出現(xiàn)的情況

user_log = pd.read_csv(’一個(gè)1.8G的文件‘) 
# 已證明8G內(nèi)存的電腦不行，在jupyter種操作的時(shí)候結(jié)果如下：

---------------------------------------------------------------------------
MemoryError                               Traceback (most recent call last)
<ipython-input-26-126c6dffbe38> in <module>()
----> 1 user_log = pd.read_csv(path6)
      2 user_log.sample(5)

E:\miniconda\envs\course_py35\lib\site-packages\pandas\io\parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)
    653                     skip_blank_lines=skip_blank_lines)
    654 
--> 655         return _read(filepath_or_buffer, kwds)
    656 
    657     parser_f.__name__ = name

E:\miniconda\envs\course_py35\lib\site-packages\pandas\io\parsers.py in _read(filepath_or_buffer, kwds)
    409 
    410     try:
--> 411         data = parser.read(nrows)
    412     finally:
    413         parser.close()

E:\miniconda\envs\course_py35\lib\site-packages\pandas\io\parsers.py in read(self, nrows)
   1021             new_rows = len(index)
   1022 
-> 1023         df = DataFrame(col_dict, columns=columns, index=index)
   1024 
   1025         self._currow += new_rows

E:\miniconda\envs\course_py35\lib\site-packages\pandas\core\frame.py in __init__(self, data, index, columns, dtype, copy)
    273                                  dtype=dtype, copy=copy)
    274         elif isinstance(data, dict):
--> 275             mgr = self._init_dict(data, index, columns, dtype=dtype)
    276         elif isinstance(data, ma.MaskedArray):
    277             import numpy.ma.mrecords as mrecords

E:\miniconda\envs\course_py35\lib\site-packages\pandas\core\frame.py in _init_dict(self, data, index, columns, dtype)
    409             arrays = [data[k] for k in keys]
    410 
--> 411         return _arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)
    412 
    413     def _init_ndarray(self, values, index, columns, dtype=None, copy=False):

E:\miniconda\envs\course_py35\lib\site-packages\pandas\core\frame.py in _arrays_to_mgr(arrays, arr_names, index, columns, dtype)
   5504     axes = [_ensure_index(columns), _ensure_index(index)]
   5505 
-> 5506     return create_block_manager_from_arrays(arrays, arr_names, axes)
   5507 
   5508 

E:\miniconda\envs\course_py35\lib\site-packages\pandas\core\internals.py in create_block_manager_from_arrays(arrays, names, axes)
   4307 
   4308     try:
-> 4309         blocks = form_blocks(arrays, names, axes)
   4310         mgr = BlockManager(blocks, axes)
   4311         mgr._consolidate_inplace()

E:\miniconda\envs\course_py35\lib\site-packages\pandas\core\internals.py in form_blocks(arrays, names, axes)
   4379 
   4380     if len(int_items):
-> 4381         int_blocks = _multi_blockify(int_items)
   4382         blocks.extend(int_blocks)
   4383 

E:\miniconda\envs\course_py35\lib\site-packages\pandas\core\internals.py in _multi_blockify(tuples, dtype)
   4448     for dtype, tup_block in grouper:
   4449 
-> 4450         values, placement = _stack_arrays(list(tup_block), dtype)
   4451 
   4452         block = make_block(values, placement=placement)

E:\miniconda\envs\course_py35\lib\site-packages\pandas\core\internals.py in _stack_arrays(tuples, dtype)
   4491     shape = (len(arrays),) + _shape_compat(first)
   4492 
-> 4493     stacked = np.empty(shape, dtype=dtype)
   4494     for i, arr in enumerate(arrays):
   4495         stacked[i] = _asarray_compat(arr)

MemoryError: 

目的

查看任務(wù)管理器，內(nèi)存使用量達(dá)到了90%以上，如何在不shutdown當(dāng)前文件的情況下回收讀取這個(gè)文件時(shí)占用的內(nèi)存？