Pandas: Pivoting and plotting workflow

Disclaimer: I am very new to Pandas.

I am doing numerical simulations and would like to use Pandas for the final data-evaluation. To keep things simple let's assume the following setup:

My simulations take a few input parameters (E.g. max, and size). The simulation then produces a number of observables as functions of time (E.g. f1(t), f2(t)). In the end, the results of three different simulations could look like this:

t1 = np.linspace(0, 2, 15)
t2 = np.linspace(0, 2, 21)
t3 = np.linspace(0, 1.5, 16)
df1 = pd.DataFrame({'max': t1.max(), 'size': t1.size, 't': t1, 'f1': t1**2+0, 'f2': t1**3+0})
df2 = pd.DataFrame({'max': t2.max(), 'size': t2.size, 't': t2, 'f1': t2**2+1, 'f2': t2**3+1})
df3 = pd.DataFrame({'max': t3.max(), 'size': t3.size, 't': t3, 'f1': t3**2+2, 'f2': t3**3+2})

Where max, and size are the parameters to each simulation, t is the time axis, and f1, and f2 are the observables.

Say, as a first task, I would like to plot the values of f1 as a function of t for each set of parameters. After spending some time with the docs I found that the pivot_table function can rearrange my data in the right way.

df = pd.concat([df1, df2, df3])
df_ms = pd.pivot_table(df, index=['t'], values=['f1', 'f2'], columns=['max', 'size'])

Intermediate question: Is this the best way to do this? I know that DataFrame takes an index argument in its constructor. Would it be better to define t as the index at that point? (I couldn't get it working together with pivot_table)

Now we can use the plot method to plot the resulting data.

df_ms['f1'].plot()

The result, however, is unexpected. I understand that some data is missing, as pandas is forced to introduce NaNs when aligning the different t axes.

My question: Why doesn't the green curve show up at all? And why are the blue and red patches aligned? Is there a simple way to skip the NaNs in the plot, along the lines of what you would get by simply calling plt.plot(t, f1) in matplotlib?

I know that it is possible to fill the NaNs by interpolation. For the given case second order splines are quite ideal.

df_ms['f1'].interpolate(method='spline', order=2).plot()

However, I am wondering why this should be necessary for simply plotting the data. Matplotlib's internal linear interpolation would be sufficient...