How to construct time series with frequencies different from the original?

In R, you can use the lubridate package, which will make it much easier to manipulate dates together with dplyr .

See an example:

library(lubridate)

dados <- data.frame(
  data = seq(dmy('01/01/1900'),dmy('31/12/2010'), by = '1 day'),
  valor = 1:40542
  )

Calculating the measurements for year :

> library(dplyr)
> dados %>% 
+   group_by(year(data)) %>% 
+   summarise(media = mean(valor), minimo = min(valor), maximo = max(valor))
Source: local data frame [111 x 4]

   year(data)  media minimo maximo
1        1900  183.0      1    365
2        1901  548.0    366    730
3        1902  913.0    731   1095
4        1903 1278.0   1096   1460
5        1904 1643.5   1461   1826
6        1905 2009.0   1827   2191
7        1906 2374.0   2192   2556
8        1907 2739.0   2557   2921
9        1908 3104.5   2922   3287
10       1909 3470.0   3288   3652
..        ...    ...    ...    ...

Calculating by month of the year :

> dados %>% group_by(year(data), month(data)) %>% 
+   summarise(media = mean(valor), min = min(valor), maximo = max(valor))
Source: local data frame [1,332 x 5]
Groups: year(data)

   year(data) month(data) media min maximo
1        1900           1  16.0   1     31
2        1900           2  45.5  32     59
3        1900           3  75.0  60     90
4        1900           4 105.5  91    120
5        1900           5 136.0 121    151
6        1900           6 166.5 152    181
7        1900           7 197.0 182    212
8        1900           8 228.0 213    243
9        1900           9 258.5 244    273
10       1900          10 289.0 274    304
..        ...         ...   ... ...    ...

See all the elements of a date that you can extract:

Inthislinkyouhaveadetailedexplanationoflubridate: link

Or if you prefer the data.table package and a little regex:)

library(data.table)
library(lubridate)
library(stringr)

dTbl = data.table(data=seq(dmy('01/01/1900'),
                           dmy('31/12/2010'),
                           by='1 day'),
                  valor=1:40542)

dTbl[, year := str_extract(data, perl('^[0-9]+(?=-)'))]
dTbl[, month := str_extract(data, perl('(?<=-)[0-9]+(?=-)'))]

dTbl[, .(median=median(as.numeric(valor)),
         mean=mean(valor),
         min=min(valor),
         max=max(valor)), by=year]

dTbl[, .(median=median(as.numeric(valor)),
         mean=mean(valor),
         min=min(valor),
         max=max(valor)), by=.(year, month)]

In Python you can use the "Pandas" package for manipulating dataframes.

In this case, I suggest grouping by year and then picking up the information you need.

For more examples and reference, see the link: link