BUG: hash_pandas_object ignores column name values

[lmeyerov]

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Reproducible Example

import hashlib, pandas as pd

df = pd.DataFrame({'s': ['a', 'b', 'c', 'd'], 'd': ['b', 'c', 'd', 'e'], 'i': [0, 2, 4, 6]})

df_renamed = df.rename(columns={'s': 'ss'})

hash_df = hashlib.sha256(pd.util.hash_pandas_object(df, index=True).values).hexdigest()
hash_df_renamed = hashlib.sha256(pd.util.hash_pandas_object(df_renamed, index=True).values).hexdigest()

assert hash_df != hash_df_renamed

Issue Description

When hashing a df, the column names are ignored

Expected Behavior

I expected dfs with diff column names to hash differently. If ignoring col names is desired, I'd expect a default-off flag for ignoring col names in the hash calc.

Installed Versions

INSTALLED VERSIONS

commit : 66e3805
python : 3.7.13.final.0
python-bits : 64
OS : Linux
OS-release : 5.4.144+
Version : #1 SMP Tue Dec 7 09:58:10 PST 2021
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 1.3.5
numpy : 1.21.5
pytz : 2018.9
dateutil : 2.8.2
pip : 21.1.3
setuptools : 57.4.0
Cython : 0.29.28
pytest : 3.6.4
hypothesis : None
sphinx : 1.8.6
blosc : None
feather : 0.4.1
xlsxwriter : None
lxml.etree : 4.2.6
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.7.6.1 (dt dec pq3 ext lo64)
jinja2 : 2.11.3
IPython : 5.5.0
pandas_datareader: 0.9.0
bs4 : 4.6.3
bottleneck : 1.3.4
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : 3.2.2
numexpr : 2.8.1
odfpy : None
openpyxl : 3.0.9
pandas_gbq : 0.13.3
pyarrow : 6.0.1
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.4.32
tables : 3.7.0
tabulate : 0.8.9
xarray : 0.18.2
xlrd : 1.1.0
xlwt : 1.3.0
numba : 0.51.2

[rhshadrach]

By doing .values you are getting the underlying NumPy array. This doesn't have column names.

[rhshadrach]

E.g.

df = pd.DataFrame({'s': ['a', 'b', 'c', 'd'], 'd': ['b', 'c', 'd', 'e'], 'i': [0, 2, 4, 6]})
print(pd.util.hash_pandas_object(df, index=True).values)

gives

[ 7494370580092021340  5889295906979463579  1670643539686186963
 10982910506778653110]

[lmeyerov]

That makes sense

We're currently doing a second explicit round of hashing to include df.columns -- is there a more natural way to get a full hash? Ultimately the goal is to get a hash of the df, not the individual .values

[rhshadrach]

Perhaps this is an odd solution, but one thought is to use to_parquet with a byte stream.

buffer = BytesIO()
df.to_parquet(buffer)
hash_df = hashlib.sha256(buffer.getbuffer()).hexdigest()

This will fail if the DataFrame contains complex objects (e.g. lists) as those can't be handled by parquet.

[lmeyerov]

Yeah we already have a a try/except here (for arrow conversion) :)

Interestingly, on a quick microbenchmark, parquet path is about ~10% faster too!

[rhshadrach]

@Imeyerov - Can you describe your use case for hashing a DataFrame? In general one typically doesn't hash mutable objects (especially for us in e.g dict keys).

Certainly DataFrames should not be hashable (i.e. hash(df) fails), but perhaps there is a use case for pandas to provide a hash value for DataFrame, or at least make obtaining such values more straight foward.

[Gabriel-ROBIN]

Interesting, in my use case, I need to hash the dataframe and then save it in parquet. Can I save the buffer on disk ? Or do I need to call to_parquet again ?