In [4]:

    

import numpy as np
import pandas as pd

dates = pd.date_range('20161010', periods=6)
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=['A', 'B', 'C', 'D'])

print df.A, df['A']


    

    




2016-10-10    2.152774
2016-10-11   -1.346165
2016-10-12   -0.788369
2016-10-13   -0.614672
2016-10-14    0.365911
2016-10-15    0.031849
Freq: D, Name: A, dtype: float64 2016-10-10    2.152774
2016-10-11   -1.346165
2016-10-12   -0.788369
2016-10-13   -0.614672
2016-10-14    0.365911
2016-10-15    0.031849
Freq: D, Name: A, dtype: float64

In [5]:

    

print df.loc['20161010']


    

    




A    2.152774
B   -0.394939
C    0.450295
D    1.111433
Name: 2016-10-10 00:00:00, dtype: float64

In [7]:

    

print(df.loc[:,['A','B']])


    

    




                   A         B
2016-10-10  2.152774 -0.394939
2016-10-11 -1.346165 -0.207410
2016-10-12 -0.788369 -1.396438
2016-10-13 -0.614672 -0.568813
2016-10-14  0.365911  1.558348
2016-10-15  0.031849 -1.859374

In [11]:

    

# select by position: iloc
print(df.iloc[[1,2,4],[0,2]])


    

    




                   A         C
2016-10-11 -1.346165 -1.240596
2016-10-12 -0.788369 -1.144332
2016-10-14  0.365911  0.877368

In [12]:

    

# mixed selection: ix
print(df.ix[:3, ['A', 'C']])


    

    




                   A         C
2016-10-10  2.152774  0.450295
2016-10-11 -1.346165 -1.240596
2016-10-12 -0.788369 -1.144332