In [1]:
import graphlab
In [45]:
import random, datetime
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import statsmodels.api as sm
from scipy.stats import norm
from scipy.stats.stats import pearsonr
In [3]:
str(3)
Out[3]:
'3'
In [4]:
"danni zhou"
Out[4]:
'danni zhou'
In [5]:
int('5')
Out[5]:
5
In [6]:
float('7.1')
Out[6]:
7.1
In [7]:
range(10)
Out[7]:
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
In [8]:
range(1,10)
Out[8]:
[1, 2, 3, 4, 5, 6, 7, 8, 9]
In [9]:
dir
Out[9]:
<function dir>
In [10]:
dir(str)[-10:]
Out[10]:
['rstrip',
 'split',
 'splitlines',
 'startswith',
 'strip',
 'swapcase',
 'title',
 'translate',
 'upper',
 'zfill']
In [11]:
help(str)

Help on class str in module __builtin__:

class str(basestring)
 |  str(object='') -> string
 |  
 |  Return a nice string representation of the object.
 |  If the argument is a string, the return value is the same object.
 |  
 |  Method resolution order:
 |      str
 |      basestring
 |      object
 |  
 |  Methods defined here:
 |  
 |  __add__(...)
 |      x.__add__(y) <==> x+y
 |  
 |  __contains__(...)
 |      x.__contains__(y) <==> y in x
 |  
 |  __eq__(...)
 |      x.__eq__(y) <==> x==y
 |  
 |  __format__(...)
 |      S.__format__(format_spec) -> string
 |      
 |      Return a formatted version of S as described by format_spec.
 |  
 |  __ge__(...)
 |      x.__ge__(y) <==> x>=y
 |  
 |  __getattribute__(...)
 |      x.__getattribute__('name') <==> x.name
 |  
 |  __getitem__(...)
 |      x.__getitem__(y) <==> x[y]
 |  
 |  __getnewargs__(...)
 |  
 |  __getslice__(...)
 |      x.__getslice__(i, j) <==> x[i:j]
 |      
 |      Use of negative indices is not supported.
 |  
 |  __gt__(...)
 |      x.__gt__(y) <==> x>y
 |  
 |  __hash__(...)
 |      x.__hash__() <==> hash(x)
 |  
 |  __le__(...)
 |      x.__le__(y) <==> x<=y
 |  
 |  __len__(...)
 |      x.__len__() <==> len(x)
 |  
 |  __lt__(...)
 |      x.__lt__(y) <==> x<y
 |  
 |  __mod__(...)
 |      x.__mod__(y) <==> x%y
 |  
 |  __mul__(...)
 |      x.__mul__(n) <==> x*n
 |  
 |  __ne__(...)
 |      x.__ne__(y) <==> x!=y
 |  
 |  __repr__(...)
 |      x.__repr__() <==> repr(x)
 |  
 |  __rmod__(...)
 |      x.__rmod__(y) <==> y%x
 |  
 |  __rmul__(...)
 |      x.__rmul__(n) <==> n*x
 |  
 |  __sizeof__(...)
 |      S.__sizeof__() -> size of S in memory, in bytes
 |  
 |  __str__(...)
 |      x.__str__() <==> str(x)
 |  
 |  capitalize(...)
 |      S.capitalize() -> string
 |      
 |      Return a copy of the string S with only its first character
 |      capitalized.
 |  
 |  center(...)
 |      S.center(width[, fillchar]) -> string
 |      
 |      Return S centered in a string of length width. Padding is
 |      done using the specified fill character (default is a space)
 |  
 |  count(...)
 |      S.count(sub[, start[, end]]) -> int
 |      
 |      Return the number of non-overlapping occurrences of substring sub in
 |      string S[start:end].  Optional arguments start and end are interpreted
 |      as in slice notation.
 |  
 |  decode(...)
 |      S.decode([encoding[,errors]]) -> object
 |      
 |      Decodes S using the codec registered for encoding. encoding defaults
 |      to the default encoding. errors may be given to set a different error
 |      handling scheme. Default is 'strict' meaning that encoding errors raise
 |      a UnicodeDecodeError. Other possible values are 'ignore' and 'replace'
 |      as well as any other name registered with codecs.register_error that is
 |      able to handle UnicodeDecodeErrors.
 |  
 |  encode(...)
 |      S.encode([encoding[,errors]]) -> object
 |      
 |      Encodes S using the codec registered for encoding. encoding defaults
 |      to the default encoding. errors may be given to set a different error
 |      handling scheme. Default is 'strict' meaning that encoding errors raise
 |      a UnicodeEncodeError. Other possible values are 'ignore', 'replace' and
 |      'xmlcharrefreplace' as well as any other name registered with
 |      codecs.register_error that is able to handle UnicodeEncodeErrors.
 |  
 |  endswith(...)
 |      S.endswith(suffix[, start[, end]]) -> bool
 |      
 |      Return True if S ends with the specified suffix, False otherwise.
 |      With optional start, test S beginning at that position.
 |      With optional end, stop comparing S at that position.
 |      suffix can also be a tuple of strings to try.
 |  
 |  expandtabs(...)
 |      S.expandtabs([tabsize]) -> string
 |      
 |      Return a copy of S where all tab characters are expanded using spaces.
 |      If tabsize is not given, a tab size of 8 characters is assumed.
 |  
 |  find(...)
 |      S.find(sub [,start [,end]]) -> int
 |      
 |      Return the lowest index in S where substring sub is found,
 |      such that sub is contained within S[start:end].  Optional
 |      arguments start and end are interpreted as in slice notation.
 |      
 |      Return -1 on failure.
 |  
 |  format(...)
 |      S.format(*args, **kwargs) -> string
 |      
 |      Return a formatted version of S, using substitutions from args and kwargs.
 |      The substitutions are identified by braces ('{' and '}').
 |  
 |  index(...)
 |      S.index(sub [,start [,end]]) -> int
 |      
 |      Like S.find() but raise ValueError when the substring is not found.
 |  
 |  isalnum(...)
 |      S.isalnum() -> bool
 |      
 |      Return True if all characters in S are alphanumeric
 |      and there is at least one character in S, False otherwise.
 |  
 |  isalpha(...)
 |      S.isalpha() -> bool
 |      
 |      Return True if all characters in S are alphabetic
 |      and there is at least one character in S, False otherwise.
 |  
 |  isdigit(...)
 |      S.isdigit() -> bool
 |      
 |      Return True if all characters in S are digits
 |      and there is at least one character in S, False otherwise.
 |  
 |  islower(...)
 |      S.islower() -> bool
 |      
 |      Return True if all cased characters in S are lowercase and there is
 |      at least one cased character in S, False otherwise.
 |  
 |  isspace(...)
 |      S.isspace() -> bool
 |      
 |      Return True if all characters in S are whitespace
 |      and there is at least one character in S, False otherwise.
 |  
 |  istitle(...)
 |      S.istitle() -> bool
 |      
 |      Return True if S is a titlecased string and there is at least one
 |      character in S, i.e. uppercase characters may only follow uncased
 |      characters and lowercase characters only cased ones. Return False
 |      otherwise.
 |  
 |  isupper(...)
 |      S.isupper() -> bool
 |      
 |      Return True if all cased characters in S are uppercase and there is
 |      at least one cased character in S, False otherwise.
 |  
 |  join(...)
 |      S.join(iterable) -> string
 |      
 |      Return a string which is the concatenation of the strings in the
 |      iterable.  The separator between elements is S.
 |  
 |  ljust(...)
 |      S.ljust(width[, fillchar]) -> string
 |      
 |      Return S left-justified in a string of length width. Padding is
 |      done using the specified fill character (default is a space).
 |  
 |  lower(...)
 |      S.lower() -> string
 |      
 |      Return a copy of the string S converted to lowercase.
 |  
 |  lstrip(...)
 |      S.lstrip([chars]) -> string or unicode
 |      
 |      Return a copy of the string S with leading whitespace removed.
 |      If chars is given and not None, remove characters in chars instead.
 |      If chars is unicode, S will be converted to unicode before stripping
 |  
 |  partition(...)
 |      S.partition(sep) -> (head, sep, tail)
 |      
 |      Search for the separator sep in S, and return the part before it,
 |      the separator itself, and the part after it.  If the separator is not
 |      found, return S and two empty strings.
 |  
 |  replace(...)
 |      S.replace(old, new[, count]) -> string
 |      
 |      Return a copy of string S with all occurrences of substring
 |      old replaced by new.  If the optional argument count is
 |      given, only the first count occurrences are replaced.
 |  
 |  rfind(...)
 |      S.rfind(sub [,start [,end]]) -> int
 |      
 |      Return the highest index in S where substring sub is found,
 |      such that sub is contained within S[start:end].  Optional
 |      arguments start and end are interpreted as in slice notation.
 |      
 |      Return -1 on failure.
 |  
 |  rindex(...)
 |      S.rindex(sub [,start [,end]]) -> int
 |      
 |      Like S.rfind() but raise ValueError when the substring is not found.
 |  
 |  rjust(...)
 |      S.rjust(width[, fillchar]) -> string
 |      
 |      Return S right-justified in a string of length width. Padding is
 |      done using the specified fill character (default is a space)
 |  
 |  rpartition(...)
 |      S.rpartition(sep) -> (head, sep, tail)
 |      
 |      Search for the separator sep in S, starting at the end of S, and return
 |      the part before it, the separator itself, and the part after it.  If the
 |      separator is not found, return two empty strings and S.
 |  
 |  rsplit(...)
 |      S.rsplit([sep [,maxsplit]]) -> list of strings
 |      
 |      Return a list of the words in the string S, using sep as the
 |      delimiter string, starting at the end of the string and working
 |      to the front.  If maxsplit is given, at most maxsplit splits are
 |      done. If sep is not specified or is None, any whitespace string
 |      is a separator.
 |  
 |  rstrip(...)
 |      S.rstrip([chars]) -> string or unicode
 |      
 |      Return a copy of the string S with trailing whitespace removed.
 |      If chars is given and not None, remove characters in chars instead.
 |      If chars is unicode, S will be converted to unicode before stripping
 |  
 |  split(...)
 |      S.split([sep [,maxsplit]]) -> list of strings
 |      
 |      Return a list of the words in the string S, using sep as the
 |      delimiter string.  If maxsplit is given, at most maxsplit
 |      splits are done. If sep is not specified or is None, any
 |      whitespace string is a separator and empty strings are removed
 |      from the result.
 |  
 |  splitlines(...)
 |      S.splitlines(keepends=False) -> list of strings
 |      
 |      Return a list of the lines in S, breaking at line boundaries.
 |      Line breaks are not included in the resulting list unless keepends
 |      is given and true.
 |  
 |  startswith(...)
 |      S.startswith(prefix[, start[, end]]) -> bool
 |      
 |      Return True if S starts with the specified prefix, False otherwise.
 |      With optional start, test S beginning at that position.
 |      With optional end, stop comparing S at that position.
 |      prefix can also be a tuple of strings to try.
 |  
 |  strip(...)
 |      S.strip([chars]) -> string or unicode
 |      
 |      Return a copy of the string S with leading and trailing
 |      whitespace removed.
 |      If chars is given and not None, remove characters in chars instead.
 |      If chars is unicode, S will be converted to unicode before stripping
 |  
 |  swapcase(...)
 |      S.swapcase() -> string
 |      
 |      Return a copy of the string S with uppercase characters
 |      converted to lowercase and vice versa.
 |  
 |  title(...)
 |      S.title() -> string
 |      
 |      Return a titlecased version of S, i.e. words start with uppercase
 |      characters, all remaining cased characters have lowercase.
 |  
 |  translate(...)
 |      S.translate(table [,deletechars]) -> string
 |      
 |      Return a copy of the string S, where all characters occurring
 |      in the optional argument deletechars are removed, and the
 |      remaining characters have been mapped through the given
 |      translation table, which must be a string of length 256 or None.
 |      If the table argument is None, no translation is applied and
 |      the operation simply removes the characters in deletechars.
 |  
 |  upper(...)
 |      S.upper() -> string
 |      
 |      Return a copy of the string S converted to uppercase.
 |  
 |  zfill(...)
 |      S.zfill(width) -> string
 |      
 |      Pad a numeric string S with zeros on the left, to fill a field
 |      of the specified width.  The string S is never truncated.
 |  
 |  ----------------------------------------------------------------------
 |  Data and other attributes defined here:
 |  
 |  __new__ = <built-in method __new__ of type object>
 |      T.__new__(S, ...) -> a new object with type S, a subtype of T
In [12]:
x = ' Hello WorlD  '
dir(x)[-10:]
Out[12]:
['rstrip',
 'split',
 'splitlines',
 'startswith',
 'strip',
 'swapcase',
 'title',
 'translate',
 'upper',
 'zfill']
In [13]:
x.lower()
Out[13]:
' hello world  '
In [14]:
x.upper()
Out[14]:
' HELLO WORLD  '
In [15]:
x.rstrip()
Out[15]:
' Hello WorlD'
In [16]:
x.strip()
Out[16]:
'Hello WorlD'
In [17]:
x.replace('lo', '')
Out[17]:
' Hel WorlD  '
In [18]:
x.split('lo')
Out[18]:
[' Hel', ' WorlD  ']
In [19]:
','.join(['a', 'b'])
Out[19]:
'a,b'
In [20]:
x = 'hello world'
type(x)
Out[20]:
str
In [21]:
l = [1,2,3,3] # list
t = (1, 2, 3, 3) # tuple
s = set([1,2,3,3]) # set
d = {'a':1,'b':2,'c':3} # dict
a = np.array(l) # array
print(l, t, s, d, a)

([1, 2, 3, 3], (1, 2, 3, 3), set([1, 2, 3]), {'a': 1, 'c': 3, 'b': 2}, array([1, 2, 3, 3]))
In [22]:
l = [1,2,3,3] # list
l.append(4)
l
Out[22]:
[1, 2, 3, 3, 4]
In [23]:
d = {'a':1,'b':2,'c':3} # dict
d.keys()
Out[23]:
['a', 'c', 'b']
In [24]:
d = {'a':1,'b':2,'c':3} # dict
d.values()
Out[24]:
[1, 3, 2]
In [25]:
d = {'a':1,'b':2,'c':3} # dict
d['b']
Out[25]:
2
In [26]:
d = {'a':1,'b':2,'c':3} # dict
d.items()
Out[26]:
[('a', 1), ('c', 3), ('b', 2)]
In [27]:
def devidePlus(m, n): 
    y = m/n + 1 
    return y
In [28]:
range(10)
Out[28]:
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
In [29]:
range(1, 10)
Out[29]:
[1, 2, 3, 4, 5, 6, 7, 8, 9]
In [30]:
for i in range(10):
    print(i, i*10, i**2)

(0, 0, 0)
(1, 10, 1)
(2, 20, 4)
(3, 30, 9)
(4, 40, 16)
(5, 50, 25)
(6, 60, 36)
(7, 70, 49)
(8, 80, 64)
(9, 90, 81)
In [31]:
for i in range(10):
    print(i*10)

0
10
20
30
40
50
60
70
80
90
In [32]:
for i in range(10):
    print(devidePlus(i, 2))

1
1
2
2
3
3
4
4
5
5
In [33]:
r = [devidePlus(i, 2)  for i in range(10)]
r
Out[33]:
[1, 1, 2, 2, 3, 3, 4, 4, 5, 5]
In [34]:
m1 = map(devidePlus, [4,3,2], [2, 1, 5])
print(*m1)

  File "<ipython-input-34-8fe2b28c59c2>", line 2
    print(*m1)
          ^
SyntaxError: invalid syntax
In [35]:
m1 = map(devidePlus, [4,3,2], [2, 1, 5])
print(m1)

[3, 4, 1]
In [36]:
m1 = map(devidePlus, [4,3,2], [2, 1, 5])
print(float(m1))

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-36-95afd3c943bb> in <module>()
      1 m1 = map(devidePlus, [4,3,2], [2, 1, 5])
----> 2 print(float(m1))

TypeError: float() argument must be a string or a number
In [37]:
m2 = map(lambda x, y: x + y, [1, 3, 5, 7, 9], [2, 4, 6, 8, 10])
print(m2)

[3, 7, 11, 15, 19]
In [38]:
m3 = map(lambda x, y, z: x + y - z, [1, 3, 5, 7, 9], [2, 4, 6, 8, 10], [3, 3, 2, 2, 5])
print(m3)

[0, 4, 9, 13, 14]
In [39]:
j = 5
if j%2 == 1:
    print(r'余数是1')
elif j%2 ==0:
    print(r'余数是0')
else:
    print(r'余数既不是1也不是0')

余数是1
In [40]:
x = 5
if x < 5:
    y = -1
    z = 5
elif x > 5:
    y = 1
    z = 11
else:
    y = 0
    z = 10
print(x, y, z)

(5, 0, 10)
In [41]:
j = 0
while j <10:
    print(j)
    j+=1 

0
1
2
3
4
5
6
7
8
9
In [42]:
j = 0
while j <10:
    if j%2 != 0: 
        print(j**2)
    j+=1

1
9
25
49
81
In [43]:
j = 0
while j <50:
    if j == 30:
        break
    if j%2 != 0: 
        print(j**2)
    j+=1 

1
9
25
49
81
121
169
225
289
361
441
529
625
729
841
In [44]:
a = 4
while a: # 0, None, False
    print(a) 
    a -= 1
    if a < 0:
        a = None

4
3
2
1
In [45]:
def devidePlus(m, n): 
    return m/n+ 1 

for i in [2, 0, 5]:
    try:
        print(devidePlus(4, i))
    except Exception as e:
        print(e)
        pass

3
integer division or modulo by zero
1
In [46]:
alist = [[1,1], [0, 0, 1]]
for aa in alist:
    try:
        for a in aa:
            print(10 / a)
    except Exception as e:
        print(e)
        pass

10
10
integer division or modulo by zero
In [47]:
alist = [[1,1], [0, 0, 1]]
for aa in alist:
    for a in aa:
        try:
            print(10 / a)
        except Exception as e:
            print(e)
            pass

10
10
integer division or modulo by zero
integer division or modulo by zero
10
In [48]:
data =[[i, i**2, i**3] for i in range(10)] 
data
Out[48]:
[[0, 0, 0],
 [1, 1, 1],
 [2, 4, 8],
 [3, 9, 27],
 [4, 16, 64],
 [5, 25, 125],
 [6, 36, 216],
 [7, 49, 343],
 [8, 64, 512],
 [9, 81, 729]]
In [49]:
for i in data:
    print('\t'.join(map(str, i)))

0	0	0
1	1	1
2	4	8
3	9	27
4	16	64
5	25	125
6	36	216
7	49	343
8	64	512
9	81	729
In [50]:
type(data)
Out[50]:
list
In [51]:
len(data)
Out[51]:
10
In [52]:
data[0]
Out[52]:
[0, 0, 0]
In [34]:
help(f.write)

Help on built-in function write:

write(text, /) method of _io.TextIOWrapper instance
    Write string to stream.
    Returns the number of characters written (which is always equal to
    the length of the string).
In [32]:
data =[[i, i**2, i**3] for i in range(10000)] 

f = open("documents/STUDY/研一下/计算新闻传播学/cjc-gh-pages/data/data_write_to_file.txt", "w")
for i in data:
    f.write('\t'.join(map(str,i)) + '\n')
f.close()
In [55]:
data =[[i, i**2, i**3] for i in range(10000)] 

f = open("documents/STUDY/研一下/计算新闻传播学/cjc-gh-pages/data/data_write_to_file.txt", "w")
for i in data:
    f.write('\t'.join(map(str,i)) + '\n')
f.close()
In [56]:
with open('documents/STUDY/研一下/计算新闻传播学/cjc-gh-pages/data/data_write_to_file.txt','r') as f:
    data = f.readlines()
data[:5]
Out[56]:
['0\t0\t0\n', '1\t1\t1\n', '2\t4\t8\n', '3\t9\t27\n', '4\t16\t64\n']
In [57]:
with open('documents/STUDY/研一下/计算新闻传播学/cjc-gh-pages/data/data_write_to_file.txt','r') as f:
    data = f.readlines(1000)
len(data)
Out[57]:
459
In [35]:
with open('../data/data_write_to_file.txt','r') as f:
    print(f.readline())

---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
<ipython-input-35-9a736ae31c1e> in <module>()
----> 1 with open('../data/data_write_to_file.txt','r') as f:
      2     print(f.readline())

FileNotFoundError: [Errno 2] No such file or directory: '../data/data_write_to_file.txt'
In [59]:
with open('documents/STUDY/研一下/计算新闻传播学/cjc-gh-pages/data/data_write_to_file.txt','r') as f:
    print(f.readline())

0	0	0
In [60]:
f = [1, 2, 3, 4, 5]
for k, i in enumerate(f):
    print(k, i)

(0, 1)
(1, 2)
(2, 3)
(3, 4)
(4, 5)
In [61]:
with open('documents/STUDY/研一下/计算新闻传播学/cjc-gh-pages/data/data_write_to_file.txt','r') as f:
    for i in f:
        print(i)

0	0	0

1	1	1

2	4	8

3	9	27

4	16	64

5	25	125

6	36	216

7	49	343

8	64	512

9	81	729

10	100	1000

11	121	1331

12	144	1728

13	169	2197

14	196	2744

15	225	3375

16	256	4096

17	289	4913

18	324	5832

19	361	6859

20	400	8000

21	441	9261

22	484	10648

23	529	12167

24	576	13824

25	625	15625

26	676	17576

27	729	19683

28	784	21952

29	841	24389

30	900	27000

31	961	29791

32	1024	32768

33	1089	35937

34	1156	39304

35	1225	42875

36	1296	46656

37	1369	50653

38	1444	54872

39	1521	59319

40	1600	64000

41	1681	68921

42	1764	74088

43	1849	79507

44	1936	85184

45	2025	91125

46	2116	97336

47	2209	103823

48	2304	110592

49	2401	117649

50	2500	125000

51	2601	132651

52	2704	140608

53	2809	148877

54	2916	157464

55	3025	166375

56	3136	175616

57	3249	185193

58	3364	195112

59	3481	205379

60	3600	216000

61	3721	226981

62	3844	238328

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In [62]:
with open('documents/STUDY/研一下/计算新闻传播学/cjc-gh-pages/data/data_write_to_file.txt','r') as f:
    for k, i in enumerate(f):
        if k%2000 == 0:
            print(i)

0	0	0

2000	4000000	8000000000

4000	16000000	64000000000

6000	36000000	216000000000

8000	64000000	512000000000
In [63]:
data = []
line = '0\t0\t0\n'
line = line.replace('\n', '')
line = line.split('\t')
line = [int(i) for i in line] # convert str to int
data.append(line)
data
Out[63]:
[[0, 0, 0]]
In [64]:
data = []
with open('../data/data_write_to_file.txt','r') as f:
    for line in f:
        data.append(line)
data

---------------------------------------------------------------------------
IOError                                   Traceback (most recent call last)
<ipython-input-64-a23d32f20eb6> in <module>()
      1 data = []
----> 2 with open('../data/data_write_to_file.txt','r') as f:
      3     for line in f:
      4         data.append(line)
      5 data

IOError: [Errno 2] No such file or directory: '../data/data_write_to_file.txt'
In [65]:
data = []
with open('documents/STUDY/研一下/计算新闻传播学/cjc-gh-pages/data/data_write_to_file.txt','r') as f:
    for line in f:
        data.append(line)
data
Out[65]:
['0\t0\t0\n',
 '1\t1\t1\n',
 '2\t4\t8\n',
 '3\t9\t27\n',
 '4\t16\t64\n',
 '5\t25\t125\n',
 '6\t36\t216\n',
 '7\t49\t343\n',
 '8\t64\t512\n',
 '9\t81\t729\n',
 '10\t100\t1000\n',
 '11\t121\t1331\n',
 '12\t144\t1728\n',
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 '973\t946729\t921167317\n',
 '974\t948676\t924010424\n',
 '975\t950625\t926859375\n',
 '976\t952576\t929714176\n',
 '977\t954529\t932574833\n',
 '978\t956484\t935441352\n',
 '979\t958441\t938313739\n',
 '980\t960400\t941192000\n',
 '981\t962361\t944076141\n',
 '982\t964324\t946966168\n',
 '983\t966289\t949862087\n',
 '984\t968256\t952763904\n',
 '985\t970225\t955671625\n',
 '986\t972196\t958585256\n',
 '987\t974169\t961504803\n',
 '988\t976144\t964430272\n',
 '989\t978121\t967361669\n',
 '990\t980100\t970299000\n',
 '991\t982081\t973242271\n',
 '992\t984064\t976191488\n',
 '993\t986049\t979146657\n',
 '994\t988036\t982107784\n',
 '995\t990025\t985074875\n',
 '996\t992016\t988047936\n',
 '997\t994009\t991026973\n',
 '998\t996004\t994011992\n',
 '999\t998001\t997002999\n',
 ...]
In [66]:
data = []
with open('documents/STUDY/研一下/计算新闻传播学/cjc-gh-pages/data/data_write_to_file.txt','r') as f:
    for line in f:
        line = line.replace('\n', '').split('\t')
        line = [int(i) for i in line]
        data.append(line)
data
Out[66]:
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 [978, 956484, 935441352],
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 [987, 974169, 961504803],
 [988, 976144, 964430272],
 [989, 978121, 967361669],
 [990, 980100, 970299000],
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 [992, 984064, 976191488],
 [993, 986049, 979146657],
 [994, 988036, 982107784],
 [995, 990025, 985074875],
 [996, 992016, 988047936],
 [997, 994009, 991026973],
 [998, 996004, 994011992],
 [999, 998001, 997002999],
 ...]
In [67]:
import pandas as pd
In [68]:
help(pd.read_csv)

Help on function read_csv in module pandas.io.parsers:

read_csv(filepath_or_buffer, sep=',', delimiter=None, header='infer', names=None, index_col=None, usecols=None, squeeze=False, prefix=None, mangle_dupe_cols=True, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=False, infer_datetime_format=False, keep_date_col=False, date_parser=None, dayfirst=False, iterator=False, chunksize=None, compression='infer', thousands=None, decimal='.', lineterminator=None, quotechar='"', quoting=0, escapechar=None, comment=None, encoding=None, dialect=None, tupleize_cols=None, error_bad_lines=True, warn_bad_lines=True, skipfooter=0, skip_footer=0, doublequote=True, delim_whitespace=False, as_recarray=None, compact_ints=None, use_unsigned=None, low_memory=True, buffer_lines=None, memory_map=False, float_precision=None)
    Read CSV (comma-separated) file into DataFrame
    
    Also supports optionally iterating or breaking of the file
    into chunks.
    
    Additional help can be found in the `online docs for IO Tools
    <http://pandas.pydata.org/pandas-docs/stable/io.html>`_.
    
    Parameters
    ----------
    filepath_or_buffer : str, pathlib.Path, py._path.local.LocalPath or any object with a read() method (such as a file handle or StringIO)
        The string could be a URL. Valid URL schemes include http, ftp, s3, and
        file. For file URLs, a host is expected. For instance, a local file could
        be file ://localhost/path/to/table.csv
    sep : str, default ','
        Delimiter to use. If sep is None, the C engine cannot automatically detect
        the separator, but the Python parsing engine can, meaning the latter will
        be used and automatically detect the separator by Python's builtin sniffer
        tool, ``csv.Sniffer``. In addition, separators longer than 1 character and
        different from ``'\s+'`` will be interpreted as regular expressions and
        will also force the use of the Python parsing engine. Note that regex
        delimiters are prone to ignoring quoted data. Regex example: ``'\r\t'``
    delimiter : str, default ``None``
        Alternative argument name for sep.
    delim_whitespace : boolean, default False
        Specifies whether or not whitespace (e.g. ``' '`` or ``'    '``) will be
        used as the sep. Equivalent to setting ``sep='\s+'``. If this option
        is set to True, nothing should be passed in for the ``delimiter``
        parameter.
    
        .. versionadded:: 0.18.1 support for the Python parser.
    
    header : int or list of ints, default 'infer'
        Row number(s) to use as the column names, and the start of the
        data.  Default behavior is to infer the column names: if no names
        are passed the behavior is identical to ``header=0`` and column
        names are inferred from the first line of the file, if column
        names are passed explicitly then the behavior is identical to
        ``header=None``. Explicitly pass ``header=0`` to be able to
        replace existing names. The header can be a list of integers that
        specify row locations for a multi-index on the columns
        e.g. [0,1,3]. Intervening rows that are not specified will be
        skipped (e.g. 2 in this example is skipped). Note that this
        parameter ignores commented lines and empty lines if
        ``skip_blank_lines=True``, so header=0 denotes the first line of
        data rather than the first line of the file.
    names : array-like, default None
        List of column names to use. If file contains no header row, then you
        should explicitly pass header=None. Duplicates in this list will cause
        a ``UserWarning`` to be issued.
    index_col : int or sequence or False, default None
        Column to use as the row labels of the DataFrame. If a sequence is given, a
        MultiIndex is used. If you have a malformed file with delimiters at the end
        of each line, you might consider index_col=False to force pandas to _not_
        use the first column as the index (row names)
    usecols : array-like or callable, default None
        Return a subset of the columns. If array-like, all elements must either
        be positional (i.e. integer indices into the document columns) or strings
        that correspond to column names provided either by the user in `names` or
        inferred from the document header row(s). For example, a valid array-like
        `usecols` parameter would be [0, 1, 2] or ['foo', 'bar', 'baz'].
    
        If callable, the callable function will be evaluated against the column
        names, returning names where the callable function evaluates to True. An
        example of a valid callable argument would be ``lambda x: x.upper() in
        ['AAA', 'BBB', 'DDD']``. Using this parameter results in much faster
        parsing time and lower memory usage.
    as_recarray : boolean, default False
        .. deprecated:: 0.19.0
           Please call `pd.read_csv(...).to_records()` instead.
    
        Return a NumPy recarray instead of a DataFrame after parsing the data.
        If set to True, this option takes precedence over the `squeeze` parameter.
        In addition, as row indices are not available in such a format, the
        `index_col` parameter will be ignored.
    squeeze : boolean, default False
        If the parsed data only contains one column then return a Series
    prefix : str, default None
        Prefix to add to column numbers when no header, e.g. 'X' for X0, X1, ...
    mangle_dupe_cols : boolean, default True
        Duplicate columns will be specified as 'X.0'...'X.N', rather than
        'X'...'X'. Passing in False will cause data to be overwritten if there
        are duplicate names in the columns.
    dtype : Type name or dict of column -> type, default None
        Data type for data or columns. E.g. {'a': np.float64, 'b': np.int32}
        Use `str` or `object` to preserve and not interpret dtype.
        If converters are specified, they will be applied INSTEAD
        of dtype conversion.
    engine : {'c', 'python'}, optional
        Parser engine to use. The C engine is faster while the python engine is
        currently more feature-complete.
    converters : dict, default None
        Dict of functions for converting values in certain columns. Keys can either
        be integers or column labels
    true_values : list, default None
        Values to consider as True
    false_values : list, default None
        Values to consider as False
    skipinitialspace : boolean, default False
        Skip spaces after delimiter.
    skiprows : list-like or integer or callable, default None
        Line numbers to skip (0-indexed) or number of lines to skip (int)
        at the start of the file.
    
        If callable, the callable function will be evaluated against the row
        indices, returning True if the row should be skipped and False otherwise.
        An example of a valid callable argument would be ``lambda x: x in [0, 2]``.
    skipfooter : int, default 0
        Number of lines at bottom of file to skip (Unsupported with engine='c')
    skip_footer : int, default 0
        .. deprecated:: 0.19.0
           Use the `skipfooter` parameter instead, as they are identical
    nrows : int, default None
        Number of rows of file to read. Useful for reading pieces of large files
    na_values : scalar, str, list-like, or dict, default None
        Additional strings to recognize as NA/NaN. If dict passed, specific
        per-column NA values.  By default the following values are interpreted as
        NaN: '', '#N/A', '#N/A N/A', '#NA', '-1.#IND', '-1.#QNAN', '-NaN', '-nan',
        '1.#IND', '1.#QNAN', 'N/A', 'NA', 'NULL', 'NaN', 'n/a', 'nan',
        'null'.
    keep_default_na : bool, default True
        If na_values are specified and keep_default_na is False the default NaN
        values are overridden, otherwise they're appended to.
    na_filter : boolean, default True
        Detect missing value markers (empty strings and the value of na_values). In
        data without any NAs, passing na_filter=False can improve the performance
        of reading a large file
    verbose : boolean, default False
        Indicate number of NA values placed in non-numeric columns
    skip_blank_lines : boolean, default True
        If True, skip over blank lines rather than interpreting as NaN values
    parse_dates : boolean or list of ints or names or list of lists or dict, default False
    
        * boolean. If True -> try parsing the index.
        * list of ints or names. e.g. If [1, 2, 3] -> try parsing columns 1, 2, 3
          each as a separate date column.
        * list of lists. e.g.  If [[1, 3]] -> combine columns 1 and 3 and parse as
          a single date column.
        * dict, e.g. {'foo' : [1, 3]} -> parse columns 1, 3 as date and call result
          'foo'
    
        If a column or index contains an unparseable date, the entire column or
        index will be returned unaltered as an object data type. For non-standard
        datetime parsing, use ``pd.to_datetime`` after ``pd.read_csv``
    
        Note: A fast-path exists for iso8601-formatted dates.
    infer_datetime_format : boolean, default False
        If True and `parse_dates` is enabled, pandas will attempt to infer the
        format of the datetime strings in the columns, and if it can be inferred,
        switch to a faster method of parsing them. In some cases this can increase
        the parsing speed by 5-10x.
    keep_date_col : boolean, default False
        If True and `parse_dates` specifies combining multiple columns then
        keep the original columns.
    date_parser : function, default None
        Function to use for converting a sequence of string columns to an array of
        datetime instances. The default uses ``dateutil.parser.parser`` to do the
        conversion. Pandas will try to call `date_parser` in three different ways,
        advancing to the next if an exception occurs: 1) Pass one or more arrays
        (as defined by `parse_dates`) as arguments; 2) concatenate (row-wise) the
        string values from the columns defined by `parse_dates` into a single array
        and pass that; and 3) call `date_parser` once for each row using one or
        more strings (corresponding to the columns defined by `parse_dates`) as
        arguments.
    dayfirst : boolean, default False
        DD/MM format dates, international and European format
    iterator : boolean, default False
        Return TextFileReader object for iteration or getting chunks with
        ``get_chunk()``.
    chunksize : int, default None
        Return TextFileReader object for iteration.
        See the `IO Tools docs
        <http://pandas.pydata.org/pandas-docs/stable/io.html#io-chunking>`_
        for more information on ``iterator`` and ``chunksize``.
    compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer'
        For on-the-fly decompression of on-disk data. If 'infer' and
        `filepath_or_buffer` is path-like, then detect compression from the
        following extensions: '.gz', '.bz2', '.zip', or '.xz' (otherwise no
        decompression). If using 'zip', the ZIP file must contain only one data
        file to be read in. Set to None for no decompression.
    
        .. versionadded:: 0.18.1 support for 'zip' and 'xz' compression.
    
    thousands : str, default None
        Thousands separator
    decimal : str, default '.'
        Character to recognize as decimal point (e.g. use ',' for European data).
    float_precision : string, default None
        Specifies which converter the C engine should use for floating-point
        values. The options are `None` for the ordinary converter,
        `high` for the high-precision converter, and `round_trip` for the
        round-trip converter.
    lineterminator : str (length 1), default None
        Character to break file into lines. Only valid with C parser.
    quotechar : str (length 1), optional
        The character used to denote the start and end of a quoted item. Quoted
        items can include the delimiter and it will be ignored.
    quoting : int or csv.QUOTE_* instance, default 0
        Control field quoting behavior per ``csv.QUOTE_*`` constants. Use one of
        QUOTE_MINIMAL (0), QUOTE_ALL (1), QUOTE_NONNUMERIC (2) or QUOTE_NONE (3).
    doublequote : boolean, default ``True``
       When quotechar is specified and quoting is not ``QUOTE_NONE``, indicate
       whether or not to interpret two consecutive quotechar elements INSIDE a
       field as a single ``quotechar`` element.
    escapechar : str (length 1), default None
        One-character string used to escape delimiter when quoting is QUOTE_NONE.
    comment : str, default None
        Indicates remainder of line should not be parsed. If found at the beginning
        of a line, the line will be ignored altogether. This parameter must be a
        single character. Like empty lines (as long as ``skip_blank_lines=True``),
        fully commented lines are ignored by the parameter `header` but not by
        `skiprows`. For example, if comment='#', parsing '#empty\na,b,c\n1,2,3'
        with `header=0` will result in 'a,b,c' being
        treated as the header.
    encoding : str, default None
        Encoding to use for UTF when reading/writing (ex. 'utf-8'). `List of Python
        standard encodings
        <https://docs.python.org/3/library/codecs.html#standard-encodings>`_
    dialect : str or csv.Dialect instance, default None
        If provided, this parameter will override values (default or not) for the
        following parameters: `delimiter`, `doublequote`, `escapechar`,
        `skipinitialspace`, `quotechar`, and `quoting`. If it is necessary to
        override values, a ParserWarning will be issued. See csv.Dialect
        documentation for more details.
    tupleize_cols : boolean, default False
        .. deprecated:: 0.21.0
           This argument will be removed and will always convert to MultiIndex
    
        Leave a list of tuples on columns as is (default is to convert to
        a MultiIndex on the columns)
    error_bad_lines : boolean, default True
        Lines with too many fields (e.g. a csv line with too many commas) will by
        default cause an exception to be raised, and no DataFrame will be returned.
        If False, then these "bad lines" will dropped from the DataFrame that is
        returned.
    warn_bad_lines : boolean, default True
        If error_bad_lines is False, and warn_bad_lines is True, a warning for each
        "bad line" will be output.
    low_memory : boolean, default True
        Internally process the file in chunks, resulting in lower memory use
        while parsing, but possibly mixed type inference.  To ensure no mixed
        types either set False, or specify the type with the `dtype` parameter.
        Note that the entire file is read into a single DataFrame regardless,
        use the `chunksize` or `iterator` parameter to return the data in chunks.
        (Only valid with C parser)
    buffer_lines : int, default None
        .. deprecated:: 0.19.0
           This argument is not respected by the parser
    compact_ints : boolean, default False
        .. deprecated:: 0.19.0
           Argument moved to ``pd.to_numeric``
    
        If compact_ints is True, then for any column that is of integer dtype,
        the parser will attempt to cast it as the smallest integer dtype possible,
        either signed or unsigned depending on the specification from the
        `use_unsigned` parameter.
    use_unsigned : boolean, default False
        .. deprecated:: 0.19.0
           Argument moved to ``pd.to_numeric``
    
        If integer columns are being compacted (i.e. `compact_ints=True`), specify
        whether the column should be compacted to the smallest signed or unsigned
        integer dtype.
    memory_map : boolean, default False
        If a filepath is provided for `filepath_or_buffer`, map the file object
        directly onto memory and access the data directly from there. Using this
        option can improve performance because there is no longer any I/O overhead.
    
    Returns
    -------
    result : DataFrame or TextParser
In [69]:
df = pd.read_csv('documents/STUDY/研一下/计算新闻传播学/cjc-gh-pages/data/data_write_to_file.txt', 
                 sep = '\t', names = ['a', 'b', 'c'])
df[-5:]
Out[69]:
a b c
9995 9995 99900025 998500749875
9996 9996 99920016 998800479936
9997 9997 99940009 999100269973
9998 9998 99960004 999400119992
9999 9999 99980001 999700029999
In [70]:
import json
data_dict = {'a':1, 'b':2, 'c':3}
with open('documents/STUDY/研一下/计算新闻传播学/cjc-gh-pages/data/save_dict.json', 'w') as f:
    json.dump(data_dict, f)
In [71]:
dd = json.load(open("documents/STUDY/研一下/计算新闻传播学/cjc-gh-pages/data/save_dict.json"))
dd
Out[71]:
{u'a': 1, u'b': 2, u'c': 3}
In [72]:
data_list = list(range(10))
with open('documents/STUDY/研一下/计算新闻传播学/cjc-gh-pages/data/save_list.json', 'w') as f:
    json.dump(data_list, f)
In [73]:
dl = json.load(open("documents/STUDY/研一下/计算新闻传播学/cjc-gh-pages/data/save_list.json"))
dl
Out[73]:
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
In [36]:
import dill # pip install dill 
# http://trac.mystic.cacr.caltech.edu/project/pathos/wiki/dill
def myFunction(num):
    return num,num

with open('documents/STUDY/研一下/计算新闻传播学/cjc-gh-pages/data/data.pkl', 'wb') as f:
    dill.dump(myFunction, f)
In [38]:
import dill # pip install dill 
# http://trac.mystic.cacr.caltech.edu/project/pathos/wiki/dill
def myFunction(num):
    return num,num

with open('documents/STUDY/研一下/计算新闻传播学/cjc-gh-pages/data/data.pkl', 'wb') as f:
    dill.dump(myFunction, f)
In [2]:
with open('documents/STUDY/研一下/计算新闻传播学/cjc-gh-pages/data/cjc/data.pkl', 'r') as f:
    newFunction = dill.load(f)#, strictio=strictio))
newFunction('hello')

---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
<ipython-input-2-e1c7615fe18a> in <module>()
----> 1 with open('documents/STUDY/研一下/计算新闻传播学/cjc-gh-pages/data/cjc/data.pkl', 'r') as f:
      2     newFunction = dill.load(f)#, strictio=strictio))
      3 newFunction('hello')

FileNotFoundError: [Errno 2] No such file or directory: 'documents/STUDY/研一下/计算新闻传播学/cjc-gh-pages/data/cjc/data.pkl'
In [39]:
with open('documents/STUDY/研一下/计算新闻传播学/cjc-gh-pages/data/data.pkl', 'rb') as f:
    newFunction = dill.load(f)#, strictio=strictio))
newFunction('hello')
Out[39]:
('hello', 'hello')
In [40]:
with open('documents/STUDY/研一下/计算新闻传播学/cjc-gh-pages/data/data.pkl', 'rb') as f:
    newFunction = dill.load(f)
newFunction('hello')
Out[40]:
('hello', 'hello')
In [5]:
%matplotlib inline

import matplotlib.pyplot as plt
x = range(1, 100)
y = [i**-3 for i in x]
plt.plot(x, y, 'b-s')
plt.ylabel('$p(k)$', fontsize = 20)
plt.xlabel('$k$', fontsize = 20)
plt.xscale('log')
plt.yscale('log')
plt.title('Degree Distribution')
plt.show()
In [6]:
import numpy as np
t = np.arange(0., 5., 0.2)
plt.plot(t, t, 'r--')
plt.plot(t, t**2, 'bs')
plt.plot(t, t**3, 'g^')
plt.show()
In [7]:
t = np.arange(0., 5., 0.2)
plt.plot(t, t**2, 'b-s', label = '1')
plt.plot(t, t**2.5, 'r-o', label = '2')
plt.plot(t, t**3, 'g-^', label = '3')
plt.annotate(r'$\alpha = 3$', xy=(3.5, 40), xytext=(2, 80),
            arrowprops=dict(facecolor='black', shrink=0.05),
            fontsize = 20)
plt.ylabel('$f(t)$', fontsize = 20)
plt.xlabel('$t$', fontsize = 20)
plt.legend(loc=2,numpoints=1,fontsize=10)
plt.show()
In [8]:
plt.figure(1)
plt.subplot(221)
plt.plot(t, t, 'r--')
plt.text(2, 0.8*np.max(t), r'$\alpha = 1$', fontsize = 20)
plt.subplot(222)
plt.plot(t, t**2, 'bs')
plt.text(2, 0.8*np.max(t**2), r'$\alpha = 2$', fontsize = 20)
plt.subplot(223)
plt.plot(t, t**3, 'g^')
plt.text(2, 0.8*np.max(t**3), r'$\alpha = 3$', fontsize = 20)
plt.subplot(224)
plt.plot(t, t**4, 'r-o')
plt.text(2, 0.8*np.max(t**4), r'$\alpha = 4$', fontsize = 20)
plt.show()
In [9]:
def f(t):
    return np.exp(-t) * np.cos(2*np.pi*t)

t1 = np.arange(0.0, 5.0, 0.1)
t2 = np.arange(0.0, 5.0, 0.02)
In [10]:
plt.figure(1)
plt.subplot(211)
plt.plot(t1, f(t1), 'bo')
plt.plot(t2, f(t2), 'k')

plt.subplot(212)
plt.plot(t2, np.cos(2*np.pi*t2), 'r--')
plt.show()
In [11]:
import matplotlib.gridspec as gridspec
import numpy as np

t = np.arange(0., 5., 0.2)

gs = gridspec.GridSpec(3, 3)
ax1 = plt.subplot(gs[0, :])
plt.plot(t, t**2, 'b-s')
ax2 = plt.subplot(gs[1,:-1])
plt.plot(t, t**2, 'g-s')
ax3 = plt.subplot(gs[1:, -1])
plt.plot(t, t**2, 'r-o')
ax4 = plt.subplot(gs[-1,0])
plt.plot(t, t**2, 'g-^')
ax5 = plt.subplot(gs[-1,1])
plt.plot(t, t**2, 'b-<')
plt.tight_layout()
In [12]:
def OLSRegressPlot(x,y,col,xlab,ylab):
    xx = sm.add_constant(x, prepend=True)
    res = sm.OLS(y,xx).fit()
    constant, beta = res.params
    r2 = res.rsquared
    lab = r'$\beta = %.2f, \,R^2 = %.2f$' %(beta,r2)
    plt.scatter(x,y,s=60,facecolors='none', edgecolors=col)
    plt.plot(x,constant + x*beta,"red",label=lab)
    plt.legend(loc = 'upper left',fontsize=16)
    plt.xlabel(xlab,fontsize=26)
    plt.ylabel(ylab,fontsize=26)
In [41]:
x = np.random.randn(50)
y = np.random.randn(50) + 3*x
pearsonr(x,y)
fig = plt.figure(figsize=(10, 4),facecolor='white')
OLSRegressPlot(x,y,'RoyalBlue',r'$x$',r'$y$')
plt.show()
In [28]:
x = np.random.randn(50)
y = np.random.randn(50) + 3*x
pearsonr(x,y)
fig = plt.figure(figsize=(10, 4),facecolor='white')
OLSRegressPlot(x,y,'RoyalBlue',r'$x$',r'$y$')
plt.show()
In [42]:
fig = plt.figure(figsize=(7, 4),facecolor='white')
data = norm.rvs(10.0, 2.5, size=5000)
mu, std = norm.fit(data)
plt.hist(data, bins=25, normed=True, alpha=0.6, color='g')
xmin, xmax = plt.xlim()
x = np.linspace(xmin, xmax, 100)
p = norm.pdf(x, mu, std)
plt.plot(x, p, 'r', linewidth=2)
title = r"$\mu = %.2f, \,  \sigma = %.2f$" % (mu, std)
plt.title(title,size=16)
plt.show()
In [48]:
from matplotlib.dates import WeekdayLocator, DayLocator, MONDAY, DateFormatter
from matplotlib.finance import quotes_historical_yahoo_ochl, candlestick_ochl

date1 = (2014, 2, 1)
date2 = (2014, 5, 1)
quotes = quotes_historical_yahoo_ochl('INTC', date1, date2)

---------------------------------------------------------------------------
gaierror                                  Traceback (most recent call last)
~/anaconda3/lib/python3.6/urllib/request.py in do_open(self, http_class, req, **http_conn_args)
   1317                 h.request(req.get_method(), req.selector, req.data, headers,
-> 1318                           encode_chunked=req.has_header('Transfer-encoding'))
   1319             except OSError as err: # timeout error

~/anaconda3/lib/python3.6/http/client.py in request(self, method, url, body, headers, encode_chunked)
   1238         """Send a complete request to the server."""
-> 1239         self._send_request(method, url, body, headers, encode_chunked)
   1240 

~/anaconda3/lib/python3.6/http/client.py in _send_request(self, method, url, body, headers, encode_chunked)
   1284             body = _encode(body, 'body')
-> 1285         self.endheaders(body, encode_chunked=encode_chunked)
   1286 

~/anaconda3/lib/python3.6/http/client.py in endheaders(self, message_body, encode_chunked)
   1233             raise CannotSendHeader()
-> 1234         self._send_output(message_body, encode_chunked=encode_chunked)
   1235 

~/anaconda3/lib/python3.6/http/client.py in _send_output(self, message_body, encode_chunked)
   1025         del self._buffer[:]
-> 1026         self.send(msg)
   1027 

~/anaconda3/lib/python3.6/http/client.py in send(self, data)
    963             if self.auto_open:
--> 964                 self.connect()
    965             else:

~/anaconda3/lib/python3.6/http/client.py in connect(self)
    935         self.sock = self._create_connection(
--> 936             (self.host,self.port), self.timeout, self.source_address)
    937         self.sock.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)

~/anaconda3/lib/python3.6/socket.py in create_connection(address, timeout, source_address)
    703     err = None
--> 704     for res in getaddrinfo(host, port, 0, SOCK_STREAM):
    705         af, socktype, proto, canonname, sa = res

~/anaconda3/lib/python3.6/socket.py in getaddrinfo(host, port, family, type, proto, flags)
    744     addrlist = []
--> 745     for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
    746         af, socktype, proto, canonname, sa = res

gaierror: [Errno 8] nodename nor servname provided, or not known

During handling of the above exception, another exception occurred:

URLError                                  Traceback (most recent call last)
<ipython-input-48-c6548efc2d11> in <module>()
      4 date1 = (2014, 2, 1)
      5 date2 = (2014, 5, 1)
----> 6 quotes = quotes_historical_yahoo_ochl('INTC', date1, date2)

~/anaconda3/lib/python3.6/site-packages/matplotlib/finance.py in quotes_historical_yahoo_ochl(ticker, date1, date2, asobject, adjusted, cachename)
    409     return _quotes_historical_yahoo(ticker, date1, date2, asobject=asobject,
    410                              adjusted=adjusted, cachename=cachename,
--> 411                              ochl=True)
    412 
    413 

~/anaconda3/lib/python3.6/site-packages/matplotlib/finance.py in _quotes_historical_yahoo(ticker, date1, date2, asobject, adjusted, cachename, ochl)
    499     #    warnings.warn("Recommend changing to asobject=None")
    500 
--> 501     fh = fetch_historical_yahoo(ticker, date1, date2, cachename)
    502 
    503     try:

~/anaconda3/lib/python3.6/site-packages/matplotlib/finance.py in fetch_historical_yahoo(ticker, date1, date2, cachename, dividends)
    358         else:
    359             mkdirs(os.path.abspath(os.path.dirname(cachename)))
--> 360             with contextlib.closing(urlopen(url)) as urlfh:
    361                 with open(cachename, 'wb') as fh:
    362                     fh.write(urlfh.read())

~/anaconda3/lib/python3.6/urllib/request.py in urlopen(url, data, timeout, cafile, capath, cadefault, context)
    221     else:
    222         opener = _opener
--> 223     return opener.open(url, data, timeout)
    224 
    225 def install_opener(opener):

~/anaconda3/lib/python3.6/urllib/request.py in open(self, fullurl, data, timeout)
    524             req = meth(req)
    525 
--> 526         response = self._open(req, data)
    527 
    528         # post-process response

~/anaconda3/lib/python3.6/urllib/request.py in _open(self, req, data)
    542         protocol = req.type
    543         result = self._call_chain(self.handle_open, protocol, protocol +
--> 544                                   '_open', req)
    545         if result:
    546             return result

~/anaconda3/lib/python3.6/urllib/request.py in _call_chain(self, chain, kind, meth_name, *args)
    502         for handler in handlers:
    503             func = getattr(handler, meth_name)
--> 504             result = func(*args)
    505             if result is not None:
    506                 return result

~/anaconda3/lib/python3.6/urllib/request.py in http_open(self, req)
   1344 
   1345     def http_open(self, req):
-> 1346         return self.do_open(http.client.HTTPConnection, req)
   1347 
   1348     http_request = AbstractHTTPHandler.do_request_

~/anaconda3/lib/python3.6/urllib/request.py in do_open(self, http_class, req, **http_conn_args)
   1318                           encode_chunked=req.has_header('Transfer-encoding'))
   1319             except OSError as err: # timeout error
-> 1320                 raise URLError(err)
   1321             r = h.getresponse()
   1322         except:

URLError: <urlopen error [Errno 8] nodename nor servname provided, or not known>
In [47]:
fig = plt.figure(figsize=(15, 5))
ax = fig.add_subplot(1,1,1)
candlestick_ochl(ax, quotes, width=0.8, colorup='green', colordown='r', alpha=0.8)
mondays = WeekdayLocator(MONDAY)    # major ticks on the mondays
alldays = DayLocator()              # minor ticks on the days
weekFormatter = DateFormatter('%b %d')  # e.g., Jan 12
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(alldays)
ax.xaxis.set_major_formatter(weekFormatter)
ax.autoscale_view()
plt.setp( plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')
plt.title(r'$Intel \,Corporation \,Stock \,Price$',size=16)
fig.subplots_adjust(bottom=0.2)
plt.show()

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-47-d9f97a5af26d> in <module>()
      1 fig = plt.figure(figsize=(15, 5))
      2 ax = fig.add_subplot(1,1,1)
----> 3 candlestick_ochl(ax, quotes, width=0.8, colorup='green', colordown='r', alpha=0.8)
      4 mondays = WeekdayLocator(MONDAY)    # major ticks on the mondays
      5 alldays = DayLocator()              # minor ticks on the days

NameError: name 'quotes' is not defined
In [18]:
from matplotlib.dates import WeekdayLocator, DayLocator, MONDAY, DateFormatter
from matplotlib.finance import quotes_historical_yahoo_ochl, candlestick_ochl

date1 = (2014, 2, 1)
date2 = (2014, 5, 1)
quotes = quotes_historical_yahoo_ochl('INTC', date1, date2)
fig = plt.figure(figsize=(15, 5))
ax = fig.add_subplot(1,1,1)
candlestick_ochl(ax, quotes, width=0.8, colorup='green', colordown='r', alpha=0.8)
mondays = WeekdayLocator(MONDAY)    # major ticks on the mondays
alldays = DayLocator()              # minor ticks on the days
weekFormatter = DateFormatter('%b %d')  # e.g., Jan 12
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(alldays)
ax.xaxis.set_major_formatter(weekFormatter)
ax.autoscale_view()
plt.setp( plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')
plt.title(r'$Intel \,Corporation \,Stock \,Price$',size=16)
fig.subplots_adjust(bottom=0.2)
plt.show()

---------------------------------------------------------------------------
gaierror                                  Traceback (most recent call last)
~/anaconda3/lib/python3.6/urllib/request.py in do_open(self, http_class, req, **http_conn_args)
   1317                 h.request(req.get_method(), req.selector, req.data, headers,
-> 1318                           encode_chunked=req.has_header('Transfer-encoding'))
   1319             except OSError as err: # timeout error

~/anaconda3/lib/python3.6/http/client.py in request(self, method, url, body, headers, encode_chunked)
   1238         """Send a complete request to the server."""
-> 1239         self._send_request(method, url, body, headers, encode_chunked)
   1240 

~/anaconda3/lib/python3.6/http/client.py in _send_request(self, method, url, body, headers, encode_chunked)
   1284             body = _encode(body, 'body')
-> 1285         self.endheaders(body, encode_chunked=encode_chunked)
   1286 

~/anaconda3/lib/python3.6/http/client.py in endheaders(self, message_body, encode_chunked)
   1233             raise CannotSendHeader()
-> 1234         self._send_output(message_body, encode_chunked=encode_chunked)
   1235 

~/anaconda3/lib/python3.6/http/client.py in _send_output(self, message_body, encode_chunked)
   1025         del self._buffer[:]
-> 1026         self.send(msg)
   1027 

~/anaconda3/lib/python3.6/http/client.py in send(self, data)
    963             if self.auto_open:
--> 964                 self.connect()
    965             else:

~/anaconda3/lib/python3.6/http/client.py in connect(self)
    935         self.sock = self._create_connection(
--> 936             (self.host,self.port), self.timeout, self.source_address)
    937         self.sock.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)

~/anaconda3/lib/python3.6/socket.py in create_connection(address, timeout, source_address)
    703     err = None
--> 704     for res in getaddrinfo(host, port, 0, SOCK_STREAM):
    705         af, socktype, proto, canonname, sa = res

~/anaconda3/lib/python3.6/socket.py in getaddrinfo(host, port, family, type, proto, flags)
    744     addrlist = []
--> 745     for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
    746         af, socktype, proto, canonname, sa = res

gaierror: [Errno 8] nodename nor servname provided, or not known

During handling of the above exception, another exception occurred:

URLError                                  Traceback (most recent call last)
<ipython-input-18-a3201dfaca78> in <module>()
      4 date1 = (2014, 2, 1)
      5 date2 = (2014, 5, 1)
----> 6 quotes = quotes_historical_yahoo_ochl('INTC', date1, date2)
      7 fig = plt.figure(figsize=(15, 5))
      8 ax = fig.add_subplot(1,1,1)

~/anaconda3/lib/python3.6/site-packages/matplotlib/finance.py in quotes_historical_yahoo_ochl(ticker, date1, date2, asobject, adjusted, cachename)
    409     return _quotes_historical_yahoo(ticker, date1, date2, asobject=asobject,
    410                              adjusted=adjusted, cachename=cachename,
--> 411                              ochl=True)
    412 
    413 

~/anaconda3/lib/python3.6/site-packages/matplotlib/finance.py in _quotes_historical_yahoo(ticker, date1, date2, asobject, adjusted, cachename, ochl)
    499     #    warnings.warn("Recommend changing to asobject=None")
    500 
--> 501     fh = fetch_historical_yahoo(ticker, date1, date2, cachename)
    502 
    503     try:

~/anaconda3/lib/python3.6/site-packages/matplotlib/finance.py in fetch_historical_yahoo(ticker, date1, date2, cachename, dividends)
    358         else:
    359             mkdirs(os.path.abspath(os.path.dirname(cachename)))
--> 360             with contextlib.closing(urlopen(url)) as urlfh:
    361                 with open(cachename, 'wb') as fh:
    362                     fh.write(urlfh.read())

~/anaconda3/lib/python3.6/urllib/request.py in urlopen(url, data, timeout, cafile, capath, cadefault, context)
    221     else:
    222         opener = _opener
--> 223     return opener.open(url, data, timeout)
    224 
    225 def install_opener(opener):

~/anaconda3/lib/python3.6/urllib/request.py in open(self, fullurl, data, timeout)
    524             req = meth(req)
    525 
--> 526         response = self._open(req, data)
    527 
    528         # post-process response

~/anaconda3/lib/python3.6/urllib/request.py in _open(self, req, data)
    542         protocol = req.type
    543         result = self._call_chain(self.handle_open, protocol, protocol +
--> 544                                   '_open', req)
    545         if result:
    546             return result

~/anaconda3/lib/python3.6/urllib/request.py in _call_chain(self, chain, kind, meth_name, *args)
    502         for handler in handlers:
    503             func = getattr(handler, meth_name)
--> 504             result = func(*args)
    505             if result is not None:
    506                 return result

~/anaconda3/lib/python3.6/urllib/request.py in http_open(self, req)
   1344 
   1345     def http_open(self, req):
-> 1346         return self.do_open(http.client.HTTPConnection, req)
   1347 
   1348     http_request = AbstractHTTPHandler.do_request_

~/anaconda3/lib/python3.6/urllib/request.py in do_open(self, http_class, req, **http_conn_args)
   1318                           encode_chunked=req.has_header('Transfer-encoding'))
   1319             except OSError as err: # timeout error
-> 1320                 raise URLError(err)
   1321             r = h.getresponse()
   1322         except:

URLError: <urlopen error [Errno 8] nodename nor servname provided, or not known>
In [19]:
import pandas as pd
df = pd.read_csv('documents/STUDY/研一下/计算新闻传播学/cjc-gh-pages/data/data_write_to_file.txt', sep = '\t', names = ['a', 'b', 'c'])
df[:5]
Out[19]:
a b c
0 0 0 0
1 1 1 1
2 2 4 8
3 3 9 27
4 4 16 64
In [20]:
df.plot.line()
plt.yscale('log')
plt.ylabel('$values$', fontsize = 20)
plt.xlabel('$index$', fontsize = 20)
plt.show()
In [21]:
df.plot.scatter(x='a', y='b')
plt.show()
In [22]:
df.plot.hexbin(x='a', y='b', gridsize=25)
plt.show()
In [23]:
df['a'].plot.kde()
plt.show()
In [24]:
bp = df.boxplot()
plt.yscale('log')
plt.show()
In [25]:
df['c'].diff().hist()
plt.show()
In [26]:
df.plot.hist(stacked=True, bins=20)
# plt.yscale('log')
plt.show()