先看下本文目录哈
代码语言：txt复制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！
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1. 一般Python和numpy实现方式
实现：实现了两个向量的相加
代码语言：txt复制# -*- 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 c2. 上述两种实现方式比较
代码语言：txt复制#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.microsecondsres:
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
代码语言：txt复制a = arange(5)
a.dtype
a
a.shape4. 创建多维数组
代码语言：txt复制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
代码语言：txt复制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
代码语言：txt复制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 error7. 数据类型转换
代码语言：txt复制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
代码语言：txt复制a = np.array([[1,2],[3,4]])
print a.dtype.byteorder
print a.dtype.itemsize9. 字符编码
代码语言：txt复制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
代码语言：txt复制t = np.dtype('Float64')
print t.char
print t.type
print t.str
<---------------------------------------------
d
<type 'numpy.float64'>
<f811. 创建自定义数据类型
代码语言：txt复制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
代码语言：txt复制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
代码语言：txt复制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
代码语言：txt复制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
代码语言：txt复制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
代码语言：txt复制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
代码语言：txt复制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
代码语言：txt复制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
代码语言：txt复制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
代码语言：txt复制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
代码语言：txt复制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>
222. 数组的转换
代码语言：txt复制b = np.array([ 1. 1.j, 3. 2.j])
print b
print b.tolist()
print b.tostring()
print np.fromstring('x00x00x00x00x00x00xf0?x00x00x00x00x00x00xf0?x00x00x00x00x00x00x08@x00x00x00x00x00x00x00@', dtype=complex)
print np.fromstring('20:42:52',sep=':', dtype=int)
print b
print b.astype(int)
print b.astype('complex')
