numpy基操

import numpy as np

In [8]:
　　a = np.arange(15).reshape(3,5) 
In [10]:
　　a
Out[10]:
　　array([[ 0, 1, 2, 3, 4],
　　[ 5, 6, 7, 8, 9],
　　[10, 11, 12, 13, 14]])
In [11]:
　　a.shape
Out[11]:
　　(3, 5)
In [12]:
  　　a.dtype.name
Out[12]:
　　'int32'
In [16]:
　np.empty((2,3))
Out[16]:
array([[0., 0., 0.],
       [0., 0., 0.]])
In [17]:
　np.arange(10,30,5) # 10-30 步长为5
Out[17]:
array([10, 15, 20, 25])
In [18]:
　np.arange(0,2,0.3) #支持浮点数
Out[18]:
array([0. , 0.3, 0.6, 0.9, 1.2, 1.5, 1.8])
In [19]:
　np.linspace(0,2,9) # 生成 0 到 2 的 9 个数，
Out[19]:
array([0.  , 0.25, 0.5 , 0.75, 1.  , 1.25, 1.5 , 1.75, 2.  ])
In [21]:
from numpy import pi
In [27]:
x = np.linspace(0,2*pi,100) # 在有多个小数位时的使用
x
Out[27]:
array([0.        , 0.06346652, 0.12693304, 0.19039955, 0.25386607,
       0.31733259, 0.38079911, 0.44426563, 0.50773215, 0.57119866,
       0.63466518, 0.6981317 , 0.76159822, 0.82506474, 0.88853126,
       0.95199777, 1.01546429, 1.07893081, 1.14239733, 1.20586385,
       1.26933037, 1.33279688, 1.3962634 , 1.45972992, 1.52319644,
       1.58666296, 1.65012947, 1.71359599, 1.77706251, 1.84052903,
       1.90399555, 1.96746207, 2.03092858, 2.0943951 , 2.15786162,
       2.22132814, 2.28479466, 2.34826118, 2.41172769, 2.47519421,
       2.53866073, 2.60212725, 2.66559377, 2.72906028, 2.7925268 ,
       2.85599332, 2.91945984, 2.98292636, 3.04639288, 3.10985939,
       3.17332591, 3.23679243, 3.30025895, 3.36372547, 3.42719199,
       3.4906585 , 3.55412502, 3.61759154, 3.68105806, 3.74452458,
       3.8079911 , 3.87145761, 3.93492413, 3.99839065, 4.06185717,
       4.12532369, 4.1887902 , 4.25225672, 4.31572324, 4.37918976,
       4.44265628, 4.5061228 , 4.56958931, 4.63305583, 4.69652235,
       4.75998887, 4.82345539, 4.88692191, 4.95038842, 5.01385494,
       5.07732146, 5.14078798, 5.2042545 , 5.26772102, 5.33118753,
       5.39465405, 5.45812057, 5.52158709, 5.58505361, 5.64852012,
       5.71198664, 5.77545316, 5.83891968, 5.9023862 , 5.96585272,
       6.02931923, 6.09278575, 6.15625227, 6.21971879, 6.28318531])
In [28]:
f = np.sin(x)
f
Out[28]:
array([ 0.00000000e+00,  6.34239197e-02,  1.26592454e-01,  1.89251244e-01,
        2.51147987e-01,  3.12033446e-01,  3.71662456e-01,  4.29794912e-01,
        4.86196736e-01,  5.40640817e-01,  5.92907929e-01,  6.42787610e-01,
        6.90079011e-01,  7.34591709e-01,  7.76146464e-01,  8.14575952e-01,
        8.49725430e-01,  8.81453363e-01,  9.09631995e-01,  9.34147860e-01,
        9.54902241e-01,  9.71811568e-01,  9.84807753e-01,  9.93838464e-01,
        9.98867339e-01,  9.99874128e-01,  9.96854776e-01,  9.89821442e-01,
        9.78802446e-01,  9.63842159e-01,  9.45000819e-01,  9.22354294e-01,
        8.95993774e-01,  8.66025404e-01,  8.32569855e-01,  7.95761841e-01,
        7.55749574e-01,  7.12694171e-01,  6.66769001e-01,  6.18158986e-01,
        5.67059864e-01,  5.13677392e-01,  4.58226522e-01,  4.00930535e-01,
        3.42020143e-01,  2.81732557e-01,  2.20310533e-01,  1.58001396e-01,
        9.50560433e-02,  3.17279335e-02, -3.17279335e-02, -9.50560433e-02,
       -1.58001396e-01, -2.20310533e-01, -2.81732557e-01, -3.42020143e-01,
       -4.00930535e-01, -4.58226522e-01, -5.13677392e-01, -5.67059864e-01,
       -6.18158986e-01, -6.66769001e-01, -7.12694171e-01, -7.55749574e-01,
       -7.95761841e-01, -8.32569855e-01, -8.66025404e-01, -8.95993774e-01,
       -9.22354294e-01, -9.45000819e-01, -9.63842159e-01, -9.78802446e-01,
       -9.89821442e-01, -9.96854776e-01, -9.99874128e-01, -9.98867339e-01,
       -9.93838464e-01, -9.84807753e-01, -9.71811568e-01, -9.54902241e-01,
       -9.34147860e-01, -9.09631995e-01, -8.81453363e-01, -8.49725430e-01,
       -8.14575952e-01, -7.76146464e-01, -7.34591709e-01, -6.90079011e-01,
       -6.42787610e-01, -5.92907929e-01, -5.40640817e-01, -4.86196736e-01,
       -4.29794912e-01, -3.71662456e-01, -3.12033446e-01, -2.51147987e-01,
       -1.89251244e-01, -1.26592454e-01, -6.34239197e-02, -2.44929360e-16])
In [31]:
a = np.array([20,30,40,50])
b = np.arange(4)
c = a-b
c
Out[31]:
array([20, 29, 38, 47])
In [32]:
b**2
Out[32]:
array([0, 1, 4, 9], dtype=int32)
In [33]:
10*np.sin(a)
Out[33]:
array([ 9.12945251, -9.88031624,  7.4511316 , -2.62374854])
In [34]:
a<35
Out[34]:
array([ True,  True, False, False])
In [35]:
A = np.array([[1,1],[0,1]])
B = np.array([[2,0],[3,4]])
A*B
Out[35]:
array([[2, 0],
       [0, 4]])
In [36]:
A.dot(B)
Out[36]:
array([[5, 4],
       [3, 4]])
In [37]:
np.dot(A,B)
Out[37]:
array([[5, 4],
       [3, 4]])
In [39]:
a = np.ones((2,3),dtype = int)
b = np.random.random((2,3))
a *= 3
a
Out[39]:
array([[3, 3, 3],
       [3, 3, 3]])
In [40]:
b += a
b
Out[40]:
array([[3.17811736, 3.01278175, 3.89765992],
       [3.63055786, 3.48837569, 3.42782035]])
In [41]:
a += b  #TypeError: Cannot cast ufunc add output from dtype('float64') to dtype('int32') with casting rule 'same_kind'
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-41-294cacd62d6f> in <module>
----> 1 a += b

TypeError: Cannot cast ufunc add output from dtype('float64') to dtype('int32') with casting rule 'same_kind'
In [42]:
a = np.ones(3,dtype=np.int32)
b = np.linspace(0,pi,3)
b.dtype.name
Out[42]:
'float64'
In [44]:
c = a+b
c
Out[44]:
array([1.        , 2.57079633, 4.14159265])
In [45]:
a = np.random.random((2,3))
a
Out[45]:
array([[0.23300036, 0.61133323, 0.7632916 ],
       [0.5514208 , 0.67509734, 0.55061737]])
In [46]:
a.sum()
Out[46]:
3.384760707418866
In [47]:
a.min()
Out[47]:
0.23300036373497113
In [48]:
b = np.arange(12).reshape(3,4)
b
Out[48]:
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])
In [49]:
b.sum(axis=0)
Out[49]:
array([12, 15, 18, 21])
In [50]:
b.min(axis=1)
Out[50]:
array([0, 4, 8])
In [51]:
b.cumsum(axis=1)
Out[51]:
array([[ 0,  1,  3,  6],
       [ 4,  9, 15, 22],
       [ 8, 17, 27, 38]], dtype=int32)
In [52]:
a = np.arange(10)**3
a
Out[52]:
array([  0,   1,   8,  27,  64, 125, 216, 343, 512, 729], dtype=int32)
In [54]:
a[:6:2] = -1000
a
Out[54]:
array([-1000,     1, -1000,    27, -1000,   125,   216,   343,   512,
         729], dtype=int32)
In [55]:
a[::-1]
Out[55]:
array([  729,   512,   343,   216,   125, -1000,    27, -1000,     1,
       -1000], dtype=int32)
In [58]:
for i in a:
    print(i**(1/3.))
nan
1.0
nan
3.0
nan
5.0
5.999999999999999
6.999999999999999
7.999999999999999
8.999999999999998
D:work01softwareanlibsite-packagesipykernel_launcher.py:2: RuntimeWarning: invalid value encountered in power
In [59]:
def f(x,y):
    return 10*x + y
b = np.fromfunction(f,(5,4),dtype=int)
b
Out[59]:
array([[ 0,  1,  2,  3],
       [10, 11, 12, 13],
       [20, 21, 22, 23],
       [30, 31, 32, 33],
       [40, 41, 42, 43]])
In [61]:
b[1,2]   # [1,2] 里面是索引
Out[61]:
12
In [62]:
b[0:5,1] # 每行的第 2 个元素
Out[62]:
array([ 1, 11, 21, 31, 41])
In [63]:
b[-1] # 最后一行，等价于 b[-1,:]
Out[63]:
array([40, 41, 42, 43])
In [64]:
c = np.array([[[0,1,2],[10,12,13]],[[100,101,102],[110,112,113]]])
c.shape
Out[64]:
(2, 2, 3)
In [65]:
c[1,...]
Out[65]:
array([[100, 101, 102],
       [110, 112, 113]])
In [68]:
c[1,:,:]
Out[68]:
array([[100, 101, 102],
       [110, 112, 113]])
In [69]:
c[...,2]
Out[69]:
array([[  2,  13],
       [102, 113]])
In [75]:
a = np.arange(12).reshape(3,4)     #  bool切片需要保证  元素个数对应行（bool数组的个数等于被切数组的行）或者列
b1 = np.array([True,True,True])
b2 = np.array([True,True,False,True])
a[b1,b2]     #   神奇的一幕，每行只取了一个元素
Out[75]:
array([ 0,  5, 11])
In [76]:
for row in a:
    print(row)
[0 1 2 3]
[4 5 6 7]
[ 8  9 10 11]
In [77]:
for element in a.flat:   #  flat属性，是数组元素的一个迭代器
    print(element)
0
1
2
3
4
5
6
7
8
9
10
11
In [78]:
# 数组赋值
a = np.arange(5)
In [79]:
a
Out[79]:
array([0, 1, 2, 3, 4])
In [80]:
a[[1,3,4]] = 0
In [81]:
a
Out[81]:
array([0, 0, 2, 0, 0])
In [82]:
a = np.arange(5)
In [83]:
a[[0,0,2]] = [1,2,3]
a
Out[83]:
array([2, 1, 3, 3, 4])
In [84]:
a = np.arange(5)
a[[0,0,2]] += 1
a    # 注意，使用 += 的时候，即使索引0出现了两次，但是仅增加一次，这是因为Python要求 a+=1 和 a=a+1 等同
Out[84]:
array([1, 1, 3, 3, 4])
In [87]:
# 数组的组合 和 拆分
a = np.floor(10*np.random.random((2,2)))
b = np.floor(10*np.random.random((2,2)))
a
Out[87]:
array([[3., 3.],
       [7., 8.]])
In [88]:
b
Out[88]:
array([[1., 6.],
       [5., 7.]])
In [92]:
np.vstack((a,b))    #  纵向拼接，增加样本个数
Out[92]:
array([[3., 3.],
       [7., 8.],
       [1., 6.],
       [5., 7.]])
In [93]:
np.hstack((a,b))   #横向拼接， 增加特征量
Out[93]:
array([[3., 3., 1., 6.],
       [7., 8., 5., 7.]])
In [100]:
# 函数column_stack：仅支持以序列顺序将多个一维数组或者一个二维数组按对位组合成新的二维数组
a = np.array((1,2,3))
b = np.array((2,3,4))
np.column_stack((a,b))
Out[100]:
array([[1, 2],
       [2, 3],
       [3, 4]])
In [95]:
a = np.floor(10*np.random.random((2,12)))
a
Out[95]:
array([[3., 2., 5., 2., 1., 9., 3., 2., 0., 3., 0., 9.],
       [5., 9., 8., 0., 6., 3., 4., 3., 6., 1., 5., 8.]])
In [96]:
np.hsplit(a,3)    #  将数组a切割成3份
Out[96]:
[array([[3., 2., 5., 2.],
        [5., 9., 8., 0.]]), array([[1., 9., 3., 2.],
        [6., 3., 4., 3.]]), array([[0., 3., 0., 9.],
        [6., 1., 5., 8.]])]
In [97]:
np.hsplit(a,(3,4))   #  在数组a的第3索引前和第4索引前切割
Out[97]:
[array([[3., 2., 5.],
        [5., 9., 8.]]), array([[2.],
        [0.]]), array([[1., 9., 3., 2., 0., 3., 0., 9.],
        [6., 3., 4., 3., 6., 1., 5., 8.]])]
In [98]:
x = np.arange(9)
In [99]:
np.split(x,[3,5,6,10])   #  直接用split 更加灵活

Out[99]:
[array([0, 1, 2]),
 array([3, 4]),
 array([5]),
 array([6, 7, 8]),
 array([], dtype=int32)]
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原文地址：https://www.cnblogs.com/pyyolo/p/12167157.html