如何减少Python脚本的执行时间

我有一个巨大的数据集，其中包含大约4亿条记录，这些记录将记录从行转换到列

输入数据集：

+------+------------------------------------------------------------------+----------------------------------+--+
| HHID |                             VAL_CD64                             |             VAL_CD32             |  |
+------+------------------------------------------------------------------+----------------------------------+--+
|  203 | 8c5bfd9b6755ffcdb85dc52a701120e0876640b69b2df0a314dc9e7c2f8f58a5 | 373aeda34c0b4ab91a02ecf55af58e15 |  |
|  203 | 0511dc19cb09f8f4ba3d140754dafb1471dacdbb6747cdb5a2bc38e278d229c8 | 6f3606577eadacef1b956307558a1efd |  |
|  203 | a18adc1bcae1b570a610b13565b82e5647f05fef8a4680bd6ccdd717cdd34af7 | 332321ab150879e930869c15b1d10c83 |  |
|  720 | f6c581becbac4ec1291dc4b9ce566334b1cb2c85e234e489e7fd5e1393bd8751 | 2c4f97a04f02db5a36a85f48dab39b5b |  |
|  720 | abad845107a699f5f99575f8ed43e0440d87a8fc7229c1a1db67793561f0f1c3 | 2111293e946703652070968b224875c9 |  |
|  348 | 25c7cf022e6651394fa5876814a05b8e593d8c7f29846117b8718c3dd951e496 | 5c80a555fcda02d028fc60afa29c4a40 |  |
|  348 | 67d9c0a4bb98900809bcfab1f50bef72b30886a7b48ff0e9eccf951ef06542f9 | 6c10cd11b805fa57d2ca36df91654576 |  |
|  348 | 05f1e412e7765c4b54a9acfd70741af545564f6fdfe48b073bfd3114640f5e37 | 6040b29107adf1a41c4f5964e0ff6dcb |  |
|  403 | 3e8da3d63c51434bcd368d6829c7cee490170afc32b5137be8e93e7d02315636 | 71a91c4768bd314f3c9dc74e9c7937e8 |  |
+------+------------------------------------------------------------------+----------------------------------+--+

+------+------------------------------------------------------------------+------------------------------------------------------------------+------------------------------------------------------------------+----------------------------------+----------------------------------+----------------------------------+--+
| HHID |                            VAL1_CD64                             |                            VAL2_CD64                             |                            VAL3_CD64                             |            VAL1_CD32             |            VAL2_CD32             |            VAL3_CD32             |  |
+------+------------------------------------------------------------------+------------------------------------------------------------------+------------------------------------------------------------------+----------------------------------+----------------------------------+----------------------------------+--+
|  203 | 8c5bfd9b6755ffcdb85dc52a701120e0876640b69b2df0a314dc9e7c2f8f58a5 | 0511dc19cb09f8f4ba3d140754dafb1471dacdbb6747cdb5a2bc38e278d229c8 | a18adc1bcae1b570a610b13565b82e5647f05fef8a4680bd6ccdd717cdd34af7 | 373aeda34c0b4ab91a02ecf55af58e15 | 6f3606577eadacef1b956307558a1efd | 332321ab150879e930869c15b1d10c83 |  |
|  720 | f6c581becbac4ec1291dc4b9ce566334b1cb2c85e234e489e7fd5e1393bd8751 | abad845107a699f5f99575f8ed43e0440d87a8fc7229c1a1db67793561f0f1c3 |                                                                  | 2c4f97a04f02db5a36a85f48dab39b5b | 2111293e946703652070968b224875c9 |                                  |  |
|  348 | 25c7cf022e6651394fa5876814a05b8e593d8c7f29846117b8718c3dd951e496 | 67d9c0a4bb98900809bcfab1f50bef72b30886a7b48ff0e9eccf951ef06542f9 | 05f1e412e7765c4b54a9acfd70741af545564f6fdfe48b073bfd3114640f5e37 | 5c80a555fcda02d028fc60afa29c4a40 | 6c10cd11b805fa57d2ca36df91654576 | 6040b29107adf1a41c4f5964e0ff6dcb |  |
|  403 | 3e8da3d63c51434bcd368d6829c7cee490170afc32b5137be8e93e7d02315636 |                                                                  |                                                                  | 71a91c4768bd314f3c9dc74e9c7937e8 |                                  |                                  |  |
+------+------------------------------------------------------------------+------------------------------------------------------------------+------------------------------------------------------------------+----------------------------------+----------------------------------+----------------------------------+--+

输出数据集：

+------+------------------------------------------------------------------+----------------------------------+--+
| HHID |                             VAL_CD64                             |             VAL_CD32             |  |
+------+------------------------------------------------------------------+----------------------------------+--+
|  203 | 8c5bfd9b6755ffcdb85dc52a701120e0876640b69b2df0a314dc9e7c2f8f58a5 | 373aeda34c0b4ab91a02ecf55af58e15 |  |
|  203 | 0511dc19cb09f8f4ba3d140754dafb1471dacdbb6747cdb5a2bc38e278d229c8 | 6f3606577eadacef1b956307558a1efd |  |
|  203 | a18adc1bcae1b570a610b13565b82e5647f05fef8a4680bd6ccdd717cdd34af7 | 332321ab150879e930869c15b1d10c83 |  |
|  720 | f6c581becbac4ec1291dc4b9ce566334b1cb2c85e234e489e7fd5e1393bd8751 | 2c4f97a04f02db5a36a85f48dab39b5b |  |
|  720 | abad845107a699f5f99575f8ed43e0440d87a8fc7229c1a1db67793561f0f1c3 | 2111293e946703652070968b224875c9 |  |
|  348 | 25c7cf022e6651394fa5876814a05b8e593d8c7f29846117b8718c3dd951e496 | 5c80a555fcda02d028fc60afa29c4a40 |  |
|  348 | 67d9c0a4bb98900809bcfab1f50bef72b30886a7b48ff0e9eccf951ef06542f9 | 6c10cd11b805fa57d2ca36df91654576 |  |
|  348 | 05f1e412e7765c4b54a9acfd70741af545564f6fdfe48b073bfd3114640f5e37 | 6040b29107adf1a41c4f5964e0ff6dcb |  |
|  403 | 3e8da3d63c51434bcd368d6829c7cee490170afc32b5137be8e93e7d02315636 | 71a91c4768bd314f3c9dc74e9c7937e8 |  |
+------+------------------------------------------------------------------+----------------------------------+--+

+------+------------------------------------------------------------------+------------------------------------------------------------------+------------------------------------------------------------------+----------------------------------+----------------------------------+----------------------------------+--+
| HHID |                            VAL1_CD64                             |                            VAL2_CD64                             |                            VAL3_CD64                             |            VAL1_CD32             |            VAL2_CD32             |            VAL3_CD32             |  |
+------+------------------------------------------------------------------+------------------------------------------------------------------+------------------------------------------------------------------+----------------------------------+----------------------------------+----------------------------------+--+
|  203 | 8c5bfd9b6755ffcdb85dc52a701120e0876640b69b2df0a314dc9e7c2f8f58a5 | 0511dc19cb09f8f4ba3d140754dafb1471dacdbb6747cdb5a2bc38e278d229c8 | a18adc1bcae1b570a610b13565b82e5647f05fef8a4680bd6ccdd717cdd34af7 | 373aeda34c0b4ab91a02ecf55af58e15 | 6f3606577eadacef1b956307558a1efd | 332321ab150879e930869c15b1d10c83 |  |
|  720 | f6c581becbac4ec1291dc4b9ce566334b1cb2c85e234e489e7fd5e1393bd8751 | abad845107a699f5f99575f8ed43e0440d87a8fc7229c1a1db67793561f0f1c3 |                                                                  | 2c4f97a04f02db5a36a85f48dab39b5b | 2111293e946703652070968b224875c9 |                                  |  |
|  348 | 25c7cf022e6651394fa5876814a05b8e593d8c7f29846117b8718c3dd951e496 | 67d9c0a4bb98900809bcfab1f50bef72b30886a7b48ff0e9eccf951ef06542f9 | 05f1e412e7765c4b54a9acfd70741af545564f6fdfe48b073bfd3114640f5e37 | 5c80a555fcda02d028fc60afa29c4a40 | 6c10cd11b805fa57d2ca36df91654576 | 6040b29107adf1a41c4f5964e0ff6dcb |  |
|  403 | 3e8da3d63c51434bcd368d6829c7cee490170afc32b5137be8e93e7d02315636 |                                                                  |                                                                  | 71a91c4768bd314f3c9dc74e9c7937e8 |                                  |                                  |  |
+------+------------------------------------------------------------------+------------------------------------------------------------------+------------------------------------------------------------------+----------------------------------+----------------------------------+----------------------------------+--+

我当前的Python代码是：

import pandas as pd
import os
import shutil
import glob
import time

start=time.time()

print('\nFile Processing Started\n')

path=r'Sample Data'
input_file=r'test'
output_file=r'test_MOD'
chunk=pd.read_csv(input_file+'.psv',sep='|',chunksize=400000,dtype={"HHID":"string","VAL_CD64":"string","VAL_CD32":"string"})

chunk_list=[]
for c_no in chunk:
    chunk_list.append(c_no)

file_no=1
rec_cnt=0
for i in chunk_list:
    start2=time.time()
    rec_cnt=rec_cnt+len(i)
    rec_cnt2=0
    rec_cnt2=len(i)
    df=pd.DataFrame(i)
    df_ = df.groupby('HHID').agg({'VAL_CD64': list, 'VAL_CD32': list})
    
    data = []
    for col in df_.columns:
        d = pd.DataFrame(df_[col].values.tolist(), index=df_.index)
        d.columns = [f'{col}_{i}' for i in map(str, range(1, len(d.columns)+1))]
        data.append(d)
    
    res = pd.concat(data, axis=1)
    res.to_csv(output_file+str(file_no)+'.psv',index=True,sep='|')
    
    with open(output_file+str(file_no)+'.psv','r') as istr:
        with open(input_file+str(file_no)+'.psv','w') as ostr:
            for line in istr:
                line=line.strip('\n')+'|'
                print(line,file=ostr)
    os.remove(output_file+str(file_no)+'.psv')
    file_no+=1
    end2=time.time()
    duration2=end2-start2
    print("\nProcessed "+ str(rec_cnt2)+ " records in "+ str(round((duration2),2))+ " seconds. \nTotal Processed Records: "+str(rec_cnt))

os.remove(input_file+'.psv')
allFiles = glob.glob(path + "/*.psv")
allFiles.sort()  # glob lacks reliable ordering, so impose your own if output order matters
with open(os.path.join(path,'someoutputfile.csv'), 'wb') as outfile:
    for i, fname in enumerate(allFiles):
        with open(fname, 'rb') as infile:
            if i != 0:
                infile.readline()  # Throw away header on all but first file
            # Block copy rest of file from input to output without parsing
            shutil.copyfileobj(infile, outfile)

test=os.listdir(path)
for item in test:
    if item.endswith(".psv"):
        os.remove(os.path.join(path,item))

final_file_name=input_file+'.psv'

os.rename(os.path.join(path,'someoutputfile.csv'),final_file_name)

end=time.time()
duration=end-start
print("\n"+ str(rec_cnt)+ " records added in "+ str(round((duration),2))+ " seconds. \n")

但是，此脚本需要花费大量时间来处理记录。处理一个4亿记录文件花了16个小时

---

有什么方法可以减少执行时间，加快整个过程吗？

pivot不是这样做的吗？即

df1 = df.assign(cols = df.groupby('HHID').cumcount() + 1).\
         pivot_table(index='HHID', columns = 'cols', values = ['VAL_CD64', 'VAL_CD32'], 
                     aggfunc = lambda x:x)
df1.columns = [i + '_' + str(j) for i,j in df1.columns]
df1.reset_index()

  HHID                       VAL_CD32_1                       VAL_CD32_2                       VAL_CD32_3                                       VAL_CD64_1
0  203 373aeda34c0b4ab91a02ecf55af58e15 6f3606577eadacef1b956307558a1efd 332321ab150879e930869c15b1d10c83 8c5bfd9b6755ffcdb85dc52a701120e0876640b69b2df...
1  348 5c80a555fcda02d028fc60afa29c4a40 6c10cd11b805fa57d2ca36df91654576 6040b29107adf1a41c4f5964e0ff6dcb 25c7cf022e6651394fa5876814a05b8e593d8c7f29846...
2  403 71a91c4768bd314f3c9dc74e9c7937e8                              NaN                              NaN 3e8da3d63c51434bcd368d6829c7cee490170afc32b51...
3  720 2c4f97a04f02db5a36a85f48dab39b5b 2111293e946703652070968b224875c9                              NaN f6c581becbac4ec1291dc4b9ce566334b1cb2c85e234e...

---

除非RAM非常小，否则可以将

chunksize

增加到

10000

，

100000

，

500000

甚至更多。您可以一直尝试，直到出现内存错误

---

由于代码取决于块的数量，增加该值应该可以提高运行时间。

您可以在C模块（下面的链接）中编写处理逻辑，并从Python代码中使用它，这样您就可以使用C-Speed和Pythons简单性

---

我在脚本中使用了400000的chunksize，这花费了16小时来处理4亿行。当我将此数据帧输出到csv文件时，我得到以下输出：。我国农村居民生活水平（1244）维维权（VAL）维维权（CD32）维权（CD32）3）维权（VAL）维权（CD64）维权（VAL）维权（CD64）维权（维权（CD64）维权（维权）维权（CD32）维权（CD32）维权（瓦权）维权（CD64）维权（维权）维权（维权（CD64）维权（维权）维权（维权（CD64）维权（维权）维权（维权）维权（CD64）维权（维权（维权）维权）维权（CD64）维权（维权）维权（维权（维权）维权（维权）维权）维权）维权（维权维权（维权（CD64）维权（维权）维权）维权）维权（维权维权维权维权维权维权维权维权维权维权维权（CD69B2DF0A314DC9E7C2F8F58A5|“1 0511dc19cb09f8f4ba3d140754dafb1471dacdbb6747cd。。。名称：VAL_CD64，数据类型：字符串“|”2 a18adc1bcae1b570a610b13565b82e5647f05fef8a4680。。。Name:VAL_CD64，dtype:string“@AbhinavDhiman您是否使用了df1.to_csv（）？使用这个：df1.to_csv（output_file+str（file_no）+'.psv'，index=True，sep='|'）我的代码正在Unix服务器上运行，该服务器没有安装C解释器。我没有安装C解释器的权限。