Boolean masks on dataframes with indexes with timezones introduces NaNs · Issue #16889 · pandas-dev/pandas (original) (raw)

Code Sample, a copy-pastable example if possible

import pandas as pd import numpy as np mask = np.array([False, True, True, False])

idx = pd.date_range('20010101', '20020101')[:4].tz_localize('UTC') df = pd.DataFrame({'a' : np.arange(4)}, index=idx).astype('float64') fill = 2 * df df.loc[mask, :] = fill.loc[mask, :] print df

idx = pd.date_range('20010101', '20020101')[:4] df = pd.DataFrame({'a' : np.arange(4)}, index=idx).astype('float64') fill = 2 * df df.loc[mask, :] = fill.loc[mask, :] print df

Problem description

This is the output that I get:

                            a
2001-01-01 00:00:00+00:00   0
2001-01-02 00:00:00+00:00 NaN
2001-01-03 00:00:00+00:00 NaN
2001-01-04 00:00:00+00:00   3
            a
2001-01-01  0
2001-01-02  2
2001-01-03  4
2001-01-04  3

In the first case, a timezone of UTC seems to cause the masking operation to introduce NaNs. The second case is correct. This problem seems to be introduced in pandas 0.17.1, whereas with 0.16 I get the following output below

Expected Output

                           a
2001-01-01 00:00:00+00:00  0
2001-01-02 00:00:00+00:00  2
2001-01-03 00:00:00+00:00  4
2001-01-04 00:00:00+00:00  3
            a
2001-01-01  0
2001-01-02  2
2001-01-03  4
2001-01-04  3

Output of `pd.show_versions()`

INSTALLED VERSIONS

commit: None
python: 2.7.13.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8