Consider the pd.Series s with pd.MultiIndex mux
lol = [list('aabc'), list('xyyz'), [1, 2, 3, 3]]
mux = pd.MultiIndex.from_arrays(lol, names='l1 l2 l3'.split())
s = pd.Series(range(1001, 1005), mux)
s
l1 l2 l3
a x 1 1001
y 2 1002
b y 3 1003
c z 3 1004
dtype: int64
According to the documentation for unstack
fill_value : replace NaN with this value if the unstack produces missing values
And when I unstack
s.unstack()
l3 1 2 3
l1 l2
a x 1001.0 NaN NaN
y NaN 1002.0 NaN
b y NaN NaN 1003.0
c z NaN NaN 1004.0
I do get NaNs.
So I try fill_value=0
s.unstack(fill_value=0)
l3 1 2 3
l1 l2
a x 1001 0 0
y 0 1002 0
b y 0 0 1003
c z 0 0 1004
Sure enough, the NaNs were filled with 0.
However, if I want to unstack more that one level at a time.
s.unstack(['l2', 'l3'], fill_value=0)
l2 x y z
l3 1 2 3 3
l1
a 1001.0 1002.0 NaN NaN
b NaN NaN 1003.0 NaN
c NaN NaN NaN 1004.0
My fill_value is ignored.
Why? And what is a work around?
Try this:
In [3]: s.unstack(['l2', 'l3']).fillna(0)
Out[3]:
l2 x y z
l3 1 2 3 3
l1
a 1001.0 1002.0 0.0 0.0
b 0.0 0.0 1003.0 0.0
c 0.0 0.0 0.0 1004.0
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