pandas 将年份和年份中的日期转换为熊猫中的日期时间索引
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Converting year and day of year into datetime index in pandas
提问by user308827
I have a dataframe:
我有一个数据框：
year doy
2000 49
2000 65
2000 81
2001 97
2001 113
2001 129
2001 145
2001 161
I want to create a datetime index for this dataframe. Here is what I am doing:
我想为这个数据框创建一个日期时间索引。这是我在做什么：
df.index = pandas.DatetimeIndex(df['doy'].apply(lambda x: date(2000, 1, 1)+ relativedelta(days=int(x)-1)))
However, this creates a datetime index which only uses 2000 as year. How can I fix that?
但是，这会创建一个仅使用 2000 作为年份的日期时间索引。我该如何解决？
采纳答案by unutbu
You can use NumPy datetime64/timedelta64 arithmeticto find the desired dates:
您可以使用NumPy datetime64/timedelta64 算法来查找所需的日期：
In [97]: (np.asarray(df['year'], dtype='datetime64[Y]')-1970)+(np.asarray(df['doy'], dtype='timedelta64[D]')-1)
Out[97]:
array(['2000-02-18', '2000-03-05', '2000-03-21', '2001-04-07',
'2001-04-23', '2001-05-09', '2001-05-25', '2001-06-10'], dtype='datetime64[D]')
Since composing dates given various parts of dates (e.g. years, months, days, weeks, hours, etc.) is a common problem, here is a utility function to make it easier:
由于给定日期的各个部分（例如年、月、日、周、小时等）来组合日期是一个常见问题，因此这里有一个实用函数来简化它：
def compose_date(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)
df = pd.DataFrame({'doy': [49, 65, 81, 97, 113, 129, 145, 161],
'year': [2000, 2000, 2000, 2001, 2001, 2001, 2001, 2001]})
df.index = compose_date(df['year'], days=df['doy'])
yields
产量
doy year
2000-02-18 49 2000
2000-03-05 65 2000
2000-03-21 81 2000
2001-04-07 97 2001
2001-04-23 113 2001
2001-05-09 129 2001
2001-05-25 145 2001
2001-06-10 161 2001
回答by Alex
You can use the date specifier %jto extract the day of year. So combine the two columns, shift the year, and convert to datetime!
您可以使用日期说明符%j来提取一年中的哪一天。所以将两列组合起来，移动年份，然后转换为日期时间！
pd.to_datetime(df['year'] * 1000 + df['doy'], format='%Y%j')
returns
回报
0 2000-02-18
1 2000-03-05
2 2000-03-21
3 2001-04-07
4 2001-04-23
5 2001-05-09
6 2001-05-25
7 2001-06-10
dtype: datetime64[ns]
