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Python中,我有一个使用pandas生成(或从CSV文件中读取)的日期数组,我想在每个日期添加一年.我可以使用pandas但不使用numpy.我究竟做错了什么？或者它是熊猫或numpy中的错误？
谢谢！
import numpy as np
import pandas as pd
from pandas.tseries.offsets import DateOffset
# Generate range of dates using pandas.
dates = pd.date_range('1980-01-01','2015-01-01')
# Add one year using pandas.
dates2 = dates + DateOffset(years=1)
# Convert result to numpy. THIS WORKS!
dates2_np = dates2.values
# Convert original dates to numpy array.
dates_np = dates.values
# Add one year using numpy. THIS FAILS!
dates3 = dates_np + np.timedelta64(1,'Y')
# TypeError: Cannot get a common Metadata divisor for NumPy datetime Metadata [ns] and [Y] because they have incompatible nonlinear base time units
解决方法
将np.timedelta64(1,’Y’)添加到dtype datetime64 [ns]的数组中不起作用,因为一年不对应于固定的纳秒数.有时一年是365天,有时是366天,有时甚至还有一个额外的闰秒. (注意额外的闰秒,例如2015-06-30 23:59:60发生的闰秒,不能表示为NumPy datetime64s.)
我知道在NumPy datetime64 [ns]数组中添加一年的最简单方法是将其分解为组成部分,例如年,月和日,对整数数组进行计算,然后重构datetime64数组：
def year(dates):
"Return an array of the years given an array of datetime64s"
return dates.astype('M8[Y]').astype('i8') + 1970
def month(dates):
"Return an array of the months given an array of datetime64s"
return dates.astype('M8[M]').astype('i8') % 12 + 1
def day(dates):
"Return an array of the days of the month given an array of datetime64s"
return (dates - dates.astype('M8[M]')) / np.timedelta64(1,'D') + 1
def combine64(years,months=1,days=1,weeks=None,hours=None,minutes=None,seconds=None,milliseconds=None,microseconds=None,nanoseconds=None):
years = np.asarray(years) - 1970
months = np.asarray(months) - 1
days = np.asarray(days) - 1
types = ('<M8[Y]','<m8[M]','<m8[D]','<m8[W]','<m8[h]','<m8[m]','<m8[s]','<m8[ms]','<m8[us]','<m8[ns]')
vals = (years,months,days,weeks,hours,minutes,seconds,milliseconds,microseconds,nanoseconds)
return sum(np.asarray(v,dtype=t) for t,v in zip(types,vals)
if v is not None)
# break the datetime64 array into constituent parts
years,days = [f(dates_np) for f in (year,month,day)]
# recompose the datetime64 array after adding 1 to the years
dates3 = combine64(years+1,days)
产量
In [185]: dates3 Out[185]: array(['1981-01-01','1981-01-02','1981-01-03',...,'2015-12-30','2015-12-31','2016-01-01'],dtype='datetime64[D]')
尽管看起来代码太多,但它实际上比添加1年的DateOffset更快：
In [206]: %timeit dates + DateOffset(years=1) 1 loops,best of 3: 285 ms per loop In [207]: %%timeit .....: years,day)] .....: combine64(years+1,days) .....: 100 loops,best of 3: 2.65 ms per loop
当然,pd.tseries.offsets提供了一整套补偿,在使用NumPy datetime64s时没有简单的副本.