如何将 Pandas 索引转换为月份名称
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How to convert Pandas Index to month name
提问by I am not George
I am trying to change my Date index in the following time series to the month name.
我正在尝试将以下时间序列中的日期索引更改为月份名称。
website = dfFinal.groupby(['Date','Website'])
websiteGroup = website['Visits'].aggregate(np.sum).unstack()
Website A B C
Date
2015-01-01 18185 805769 NaN
2015-02-01 73236 944458 NaN
2015-03-01 101737 1003966 NaN
2015-04-01 101018 861229 NaN
2015-05-01 77724 845223 NaN
2015-06-01 111503 966043 NaN
2015-07-01 115413 937184 NaN
2015-08-01 115215 890457 1649
for example I want it to look like this:
例如，我希望它看起来像这样：
Website A B C
Date
January 18185 805769 NaN
February 73236 944458 NaN
March 101737 1003966 NaN
April 101018 861229 NaN
May 77724 845223 NaN
June 111503 966043 NaN
July 115413 937184 NaN
August 115215 890457 1649
I want to be able to this so my plot ticks will be the month name instead of the datetime.
我希望能够做到这一点，所以我的情节刻度将是月份名称而不是日期时间。
Thanks
谢谢
edit//
编辑//
same scenario but solution does not work on it:
相同的场景，但解决方案对它不起作用：
systemType = dfFinal.groupby(['Date','Website','Type'])
systemGroup = systemType['Visits'].aggregate(np.sum)
systemGroup = systemGroup.groupby(level=[0,1]).apply(lambda x: 100*x/float(x.sum())).unstack()
Type Other Windows Mobile Windows PC
Date Website
2015-01-01 A 0.637888 0.005499 48.814957
B 0.686549 0.016506 54.176073
2015-02-01 A 0.742804 0.020482 49.811568
B 0.651802 0.014506 57.014288
2015-03-01 A 0.668390 0.014744 50.087972
B 0.573924 0.015937 59.906013
2015-04-01 A 0.662258 0.015839 49.310024
B 0.583933 0.013469 59.490449
2015-05-01 A 0.666461 0.020586 48.522979
B 0.577954 0.017983 58.838200
systemGroup = systemGroup.rename(index=lambda x: x.strftime('%B'))
gives me an error
给我一个错误
AttributeError: 'str' object has no attribute 'strftime'
采纳答案by TomAugspurger
If you have a DatetimeIndex, you can use
如果您有 DatetimeIndex，则可以使用
websiteGroup.rename(index=lambda x: x.strftime('%B'))
.renamecan take a function, and we'll use the '%B'code for the full month name.
.rename可以接受一个函数，我们将使用'%B'完整月份名称的代码。
回答by jezrael
websiteGroup.index = websiteGroup.index.strftime('%B')
print (websiteGroup)
A B C
January 18185 805769 NaN
February 73236 944458 NaN
March 101737 1003966 NaN
April 101018 861229 NaN
May 77724 845223 NaN
June 111503 966043 NaN
July 115413 937184 NaN
August 115215 890457 1649.0
df = websiteGroup.set_index(websiteGroup.index.strftime('%b'))
print (df)
A B C
Jan 18185 805769 NaN
Feb 73236 944458 NaN
Mar 101737 1003966 NaN
Apr 101018 861229 NaN
May 77724 845223 NaN
Jun 111503 966043 NaN
Jul 115413 937184 NaN
Aug 115215 890457 1649.0
Also for assign new values in index is possible use set_index:
也可以用于在索引中分配新值set_index：
df = websiteGroup.set_index(websiteGroup.index.strftime('%B'))
print (df)
A B C
January 18185 805769 NaN
February 73236 944458 NaN
March 101737 1003966 NaN
April 101018 861229 NaN
May 77724 845223 NaN
June 111503 966043 NaN
July 115413 937184 NaN
August 115215 890457 1649.0
EDIT:
编辑：
For versions pandas 0.23.0is possible use DatetimeIndex.month_name:
对于版本pandas 0.23.0可以使用DatetimeIndex.month_name：
websiteGroup.index = websiteGroup.index.month_name()
print (websiteGroup)
A B C
Website
January 18185 805769 NaN
February 73236 944458 NaN
March 101737 1003966 NaN
April 101018 861229 NaN
May 77724 845223 NaN
June 111503 966043 NaN
July 115413 937184 NaN
August 115215 890457 1649.0
回答by onlyphantom
Pandas version 0.23.0 and onwards (as of this writing, it's 0.24.2) provide a built-in method: .month_name. From its official documentation:
Pandas 0.23.0 及更高版本（在撰写本文时，它是 0.24.2）提供了一个内置方法：.month_name. 从它的官方文档：
pandas.DatetimeIndex.month_namereturns the month names of the DateTimeIndex with specified locale.
pandas.DatetimeIndex.month_name返回具有指定语言环境的 DateTimeIndex 的月份名称。
Consider the following DataFrame:
考虑以下数据帧：
aapl.tail()
# returns:
Attributes High Low Open Close Volume Adj Close
2019-03-27 189.76 186.55 188.75 188.47 29848400.0 188.47
2019-03-28 189.56 187.53 188.95 188.72 20780400.0 188.72
2019-03-29 190.08 188.54 189.83 189.95 23564000.0 189.95
2019-03-30 190.08 188.54 189.83 189.95 23564000.0 189.95
2019-03-31 190.08 188.54 189.83 189.95 23564000.0 189.95
The DataFrame has a DateTimeIndex, so we can apply .month_nameon the index like so:
DataFrame 有一个 DateTimeIndex，所以我们可以.month_name像这样应用索引：
aapl.index = aapl.index.month_name()
aapl.tail()
# returns:
Attributes High Low Open Close Volume Adj Close
March 189.76 186.55 188.75 188.47 29848400.0 188.47
March 189.56 187.53 188.95 188.72 20780400.0 188.72
March 190.08 188.54 189.83 189.95 23564000.0 189.95
March 190.08 188.54 189.83 189.95 23564000.0 189.95
March 190.08 188.54 189.83 189.95 23564000.0 189.95
Prior to 0.23.0 you would use .month()and other answers referencing the use of .strftime('%B')is the way to go.
在 0.23.0 之前，您将使用.month()和其他参考使用的答案.strftime('%B')是要走的路。
回答by multivac
You can parse each date string with datetime.strptimeand print the month name with datetime.strftime('%B'):
您可以使用以下命令解析每个日期字符串datetime.strptime并打印月份名称datetime.strftime('%B')：
>>> d = datetime.datetime.strptime('2015-01-01', '%Y-%m-%d')
>>> d.strftime('%B')
'January'
