29 10][[ 4  7  5  6] [20 23 21 22] [28 31 29 30] [ 8 11  9 10]][[ 4  7  5  6] [20 23 21 22] [28 31 29 30] [ 8 11  9 10]]Process finished with exit code 0

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Transposing arrays and swapping axes

arr = np.arange(15).reshape((3, 5))print(arr)# 转置print(arr.T)arr = np.random.randn(6, 3)# 点乘print(np.dot(arr.T, arr))# 高维数组需要一个 由轴编号 组成的元组才能对这些轴进行转置（费脑子）arr = np.arange(16).reshape((2, 2, 4))print(arr)print(arr.transpose((1, 0, 2)))# np.ndarray.swapaxes() 接受一对 轴编号print(arr)print(arr.swapaxes(1, 2))# -----------------------------------------------------------

[[ 0  1  2  3  4] [ 5  6  7  8  9] [10 11 12 13 14]][[ 0  5 10] [ 1  6 11] [ 2  7 12] [ 3  8 13] [ 4  9 14]][[  3.9802  -0.2502   3.8607] [ -0.2502   7.7333   2.5982] [  3.8607   2.5982  10.7432]][[[ 0  1  2  3]  [ 4  5  6  7]] [[ 8  9 10 11]  [12 13 14 15]]][[[ 0  1  2  3]  [ 8  9 10 11]] [[ 4  5  6  7]  [12 13 14 15]]][[[ 0  1  2  3]  [ 4  5  6  7]] [[ 8  9 10 11]  [12 13 14 15]]][[[ 0  4]  [ 1  5]  [ 2  6]  [ 3  7]] [[ 8 12]  [ 9 13]  [10 14]  [11 15]]]Process finished with exit code 0

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Universal Functions: Fast element-wise array functions

快速的元素级数组函数

arr = np.arange(10)# 开方print(np.sqrt(arr))# 自然对数的底数 e = 2.718..... 的 x 次幂print(np.exp(arr))x = np.random.randn(8)y = np.random.randn(8)print(x)print(y)# 接受两个数组print(np.maximum(x, y)) # element-wise maximumarr = np.random.randn(7) * 5print(arr)# 接受 一个数组，返回 两个数组，（将输入数组的 小数部分 与 整数部分 拆分）print(np.modf(arr))# -----------------------------------------------------------

[ 0.      1.      1.4142  1.7321  2.      2.2361  2.4495  2.6458  2.8284  3.    ][    1.         2.7183     7.3891    20.0855    54.5982   148.4132   403.4288  1096.6332  2980.958   8103.0839][-0.9039  0.6059  0.1219 -1.4536 -1.1021 -0.0681  0.2509  0.7939][ 0.9495 -1.8458  1.0095 -1.2951  0.3019 -1.5155 -0.1693 -0.4673][ 0.9495  0.6059  1.0095 -1.2951  0.3019 -0.0681  0.2509  0.7939][ 0.6628 -7.8998 -6.6542  3.9569 -1.4966  2.8784  3.5424](array([ 0.6628, -0.8998, -0.6542,  0.9569, -0.4966,  0.8784,  0.5424]), array([ 0., -7., -6.,  3., -1.,  2.,  3.]))Process finished with exit code 0

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Data processing using arrays

先不看了，累了…