TypeError：参数必须是字符串或数字 [英] TypeError: argument must be a string or number

问题描述

我正在使用以下代码：

cat_cols = ['MSZoning','Alley','LotShape','LandContour','Utilities','LotConfig','LandSlope','Neighborhood','Condition1','Condition2','BldgType','HouseStyle','RoofStyle','RoofMatl','Exterior1st','Exterior2nd','MasVnrType','ExterQual','ExterCond','Foundation','BsmtQual','BsmtCond','BsmtExposure','BsmtFinType1','BsmtFinType2','Heating','HeatingQC','CentralAir','Electrical','KitchenQual','Functional','FireplaceQu','GarageType','GarageFinish','GarageQual','GarageCond','PavedDrive','PoolQC','Fence','MiscFeature','SaleType','SaleCondition']

from sklearn.preprocessing import LabelEncoder
le=LabelEncoder()

for col in cat_cols:
    if col in dataset_train.columns:
        i = dataset_train.columns.get_loc(col)
        dataset_train.iloc[:,i] =le.fit_transform(dataset_train.iloc[:,i])

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出现错误，如belo所示w：

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It gives an error as shown below:

TypeError：参数必须是字符串或数字

TypeError: argument must be a string or number

推荐答案

这是解决问题的方法

这是我编写的代码。 （ps：幸运的是我有房价预测数据集：D）

this is the code I wrote. (ps: luckily i have the house price prediction dataset with me :D")

from sklearn.preprocessing import LabelEncoder

path="....\house pricing"
filepath=os.path.join(path,"train.csv")

dataset_train=pd.read_csv(filepath)
dataset_train

cat_features=[x for x in dataset_train.columns if dataset_train[x].dtype=="object"]

le=LabelEncoder()

for col in cat_features:
    if col in dataset_train.columns:
        i = dataset_train.columns.get_loc(col)
        dataset_train.iloc[:,i] = dataset_train.apply(lambda i:le.fit_transform(i.astype(str)), axis=0, result_type='expand')

因此，您只需要修改

dataset_train.iloc[:,i] =le.fit_transform(dataset_train.iloc[:,i])

与

dataset_train.iloc[:,i] = dataset_train.apply(lambda i:le.fit_transform(dataset_train[i].astype(str)), axis=0, result_type='expand')

上面的lamda函数将转换每列及其数据点（行 axis = 0 ）更改为 str然后将其通过 le或LableEncoder函数通过 fit_transform对其进行标签编码。

The above lamda function will convert each column and its data points(row wise axis=0) to "str" and then pass it through the "le" or LableEncoder function via the "fit_transform" to LabelEncode it.

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