Sklearn LabelEncoder在sort中抛出TypeError

Question

我正在使用Kaggle的Titanic数据集学习机器学习。我正在使用sklearn的LabelEncoder将文本数据转换为数字标签。以下代码适用于＆＃34; Sex＆＃34;但不是为了＃34;已经开始＆＃34;。

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

这是我得到的错误

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'

Answer 1

我自己解决了。问题是特定功能具有NaN值。用数值替换它仍会引发错误，因为它具有不同的数据类型。所以我用字符值

替换它

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

Answer 2

尝试此功能，您需要传递Pandas Dataframe。它将查看列的类型和编码。所以你自己也不需要费心去检查这些类型。

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