Interpolate missing values in pandas DataFrame

If we have a dataframe with dates and flows - with missing values, as example below:

        0
2019-01-31 50.208308
2019-02-28 50.623457
2019-03-31 56.203933
2019-04-30 NaN
2019-05-31 NaN
2019-06-30 117.727655
2019-07-31 62.273259
2019-08-31 49.054898
2019-09-30 55.612575
2019-10-31 54.187409

We can use the function pandas interpolate, and interpolate the data with different methods

dfIn.interpolate() - will fill noData with linear interpolation;
dfIn.interpolate(method='polynomial', order=3) - will fill noData with 3rd degree polinomial interpolation;

Result:
                linear  polinomial    original
2019-01-31   50.208308   50.208308   50.208308
2019-02-28   50.623457   50.623457   50.623457
2019-03-31   56.203933   56.203933   56.203933
2019-04-30   76.711840   89.513986         NaN
2019-05-31   97.219748  124.233259         NaN
2019-06-30  117.727655  117.727655  117.727655
2019-07-31   62.273259   62.273259   62.273259
2019-08-31   49.054898   49.054898   49.054898
2019-09-30   55.612575   55.612575   55.612575
2019-10-31   54.187409   54.187409   54.187409

Simple code to generate synthetic time series data in Python / Pandas

Here is a simple code to generate synthetic time series.

import numpy as np
import pandas as pd

med = 15.5
dp = 8.2
sDays = np.arange('2001-01', '2016-12', dtype='datetime64[D]')
nDays = len(sDays)

s1 = np.random.gumbel(loc=med,scale=dp,size=nDays)
s1[s1 < 0] = 0

dfSint = pd.DataFrame({'Q':s1},index=sDays)
dfSint.plot()