In [2]:

    

import pandas as pd
import numpy as np


    

In [3]:

    

pd.Timestamp('9/1/2016 10:05AM')


    

    
Out[3]:





Timestamp('2016-09-01 10:05:00')

In [4]:

    

pd.Period('1/2016')


    

    
Out[4]:





Period('2016-01', 'M')

In [5]:

    

pd.Period('3/5/2016')


    

    
Out[5]:





Period('2016-03-05', 'D')

In [8]:

    

t1 = pd.Series(list('abc'), [pd.Timestamp('2016-09-01'),
                            pd.Timestamp('2016-09-02'), pd.Timestamp('2016-09-03')])
t1


    

    
Out[8]:





2016-09-01    a
2016-09-02    b
2016-09-03    c
dtype: object

In [9]:

    

type(t1.index)


    

    
Out[9]:





pandas.tseries.index.DatetimeIndex

In [10]:

    

t2 = pd.Series(list('def'), [pd.Period('2016-09'), pd.Period('2016-10'), pd.Period('2016-11')])
t2


    

    
Out[10]:





2016-09    d
2016-10    e
2016-11    f
Freq: M, dtype: object

In [11]:

    

type(t2.index)


    

    
Out[11]:





pandas.tseries.period.PeriodIndex

In [14]:

    

d1 = ['2 June 2013', 'Aug 29, 2014', '2015-06-26', '7/12/16']
ts3 = pd.DataFrame(data=np.random.randint(10,100,(4,2)), index=d1, columns=list('ab'))
ts3


    

    
Out[14]:






  

    

      

      
a
      
b
    
  
  

    

      
2 June 2013
      
60
      
73
    
    

      
Aug 29, 2014
      
58
      
30
    
    

      
2015-06-26
      
91
      
74
    
    

      
7/12/16
      
86
      
71
    
  

In [16]:

    

ts3.index = pd.to_datetime(ts3.index)
ts3


    

    
Out[16]:






  

    

      

      
a
      
b
    
  
  

    

      
2013-06-02
      
60
      
73
    
    

      
2014-08-29
      
58
      
30
    
    

      
2015-06-26
      
91
      
74
    
    

      
2016-07-12
      
86
      
71
    
  

In [17]:

    

pd.to_datetime('4.7.12', dayfirst=True)


    

    
Out[17]:





Timestamp('2012-07-04 00:00:00')

In [18]:

    

pd.Timestamp('9/3/2016') - pd.Timestamp('9/1/2016')


    

    
Out[18]:





Timedelta('2 days 00:00:00')

In [20]:

    

pd.Timestamp('9/2/2016 8:10AM') + pd.Timedelta('12D 3H')


    

    
Out[20]:





Timestamp('2016-09-14 11:10:00')

In [22]:

    

dates = pd.date_range('10-01-2016', periods=9, freq='2W-SUN')
dates


    

    
Out[22]:





DatetimeIndex(['2016-10-02', '2016-10-16', '2016-10-30', '2016-11-13',
               '2016-11-27', '2016-12-11', '2016-12-25', '2017-01-08',
               '2017-01-22'],
              dtype='datetime64[ns]', freq='2W-SUN')

In [23]:

    

df = pd.DataFrame({'Count 1': 100 + np.random.randint(-5, 10, 9).cumsum(),
                  'Count 2': 120 + np.random.randint(-5, 10, 9)}, index=dates)
df


    

    
Out[23]:






  

    

      

      
Count 1
      
Count 2
    
  
  

    

      
2016-10-02
      
106
      
119
    
    

      
2016-10-16
      
105
      
128
    
    

      
2016-10-30
      
106
      
128
    
    

      
2016-11-13
      
112
      
129
    
    

      
2016-11-27
      
121
      
115
    
    

      
2016-12-11
      
129
      
127
    
    

      
2016-12-25
      
127
      
121
    
    

      
2017-01-08
      
128
      
120
    
    

      
2017-01-22
      
136
      
129
    
  

In [25]:

    

df.index.weekday_name


    

    
Out[25]:





array(['Sunday', 'Sunday', 'Sunday', 'Sunday', 'Sunday', 'Sunday',
       'Sunday', 'Sunday', 'Sunday'], dtype=object)

In [26]:

    

df.diff()


    

    
Out[26]:






  

    

      

      
Count 1
      
Count 2
    
  
  

    

      
2016-10-02
      
NaN
      
NaN
    
    

      
2016-10-16
      
-1.0
      
9.0
    
    

      
2016-10-30
      
1.0
      
0.0
    
    

      
2016-11-13
      
6.0
      
1.0
    
    

      
2016-11-27
      
9.0
      
-14.0
    
    

      
2016-12-11
      
8.0
      
12.0
    
    

      
2016-12-25
      
-2.0
      
-6.0
    
    

      
2017-01-08
      
1.0
      
-1.0
    
    

      
2017-01-22
      
8.0
      
9.0
    
  

In [27]:

    

df.resample('M').mean()


    

    
Out[27]:






  

    

      

      
Count 1
      
Count 2
    
  
  

    

      
2016-10-31
      
105.666667
      
125.0
    
    

      
2016-11-30
      
116.500000
      
122.0
    
    

      
2016-12-31
      
128.000000
      
124.0
    
    

      
2017-01-31
      
132.000000
      
124.5
    
  

In [30]:

    

df['2017']


    

    
Out[30]:






  

    

      

      
Count 1
      
Count 2
    
  
  

    

      
2017-01-08
      
128
      
120
    
    

      
2017-01-22
      
136
      
129
    
  

In [31]:

    

df['2016-12']


    

    
Out[31]:






  

    

      

      
Count 1
      
Count 2
    
  
  

    

      
2016-12-11
      
129
      
127
    
    

      
2016-12-25
      
127
      
121
    
  

In [32]:

    

df['2016-12':]


    

    
Out[32]:






  

    

      

      
Count 1
      
Count 2
    
  
  

    

      
2016-12-11
      
129
      
127
    
    

      
2016-12-25
      
127
      
121
    
    

      
2017-01-08
      
128
      
120
    
    

      
2017-01-22
      
136
      
129
    
  

In [40]:

    

df.asfreq('M', method='ffill')


    

    
Out[40]:






  

    

      

      
Count 1
      
Count 2
    
  
  

    

      
2016-10-31
      
106
      
128
    
    

      
2016-11-30
      
121
      
115
    
    

      
2016-12-31
      
127
      
121
    
  

In [44]:

    

import matplotlib.pyplot as plt
%matplotlib inline
df.plot()


    

    
Out[44]:





<matplotlib.axes._subplots.AxesSubplot at 0x232e3d37f28>






    

In [ ]: