我正在尝试预处理我的数据集，但运行时出现错误[关闭]

Question

错误！预处理阶段 Wednesday-workingHours.pcap_ISCX 文件已完成。

这是我收到的错误，这是我尝试使用的代码，它应该可以有效运行：

import pandas as pd
import os
from sklearn import preprocessing
import time
seconds = time.time()
%matplotlib inline

print("This process may take 5 to 10 minutes, depending on the performance of your computer.\n\n\n")
number="0123456789"
# CSV files names:
csv_files=["Monday-WorkingHours.pcap_ISCX",
        "Tuesday-WorkingHours.pcap_ISCX",
        "Wednesday-workingHours.pcap_ISCX",
        "Thursday-WorkingHours-Morning-WebAttacks.pcap_ISCX",
        "Thursday-WorkingHours-Afternoon-Infilteration.pcap_ISCX",
        "Friday-WorkingHours-Morning.pcap_ISCX",
        "Friday-WorkingHours-Afternoon-PortScan.pcap_ISCX",
        "Friday-WorkingHours-Afternoon-DDos.pcap_ISCX",]

# Headers of column
main_labels=["Flow ID","Source IP","Source Port","Destination IP","Destination Port","Protocol","Timestamp","Flow Duration","Total Fwd Packets",
"Total Backward Packets","Total Length of Fwd Packets","Total Length of Bwd Packets","Fwd Packet Length Max","Fwd Packet Length Min",
"Fwd Packet Length Mean","Fwd Packet Length Std","Bwd Packet Length Max","Bwd Packet Length Min","Bwd Packet Length Mean","Bwd Packet Length Std",
"Flow Bytes/s","Flow Packets/s","Flow IAT Mean","Flow IAT Std","Flow IAT Max","Flow IAT Min","Fwd IAT Total","Fwd IAT Mean","Fwd IAT Std","Fwd IAT Max",
"Fwd IAT Min","Bwd IAT Total","Bwd IAT Mean","Bwd IAT Std","Bwd IAT Max","Bwd IAT Min","Fwd PSH Flags","Bwd PSH Flags","Fwd URG Flags","Bwd URG Flags",
"Fwd Header Length","Bwd Header Length","Fwd Packets/s","Bwd Packets/s","Min Packet Length","Max Packet Length","Packet Length Mean","Packet Length Std",
"Packet Length Variance","FIN Flag Count","SYN Flag Count","RST Flag Count","PSH Flag Count","ACK Flag Count","URG Flag Count","CWE Flag Count",
"ECE Flag Count","Down/Up Ratio","Average Packet Size","Avg Fwd Segment Size","Avg Bwd Segment Size","faulty-Fwd Header Length","Fwd Avg Bytes/Bulk",
"Fwd Avg Packets/Bulk","Fwd Avg Bulk Rate","Bwd Avg Bytes/Bulk","Bwd Avg Packets/Bulk","Bwd Avg Bulk Rate","Subflow Fwd Packets","Subflow Fwd Bytes",
"Subflow Bwd Packets","Subflow Bwd Bytes","Init_Win_bytes_forward","Init_Win_bytes_backward","act_data_pkt_fwd",
"min_seg_size_forward","Active Mean","Active Std","Active Max","Active Min","Idle Mean","Idle Std","Idle Max","Idle Min","Label","External IP"]

main_labels2=main_labels
main_labels=( ",".join( i for i in main_labels ) )
main_labels=main_labels+"\n"
flag=True
for i in range(len(csv_files)):
    ths = open(str(i)+".csv", "w")
    ths.write(main_labels)
    with open("./CSVs/"+csv_files[i]+".csv", "r") as file:
        while True:
            try:
                line=file.readline()
                if  line[0] in number:# this line eliminates the headers of CSV files and incomplete streams .
                    if " – " in str(line): ##  if there is "–" character ("–", Unicode code:8211) in the flow ,  it will be chanced with "-" character ( Unicode code:45).
                        line=(str(line).replace(" – "," - "))
                    line=(str(line).replace("inf","0"))
                    line=(str(line).replace("Infinity","0"))
                    line=(str(line).replace("NaN","0"))
                    
                    ths.write(str(line))
                else:
                    continue                       
            except:
                break
    ths.close()


    df=pd.read_csv(str(i)+".csv",low_memory=False)
    df=df.fillna(0)

    string_features=["Flow Bytes/s","Flow Packets/s"]
    for ii in string_features: #Some data in the "Flow Bytes / s" and "Flow Packets / s" columns are not numeric. Fixing this bug in this loop
        df[ii]=df[ii].replace('Infinity', -1)
        df[ii]=df[ii].replace('NaN', 0)
        number_or_not=[]
        for iii in df[ii]:
            try:
                k=int(float(iii))
                number_or_not.append(int(k))
            except:
                number_or_not.append(iii)
        df[ii]=number_or_not



    string_features=[]
    for j in main_labels2: # In this section, non-numeric (string and / or categorical) properties (columns) are detected.
        if df[j].dtype=="object":
            string_features.append(j)
    try:
        string_features.remove('Label')#The "Label" property was removed from the list. Because it has to remain "categorical" for using with different machine learning approach.
    except:
        print("error!")
    labelencoder_X = preprocessing.LabelEncoder()



    for ii in string_features: ## In this loop, non-numeric (string and/or categorical) properties converted to numeric features.
        try:
            df[ii]=labelencoder_X.fit_transform(df[ii])
        except:
            df[ii]=df[ii].replace('Infinity', -1)
    df=df.drop(main_labels2[61], axis=1) ## Column 61 is deleted because it is unnecessary, column 41 ("Fwd Header Length" feature) had be mistakenly rewritten.



    ##All CSV files are merged into a single file.
    if flag:
        df.to_csv('all_data.csv' ,index = False)
        flag=False
    else:
        df.to_csv('all_data.csv' ,index = False,header=False,mode="a")
    os.remove(str(i)+".csv")
    print("The pre-processing phase of the ",csv_files[i]," file is completed.\n")
    

print("mission accomplished!")
print("Total operation time: = ",time.time()- seconds ,"seconds")

我做错了什么吗？有办法解决吗？

Answer 1

 try:
    string_features.remove('Label')#The "Label" property was removed from the list. Because it has to remain "categorical" for using with different machine learning approach.
except:
    print("error!")

在您的代码中，您只需打印并且不退出或再次引发异常，以便代码继续。

 try:
    string_features.remove('Label')#The "Label" property was removed from the list. Because it has to remain "categorical" for using with different machine learning approach.
except Exception as e:
    print("error!")
    raise e

这样捕获的异常会再次引发，你会看到它。