Store large dictionary to file in Python
With 60,000 dimensions do you mean 60,000 elements? if this is the case and the numbers are 1..10 then a reasonably compact but still efficient approach is to use a dictionary of Python array.array objects with 1 byte per element (type 'B').
The size in memory should be about 60,000 entries x 60,000 bytes, totaling 3.35Gb of data.
That data structure is pickled to about the same size to disk too.
I have a dictionary with many entries and a huge vector as values. These vectors can be 60.000 dimensions large and I have about 60.000 entries in the dictionary. To save time, I want to store this after computation. However, using a pickle led to a huge file. I have tried storing to JSON, but the file remains extremely large (like 10.5 MB on a sample of 50 entries with less dimensions). I have also read about sparse matrices. As most entries will be 0, this is a possibility. Will this reduce the filesize? Is there any other way to store this information? Or am I just unlucky?
Update:
Thank you all for the replies. I want to store this data as these are word counts. For example, when given sentences, I store the amount of times word 0 (at location 0 in the array) appears in the sentence. There are obviously more words in all sentences than appear in one sentence, hence the many zeros. Then, I want to use this array tot train at least three, maybe six classifiers. It seemed easier to create the arrays with word counts and then run the classifiers over night to train and test. I use sklearn for this. This format was chosen to be consistent with other feature vector formats, which is why I am approaching the problem this way. If this is not the way to go, in this case, please let me know. I am very much aware that I have much to learn in coding efficiently! 
I also started implementing sparse matrices. The file is even bigger now (testing with a sample set of 300 sentences).
Update 2:
Thank you all for the tips. John Mee was right by not needing to store the data. Both he and Mike McKerns told me to use sparse matrices, which sped up calculation significantly! So thank you for your input. Now I have a new tool in my arsenal!