Python pandas Date

import pandas as pd
import numpy as np
#
#Timestamp
pd.Timestamp(‘9/1/2016 10:05AM‘)
#output: Timestamp(‘2016-09-01 10:05:00‘)
#
#Period
pd.Period(‘1/2016‘)
#output: Period(‘2016-01‘, ‘M‘)
pd.Period(‘3/5/2016‘)
#output: Period(‘2016-03-05‘, ‘D‘)
#
#DatetimeIndex
t1 = pd.Series(list(‘abc‘), [pd.Timestamp(‘2016-09-01‘), pd.Timestamp(‘2016-09-02‘), pd.Timestamp(‘2016-09-03‘)])
t1
"""
2016-09-01    a
2016-09-02    b
2016-09-03    c
dtype: object
"""
type(t1.index)
#pandas.tseries.index.DatetimeIndex

#
#PeriodIndex
t2 = pd.Series(list(‘def‘), [pd.Period(‘2016-09‘), pd.Period(‘2016-10‘), pd.Period(‘2016-11‘)])
t2
"""
2016-09    d
2016-10    e
2016-11    f
Freq: M, dtype: object
"""
type(t2.index)
# pandas.tseries.period.PeriodIndex

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

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

pd.to_datetime(‘4.7.12‘, dayfirst=True)
#output: Timestamp(‘2012-07-04 00:00:00‘)

##Timedeltas
pd.Timestamp(‘9/3/2016‘)-pd.Timestamp(‘9/1/2016‘)
# Timedelta(‘2 days 00:00:00‘)
pd.Timestamp(‘9/2/2016 8:10AM‘) + pd.Timedelta(‘12D 3H‘)
# Timestamp(‘2016-09-14 11:10:00‘)

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

df.index.weekday_name
"""
array([‘Sunday‘, ‘Sunday‘, ‘Sunday‘, ‘Sunday‘, ‘Sunday‘, ‘Sunday‘,
       ‘Sunday‘, ‘Sunday‘, ‘Sunday‘], dtype=object)
"""
df.diff()

df.resample(‘M‘).mean()

df[‘2017‘]

df[‘2016-12‘]

df[‘2016-12‘:]