You were almost there !
Basically the fit method, prepare the encoder (fit on your data i.e. prepare the mapping) but don't transform the data.
You have to call transform to transform the data , or use fit_transform which fit and transform the same data.
enc = OrdinalEncoder()
enc.fit(df[["Sex","Blood", "Study"]])
df[["Sex","Blood", "Study"]] = enc.transform(df[["Sex","Blood", "Study"]])
or directly
enc = OrdinalEncoder()
df[["Sex","Blood", "Study"]] = enc.fit_transform(df[["Sex","Blood", "Study"]])
Note: The values won't be the one that you provided, since internally the fit method use numpy.unique which gives result sorted in alphabetic order and not by order of appearance.
As you can see from enc.categories_
[array(['F', 'M'], dtype=object),
array(['A', 'AB', 'B', 'O'], dtype=object),
array(['Biology', 'English', 'Math', 'Science'], dtype=object)]```
Each value in the array is encoded by it's position. (F will be encoded as 0 , M as 1)
Answer from abcdaire on Stack OverflowYou were almost there !
Basically the fit method, prepare the encoder (fit on your data i.e. prepare the mapping) but don't transform the data.
You have to call transform to transform the data , or use fit_transform which fit and transform the same data.
enc = OrdinalEncoder()
enc.fit(df[["Sex","Blood", "Study"]])
df[["Sex","Blood", "Study"]] = enc.transform(df[["Sex","Blood", "Study"]])
or directly
enc = OrdinalEncoder()
df[["Sex","Blood", "Study"]] = enc.fit_transform(df[["Sex","Blood", "Study"]])
Note: The values won't be the one that you provided, since internally the fit method use numpy.unique which gives result sorted in alphabetic order and not by order of appearance.
As you can see from enc.categories_
[array(['F', 'M'], dtype=object),
array(['A', 'AB', 'B', 'O'], dtype=object),
array(['Biology', 'English', 'Math', 'Science'], dtype=object)]```
Each value in the array is encoded by it's position. (F will be encoded as 0 , M as 1)
I think it is important to point out that this is not an example for an ordinal encoding of variables. Sex, Blood and Study should all not have an ordinal scale (and was also not suggested by the person, who asked the question). Ordinal data has a ranking (see e.g. https://en.wikipedia.org/wiki/Ordinal_data) Those examples here do not have a ranking.
In the case that your variable is a target variable you can use the LabelEncoder.(https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html)
Then you can do something like:
from sklearn.preprocessing import LabelEncoder
for col in ["Sex","Blood", "Study"]:
df[col] = LabelEncoder().fit_transform(df[col])
If your variables are features you should use the Ordinalencoder for accomplishing this. (See comments to my answer).
The naming for the Ordinalencoder is quite unfortunate as "ordinal" is seen from a mathematical and not a statistical naming perspective.
More on the difference between ordinal- and labelencoder in sklearn: https://datascience.stackexchange.com/questions/39317/difference-between-ordinalencoder-and-labelencoder
The main distinction between LabelEncoder and OrdinalEncoder is their purpose:
LabelEncodershould be used for target variables,OrdinalEncodershould be used for feature variables.
In general they work the same, but:
LabelEncoderneeds y: array-like of shape [n_samples],OrdinalEncoderneeds X: array-like, shape [n_samples, n_features].
If you just want to encode your categorical variable's values to 0, 1, ..., n, use LabelEncoder the same way you did with X1 and X2.
labelencoder_X_3 = LabelEncoder()
X[:, 2] = labelencoder_X_3.fit_transform(X[:, 2])
But I would transform all three variables with OrdinalEncoder at the same time:
ordinalencoder_X = OrdinalEncoder()
X[:, 0:3] = ordinalencoder_X.fit_transform(X[:, 0:3])
Instead of using Ordinal Encoder, One other option is to use Pandas Applymap function and pass the mapping dictionary using Lambda Function.
Here is the mapping dictionary:
mapping = { "Tiny Mongra" : 0,"Mini Mongra" : 1,"Mongra":2,"Super Mongra" : 3,"Mini
Dubar":4,"Dubar":5,"Super Dubar":6,"Mini Tibar":7,"Tibar":8,"Super Tibar":9,"2nd
Wand":10,"Super 2nd Wand" :11,"1st Wand":12}
Lets say below is my dataframe:
df = pd.DataFrame(['Tiny Mongra', 'Mini Dubar' ,'Mongra', '1st Wand' ,'1st Wand'
,'Dubar' ,'2nd Wand','Tibar', 'Mongra', 'Super Dubar', '1st Wand', '1st Wand', '1st
Wand' ,'1st Wand','1st Wand', '2nd Wand' ,'Super Dubar' ,'Super Tibar' ,'1st Wand',
'1st Wand'], columns = ['category'])
Then you can create another encoded mapping column using below code:
df['mapped_category'] = df.applymap(lambda x : mapping[x])