Question

我是一个使用python广告的初学者，尝试半自动导入YNAB。

如果我使用“ askopenfilename”并将每个csv分开，则可以使用它，但是我无法使其与askopenfilenames一起使用并在for项内循环。

然后我收到以下错误消息： ValueError：无效的文件路径或缓冲区对象类型：

附上python代码：

import pandas as pd
import numpy as np
from tkinter import filedialog
from tkinter import *
root = Tk()
root.filenames = filedialog.askopenfilenames(initialdir="C:/Users/dangeph/Desktop/Favoriten/Privat/YNAB/Download_Finanzblick/", title="Select file",
                                           filetypes=(("csv files", "*.csv"), ("all files", "*.*")))
print(root.filenames)
for item in root.filenames:
    print(item)
from pandas.core.tools.numeric import to_numeric
df = pd.read_csv(root.filenames, sep=';', usecols=(0, 1, 2, 3, 4),
                 encoding='utf-8', decimal=',')
df.columns = ['Date', 'Payee', 'Verwendungszweck', 'Buchungstext', 'Betrag']
df['Memo'] = df[['Buchungstext', 'Verwendungszweck']].apply(lambda x: ' -- '.join(x), axis=1)
Betrag = df.Betrag.astype(int)
df['Inflow'] = np.where(df.Betrag > 0, df.Betrag, "")
df['Outflow'] = np.where(df.Betrag < 0, df.Betrag * (-1), "")
path = (root.filenames.replace('Download_Finanzblick', 'Converted_YNAB')[:-4] + '-converted.csv')
print(path)
df.to_csv(path, sep=';', index=False,
          columns=['Date', 'Payee', 'Memo', 'Inflow', 'Outflow'], decimal='.')
next(item)

和错误消息：

“ C：\ Program Files（x86）\ Microsoft Visual   Studio \ Shared \ Anaconda3_64 \ python.exe”   “ C：/用户/ dangeph /存储库/ Finanzblick YNAB / Testfile.py”   （'C：/ Users / dangeph / Desktop / Favoriten / Privat / YNAB / Download_Finanzblick / 2019_YY / Amazon   -Kopie.csv'，'C：/ Users / dangeph / Desktop / Favoriten / Privat / YNAB / Download_Finanzblick / 2019_YY / Amazon   -Kopie-converted.csv'，'C：/Users/dangeph/Desktop/Favoriten/Privat/YNAB/Download_Finanzblick/2019_YY/Amazon.csv'，   'C：/Users/dangeph/Desktop/Favoriten/Privat/YNAB/Download_Finanzblick/2019_YY/Amazon-converted.csv'，   'C：/Users/dangeph/Desktop/Favoriten/Privat/YNAB/Download_Finanzblick/2019_YY/DB.csv'，   'C：/Users/dangeph/Desktop/Favoriten/Privat/YNAB/Download_Finanzblick/2019_YY/DiBA.csv'，   'C：/Users/dangeph/Desktop/Favoriten/Privat/YNAB/Download_Finanzblick/2019_YY/DKB.csv'，   'C：/Users/dangeph/Desktop/Favoriten/Privat/YNAB/Download_Finanzblick/2019_YY/DKB-Visa.csv'，   'C：/Users/dangeph/Desktop/Favoriten/Privat/YNAB/Download_Finanzblick/2019_YY/Paypal.csv'，   'C：/Users/dangeph/Desktop/Favoriten/Privat/YNAB/Download_Finanzblick/2019_YY/VB.csv'）   C：/用户/ dangeph /桌面/收藏夹/ Privat / YNAB / Download_Finanzblick / 2019_YY / Amazon   -Kopie.csv C：/ Users / dangeph / Desktop / Favoriten / Privat / YNAB / Download_Finanzblick / 2019_YY / Amazon   -Kopie-converted.csv C：/Users/dangeph/Desktop/Favoriten/Privat/YNAB/Download_Finanzblick/2019_YY/Amazon.csv   C：/用户/ dangeph /桌面/收藏夹/Privat/YNAB/Download_Finanzblick/2019_YY/Amazon-converted.csv   C：/用户/ dangeph /桌面/收藏夹/Privat/YNAB/Download_Finanzblick/2019_YY/DB.csv   C：/用户/ dangeph /桌面/收藏夹/Privat/YNAB/Download_Finanzblick/2019_YY/DiBA.csv   C：/用户/ dangeph /桌面/收藏夹/Privat/YNAB/Download_Finanzblick/2019_YY/DKB.csv   C：/用户/ dangeph /桌面/收藏夹/Privat/YNAB/Download_Finanzblick/2019_YY/DKB-Visa.csv   C：/Users/dangeph/Desktop/Favoriten/Privat/YNAB/Download_Finanzblick/2019_YY/Paypal.csv   C：/用户/ dangeph /桌面/收藏夹/Privat/YNAB/Download_Finanzblick/2019_YY/VB.csv   追溯（最近一次通话）：文件   “ C：/ Users / dangeph / Repository / Finanzblick YNAB / Testfile.py”，第21行，   在       编码='utf-8'，十进制='，'）文件“ C：\ Program Files（x86）\ Microsoft Visual   Studio \ Shared \ Anaconda3_64 \ lib \ site-packages \ pandas \ io \ parsers.py”，   parser_f中的第678行       返回_read（filepath_or_buffer，kwds）文件“ C：\ Program Files（x86）\ Microsoft Visual   Studio \ Shared \ Anaconda3_64 \ lib \ site-packages \ pandas \ io \ parsers.py”，   _read中的第424行       filepath_or_buffer，编码，压缩）“ C：\ Program Files（x86）\ Microsoft Visual   Studio \ Shared \ Anaconda3_64 \ lib \ site-packages \ pandas \ io \ common.py”，   第218行，位于get_filepath_or_buffer中       引发ValueError（msg.format（_type = type（filepath_or_buffer）））ValueError：无效的文件路径或缓冲区对象类型：

以退出代码1完成的过程

ValueError：无效的文件路径或缓冲区对象类型：<class'tuple'=“”>

0 个答案: