[Python+Altair (Vega-lite)]: Streamgraph from excel-like Pandas' Dataframe

I have a Pandas' Dataframe I would like to visualize using Altair, but it looks like the library syntax is yet above my skills:

                 Time     WL1       WL2      WL3      WL4
0 2017-04-05 09:15:00  103448.0  100776.0  71581.1  81118.2
1 2017-04-05 09:30:00  114730.0  113206.0  68600.1  78322.2
2 2017-04-05 09:45:00  117252.0  114189.0  68208.5  77261.0
3 2017-04-05 10:00:00  115569.0  111300.0  67454.7  75347.4
4 2017-04-05 10:15:00  117504.0  114956.0  68709.9  76345.1  
5 2017-04-05 10:30:00  116123.0  112637.0  66210.9  74544.4
6 2017-04-05 10:45:00  111050.0  106342.0  61699.3  70165.2
7 2017-04-05 11:00:00  111221.0  108409.0  66124.6  74842.1
8 2017-04-05 11:15:00  111604.0  106853.0  66950.5  75782.9
9 2017-04-05 11:30:00  113026.0  108071.0  68222.2  77848.0

I cannot find the syntax to create a Streamgraph like this, taken from Altair's tutorial (06-AreaCharts):

I can visualize the single lines but it is not the best representation for my data:

LayeredChart(data,
    layers=[
        Chart().mark_line().encode(
            x='Time:T',
            y='WL1:Q',
        ),

        Chart().mark_line().encode(
            x='Time:T',
            y='WL2:Q',
        ),
        Chart().mark_line().encode(
            x='Time:T',
            y='WL3:Q',
        ),

        Chart().mark_line().encode(
            x='Time:T',
            y='WL4:Q',
        ),
    ]
)

Is there a way to create a Streamgraph without going through a LayeredChart, directly from the multiple-column dataframe?

This page contains the code for the stream graph.

By the way, you may want to compare the code with the vega-lite json spec to allow you to better understand the Altair syntax.