In [2]:

    

import numpy as np
np.random.seed(0) ## seed 설정


    

In [3]:

    

np.random.rand(5)


    

    
Out[3]:





array([ 0.5488135 ,  0.71518937,  0.60276338,  0.54488318,  0.4236548 ])

In [5]:

    

x = np.arange(5)
x


    

    
Out[5]:





array([0, 1, 2, 3, 4])

In [9]:

    

np.random.shuffle(x) ## 데이터 순서바꾸기
x


    

    
Out[9]:





array([1, 4, 2, 3, 0])

In [16]:

    

np.random.choice(5,4, replace=False)


    

    
Out[16]:





array([3, 2, 1, 4])

In [21]:

    

np.random.choice(5,10, p=[0.1,0,0.2,0.5,0.2])


    

    
Out[21]:





array([3, 4, 4, 3, 0, 3, 0, 3, 4, 3])

In [25]:

    

#rand 0부터 1사이의 균일분포, randn 가우시안, randint 균일분포의 난수
a = np.random.rand(10)
b = np.random.randn(10)
c = np.random.randint(10, size=10)
print a,b,c


    

    




 [ 0.98257496  0.37329075  0.42007537  0.05058812  0.36549611  0.01662797
  0.23074234  0.7649117   0.94412352  0.74999925] [ -1.38282715e-01  -2.12492027e+00  -1.03001883e+00  -5.17451865e-01
  -1.41800012e-01  -1.27648021e+00   1.48151328e+00   1.65927664e+00
   1.39611962e-04   1.50583836e+00] [8 4 4 0 9 3 7 3 2 1]

In [27]:

    

from sklearn.datasets import load_boston
import pandas as pd
boston = load_boston()
dfx = pd.DataFrame(boston.data, columns = boston.feature_names)
dfy = pd.DataFrame(boston.target, columns = ['MEDV'])


    

In [29]:

    

df = pd.concat([dfx,dfy], axis = 1)
df.tail()


    

    
Out[29]:






  

    

      

      
CRIM
      
ZN
      
INDUS
      
CHAS
      
NOX
      
RM
      
AGE
      
DIS
      
RAD
      
TAX
      
PTRATIO
      
B
      
LSTAT
      
MEDV
    
  
  

    

      
501
      
0.06263
      
0.0
      
11.93
      
0.0
      
0.573
      
6.593
      
69.1
      
2.4786
      
1.0
      
273.0
      
21.0
      
391.99
      
9.67
      
22.4
    
    

      
502
      
0.04527
      
0.0
      
11.93
      
0.0
      
0.573
      
6.120
      
76.7
      
2.2875
      
1.0
      
273.0
      
21.0
      
396.90
      
9.08
      
20.6
    
    

      
503
      
0.06076
      
0.0
      
11.93
      
0.0
      
0.573
      
6.976
      
91.0
      
2.1675
      
1.0
      
273.0
      
21.0
      
396.90
      
5.64
      
23.9
    
    

      
504
      
0.10959
      
0.0
      
11.93
      
0.0
      
0.573
      
6.794
      
89.3
      
2.3889
      
1.0
      
273.0
      
21.0
      
393.45
      
6.48
      
22.0
    
    

      
505
      
0.04741
      
0.0
      
11.93
      
0.0
      
0.573
      
6.030
      
80.8
      
2.5050
      
1.0
      
273.0
      
21.0
      
396.90
      
7.88
      
11.9
    
  

In [5]:

    

"{name} is so sexy".format(name = "jiyong")


    

    
Out[5]:





'jiyong is so sexy'

In [6]:

    

"{name} is so sexy. he is {age} years old".format(name = "GD", age="28")


    

    
Out[6]:





'GD is so sexy. he is 28 years old'

In [7]:

    

animals = ["cat", "dog"]


    

In [9]:

    

animals.append("people")


    

In [13]:

    

animals.pop(1)


    

    
Out[13]:





'dog'

In [17]:

    

## tuple - element 값이 변하지 않음


    

In [21]:

    

width_and_height = (12,33)


    

In [ ]:

    

# set - 집합 , 순서가 없음 element가 uniq 해야함


    

In [22]:

    

name_set = set(["jy","jy","jp","jj"])


    

In [23]:

    

name_set


    

    
Out[23]:





{'jj', 'jp', 'jy'}

In [29]:

    

a_dict = {"name" : "jy", "age" : 23}


    

In [30]:

    

a_dict["name"]


    

    
Out[30]:





'jy'

In [34]:

    

True & True # And


    

    
Out[34]:





True

In [38]:

    

True | False # Or


    

    
Out[38]:





True