Python Pandas 中最接近因子变量的等价物
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Closest equivalent of a factor variable in Python Pandas
提问by Amelio Vazquez-Reina
What is the closest equivalent to an R Factor variablein Python pandas?
什么是最接近相当于R因素变量在Python的大Pandas？
采纳答案by sriramn
This question seems to be from a year back but since it is still open here's an update. pandas has introduced a categoricaldtype and it operates very similar to factorsin R. Please see this link for more information:
这个问题似乎是一年前的，但由于它仍然开放，这里有一个更新。pandas 引入了一个categoricaldtype，它的操作与factorsR 中的非常相似。有关更多信息，请参阅此链接：
http://pandas-docs.github.io/pandas-docs-travis/categorical.html
http://pandas-docs.github.io/pandas-docs-travis/categorical.html
Reproducing a snippet from the link above showing how to create a "factor" variable in pandas.
从上面的链接中复制一个片段，展示如何在 Pandas 中创建一个“因子”变量。
In [1]: s = Series(["a","b","c","a"], dtype="category")
In [2]: s
Out[2]:
0 a
1 b
2 c
3 a
dtype: category
Categories (3, object): [a < b < c]
回答by badgley
If you're looking to do modeling etc, lots of goodies for factor within the patsy library. I will admit to having struggled with this myself. I found these slideshelpful. Wish I could give a better example, but this is as far as I've gotten myself.
如果您想进行建模等工作，那么patsy 库中有很多关于 factor 的好东西。我承认我自己也曾为此挣扎过。我发现这些幻灯片很有帮助。希望我能举一个更好的例子，但这是我自己得到的。
回答by jpcsoup
If you're looking to map a categorical variable to a number as R does, Pandas implemented a function that will give you just that: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.factorize.html
如果您想像 R 一样将分类变量映射到数字，Pandas 实现了一个函数，该函数将为您提供：https: //pandas.pydata.org/pandas-docs/stable/reference/api/pandas。分解.html
import pandas as pd
df = pd.read_csv('path_to_your_file')
df['new_factor'], _ = pd.factorize(df['old_categorical'], sort=True)
This function returns both the enumerated mapping as well as a list of unique values. If you're just doing variable assignment, you'll have to throw the latter away as above.
此函数返回枚举映射以及唯一值列表。如果你只是在做变量赋值，你就必须像上面一样把后者扔掉。
If you want a homegrown solution, you can use a combination of a set and a dictionary within a function. This method is a bit easier to apply over multiple columns, but you do have to note that None, NaN, etc. will be a included as a category in this method:
如果你想要一个自产的解决方案，你可以在一个函数中使用集合和字典的组合。这种方法更容易应用于多列，但您必须注意 None、NaN 等将作为类别包含在此方法中：
def factor(var):
var_set = set(var)
var_set = {x: y for x, y in [pair for pair in zip(var_set, range(len(var_set)))]}
return [var_set[x] for x in var]
df['new_factor1'] = df['old_categorical1'].apply(factor)
df[['new_factor2', 'new_factor3']] = df[['old_categorical2', 'old_categorical3']].apply(factor)
回答by Dan Krahenbuhl
C # array containing category data
V # array containing numerical data
H = np.unique(C)
mydict = {}
for h in H:
mydict[h] = V[C==h]
boxplot(mydict.values(), labels=mydict.keys())
