Python tutorials (Python learning route): Pandas library based on the analysis - Detailed processing time series
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()：
{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()：
{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()：
{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()：
{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()：
{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()：
{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')
"""
复制代码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：<class 'pandas._libs.tslibs.timestamps.Timestamp'>
#dt=pd.to_datetime(2019,1,1) 不支持
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：<class 'pandas._libs.tslibs.timestamps.Timestamp'>
#pd.to_datetime生成自定义时间序列
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)
#时间戳转换为period月时期
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：<class 'pandas._libs.tslibs.period.Period'>
复制代码3, and generates a target conversion period
#定义时期period
per=pd.Period('2019')
print(f'pd.Period()：{per}')
#pd.Period()：2019
per_del=pd.Period('2019')-pd.Period('2018')
print(f'2019和2018间隔{per_del}年')#可以直接+、-整数（代表年）
#2019和2018间隔1年
#时期转换为时间戳
print(per.to_timestamp(how='end'))#2019-12-31 00:00:00
print(per.to_timestamp(how='start'))#2019-01-01 00:00:00
复制代码4, generation interval Timedelta
#生成时间间隔Timedelta
print(pd.Timedelta(days=5, minutes=50, seconds=20, milliseconds=10, microseconds=10, nanoseconds=10))
#5 days 00:50:20.010010
#获取当前时间
now=pd.datetime.now()
#计算当前时间往后50天的日期
dt=now+pd.Timedelta(days=50)
print(f'当前时间是{now}, 50天后时间是{dt}')
#当前时间是2019-06-08 17:59:31.726065, 50天后时间是2019-07-28 17:59:31.726065
#只显示年月日
print(dt.strftime('%Y-%m-%d'))#2019-07-28
复制代码5, frequency conversion and resampling
#asfreq 按季度显示索引值
#'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 按季度统计并显示
print(tsdat_series.resample('Q').sum().to_period('Q'))
"""
2018Q1 190
Freq: Q-DEC, dtype: int64
"""
#groupby 按周进行汇总求平均值
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
"""
复制代码Before there with you tutorials talked about pandas, we do not have a place to go back and review what can be understood, more Python tutorials and Python learning routes will continue to share with you!
Reproduced in: https: //juejin.im/post/5cff0d32e51d45105e02129b