Python df_的索引器有效

我正在使用python 3.6.8

我使用循环将某些列中的值转换为int：

for i in cols:
    df_valid[[i]] = df_valid[[i]].astype(int)

其中显示了给定的错误

错误：

索引器错误：只有整数、片（`:`）、省略号（`…`）、numpy.newaxis（`None`）以及整数或布尔数组是有效的索引

如下面的完整代码所示，我在df_列车上使用了相同的方法。但是，它没有产生任何错误。我想这一定是因为

df\u valid=inputer.transform（df\u valid）

。但是，我无法解决它

您能否帮助并提供解决此错误的方向

我的完整代码如下所示：

import argparse
import os

import joblib
import pandas as pd
from sklearn.impute import KNNImputer
from sklearn import metrics

import config
import model_dispatcher


def run(fold, model):

 df = pd.read_csv(config.TRAINING_FILE)

 df["Gender"] = df["Gender"].map({"Male": 1, "Female": 0})
 df["Married"] = df["Married"].map({"No": 0, "Yes": 1})
 df["Self_Employed"] = df["Self_Employed"].map({"No": 0, "Yes": 1})
 df["Dependents"] = df["Dependents"].map({"0": 0, "1": 1, "2": 2, "3+": 3})
 df["Education"] = df["Education"].map({"Graduate": 1, "Not Graduate": 0})
 df["Loan_Status"] = df["Loan_Status"].map({"N": 0, "Y": 1})

 cols = ["Gender",
        "Married",
        "Dependents",
        "Education",
        "Self_Employed",
        "Credit_History",
        "Loan_Status"]

 dummy = pd.get_dummies(df["Property_Area"])
 df = pd.concat([df, dummy], axis=1)
 df = df.drop(["Loan_ID", "Property_Area"], axis=1)

 df_train = df[df.kfold != fold].reset_index(drop=True)

 df_valid = df[df.kfold == fold].reset_index(drop=True)

 imputer = KNNImputer(n_neighbors=18)
 df_train = pd.DataFrame(imputer.fit_transform(df_train),
                        columns=df_train.columns)
 for i in cols:
    df_train[[i]] = df_train[[i]].astype(int)

 df_valid = imputer.transform(df_valid)
 for i in cols:
    df_valid[[i]] = df_valid[[i]].astype(int)

 df_train['GxM'] = df_train.apply(lambda row:
                                 (row['Gender']*row['Married']),
                                 axis=1)
 df_train['Income_sum'] = (
                        df_train.apply(lambda row:
                                       (row['ApplicantIncome'] +
                                        row['CoapplicantIncome']),
                                       axis=1))
 df_train['DxE'] = df_train.apply(lambda row: (row['Education'] *
                                              row['Dependents']),
                                 axis=1)
 df_train['DxExG'] = (
                    df_train.apply(lambda row:
                                   (row['Education'] *
                                    row['Dependents'] *
                                    row['Gender']),
                                   axis=1))

 df_valid['GxM'] = df_valid.apply(lambda row:
                                 (row['Gender']*row['Married']),
                                 axis=1)
 df_valid['Income_sum'] = (
                        df_valid.apply(lambda row:
                                       (row['ApplicantIncome'] +
                                        row['CoapplicantIncome']),
                                       axis=1))
 df_valid['DxE'] = df_valid.apply(lambda row: (row['Education'] *
                                              row['Dependents']),
                                 axis=1)
 df_valid['DxExG'] = (
                    df_valid.apply(lambda row:
                                   (row['Education'] *
                                    row['Dependents'] *
                                    row['Gender']),
                                   axis=1))

 X_train = df_train.drop("Loan_Status", axis=1).values
 y_train = df_train.Loan_Status.values

 X_valid = df_valid.drop("Loan_Status", axis=1).values
 y_valid = df_valid.Loan_Status.values

 clf = model_dispatcher.models[model]

 clf.fit(X_train, y_train)

 preds = clf.predict(X_valid)

 rascore = metrics.roc_auc_score(y_valid, preds)
 print(f"Fold = {fold}, ROC-AUC = {rascore}")

 joblib.dump(
    clf,
    os.path.join(config.MODEL_OUTPUT, f"dt_{fold}.bin")
 )

if __name__ == "__main__":

    parser = argparse.ArgumentParser()

    parser.add_argument("--fold", type=int)

    parser.add_argument("--model", type=str)

    args = parser.parse_args()

    run (fold=args.fold, model=args.model)

---

要将所有列转换为整数格式，只需给出：

df_valid.apply(pd.to_numeric).dtypes

有关pd.to_数值的更多详细信息，请参阅

---

您可能还想在此阅读更多关于将数据转换为不同数据类型的信息

我刚刚整理了一些代码。请看看是否可以。附言：我没有投反对票。我想这里有很多代码。你可能只想分享一段相关的代码，这样我们就可以清楚地知道我们需要寻找什么。如果你有太多的噪音（太多的代码），它会分散你对主要问题的注意力。我已经澄清了这个问题。看看它看起来是否正常。你是想写

df\u valid[i]

而不是

df\u valid[[i]]

。另外，如果您想将所有列转换为整数，您不必像这样循环它们。您可以给

df\u valid.apply（pd.to\u numeric.dtypes）

将所有列转换为整数datatype@JoeFerndz谢谢你的帮助。pd.to_数字作品！：）