有什么方法可以在 Python pandas 中获取标签编码器的映射？
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Any way to get mappings of a label encoder in Python pandas?
提问by Gingerbread
I am converting strings to categorical values in my dataset using the following piece of code.
我正在使用以下代码将字符串转换为数据集中的分类值。
data['weekday'] = pd.Categorical.from_array(data.weekday).labels
For eg,
例如，
index weekday
0 Sunday
1 Sunday
2 Wednesday
3 Monday
4 Monday
5 Thursday
6 Tuesday
After encoding the weekday, my dataset appears like this:
对工作日进行编码后，我的数据集如下所示：
index weekday
0 3
1 3
2 6
3 1
4 1
5 4
6 5
Is there any way I can know that Sunday has been mapped to 3, Wednesday to 6 and so on?
有什么办法可以知道星期日已映射到 3、星期三到 6 等等？
采纳答案by Algor Troy
The best way of doing this can be to use label encoder of sklearn library.
最好的方法是使用 sklearn 库的标签编码器。
Something like this:
像这样的东西：
from sklearn import preprocessing
le = preprocessing.LabelEncoder()
le.fit(["paris", "paris", "tokyo", "amsterdam"])
list(le.classes_)
le.transform(["tokyo", "tokyo", "paris"])
list(le.inverse_transform([2, 2, 1]))
回答by chinskiy
You can create additional dictionary with mapping:
您可以使用映射创建附加字典：
from sklearn import preprocessing
le = preprocessing.LabelEncoder()
le.fit(data['name'])
le_name_mapping = dict(zip(le.classes_, le.transform(le.classes_)))
print(le_name_mapping)
{'Tom': 0, 'Nick': 1, 'Kate': 2}
回答by Abhishek
A simple & elegant way to do the same.
一个简单而优雅的方法来做同样的事情。
cat_list = ['Sun', 'Sun', 'Wed', 'Mon', 'Mon']
encoded_data, mapping_index = pd.Series(cat_list).factorize()
and you are done, check below
和你做，请查看下面
print(encoded_data)
print(mapping_index)
print(mapping_index.get_loc("Mon"))
回答by John Zwinck
First, make a categorical series:
首先，制作一个分类系列：
weekdays = pd.Series(['Sun', 'Sun', 'Wed', 'Mon', 'Mon']).astype('category')
Then, inspect its "categories":
然后，检查它的“类别”：
weekdays.cat.categories.get_loc('Sun')
回答by ssm
There are many ways of doing this. You can consider pd.factorize, sklearn.preprocessing.LabelEncoderetc. However, in this specific case, you have two options which will suit you best:
有很多方法可以做到这一点。你可以考虑pd.factorize，sklearn.preprocessing.LabelEncoder等等。然而，在这种特殊情况下，你必须将最适合您两种选择：
Going by your own method, you can add the categories:
按照您自己的方法，您可以添加类别：
pd.Categorical( df.weekday, [
'Sunday', 'Monday', 'Tuesday',
'Wednesday', 'Thursday', 'Friday',
'Saturday'] ).labels
The other option is to map values directly using a dict
另一种选择是使用直接映射值 dict
df.weekday.map({
'Sunday': 0,
'Monday': 1,
# ... and so on. You get the idea ...
})
回答by Vikas Gupta
If you have numerical and categorical both type of data in dataframe You can use : here X is my dataframe having categorical and numerical both variables
如果您在数据框中有数字和分类两种类型的数据，您可以使用：这里 X 是我的数据框，具有分类和数字两种变量
from sklearn import preprocessing
le = preprocessing.LabelEncoder()
for i in range(0,X.shape[1]):
if X.dtypes[i]=='object':
X[X.columns[i]] = le.fit_transform(X[X.columns[i]])
Or you can try this:
或者你可以试试这个：
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
data = data.apply(le.fit_transform)
Note: This technique is good if you are not interested in converting them back.
注意：如果您对将它们转换回来不感兴趣，则此技术很好。
回答by Alexandr Kosolapov
train['cat'] = train['cat'].map(list(train['cat'].value_counts().to_frame().reset_index().reset_index().set_index('index').to_dict().values())[0])
