Pandas Dataframe Group Year Index By Decade

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

Solution 1:

To get the decade, you can integer-divide the year by 10 and then multiply by 10. For example, if you're starting from

>>>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 DatetimeIndex so it's really easy):

>>>df.groupby(df.index.year).sum().head()A2001   3542002  10742003  17942004  25142005  3234

or you could do the (x//10)*10 trick:

>>>df.groupby((df.index.year//10)*10).sum()A2000   291062010  1007402020  1727402030  2447402040   77424

If you don't have something on which you can use .year, you could still do lambda x: (x.year//10)*10).

Solution 2:

if your Data Frame has Headers say : DataFrame ['Population','Salary','vehicle count']

Make your index as Year: 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

datafame=dataframe.resample('10AS').sum()

Solution 3:

Use the year attribute of index:

df.groupby(df.index.year)

Solution 4:

lets say your date column goes by the name Date, then you can group up

dataframe.set_index('Date').ix[:,0].resample('10AS', how='count')

Note: the ix - here chooses the first column in your dataframe

You get the various offsets: http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases