Update (April 2018): Use feather format.
How to save & load large pandas dataframes
I have recently started using Pandas for many projects, but one feature which I felt was missing was a native file format the data. This is especially important as the data grows.
Numpy has a simple data format which is just a header plus a memory dump, which is great as it allows you to memory-map the data into memory. Pandas does not have the same thing.
After looking at the code a little bit, I realized it’s pretty easy to fake it though:
- The data in a Pandas DataFrame is held in a numpy array.
- You can save that array using the numpy format.
- The numpy code does not care about the file beyond the header: it just maps the rest of the data into memory.
- In particular, it does not care if there is something in the file after the data. Thus, you can save the Pandas extra-data after the numpy array on disk.
I wrote this up. Here is the writing code:
np.save(open(fname, 'w'), data) meta = data.index,data.columns s = pickle.dumps(meta) s = s.encode('string_escape') with open(fname, 'a') as f: f.seek(0, 2) f.write(s)
We save the array to disk with the numpy machinery, then seek to the end and write out the metadata.
Here is the corresponding loading code:
values = np.load(fname, mmap_mode='r') with open(fname) as f: numpy.lib.format.read_magic(f) numpy.lib.format.read_array_header_1_0(f) f.seek(values.dtype.alignment*values.size, 1) meta = pickle.loads(f.readline().decode('string_escape')) frame = pd.DataFrame(values, index=meta, columns=meta)
Check out this gist for a better version of these, which also supports pandas.Series.
As an added bonus, you can load the saved data as a numpy array directly if you do not care for the metadata:
save_pandas('data.pdy', data) raw = np.load('data.pdy')
Note: I’m not sure this code covers all Pandas cases. It fits in my use case and I hope it can be useful for you, but feel free to point out shortcomings (or improvements) in the comments.