Pandas .factorize()方法通过标识不同的值来帮助获得数组的数字表示形式。该方法可以同时使用pandas.factorize()和Series.factorize()。
参数：
values :1D sequence.
sort :[bool, Default is False] Sort uniques and shuffle labels.
na_sentinel:[ int, default -1] Missing Values to mark ‘not found’.返回： Numeric representation of array
代码：解释factorize()方法的用法
# importing libraries
import numpy as np
import pandas as pd
from pandas.api.types import CategoricalDtype
labels, uniques = pd.factorize(['b', 'd', 'd', 'c', 'a', 'c', 'a', 'b'])
print("Numeric Representation:\n", labels)
print("Unique Values:\n", uniques)
# sorting the numerics
label1, unique1 = pd.factorize(['b', 'd', 'd', 'c', 'a', 'c', 'a', 'b'],
sort = True)
print("\n\nNumeric Representation:\n", label1)
print("Unique Values:\n", unique1)
# Missing values indicated
label2, unique2 = pd.factorize(['b', None, 'd', 'c', None, 'a', ],
na_sentinel = -101)
print("\n\nNumeric Representation:\n", label2)
print("Unique Values:\n", unique2)
# When factorizing pandas object; unique will differ
a = pd.Categorical(['a', 'a', 'c'], categories =['a', 'b', 'c'])
label3, unique3 = pd.factorize(a)
print("\n\nNumeric Representation:\n", label3)
print("Unique Values:\n", unique3)
相关用法
注：本文由纯净天空筛选整理自Mohit Gupta_OMG 大神的英文原创作品 Python | Pandas.factorize()。非经特殊声明，原始代码版权归原作者所有，本译文未经允许或授权，请勿转载或复制。