NameError Traceback（最近一次调用最后一次）

Question

我一直在尝试运行下面的这个程序，但没有成功，因为出现了回溯错误，因为该函数已明确定义，所以我不明白该错误。任何建议都非常感激。我在 Windows10、Jupyter Notebook、Python3 上运行。我希望这些信息足以提供一些指导。

"""
Multi processing in Python, threading
"""

import pandas as pd
import timeit, sys
import numpy as np
import multiprocess as mp

df = pd.read_csv("C:/files/data.csv", encoding="UTF-8")

df['start-digits'] = None

startTime = timeit.default_timer()

def strip_digits(x):
    return str(x)[:4]

def zip_handler(x):
    x['start-digits'] = x['Zip Codes'].apply(strip_digits)
    return x

def parallelize(dataframe, func):
    dataframe_split = np.array_split(dataframe, partitions)
    pool = mp.Pool(cores)
    dataframe_return = pd.concat(pool.map(func, dataframe_split), ignore_index=True)
    pool.close()
    
    return dataframe_return
    
if __name__ == '__main__':
    #mp.set_start_method('spawn')
    
    cores = mp.cpu_count()
    partitions = cores
    
    df = parallelize(df, zip_handler)

    group_dataframe = df.groupby(['Zip Codes'])
    size_of_group = group_dataframe.size()

    print(size_of_group)
    print(group_dataframe.get_group(2443))

    stopTime = timeit.default_timer() - startTime
    round_time = round(stopTime, 6)

    print('')
    print(round_time/60, 'min handle time')
    print('cpu threads', cores)
    print('split size', partitions)

---

RemoteTraceback                           Traceback (most recent call last)
RemoteTraceback: 
"""
Traceback (most recent call last):
  File "C:\Users\trevo\anaconda3\lib\site-packages\multiprocess\pool.py", line 125, in worker
    result = (True, func(*args, **kwds))
  File "C:\Users\trevo\anaconda3\lib\site-packages\multiprocess\pool.py", line 48, in mapstar
    return list(map(*args))
  File "<ipython-input-10-e6bb79e2f39d>", line 20, in zip_handler
NameError: name 'strip_digits' is not defined
"""

The above exception was the direct cause of the following exception:

NameError                                 Traceback (most recent call last)
<ipython-input-10-e6bb79e2f39d> in <module>
     35     partitions = cores
     36 
---> 37     df = parallelize(df, zip_handler)
     38 
     39     group_dataframe = df.groupby(['Zip Codes'])

<ipython-input-10-e6bb79e2f39d> in parallelize(dataframe, func)
     24     dataframe_split = np.array_split(dataframe, partitions)
     25     pool = mp.Pool(cores)
---> 26     dataframe_return = pd.concat(pool.map(func, dataframe_split), ignore_index=True)
     27     pool.close()
     28 

~\anaconda3\lib\site-packages\multiprocess\pool.py in map(self, func, iterable, chunksize)
    362         in a list that is returned.
    363         '''
--> 364         return self._map_async(func, iterable, mapstar, chunksize).get()
    365 
    366     def starmap(self, func, iterable, chunksize=None):

~\anaconda3\lib\site-packages\multiprocess\pool.py in get(self, timeout)
    769             return self._value
    770         else:
--> 771             raise self._value
    772 
    773     def _set(self, i, obj):

NameError: name 'strip_digits' is not defined

Answer 1

import os
import timeit
import multiprocessing as mp
import concurrent.futures
import numpy as np
import pandas as pd


# Define the strip_digits function
def strip_digits(x):
    return str(x)[:4]

# Define the zip_handler function
def zip_handler(x):
    x['start-digits'] = x['Zip Codes'].apply(strip_digits)
    return x

def parallelize(dataframe, func):
    dataframe_split = np.array_split(dataframe, partitions)
    pool = mp.Pool(cores)
    dataframe_return = pd.concat(pool.map(func, dataframe_split), 
    ignore_index=True)
    pool.close()    
    return dataframe_return
    
if __name__ == '__main__':
    cores = mp.cpu_count()
    partitions = cores
    
    # Specify the file path
    file_path = "data/Test.csv"
    
    # Check if the file exists
    if os.path.exists(file_path):
        # Load your data into the df DataFrame
        df = pd.read_csv(file_path, sep=";")
        
        # Call the parallelize function with df and zip_handler
        df = parallelize(df, zip_handler)

        group_dataframe = df.groupby(['Zip Codes'])
        size_of_group = group_dataframe.size()

        print(size_of_group)
        print(group_dataframe.get_group(2443))

        stopTime = timeit.default_timer()
        round_time = round(stopTime, 6)

        print('')
        print(round_time/60, 'min handle time')
        print('cpu threads', cores)
        print('split size', partitions)
    else:
        print(f"File not found: {file_path}")