HDF5 taking more space than CSV?

Question

Consider the following example:

Prepare the data:

import string import random import pandas as pd  matrix = np.random.random((100, 3000)) my_cols = [random.choice(string.ascii_uppercase) for x in range(matrix.shape[1])] mydf = pd.DataFrame(matrix, columns=my_cols) mydf['something'] = 'hello_world' 

Set the highest compression possible for HDF5:

store = pd.HDFStore('myfile.h5',complevel=9, complib='bzip2') store['mydf'] = mydf store.close() 

Save also to CSV:

mydf.to_csv('myfile.csv', sep=':') 

The result is:

• myfile.csv is 5.6 MB big
• myfile.h5 is 11 MB big

The difference grows bigger as the datasets get larger.

I have tried with other compression methods and levels. Is this a bug? (I am using Pandas 0.11 and the latest stable version of HDF5 and Python).

Answer 1

Copy of my answer from the issue: https://github.com/pydata/pandas/issues/3651

Your sample is really too small. HDF5 has a fair amount of overhead with really small sizes (even 300k entries is on the smaller side). The following is with no compression on either side. Floats are really more efficiently represented in binary (that as a text representation).

In addition, HDF5 is row based. You get MUCH efficiency by having tables that are not too wide but are fairly long. (Hence your example is not very efficient in HDF5 at all, store it transposed in this case)

I routinely have tables that are 10M+ rows and query times can be in the ms. Even the below example is small. Having 10+GB files is quite common (not to mention the astronomy guys who 10GB+ is a few seconds!)

-rw-rw-r--  1 jreback users 203200986 May 19 20:58 test.csv -rw-rw-r--  1 jreback users  88007312 May 19 20:59 test.h5  In [1]: df = DataFrame(randn(1000000,10))  In [9]: df Out[9]:  <class 'pandas.core.frame.DataFrame'> Int64Index: 1000000 entries, 0 to 999999 Data columns (total 10 columns): 0    1000000  non-null values 1    1000000  non-null values 2    1000000  non-null values 3    1000000  non-null values 4    1000000  non-null values 5    1000000  non-null values 6    1000000  non-null values 7    1000000  non-null values 8    1000000  non-null values 9    1000000  non-null values dtypes: float64(10)  In [5]: %timeit df.to_csv('test.csv',mode='w') 1 loops, best of 3: 12.7 s per loop  In [6]: %timeit df.to_hdf('test.h5','df',mode='w') 1 loops, best of 3: 825 ms per loop  In [7]: %timeit pd.read_csv('test.csv',index_col=0) 1 loops, best of 3: 2.35 s per loop  In [8]: %timeit pd.read_hdf('test.h5','df') 10 loops, best of 3: 38 ms per loop 

I really wouldn't worry about the size (I suspect you are not, but are merely interested, which is fine). The point of HDF5 is that disk is cheap, cpu is cheap, but you can't have everything in memory at once so we optimize by using chunking