pandas.factorize()

pandas官网  http://pandas.pydata.org/pandas-docs/stable/generated/pandas.factorize.html

pandas.factorize(values, sort=False, order=None, na_sentinel=-1, size_hint=None)

Encode the object as an enumerated type or categorical variable.

作用是将object型变量转换成枚举型或者类别型

Prameters:

values : sequence A 1-D seqeunce. Sequences that aren’t pandas objects are coereced to ndarrays before factorization. sort : bool, default False Sort uniques and shuffle labels to maintain the relationship. order Deprecated since version 0.23.0: This parameter has no effect and is deprecated. na_sentinel : int, default -1 Value to mark “not found”. size_hint : int, optional Hint to the hashtable sizer.

Returns:

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

Example

1、 pd.factorize(values)

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

2、 pd.factorize(values, sort = True)

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

3、Missing values are indicated in labels with na_sentinel (-1 by default). Note that missing values are never included in uniques.

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

4、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'])
>>> labels, uniques = pd.factorize(cat)
>>> labels
array([0, 0, 1])
>>> uniques
[a, c]
Categories (3, object): [a, b, c]
Notice that 'b' is in uniques.categories, desipite not being present in cat.values.

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

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