Incompatibility between pandas.infer_freq and pandas.to_timedelta

[idanivanov]

A small, complete example of the issue

The output of pd.infer_freq is "D" while pd.to_timedelta expects "1D".

import pandas as pd
dates = pd.date_range(start='2016-10-01', end='2016-10-10', freq='1D')
freq = pd.infer_freq(dates)
delta = pd.to_timedelta(freq)

Expected Output

delta = Timedelta('1 days 00:00:00')

>>> import pandas as pd
>>> dates = pd.date_range(start='2016-10-01', end='2016-10-10', freq='1D')
>>> dates
DatetimeIndex(['2016-10-01', '2016-10-02', '2016-10-03', '2016-10-04',
               '2016-10-05', '2016-10-06', '2016-10-07', '2016-10-08',
               '2016-10-09', '2016-10-10'],
              dtype='datetime64[ns]', freq='D')
>>> freq = pd.infer_freq(dates)
>>> freq
'D'
>>> delta = pd.to_timedelta(freq)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/iivanov/anaconda2/lib/python2.7/site-packages/pandas/util/decorators.py", line 91, in wrapper
    return func(*args, **kwargs)
  File "/home/iivanov/anaconda2/lib/python2.7/site-packages/pandas/tseries/timedeltas.py", line 102, in to_timedelta
    box=box, errors=errors)
  File "/home/iivanov/anaconda2/lib/python2.7/site-packages/pandas/tseries/timedeltas.py", line 148, in _coerce_scalar_to_timedelta_type
    result = tslib.convert_to_timedelta(r, unit, errors)
  File "pandas/tslib.pyx", line 2941, in pandas.tslib.convert_to_timedelta (pandas/tslib.c:52631)
  File "pandas/tslib.pyx", line 3256, in pandas.tslib.convert_to_timedelta64 (pandas/tslib.c:56028)
  File "pandas/tslib.pyx", line 3188, in pandas.tslib.parse_timedelta_string (pandas/tslib.c:54877)
ValueError: unit abbreviation w/o a number

Output of pd.show_versions()

## INSTALLED VERSIONS

commit: None
python: 2.7.12.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-42-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8

pandas: 0.18.1
nose: 1.3.7
pip: 8.1.2
setuptools: 26.1.1
Cython: 0.24.1
numpy: 1.11.1
scipy: 0.18.0
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.1
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: 1.1.0
tables: 3.2.3.1
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.2
lxml: 3.6.4
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.40.0
pandas_datareader: None

[chris-b1]

pd.to_timedelta can only handle fixed deltas (basically days and finer) - it isn't an API guarantee that frequency strings can be parsed as a delta, and in fact some frequencies can't be converted to a timedelta.

In [254]: dates = pd.bdate_range(start='2014-01-01', periods=10)

In [255]: pd.infer_freq(dates)
Out[255]: 'B'

If you need to convert a frequency string into a DateOffset object, which can be a fixed or relative delta, use the to_offset function.

In [253]: from pandas.tseries.frequencies import to_offset

In [256]: to_offset('D')
Out[256]: <Day>

In [257]: to_offset('2D')
Out[257]: <2 * Days>

In [258]: to_offset('B')
Out[258]: <BusinessDay>

[jorisvandenbossche]

BTW, you can convert such an offset to a timedelta for certain types:

In [19]: pd.to_timedelta(to_offset('D'))
Out[19]: Timedelta('1 days 00:00:00')

In [20]: pd.to_timedelta(to_offset('B'))
...
ValueError: Invalid type for timedelta scalar: <class 'pandas.tseries.offsets.BusinessDay'>

(or alternative to_offset('D').delta)
As @chris-b1 noted above, relative offsets cannot be converted to a timedelta, so this also errors.

[idanivanov]

Thank you for the comments! It makes sense now.