CODEWITHSUNDEEP
pythonpython-3.xpandas
bkoodaa
bkoodaa

Reputation: 5361

Filter Pandas Series using both index and value

Is there a clean way to filter Pandas Series using a custom function that takes as input both index and value?

Here is a piece of code that achieves what I want to do:

series = pd.Series({"id5":88, "id3":40})
def custom(k,v):
    if k=="id5":
        return v>20
    else:
        return v>50

filtered_indexes = []
filtered_values = []
for k,v in series.iteritems():
    if custom(k,v):
        filtered_indexes.append(k)
        filtered_values.append(v)
filtered_series = pd.Series(data=filtered_values, index=filtered_indexes)

My question is: can the same be achieved cleaner and/or more efficiently with syntax like

series.filter(lambda x: custom(x.index, x.value))

Upvotes: 2

Views: 4548

Answers (2)

jpp
jpp

Reputation: 164843

You can vectorise your logic as below. This avoids inefficient lambda loops and may also make your code cleaner.

res = series[((series.index == 'id5') & (series > 20)) |
             ((series.index != 'id5') & (series > 50))]

Result:

id5    88
dtype: int64

For readability, you may wish to separate the Boolean criteria:

c1 = ((series.index == 'id5') & (series > 20))
c2 = ((series.index != 'id5') & (series > 50))

res = series[c1 | c2]

Upvotes: 1

jezrael
jezrael

Reputation: 863751

There is problem Series.apply have no accesses to index and DataFrame.filter is not implemented for Series.

It is possible, but need create DataFrame:

s = series[series.to_frame().apply(lambda x: custom(x.name, x), axis=1).squeeze()]
print (s)
id5    88
dtype: int64

Or use groupby with filtration:

s = series.groupby(level=0).filter(lambda x: custom(x.name, x)[0])
print (s)
id5    88
dtype: int64

Upvotes: 1

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