factorize_array(values: 'np.ndarray', na_sentinel: 'int' = -1, size_hint: 'int | None' = None, na_value=None, mask: 'np.ndarray | None' = None) -> 'tuple[npt.NDArray[np.intp], np.ndarray]'
This doesn't do any coercion of types or unboxing before factorization.
Passed through to the hashtable's 'get_labels' method
A value in :None:None:`values` to consider missing. Note: only use this parameter when you know that you don't have any values pandas would consider missing in the array (NaN for float data, iNaT for datetimes, etc.).
If not None, the mask is used as indicator for missing values (True = missing, False = valid) instead of :None:None:`na_value` or condition "val != val".
Factorize a numpy array to codes and uniques.
The following pages refer to to this document either explicitly or contain code examples using this.
pandas.core.reshape.merge._factorize_keys
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