When using the Python data analysis, often encounter time and date format conversion processing, analysis and data mining in particular time-related, such as quantitative trading is to look for changes of stock prices from the historical data. Python comes processing time module datetime, NumPy library also provides a corresponding method, as the data analysis Pandas BANK Python environment, but also provides a powerful data processing date, the processing tool is a time series.
1, generating a sequence of dates
Mainly provides pd.data_range () and pd.period_range () two methods, parameters are given start time, end time, and the number of times generated time-frequency (freq = 'M' month, 'D' day, 'W ', weeks,' the Y ') and other.
The two main difference is that pd.date_range () is generated sequence DatetimeIndex date format; pd.period_range () is generated sequence PeriodIndex date format.
The following months by generating time-series sequence and to compare the periphery:
date_rng = pd.date_range('2019-01-01', freq='M', periods=12)
print(f'month date_range()：\n{date_rng}')
date_range()：
DatetimeIndex(['2019-01-31', '2019-02-28', '2019-03-31', '2019-04-30',
'2019-05-31', '2019-06-30', '2019-07-31', '2019-08-31',
'2019-09-30', '2019-10-31', '2019-11-30', '2019-12-31'],
dtype='datetime64[ns]', freq='M')
period_rng = pd.period_range('2019/01/01', freq='M', periods=12)
print(f'month period_range()：\n{period_rng}')
period_range()：
PeriodIndex(['2019-01', '2019-02', '2019-03', '2019-04', '2019-05', '2019-06',
'2019-07', '2019-08', '2019-09', '2019-10', '2019-11', '2019-12'],
dtype='period[M]', freq='M')
date_rng = pd.date_range('2019-01-01', freq='W-SUN', periods=12)
print(f'week date_range()：\n{date_rng}')
week date_range()：
DatetimeIndex(['2019-01-06', '2019-01-13', '2019-01-20', '2019-01-27',
'2019-02-03', '2019-02-10', '2019-02-17', '2019-02-24',
'2019-03-03', '2019-03-10', '2019-03-17', '2019-03-24'],
dtype='datetime64[ns]', freq='W-SUN')
period_rng=pd.period_range('2019-01-01',freq='W-SUN',periods=12)
print(f'week period_range()：\n{period_rng}')
week period_range()：
PeriodIndex(['2018-12-31/2019-01-06', '2019-01-07/2019-01-13',
'2019-01-14/2019-01-20', '2019-01-21/2019-01-27',
'2019-01-28/2019-02-03', '2019-02-04/2019-02-10',
'2019-02-11/2019-02-17', '2019-02-18/2019-02-24',
'2019-02-25/2019-03-03', '2019-03-04/2019-03-10',
'2019-03-11/2019-03-17', '2019-03-18/2019-03-24'],
dtype='period[W-SUN]', freq='W-SUN')
date_rng = pd.date_range('2019-01-01 00:00:00', freq='H', periods=12)
print(f'hour date_range()：\n{date_rng}')
hour date_range()：
DatetimeIndex(['2019-01-01 00:00:00', '2019-01-01 01:00:00',
'2019-01-01 02:00:00', '2019-01-01 03:00:00',
'2019-01-01 04:00:00', '2019-01-01 05:00:00',
'2019-01-01 06:00:00', '2019-01-01 07:00:00',
'2019-01-01 08:00:00', '2019-01-01 09:00:00',
'2019-01-01 10:00:00', '2019-01-01 11:00:00'],
dtype='datetime64[ns]', freq='H')
period_rng=pd.period_range('2019-01-01 00:00:00',freq='H',periods=12)
print(f'hour period_range()：\n{period_rng}')
hour period_range()：
PeriodIndex(['2019-01-01 00:00', '2019-01-01 01:00', '2019-01-01 02:00',
'2019-01-01 03:00', '2019-01-01 04:00', '2019-01-01 05:00',
'2019-01-01 06:00', '2019-01-01 07:00', '2019-01-01 08:00',
'2019-01-01 09:00', '2019-01-01 10:00', '2019-01-01 11:00'],
dtype='period[H]', freq='H')
2, and generates a conversion Timestamp object
Creating a timestamp Timestamp object has pd.Timestamp () method and pd.to_datetime () method. As follows:
ts=pd.Timestamp(2019,1,1)
print(f'pd.Timestamp()-1：{ts}')
#pd.Timestamp()-1：2019-01-01 00:00:00
ts=pd.Timestamp(dt(2019,1,1,hour=0,minute=1,second=1))
print(f'pd.Timestamp()-2：{ts}')
#pd.Timestamp()-2：2019-01-01 00:01:01
ts=pd.Timestamp(2019-1-1 0:1:1)
print(f'pd.Timestamp()-3：{ts}')
#pd.Timestamp()-3：2019-01-01 00:01:01
print(f'pd.Timestamp()-type：{type(ts)}')
#pd.Timestamp()-type：
# Dt = pd.to_datetime (2019,1,1) is not supported
dt=pd.to_datetime(dt(2019,1,1,hour=0,minute=1,second=1))
print(f'pd.to_datetime()-1：{dt}')
#pd.to_datetime()-1：2019-01-01 00:01:01
dt=pd.to_datetime(2019-1-1 0:1:1)
print(f'pd.to_datetime()-2：{dt}')
#pd.to_datetime()-2：2019-01-01 00:01:01
print(f'pd.to_datetime()-type：{type(dt)}')
#pd.to_datetime()-type：
# Pd.to_datetime generate custom time series
dtlist=pd.to_datetime([2019-1-1 0:1:1, 2019-3-1 0:1:1])
print(f'pd.to_datetime()-list：{dtlist}')
#pd.to_datetime()-list：DatetimeIndex(['2019-01-01 00:01:01', '2019-03-01 00:01:01'], dtype='datetime64[ns]', freq=None)
# Timestamp converted to period-month period
pr = ts.to_period('M')
print(f'ts.to_period()：{pr}')
#ts.to_period()：2019-01
print(f'pd.to_period()-type：{type(pr)}')
#pd.to_period()-type：
3, and generates a target conversion period
#Define time period
per=pd.Period('2019')
print(f'pd.Period()：{per}')
#pd.Period()：2019
per_del=pd.Period('2019')-pd.Period('2018')
print (f'2019 and 2018 of spacer {per_del} ') can be directly # + - integer (on behalf)
# 2019 and 2018 gap year
# Time to a timestamp
print(per.to_timestamp(how='end'))#2019-12-31 00:00:00
print(per.to_timestamp(how='start'))#2019-01-01 00:00:00
4, generation interval Timedelta
# Generation interval Timedelta
print(pd.Timedelta(days=5, minutes=50, seconds=20, milliseconds=10, microseconds=10, nanoseconds=10))
#5 days 00:50:20.010010
# Get the current time
now=pd.datetime.now()
# Calculate the current time 50 days later date
dt=now+pd.Timedelta(days=50)
print (f 'current time is {now}, 50 days after the time {dt}')
# The current time is 2019-06-0817: 59: 31.726065, 50 days after the time 2019-07-2817: 59: 31.726065
# Only display date
print(dt.strftime('%Y-%m-%d'))#2019-07-28
5, frequency conversion and resampling
#asfreq display the index value on a quarterly basis
#'DatetimeIndex' object has no attribute 'asfreq'
date=pd.date_range('1/1/2018', periods=20, freq='D')
tsdat_series=pd.Series(range(20),index=date)
tsp_series=tsdat_series.to_period('D')
print(tsp_series.index.asfreq('Q'))
date=pd.period_range('1/1/2018', periods=20, freq='D')
tsper_series=pd.Series(range(20),index=date)
print(tsper_series.index.asfreq('Q'))
PeriodIndex(['2018Q1', '2018Q1', '2018Q1', '2018Q1', '2018Q1', '2018Q1',
'2018Q1', '2018Q1', '2018Q1', '2018Q1', '2018Q1', '2018Q1',
'2018Q1', '2018Q1', '2018Q1', '2018Q1', '2018Q1', '2018Q1',
'2018Q1', '2018Q1'],
dtype='period[Q-DEC]', freq='Q-DEC')
#resample quarterly statistics and display
print(tsdat_series.resample('Q').sum().to_period('Q'))
2018Q1 190
Freq: Q-DEC, dtype: int64
#groupby summarize averaging weekly
print(tsdat_series.groupby(lambda x:x.weekday).mean())
0 7.0
1 8.0
2 9.0
3 10.0
4 11.0
5 12.0
6 9.5
dtype: float64
Reproduced in: https: //juejin.im/post/5cfe0230f265da1b8d161155