Inspired by this answer and the lack of an easy answer to this question I found myself writing a little syntactic sugar to make life easier to filter by MultiIndex level.
def _filter_series(x, level_name, filter_by):
"""
Filter a pd.Series or pd.DataFrame x by `filter_by` on the MultiIndex level
`level_name`
Uses `pd.Index.get_level_values()` in the background. `filter_by` is either
a string or an iterable.
"""
if isinstance(x, pd.Series) or isinstance(x, pd.DataFrame):
if type(filter_by) is str:
filter_by = [filter_by]
index = x.index.get_level_values(level_name).isin(filter_by)
return x[index]
else:
print "Not a pandas object"
But if I know the pandas development team (and I'm starting to, slowly!) there's already a nice way to do this, and I just don't know what it is yet!
Am I right?
This is very easy using the new multi-index slicers in master/0.14 (releasing soon), see here
There is an open issue to make this syntatically easier (its not hard to do), see here eg something like this: df.loc[{ 'third' : ['C1','C3'] }]
I think is reasonable
Here's how you can do it (requires master/0.14):
In [2]: def mklbl(prefix,n):
...: return ["%s%s" % (prefix,i) for i in range(n)]
...:
In [11]: index = MultiIndex.from_product([mklbl('A',4),
mklbl('B',2),
mklbl('C',4),
mklbl('D',2)],names=['first','second','third','fourth'])
In [12]: columns = ['value']
In [13]: df = DataFrame(np.arange(len(index)*len(columns)).reshape((len(index),len(columns))),index=index,columns=columns).sortlevel()
In [14]: df
Out[14]:
value
first second third fourth
A0 B0 C0 D0 0
D1 1
C1 D0 2
D1 3
C2 D0 4
D1 5
C3 D0 6
D1 7
B1 C0 D0 8
D1 9
C1 D0 10
D1 11
C2 D0 12
D1 13
C3 D0 14
D1 15
A1 B0 C0 D0 16
D1 17
C1 D0 18
D1 19
C2 D0 20
D1 21
C3 D0 22
D1 23
B1 C0 D0 24
D1 25
C1 D0 26
D1 27
C2 D0 28
D1 29
C3 D0 30
D1 31
A2 B0 C0 D0 32
D1 33
C1 D0 34
D1 35
C2 D0 36
D1 37
C3 D0 38
D1 39
B1 C0 D0 40
D1 41
C1 D0 42
D1 43
C2 D0 44
D1 45
C3 D0 46
D1 47
A3 B0 C0 D0 48
D1 49
C1 D0 50
D1 51
C2 D0 52
D1 53
C3 D0 54
D1 55
B1 C0 D0 56
D1 57
C1 D0 58
D1 59
...
[64 rows x 1 columns]
Create an indexer across all of the levels, selecting all entries
In [15]: indexer = [slice(None)]*len(df.index.names)
Make the level we care about only have the entries we care about
In [16]: indexer[df.index.names.index('third')] = ['C1','C3']
Select it (its important that this is a tuple!)
In [18]: df.loc[tuple(indexer),:]
Out[18]:
value
first second third fourth
A0 B0 C1 D0 2
D1 3
C3 D0 6
D1 7
B1 C1 D0 10
D1 11
C3 D0 14
D1 15
A1 B0 C1 D0 18
D1 19
C3 D0 22
D1 23
B1 C1 D0 26
D1 27
C3 D0 30
D1 31
A2 B0 C1 D0 34
D1 35
C3 D0 38
D1 39
B1 C1 D0 42
D1 43
C3 D0 46
D1 47
A3 B0 C1 D0 50
D1 51
C3 D0 54
D1 55
B1 C1 D0 58
D1 59
C3 D0 62
D1 63
[32 rows x 1 columns]
I actually upvoted joris's answer... but unfortunately the refactoring he mentions has not happened in 0.14 and is not happening in 0.17 neither. So for the moment let me suggest a quick and dirty solution (obviously derived from Jeff's one):
def filter_by(df, constraints):
"""Filter MultiIndex by sublevels."""
indexer = [constraints[name] if name in constraints else slice(None)
for name in df.index.names]
return df.loc[tuple(indexer)] if len(df.shape) == 1 else df.loc[tuple(indexer),]
pd.Series.filter_by = filter_by
pd.DataFrame.filter_by = filter_by
... to be used as
df.filter_by({'level_name' : value})
where value
can be indeed a single value, but also a list, a slice...
(untested with Panels and higher dimension elements, but I do expect it to work)
You have the filter
method that can do things like this. Eg with the example that was asked in the linked SO question:
In [188]: df.filter(like='0630', axis=0)
Out[188]:
sales cogs net_pft
STK_ID RPT_Date
876 20060630 857483000 729541000 67157200
20070630 1146245000 1050808000 113468500
20080630 1932470000 1777010000 133756300
2254 20070630 501221000 289167000 118012200
The filter method is refactored at the moment (in upcoming 0.14), and a level
keyword will be added (because now you can have a problem if the same labels appear in different levels of the index).
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