Numpy的主要功能：
可以观察以上的规律，会发现，代码类型的简写，计量都是以8作为起始1的。
# -*- 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', ''], 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')