我正在处理一个2.5 GB大的 csv 文件。 2.5 GB的表格如下所示：
columns=[ka,kb_1,kb_2,timeofEvent,timeInterval]
0:'3M' '2345' '2345' '2014-10-5',3000
1:'3M' '2958' '2152' '2015-3-22',5000
2:'GE' '2183' '2183' '2012-12-31',515
3:'3M' '2958' '2958' '2015-3-10',395
4:'GE' '2183' '2285' '2015-4-19',1925
5:'GE' '2598' '2598' '2015-3-17',1915
我希望通过ka和kb_1分组来得到这样的结果：
columns=[ka,kb,errorNum,errorRate,totalNum of records]
'3M','2345',0,0%,1
'3M','2958',1,50%,2
'GE','2183',1,50%,2
'GE','2598',0,0%,1
（错误记录的定义：当 kb_1 != kb_2 时，相应的记录被视为异常记录）
我的电脑是ubuntu 12.04， 16 GB内存且 free -m 返回
total used free shared buffers cached
Mem: 112809 14476 98333 0 128 10823
-/+ buffers/cache: 3524 109285
Swap:
0 0 0
我的python文件名为 bigData.py
import pandas as pd
import numpy as np
import sys,traceback,os
cksize=98333 # or 1024, either chunk size didn't work at all
try:
dfs = pd.DataFrame()
reader=pd.read_table('data/petaJoined.csv', chunksize=cksize)
for chunk in reader:#when executed this line,error occur!
pass
#temp=tb_createTopRankTable(chunk)
#dfs.append(temp)
#df=tb_createTopRankTable(dfs)
except:
traceback.print_exc(file=sys.stdout)
ipdb> pd.__version__
'0.16.0'
我使用以下命令监视内存使用情况：
top
ps -C python -o %cpu,%mem,cmd
由于崩溃需要大约2秒钟，因此我可以看到 mem 使用率已达到90％，并且 CPU 使用率达到100％
当我执行python bigData.py时，会产生以下错误：
/usr/local/lib/python2.7/dist-packages/pytz/__init__.py:29: UserWarning: Module dateutil was already imported from /usr/local/lib/python2.7/dist-packages/dateutil/__init__.pyc, but /usr/lib/python2.7/dist-packages is being added to sys.path
from pkg_resources import resource_stream
/usr/local/lib/python2.7/dist-packages/pytz/__init__.py:29: UserWarning: Module pytz was already imported from /usr/local/lib/python2.7/dist-packages/pytz/__init__.pyc, but /usr/lib/python2.7/dist-packages is being added to sys.path
from pkg_resources import resource_stream
Traceback (most recent call last):
File "bigData.py", line 10, in <module>
for chunk in reader:
File "/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.py", line 691, in __iter__
yield self.read(self.chunksize)
File "/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.py", line 715, in read
ret = self._engine.read(nrows)
File "/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.py", line 1164, in read
data = self._reader.read(nrows)
File "pandas/parser.pyx", line 758, in pandas.parser.TextReader.read (pandas/parser.c:7411)
File "pandas/parser.pyx", line 792, in pandas.parser.TextReader._read_low_memory (pandas/parser.c:7819)
File "pandas/parser.pyx", line 833, in pandas.parser.TextReader._read_rows (pandas/parser.c:8268)
File "pandas/parser.pyx", line 820, in pandas.parser.TextReader._tokenize_rows (pandas/parser.c:8142)
File "pandas/parser.pyx", line 1758, in pandas.parser.raise_parser_error (pandas/parser.c:20728)
CParserError: Error tokenizing data. C error: out of memory
Segmentation fault (core dumped)
或
/usr/local/lib/python2.7/dist-packages/pytz/__init__.py:29: UserWarning: Module dateutil was already imported from /usr/local/lib/python2.7/dist-packages/dateutil/__init__.pyc, but /usr/lib/python2.7/dist-packages is being added to sys.path
from pkg_resources import resource_stream
/usr/local/lib/python2.7/dist-packages/pytz/__init__.py:29: UserWarning: Module pytz was already imported from /usr/local/lib/python2.7/dist-packages/pytz/__init__.pyc, but /usr/lib/python2.7/dist-packages is being added to sys.path
from pkg_resources import resource_stream
Traceback (most recent call last):
File "bigData.py", line 10, in <module>
for chunk in reader:
File "/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.py", line 691, in __iter__
yield self.read(self.chunksize)
File "/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.py", line 715, in read
ret = self._engine.read(nrows)
File "/usr/local/lib/python2.7/dist-packages/pandas/io/parsers.py", line 1164, in read
data = self._reader.read(nrows)
File "pandas/parser.pyx", line 758, in pandas.parser.TextReader.read (pandas/parser.c:7411)
File "pandas/parser.pyx", line 792, in pandas.parser.TextReader._read_low_memory (pandas/parser.c:7819)
File "pandas/parser.pyx", line 833, in pandas.parser.TextReader._read_rows (pandas/parser.c:8268)
File "pandas/parser.pyx", line 820, in pandas.parser.TextReader._tokenize_rows (pandas/parser.c:8142)
File "pandas/parser.pyx", line 1758, in pandas.parser.raise_parser_error (pandas/parser.c:20728)
CParserError: Error tokenizing data. C error: out of memory
*** glibc detected *** python: free(): invalid pointer: 0x00007f750d2a4c0e ***
====== Backtrace: ========
/lib/x86_64-linux-gnu/libc.so.6(+0x7db26)[0x7f7511529b26]
/usr/local/lib/python2.7/dist-packages/pandas/parser.so(+0x4d5a1)[0x7f750d29d5a1]
/usr/local/lib/python2.7/dist-packages/pandas/parser.so(parser_cleanup+0x15)[0x7f750d29de45]
/usr/local/lib/python2.7/dist-packages/pandas/parser.so(parser_free+0x9)[0x7f750d29e039]
/usr/local/lib/python2.7/dist-packages/pandas/parser.so(+0xb43e)[0x7f750d25b43e]
....
python(PyDict_SetItem+0x49)[0x577749]
python(_PyModule_Clear+0x149)[0x4cafb9]
python(PyImport_Cleanup+0x477)[0x4cb4f7]
python(Py_Finalize+0x18e)[0x549f0e]
python(Py_Main+0x3bc)[0x56b56c]
/lib/x86_64-linux-gnu/libc.so.6(__libc_start_main+0xed)[0x7f75114cd76d]
python[0x41bb11]
======= Memory map: ========
00400000-00670000 r-xp 00000000 08:01 26612 /usr/bin/python2.7
0086f000-00870000 r--p 0026f000 08:01 26612 /usr/b.......
008d9000-008eb000 rw-p 00000000 00:00 0
01ddb000-036f7000 rw-p 00000000 00:00 0 [heap]
7f748c179000-7f74cc17a000 rw-p 00000000 00:00 0
7f7504000000-7f7504021000 rw-p 00000000 00:00 0
7f7504021000-7f7508000000 ---p 00000000 00:00 0
7f750bf83000-7f750c285000 rw-p 00000000 00:00 0
7f750c285000-7f750c586000 rw-p 00000000 00:00 0
7f750c586000-7f750c707000 rw-p 00000000 00:00 0
7f750c707000-7f750c711000 r-xp 00000000 08:01 533205 /usr/local/lib/python2.7/dist-packages/pandas/_testing.so
7f750c711000-7f750c911000 ---p 0000a000 08:01 533205 /usr/local/lib/python2.7/dist-packages/pandas/_testing.so
7f750c911000-7f750c912000 r--p 0000a000 08:01 533205 /usr/local/lib/python2.7/dist-packages/pandas/_testing.so
7f750c912000-7f750c913000 rw-p 0000b000 08:01 533205 /usr/local/lib/python2.7/dist-packages/pandas/_testing.so
7f750c913000-7f750c914000 rw-p 00000000 00:00 0
7f750c914000-7f750c918000 r-xp 00000000 08:01 2331 /lib/x86_64-linux-gnu/libuuid.so.1.3.0
7f750c918000-7f750cb17000 ---p 00004000 08:01 2331 /lib/x86_64-linux-gnu/libuuid.so.1.3.0
7f750cb17000-7f750cb18000 r--p 00003000 08:01 2331 /lib/x86_64-linux-gnu/libuuid.so.1.3.0
7f750cb18000-7f750cb19000 rw-p 00004000 08:01 2331 /lib/x86_64-linux-gnu/libuuid.so.1.3.0
7f750cb19000-7f750cb34000 r-xp 00000000 08:01 533071 /usr/local/lib/python2.7/dist-packages/pandas/msgpack.so
7f750cb34000-7f750cd33000 ---p 0001b000 08:01 533071 /usr/local/lib/python2.7/dist-packages/pandas/msgpack.so
7f750cd33000-7f750cd34000 r--p 0001a000 08:01 533071 /usr/local/lib/python2.7/dist-packages/pandas/msgpack.so
7f750cd34000-7f750cd38000 rw-p 0001b000 08:01 533071 /usr/local/lib/python2.7/dist-packages/pandas/msgpack.so
7f750cd38000-7f750d039000 rw-p 00000000 00:00 0
7f750d039000-7f750d04e000 r-xp 00000000 08:01 533070 /usr/local/lib/python2.7/dist-packages/pandas/json.so
7f750d04e000-7f750d24e000 ---p 00015000 08:01 533070 /usr/local/lib/python2.7/dist-packages/pandas/json.so
7f750d24e000-7f750d24f000 r--p 00015000 08:01 533070 /usr/local/lib/python2.7/dist-packages/pandas/json.so
7f750d24f000-7f750d250000 rw-p 00016000 08:01 533070 /usr/local/lib/python2.7/dist-packages/pandas/json.so
7f750d250000-7f750d2a9000 r-xp 00000000 08:01 533270 /usr/local/lib/python2.7/dist-packages/pandas/parser.so
7f750d2a9000-7f750d4a8000 ---p 00059000 08:01 533270 /usr/local/lib/python2.7/dist-packages/pandas/parser.so
7f750d4a8000-7f750d4a9000 r--p 00058000 08:01 533270 /usr/local/lib/python2.7/dist-packages/pandas/parser.so
7f750d4a9000-7f750d4af000 rw-p 00059000 08:01 533270 /usr/local/lib/python2.7/dist-packages/pandas/parser.so
7f750d4af000-7f750d591000 r-xp 00000000 08:01 49584 /usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.16
7f750d591000-7f750d790000 ---p 000e2000 08:01 49584 /usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.16
7f750d790000-7f750d798000 r--p 000e1000 08:01 49584 /usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.16
7f750d798000-7f750d79a000 rw-p 000e9000 08:01 49584 /usr/lib/x86_64-linux-gnu/libstdc++.so.6.0.16
7f750d79a000-7f750d7af000 rw-p 00000000 00:00 0
7f750d7af000-7f750d7f1000 r-xp 00000000 08:01 530477 /usr/lib/pyshared/python2.7/matplotlib/_path.so
7f750d7f1000-7f750d9f1000 ---p 00042000 08:01 530477 /usr/lib/pyshared/python2.7/matplotlib/_path.so
7f750d9f1000-7f750d9f3000 r--p 00042000 08:01 530477 /usr/lib/pyshared/python2.7/matplotlib/_path.so
7f750d9f3000-7f750d9f4000 rw-p 00044000 08:01 530477 /usr/lib/pyshared/python2.7/matplotlib/_path.so
7f750d9f4000-7f750da2d000 r-xp 00000000 08:01 533269 /usr/local/lib/python2.7/dist-packages/pandas/_sparse.so
7f750da2d000-7f750dc2c000 ---p 00039000 08:01 533269 /usr/local/lib/python2.7/dist-packages/pandas/_sparse.so
7f750dc2c000-7f750dc2d000 r--p 00038000 08:01 533269 /usr/local/lib/python2.7/dist-packages/pandas/_sparse.so
7f750dc2d000-7f750dc31000 rw-p 00039000 08:01 533269 /usr/local/lib/python2.7/dist-packages/pandas/_sparse.so
7f750dc31000-7f750dc7d000 r-xp 00000000 08:01 533447 /usr/local/lib/python2.7/dist-packages/pandas/_period.so
7f750dc7d000-7f750de7c000 ---p 0004c000 08:01 533447 /usr/local/lib/python2.7/dist-packages/pandas/_period.so
7f750de7c000-7f750de7d000 r--p 0004b000 08:01 533447 /usr/local/lib/python2.7/dist-packages/pandas/_period.so
7f750de7d000-7f750de86000 rw-p 0004c000 08:01 533447 /usr/local/lib/python2.7/dist-packages/pandas/_period.so
7f750de86000-7f750de87000 rw-p 00000000 00:00 0
7f750de87000-7f750debc000 r-xp 00000000 08:01 533203 /usr/local/lib/python2.7/dist-packages/pandas/index.so
7f750debc000-7f750e0bb000 ---p 00035000 08:01 533203 /usr/local/lib/python2.7/dist-packages/pandas/index.so
7f750e0bb000-7f750e0bc000 r--p 00034000 08:01 533203 /usr/local/lib/python2.7/dist-packages/pandas/index.so
7f750e0bc000-7f750e0c0000 rw-p 00035000 08:01 533203 /usr/local/lib/python2.7/dist-packages/pandas/index.so
7f750e0c0000-7f750e295000 r-xp 00000000 08:01 533278 /usr/local/lib/python2.7/dist-packages/pandas/algos.so
7f750e295000-7f750e494000 ---p 001d5000 08:01 533278 /usr/local/lib/python2.7/dist-packages/pandas/algos.so
7f750e494000-7f750e495000 r--p 001d4000 08:01 533278 /usr/local/lib/python2.7/dist-packages/pandas/algos.so
7f750e495000-7f750e4a9000 rw-p 001d5000 08:01 533278 /usr/local/lib/python2.7/dist-packages/pandas/algos.so
7f750e4a9000-7f750e4ac000 rw-p 00000000 00:00 0
7f750e4ac000-7f750e4b2000 r-xp 00000000 08:01 48831 /usr/lib/python2.7/lib-dynload/_csv.so
7f750e4b2000-7f750e6b1000 ---p 00006000 08:01 48831 /usr/lib/python2.7/lib-dynload/_csv.so
7f750e6b1000-7f750e6b2000 r--p 00005000 08:01 48831 /usr/lib/python2.7/lib-dynload/_csv.so
7f750e6b2000-7f750e6b4000 rw-p 00006000 08:01 48831 /usr/lib/python2.7/lib-dynload/_csv.so
7f750e6b4000-7f750e782000 r-xp 00000000 08:01 533449 /usr/local/lib/python2.7/dist-packages/pandas/lib.so
7f750e782000-7f750e981000 ---p 000ce000 08:01 533449 /usr/local/lib/python2.7/dist-packages/pandas/lib.so
7f750e981000-7f750e982000 r--p 000cd000 08:01 533449 /usr/local/lib/python2.7/dist-packages/pandas/lib.so
7f750e982000-7f750e990000 rw-p 000ce000 08:01 533449 /usr/local/lib/python2.7/dist-packages/pandas/lib.so
7f750e990000-7f750e992000 rw-p 00000000 00:00 0
7f750e992000-7f750ea8f000 r-xp 00000000 08:01 533271 /usr/local/lib/python2.7/dist-packages/pandas/tslib.so
7f750ea8f000-7f750ec8e000 ---p 000fd000 08:01 533271 /usr/local/lib/python2.7/dist-packages/pandas/tslib.so
7f750ec8e000-7f750ec8f000 r--p 000fc000 08:01 533271 /usr/local/lib/python2.7/dist-packages/pandas/tslib.so
7f750ec8f000-7f750eca1000 rw-p 000fd000 08:01 533271 /usr/local/lib/python2.7/dist-packages/pandas/tslib.so
7f750eca1000-7f750eca4000 rw-p 00000000 00:00 0
7f750eca4000-7f750ecc5000 r-xp 00000000 08:01 48837 /usr/lib/python2.7/lib-dynload/_ctypes.so
7f750ecc5000-7f750eec4000 ---p 00021000 08:01 48837 /usr/lib/python2.7/lib-dynload/_ctypes.so
7f750eec4000-7f750eec5000 r--p 00020000 08:01 48837 /usr/lib/python2.7/lib-dynload/_ctypes.so
7f750eec5000-7f750eec9000 rw-p 00021000 08:01 48837 /usr/lib/python2.7/lib-dynload/_ctypes.so
7f750eec9000-7f750eeca000 rw-p 00000000 00:00 0
7f750eeca000-7f750ef24000 r-xp 00000000 08:01 532046 /usr/local/lib/python2.7/dist-packages/numpy/random/mtrand.so
7f750ef24000-7f750f123000 ---p 0005a000 08:01 532046 /usr/local/lib/python2.7/dist-packages/numpy/random/mtrand.so
7f750f123000-7f750f124000 r--p 00059000 08:01 532046 /usr/local/lib/python2.7/dist-packages/numpy/random/mtrand.so
7f750f124000-7f750f15c000 rw-p 0005a000 08:01 532046 /usr/local/lib/python2.7/dist-packages/numpy/random/mtrand.so
7f750f15c000-7f750f15d000 rw-p 00000000 00:00 0
7f750f15d000-7f750f166000 r-xp 00000000 08:01 532085 /usr/local/lib/python2.7/dist-packages/numpy/fft/fftpack_lite.so
7f750f166000-7f750f365000 ---p 00009000 08:01 532085 /usr/local/lib/python2.7/dist-packages/numpy/fft/fftpack_lite.so
7f750f365000-7f750f366000 r--p 00008000 08:01 532085 /usr/local/lib/python2.7/dist-packages/numpy/fft/fftpack_lite.so
7f750f366000-7f750f367000 rw-p 00009000 08:01 532085 /usr/local/lib/python2.7/dist-packages/numpy/fft/fftpack_lite.so
7f750f367000-7f750f368000 r-xp 00000000 08:01 48818 /usr/lib/python2.7/lib-dynload/future_builtins.so
7f750f368000-7f750f567000 ---p 00001000 08:01 48818 /usr/lib/python2.7/lib-dynload/future_builtins.so
7f750f567000-7f750f568000 r--p 00000000 08:01 48818 /usr/lib/python2.7/lib-dynload/future_builtins.so
7f750f568000-7f750f569000 rw-p 00001000 08:01 48818 /usr/lib/python2.7/lib-dynload/future_builtins.so
7f750f569000-7f750f588000 r-xp 00000000 08:01 48815 /usr/lib/python2.7/lib-dynload/_io.so
7f750f588000-7f750f787000 ---p 0001f000 08:01 48815 /usr/lib/python2.7/lib-dynload/_io.so
7f750f787000-7f750f788000 r--p 0001e000 08:01 48815 /usr/lib/python2.7/lib-dynload/_io.so
7f750f788000-7f750f791000 rw-p 0001f000 08:01 48815 /usr/lib/python2.7/lib-dynload/_io.so
7f750f791000-7f750f907000 r-xp 00000000 08:01 532132 /usr/local/lib/python2.7/dist-packages/numpy/linalg/_umath_linalg.so
7f750f907000-7f750fb06000 ---p 00176000 08:01 532132 /usr/local/lib/python2.7/dist-packages/numpy/linalg/_umath_linalg.so
7f750fb06000-7f750fb07000 r--p 00175000 08:01 532132 /usr/local/lib/python2.7/dist-packages/numpy/linalg/_umath_linalg.so
7f750fb07000-7f750fb08000 rw-p 00176000 08:01 532132 /usr/local/lib/python2.7/dist-packages/numpy/linalg/_umath_linalg.so
7f750fb08000-7f750fba4000 rw-p 00000000 00:00 0
7f750fba4000-7f750fd01000 r-xp 00000000 08:01 532128 /usr/local/lib/python2.7/dist-packages/numpy/linalg/lapack_lite.so
7f750fd01000-7f750ff00000 ---p 0015d000 08:01 532128 /usr/local/lib/python2.7/dist-packages/numpy/linalg/lapack_lite.so
7f750ff00000-7f750ff01000 r--p 0015c000 08:01 532128 /usr/local/lib/python2.7/dist-packages/numpy/linalg/lapack_lite.so
7f750ff01000-7f750ff02000 rw-p 0015d000 08:01 532128 /usr/local/lib/python2.7/dist-packages/numpy/linalg/lapack_lite.so
7f750ff02000-7f750ff9d000 rw-p 00000000 00:00 0
7f750ff9d000-7f750ffa3000 r-xp 00000000 08:01 532067 /usr/local/lib/python2.7/dist-packages/numpy/lib/_compiled_base.so
7f750ffa3000-7f75101a2000 ---p 00006000 08:01 532067 /usr/local/lib/python2.7/dist-packages/numpy/lib/_compiled_base.so
7f75101a2000-7f75101a3000 r--p 00005000 08:01 532067 /usr/local/lib/python2.7/dist-packages/numpy/lib/_compiled_base.so
7f75101a3000-7f75101a4000 rw-p 00006000 08:01 532067 /usr/local/lib/python2.7/dist-packages/numpy/lib/_compiled_base.so
7f7510265000-7f751028f000 r-xp 00000000 08:01 532108 /usr/local/lib/python2.7/dist-packages/numpy/core/scalarmath.so
7f751028f000-7f751048e000 ---p 0002a000 08:01 532108 /usr/local/lib/python2.7/dist-packages/numpy/core/scalarmath.so
7f751048e000-7f751048f000 r--p 00029000 08:01 532108 /usr/local/lib/python2.7/dist-packages/numpy/core/scalarmath.so
7f751048f000-7f7510491000 rw-p 0002a000 08:01 532108 /usr/local/lib/python2.7/dist-packages/numpy/core/scalarmath.so
7f7510491000-7f75104d2000 rw-p 00000000 00:00 0
7f75104d2000-7f75104d5000 r-xp 00000000 08:01 48833 /usr/lib/python2.7/lib-dynload/_heapq.so
7f75104d5000-7f75106d4000 ---p 00003000 08:01 48833 /usr/lib/python2.7/lib-dynload/_heapq.so
7f75106d4000-7f75106d5000 r--p 00002000 08:01 48833 /usr/lib/python2.7/lib-dynload/_heapq.so
7f75106d5000-7f75106d7000 rw-p 00003000 08:01 48833 /usr/lib/python2.7/lib-dynload/_heapq.so
7f75106d7000-7f751073e000 r-xp 00000000 08:01 532118 /usr/local/lib/python2.7/dist-packages/numpy/core/umath.so
7f751073e000-7f751093d000 ---p 00067000 08:01 532118 /usr/local/lib/python2.7/dist-packages/numpy/core/umath.so
7f751093d000-7f751093e000 r--p 00066000 08:01 532118 /usr/local/lib/python2.7/dist-packages/numpy/core/umath.so
7f751093e000-7f7510942000 rw-p 00067000 08:01 532118 /usr/local/lib/python2.7/dist-packages/numpy/core/umath.so
7f7510942000-7f7510944000 rw-p 00000000 00:00 0
7f7510944000-7f7510958000 r-xp 00000000 08:01 48804 /usr/lib/python2.7/lib-dynload/datetime.so
7f7510958000-7f7510b57000 ---p 00014000 08:01 48804 /usr/lib/python2.7/lib-dynload/datetime.so
7f7510b57000-7f7510b58000 r--p 00013000 08:01 48804 /usr/lib/python2.7/lib-dynload/datetime.so
7f7510b58000-7f7510b5c000 rw-p 00014000 08:01 48804 /usr/lib/python2.7/lib-dynload/datetime.so
7f7510b5c000-7f7510caf000 r-xp 00000000 08:01 532106 /usr/local/lib/python2.7/dist-packages/numpy/core/multiarray.so
7f7510caf000-7f7510eae000 ---p 00153000 08:01 532106 /usr/local/lib/python2.7/dist-packages/numpy/core/multiarray.so
7f7510eae000-7f7510eb0000 r--p 00152000 08:01 532106 /usr/local/lib/python2.7/dist-packages/numpy/core/multiarray.so
7f7510eb0000-7f7510ebd000 rw-p 00154000 08:01 532106 /usr/local/lib/python2.7/dist-packages/numpy/core/multiarray.so
7f7510ebd000-7f7510ecf000 rw-p 00000000 00:00 0
7f7510ecf000-7f7510f08000 r-xp 00000000 08:01 533450 /usr/local/lib/python2.7/dist-packages/pandas/hashtable.so
7f7510f08000-7f7511107000 ---p 00039000 08:01 533450 /usr/local/lib/python2.7/dist-packages/pandas/hashtable.so
7f7511107000-7f7511108000 r--p 00038000 08:01 533450 /usr/local/lib/python2.7/dist-packages/pandas/hashtable.so
7f7511108000-7f751110c000 rw-p 00039000 08:01 533450 /usr/local/lib/python2.7/dist-packages/pandas/hashtable.so
7f751110c000-7f751110d000 rw-p 00000000 00:00 0
7f751110d000-7f7511296000 r--p 00000000 08:01 58562 /usr/lib/locale/locale-archive
7f7511296000-7f75112ab000 r-xp 00000000 08:01 2312 /lib/x86_64-linux-gnu/libgcc_s.so.1
7f75112ab000-7f75114aa000 ---p 00015000 08:01 2312 /lib/x86_64-linux-gnu/libgcc_s.so.1
7f75114aa000-7f75114ab000 r--p 00014000 08:01 2312 /lib/x86_64-linux-gnu/libgcc_s.so.1
7f75114ab000-7f75114ac000 rw-p 00015000 08:01 2312 /lib/x86_64-linux-gnu/libgcc_s.so.1
7f75114ac000-7f7511660000 r-xp 00000000 08:01 2327 /lib/x86_64-linux-gnu/libc-2.15.so
7f7511660000-7f751185f000 ---p 001b4000 08:01 2327 /lib/x86_64-linux-gnu/libc-2.15.so
7f751185f000-7f7511863000 r--p 001b3000 08:01 2327 /lib/x86_64-linux-gnu/libc-2.15.so
7f7511863000-7f7511865000 rw-p 001b7000 08:01 2327 /lib/x86_64-linux-gnu/libc-2.15.so
7f7511865000-7f751186a000 rw-p 00000000 00:00 0
7f751186a000-7f7511965000 r-xp 00000000 08:01 2400 /lib/x86_64-linux-gnu/libm-2.15.so
7f7511965000-7f7511b64000 ---p 000fb000 08:01 2400 /lib/x86_64-linux-gnu/libm-2.15.so
7f7511b64000-7f7511b65000 r--p 000fa000 08:01 2400 /lib/x86_64-linux-gnu/libm-2.15.so
7f7511b65000-7f7511b66000 rw-p 000fb000 08:01 2400 /lib/x86_64-linux-gnu/libm-2.15.so
7f7511b66000-7f7511b7c000 r-xp 00000000 08:01 2288 /lib/x86_64-linux-gnu/libz.so.1.2.3.4
7f7511b7c000-7f7511d7b000 ---p 00016000 08:01 2288 /lib/x86_64-linux-gnu/libz.so.1.2.3.4
7f7511d7b000-7f7511d7c000 r--p 00015000 08:01 2288 /lib/x86_64-linux-gnu/libz.so.1.2.3.4
7f7511d7c000-7f7511d7d000 rw-p 00016000 08:01 2288 /lib/x86_64-linux-gnu/libz.so.1.2.3.4
7f7511d7d000-7f7511f2f000 r-xp 00000000 08:01 2279 /lib/x86_64-linux-gnu/libcrypto.so.1.0.0
7f7511f2f000-7f751212e000 ---p 001b2000 08:01 2279 /lib/x86_64-linux-gnu/libcrypto.so.1.0.0
7f751212e000-7f7512149000 r--p 001b1000 08:01 2279 /lib/x86_64-linux-gnu/libcrypto.so.1.0.0
7f7512149000-7f7512154000 rw-p 001cc000 08:01 2279 /lib/x86_64-linux-gnu/libcrypto.so.1.0.0
7f7512154000-7f7512158000 rw-p 00000000 00:00 0
7f7512158000-7f75121ac000 r-xp 00000000 08:01 2393 /lib/x86_64-linux-gnu/libssl.so.1.0.0
7f75121ac000-7f75123ac000 ---p 00054000 08:01 2393 /lib/x86_64-linux-gnu/libssl.so.1.0.0
7f75123ac000-7f75123af000 r--p 00054000 08:01 2393 /lib/x86_64-linux-gnu/libssl.so.1.0.0
7f75123af000-7f75123b6000 rw-p 00057000 08:01 2393 /lib/x86_64-linux-gnu/libssl.so.1.0.0
7f75123b6000-7f75123b8000 r-xp 00000000 08:01 2283 /lib/x86_64-linux-gnu/libutil-2.15.so
7f75123b8000-7f75125b7000 ---p 00002000 08:01 2283 /lib/x86_64-linux-gnu/libutil-2.15.so
7f75125b7000-7f75125b8000 r--p 00001000 08:01 2283 /lib/x86_64-linux-gnu/libutil-2.15.so
7f75125b8000-7f75125b9000 rw-p 00002000 08:01 2283 /lib/x86_64-linux-gnu/libutil-2.15.so
7f75125b9000-7f75125bb000 r-xp 00000000 08:01 2406
/lib/x86_64-linux-gnu/ld-2.15.so
7f7512a2d000-7f7512b31000 rw-p 00000000 00:00 0
7f7512b62000-7f7512bea000 rw-p 00000000 00:00 0
7f7512bf7000-7f7512bf9000 rw-p 00000000 00:00 0
7f7512bf9000-7f7512bfa000 rwxp 00000000 00:00 0
7f7512bfa000-7f7512bfc000 rw-p 00000000 00:00 0
7f7512bfc000-7f7512bfd000 r--p 00022000 08:01 2260 /lib/x86_64-linux-gnu/ld-2.15.so
7f7512bfd000-7f7512bff000 rw-p 00023000 08:01 2260 /lib/x86_64-linux-gnu/ld-2.15.so
7ffcf454c000-7ffcf4585000 rw-p 00000000 00:00 0 [stack]
7ffcf459b000-7ffcf459d000 r-xp 00000000 00:00 0 [vdso]
ffffffffff600000-ffffffffff601000 r-xp 00000000 00:00 0 [vsyscall]
Aborted (core dumped)
使用下面的代码，没有内存问题，但下面的代码可以做什么，我的意思是做分组和数据聚合
with open("data/petaJoined.csv", "r") as content:
for line in content:
#print line
pass
#do stuff with line`
content.close()
任何人都知道发生了什么事？
实际上我想达到Pandas read csv out of memory
中显示的结果也许有解决方案？
注意我已经使用chunk读取csv，但仍然存在内存错误
然后，我更改了块大小，以另一种方式获取 bigData.py 文件
import pandas as pd
import numpy as np
import sys, traceback, os
import etl2 # my self processing flow
reload(etl2)
def iter_chunks(n,df):
while True:
try:
yield df.get_chunk(n)
except StopIteration:
break
cksize=5
try:
dfs = pd.DataFrame()
reader=pd.read_table( 'data/petaJoined.csv',
chunksize = cksize,
low_memory = False,
iterator = True
) # choose as appropriate
for chunk in iter_chunks(cksize,reader):
temp=etl2.tb_createTopRankTable(chunk)
dfs.append(temp)
df=tb_createTopRankTable(dfs)
#
# for chunk in reader:
# pass
# temp=tb_createTopRankTable(chunk)
# dfs.append(temp)
# df=tb_createTopRankTable(dfs)
except:
traceback.print_exc(file=sys.stdout)
但是，运行一段时间后会出现分段错误
def tb_createTopRankTable(df):
try:
key='name1'
key2='name2'
df2 = df.groupby([key,key2])['isError'].agg({ 'errorNum': 'sum','totalParcel': 'count' })
df2['errorRate'] = df2['errorNum'] / df2['totalParcel']
return df2
答案 0 :(得分：2)
根据您的代码段逐行阅读。
我认为kb_2是错误指示符，
groups={}
with open("data/petaJoined.csv", "r") as large_file:
for line in large_file:
arr=line.split('\t')
#assuming this structure: ka,kb_1,kb_2,timeofEvent,timeInterval
k=arr[0]+','+arr[1]
if not (k in groups.keys())
groups[k]={'record_count':0, 'error_sum': 0}
groups[k]['record_count']=groups[k]['record_count']+1
groups[k]['error_sum']=groups[k]['error_sum']+float(arr[2])
for k,v in groups.items:
print ('{group}: {error_rate}'.format(group=k,error_rate=v['error_sum']/v['record_count']))
此代码段将所有组存储在字典中，并在读取整个文件后计算错误率。
如果组的组合太多，则会遇到内存不足的异常。
答案 1 :(得分：1)
问：任何人都知道发生了什么事？
答：是的。 RAM内对象的所有数据存储器开销的总和！＆lt; RAM
如果要在更高（更抽象）的层上实现某些附加功能，则添加一些额外开销是任何正式抽象的自然部分。这意味着选择任何数据集的更抽象/更丰富的特征表示，记忆和记忆越多。处理开销是可以预期的。
ITEMasINT = 32345
ITEMasTUPLE = ( 32345, )
ITEMasLIST = [ 32345, ]
ITEMasARRAY = np.array( [ 32345, ] )
ITEMasDICT = { 0: 32345, }
######## .__sizeof__() -> int\nsize of object in memory, in bytes'
ITEMasINT.__sizeof__() -> 12 #_____ 100% _ trivial INT
ITEMasTUPLE.__sizeof__() -> 16 # 133% _ en-tuple-d
ITEMasLIST.__sizeof__() -> 24 # 200% _ list-ed
ITEMasARRAY.__sizeof__() -> 40 # 333% _ numpy-wrapped
ITEMasDICT.__sizeof__() -> 124 # 1033% _ hash-associated asDict
如果个人经验不足，请检查将输入（已经不小）数据重新包装到pandas开销中的“成本”：
CParserError: Error tokenizing data. C error: out of memory
Segmentation fault (core dumped)
和
CParserError: Error tokenizing data. C error: out of memory
*** glibc detected *** python: free(): ...
...
..
.
Aborted (core dumped)
问：也许会有解决方案？
只需遵循计算策略并部署内存高效的＆amp; csv-input的快速处理（它仍然是 fileIO 具有大约8-15 ms的访问时间和相当低的性能流数据流，即使你使用SSD设备960MB / s的峰值传输速率，你的阻塞事实是内存分配限制...... 所以请耐心等待输入流，不要碰到任何一个主要的内存屏障RAM超级对象（最初会被引入（如果它在实例化过程中没有崩溃），以计算普通sum/nROWs）。
逐行或块排列读取允许您即时计算结果并使用基于寄存器（asDict等结果的临时存储）滑动窗口计算策略快速和内存效率。 （Uri为此提供了一个示例）
这种主要方法用于实时约束系统和片上系统设计，用于处理超过半个世纪的大数据流，所以没有什么新的太阳报了
如果您的结果的大小无法容纳在RAM中，那么即使开始处理任何输入文件也没有意义，是吗？
处理BigData 既不关于COTS-dataObjects 的超级扩展 也不关于找到最佳或性感“一线”......
BigData需要对如何快速处理和智能处理的方式有很多了解，以避免即使是很小的开销的极端成本，这些开销只能在几个GB的小 - 上做出主要错误。大数据但会杀死任何人的预算和在较大的操场上尝试过同样的努力。