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

Matplotlib template for Precipitation and Flow over time - Hidrograms and Hyetographs

This code works in python3, to plot a hyetograph - precipitation event (sP), together with a hydrograph (sQ), both in the same time series (t).

#-*-coding:utf-8-*-;
import matplotlib.pyplot as plt
from matplotlib import gridspec
import numpy as np

def plotHH(t,sP,sQ):
    fig = plt.figure()
    gs = gridspec.GridSpec(2, 1, height_ratios=[1, 2])
    
    # HYDROGRAM CHART
    ax = plt.subplot(gs[1])
    ax.plot(t,sQ)
    ax.set_ylabel(u'Q(m³/s)', color='b')
    ax.set_xlabel('Time (min.)')
    ax.tick_params(axis='y', colors='b')
    ax.xaxis.grid(b=True, which='major', color='.7', linestyle='-')
    ax.yaxis.grid(b=True, which='major', color='.7', linestyle='-')
    ax.set_xlim(min(t), max(t))
    ax.set_ylim(0, max(sQ)*1.2)

    # PRECIPITATION/HYETOGRAPH CHART
    ax2 = plt.subplot(gs[0])
    ax2.bar(t, sP, 1, color='#b0c4de')
    ax2.xaxis.grid(b=True, which='major', color='.7', linestyle='-')
    ax2.yaxis.grid(b=True, which='major', color='0.7', linestyle='-')
    ax2.set_ylabel('P(mm)')
    ax2.set_xlim(min(t), max(t))
    plt.setp(ax2.get_xticklabels(), visible=False)
    
    plt.tight_layout()
    ax2.invert_yaxis()
    plt.gcf().subplots_adjust(bottom=0.15)
    plt.show()
    #plt.savefig(filename,format='pdf')
    plt.close(fig)