python - Pandas, Filling between dates with average change between previous rows -

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i think best illustrated examples. lets have dataframe such:

                 295340      299616 2014-11-02   304.904110  157.123288 2014-12-02   597.303413  305.488493 2015-01-02   896.310372  454.614630 2015-02-02  1192.379580  599.588466 2015-02-04  1211.285484         nan 2015-03-02          nan  726.622932

now let's want reindex this, such:

rng = pd.date_range(df.index[0], df.index[-1])  df.reindex(rng)                  295340      299616 2014-11-02   304.904110  157.123288 2014-11-03          nan         nan 2014-11-04          nan         nan 2014-11-05          nan         nan ...  2014-11-29          nan         nan 2014-11-30          nan         nan 2014-12-01          nan         nan 2014-12-02   597.303413  305.488493

now if @ 295340, see difference between values (597.30-304.90) = 292.39.

the amount of days between 2 values 31. average increase 9.43 day.

so want such:

                 295340      299616 2014-11-02   304.904110  157.123288 2014-11-03   314.336345         nan 2014-11-04   323.768581         nan 2014-11-05   333.200816         nan

the way calculated was:

304.904110 + (((597.303413-304.904110) / 31) * n)

where n 1 first row since row 1, 2 after, etc.

i want columns filled way, 299616 same method , such.

any ideas efficient possible? know of ways this, nothing seems efficient , seems there should type of fillna() or works type of finance related problem.

note: columns not spaced out same. each 1 can have numbers anywhere within range of dates, can't assume next number each column @ x date.

you can use dataframe.interpolate "time" method after resample. (it won't give quite numbers gave, because there 30 days between 2 nov , 2 dec, not 31):

>>> dnew = df.resample("1d").interpolate("time") >>> dnew.head(100)                  295340      299616 2014-11-02   304.904110  157.123288 2014-11-03   314.650753  162.068795 [...] 2014-11-28   558.316839  285.706466 2014-11-29   568.063483  290.651972 2014-11-30   577.810126  295.597479 2014-12-01   587.556770  300.542986 2014-12-02   597.303413  305.488493 2014-12-03   606.948799  310.299014 [...] 2014-12-30   867.374215  440.183068 2014-12-31   877.019600  444.993589 2015-01-01   886.664986  449.804109 2015-01-02   896.310372  454.614630 [...] 2015-02-01  1182.828960  594.911891 2015-02-02  1192.379580  599.588466 [...]

the downside here it'll extrapolate using last value @ end:

[...] 2015-01-31  1173.278341  590.235315 2015-02-01  1182.828960  594.911891 2015-02-02  1192.379580  599.588466 2015-02-03  1201.832532  604.125411 2015-02-04  1211.285484  608.662356 2015-02-05  1211.285484  613.199302 2015-02-06  1211.285484  617.736247 [...]

so you'd have decide how want handle that.


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