Python pandas Date

Posted 2020-11-20 卷积

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Pandas主要有4中与时间相关的类型。Timestamp, Period, DatetimeIndex,PeriodIndex.

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

1. 关于时间类型的转换

#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\')

2. 时间间隔

##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\')

3. Dataframe中的时间

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()