In [13]:

    

import numpy as np
import pandas as pd

s = pd.Series([1,3,6,np.nan,44,1])
dates = pd.date_range('20161010', periods=6)

df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=['a','b','c','d'])

df2 = pd.DataFrame({'A' : 1.,
                       'B' : pd.Timestamp('20130102'),
                        'C' : pd.Series(1,index=list(range(4)),dtype='float32'),
                        'D' : np.array([3] * 4,dtype='int32'),
                        'E' : pd.Categorical(["test","train","test","train"]),
                        'F' : 'foo'})
print(df2)
print(df2.dtypes)

print(df2.index)
print(df2.columns)
print(df2.values)
print(df.describe())

print(df.T)
print(df.sort_index(axis=1, ascending=False))
print(df.sort_values(by='B'))


    

    




     A          B    C  D      E    F
0  1.0 2013-01-02  1.0  3   test  foo
1  1.0 2013-01-02  1.0  3  train  foo
2  1.0 2013-01-02  1.0  3   test  foo
3  1.0 2013-01-02  1.0  3  train  foo
A           float64
B    datetime64[ns]
C           float32
D             int32
E          category
F            object
dtype: object
Int64Index([0, 1, 2, 3], dtype='int64')
Index([u'A', u'B', u'C', u'D', u'E', u'F'], dtype='object')
[[1.0 Timestamp('2013-01-02 00:00:00') 1.0 3 'test' 'foo']
 [1.0 Timestamp('2013-01-02 00:00:00') 1.0 3 'train' 'foo']
 [1.0 Timestamp('2013-01-02 00:00:00') 1.0 3 'test' 'foo']
 [1.0 Timestamp('2013-01-02 00:00:00') 1.0 3 'train' 'foo']]
              a         b         c         d
count  6.000000  6.000000  6.000000  6.000000
mean   0.297567  0.538887 -0.087728  0.358686
std    1.386077  0.673955  0.752533  0.591750
min   -1.219325 -0.407863 -1.466573 -0.258363
25%   -0.474381  0.164730 -0.241454 -0.117245
50%    0.185964  0.502060  0.115877  0.272942
75%    0.441260  0.969125  0.398671  0.737387
max    2.800937  1.459967  0.576078  1.219699