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Showing content from https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.factorize.html below:

pandas.factorize — pandas 2.3.1 documentation

pandas.factorize(values, sort=False, use_na_sentinel=True, size_hint=None)[source]#

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. factorize is available as both a top-level function pandas.factorize(), and as a method Series.factorize() and Index.factorize().

Parameters:

valuessequence

A 1-D sequence. Sequences that aren’t pandas objects are coerced to ndarrays before factorization.

sortbool, default False

Sort uniques and shuffle codes to maintain the relationship.

use_na_sentinelbool, default True

If True, the sentinel -1 will be used for NaN values. If False, NaN values will be encoded as non-negative integers and will not drop the NaN from the uniques of the values.

Added in version 1.5.0.

size_hintint, optional

Hint to the hashtable sizer.

Returns:

codesndarray

An integer ndarray that’s an indexer into uniques. uniques.take(codes) will have the same values as values.

uniquesndarray, Index, or Categorical

The unique valid values. When values is Categorical, uniques is a Categorical. When values is some other pandas object, an Index is returned. Otherwise, a 1-D ndarray is returned.

Note

Even if there’s a missing value in values, uniques will not contain an entry for it.

See also

cut

Discretize continuous-valued array.

unique

Find the unique value in an array.

Notes

Reference the user guide for more examples.

Examples

These examples all show factorize as a top-level method like pd.factorize(values). The results are identical for methods like Series.factorize().

>>> codes, uniques = pd.factorize(np.array(['b', 'b', 'a', 'c', 'b'], dtype="O"))
>>> codes
array([0, 0, 1, 2, 0])
>>> uniques
array(['b', 'a', 'c'], dtype=object)

With sort=True, the uniques will be sorted, and codes will be shuffled so that the relationship is the maintained.

>>> codes, uniques = pd.factorize(np.array(['b', 'b', 'a', 'c', 'b'], dtype="O"),
...                               sort=True)
>>> codes
array([1, 1, 0, 2, 1])
>>> uniques
array(['a', 'b', 'c'], dtype=object)

When use_na_sentinel=True (the default), missing values are indicated in the codes with the sentinel value -1 and missing values are not included in uniques.

>>> codes, uniques = pd.factorize(np.array(['b', None, 'a', 'c', 'b'], dtype="O"))
>>> codes
array([ 0, -1,  1,  2,  0])
>>> uniques
array(['b', 'a', 'c'], dtype=object)

Thus far, we’ve only factorized lists (which are internally coerced to NumPy arrays). When factorizing pandas objects, the type of uniques will differ. For Categoricals, a Categorical is returned.

>>> cat = pd.Categorical(['a', 'a', 'c'], categories=['a', 'b', 'c'])
>>> codes, uniques = pd.factorize(cat)
>>> codes
array([0, 0, 1])
>>> uniques
['a', 'c']
Categories (3, object): ['a', 'b', 'c']

Notice that 'b' is in uniques.categories, despite not being present in cat.values.

For all other pandas objects, an Index of the appropriate type is returned.

>>> cat = pd.Series(['a', 'a', 'c'])
>>> codes, uniques = pd.factorize(cat)
>>> codes
array([0, 0, 1])
>>> uniques
Index(['a', 'c'], dtype='object')

If NaN is in the values, and we want to include NaN in the uniques of the values, it can be achieved by setting use_na_sentinel=False.

>>> values = np.array([1, 2, 1, np.nan])
>>> codes, uniques = pd.factorize(values)  # default: use_na_sentinel=True
>>> codes
array([ 0,  1,  0, -1])
>>> uniques
array([1., 2.])

>>> codes, uniques = pd.factorize(values, use_na_sentinel=False)
>>> codes
array([0, 1, 0, 2])
>>> uniques
array([ 1.,  2., nan])

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