Assigning values to Pandas Multiindex DataFrame by index level

Question

I have a Pandas multiindex dataframe and I need to assign values to one of the columns from a series. The series shares its index with the first level of the index of the dataframe.

import pandas as pd
import numpy as np
idx0 = np.array(['bar', 'bar', 'bar', 'baz', 'foo', 'foo'])
idx1 = np.array(['one', 'two', 'three', 'one', 'one', 'two'])
df = pd.DataFrame(index = [idx0, idx1], columns = ['A', 'B'])
s = pd.Series([True, False, True],index = np.unique(idx0))
print df
print s

out:

             A    B
bar one    NaN  NaN
    two    NaN  NaN
    three  NaN  NaN
baz one    NaN  NaN
foo one    NaN  NaN
    two    NaN  NaN

bar     True
baz    False
foo     True
dtype: bool

These don't work:

df.A = s # does not raise an error, but does nothing
df.loc[s.index,'A'] = s # raises an error

expected output:

             A     B
bar one    True   NaN
    two    True   NaN
    three  True   NaN
baz one    False  NaN
foo one    True   NaN
    two    True   NaN

Answer 1

df.A = s does not raise an error, but does nothing

Indeed this should have worked.^{Your point is actually related to mine.}

ᐊᐊ The workaround ᐊᐊ

>>> s.index = pd.Index((c,) for c in s.index)  # ᐊᐊᐊᐊᐊᐊᐊᐊ
>>> df.A = s
>>> df
               A    B
bar one     True  NaN
    two     True  NaN
    three   True  NaN
baz one    False  NaN
foo one     True  NaN
    two     True  NaN

---

Why does the above work ?

Because when you do directly df.A = s without the workaround, you are actually trying to assign pandas.Index-contained coordinates within a subclass instance,^{which somehow looks like a "counter-opposition" to the LS principle} i.e. an instance of pandas.MultiIndex. I mean, look for yourself:

>>> type(s.index).__name__
'Index'

whereas

>>> type(df.index).__name__
'MultiIndex'

Hence this workaround that consists in turning s's index into a 1-dimensional pandas.MultiIndex instance.

>>> s.index = pd.Index((c,) for c in s.index)
>>> type(s.index).__name__
'MultiIndex'

and nothing has perceptibly changed

>>> s
bar     True
baz    False
foo     True
dtype: bool

---

A thought: From many views (mathematical, ontological) all this somehow shows that pandas.Index should have been designed as a subclass of pandas.MultiIndex, not the opposite, as it is currently.

Answer 2

Series (and dictionaries) can be used just like functions with map and apply (thanks to @normanius for improving the syntax):

df['A'] = pd.Series(df.index.get_level_values(0)).map(s).values

Or similarly:

df['A'] = df.reset_index(level=0)['level_0'].map(s).values

Results:

A    B
bar one     True  NaN
    two     True  NaN
    three   True  NaN
baz one    False  NaN
foo one     True  NaN
    two     True  NaN