Python 添加 def myfucntion() 時腳本不起作用：

Question

我有一些代碼在我單獨運行時可以工作，但是當我將其定義為 function 時運行它時不起作用。 我沒有收到任何錯誤，所以它確實運行了，但是它不會拉回最新文件或更新 CSV 文件，它只是說和前一天一樣。

這是為了更新報告，之前同事運行它時正在工作，但我自己無法讓它工作。 下面的代碼是有效的：

def typose(): 
    today = datetime.today().strftime('%d%m%Y')
    

    yesterday = datetime.now() - timedelta(1)
    yesterday1 = yesterday.strftime('%d%m%Y')
  
    




###############################################################################
#################### ESTABLISH CONNECTION TO ESENDEX SFTP #####################
###############################################################################    


# Open a transport
# host,port = "sftp.esendex.com",22
    host,port = "10.132.0.1",22
    transport = paramiko.Transport((host,port))

# Auth    
    username,password = "bocsurveys","lfxDmr4i"
    transport.connect(None,username,password)

# Go!    
    sftp = paramiko.SFTPClient.from_transport(transport)


###############################################################################
######################## PICK UP THE FILE FOR THE SMS #########################
############################### FROM ESENDEX ##################################

# Download the SMS
    filepathsms = "/FromEsendex/CX_Survey_SMS_output_2_"+today+".csv"
    localpathsms = "C:/Users/l0ad06/Desktop/Daily Feedback from Esendex/CX_Survey_SMS_output_2_"+today+".csv"
    sftp.get(filepathsms ,localpathsms)

    filepathsms2 = "/FromEsendex/CX_Survey_SMS_output_1_"+yesterday1+".csv"
    localpathsms2 = "C:/Users/l0ad06/Desktop/Daily Feedback from Esendex/CX_Survey_SMS_output_1_"+yesterday1+".csv"
    sftp.get(filepathsms2 ,localpathsms2)


    filename = "C:/Users/l0ad06/Desktop/Daily Feedback from Esendex/CX_Survey_SMS_output_2_"+today+".csv"
    filename2 = "C:/Users/l0ad06/Desktop/Daily Feedback from Esendex/CX_Survey_SMS_output_1_"+yesterday1+".csv"



###############################################################################
################## CREATING ONE RECORD PER DELIVERY NUMBER ####################
###############################################################################
    ##df1 = pandas.read_csv(filename,
    ##                usecols= ['Question Label','Answer Label',
    ##                         'Answer DateTime','Delivery Number',
    ##                         'ShipTo Number'], encoding= 'unicode_escape')
    
    df1 = pandas.read_csv(filename, usecols =[2,4,5,12,23],
                   encoding= 'unicode_escape')


    df1 = df1.rename(columns= {df1.columns[0]: "Question Label",
                             df1.columns[1]: "Answer Label",
                             df1.columns[2]: "Answer DateTime",
                             df1.columns[3]: "Delivery Number",
                             df1.columns[4]: "ShipTo Number"})

     
    
# Filter only the records with scores
    clean_data1 = df1[df1['Question Label'] != 2]
    clean_data1 = clean_data1[clean_data1["Question Label"].notnull()]
    clean_data2 = clean_data1[clean_data1['Answer Label'] != 'Error']

    clean_df1 = pandas.DataFrame(clean_data2,
                       columns = ['Answer Label',
                                  'Answer DateTime',
                                  'Delivery Number',
                                  'ShipTo Number'])

# Rename the columns
    cleandf1 = clean_df1.rename(columns={"Answer Label": "Score",
                        "Answer DateTime": "Created",
                        "Delivery Number": "Delivery",
                        "ShipTo Number": "ShipTo" }) 




    ##df2 = pandas.read_csv(filename,
    ##               usecols= ['Question Label',
    ##                       'Answer DateTime',
    ##                     'Answer Text',
    ##                   'Delivery Number',
    ##                 'ShipTo Number'], encoding= 'unicode_escape')
    
    
    df2 = pandas.read_csv(filename, usecols =[2,5,6,12,23],
                   encoding= 'unicode_escape')
  

    df2 = df2.rename(columns= {df2.columns[0]: "Question Label",
                             df2.columns[1]: "Answer DateTime",
                             df2.columns[2]: "Answer Text",
                             df2.columns[3]: "Delivery Number",
                             df2.columns[4]: "ShipTo Number"})


# Filter only the records with comments
    clean_data3 = df2[df2['Question Label'] != 1]
    clean_data3 = clean_data3[clean_data3["Question Label"].notnull()]
    clean_df2 = pandas.DataFrame(clean_data3,
                       columns = ['Answer Text',
                                'Delivery Number',
                                'ShipTo Number'])

# Rename the columns
    cleandf2 = clean_df2.rename(columns={"Answer Text": "Comment",
                                   "Delivery Number": "Delivery",
                                   "ShipTo Number": "ShipTo" }) 





  ##  df3 = pandas.read_csv(filename,
  ##                usecols= ['Classification Code','Classification Text',
  ##                              'Country Code',
  ##                            'Customer Post Code','Delivery Number',
  ##                             'GroupTo Code','GroupTo Name',
  ##                            'PGI Date','Plant Code',
  ##                           'Plant Name','Pricing Area',
  ##                           'Pricing Area Text','Sales Organisation',
  ##                         'ShipTo Number'], encoding= 'unicode_escape')
    
    df3 = pandas.read_csv(filename,
                    usecols= [7,8,9,11,12,13,14,16,17,18,19,20,22,23], encoding= 'unicode_escape')
  



    df3 = df3.rename(columns = {df3.columns[0]: "Classification Code",
                              df3.columns[1]: "Classification Text",
                              df3.columns[2]: "Country Code",
                              df3.columns[3]: "Customer Post Code",
                              df3.columns[4]: "Delivery Number",
                              df3.columns[5]: "GroupTo Code",
                              df3.columns[6]: "GroupTo Name",
                              df3.columns[7]: "PGI Date",
                              df3.columns[8]: "Plant Code",
                              df3.columns[9]: "Plant Name",
                              df3.columns[10]: "Pricing Area",
                              df3.columns[11]: "Pricing Area Text",
                              df3.columns[12]: "Sales Organisation",
                              df3.columns[13]: "ShipTo Number"})


    # dropping ALL duplicte values 
    clean_df3 = df3.drop_duplicates() 
   

    cleandf3 = clean_df3.rename(columns={"Classification Code": "Classification_Code",
                                 "Classification Text": "Classification_Text",
                                 "Customer Post Code": "Customer_Postcode",
                                 "Country Code": "Country_Code",
                                 "Delivery Number": "Delivery",
                                 "GroupTo Code": "Group_To",
                                 "GroupTo Name": "Group_To_Name",
                                 "PGI Date": "PGI_Date",
                                 "Plant Code": "Plant",
                                 "Plant Name": "Plant_Name",
                                 "Pricing Area": "Pricing_Area",
                                 "Pricing Area Text": "Pricing_Area_Description",
                                 "Sales Organisation": "Sales_Organisation",
                                 "ShipTo Number": "ShipTo"  }) 




# Join the tables
    result1 = pandas.merge(cleandf1, cleandf2, how='left', on=['Delivery','ShipTo'])


    result2 = pandas.merge(result1, cleandf3, how='left', on=['Delivery','ShipTo'])

# Check the data types
    result2.dtypes

    result2['Created'] = pandas.to_datetime(yesterday)
    
  
# Change the data types
    result2 = result2.astype({'Score': 'str',
                           'Created': 'datetime64[ns]',
                           'Delivery': 'int64',
                           'ShipTo': 'int64',
                           'Comment':'str',
                           'Classification_Code':'str',
                           'Classification_Text':'str',
                           'Country_Code':'str',
                         'Customer_Postcode':'str',
                         'Group_To': 'float',
                         'Group_To_Name':'str',  
                        'PGI_Date': 'int64',
                         'Plant':'int64',
                         'Plant_Name':'str', 
                         'Pricing_Area':'str', 
                         'Pricing_Area_Description':'str', 
                         'Sales_Organisation': 'str'
                         })


    

# Add a column that will give us the channel
    result2['Channel'] = 'SMS'

# Export to a csv
    result2.to_csv(r'C:/Users/l0ad06/Desktop/Daily Feedback from Esendex/CX_Survey_SMS_output_2_'+today+'.csv', index = False)

    schedule.every().day.at("09:00").do(typose)

有誰知道為什么當我添加def typose():?

Answer 1

您的縮進似乎不正確

您的代碼：

     def typose():
       today = datetime.today().strftime('%d%m%Y')

嘗試這個：

def typose():
    today = datetime.today().strftime('%d%m%Y')

Answer 2

有可能是由於以下原因造成的問題！

1. 縮進

您需要而不是執行以下操作

 def typose():
     today = datetime.today().strftime('%d%m%Y')

2. 檢查調用 function

嘗試檢查以下命令是否確實運行了代碼，您可以通過執行以下簡單測試來檢查： schedule.every().day.at("09:00").do(typose)為typose()如果它工作，然后schedule.every().day.at("09:00").do(typose)不運行或調用 function 錯字，所以你可以將它更改為 if 語句，如果它是True它運行代碼。

3. 檢查 function 代碼

鍵入錯誤的值可能會出現問題，該值執行沒有錯誤，但不能正常工作！

如果這沒有幫助，請告訴我