问MemoryError将两个数据帧与pandas和dasks合并-我该怎么做？

EN

import pandas as pd

df1 = pd.read_cvs("first1.csv")
df2 = pd.read_csv("second2.csv")
print(df1.shape) # output: (4757076, 4)
print(df2.shape) # output: (428764, 45)


df1.head 

    column1  begin    end    category
0  class1  10001  10468    third
1  class1  10469  11447     third
2  class1  11505  11675     fourth
3  class2  15265  15355   seventh
4  class2  15798  15849   second


print(df2.shape) # (428764, 45)
   column1  begin    .... 
0  class1  10524   .... 
1  class1  10541   ....
2  class1  10549  ....
3  class1  10565  ...
4  class1  10596  ...

import pandas as pd
df1 = pd.read_cvs("first1.csv")
df2 = pd.read_csv("second2.csv")
merged = pd.merge(df1, df2, on="column1", how="outer", suffixes=("","_repeated")

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/nfs/sw/python/python-3.5.1/lib/python3.5/site-packages/pandas/tools/merge.py", line 39, in merge
    return op.get_result()
  File "/nfs/sw/python/python-3.5.1/lib/python3.5/site-packages/pandas/tools/merge.py", line 217, in get_result
    join_index, left_indexer, right_indexer = self._get_join_info()
  File "/nfs/sw/python/python-3.5.1/lib/python3.5/site-packages/pandas/tools/merge.py", line 353, in _get_join_info
    sort=self.sort, how=self.how)
  File "/nfs/sw/python/python-3.5.1/lib/python3.5/site-packages/pandas/tools/merge.py", line 559, in _get_join_indexers
    return join_func(lkey, rkey, count, **kwargs)
  File "pandas/src/join.pyx", line 160, in pandas.algos.full_outer_join (pandas/algos.c:61256)
MemoryError

That didn't work. Let's try with dask:


import pandas as pd
import dask.dataframe as dd
from numpy import nan


ddf1 = dd.from_pandas(df1, npartitions=2)
ddf2 = dd.from_pandas(df2, npartitions=2)

merged = dd.merge(ddf1, ddf2, on="column1", how="outer", suffixes=("","_repeat")).compute(num_workers=60)

Here's the error I get:

Traceback (most recent call last):
  File "repeat_finder.py", line 15, in <module>
    merged = dd.merge(ddf1, ddf2,on="column1", how="outer", suffixes=("","_repeat")).compute(num_workers=60)
  File "/path/python3.5/site-packages/dask/base.py", line 78, in compute
    return compute(self, **kwargs)[0]
  File "/path/python3.5/site-packages/dask/base.py", line 178, in compute
    results = get(dsk, keys, **kwargs)
  File "/path/python3.5/site-packages/dask/threaded.py", line 69, in get
    **kwargs)
  File "/path/python3.5/site-packages/dask/async.py", line 502, in get_async
    raise(remote_exception(res, tb))
dask.async.MemoryError: 

Traceback
---------
  File "/path/python3.5/site-packages/dask/async.py", line 268, in execute_task
    result = _execute_task(task, data)
  File "/path/python3.5/site-packages/dask/async.py", line 249, in _execute_task
    return func(*args2)
  File "/path/python3.5/site-packages/dask/dataframe/methods.py", line 221, in merge
    suffixes=suffixes, indicator=indicator)
  File "/path/python3.5/site-packages/pandas/tools/merge.py", line 59, in merge
    return op.get_result()
  File "/path/python3.5/site-packages/pandas/tools/merge.py", line 503, in get_result
    join_index, left_indexer, right_indexer = self._get_join_info()
  File "/path/python3.5/site-packages/pandas/tools/merge.py", line 667, in _get_join_info
    right_indexer) = self._get_join_indexers()
  File "/path/python3.5/site-packages/pandas/tools/merge.py", line 647, in _get_join_indexers
    how=self.how)
  File "/path/python3.5/site-packages/pandas/tools/merge.py", line 876, in _get_join_indexers
    return join_func(lkey, rkey, count, **kwargs)
  File "pandas/src/join.pyx", line 226, in pandas._join.full_outer_join (pandas/src/join.c:11286)
  File "pandas/src/join.pyx", line 231, in pandas._join._get_result_indexer (pandas/src/join.c:11474)
  File "path/python3.5/site-packages/pandas/core/algorithms.py", line 1072, in take_nd
    out = np.empty(out_shape, dtype=dtype, order='F')

import pandas as pd
import dask.dataframe as dd

df1 = pd.read_cvs("first1.csv")
df2 = pd.read_csv("second2.csv")

ddf1 = dd.from_pandas(df1, npartitions=2)
ddf2 = dd.from_pandas(df2, npartitions=2)

merged = dd.merge(ddf1, ddf2, on="column1", how="outer", suffixes=("","_repeat")).compute(num_workers=60)
merged = merged[(ddf1.column1 == row.column1) & (ddf2.begin >= ddf1.begin) & (ddf2.begin <= ddf1.end)]
merged = dd.merge(ddf2, merged, on = ["column1"]).compute(num_workers=60)
merged.to_csv("output.csv", index=False)