数据科学家极力推荐核心计算工具-Numpy的前世今生（上）

原创

Python编程爱好者

修改于 2020-09-04 10:03:58

4980

修改于 2020-09-04 10:03:58

先看下本文目录哈

1. 一般Python和numpy实现方式
2. 上述两种实现方式比较
3. numpy数组
4. 创建多维数组
5. 选取数组元素
6. 数据类型
7. 数据类型转换
8. 数据类型对象
9. 字符编码
10. dtype类的属性
11. 创建自定义数据类型
12. 数组与标量的运算
13. 一维数组的索引与切片
14. 多维数组的切片与索引
15. 布尔型索引
16. 花式索引
17. 数组转置
18. 改变数组的维度
19. 组合数组
20. 数组的分割
21. 数组的属性
22. 数组的转换

然后，重磅！今天给大家拿到Python的核心资料！实实在在在工业界会要用到！

公众号后台回复“Python数据科学”全部获取得到！

人生苦短我用python！这不是吹牛，为什么？大家看看其他语言之父们！

Java之父——James Gosling

vb.net之父 ——lan Cooper

PHP之父 ——Rasmus Lerdorf

Go语言之父 ——rob pike

C++之父 ——Bjarne Stroustrupt

最后是重磅的Python之父！

Python在发展接近三十年里，逐渐发展为各行各业的网红语言!

无论是哪个方向工业界 Python 都为其发展带了不可磨灭的功劳！

1. 一般Python和numpy实现方式

实现：实现了两个向量的相加

# -*- coding: utf-8 -*-

# 此处两种操作方式：
# 第一种对于每一个元素的操作，第二种是对于整体的操作
# 向量相加-Python
def pythonsum(n):
    a = range(n)
    b = range(n)
    c = []
    for i in range(len(a)):
        a[i] = i ** 2
        b[i] = i ** 3
        c.append(a[i] + b[i])
    return c

#向量相加-NumPy
import numpy as np

def numpysum(n):
    a = np.arange(n) ** 2
    b = np.arange(n) ** 3
    c = a + b
    return c

2. 上述两种实现方式比较

#效率比较
import sys
from datetime import datetime
import numpy as np

size = 1000

start = datetime.now()
c = pythonsum(size)
delta = datetime.now() - start
print "The last 2 elements of the sum", c[-2:]
print "PythonSum elapsed time in microseconds", delta.microseconds

start = datetime.now()
c = numpysum(size)
delta = datetime.now() - start
print "The last 2 elements of the sum", c[-2:]
print "NumPySum elapsed time in microseconds", delta.microseconds

res:

The last 2 elements of the sum 995007996, 998001000

PythonSum elapsed time in microseconds 1110

The last 2 elements of the sum 995007996 998001000

NumPySum elapsed time in microseconds 4052

3. numpy数组

a = arange(5)
a.dtype

a
a.shape

4. 创建多维数组

m = np.array([np.arange(2), np.arange(2)])

print m

print m.shape

print m.dtype

np.zeros(10)
np.zeros((3, 6))
np.empty((2, 3, 2))
np.arange(15)

5. 选取数组元素

a = np.array([[1,2],[3,4]])

print "In: a"
print a

print "In: a[0,0]"
print a[0,0]

print "In: a[0,1]"
print a[0,1]

print "In: a[1,0]"
print a[1,0]

print "In: a[1,1]"
print a[1,1]

6. 数据类型

print "In: float64(42)"
print np.float64(42)

print "In: int8(42.0)"
print np.int8(42.0)

print "In: bool(42)"
print np.bool(42)

print np.bool(0)

print "In: bool(42.0)"
print np.bool(42.0)

print "In: float(True)"
print np.float(True)
print np.float(False)

print "In: arange(7, dtype=uint16)"
print np.arange(7, dtype=np.uint16)


print "In: int(42.0 + 1.j)"
try:
   print np.int(42.0 + 1.j)
except TypeError:
   print "TypeError"
#Type error

print "In: float(42.0 + 1.j)"
print float(42.0 + 1.j)
#Type error

7. 数据类型转换

arr = np.array([1, 2, 3, 4, 5])
arr.dtype
float_arr = arr.astype(np.float64)
float_arr.dtype

arr = np.array([3.7, -1.2, -2.6, 0.5, 12.9, 10.1])
arr
arr.astype(np.int32)

numeric_strings = np.array(['1.25', '-9.6', '42'], dtype=np.string_)
numeric_strings.astype(float)

8. 数据类型对象

a = np.array([[1,2],[3,4]])

print a.dtype.byteorder

print a.dtype.itemsize

9. 字符编码

print np.arange(7, dtype='f')
print np.arange(7, dtype='D')

print np.dtype(float)

print np.dtype('f')

print np.dtype('d')


print np.dtype('f8')

print np.dtype('Float64')

10. dtype类的属性

t = np.dtype('Float64')
print t.char
print t.type
print t.str
<---------------------------------------------
d
<type 'numpy.float64'>
<f8

11. 创建自定义数据类型

t = np.dtype([('name', np.str_, 40), ('numitems', np.int32), ('price', np.float32)])
print t

print t['name']

itemz = np.array([('Meaning of life DVD', 42, 3.14), ('Butter', 13, 2.72)], dtype=t)

print itemz[1]
<---------------------------------------------
[('name', 'S40'), ('numitems', '<i4'), ('price', '<f4')]
|S40
('Butter', 13, 2.72)

12. 数组与标量的运算

arr = np.array([[1., 2., 3.], [4., 5., 6.]])
arr
arr * arr
arr - arr

1 / arr
arr ** 0.5
<---------------------------------------------
array([[1.        , 1.41421356, 1.73205081],
       [2.        , 2.23606798, 2.44948974]])

13. 一维数组的索引与切片

a = np.arange(9)
print a
print a[3:7]

print a[:7:2]

print a[::-1]

s = slice(3,7,2)
print a[s]

s = slice(None, None, -1)
print a[s]
<----------------------------------------
a: [0 1 2 3 4 5 6 7 8]
a[3:7]: [3 4 5 6]
a[:7:2]: [0 2 4 6]
a[::-1]: [8 7 6 5 4 3 2 1 0]
a[s]: [3 5]
a[s]: [8 7 6 5 4 3 2 1 0]

14. 多维数组的切片与索引

b = np.arange(24).reshape(2,3,4)

print b.shape
print b
print b[0,0,0]
print b[:,0,0]
print b[0]
print b[0, :, :]
print b[0, ...]
print b[0,1]
print b[0,1,::2]
print b[...,1]
print b[:,1]
print b[0,:,1]
print b[0,:,-1] 
print b[0,::-1, -1]
print b[0,::2,-1]
print b[::-1]

s = slice(None, None, -1)
print b[(s, s, s)]
<-----------------------------------------------
b.shape:
(2, 3, 4)

b:
[[[ 0  1  2  3]
  [ 4  5  6  7]
  [ 8  9 10 11]]

 [[12 13 14 15]
  [16 17 18 19]
  [20 21 22 23]]]

b[0,0,0]:
0

b[:,0,0]:
[ 0 12]

b[0]:
[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]]

b[0, :, :]:
[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]]

b[0, ...]:
[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]]

b[0,1]:
[4 5 6 7]

b[0,1,::2]:
[4 6]

b[...,1]:
[[ 1  5  9]
 [13 17 21]]

b[:,1]:
[[ 4  5  6  7]
 [16 17 18 19]]

b[0,:,1]:
[1 5 9]

b[0,:,-1]:
[ 3  7 11]

b[0,::-1, -1]:
[11  7  3]

b[0,::2,-1]:
[ 3 11]

b[::-1]:
[[[12 13 14 15]
  [16 17 18 19]
  [20 21 22 23]]

 [[ 0  1  2  3]
  [ 4  5  6  7]
  [ 8  9 10 11]]]

b[(s, s, s)]:
[[[23 22 21 20]
  [19 18 17 16]
  [15 14 13 12]]

 [[11 10  9  8]
  [ 7  6  5  4]
  [ 3  2  1  0]]]

15. 布尔型索引

names = np.array(['Bob', 'Joe', 'Will', 'Bob', 'Will', 'Joe', 'Joe'])
data = randn(7, 4)
names
data

names == 'Bob'
data[names == 'Bob']

data[names == 'Bob', 2:]
data[names == 'Bob', 3]

names != 'Bob'
data[-(names == 'Bob')]

mask = (names == 'Bob') | (names == 'Will')
mask
data[mask]

data[data < 0] = 0
data

data[names != 'Joe'] = 7
data

<--------------------------------------------------
['Bob' 'Joe' 'Will' 'Bob' 'Will' 'Joe' 'Joe']
[[ 1.43829891 -1.83591387  0.63309836 -0.0836829 ]
 [ 0.26632654 -0.22359825  0.27609837  0.37220043]
 [ 0.98970563  0.31626285  0.80613492 -2.52762618]
 [-0.95268723  0.55888808 -0.37982142 -0.79270072]
 [ 0.00445215 -0.55879136  0.41136902 -0.3590782 ]
 [-0.49665784 -0.09281634  0.65459855  1.35881415]
 [ 0.21105429 -0.99353232  1.29098127 -1.25913777]]
[ True False  True  True  True False False]
[[ 1.43829891 -1.83591387  0.63309836 -0.0836829 ]
 [ 0.98970563  0.31626285  0.80613492 -2.52762618]
 [-0.95268723  0.55888808 -0.37982142 -0.79270072]
 [ 0.00445215 -0.55879136  0.41136902 -0.3590782 ]]
[[1.43829891 0.         0.63309836 0.        ]
 [0.26632654 0.         0.27609837 0.37220043]
 [0.98970563 0.31626285 0.80613492 0.        ]
 [0.         0.55888808 0.         0.        ]
 [0.00445215 0.         0.41136902 0.        ]
 [0.         0.         0.65459855 1.35881415]
 [0.21105429 0.         1.29098127 0.        ]]
[[7.         7.         7.         7.        ]
 [0.26632654 0.         0.27609837 0.37220043]
 [7.         7.         7.         7.        ]
 [7.         7.         7.         7.        ]
 [7.         7.         7.         7.        ]
 [0.         0.         0.65459855 1.35881415]
 [0.21105429 0.         1.29098127 0.        ]]

16. 花式索引

arr = np.empty((8, 4))
for i in range(8):
    arr[i] = i
arr

arr[[4, 3, 0, 6]]

arr[[-3, -5, -7]]

arr = np.arange(32).reshape((8, 4))
arr
arr[[1, 5, 7, 2], [0, 3, 1, 2]]

arr[[1, 5, 7, 2]][:, [0, 3, 1, 2]]

arr[np.ix_([1, 5, 7, 2], [0, 3, 1, 2])]
<---------------------------------------------
arr = np.empty((8, 4))
print arr
array([[-3.10503618e+231, -3.10503618e+231,  3.32457344e-309,2.14057207e-314],
       [-3.10503618e+231, -3.10503618e+231,  2.14038712e-314,1.27319747e-313],
       [ 1.27319747e-313,  1.27319747e-313,  2.12199579e-314,1.91163808e-313],
       [ 2.14059464e-314,  2.12199580e-314,  3.18573536e-313,2.14059516e-314],
       [ 2.12199580e-314,  1.25160619e-308,  0.00000000e+000,0.00000000e+000],
       [ 0.00000000e+000,  0.00000000e+000,  0.00000000e+000,0.00000000e+000],
       [ 0.00000000e+000,  0.00000000e+000,  0.00000000e+000,0.00000000e+000],
       [ 0.00000000e+000,  0.00000000e+000,  2.12199579e-314,2.14062641e-314]])


for i in range(8):
    arr[i] = i
print arr
[[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.]]

## 同时选取多行，甚至多列，换位
print arr[[4, 3, 0, 6]]  ### 注意与arr[4]的不同
[[4. 4. 4. 4.]
 [3. 3. 3. 3.]
 [0. 0. 0. 0.]
 [6. 6. 6. 6.]]

print arr[[-3, -5, -7]]  ### 注意与arr[4]的不同
[[5. 5. 5. 5.]
 [3. 3. 3. 3.]
 [1. 1. 1. 1.]]

arr = np.arange(32).reshape((8, 4))
print arr
[[ 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]]
print arr[[1, 5, 7, 2], [0, 3, 1, 2]]
[ 4 23 29 10]

print arr[[1, 5, 7, 2]][:, [0, 3, 1, 2]]
[[ 4  7  5  6]
 [20 23 21 22]
 [28 31 29 30]
 [ 8 11  9 10]]
 
print arr[np.ix_([1, 5, 7, 2], [0, 3, 1, 2])]
[[ 4  7  5  6]
 [20 23 21 22]
 [28 31 29 30]
 [ 8 11  9 10]]

17. 数组转置

arr = np.arange(15).reshape((3, 5))
arr
arr.T
<--------------------------------------
array([[ 0,  5, 10],
       [ 1,  6, 11],
       [ 2,  7, 12],
       [ 3,  8, 13],
       [ 4,  9, 14]])

18. 改变数组的维度

b = np.arange(24).reshape(2,3,4)   ## 与resize()的区别，resize会改变

print b

print b.ravel()

print b.flatten()

b.shape = (6,4)

print b

print b.transpose() # 转置

b.resize((2,12))  ## 和reshape()一样，resize会改变原数据

print b

# numpy中的ravel()、flatten()、squeeze()都有将多维数组转换为一维数组的功能，区别： 
# ravel()：如果没有必要，不会产生源数据的副本 
# flatten()：返回源数据的副本 
# squeeze()：只能对维数为1的维度降维

19. 组合数组

a = np.arange(9).reshape(3,3)

print a

b = 2 * a

print b

print np.hstack((a, b))

print np.concatenate((a, b), axis=1) 

print np.vstack((a, b))

print np.concatenate((a, b), axis=0)

print np.dstack((a, b))  # 深度合并

oned = np.arange(2)

#-------------另外一种实现--------------------
print oned

twice_oned = 2 * oned

print twice_oned

print np.column_stack((oned, twice_oned)) 

print np.column_stack((a, b))

print np.column_stack((a, b)) == np.hstack((a, b))

print np.row_stack((oned, twice_oned))

print np.row_stack((a, b))

print np.row_stack((a,b)) == np.vstack((a, b))

20. 数组的分割

a = np.arange(9).reshape(3, 3)
print a
print np.hsplit(a, 3)
print np.split(a, 3, axis=1)
<----------------------------------------------------
[[0 1 2]
 [3 4 5]
 [6 7 8]]
 
[
 array([[0],[3],[6]]), 
 array([[1],[4],[7]]), 
 array([[2],[5],[8]])
]

[
 array([[0],[3],[6]]), 
 array([[1],[4],[7]]), 
 array([[2],[5],[8]])
]

print np.vsplit(a, 3)
print np.split(a, 3, axis=0)
c = np.arange(27).reshape(3, 3, 3)
print c
print np.dsplit(c, 3)
<------------------------------------------------
[array([[0, 1, 2]]), array([[3, 4, 5]]), array([[6, 7, 8]])]
[array([[0, 1, 2]]), array([[3, 4, 5]]), array([[6, 7, 8]])]
[[[ 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]]]
[array([[[ 0],
        [ 3],
        [ 6]],

       [[ 9],
        [12],
        [15]],

       [[18],
        [21],
        [24]]]), array([[[ 1],
        [ 4],
        [ 7]],

       [[10],
        [13],
        [16]],

       [[19],
        [22],
        [25]]]), array([[[ 2],
        [ 5],
        [ 8]],

       [[11],
        [14],
        [17]],

       [[20],
        [23],
        [26]]])]

21. 数组的属性

b=np.arange(24).reshape(2,12)
print b.ndim
print b.size
print b.itemsize
print b.nbytes

b = np.array([ 1.+1.j,  3.+2.j])
print b.real
print b.imag

b=np.arange(4).reshape(2,2)
print b.flat
print b.flat[2]
<--------------------------------------------
2
24
8
192
[1. 3.]
[1. 2.]
<numpy.flatiter object at 0x7fdb1d4eae00>
2

22. 数组的转换

b = np.array([ 1.+1.j,  3.+2.j])
print b

print b.tolist()

print b.tostring()

print np.fromstring('\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\x08@\x00\x00\x00\x00\x00\x00\x00@', dtype=complex)

print np.fromstring('20:42:52',sep=':', dtype=int)

print b

print b.astype(int)

print b.astype('complex')

原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。

如有侵权，请联系 cloudcommunity@tencent.com 删除。

机器学习