仅在从工作代码构建 .exe 时：AttributeError: Can only use .dt accessor with datetimelike values

Question

我有一个基于 pandas 的工作 python 脚本。 将类似的脚本转换为 .exe 在我的计算机上工作。不幸的是，我家的电脑并非如此。我尝试了 pyinstaller 和 py2exe 都出现了这个错误。 在我看来，转换产生了许多错误（我已经修复了其中一些），所以我认为这最终与日期时间问题无关。

import pandas as pd
import os
import glob
from datetime import datetime
import shutil
import os.path

try:
    parentfolder = os.path.dirname(__file__)
    parentfolder = os.path.abspath(os.path.join(parentfolder, '..'))#parentfolder der skriptdatei
except NameError:  # We are the main py2exe script, not a module
    import sys 
    parentfolder = os.path.dirname(sys.argv[0])
    parentfolder = os.path.abspath(os.path.join(parentfolder, '..'))#parentfolder der skriptdatei

today = datetime.now()
day1 = today.strftime("%d-%m-%Y")
time1= today.strftime("%d-%m-%Y_%H-%M-%S")
day1=day1+'_cleaned'

logname="logfile_" + time1 + ".txt"

resultfolder=os.path.join(parentfolder, day1)
logfile = os.path.join(resultfolder, logname)
if os.path.exists(resultfolder):
    shutil.rmtree(resultfolder) #deletes folder and all subfolders
os.makedirs(resultfolder)
    
pd.set_option('display.max_columns', 5)
pd.set_option('display.max_colwidth', 99)

f = open(logfile, "w")
f.close()
all_files = glob.glob(parentfolder + "/*.xls")
filecounter=0
first_run_counter=0
first_day_counter=0
for filename in all_files:
    file_name=(os.path.splitext(os.path.basename(filename))[0])
    
    writepath = os.path.join(resultfolder, '{}.xlsx'.format(str(file_name)+"_py"))
    writer = pd.ExcelWriter(writepath, engine = 'xlsxwriter')
    with open(logfile, "a") as file:
        file.write("{} \n".format(str(file_name)))  
    
    filecounter +=  1
    if filecounter > 1:
        print("WARNING, JUST CONVERT 1 FILE")
        break
    list1 = []
    dfs_by_day= []    
    df = pd.read_excel(filename,header=None,parse_dates=False)#ohne header einlesen ,decimal=","
    #df=df.convert_dtypes(convert_string=True)
    df_help=df.copy()   
    df_help[1] = df_help[1].astype(str)
    df_help[0] = df_help[0].astype(str)
    #####datei ordnen,filtern etc
    df.dropna(axis=0,how='any',thresh=None,subset=None,inplace=True)#löscht zeilen mit leeren zellen    
    df.drop_duplicates(inplace=True) #dropt auch doppelte header?!
    df.reset_index(drop=True, inplace=True)
    
    new_header = df.iloc[0] #grab the first row for the header
    df = df[1:] #take the data less the header row
    df.columns = new_header#nimmt 2, reihe als header
    
    df = df.sort_values(['Date (MM/DD/YYYY)','Time (HH:mm:ss)'], ascending=[True,True])
    df.reset_index(drop=True, inplace=True)
    
    df.rename(columns={'Date (MM/DD/YYYY)':'Date (DD/MM/YYYY)'}, inplace=True)
    #df['Date (DD/MM/YYYY)']=df['Date (DD/MM/YYYY)'].astype(str)#WICHTIG! datumsangabe unabhängig von / oder . machen
    #df['Date (DD/MM/YYYY)'] = df['Date (DD/MM/YYYY)'].str.replace('/','.')#/ mit . ersetzen
    df_help2=df.copy() #deepcopy vom noch nicht datetime, aber getrimmten dataframe
    #################################################################### datei in tage aufspalten
    ##df_help2['Date (DD/MM/YYYY)'] = pd.to_datetime(df_help2['Date (DD/MM/YYYY)'],format='%d.%m.%Y')#EVTL FORMAT EINFÜGEN ,format='%d.%m.%Y'
    df_help2['next day'] = (df_help2['Date (DD/MM/YYYY)'].diff()).dt.days > 0 #ob neue zeile=neuer tag
    ###############datumsangabe unabhängig von / oder . machen
    for i in range(df_help2.shape[0]):
        if df_help2.at[i,'next day'] == True:
            list1.append(i)    
    #spaltalgorithmus gesamtfile in tage
    l_mod = [0] + list1 + [df.shape[0]]
    dfs_by_day = [df.iloc[l_mod[n]:l_mod[n+1]] for n in range(len(l_mod)-1)]  
    ################################################################# tage in runs aufspalten

    for j in dfs_by_day: 
        memo=0
        run_counter=1
        
        df1 = j
        df1=df1.reset_index(drop=True)
        
        df_help4 = df1.iloc[0:1,0:2].reset_index(drop=True).copy()     
        
        df1['Date (DD/MM/YYYY)'] = df1['Date (DD/MM/YYYY)'].dt.strftime('%d.%m.%Y')
        list3=[]
                
        dfdate= str(df1.at[0,'Date (DD/MM/YYYY)']) 
        print(dfdate)

        df_help3=df1.copy() #deepcopy für tageszeitanalyse/runs
        df_help3['Time (HH:mm:ss)'] = pd.to_datetime(df_help3['Time (HH:mm:ss)'],format='%H:%M:%S')
        df_help3['next run'] = (df_help3['Time (HH:mm:ss)'].diff()).dt.seconds > 2000
        df_help3.reset_index(drop=True, inplace=True)
        for i in range(df_help3.shape[0]):
            if df_help3.at[i,'next run'] == True:
                list3.append(i)   
        ###algorithmus spaltet tag in runs auf
        l_mod2 = [0] + list3 + [df1.shape[0]]
        dfs_by_run = [df1.iloc[l_mod2[n]:l_mod2[n+1]] for n in range(len(l_mod2)-1)]
                      
        for k in dfs_by_run:
            df_run = k
            df_run['Depth m'] = pd.to_numeric(df_run['Depth m'])
            df_run['depth rounded'] = df_run['Depth m'].astype(int) #rundet
            df_run=df_run.reset_index(drop=True)  
          
            df_run = df_run.drop_duplicates(subset=['depth rounded'], keep='last')#letzter wert   
                        
            del df_run['depth rounded']
            df_run=df_run.dropna(axis=0,how='any',thresh=2)
            df_run=df_run.reset_index(drop=True) 
            run_name = str(dfdate) +'_run' + str(run_counter)            
            #####sensortoresultfile
            if first_run_counter==0:
                last_df=df_run.copy()
                last_df=last_df[0:0]
                
            last_df=last_df.append(df_run)
            first_run_counter+=1
            with open(logfile, "a") as file:
                file.write("{0} has {1} last measurement(s) \n".format(run_name,df_run.shape[0]))    
            run_counter+=1
        #alle daten raw aber mit sensor und header pro tag
        df_help4['Time (HH:mm:ss)'] = df_help4['Time (HH:mm:ss)'].astype(str)
        df_help4['Date (DD/MM/YYYY)'] = df_help4['Date (DD/MM/YYYY)'].astype(str)
        for i in range(df_help.shape[0]):
            if df_help4.at[0,'Date (DD/MM/YYYY)'] == df_help.at[i,0]:                         
                if df_help4.at[0,'Time (HH:mm:ss)'] == df_help.at[i,1]:   
                    memo=i
                    break
        for n in reversed(list(range(memo))):   
            if df_help.at[n,3] == 'SENSOR SERIAL NUMBER:':
                sensor_info=df_help.iloc[n:n+1,:]
                sensor_info.reset_index(drop=True,inplace=True)
                break                    
        sensor_info.at[0,0:2]='-'
        df1 = df1.columns.to_frame().T.append(df1, ignore_index=True)#fügt header als zeile ganz oben hinzu
        df1.columns = range(len(df1.columns))#header neu 0 bis n
        if first_day_counter==0:
            raw_df=df1.copy()
            raw_df=raw_df[0:0]
        sensor_info.columns= range(len(df1.columns))        
        df1 = pd.concat([df1.iloc[:(0)], sensor_info, df1.iloc[0:]]).reset_index(drop=True) 
        raw_df=raw_df.append(df1) 
        first_day_counter += 1   
        
    last_df.to_excel(writer, sheet_name='{}'.format("last"),header=False, index = False)
    #raw_df['Date (DD/MM/YYYY)'] = raw_df['Date (DD/MM/YYYY)'].dt.strftime('%d.%m.%Y')
    raw_df.to_excel(writer, sheet_name='{}'.format("raw"),header=False, index = False)

    writer.save()
with open(logfile, "a") as file:
    file.write("total number of last measurements: {} \n".format(last_df.shape[0]))  
    file.write("total number of raw measurements: {} \n".format(raw_df.shape[0]))
f.close()

错误：

Traceback (most recent call last):
  File "tsk-py-convert.py", line 95, in <module>
  File "pandas\core\generic.pyc", line 5458, in __getattr__
  File "pandas\core\accessor.pyc", line 180, in __get__
  File "pandas\core\indexes\accessors.pyc", line 494, in __new__
AttributeError: Can only use .dt accessor with datetimelike values

Answer 1

在 spyder 中，代码使用的是旧版 pandas (0.23.4)。我的代码似乎不适用于新版本。我在 windows 上安装了最新的 pandas 版本 pip，现在手动安装了 anaconda 的版本（0.23.4）。 我现在可以通过 cmd、IDLE 和使用 pyinstaller 创建的 .exe 运行代码了！