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Compression using an optimized algorithm (lzhw) developed from Lempel-Ziv, Huffman and LZ-Welch techniques

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Compression library to compress big lists and/or pandas dataframes using an optimized algorithm (lzhw) developed from Lempel-Ziv, Huffman and LZ-Welch techniques.

How lzhw Works

The library's main goal is to compress data frames, excel and csv files so that they consume less space to overcome memory errors. Also to enable dealing with large files that can cause memory errors when reading them in python or that cause slow operations. With lzhw, we can read compressed files and do operations column by column only on columns that we are interesred in.

The algorithm is a mix of the famous lempel-ziv and huffman coding algorithm with some use of lempel-ziv-welch algorithm. The algorithm starts with an input stream for example this one:

example = ["to", "be", "or", "not", "to", "be", "or", "to", "be", "or", "not"] * 2
# tobeornottobeortobeornottobeornottobeortobeornot

lzhw uses lz78 to discover repeated sequences in the stream and construct a dictionary, but instead of returning their codes from the dictionary, it glues the sequence together in one cell. Then we will have a shorter list with repeated sequences glued together, where Huffman Coding can come to the game encoding them.

The function that performs lempel-ziv and returning a shorter list without the codes I named it lz20 :D.

lz20_ex = lzhw.lz20(example)
# ['to', 'be', 'or', 'not', 'to', 'be', 'or', 'to', 'be', 'or', 
#  'not', 'to', 'be or', 'not', 'to be', 'or', 'to be or', 'not']

Now huffman coding will give us:

huff20 = lzhw.huffman_coding(Counter(lz20_ex))
# {'to': '10', 'be': '110', 'or': '01', 'not': '00', 
#  'be or': '11110', 'to be': '11111', 'to be or': '1110'}

This will give us:

"".join([huff20.get(i) for i in lz20_ex])
# 10110010010110011011001001011110001111101111000

Which has a length of 47 bits.

Then it uses lempel-ziv-welch (lzw) to compress the keys of the dictionaries so that no need to store them as strings, i.e. extra space saving.

from sys import getsizeof
some_key = "to be or"
# 57

lzw_key = lzhw.lzw_compress(some_key)
# 5794278370027331706482

# 36

Let's see if we store the keys of the dictionary as string how much size they will have in comparison of the integer type.

keys = list(huff20.keys())
# 144

comp_keys = [lzhw.lzw_compress(k) for k in keys]
# 128

All of the steps can be done at once using LZHW class as follows and as shown in the Quick Start section:

lzhw_comp = lzhw.LZHW(example)
# 110110010010110011011001001011110001111101111000 
# the first 1 is a to save the preceeding 0s as compressed data is stored as an integer
# 238786645532536

print(lzhw_comp.sequences) # dictionary's keys and values are stored as integers as well
# {6: 321647, 14: 312421, 5: 319090, 4: 163110516, 62: 41932445245042,
#  63: 43170737996901, 30: 5794278370027331706482}

Quick Start

pip install lzhw
import lzhw

sample_data = ["Sunny", "Sunny", "Overcast", "Rain", "Rain", "Rain", "Overcast", 
               "Sunny", "Sunny", "Rain", "Sunny", "Overcast", "Overcast", "Rain", 
               "Rain", "Rain", "Sunny", "Sunny", "Overcaste"]

compressed = lzhw.LZHW(sample_data)
## let's see how the compressed object looks like:
# 1111101101010011111101101010011100000010

## its size
# 32

## size of original
from sys import getsizeof
# 216

# space saving from original to compressed is 85%

## Let's decompress and check whether there is any information loss
decomp = compressed.decompress()
print(decomp == sample_data)
# True

As we saw, the LZHW class has saved 85% of the space used to store the original list without any loss. The class has also some useful helper methods as space_saving, size, and decompress() to revert back to original.

Another example with numeric data.

from random import sample, choices

numbers = choices(sample(range(0, 5), 5), k = 20)
comp_num = lzhw.LZHW(numbers)

print(getsizeof(numbers) > comp_num.size())
# True

print(numbers == list(map(int, comp_num.decompress()))) ## make it int again
# True

# space saving from original to compressed is 88%

Let's look at how the compressed object is stored and how it looks like when printed: LZHW class has an attribute called compressed which is the integer of the encoded bitstring

print(comp_num.compressed) # how the compressed is saved (as integer of the bit string)
# 103596881534874

# 10111100011100010000111010100101101111110011010

We can also write the compressed data to files using save_to_file method, and read it back and decompress it using decompress_from_file function.

status = ["Good", "Bad", "Bad", "Bad", "Good", "Good", "Average", "Average", "Good",
          "Average", "Average", "Bad", "Average", "Good", "Bad", "Bad", "Good"]
comp_status = lzhw.LZHW(status)
decomp_status = lzhw.decompress_from_file("status.txt")
print(status == decomp_status)
# True

Compressing DataFrames

lzhw doesn't work only on lists, it also compress pandas dataframes and save it into compressed files to decompress them later.

import pandas as pd

df = pd.DataFrame({"a": [1, 1, 2, 2, 1, 3, 4, 4],
                   "b": ["A", "A", "B", "B", "A", "C", "D", "D"]})
comp_df = lzhw.CompressedDF(df)
# 100%|██████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2003.97it/s]

Let's check space saved by compression

comp_space = 0
for i in range(len(comp_df.compressed)):
	comp_space += comp_df.compressed[i].size()

print(comp_space, getsizeof(df))
# 56 712

## Test information loss
print(comp_df.compressed[0].decompress() == list(map(str, df.a)))
# True

Saving and Loading Compressed DataFrames

With lzhw we can save a data frame into a compressed file and then read it again using save_to_file method and decompress_df_from_file function.

## Save to file

## Load the file
original = lzhw.decompress_df_from_file("comp_df.txt")
# 100%|██████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 2004.93it/s]

#   a  b
#0  1  A
#1  1  A
#2  2  B
#3  2  B
#4  1  A
#5  3  C
#6  4  D
#7  4  D

Compressing Bigger DataFrames

Let's try to compress a real-world dataframe german_credit.xlsx file. Original txt file is 219 KB on desk.

gc_original = pd.read_excel("examples/german_credit.xlsx")
comp_gc = lzhw.CompressedDF(gc_original)
# 100%|█████████████████████████████████████████████████████████████████████████████████| 62/62 [00:00<00:00, 257.95it/s]

## Compare sizes in Python:
comp_space = 0
for i in range(len(comp_gc.compressed)):
	comp_space += comp_gc.compressed[i].size()

print(comp_space, getsizeof(gc_original))
# 12932 548852

print(comp_gc.compressed[0].decompress() == list(map(str, gc_original.iloc[:, 0])))
# True

Huge space saving, 97%, with no information loss!

Let's now write the compressed dataframe into a file and compare the sizes of the files.


Checking the size of the compressed file, it is 87 KB. Meaning that in total we saved around 60%. Future versions will be optimized to save more space.

Let's now check when we reload the file, will we lose any information or not.

## Load the file
gc_original2 = lzhw.decompress_df_from_file("gc_compressed.txt")
# 100%|█████████████████████████████████████████████████████████████████████████████████| 62/62 [00:00<00:00, 259.46it/s]

print(list(gc_original2.iloc[:, 13]) == list(map(str, gc_original.iloc[:, 13])))
# True

print(gc_original.shape == gc_original2.shape)
# True

Perfect! There is no information loss at all.

(De)Compressing specific columns from a dataframe

With lzhw you can choose what columns you are interested in compressing from a data frame. CompressedDF class has an argument selected_cols.

gc_original = pd.read_excel("examples/german_credit.xlsx")
comp_gc = lzhw.CompressedDF(gc_original, selected_cols = [0, 3, 4, 7])
# 100%|███████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 401.11it/s]

Also when you have a compressed file that you want to decompress, you don't have to decompress it all, you can choose only specific columns to decompress. By this you can deal with large compressed files and do operations column by column quickly and avoid memory errors decompress_df_from_file function has the same argument selected_cols.

gc_original2 = lzhw.decompress_df_from_file("gc_compressed.txt", selected_cols = [0, 4])
# 100%|████████████████████████████████████████████████████████████████████████████████| 62/62 [00:00<00:00, 3348.53it/s]

#	Duration	Age
#0	       6	67
#1	      48	22
#2	      12	49
#3	      42	45
#4	      24	53

Let's compare this subset with the original df.

gc_original.iloc[:, [0, 4]].head()
#	Duration	Age
#0	       6	67
#1	      48	22
#2	      12	49
#3	      42	45
#4	      24	53


selected_cols has "all" as its default value.

Using the lzhw Command Line Interface

In lzhw_cli folder, there is a python script that can work on command line to compress and decompress files.

Here is the file:


usage: [-h] [-d] -f INPUT -o OUTPUT error: the following arguments are required: -f/--input, -o/--output

Getting help to see what it does and its arguments:

$python -h

usage: [-h] [-d] -f INPUT -o OUTPUT

Data Frame Compressor

optional arguments:
  -h, --help            show this help message and exit
  -d, --decompress      decompress input into output
  -f INPUT, --input INPUT
                        input file to be (de)compressed
  -o OUTPUT, --output OUTPUT
                        output where to save result

How to compress:

$python -f "file_to_compress" -o "output"

compressed successfully

How to decompress:

$python -d -f "file_to_decompress" -o "output"

decompressed successfully

Helper Functions

Aside from the functions and classes discussed, the library also has some more compression functions that can be used as standalone.


lz78 which performs the famous lempel-ziv78 algorithm:

import random
example = random.choices(["A", "B", "C"], k = 20)
['A', 'A', 'C', 'C', 'A', 'A', 'C', 'C', 'C', 'B', 'B', 'A', 'B', 'B', 'C', 'C', 'B', 'C', 'C', 'B']

lz78_comp, symb_dict = lzhw.lz78(example)
# ['1', '1', 'C', '3', '1', 'A', '3', 'C', '3', 'B', 
#  '7', '1', 'B', '7', 'C', '6', 'C', 'C B']

# {'A': '1', 'A C': '2', 'C': '3', 'A A': '4', 'C C': '5', 
   'C B': '6', 'B': '7', 'A B': '8', 'B C': '9', 'C B C': '10'}


Huffman Coding function which takes a Counter object and encodes each symbol accordingly.

from collections import Counter
huffs = lzhw.huffman_coding(Counter(example))
# {'A': '10', 'C': '0', 'B': '11'}

lzw_compress() and lzw_decompress()

They perform lempel-ziv-welch compressing and decompressing

print(lzhw.lzw_compress("Hello World"))
# 723201696971929295664359987300

print(lzhw.lzw_decompress(lzw.lzw_compress("Hello World")))
# Hello World

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