Pandas How to filter a Series

A fast way of doing this is to reconstruct using numpy to slice the underlying arrays. See timings below.

mask = s.values != 1
pd.Series(s.values[mask], s.index[mask])

0
383    3.000000
737    9.000000
833    8.166667
dtype: float64

naive timing

In [5]:

import pandas as pd

test = {
383:    3.000000,
663:    1.000000,
726:    1.000000,
737:    9.000000,
833:    8.166667
}

s = pd.Series(test)
s = s[s != 1]
s
Out[0]:
383    3.000000
737    9.000000
833    8.166667
dtype: float64

From pandas version 0.18+ filtering a series can also be done as below

test = {
383:    3.000000,
663:    1.000000,
726:    1.000000,
737:    9.000000,
833:    8.166667
}

pd.Series(test).where(lambda x : x!=1).dropna()

Checkout: http://pandas.pydata.org/pandas-docs/version/0.18.1/whatsnew.html#method-chaininng-improvements

As DACW pointed out, there are method-chaining improvements in pandas 0.18.1 that do what you are looking for very nicely.

Rather than using .where, you can pass your function to either the .loc indexer or the Series indexer [] and avoid the call to .dropna:

test = pd.Series({
383:    3.000000,
663:    1.000000,
726:    1.000000,
737:    9.000000,
833:    8.166667
})

test.loc[lambda x : x!=1]

test[lambda x: x!=1]

Similar behavior is supported on the DataFrame and NDFrame classes.