pandas 和scikit-learn:KeyError:[....]不在索引中 [英] Pandas and scikit-learn: KeyError: [....] not in index

问题描述

我不明白为什么在运行此代码时出现错误KeyError: '[ 1351 1352 1353 ... 13500 13501 13502] not in index':

I do not understand why do I get the error KeyError: '[ 1351 1352 1353 ... 13500 13501 13502] not in index' when I run this code:

cv = KFold(n_splits=10)

for train_index, test_index in cv.split(X):
    f_train_X, f_valid_X = X[train_index], X[test_index]
    f_train_y, f_valid_y = y[train_index], y[test_index]

我使用X(熊猫数据框)拆分我cv.split(X).

I use X (a Pandas dataframe) to split I cv.split(X).

X.shape
y.shape
Out: (13503, 17)
Out: (13503,)

推荐答案

问题是您尝试使用X[train_index]为X编制索引的方式. 由于具有pandas数据框，因此需要使用.loc或.iloc.

The problem is the way you are trying to index the X using X[train_index]. You need to use .loc or .iloc since you have pandas dataframe.

cv = KFold(n_splits=10)

for train_index, test_index in cv.split(X):
    f_train_X, f_valid_X = X.iloc[train_index], X.iloc[test_index]
    f_train_y, f_valid_y = y.iloc[train_index], y.iloc[test_index]

第一种方式:使用iloc

的示例

1st way: Example using iloc

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randint(0,100,size=(100, 4)), columns=list('ABCD'))

df[[1,2]]
#KeyError: '[1 2] not in index'

df.iloc[[1,2]]
#    A   B   C   D
#1  25  97  78  74
#2   6  84  16  21

第二种方式:例如，将熊猫预先转换为numpy

df = df.values

#now this should work fine
df[[1,2]]
#array([[25, 97, 78, 74],
#      [ 6, 84, 16, 21]])

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