# -*- coding: utf-8 -*-
#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 = numpy.arange(n) ** 2
b = numpy.arange(n) ** 3
c = a + b
return c
#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
#numpy数组
a = arange(5)
a.dtype
a
a.shape
#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)
#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]
#numpy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
# 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)
#数据类型对象
a = np.array([[1,2],[3,4]])
print a.dtype.byteorder
print a.dtype.itemsize
#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')
#dtype类的属性
t = np.dtype('Float64')
print t.char
print t.type
print t.str
#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]
#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
#一维数组的索引与切片
a = np.arange(9)
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]
#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)]
#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
#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])]
#数组转置
arr = np.arange(15).reshape((3, 5))
arr
arr.T
#改变数组的维度
b = np.arange(24).reshape(2,3,4)
print b
print b.ravel()
print b.flatten()
b.shape = (6,4)
print b
print b.transpose()
b.resize((2,12))
print b
#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))
oned = np.arange(2)
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))
#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)
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)
#数组的属性
b=np.arange(24).reshape(2,12)
b.ndim
b.size
b.itemsize
b.nbytes
b = np.array([ 1.+1.j, 3.+2.j])
b.real
b.imag
b=np.arange(4).reshape(2,2)
b.flat
b.flat[2]
#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')
