In [2]:

    

import statsmodels.api as sm
import statsmodels.formula.api as smf
from sklearn.datasets import make_classification


    

In [3]:

    

X, y = make_classification(n_samples=10000, n_features=10,n_informative=10, n_redundant=0,random_state=101)


    

In [4]:

    

Xc=sm.add_constant(X)
logistic_regression=sm.Logit(y,Xc)
fitted_model=logistic_regression.fit()


    

    




Optimization terminated successfully.
         Current function value: 0.438685
         Iterations 7

In [5]:

    

fitted_model.summary()


    

    
Out[5]:





Logit Regression Results

  
Dep. Variable:
         
y
        
  No. Observations:  
  
 10000
 


  
Model:
               
Logit
      
  Df Residuals:      
  
  9989
 


  
Method:
               
MLE
       
  Df Model:          
  
    10
 


  
Date:
          
Mon, 27 Feb 2017
 
  Pseudo R-squ.:     
  
0.3671
 


  
Time:
              
12:02:59
     
  Log-Likelihood:    
 
 -4386.8


  
converged:
           
True
       
  LL-Null:           
 
 -6931.5


  
 
                      
 
        
  LLR p-value:       
  
 0.000
 




    
       
coef
     
std err
      
z
      
P>|z|
 
[95.0% Conf. Int.]
 


  
const
 
    0.4299
 
    0.039
 
   11.023
 
 0.000
 
    0.353     0.506


  
x1
    
    0.0671
 
    0.015
 
    4.410
 
 0.000
 
    0.037     0.097


  
x2
    
   -0.7828
 
    0.019
 
  -41.947
 
 0.000
 
   -0.819    -0.746


  
x3
    
    0.1221
 
    0.016
 
    7.815
 
 0.000
 
    0.091     0.153


  
x4
    
    0.2841
 
    0.016
 
   18.150
 
 0.000
 
    0.253     0.315


  
x5
    
    0.1469
 
    0.014
 
   10.283
 
 0.000
 
    0.119     0.175


  
x6
    
   -0.3414
 
    0.019
 
  -17.636
 
 0.000
 
   -0.379    -0.303


  
x7
    
    0.0503
 
    0.014
 
    3.481
 
 0.000
 
    0.022     0.079


  
x8
    
   -0.1393
 
    0.014
 
   -9.642
 
 0.000
 
   -0.168    -0.111


  
x9
    
    0.1127
 
    0.014
 
    7.931
 
 0.000
 
    0.085     0.141


  
x10
   
   -0.4792
 
    0.018
 
  -27.340
 
 0.000
 
   -0.514    -0.445

In [6]:

    

import urllib


    

In [7]:

    

dir(urllib.parse)


    

    
Out[7]:





['DefragResult',
 'DefragResultBytes',
 'MAX_CACHE_SIZE',
 'ParseResult',
 'ParseResultBytes',
 'Quoter',
 'ResultBase',
 'SplitResult',
 'SplitResultBytes',
 '_ALWAYS_SAFE',
 '_ALWAYS_SAFE_BYTES',
 '_DefragResultBase',
 '_NetlocResultMixinBase',
 '_NetlocResultMixinBytes',
 '_NetlocResultMixinStr',
 '_ParseResultBase',
 '_ResultMixinBytes',
 '_ResultMixinStr',
 '_SplitResultBase',
 '__all__',
 '__builtins__',
 '__cached__',
 '__doc__',
 '__file__',
 '__loader__',
 '__name__',
 '__package__',
 '__spec__',
 '_asciire',
 '_coerce_args',
 '_decode_args',
 '_encode_result',
 '_hexdig',
 '_hextobyte',
 '_hostprog',
 '_implicit_encoding',
 '_implicit_errors',
 '_noop',
 '_parse_cache',
 '_portprog',
 '_safe_quoters',
 '_splitnetloc',
 '_splitparams',
 '_typeprog',
 'clear_cache',
 'collections',
 'namedtuple',
 'non_hierarchical',
 'parse_qs',
 'parse_qsl',
 'quote',
 'quote_from_bytes',
 'quote_plus',
 're',
 'scheme_chars',
 'splitattr',
 'splithost',
 'splitnport',
 'splitpasswd',
 'splitport',
 'splitquery',
 'splittag',
 'splittype',
 'splituser',
 'splitvalue',
 'sys',
 'to_bytes',
 'unquote',
 'unquote_plus',
 'unquote_to_bytes',
 'unwrap',
 'urldefrag',
 'urlencode',
 'urljoin',
 'urlparse',
 'urlsplit',
 'urlunparse',
 'urlunsplit',
 'uses_fragment',
 'uses_netloc',
 'uses_params',
 'uses_query',
 'uses_relative']

In [8]:

    

import csv


    

In [12]:

    

dir(csv)


    

    
Out[12]:





['Dialect',
 'DictReader',
 'DictWriter',
 'Error',
 'QUOTE_ALL',
 'QUOTE_MINIMAL',
 'QUOTE_NONE',
 'QUOTE_NONNUMERIC',
 'Sniffer',
 'StringIO',
 '_Dialect',
 '__all__',
 '__builtins__',
 '__cached__',
 '__doc__',
 '__file__',
 '__loader__',
 '__name__',
 '__package__',
 '__spec__',
 '__version__',
 'excel',
 'excel_tab',
 'field_size_limit',
 'get_dialect',
 'list_dialects',
 're',
 'reader',
 'register_dialect',
 'unix_dialect',
 'unregister_dialect',
 'writer']