Sklearn LabelEncoder throws TypeError in sort

问题

I am learning machine learning using Titanic dataset from Kaggle. I am using LabelEncoder of sklearn to transform text data to numeric labels. The following code works fine for "Sex" but not for "Embarked".

encoder = preprocessing.LabelEncoder()
features["Sex"] = encoder.fit_transform(features["Sex"])
features["Embarked"] = encoder.fit_transform(features["Embarked"])

This is the error I got

Traceback (most recent call last):
  File "../src/script.py", line 20, in <module>
    features["Embarked"] = encoder.fit_transform(features["Embarked"])
  File "/opt/conda/lib/python3.6/site-packages/sklearn/preprocessing/label.py", line 131, in fit_transform
    self.classes_, y = np.unique(y, return_inverse=True)
  File "/opt/conda/lib/python3.6/site-packages/numpy/lib/arraysetops.py", line 211, in unique
    perm = ar.argsort(kind='mergesort' if return_index else 'quicksort')
TypeError: '>' not supported between instances of 'str' and 'float'

回答1:

I solved it myself. The problem was that the particular feature had NaN values. Replacing it with a numerical value it will still throw an error since it is of different datatypes. So I replaced it with a character value

 features["Embarked"] = encoder.fit_transform(features["Embarked"].fillna('0'))

回答2:

Try this function, you’ll need to pass a Pandas Dataframe. It will look at the type of your column and encode. So you won’t need to even bother checking the types yourself.

def encoder(data):
'''Map the categorical variables to numbers to work with scikit learn'''
for col in data.columns:
    if data.dtypes[col] == "object":
        le = preprocessing.LabelEncoder()
        le.fit(data[col])
        data[col] = le.transform(data[col])
return data

来源：https://stackoverflow.com/questions/43956705/sklearn-labelencoder-throws-typeerror-in-sort