Pandas How to filter a Series

I have a Series like this after doing groupby('name') and used mean() function on other column

name
383      3.000000
663      1.000000
726      1.000000
737      9.000000
833      8.166667

Could anyone please show me how to filter out the rows with 1.000000 mean values? Thank you and I greatly appreciate your help.

Solution 1:

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

Solution 2:

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

Solution 3:

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.

Solution 4:

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