CODEWITHSUNDEEP
pythonpandasdataframegroup-by
alernerdev
alernerdev

Reputation: 2064

how to convert monthly data to quarterly in pandas

I have monthly data. I want to convert it to "periods" of 3 months where q1 starts in January. So in the example below, the first 3 month aggregation would translate into start of q2 (desired format: 1996q2). And the data value that results from mushing together 3 monthly values is a mean (average) of 3 columns. Conceptually, not complicated. Does anyone know how to do it in one swoop? Potentially, I could do a lot of hard work through looping and just hardcode the hell out of it, but I am new to pandas and looking for something more clever than brute force.

1996-04   1996-05 1996-06  1996-07 .....
25          19       37      40

So I am looking for:

1996q2  1996q3   1996q4  1997q1  1997q2 .....
 avg      avg      avg     ...     ...

Upvotes: 28

Views: 26678

Answers (3)

Shravana Kumar Yadav
Shravana Kumar Yadav

Reputation: 1

If you want your quarters start from April month instead of January, then you have to pass slightly different value to the argument 'feq', which is freq='Q-MAR' and it will calculate q1 as (April to June), q2 as (July to Sept), q3 as (Oct to Dec) and q4 (Jan to Mar)

enter image description here

Upvotes: 0

Sayyor Y
Sayyor Y

Reputation: 1314

Just to add to @MaxU's answer above, to convert the resulting PeriodIndex columns back to str and add spaces between year and quarter number (i.e. 1999 Q1, not 1999Q1 ), you can do this:

res = res.columns.to_series().astype(str)

Upvotes: 0

MaxU - stand with Ukraine
MaxU - stand with Ukraine

Reputation: 210852

you can use pd.PeriodIndex(..., freq='Q') in conjunction with groupby(..., axis=1):

In [63]: df
Out[63]:
   1996-04  1996-05  2000-07  2000-08  2010-10  2010-11  2010-12
0        1        2        3        4        1        1        1
1       25       19       37       40        1        2        3
2       10       20       30       40        4        4        5

In [64]: df.groupby(pd.PeriodIndex(df.columns, freq='Q'), axis=1).mean()
Out[64]:
   1996Q2  2000Q3    2010Q4
0     1.5     3.5  1.000000
1    22.0    38.5  2.000000
2    15.0    35.0  4.333333

UPDATE: to get columns in a resulting DF as strings intead of period dtype:

In [66]: res = (df.groupby(pd.PeriodIndex(df.columns, freq='Q'), axis=1)
                  .mean()
                  .rename(columns=lambda c: str(c).lower()))

In [67]: res
Out[67]:
   1996q2  2000q3    2010q4
0     1.5     3.5  1.000000
1    22.0    38.5  2.000000
2    15.0    35.0  4.333333

In [68]: res.columns.dtype
Out[68]: dtype('O')

Upvotes: 53

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