Remove rows of zeros from a Pandas series

Question

I have a number Pandas Series with 601 rows indexed by date as seen below. The values are zero up until a point, after which all the values are non zero. This point varies with each Series but I would like a way to remove all the rows where the value is zero while keeping the integrity of the date index.

Name: users, dtype: float64 dates
2015-08-17 14:29:59-04:00    18
2015-08-16 14:29:59-04:00     3
2015-08-15 14:29:59-04:00    11
2015-08-14 14:29:59-04:00    12
2015-08-13 14:29:59-04:00     8
2015-08-12 14:29:59-04:00    10
2015-08-11 14:29:59-04:00     6
2015-08-10 14:29:59-04:00     6
2015-08-09 14:29:59-04:00     7
2015-08-08 14:29:59-04:00     7
2015-08-07 14:29:59-04:00    13
2015-08-06 14:29:59-04:00    16
2015-08-05 14:29:59-04:00    12
2015-08-04 14:29:59-04:00    14
2015-08-03 14:29:59-04:00     5
2015-08-02 14:29:59-04:00     5
2015-08-01 14:29:59-04:00     8
2015-07-31 14:29:59-04:00     6
2015-07-30 14:29:59-04:00     7
2015-07-29 14:29:59-04:00     9
2015-07-28 14:29:59-04:00     7
2015-07-27 14:29:59-04:00     5
2015-07-26 14:29:59-04:00     4
2015-07-25 14:29:59-04:00     8
2015-07-24 14:29:59-04:00     8
2015-07-23 14:29:59-04:00     8
2015-07-22 14:29:59-04:00     9
2015-07-21 14:29:59-04:00     5
2015-07-20 14:29:59-04:00     7
2015-07-19 14:29:59-04:00     6
                             ..
2014-01-23 13:29:59-05:00     0
2014-01-22 13:29:59-05:00     0
2014-01-21 13:29:59-05:00     0
2014-01-20 13:29:59-05:00     0
2014-01-19 13:29:59-05:00     0
2014-01-18 13:29:59-05:00     0
2014-01-17 13:29:59-05:00     0
2014-01-16 13:29:59-05:00     0
2014-01-15 13:29:59-05:00     0
2014-01-14 13:29:59-05:00     0
2014-01-13 13:29:59-05:00     0
2014-01-12 13:29:59-05:00     0
2014-01-11 13:29:59-05:00     0
2014-01-10 13:29:59-05:00     0
2014-01-09 13:29:59-05:00     0
2014-01-08 13:29:59-05:00     0
2014-01-07 13:29:59-05:00     0
2014-01-06 13:29:59-05:00     0
2014-01-05 13:29:59-05:00     0
2014-01-04 13:29:59-05:00     0
2014-01-03 13:29:59-05:00     0
2014-01-02 13:29:59-05:00     0
2014-01-01 13:29:59-05:00     0
2013-12-31 13:29:59-05:00     0
2013-12-30 13:29:59-05:00     0
2013-12-29 13:29:59-05:00     0
2013-12-28 13:29:59-05:00     0
2013-12-27 13:29:59-05:00     0
2013-12-26 13:29:59-05:00     0
2013-12-25 13:29:59-05:00     0

Answer 1

Just filter them out:

users[users!=0]

This will preserve your index also

Or

users[users > 0]

if it's positive values you're after:

In [38]:
s[s>0]

Out[38]:
2015-08-17 18:29:59    18
2015-08-16 18:29:59     3
2015-08-15 18:29:59    11
2015-08-14 18:29:59    12
2015-08-13 18:29:59     8
2015-08-12 18:29:59    10
2015-08-11 18:29:59     6
2015-08-10 18:29:59     6
2015-08-09 18:29:59     7
2015-08-08 18:29:59     7
2015-08-07 18:29:59    13
2015-08-06 18:29:59    16
2015-08-05 18:29:59    12
2015-08-04 18:29:59    14
2015-08-03 18:29:59     5
2015-08-02 18:29:59     5
2015-08-01 18:29:59     8
2015-07-31 18:29:59     6
2015-07-30 18:29:59     7
2015-07-29 18:29:59     9
2015-07-28 18:29:59     7
2015-07-27 18:29:59     5
2015-07-26 18:29:59     4
2015-07-25 18:29:59     8
2015-07-24 18:29:59     8
2015-07-23 18:29:59     8
2015-07-22 18:29:59     9
2015-07-21 18:29:59     5
2015-07-20 18:29:59     7
2015-07-19 18:29:59     6
Name: 1, dtype: int64

Answer 2

if ds is you DataSeries: ds!=0 will return a boolean vector of rows with values different than zero.

ds[ds!=0] are the rows, with the index preserved

Note that missing values (NaN) will not be filtered.

To filter both, use: ds[(ds!=0)&(pd.isnull(ds))]