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Python package to compress numerical series into strings
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Simple way to compress and decompress numerical series.

  • Easily gets you above 80% compression ratio
  • You can specify the precision you need for floating points (up to 10 decimal points)
  • Useful to store or transmit stock prices, monitoring & other time series data in compressed string format

Compression algorithm is based on google encoded polyline format. I modified it to preserve arbitrary precision and apply it to any numerical series. The work is motivated by usefulness of time aware polyline built by Arjun Attam at HyperTrack. After building this I came across arrays that are much efficient than lists in terms memory footprint. You might consider using that over numcompress if you don't care about conversion to string for transmitting or storing purpose.


pip install numcompress


from numcompress import compress, decompress

# Integers
>>> compress([14578, 12759, 13525])

>>> decompress('B_twxZnv_nB_bwm@')
[14578.0, 12759.0, 13525.0]

# precision argument specifies how many decimal points to preserve, defaults to 3

# Floats - lossless compression
>>> compress([145.7834, 127.5989, 135.2569], precision=4)

>>> decompress('Csi~wAhdbJgqtC')
[145.7834, 127.5989, 135.2569]

# Floats - lossy compression
>>> compress([145.7834, 127.5989, 135.2569], precision=2)

>>> decompress('Acn[rpB{n@')
[145.78, 127.6, 135.26]

Compression Ratio

Test # of Numbers Compression ratio
Integers 10k 91.14%
Floats 10k 81.35%

You can run the test suite with -s switch to see the compression ratio. You can even modify the tests to see what kind of compression ratio you will get for your own input.

pytest -s

Here's a quick example showing compression ratio:

>>> series = random.sample(range(1, 100000), 50000)  # generate 50k random numbers between 1 and 100k
>>> text = compress(series)  # apply compression

>>> original_size = sum(sys.getsizeof(i) for i in series)
>>> original_size

>>> compressed_size = sys.getsizeof(text)
>>> compressed_size

>>> compression_ratio = ((original_size - compressed_size) * 100.0) / original_size
>>> compression_ratio

We get ~76% compression for 50k random numbers between 1 & 100k. This ratio increases for real world numerical series as the difference between consecutive numbers tends to be lower. Think of stock prices, monitoring & other time series data.


If you see any problem, open an issue or send a pull request. You can write to me at

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