python - 减少 pandas groupby 函数的内存占用

问题描述

我有一个巨大的数据集，其内容如下：

+------+------------------------------------------------------------------+----------------------------------+--+
| 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 |  |
+------+------------------------------------------------------------------+----------------------------------+--+

我正在处理文件，以便以以下给定格式输出：

+------+------------------------------------------------------------------+------------------------------------------------------------------+------------------------------------------------------------------+----------------------------------+----------------------------------+----------------------------------+--+
| 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 |                                  |                                  |  |
+------+------------------------------------------------------------------+------------------------------------------------------------------+------------------------------------------------------------------+----------------------------------+----------------------------------+----------------------------------+--+

我目前的代码是：

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

start=time.time()

print('\nFile Processing Started\n')

path=r'C:\Users\xyz\Sample Data'
input_file=r'C:\Users\xyz\Sample Data\test'
output_file=r'C:\Users\xyz\Sample Data\test_MOD'
chunk=pd.read_csv(input_file+'.psv',sep='|',chunksize=10000,dtype={"HH_ID":"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('HH_ID').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.columns=['MAID1_SHA256', 'MAID2_SHA256', 'MAID3_SHA256', 'MAID1_MD5','MAID2_MD5', 'MAID3_MD5']
    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() 
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()  
            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，这段代码需要花费大量时间来处理在 unix 上运行的 4 亿条记录文件，大约需要 18-19 小时。如果我尝试处理 7 亿条记录文件，整个脚本都会被杀死。通过我的谷歌搜索，我相信它由于 groupby 函数的高内存使用而被杀死。

有什么办法可以减少这个程序的内存占用，让一个7亿的文件可以通过它来处理？

标签： pythonpython-3.xpandas

python - 减少 pandas groupby 函数的内存占用

问题描述

解决方案

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