Description
XREF: #33208 (comment)
Note:
I'm using "hours" here for the examples, but this applies to
- hours
- minutes
- milliseconds
Short explanation:
Not all components are exposed directly:
Example:
In [1]: import pandas as pd
In [2]: s = pd.Series(pd.timedelta_range(start="1 day", periods=5, freq="H"))
In [3]: s.dt.components
Out[3]:
days hours minutes seconds milliseconds microseconds nanoseconds
0 1 0 0 0 0 0 0
1 1 1 0 0 0 0 0
2 1 2 0 0 0 0 0
3 1 3 0 0 0 0 0
4 1 4 0 0 0 0 0
In [4]: s.dt.days
Out[4]:
0 1
1 1
2 1
3 1
4 1
dtype: int64
In [5]: s.dt.hours
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-5-86f203890f2a> in <module>
----> 1 s.dt.hours
AttributeError: 'TimedeltaProperties' object has no attribute 'hours'
Long explanation:
If we create a series that contains a timedelta, we can access only some of the components directly.
Example setup:
In [1]: import pandas as pd
In [2]: s = pd.Series(pd.timedelta_range(start="1 day", periods=5, freq="H"))
In [3]: s
Out[3]:
0 1 days 00:00:00
1 1 days 01:00:00
2 1 days 02:00:00
3 1 days 03:00:00
4 1 days 04:00:00
dtype: timedelta64[ns]
In [4]: s.dt.components
Out[4]:
days hours minutes seconds milliseconds microseconds nanoseconds
0 1 0 0 0 0 0 0
1 1 1 0 0 0 0 0
2 1 2 0 0 0 0 0
3 1 3 0 0 0 0 0
4 1 4 0 0 0 0 0
We can access "days" and "seconds" directly, for example:
In [5]: s.dt.days
Out[5]:
0 1
1 1
2 1
3 1
4 1
dtype: int64
In [6]: s.dt.seconds
Out[6]:
0 0
1 3600
2 7200
3 10800
4 14400
dtype: int64
But if we try to access "hours" for example directly:
In [7]: s.dt.hours
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-7-86f203890f2a> in <module>
----> 1 s.dt.hours
AttributeError: 'TimedeltaProperties' object has no attribute 'hours'
Output of pd.show_versions()
:
INSTALLED VERSIONS
commit : 37dc5dc
python : 3.7.6.final.0
python-bits : 64
OS : Linux
OS-release : 5.5.13.a-1-hardened
Version : #1 SMP PREEMPT Wed, 25 Mar 2020 21:46:24 +0000
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.1.0.dev0+1089.g37dc5dc39
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 46.0.0.post20200311
Cython : 0.29.16
pytest : 5.4.1
hypothesis : 5.8.0
sphinx : 2.4.4
blosc : None
feather : None
xlsxwriter : 1.2.8
lxml.etree : 4.5.0
html5lib : 1.0.1
pymysql : None
psycopg2 : None
jinja2 : 2.11.1
IPython : 7.13.0
pandas_datareader: None
bs4 : 4.8.2
bottleneck : 1.3.2
fastparquet : 0.3.3
gcsfs : None
matplotlib : 3.2.1
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.1
pandas_gbq : None
pyarrow : 0.16.0
pytables : None
pyxlsb : None
s3fs : 0.4.2
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : 3.6.1
tabulate : 0.8.7
xarray : 0.15.1
xlrd : 1.2.0
xlwt : 1.3.0
numba : 0.48.0