pyspark.pandas.Series.factorize¶
-
Series.factorize(sort: bool = True, na_sentinel: Optional[int] = - 1) → Tuple[IndexOpsLike, pandas.core.indexes.base.Index]¶ Encode the object as an enumerated type or categorical variable.
This method is useful for obtaining a numeric representation of an array when all that matters is identifying distinct values.
- Parameters
- sortbool, default True
- na_sentinelint or None, default -1
Value to mark “not found”. If None, will not drop the NaN from the uniques of the values.
Deprecated since version 3.4.0.
- Returns
- codesSeries or Index
A Series or Index that’s an indexer into uniques.
uniques.take(codes)will have the same values as values.- uniquespd.Index
The unique valid values.
Note
Even if there’s a missing value in values, uniques will not contain an entry for it.
Examples
>>> psser = ps.Series(['b', None, 'a', 'c', 'b']) >>> codes, uniques = psser.factorize() >>> codes 0 1 1 -1 2 0 3 2 4 1 dtype: int32 >>> uniques Index(['a', 'b', 'c'], dtype='object')
>>> codes, uniques = psser.factorize(na_sentinel=None) >>> codes 0 1 1 3 2 0 3 2 4 1 dtype: int32 >>> uniques Index(['a', 'b', 'c', None], dtype='object')
>>> codes, uniques = psser.factorize(na_sentinel=-2) >>> codes 0 1 1 -2 2 0 3 2 4 1 dtype: int32 >>> uniques Index(['a', 'b', 'c'], dtype='object')
For Index:
>>> psidx = ps.Index(['b', None, 'a', 'c', 'b']) >>> codes, uniques = psidx.factorize() >>> codes Int64Index([1, -1, 0, 2, 1], dtype='int64') >>> uniques Index(['a', 'b', 'c'], dtype='object')