操做 numpy 数组的经常使用函数

操做 numpy 数组的经常使用函数

where

使用 where 函数能将索引掩码转换成索引位置：

indices = where(mask)
indices

=> (array([11, 12, 13, 14]),) x[indices] # this indexing is equivalent to the fancy indexing x[mask] => array([ 5.5, 6. , 6.5, 7. ]) 

diag

使用 diag 函数可以提取出数组的对角线：

diag(A)

=> array([ 0, 11, 22, 33, 44]) diag(A, -1) array([10, 21, 32, 43]) 

take

take 函数与高级索引（fancy indexing）用法类似：

v2 = arange(-3,3) v2 => array([-3, -2, -1, 0, 1, 2]) row_indices = [1, 3, 5] v2[row_indices] # fancy indexing => array([-2, 0, 2]) v2.take(row_indices) => array([-2, 0, 2]) 

可是 take 也能够用在 list 和其它对象上：

take([-3, -2, -1, 0, 1, 2], row_indices) => array([-2, 0, 2]) 

choose

选取多个数组的部分组成新的数组：

which = [1, 0, 1, 0] choices = [[-2,-2,-2,-2], [5,5,5,5]] choose(which, choices) => array([ 5, -2, 5, -2]) 

线性代数

矢量化是用 Python/Numpy 编写高效数值计算代码的关键，这意味着在程序中尽可能选择使用矩阵或者向量进行运算，好比矩阵乘法等。

标量运算

咱们可使用通常的算数运算符，好比加减乘除，对数组进行标量运算。

v1 = arange(0, 5) v1 * 2 => array([0, 2, 4, 6, 8]) v1 + 2 => array([2, 3, 4, 5, 6]) A * 2, A + 2 => (array([[ 0, 2, 4, 6, 8], [20, 22, 24, 26, 28], [40, 42, 44, 46, 48], [60, 62, 64, 66, 68], [80, 82, 84, 86, 88]]), array([[ 2, 3, 4, 5, 6], [12, 13, 14, 15, 16], [22, 23, 24, 25, 26], [32, 33, 34, 35, 36], [42, 43, 44, 45, 46]])) 

Element-wise(逐项乘) 数组-数组 运算

当咱们在矩阵间进行加减乘除时，它的默认行为是 element-wise(逐项乘) 的:

A * A # element-wise multiplication => array([[ 0, 1, 4, 9, 16], [ 100, 121, 144, 169, 196], [ 400, 441, 484, 529, 576], [ 900, 961, 1024, 1089, 1156], [1600, 1681, 1764, 1849, 1936]]) v1 * v1 => array([ 0, 1, 4, 9, 16]) A.shape, v1.shape => ((5, 5), (5,)) A * v1 => array([[ 0, 1, 4, 9, 16], [ 0, 11, 24, 39, 56], [ 0, 21, 44, 69, 96], [ 0, 31, 64, 99, 136], [ 0, 41, 84, 129, 176]]) 

矩阵代数

矩阵乘法要怎么办? 有两种方法。

1.使用 dot 函数进行 矩阵－矩阵，矩阵－向量，数量积乘法：

dot(A, A)

=> array([[ 300, 310, 320, 330, 340], [1300, 1360, 1420, 1480, 1540], [2300, 2410, 2520, 2630, 2740], [3300, 3460, 3620, 3780, 3940], [4300, 4510, 4720, 4930, 5140]]) dot(A, v1) => array([ 30, 130, 230, 330, 430]) dot(v1, v1) => 30 

2.将数组对象映射到 matrix 类型。

M = matrix(A)
v = matrix(v1).T # make it a column vector v => matrix([[0], [1], [2], [3], [4]]) M * M => matrix([[ 300, 310, 320, 330, 340], [1300, 1360, 1420, 1480, 1540], [2300, 2410, 2520, 2630, 2740], [3300, 3460, 3620, 3780, 3940], [4300, 4510, 4720, 4930, 5140]]) M * v => matrix([[ 30], [130], [230], [330], [430]]) # inner product v.T * v => matrix([[30]]) # with matrix objects, standard matrix algebra applies v + M*v => matrix([[ 30], [131], [232], [333], [434]]) 

加减乘除不兼容的维度时会报错：

v = matrix([1,2,3,4,5,6]).T shape(M), shape(v) => ((5, 5), (6, 1)) M * v => Traceback (most recent call last): File "<ipython-input-9-995fb48ad0cc>", line 1, in <module> M * v File "/Applications/Spyder-Py2.app/Contents/Resources/lib/python2.7/numpy/matrixlib/defmatrix.py", line 341, in __mul__ return N.dot(self, asmatrix(other)) ValueError: shapes (5,5) and (6,1) not aligned: 5 (dim 1) != 6 (dim 0) 

查看其它运算函数: inner, outer, cross, kron, tensordot。 可使用 help(kron)。

数组/矩阵 变换

以前咱们使用 .T 对 v 进行了转置。 咱们也可使用 transpose 函数完成一样的事情。

让咱们看看其它变换函数：

C = matrix([[1j, 2j], [3j, 4j]])
C

=> matrix([[ 0.+1.j,  0.+2.j],
           [ 0.+3.j,  0.+4.j]])

共轭：

conjugate(C)

=> matrix([[ 0.-1.j, 0.-2.j], [ 0.-3.j, 0.-4.j]]) 

共轭转置：

C.H

=> matrix([[ 0.-1.j, 0.-3.j], [ 0.-2.j, 0.-4.j]]) 

real 与 imag 可以分别获得复数的实部与虚部：

real(C) # same as: C.real => matrix([[ 0., 0.], [ 0., 0.]]) imag(C) # same as: C.imag => matrix([[ 1., 2.], [ 3., 4.]]) 

angle 与 abs 能够分别获得幅角和绝对值：

angle(C+1) # heads up MATLAB Users, angle is used instead of arg => array([[ 0.78539816, 1.10714872], [ 1.24904577, 1.32581766]]) abs(C) => matrix([[ 1., 2.], [ 3., 4.]]) 

矩阵计算

矩阵求逆

from scipy.linalg import *
inv(C) # equivalent to C.I => matrix([[ 0.+2.j , 0.-1.j ], [ 0.-1.5j, 0.+0.5j]]) C.I * C => matrix([[ 1.00000000e+00+0.j, 4.44089210e-16+0.j], [ 0.00000000e+00+0.j, 1.00000000e+00+0.j]]) 

行列式

linalg.det(C)

=> (2.0000000000000004+0j) linalg.det(C.I) => (0.50000000000000011+0j) 

数据处理

将数据集存储在 Numpy 数组中能很方便地获得统计数据。为了有个感性地认识，让咱们用 numpy 来处理斯德哥尔摩天气的数据。

# reminder, the tempeature dataset is stored in the data variable: shape(data) => (77431, 7) 

平均值

# the temperature data is in column 3 mean(data[:,3]) => 6.1971096847515925 

过去200年里斯德哥尔摩的日均温度大约是 6.2 C。

标准差 与 方差

std(data[:,3]), var(data[:,3]) => (8.2822716213405663, 68.596023209663286) 

最小值 与 最大值

# lowest daily average temperature data[:,3].min() => -25.800000000000001 # highest daily average temperature data[:,3].max() => 28.300000000000001 

总和, 总乘积 与 对角线和

d = arange(0, 10) d => array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) # sum up all elements sum(d) => 45 # product of all elements prod(d+1) => 3628800 # cummulative sum cumsum(d) => array([ 0, 1, 3, 6, 10, 15, 21, 28, 36, 45]) # cummulative product cumprod(d+1) => array([ 1, 2, 6, 24, 120, 720, 5040, 40320, 362880, 3628800]) # same as: diag(A).sum() trace(A) => 110 

对子数组的操做

咱们可以经过在数组中使用索引，高级索引，和其它从数组提取数据的方法来对数据集的子集进行操做。

举个例子，咱们会再次用到温度数据集：

!head -n 3 stockholm_td_adj.dat 1800 1 1 -6.1 -6.1 -6.1 1 1800 1 2 -15.4 -15.4 -15.4 1 1800 1 3 -15.0 -15.0 -15.0 1 

该数据集的格式是：年，月，日，日均温度，最低温度，最高温度，地点。

若是咱们只是关注一个特定月份的平均温度，好比说2月份，那么咱们能够建立一个索引掩码，只选取出咱们须要的数据进行操做：

unique(data[:,1]) # the month column takes values from 1 to 12 => array([ 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12.]) mask_feb = data[:,1] == 2 # the temperature data is in column 3 mean(data[mask_feb,3]) => -3.2121095707366085 

拥有了这些工具咱们就拥有了很是强大的数据处理能力。 像是计算每月的平均温度只须要几行代码：

months = arange(1,13)
monthly_mean = [mean(data[data[:,1] == month, 3]) for month in months] fig, ax = subplots() ax.bar(months, monthly_mean) ax.set_xlabel("Month") ax.set_ylabel("Monthly avg. temp."); 

对高维数组的操做

当诸如 min, max 等函数对高维数组进行操做时，有时咱们但愿是对整个数组进行该操做，有时则但愿是对每一行进行该操做。使用 axis 参数咱们能够指定函数的行为：

m = rand(3,3) m => array([[ 0.09260423, 0.73349712, 0.43306604], [ 0.65890098, 0.4972126 , 0.83049668], [ 0.80428551, 0.0817173 , 0.57833117]]) # global max m.max() => 0.83049668273782951 # max in each column m.max(axis=0) => array([ 0.80428551, 0.73349712, 0.83049668]) # max in each row m.max(axis=1) => array([ 0.73349712, 0.83049668, 0.80428551]) 

改变形状与大小

Numpy 数组的维度能够在底层数据不用复制的状况下进行修改，因此 reshape 操做的速度很是快，即便是操做大数组。

A

=> array([[ 0, 1, 2, 3, 4], [10, 11, 12, 13, 14], [20, 21, 22, 23, 24], [30, 31, 32, 33, 34], [40, 41, 42, 43, 44]]) n, m = A.shape B = A.reshape((1,n*m)) B => array([[ 0, 1, 2, 3, 4, 10, 11, 12, 13, 14, 20, 21, 22, 23, 24, 30, 31, 32, 33, 34, 40, 41, 42, 43, 44]]) B[0,0:5] = 5 # modify the array B => array([[ 5, 5, 5, 5, 5, 10, 11, 12, 13, 14, 20, 21, 22, 23, 24, 30, 31, 32, 33, 34, 40, 41, 42, 43, 44]]) A # and the original variable is also changed. B is only a different view of the same data => array([[ 5, 5, 5, 5, 5], [10, 11, 12, 13, 14], [20, 21, 22, 23, 24], [30, 31, 32, 33, 34], [40, 41, 42, 43, 44]]) 

咱们也可使用 flatten 函数建立一个高阶数组的向量版本，可是它会将数据作一份拷贝。

B = A.flatten()
B

=> array([ 5, 5, 5, 5, 5, 10, 11, 12, 13, 14, 20, 21, 22, 23, 24, 30, 31, 32, 33, 34, 40, 41, 42, 43, 44]) B[0:5] = 10 B => array([10, 10, 10, 10, 10, 10, 11, 12, 13, 14, 20, 21, 22, 23, 24, 30, 31, 32, 33, 34, 40, 41, 42, 43, 44]) A # now A has not changed, because B's data is a copy of A's, not refering to the same data => array([[ 5, 5, 5, 5, 5], [10, 11, 12, 13, 14], [20, 21, 22, 23, 24], [30, 31, 32, 33, 34], [40, 41, 42, 43, 44]]) 

增长一个新维度: newaxis

newaxis 能够帮助咱们为数组增长一个新维度，好比说，将一个向量转换成列矩阵和行矩阵：

v = array([1,2,3]) shape(v) => (3,) # make a column matrix of the vector v v[:, newaxis] => array([[1], [2], [3]]) # column matrix v[:,newaxis].shape => (3, 1) # row matrix v[newaxis,:].shape => (1, 3) 

叠加与重复数组

函数 repeat, tile, vstack, hstack, 与 concatenate能帮助咱们以已有的矩阵为基础建立规模更大的矩阵。

tile 与 repeat

a = array([[1, 2], [3, 4]]) # repeat each element 3 times repeat(a, 3) => array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4]) # tile the matrix 3 times tile(a, 3) => array([[1, 2, 1, 2, 1, 2], [3, 4, 3, 4, 3, 4]]) 

concatenate

b = array([[5, 6]]) concatenate((a, b), axis=0) => array([[1, 2], [3, 4], [5, 6]]) concatenate((a, b.T), axis=1) => array([[1, 2, 5], [3, 4, 6]]) 

hstack 与 vstack

vstack((a,b)) => array([[1, 2], [3, 4], [5, 6]]) hstack((a,b.T)) => array([[1, 2, 5], [3, 4, 6]]) 

浅拷贝与深拷贝

为了得到高性能，Python 中的赋值经常不拷贝底层对象，这被称做浅拷贝。

A = array([[1, 2], [3, 4]]) A => array([[1, 2], [3, 4]]) # now B is referring to the same array data as A B = A # changing B affects A B[0,0] = 10 B => array([[10, 2], [ 3, 4]]) A => array([[10, 2], [ 3, 4]]) 

若是咱们但愿避免改变原数组数据的这种状况，那么咱们须要使用 copy 函数进行深拷贝：

B = copy(A)
# now, if we modify B, A is not affected B[0,0] = -5 B => array([[-5, 2], [ 3, 4]]) A => array([[10, 2], [ 3, 4]]) 

遍历数组元素

一般状况下，咱们是但愿尽量避免遍历数组元素的。由于迭代相比向量运算要慢的多。

可是有些时候迭代又是不可避免的，这种状况下用 Python 的 for 是最方便的：

v = array([1,2,3,4]) for element in v: print(element) => 1 2 3 4 M = array([[1,2], [3,4]]) for row in M: print("row", row) for element in row: print(element) => row [1 2] 1 2 row [3 4] 3 4 

当咱们须要遍历数组而且更改元素内容的时候，可使用 enumerate 函数同时获取元素与对应的序号：

for row_idx, row in enumerate(M): print("row_idx", row_idx, "row", row) for col_idx, element in enumerate(row): print("col_idx", col_idx, "element", element) # update the matrix M: square each element M[row_idx, col_idx] = element ** 2 row_idx 0 row [1 2] col_idx 0 element 1 col_idx 1 element 2 row_idx 1 row [3 4] col_idx 0 element 3 col_idx 1 element 4 # each element in M is now squared M array([[ 1, 4], [ 9, 16]]) 

矢量化函数

像以前提到的，为了得到更好的性能咱们最好尽量避免遍历咱们的向量和矩阵，有时能够用矢量算法代替。首先要作的就是将标量算法转换为矢量算法：

def Theta(x): """ Scalar implemenation of the Heaviside step function. """ if x >= 0: return 1 else: return 0 Theta(array([-3,-2,-1,0,1,2,3])) => Traceback (most recent call last): File "<ipython-input-11-1f7d89baf696>", line 1, in <module> Theta(array([-3, -2, -1, 0, 1, 2, 3])) File "<ipython-input-10-fbb0379ab8cb>", line 2, in Theta if x >= 0: ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() 

很显然 Theta 函数不是矢量函数因此没法处理向量。

为了获得 Theta 函数的矢量化版本咱们可使用 vectorize 函数：

Theta_vec = vectorize(Theta)
Theta_vec(array([-3,-2,-1,0,1,2,3])) => array([0, 0, 0, 1, 1, 1, 1]) 

咱们也能够本身实现矢量函数:

def Theta(x): """ Vector-aware implemenation of the Heaviside step function. """ return 1 * (x >= 0) Theta(array([-3,-2,-1,0,1,2,3])) => array([0, 0, 0, 1, 1, 1, 1]) # still works for scalars as well Theta(-1.2), Theta(2.6) => (0, 1) 

数组与条件判断

M

=> array([[ 1, 4], [ 9, 16]]) if (M > 5).any(): print("at least one element in M is larger than 5") else: print("no element in M is larger than 5") => at least one element in M is larger than 5 if (M > 5).all(): print("all elements in M are larger than 5") else: print("all elements in M are not larger than 5") => all elements in M are not larger than 5 

类型转换

既然 Numpy 数组是静态类型，数组一旦生成类型就没法改变。可是咱们能够显示地对某些元素数据类型进行转换生成新的数组，使用 astype 函数（可查看功能类似的 asarray 函数）：

M.dtype

=> dtype('int64') M2 = M.astype(float) M2 => array([[ 1., 4.], [ 9., 16.]]) M2.dtype => dtype('float64') M3 = M.astype(bool) M3 => array([[ True, True], [ True, True]], dtype=bool) 

延伸阅读

• http://numpy.scipy.org
• http://scipy.org/Tentative_NumPy_Tutorial
• http://scipy.org/NumPy_for_Matlab_Users - MATLAB 用户的 Numpy 教程。