17

I have a dataframe "DF" with with 500,000 rows. Here are the data types per column:

ID      int64
time    datetime64[ns]
data    object

each entry in the "data" column is an array with size = [5,500]

When I try to save this dataframe using 

DF.to_pickle("my_filename.pkl")

it returned me the following error:

     12     """
     13     with open(path, 'wb') as f:
---> 14         pkl.dump(obj, f, protocol=pkl.HIGHEST_PROTOCOL) 

OSError: [Errno 22] Invalid argument

I also try this method but I get the same error:

import pickle


with open('my_filename.pkl', 'wb') as f:
    pickle.dump(DF, f)

I try to save 10 rows of this dataframe:

DF.head(10).to_pickle('test_save.pkl')

and I have no error at all. Therefore, it can save small DF but not large DF.

I am using python 3, ipython notebook 3 in Mac.

Please help me to solve this problem. I really need to save this DF to a pickle file. I can not find the solution in the internet.

Joseph Roxas
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4 Answers4

16

Until there is a fix somewhere on pickle/pandas side of things, I'd say a better option is to use alternative IO backend. HDF is suitable for large datasets (GBs). So you don't need to add additional split/combine logic.

df.to_hdf('my_filename.hdf','mydata',mode='w')

df = pd.read_hdf('my_filename.hdf','mydata')
volodymyr
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4

Probably not the answer you were hoping for but this is what I did......

Split the dataframe into smaller chunks using np.array_split (although numpy functions are not guaranteed to work, it does now, although there used to be a bug for it).

Then pickle the smaller dataframes.

When you unpickle them use pandas.append or pandas.concat to glue everything back together.

I agree it is a fudge and suboptimal. If anyone can suggest a "proper" answer I'd be interested in seeing it, but I think it as simple as dataframes are not supposed to get above a certain size.

Split a large pandas dataframe

Community
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Yupsiree
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2

Try to use compression. It worked for me.

data_df.to_pickle('data_df.pickle.gzde', compression='gzip')

PGorshenin
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0

I ran into this same issue and traced the cause to a memory issue. According to this recourse it's usually not actually caused by the memory itself, but the movement of too many resources into the swap space. I was able to save the large pandas file by disableing swap all together withe the command (provided in that link): 

    swapoff -a
user3843986
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