大 pandas DataFrames 的外部合并导致 MemoryError---"big data" 如何与 pandas 合并?

Outer merge on large pandas DataFrames causes MemoryError---how to do "big data" merges with pandas?

我有两个 pandas DataFrame df1df2 具有相当标准的格式:

   one  two  three   feature
A    1    2      3   feature1
B    4    5      6   feature2  
C    7    8      9   feature3   
D    10   11     12  feature4
E    13   14     15  feature5 
F    16   17     18  feature6 
...

df2 的格式相同。这些 DataFrame 的大小约为 175MB 和 140MB。

merged_df = pd.merge(df1, df2, on='feature', how='outer', suffixes=('','_features'))

我收到以下内存错误:

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 187, in pandas.algos.full_outer_join (pandas/algos.c:61680)
  File "pandas/src/join.pyx", line 196, in pandas.algos._get_result_indexer (pandas/algos.c:61978)
MemoryError

合并时 pandas 数据帧是否可能存在 "size limit"?我很惊讶这行不通。也许这是某个版本 pandas 的错误?

编辑:如评论中所述,合并列中的许多重复项很容易导致 RAM 问题。参见:Python Pandas Merge Causing Memory Overflow

现在的问题是,我们如何进行合并?似乎最好的方法是以某种方式对数据帧进行分区。

尝试为数字列指定数据类型以减小现有数据框的大小,例如:

df[['one','two', 'three']] = df[['one','two', 'three']].astype(np.int32)

这应该会显着减少内存,并有望让您执行合并。

您可以尝试先通过 unique values, merge and last concat 输出过滤 df1

如果只需要outer join,我觉得也是内存问题。但是,如果为每个循环的过滤器输出添加一些其他代码,它就可以工作。

dfs = []
for val in df.feature.unique():
    df1 = pd.merge(df[df.feature==val], df2, on='feature', how='outer', suffixes=('','_key'))
    #
    #df1 = df1[(df1.start <= df1.start_key) & (df1.end <= df1.end_key)]
    print (df1)
    dfs.append(df1)

df = pd.concat(dfs, ignore_index=True)
print (df)

其他解决方案是使用 dask.dataframe.DataFrame.merge.