#🔒 Convert Column of String Dates to Numpy Array of Floats Using Vectorization

16 messages · Page 1 of 1 (latest)

worthy sigil
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given a Pandas.DataFrame with a column consisting of string dates all in the format: mm/dd/yyyy hh:mm:ss, how could convert this column into a numpy array of type int using vectorization

dapper ginkgoBOT
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@worthy sigil

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knotty hull
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!docs pandas.to_datetime

dapper ginkgoBOT
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pandas.to\_datetime 
pandas.to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False, utc=False, format=None, exact=_NoDefault.no_default, unit=None, ...)```
Convert argument to datetime.

This function converts a scalar, array-like, [`Series`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series) or [`DataFrame`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame)/dict-like to a pandas datetime object.
knotty hull
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@worthy sigil start with this

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Then you need to decide what you want 1 to be (a second, a minute, etc.)

worthy sigil
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oh thanks

knotty hull
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Ignore anything you see about using apply. That won't be vectorized

worthy sigil
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Is this the right idea?

queued_time_col = pd.to_datetime(df['Original Time Queued'].str[11:20], format='%H:%M:%S %p', exact=True)
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ValueError: time data "03:04:47 " doesn't match format "%H:%M:%S %p", at position 0.

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Changed slice [11:20] to [11:22]:

queued_time_col = pd.to_datetime(df['Original Time Queued'].str[11:22], format='%H:%M:%S %p', exact=True)
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Works now

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!solved

dapper ginkgoBOT
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