In [1]:

    

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from pandas import DataFrame, Series
from numpy.random import randn
import re


    

In [2]:

    

def make_watcher():
    have_seen = {}
    
    def has_been_seen(x):
        if x in have_seen:
            return True
        else:
            have_seen[x] = True
            return False
    return has_been_seen


    

In [3]:

    

watcher = make_watcher()

vals = [5, 6, 1, 5, 1, 6, 3, 5]

[watcher(x) for x in vals]


    

    
Out[3]:





[False, False, False, True, True, True, False, True]

In [4]:

    

def make_counter():
    count = [0]
    def counter():
        # 增加并返回当前的count
        count[0] += 1
        return count[0]
    return counter

counter = make_counter()
counter()
counter()


    

    
Out[4]:





2

In [6]:

    

def format_and_pad(template, space):
    def formatter(x):
        return (template % x).rjust(space)
    
    return formatter

fmt = format_and_pad('%.4f', 15)
fmt(3.1415)


    

    
Out[6]:





'         3.1415'

In [7]:

    

def say_hello_then_call_f(f, *args, **kwargs):
    print 'args is', args
    print 'kwargs is', kwargs
    print("Hello! Now I'm going to call %s" % f)
    return f(*args, **kwargs)

def g(x, y, z=1):
    return (x + y) / z


    

In [8]:

    

say_hello_then_call_f(g, 1, 2, z=5.)


    

    




args is (1, 2)
kwargs is {'z': 5.0}
Hello! Now I'm going to call <function g at 0x0000000009A7D128>






    
Out[8]:





0.6

In [9]:

    

def add_numbers(x, y):
    return x + y


    

In [10]:

    

add_five = lambda y: add_numbers(5, y)
add_five(7)


    

    
Out[10]:





12

In [11]:

    

from functools import partial
add_five = partial(add_numbers, 5)
add_five(1000)


    

    
Out[11]:





1005

In [15]:

    

# 计算时间序列x的60日移动平均
ma60 = lambda x: pd.rolling_mean(x, 60)
data = DataFrame(range(600))
# 计算data中所有时间序列的60日移动平均
data.apply(ma60)


    

    




d:\Anaconda2\lib\site-packages\ipykernel\__main__.py:2: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with 
	Series.rolling(window=60,center=False).mean()
  from ipykernel import kernelapp as app






    
Out[15]:






  

    

      

      
0
    
  
  

    

      
0
      
NaN
    
    

      
1
      
NaN
    
    

      
2
      
NaN
    
    

      
3
      
NaN
    
    

      
4
      
NaN
    
    

      
5
      
NaN
    
    

      
6
      
NaN
    
    

      
7
      
NaN
    
    

      
8
      
NaN
    
    

      
9
      
NaN
    
    

      
10
      
NaN
    
    

      
11
      
NaN
    
    

      
12
      
NaN
    
    

      
13
      
NaN
    
    

      
14
      
NaN
    
    

      
15
      
NaN
    
    

      
16
      
NaN
    
    

      
17
      
NaN
    
    

      
18
      
NaN
    
    

      
19
      
NaN
    
    

      
20
      
NaN
    
    

      
21
      
NaN
    
    

      
22
      
NaN
    
    

      
23
      
NaN
    
    

      
24
      
NaN
    
    

      
25
      
NaN
    
    

      
26
      
NaN
    
    

      
27
      
NaN
    
    

      
28
      
NaN
    
    

      
29
      
NaN
    
    

      
...
      
...
    
    

      
570
      
540.5
    
    

      
571
      
541.5
    
    

      
572
      
542.5
    
    

      
573
      
543.5
    
    

      
574
      
544.5
    
    

      
575
      
545.5
    
    

      
576
      
546.5
    
    

      
577
      
547.5
    
    

      
578
      
548.5
    
    

      
579
      
549.5
    
    

      
580
      
550.5
    
    

      
581
      
551.5
    
    

      
582
      
552.5
    
    

      
583
      
553.5
    
    

      
584
      
554.5
    
    

      
585
      
555.5
    
    

      
586
      
556.5
    
    

      
587
      
557.5
    
    

      
588
      
558.5
    
    

      
589
      
559.5
    
    

      
590
      
560.5
    
    

      
591
      
561.5
    
    

      
592
      
562.5
    
    

      
593
      
563.5
    
    

      
594
      
564.5
    
    

      
595
      
565.5
    
    

      
596
      
566.5
    
    

      
597
      
567.5
    
    

      
598
      
568.5
    
    

      
599
      
569.5
    
  

600 rows × 1 columns

In [16]:

    

some_dict = {'a': 1, 'b' : 2, 'c': 3}
for key in some_dict:
    print key,


    

    




a c b

In [17]:

    

dict_iterator = iter(some_dict)
dict_iterator


    

    
Out[17]:





<dictionary-keyiterator at 0x9a80b38>

In [18]:

    

list(dict_iterator)


    

    
Out[18]:





['a', 'c', 'b']

In [19]:

    

def squares(n=10):
    print 'Generating squares from 1 to %d' % (n ** 2)
    for i in xrange(1, n + 1):
        yield i ** 2
gen = squares()
gen


    

    
Out[19]:





<generator object squares at 0x00000000099059D8>

In [20]:

    

for x in gen:
    print x,


    

    




Generating squares from 1 to 100
1 4 9 16 25 36 49 64 81 100

In [21]:

    

def make_change(amount, coins=[1, 5, 10, 25], hand=None):
    hand = [] if hand is None else hand
    if amount == 0:
        yield hand
    for coin in coins:
        # 确保我们给出的硬币没有超过总额，且组合是唯一的
        if coin > amount or (len(hand) > 0 and hand[-1] < coin):
            continue
        
        for result in make_change(amount - coin, coins=coins,
                                 hand=hand + [coin]):
            yield result


    

In [22]:

    

for way in make_change(100, coins=[10, 25, 50]):
    print way


    

    




[10, 10, 10, 10, 10, 10, 10, 10, 10, 10]
[25, 25, 10, 10, 10, 10, 10]
[25, 25, 25, 25]
[50, 10, 10, 10, 10, 10]
[50, 25, 25]
[50, 50]

In [23]:

    

len(list(make_change(100)))


    

    
Out[23]:





242

In [24]:

    

gen = (x ** 2 for x in xrange(100))
gen


    

    
Out[24]:





<generator object <genexpr> at 0x0000000009905C18>

In [25]:

    

sumx = 0
for x in gen:
    sumx += x
print sumx


    

    




328350

In [36]:

    

%reset
sum(x*1.0 for x in xrange(100))


    

    




Once deleted, variables cannot be recovered. Proceed (y/[n])? y






    
Out[36]:





4950.0

In [31]:

    

dict((i, i **2) for i in xrange(5))


    

    
Out[31]:





{0: 0, 1: 1, 2: 4, 3: 9, 4: 16}

In [37]:

    

import itertools

first_letter = lambda x: x[0]
names = ['Alan', 'Adam', 'Wes', 'Will', 'Albert', 'Steven']
for letter, namesgen in itertools.groupby(names, first_letter):
    print letter, list(namesgen) # namesgen是一个生成器


    

    




A ['Alan', 'Adam']
W ['Wes', 'Will']
A ['Albert']
S ['Steven']

In [38]:

    

for name in itertools.combinations(names, 3):
    print name


    

    




('Alan', 'Adam', 'Wes')
('Alan', 'Adam', 'Will')
('Alan', 'Adam', 'Albert')
('Alan', 'Adam', 'Steven')
('Alan', 'Wes', 'Will')
('Alan', 'Wes', 'Albert')
('Alan', 'Wes', 'Steven')
('Alan', 'Will', 'Albert')
('Alan', 'Will', 'Steven')
('Alan', 'Albert', 'Steven')
('Adam', 'Wes', 'Will')
('Adam', 'Wes', 'Albert')
('Adam', 'Wes', 'Steven')
('Adam', 'Will', 'Albert')
('Adam', 'Will', 'Steven')
('Adam', 'Albert', 'Steven')
('Wes', 'Will', 'Albert')
('Wes', 'Will', 'Steven')
('Wes', 'Albert', 'Steven')
('Will', 'Albert', 'Steven')

In [41]:

    

Wes = itertools.ifilter(lambda x: x[0] == 'Wes', itertools.permutations(names,5))
for x in Wes:
    print x


    

    




('Wes', 'Alan', 'Adam', 'Will', 'Albert')
('Wes', 'Alan', 'Adam', 'Will', 'Steven')
('Wes', 'Alan', 'Adam', 'Albert', 'Will')
('Wes', 'Alan', 'Adam', 'Albert', 'Steven')
('Wes', 'Alan', 'Adam', 'Steven', 'Will')
('Wes', 'Alan', 'Adam', 'Steven', 'Albert')
('Wes', 'Alan', 'Will', 'Adam', 'Albert')
('Wes', 'Alan', 'Will', 'Adam', 'Steven')
('Wes', 'Alan', 'Will', 'Albert', 'Adam')
('Wes', 'Alan', 'Will', 'Albert', 'Steven')
('Wes', 'Alan', 'Will', 'Steven', 'Adam')
('Wes', 'Alan', 'Will', 'Steven', 'Albert')
('Wes', 'Alan', 'Albert', 'Adam', 'Will')
('Wes', 'Alan', 'Albert', 'Adam', 'Steven')
('Wes', 'Alan', 'Albert', 'Will', 'Adam')
('Wes', 'Alan', 'Albert', 'Will', 'Steven')
('Wes', 'Alan', 'Albert', 'Steven', 'Adam')
('Wes', 'Alan', 'Albert', 'Steven', 'Will')
('Wes', 'Alan', 'Steven', 'Adam', 'Will')
('Wes', 'Alan', 'Steven', 'Adam', 'Albert')
('Wes', 'Alan', 'Steven', 'Will', 'Adam')
('Wes', 'Alan', 'Steven', 'Will', 'Albert')
('Wes', 'Alan', 'Steven', 'Albert', 'Adam')
('Wes', 'Alan', 'Steven', 'Albert', 'Will')
('Wes', 'Adam', 'Alan', 'Will', 'Albert')
('Wes', 'Adam', 'Alan', 'Will', 'Steven')
('Wes', 'Adam', 'Alan', 'Albert', 'Will')
('Wes', 'Adam', 'Alan', 'Albert', 'Steven')
('Wes', 'Adam', 'Alan', 'Steven', 'Will')
('Wes', 'Adam', 'Alan', 'Steven', 'Albert')
('Wes', 'Adam', 'Will', 'Alan', 'Albert')
('Wes', 'Adam', 'Will', 'Alan', 'Steven')
('Wes', 'Adam', 'Will', 'Albert', 'Alan')
('Wes', 'Adam', 'Will', 'Albert', 'Steven')
('Wes', 'Adam', 'Will', 'Steven', 'Alan')
('Wes', 'Adam', 'Will', 'Steven', 'Albert')
('Wes', 'Adam', 'Albert', 'Alan', 'Will')
('Wes', 'Adam', 'Albert', 'Alan', 'Steven')
('Wes', 'Adam', 'Albert', 'Will', 'Alan')
('Wes', 'Adam', 'Albert', 'Will', 'Steven')
('Wes', 'Adam', 'Albert', 'Steven', 'Alan')
('Wes', 'Adam', 'Albert', 'Steven', 'Will')
('Wes', 'Adam', 'Steven', 'Alan', 'Will')
('Wes', 'Adam', 'Steven', 'Alan', 'Albert')
('Wes', 'Adam', 'Steven', 'Will', 'Alan')
('Wes', 'Adam', 'Steven', 'Will', 'Albert')
('Wes', 'Adam', 'Steven', 'Albert', 'Alan')
('Wes', 'Adam', 'Steven', 'Albert', 'Will')
('Wes', 'Will', 'Alan', 'Adam', 'Albert')
('Wes', 'Will', 'Alan', 'Adam', 'Steven')
('Wes', 'Will', 'Alan', 'Albert', 'Adam')
('Wes', 'Will', 'Alan', 'Albert', 'Steven')
('Wes', 'Will', 'Alan', 'Steven', 'Adam')
('Wes', 'Will', 'Alan', 'Steven', 'Albert')
('Wes', 'Will', 'Adam', 'Alan', 'Albert')
('Wes', 'Will', 'Adam', 'Alan', 'Steven')
('Wes', 'Will', 'Adam', 'Albert', 'Alan')
('Wes', 'Will', 'Adam', 'Albert', 'Steven')
('Wes', 'Will', 'Adam', 'Steven', 'Alan')
('Wes', 'Will', 'Adam', 'Steven', 'Albert')
('Wes', 'Will', 'Albert', 'Alan', 'Adam')
('Wes', 'Will', 'Albert', 'Alan', 'Steven')
('Wes', 'Will', 'Albert', 'Adam', 'Alan')
('Wes', 'Will', 'Albert', 'Adam', 'Steven')
('Wes', 'Will', 'Albert', 'Steven', 'Alan')
('Wes', 'Will', 'Albert', 'Steven', 'Adam')
('Wes', 'Will', 'Steven', 'Alan', 'Adam')
('Wes', 'Will', 'Steven', 'Alan', 'Albert')
('Wes', 'Will', 'Steven', 'Adam', 'Alan')
('Wes', 'Will', 'Steven', 'Adam', 'Albert')
('Wes', 'Will', 'Steven', 'Albert', 'Alan')
('Wes', 'Will', 'Steven', 'Albert', 'Adam')
('Wes', 'Albert', 'Alan', 'Adam', 'Will')
('Wes', 'Albert', 'Alan', 'Adam', 'Steven')
('Wes', 'Albert', 'Alan', 'Will', 'Adam')
('Wes', 'Albert', 'Alan', 'Will', 'Steven')
('Wes', 'Albert', 'Alan', 'Steven', 'Adam')
('Wes', 'Albert', 'Alan', 'Steven', 'Will')
('Wes', 'Albert', 'Adam', 'Alan', 'Will')
('Wes', 'Albert', 'Adam', 'Alan', 'Steven')
('Wes', 'Albert', 'Adam', 'Will', 'Alan')
('Wes', 'Albert', 'Adam', 'Will', 'Steven')
('Wes', 'Albert', 'Adam', 'Steven', 'Alan')
('Wes', 'Albert', 'Adam', 'Steven', 'Will')
('Wes', 'Albert', 'Will', 'Alan', 'Adam')
('Wes', 'Albert', 'Will', 'Alan', 'Steven')
('Wes', 'Albert', 'Will', 'Adam', 'Alan')
('Wes', 'Albert', 'Will', 'Adam', 'Steven')
('Wes', 'Albert', 'Will', 'Steven', 'Alan')
('Wes', 'Albert', 'Will', 'Steven', 'Adam')
('Wes', 'Albert', 'Steven', 'Alan', 'Adam')
('Wes', 'Albert', 'Steven', 'Alan', 'Will')
('Wes', 'Albert', 'Steven', 'Adam', 'Alan')
('Wes', 'Albert', 'Steven', 'Adam', 'Will')
('Wes', 'Albert', 'Steven', 'Will', 'Alan')
('Wes', 'Albert', 'Steven', 'Will', 'Adam')
('Wes', 'Steven', 'Alan', 'Adam', 'Will')
('Wes', 'Steven', 'Alan', 'Adam', 'Albert')
('Wes', 'Steven', 'Alan', 'Will', 'Adam')
('Wes', 'Steven', 'Alan', 'Will', 'Albert')
('Wes', 'Steven', 'Alan', 'Albert', 'Adam')
('Wes', 'Steven', 'Alan', 'Albert', 'Will')
('Wes', 'Steven', 'Adam', 'Alan', 'Will')
('Wes', 'Steven', 'Adam', 'Alan', 'Albert')
('Wes', 'Steven', 'Adam', 'Will', 'Alan')
('Wes', 'Steven', 'Adam', 'Will', 'Albert')
('Wes', 'Steven', 'Adam', 'Albert', 'Alan')
('Wes', 'Steven', 'Adam', 'Albert', 'Will')
('Wes', 'Steven', 'Will', 'Alan', 'Adam')
('Wes', 'Steven', 'Will', 'Alan', 'Albert')
('Wes', 'Steven', 'Will', 'Adam', 'Alan')
('Wes', 'Steven', 'Will', 'Adam', 'Albert')
('Wes', 'Steven', 'Will', 'Albert', 'Alan')
('Wes', 'Steven', 'Will', 'Albert', 'Adam')
('Wes', 'Steven', 'Albert', 'Alan', 'Adam')
('Wes', 'Steven', 'Albert', 'Alan', 'Will')
('Wes', 'Steven', 'Albert', 'Adam', 'Alan')
('Wes', 'Steven', 'Albert', 'Adam', 'Will')
('Wes', 'Steven', 'Albert', 'Will', 'Alan')
('Wes', 'Steven', 'Albert', 'Will', 'Adam')

In [42]:

    

path = 'ch13/segismundo.txt'
f = open(path)
for line in f:
    pass


    

In [43]:

    

lines = [x.rstrip() for x in open(path)]
lines


    

    
Out[43]:





['Sue\xc3\xb1a el rico en su riqueza,',
 'que m\xc3\xa1s cuidados le ofrece;',
 '',
 'sue\xc3\xb1a el pobre que padece',
 'su miseria y su pobreza;',
 '',
 'sue\xc3\xb1a el que a medrar empieza,',
 'sue\xc3\xb1a el que afana y pretende,',
 'sue\xc3\xb1a el que agravia y ofende,',
 '',
 'y en el mundo, en conclusi\xc3\xb3n,',
 'todos sue\xc3\xb1an lo que son,',
 'aunque ninguno lo entiende.',
 '']

In [44]:

    

with open('tmp.txt', 'w') as handle:
    handle.writelines(x for x in open(path) if len(x) > 1)


    

In [45]:

    

open('tmp.txt').readlines()


    

    
Out[45]:





['Sue\xc3\xb1a el rico en su riqueza,\n',
 'que m\xc3\xa1s cuidados le ofrece;\n',
 'sue\xc3\xb1a el pobre que padece\n',
 'su miseria y su pobreza;\n',
 'sue\xc3\xb1a el que a medrar empieza,\n',
 'sue\xc3\xb1a el que afana y pretende,\n',
 'sue\xc3\xb1a el que agravia y ofende,\n',
 'y en el mundo, en conclusi\xc3\xb3n,\n',
 'todos sue\xc3\xb1an lo que son,\n',
 'aunque ninguno lo entiende.\n']

In [ ]: