Duan Uys
07/01/2021, 12:09 AM[DEPRECATED] Marcos Marx
Chris (deprecated profile)
Chris (deprecated profile)
Chris (deprecated profile)
Chris (deprecated profile)
epidemiology
dataset example in my source, I get these types (noticed the number columns):Chris (deprecated profile)
{"dtype":"string"}
, i can force all columns to be parsed as strings…
which results in the following schema:Chris (deprecated profile)
dtype: Type name or dict of column -> type, optional
Data type for data or columns. E.g. {‘a’: np.float64, ‘b’: np.int32, ‘c’: ‘Int64’} Use str or object together with suitable na_values settings to preserve and not interpret dtype. If converters are specified, they will be applied INSTEAD of dtype conversion.from https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html
Charles
08/26/2021, 3:53 PMCharles
08/26/2021, 3:54 PMChris (deprecated profile)
E.g. {‘a’: np.float64, ‘b’: np.int32, ‘c’: ‘Int64’}
Charles
08/26/2021, 4:02 PM