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suvayu
commented
Jan 30, 2025
suvayu
commented
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- low_res_datetime
- make package pipeable
- add tests
suvayu
commented
Jan 30, 2025
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| You can check the available ranges with `pd.Timestamp.min` and `pd.Timestamp.max`. | ||
| """ | ||
| period_parts = re.search(r'^([0-9]+) *(.*)$', freq).groups() |
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Check for match, if no match returns None. Should raise a error message then
| """ | ||
| period_parts = re.search(r'^([0-9]+) *(.*)$', freq).groups() | ||
| if len(period_parts) != 2: | ||
| raise ValueError("Can't analyze period \"{period}\".") |
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Error message is not specific. Say what is the problem, like couldn't separate number from unit, etc.
| period_parts = re.search(r'^([0-9]+) *(.*)$', freq).groups() | ||
| if len(period_parts) != 2: | ||
| raise ValueError("Can't analyze period \"{period}\".") | ||
| number_str, unit = period_parts |
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maybe merge with above and catch ValeuError
| freq_pd = pd.Timedelta(freq_np) | ||
| #print(f"freq_pd: {freq_pd}") | ||
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| date_array = np.arange(start_date_np, start_date_np + periods * freq_pd, freq_pd) |
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Have a look at pd.date_range, maybe it has the same behaviour, but code would be simpler.
pandas.DateOffset is similar to relative delta
Arrays in the absense of "value_type" default to "float" (as per the docs), here the data is text string, not convertible to a number.
- minute is abbreviated as min - convert freq to int before passing to DateOffset
Refactor the module w/ conditional imports to use Pydantic for schema generation (invoked as `python -m models.py json-blob-schema.json`), but use plain Python definitions when used as a module. Update the JSON schema
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