pandas 大熊猫数据框按十年分组年份索引
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pandas dataframe group year index by decade
提问by wiswit
suppose I have a dataframe with index as monthy timestep, I know I can use dataframe.groupby(lambda x:x.year)to group monthly data into yearly and apply other operations. Is there some way I could quick group them, let's say by decade?
假设我有一个索引作为dataframe.groupby(lambda x:x.year)每月时间步长的数据框，我知道我可以使用将每月数据分组为每年并应用其他操作。有什么方法可以让我快速将它们分组，让我们说十年？
thanks for any hints.
感谢您的任何提示。
回答by DSM
To get the decade, you can integer-divide the year by 10 and then multiply by 10. For example, if you're starting from
要获得十年，您可以将年份除以 10，然后乘以 10。例如，如果您从
>>> dates = pd.date_range('1/1/2001', periods=500, freq="M")
>>> df = pd.DataFrame({"A": 5*np.arange(len(dates))+2}, index=dates)
>>> df.head()
A
2001-01-31 2
2001-02-28 7
2001-03-31 12
2001-04-30 17
2001-05-31 22
You can group by year, as usual (here we have a DatetimeIndexso it's really easy):
您可以像往常一样按年份分组（这里我们有一个，DatetimeIndex所以这真的很容易）：
>>> df.groupby(df.index.year).sum().head()
A
2001 354
2002 1074
2003 1794
2004 2514
2005 3234
or you could do the (x//10)*10trick:
或者你可以这样做(x//10)*10：
>>> df.groupby((df.index.year//10)*10).sum()
A
2000 29106
2010 100740
2020 172740
2030 244740
2040 77424
If you don't have something on which you can use .year, you could still do lambda x: (x.year//10)*10).
如果您没有可以使用的东西.year，您仍然可以使用lambda x: (x.year//10)*10)。
回答by waitingkuo
Use the year attribute of index:
使用索引的年份属性：
df.groupby(df.index.year)
回答by Shankar ARUL - jupyterdata.com
lets say your date column goes by the name Date, then you can group up
假设您的日期列按名称排列Date，然后您可以分组
dataframe.set_index('Date').ix[:,0].resample('10AS', how='count')
dataframe.set_index('Date').ix[:,0].resample('10AS', how='count')
Note: the ix- here chooses the first column in your dataframe
注意：ix- 此处选择数据框中的第一列
You get the various offsets: http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases
你得到各种偏移量：http: //pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases
回答by Shiva_Achari
if your Data Frame has Headers say : DataFrame ['Population','Salary','vehicle count']
如果您的数据框有标题说： DataFrame ['Population','Salary','vehicle count']
Make your index as Year: DataFrame=DataFrame.set_index('Year')
将您的索引设为年份： DataFrame=DataFrame.set_index('Year')
use below code to resample data in decade of 10 years and also gives you some of all other columns within that dacade
使用下面的代码在 10 年的十年内重新采样数据，并为您提供该 dacade 中的所有其他列
datafame=dataframe.resample('10AS').sum()
datafame=dataframe.resample('10AS').sum()
