無法連接“類型”的 object<class 'numpy.ndarray'> ”； 只有 pd.Series、pd.DataFrame 和 pd.Panel（已棄用）對象有效</class>

Question

我的輸入數據采用以下形式：

    gold,Program,MethodType,CallersT,CallersN,CallersU,CallersCallersT,CallersCallersN,CallersCallersU,CalleesT,CalleesN,CalleesU,CalleesCalleesT,CalleesCalleesN,CalleesCalleesU,CompleteCallersCallees,classGold
T,chess,Inner,Low,-1,-1,Low,-1,-1,High,-1,-1,-1,-1,Low,1,Trace,
N,chess,Inner,-1,Low,-1,-1,Low,-1,-1,High,-1,-1,-1,Low,1,NoTrace,
N,chess,Inner,-1,Low,-1,-1,Low,-1,-1,High,-1,-1,-1,Low,1,NoTrace,
N,chess,Inner,-1,Low,-1,-1,Low,-1,-1,High,-1,-1,-1,Low,1,Trace,
N,chess,Inner,-1,Low,-1,-1,Low,-1,-1,High,-1,-1,-1,Low,1,NoTrace,
N,chess,Inner,-1,Low,-1,-1,Low,-1,-1,High,-1,-1,-1,Low,1,Trace,
N,chess,Inner,-1,Low,-1,-1,Low,-1,-1,High,-1,-1,-1,Low,1,Trace,
N,chess,Inner,-1,Low,-1,-1,Low,-1,-1,High,-1,-1,-1,Low,1,NoTrace,
N,chess,Inner,-1,Low,-1,-1,Low,-1,-1,Medium,Medium,-1,High,High,0,Trace,
N,chess,Inner,-1,Low,-1,-1,Low,-1,-1,Medium,Medium,-1,High,High,0,NoTrace,
N,chess,Inner,-1,Low,-1,-1,Low,-1,-1,Medium,Medium,-1,High,High,0,NoTrace,
N,chess,Inner,-1,Low,-1,-1,Low,-1,-1,Medium,Medium,-1,High,High,0,Trace,
N,chess,Inner,-1,Low,-1,-1,Low,-1,-1,Medium,Medium,-1,High,High,0,NoTrace,
T,chess,Inner,Low,-1,-1,Low,-1,-1,Medium,-1,Medium,High,-1,High,0,Trace,
T,chess,Inner,Low,-1,-1,Low,-1,-1,Medium,-1,Medium,High,-1,High,0,Trace,
N,chess,Inner,-1,Low,-1,-1,Low,-1,-1,Medium,Medium,-1,High,High,0,NoTrace,
N,chess,Inner,-1,Low,-1,-1,-1,-1,Low,Low,High,Medium,-1,Medium,0,Trace,
N,chess,Inner,-1,Low,-1,-1,-1,-1,-1,Medium,High,Low,Low,Medium,0,NoTrace,
N,chess,Inner,-1,Low,-1,-1,-1,-1,-1,Medium,High,-1,Medium,Medium,0,NoTrace,
T,chess,Inner,-1,Low,-1,-1,-1,-1,-1,Medium,High,Low,Low,Medium,0,Trace,
N,chess,Inner,-1,Low,-1,-1,-1,-1,-1,Medium,High,-1,Medium,Medium,0,NoTrace,
N,chess,Inner,-1,Low,-1,-1,-1,-1,Low,Low,High,Low,Low,Medium,0,Trace,
N,chess,Inner,Low,-1,-1,-1,-1,-1,Low,Low,High,Low,Low,Medium,0,Trace,
N,chess,Inner,-1,Low,-1,-1,-1,-1,-1,Medium,High,-1,Medium,Medium,0,NoTrace,
....
N,chess,Inner,-1,Low,-1,-1,Medium,-1,-1,Low,Low,-1,-1,-1,0,Trace,
N,chess,Inner,-1,Low,-1,-1,Medium,-1,-1,Low,Low,-1,-1,-1,0,NoTrace,
T,chess,Inner,Low,-1,-1,Low,Low,-1,Low,-1,Low,-1,-1,-1,0,Trace,
T,chess,Inner,Low,-1,-1,Medium,-1,-1,Low,-1,Low,-1,-1,-1,0,Trace,
N,chess,Inner,-1,Low,-1,-1,Medium,-1,-1,Low,Low,-1,-1,-1,0,NoTrace,

我正在讀取我的數據，並嘗試連接兩個作為原始數據集子集的數據集，這是我正在使用的代碼：

    import pandas as pd
import numpy as np
from sklearn.feature_selection import SelectFromModel
from sklearn.model_selection import train_test_split
# Feature Scaling
from sklearn.preprocessing import StandardScaler
SeparateProjectLearning=False
CompleteCallersCallees=False
PartialTrainingSetCompleteCallersCallees=True
def main():
    X_train={}
    X_test={}
    y_train={}
    y_test={}
    dataset = pd.read_csv( 'InputData.txt', sep= ',', index_col=False) 
    #convert T into 1 and N into 0
    dataset['gold'] = dataset['gold'].astype('category').cat.codes
    dataset['Program'] = dataset['Program'].astype('category').cat.codes
    dataset['classGold'] = dataset['classGold'].astype('category').cat.codes
    dataset['MethodType'] = dataset['MethodType'].astype('category').cat.codes
    
    dataset['CallersT'] = dataset['CallersT'].astype('category').cat.codes
    dataset['CallersN'] = dataset['CallersN'].astype('category').cat.codes
    dataset['CallersU'] = dataset['CallersU'].astype('category').cat.codes
    dataset['CallersCallersT'] = dataset['CallersCallersT'].astype('category').cat.codes
    dataset['CallersCallersN'] = dataset['CallersCallersN'].astype('category').cat.codes
    dataset['CallersCallersU'] = dataset['CallersCallersU'].astype('category').cat.codes
    dataset['CalleesT'] = dataset['CalleesT'].astype('category').cat.codes
    dataset['CalleesN'] = dataset['CalleesN'].astype('category').cat.codes
    dataset['CalleesU'] = dataset['CalleesU'].astype('category').cat.codes
    dataset['CalleesCalleesT'] = dataset['CalleesCalleesT'].astype('category').cat.codes
    dataset['CalleesCalleesN'] = dataset['CalleesCalleesN'].astype('category').cat.codes
    dataset['CalleesCalleesU'] = dataset['CalleesCalleesU'].astype('category').cat.codes 
    pd.set_option('display.max_columns', None)
    row_count, column_count = dataset.shape
    Xcol = dataset.iloc[:, 1:column_count]
        
              
    
    
    CompleteSet=dataset.loc[dataset['CompleteCallersCallees'] == 1]
    CompleteSet_X = CompleteSet.iloc[:, 1:column_count].values
    CompleteSet_Y = CompleteSet.iloc[:, 0].values
    X_train, X_test, y_train, y_test = train_test_split(CompleteSet_X, CompleteSet_Y, test_size = 0.2, random_state = 0)
    TestSet=dataset.loc[dataset['CompleteCallersCallees'] == 0]
    X_test1=TestSet.iloc[:, 1:column_count].values
    X_test=pd.concat(X_test1,X_test)

我想通過使用連接來構建我自己的測試集和訓練集，並且我試圖在上面的代碼中連接X_test1和X_test 。 但是，問題是我收到最后一行代碼X_test=pd.concat(X_test1,X_test)的錯誤，錯誤顯示TypeError: cannot concatenate object of type "<class 'numpy.ndarray'>"; only pd.Series, pd.DataFrame, and pd.Panel (deprecated) objs are valid TypeError: cannot concatenate object of type "<class 'numpy.ndarray'>"; only pd.Series, pd.DataFrame, and pd.Panel (deprecated) objs are valid 。 我怎樣才能解決這個問題？

Answer 1

通過在以下幾行.values添加到過濾器的末尾：

CompleteSet_X = CompleteSet.iloc[:, 1:column_count].values
CompleteSet_Y = CompleteSet.iloc[:, 0].values
X_test1=TestSet.iloc[:, 1:column_count].values

You are extracting the underlying Numpy ndarray from the Pandas Series / DataFrame the prior code extracts, just remove .values at the end and you can use concat directly with the Series or DataFrame .