arrayhascher 0.11

Description:

arrayhascher 0.11

Fast hash in 2D Arrays (Numpy/Pandas/lists/tuples)
pip install arrayhascher
Tested against Windows / Python 3.11 / Anaconda
Cython (and a C/C++ compiler) must be installed
Computes a hash value for each column in a DataFrame/NumPy Array/list/tuple.

Parameters:
- df (numpy.ndarray, pandas.Series, pandas.DataFrame, list, tuple): 2D (!) Input data to compute hash values for.
- fail_convert_to_string (bool, optional): If True, tries to convert non-string columns to strings after failed hashing. - The original data won't change!
If False, raises an exception if conversion fails. Default is True.
- whole_result (bool, optional): If True, returns an array of hash values for each element in the DataFrame/NumPy Array/list/tuple.
If False, returns a condensed array of hash values for each column.
Default is False.

Returns:
- numpy.ndarray: If `whole_result` is False, returns a condensed array of hash values for each column.
If `whole_result` is True, returns an array of hash values for each element in the DataFrame.

Example:
import pandas as pd

from arrayhascher import get_hash_column

def test_drop_duplicates(df,hashdata):
# Example of how to delete duplicates

return df.assign(__XXXX___DELETE____=hashdata).drop_duplicates(subset='__XXXX___DELETE____').drop(
columns='__XXXX___DELETE____')

# With pandas ----------------------------------------------------------------
df = pd.read_csv(
"https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv"
)
df = pd.concat([df for _ in range(10000)], ignore_index=True)
df = df.sample(len(df))
hashdata = get_hash_column(df, fail_convert_to_string=True, whole_result=False)
# Out[3]:
# array([-4123592378399267822, -20629003135630820, 1205215161148196795,
# ..., 4571993557129865534, -5454081294880889185,
# 2672790383060839465], dtype=int64)

# %timeit test_drop_duplicates(df,hashdata)
# %timeit df.drop_duplicates()
# 947 ms ± 18.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
# 2.94 s ± 10.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

# Numpy only ----------------------------------------------------------------
hashdata = get_hash_column(df.to_numpy(), fail_convert_to_string=True, whole_result=False)
print(hashdata)
# # array([-4123592378399267822, -20629003135630820, 1205215161148196795,
# # ..., 4571993557129865534, -5454081294880889185,
# # 2672790383060839465], dtype=int64)

# Works also with lists ------------------------------------------------------
get_hash_column(df[:100].to_numpy().tolist(), fail_convert_to_string=True, whole_result=False)
# array([-5436153420663104440, -1384246600780856199, 177114776690388363,
# 788413506175135724, 1442743010667139722, -6386366738900951630,
# -8610361015858259700, 3995349003546064044, 3627302932646306514,
# 3448626572271213155, -1555175565302024830, 3265835764424924148, ....
# And tuples ----------------------------------------------------------------
tuple(map(tuple, df[:100].to_numpy().tolist()))