Description
Code Sample
from random import randint
def bug_report(n=2000000, idmax=22750, prodmax=3414341):
ids = [randint(1, idmax) for _ in range(n)]
r = lambda: randint(1, prodmax)
prods = [(-1,-2,-3), (-1,-2,-3)] + [(r(), r(), r()) for _ in range(n-2)]
df = pd.DataFrame({'ids': ids, 'products': prods})
counts = df['products'].value_counts()
counts_idxs = counts[counts >= 2].index
idxs = df['products'].isin(counts_idxs)
return df[idxs]Problem description
There are several ways to trigger the bug, either of them resulting in isin returning all False whereas some indexes should be True.
Take the example above, we have the tuple (-1,-2,-3) repeated twice, and it can be checked that both counts and counts_idxs are 2 and (-1,-2,-3), respectively. Then, independently from the rest of the products, the resulting dataset from taking the idxs from isin should have, at least, 2 items. Calling the function as is, does not. Explanation, causes and possible solutions below:
Manually importing from pandas.core.algorithms import isin and settings idxs = isin(df['products'], counts[counts >= 2].index) results in the exact same behaviour.
I've tried to reproduce this same behaviour when not using tuples at all and I can't seem to succeed.
Proposed solution
This seems to be a regression in 0.20.x as using latest 0.19.x (0.19.2) works perfectly fine. Indeed, manually copying isin from 0.19.x and using it instead of 0.20.x works. One can see that a particular if was reversed/erased in
https://github.com/pandas-dev/pandas/blob/master/pandas/core/algorithms.py#L414
and
https://github.com/pandas-dev/pandas/blob/v0.19.2/pandas/core/algorithms.py#L144
https://github.com/pandas-dev/pandas/blob/v0.19.2/pandas/core/algorithms.py#L161
This results in 0.20.x relying in numpy.in1d whereas 0.19.x used lib.ismember, which is equivalent to htable.ismember_object in 0.20.x. One can confirm this becase:
htable = pandas._libs.hashtable
idxs = htable.ismember_object(df['products'].values, np.asarray(counts[counts >= 2].index))
df[idxs]works fine, whereas
idxs = np.in1d(df['products'].values, np.asarray(counts[counts >= 2].index))
all_sets[idxs]silently fails.
Now, either this is temporally fixed in pandas by not relying in in1d or an issue is submitted to numpy (which I will do once I can take a look at in1d and see what's happening). Also, one can solve it by not using tuples at all, and applying hash beforehand, for example.
I've narrowed a bit more the problem and it is not only related to n but also prodmax:
Any combination with n > 1000001 && prodmax > 1986 produces and empty dataframe:
bug_report(n=1000001, prodmax=1987)
bug_report(n=1000001)
bug_report()Whereas having n <= 1000000 or prodmax <= 1986 works just fine. Parameter values have been deduced from:
nfrom https://github.com/pandas-dev/pandas/blob/master/pandas/core/algorithms.py#L414prodmaxby binary search:
def narrow():
start = 256
end = 2048
while start + 1 < end:
print(start, end)
df = bug_report_4(n=1000001, prodmax=(start + end) // 2)
if df.empty:
end = (start + end) // 2
else:
start = (start + end) // 2
return start, df.empty
narrow()
# (1896, False)Output of pd.show_versions()
pandas: 0.20.3
pytest: 3.0.5
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.13.1
scipy: 0.19.1
xarray: None
IPython: 5.1.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.2
feather: None
matplotlib: 2.0.2
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: None
sqlalchemy: 1.1.5
pymysql: None
psycopg2: None
jinja2: 2.9.4
s3fs: None
pandas_gbq: None
pandas_datareader: None
This has been confirmed and tested in multiple pcs and environments, always Python 3.x