Get the same hash value for a Pandas DataFrame each time

Question

My goal is to get unique hash value for a DataFrame. I obtain it out of .csv file. Whole point is to get the same hash each time I call hash() on it.

My idea was that I create the function

def _get_array_hash(arr):     arr_hashable = arr.values     arr_hashable.flags.writeable = False     hash_ = hash(arr_hashable.data)     return hash_ 

that is calling underlying numpy array, set it to immutable state and get hash of the buffer.

INLINE UPD.

As of 08.11.2016, this version of the function doesn't work anymore. Instead, you should use

hash(df.values.tobytes()) 

See comments for the Most efficient property to hash for numpy array.

END OF INLINE UPD.

It works for regular pandas array:

In [12]: data = pd.DataFrame({'A': [0], 'B': [1]})  In [13]: _get_array_hash(data) Out[13]: -5522125492475424165  In [14]: _get_array_hash(data) Out[14]: -5522125492475424165  

But then I try to apply it to DataFrame obtained from a .csv file:

In [15]: fpath = 'foo/bar.csv'  In [16]: data_from_file = pd.read_csv(fpath)  In [17]: _get_array_hash(data_from_file) Out[17]: 6997017925422497085  In [18]: _get_array_hash(data_from_file) Out[18]: -7524466731745902730 

Can somebody explain me, how's that possible?

I can create new DataFrame out of it, like

new_data = pd.DataFrame(data=data_from_file.values,              columns=data_from_file.columns,              index=data_from_file.index) 

and it works again

In [25]: _get_array_hash(new_data) Out[25]: -3546154109803008241  In [26]: _get_array_hash(new_data) Out[26]: -3546154109803008241 

But my goal is to preserve the same hash value for a dataframe across application launches in order to retrieve some value from cache.

Answer 1

As of Pandas 0.20.1, you can use the little known (and poorly documented) hash_pandas_object (source code) which was recently made public in pandas.util. It returns one hash value for reach row of the dataframe (and works on series etc. too)

import pandas as pd import numpy as np  np.random.seed(42) arr = np.random.choice(['foo', 'bar', 42], size=(3,4)) df = pd.DataFrame(arr)  print(df) #      0    1   2    3 # 0   42  foo  42   42 # 1  foo  foo  42  bar # 2   42   42  42   42  from pandas.util import hash_pandas_object h = hash_pandas_object(df)  print(h) # 0     5559921529589760079 # 1    16825627446701693880 # 2     7171023939017372657 # dtype: uint64 

You can always do hash_pandas_object(df).sum() if you want an overall hash of all rows.