从 Pandas 中的 DatetimeIndex 制作月份和年份列表
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Making a list of months and years from DatetimeIndex in Pandas
提问by jenryb
I have a dataframe of information. I set the index to be the received date and time. Now I want a list
我有一个信息数据框。我将索引设置为接收的日期和时间。现在我想要一个清单
I set the df index doing this:
我设置 df 索引这样做：
df.index = pd.to_datetime(df.index, format='%m/%d/%Y %H:%M')
which gives me this:
这给了我这个：
print df.index
<class 'pandas.tseries.index.DatetimeIndex'>
[2014-07-28 09:42:08, ..., 2015-07-28 09:06:12]
Length: 15177, Freq: None, Timezone: None
I want a list of the month and years in order to use them to plot, like so: ["Jan 2015", "Feb 2015", "Mar 2015", "Apr 2015", "May 2015", "June 2015", "Jul 2015", "Aug 2014", "Sep 2014", "Oct 2014", "Nov 2014", "Dec 2014"]
我想要一个月份和年份的列表，以便使用它们进行绘图，如下所示： ["Jan 2015", "Feb 2015", "Mar 2015", "Apr 2015", "May 2015", "June 2015", "Jul 2015", "Aug 2014", "Sep 2014", "Oct 2014", "Nov 2014", "Dec 2014"]
How can I do this? I've looked into something like this:
我怎样才能做到这一点？我研究过这样的事情：
df = [datetime.datetime.strftime(n,'%b-%Y') for n in pd.DataFrame(df).resample('M').index]
But this gives me the error DataError: No numeric types to aggregate.
但这给了我错误DataError: No numeric types to aggregate。
回答by EdChum
Original answer
原答案
The following should work: convert your datetimeindex to a series, so you can call applyand use strftimeto return an array of strings:
以下应该有效：将您的 datetimeindex 转换为series，以便您可以调用apply和使用strftime来返回一个字符串数组：
In [27]:
import datetime as dt
import pandas as pd
df = pd.DataFrame(index=pd.date_range(start = dt.datetime(2014,1,1), end = dt.datetime.now(), freq='M'))
df.index.to_series().apply(lambda x: dt.datetime.strftime(x, '%b %Y'))
Out[27]:
2014-01-31 Jan 2014
2014-02-28 Feb 2014
2014-03-31 Mar 2014
2014-04-30 Apr 2014
2014-05-31 May 2014
2014-06-30 Jun 2014
2014-07-31 Jul 2014
2014-08-31 Aug 2014
2014-09-30 Sep 2014
2014-10-31 Oct 2014
2014-11-30 Nov 2014
2014-12-31 Dec 2014
2015-01-31 Jan 2015
2015-02-28 Feb 2015
2015-03-31 Mar 2015
2015-04-30 Apr 2015
2015-05-31 May 2015
2015-06-30 Jun 2015
Freq: M, dtype: object
If you want a list then just call tolist():
如果你想要一个列表，那么只需调用tolist()：
df.index.to_series().apply(lambda x: dt.datetime.strftime(x, '%b %Y')).tolist()
Updated answer
更新答案
Actually, looking at this question 2 years later, I realise the above is completely unnecessary. You can just do:
其实，两年后看这个问题，我意识到以上是完全没有必要的。你可以这样做：
In [10]:
df.index.strftime('%Y-%b')
Out[10]:
array(['2014-Jan', '2014-Feb', '2014-Mar', '2014-Apr', '2014-May',
'2014-Jun', '2014-Jul', '2014-Aug', '2014-Sep', '2014-Oct',
'2014-Nov', '2014-Dec', '2015-Jan', '2015-Feb', '2015-Mar',
'2015-Apr', '2015-May', '2015-Jun', '2015-Jul', '2015-Aug',
'2015-Sep', '2015-Oct', '2015-Nov', '2015-Dec', '2016-Jan',
'2016-Feb', '2016-Mar', '2016-Apr', '2016-May', '2016-Jun',
'2016-Jul', '2016-Aug', '2016-Sep', '2016-Oct', '2016-Nov',
'2016-Dec', '2017-Jan', '2017-Feb', '2017-Mar', '2017-Apr',
'2017-May', '2017-Jun', '2017-Jul'],
dtype='<U8')
datetimeindexsupport .dtaccessors directly without converting to a Series
datetimeindex.dt直接支持访问器，无需转换为Series
