numpy练习笔记1

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本文详细介绍NumPy库的基础使用，包括数组创建、属性获取、数据类型转换等基本操作，以及高级函数如数学运算、矩阵操作、统计分析等。通过实例演示了如何利用NumPy进行高效的数据处理。

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import numpy as np

lst = [[1,3,5],[2,4,6]]
print(type(lst))
np_lst = np.array(lst)
print(type(np_lst))
np_lst = np.array(lst,dtype = np.float32)
print(np_lst.shape)
print(np_lst.ndim)#维度
print(np_lst.dtype)
print(np_lst.itemsize)#每个元素的字节大小，64就是8，32就是4
print(np_lst.size)

输出结果：

<class 'list'>
<class 'numpy.ndarray'>
(2, 3)
2
float32
4
6

数组

print ('数组:')
print(np.zeros([2,4]))
print(np.ones([6,4]))

rand

print ('rand:')
print(np.random.rand(2,4))

randint

print ('randint:')
print(np.random.randint(1,10,3))#生成3个1——10的随机整数

randn 标准正态随机数

print ('randn:')
print(np.random.randn(2,4))

choice

print('choice:')
print(np.random.choice([10,20,30,50,46,85],3))#在这中间随机选择3个数字

distribute

print('disritube:')
print(np.random.beta(1,10,100))#生成贝塔分布

输出结果：

数组:
[[ 0.  0.  0.  0.]
 [ 0.  0.  0.  0.]]
[[ 1.  1.  1.  1.]
 [ 1.  1.  1.  1.]
 [ 1.  1.  1.  1.]
 [ 1.  1.  1.  1.]
 [ 1.  1.  1.  1.]
 [ 1.  1.  1.  1.]]
rand:
[[ 0.51134183  0.68640869  0.30745971  0.42751586]
 [ 0.39394628  0.51500959  0.93783152  0.33093498]]
randint:
[7 7 6]
randn:
[[-0.0629641   1.81308963  0.17863036  0.2995394 ]
 [ 0.38179353 -1.09422534 -0.04260484  0.12850522]]
choice:
[50 30 46]
disritube:
[ 0.30105046  0.22466447  0.00312794  0.04867815  0.23824019  0.22276963
  0.06994241  0.08153077  0.04210733  0.15298111  0.13114783  0.19231987
  0.08590524  0.02384821  0.05994102  0.01940616  0.01914576  0.08006452
  0.09477109  0.03481951  0.17449212  0.06711711  0.18885787  0.03662316
  0.1226368   0.20623612  0.1371511   0.06301977  0.05377805  0.02044071
  0.07306211  0.00730291  0.02932294  0.0193199   0.02619412  0.0204607
  0.02536301  0.02602958  0.0815886   0.07754666  0.01302227  0.15267093
  0.08529373  0.00839241  0.08830106  0.04258077  0.06510606  0.08714733
  0.09652298  0.16992335  0.00873204  0.04909573  0.01785387  0.01237347
  0.08418552  0.24211199  0.0422243   0.1793361   0.0154549   0.00799404
  0.01506692  0.17877294  0.20004141  0.13304119  0.14527565  0.08852351
  0.05173086  0.02800703  0.10004384  0.05408717  0.0157574   0.18044461
  0.04537623  0.07583842  0.15127688  0.06006866  0.04503374  0.07682175
  0.00204775  0.13950375  0.0458627   0.13266404  0.06712269  0.03587277
  0.02672294  0.05953323  0.02113297  0.016502    0.20327103  0.04900302
  0.02238234  0.00264506  0.01121504  0.18968254  0.04769414  0.01039635
  0.0551518   0.06952842  0.09926505  0.00047308]

numpy操作

import numpy as np

#numpy的操作
print(np.arange(1,11).reshape([2,-1]))#5可以用-1代替
lst = np.arange(1,11).reshape([2,-1])
print('exp')
print(np.exp(lst))#自然指数
print('exp2')
print(np.exp2(lst))#自然指数的平方
print('sqrt:')
print(np.sqrt(lst))#开方
print('sin:')
print(np.sin(lst))
print ('log:')
print(np.log(lst))

print ('sum:')
lst1 = np.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]]])
print ('axis=0:')
print(lst1.sum(axis=0))
print ('axis=1:')
print(lst1.sum(axis=1))
print ('axis=2:')
print(lst1.sum(axis=2))

print ('max:')
print ('无内容：')
print(lst1.max())
print ('axis=0:')
print(lst1.max(axis=0))
print ('axis=1:')
print(lst1.max(axis=1))
print ('axis=2:')
print(lst1.max(axis=2))

print ('add:')
lst2 = np.array([1,2,3,4])
lst3 = np.array([7,8,9,10])
print(lst2+lst3)

print('dot:')
print(np.dot(lst2.reshape([2,2]),lst3.reshape([2,2])))

print('concatenate追加：')
print (np.concatenate((lst2,lst3),axis=0))
print (np.concatenate((lst1,lst1),axis=1))
print('vstack：')
print(np.vstack((lst2,lst3)))
print('hstack:')
print(np.hstack((lst2,lst3)))
print('split:')
print(np.split(lst1,3))
print('copy:')
print(np.copy(lst1))

输出结果：

[[ 1  2  3  4  5]
 [ 6  7  8  9 10]]
exp
[[  2.71828183e+00   7.38905610e+00   2.00855369e+01   5.45981500e+01
    1.48413159e+02]
 [  4.03428793e+02   1.09663316e+03   2.98095799e+03   8.10308393e+03
    2.20264658e+04]]
exp2
[[    2.     4.     8.    16.    32.]
 [   64.   128.   256.   512.  1024.]]
sqrt:
[[ 1.          1.41421356  1.73205081  2.          2.23606798]
 [ 2.44948974  2.64575131  2.82842712  3.          3.16227766]]
sin:
[[ 0.84147098  0.90929743  0.14112001 -0.7568025  -0.95892427]
 [-0.2794155   0.6569866   0.98935825  0.41211849 -0.54402111]]
log:
[[ 0.          0.69314718  1.09861229  1.38629436  1.60943791]
 [ 1.79175947  1.94591015  2.07944154  2.19722458  2.30258509]]
sum:
axis=0:
[[27 30 33 36]
 [39 42 45 48]]
axis=1:
[[ 6  8 10 12]
 [22 24 26 28]
 [38 40 42 44]]
axis=2:
[[10 26]
 [42 58]
 [74 90]]
max:
无内容：
24
axis=0:
[[17 18 19 20]
 [21 22 23 24]]
axis=1:
[[ 5  6  7  8]
 [13 14 15 16]
 [21 22 23 24]]
axis=2:
[[ 4  8]
 [12 16]
 [20 24]]
add:
[ 8 10 12 14]
dot:
[[25 28]
 [57 64]]
concatenate追加：
[ 1  2  3  4  7  8  9 10]
[[[ 1  2  3  4]
  [ 5  6  7  8]
  [ 1  2  3  4]
  [ 5  6  7  8]]

 [[ 9 10 11 12]
  [13 14 15 16]
  [ 9 10 11 12]
  [13 14 15 16]]

 [[17 18 19 20]
  [21 22 23 24]
  [17 18 19 20]
  [21 22 23 24]]]
vstack：
[[ 1  2  3  4]
 [ 7  8  9 10]]
hstack:
[ 1  2  3  4  7  8  9 10]
split:
[array([[[1, 2, 3, 4],
        [5, 6, 7, 8]]]), array([[[ 9, 10, 11, 12],
        [13, 14, 15, 16]]]), array([[[17, 18, 19, 20],
        [21, 22, 23, 24]]])]
copy:
[[[ 1  2  3  4]
  [ 5  6  7  8]]

 [[ 9 10 11 12]
  [13 14 15 16]]

 [[17 18 19 20]
  [21 22 23 24]]]