Filling currency missing data in Pandas Dataframe

Question

I am extracting currency data from Quandl:

import Quandl
df = Quandl.get("CURRFX/USDEUR", collapse="daily") 
df = df.tail(10).reset_index()

Running the code above, you can see that currency data is of course not available during the weekends.

The dataframe doesn't show the missing days rows.

Any idea on how can I add the missing days and filling them with the average of the previous week?

UPDATE:

the code I have is:

 exchange = Quandl.get("CURRFX/USDEUR", trim_start="2016-02-01",trim_end="2016-02-28")
 exchange = exchange.drop(['High (est)','Low (est)'], axis = 1) 
 exchange = exchange.reindex(pd.date_range(start=exchange.index[0], end=exchange.index[-1])).ffill().reset_index()
 exchange = exchange.rename(columns={'index':'Day','Rate':'exchange'})

Of course, imagine I run it monday morning, I still dont have data for the whole date so the privious sunday and saturday stay empty...

How can I fill them out?

Answer 1

IIUC then you can reindex and pass a new DatetimeIndex generated from date_range and call ffill:

In [115]:
df.reindex(pd.date_range(start=df.index[0], end=df.index[-1])).ffill()

Out[115]:
                Rate  High (est)  Low (est)
1999-09-06  0.941019     0.95269    0.92949
1999-09-07  0.945500     0.95636    0.93476
1999-09-08  0.944376     0.95588    0.93301
1999-09-09  0.943697     0.95412    0.93339
1999-09-10  0.948008     0.00000    0.00000
1999-09-11  0.948008     0.00000    0.00000
1999-09-12  0.948008     0.00000    0.00000
1999-09-13  0.959838     0.97143    0.94838
1999-09-14  0.964873     0.97623    0.95365
1999-09-15  0.965378     0.97708    0.95382
1999-09-16  0.963142     0.97393    0.95248
1999-09-17  0.961971     0.00000    0.00000
1999-09-18  0.961971     0.00000    0.00000
1999-09-19  0.961971     0.00000    0.00000
1999-09-20  0.960917     0.00000    0.00000
1999-09-21  0.959907     0.97166    0.94829
1999-09-22  0.954640     0.96633    0.94309
1999-09-23  0.955403     0.96649    0.94444
1999-09-24  0.955754     0.00000    0.00000
1999-09-25  0.955754     0.00000    0.00000
1999-09-26  0.955754     0.00000    0.00000
1999-09-27  0.956281     0.96693    0.94575
1999-09-28  0.955362     0.00000    0.00000
1999-09-29  0.949949     0.96176    0.93829
1999-09-30  0.942264     0.00000    0.00000
1999-10-01  0.934798     0.00000    0.00000
1999-10-02  0.934798     0.00000    0.00000
1999-10-03  0.934798     0.00000    0.00000
1999-10-04  0.932772     0.00000    0.00000
1999-10-05  0.934479     0.94584    0.92326
...              ...         ...        ...
2016-01-17  0.917878     0.00000    0.00000
2016-01-18  0.917451     0.92788    0.90714
2016-01-19  0.918046     0.92896    0.90726
2016-01-20  0.916549     0.92688    0.90634
2016-01-21  0.918055     0.92901    0.90723
2016-01-22  0.921764     0.00000    0.00000
2016-01-23  0.921764     0.00000    0.00000
2016-01-24  0.921764     0.00000    0.00000
2016-01-25  0.923926     0.00000    0.00000
2016-01-26  0.922914     0.93427    0.91170
2016-01-27  0.920722     0.93114    0.91042
2016-01-28  0.917792     0.92875    0.90696
2016-01-29  0.917405     0.00000    0.00000
2016-01-30  0.917405     0.00000    0.00000
2016-01-31  0.917405     0.00000    0.00000
2016-02-01  0.919592     0.00000    0.00000
2016-02-02  0.918263     0.92859    0.90805
2016-02-03  0.913820     0.92514    0.90264
2016-02-04  0.903148     0.91308    0.89332
2016-02-05  0.895662     0.90574    0.88569
2016-02-06  0.895662     0.90574    0.88569
2016-02-07  0.895662     0.90574    0.88569
2016-02-08  0.896030     0.00000    0.00000
2016-02-09  0.892736     0.90329    0.88231
2016-02-10  0.888489     0.89846    0.87863
2016-02-11  0.885288     0.89508    0.87560
2016-02-12  0.885464     0.00000    0.00000
2016-02-13  0.885464     0.00000    0.00000
2016-02-14  0.885464     0.00000    0.00000
2016-02-15  0.890435     0.00000    0.00000

[6007 rows x 3 columns]