drop_duplicates destroys non-duplicated data under 0.17

[RPGillespie6]

The drop_duplicates() function in Python 3 is broken. Take the following example snippet:

import pandas as pd

raw_data = {'x': [7,6,3,3,4,8,0],'y': [0,6,5,5,9,1,2]}
df = pd.DataFrame(raw_data, columns = ['x', 'y'])

print("Before:", df)
df = df.drop_duplicates()
print("After:", df)

When run under python 2, the results are correct, but when running under python 3, pandas removes 6,6 from the frame, which is a completely unique row. When using this function with large CSV files, it causes thousands of lines of unique data loss.

See:
http://stackoverflow.com/questions/33224356/why-is-pandas-dropping-unique-rows

[jreback]

pls pd.show_versions()

[jreback]

cc @sinhrks

[oscar6echo]

In the same field - and maybe connected (?) - I ran into a Python 2 case where the .duplicated() method applied to a dataframe returned lines that were NOT duplicates. Too large to paste the example here. But wanted to mention it in case it almost the same bug under the hood.

[kawochen]

able to reproduce in master (PY3)

[dsm054]

I see the loss of 6,6 under both 2.7.6 and 3.5.0 with 0.17.0-- that is, I see no 2 vs. 3 difference in this example. (My notebook has a somewhat rare 32-bit Python linux environment, and so problems sometimes manifest differently.)

>>> df = pd.DataFrame([[1,0],[0,2]])
>>> df
   0  1
0  1  0
1  0  2
>>> df.duplicated()
0    False
1     True
dtype: bool
>>> df.astype(str).duplicated()
0    False
1    False
dtype: bool
>>> df.astype(np.float64).duplicated()
0    False
1    False
dtype: bool

[dsm054]

I'm not convinced that the comment # if we have integers we can directly index with these before the int shortcut in f is correct. Not sure that get_group_index plays nicely with the integers if they're not ranked by column. For example:

In [202]: get_group_index([np.array([1,0]), np.array([0,2])], np.array([2,2]), False, False)
Out[202]: array([2, 2], dtype=int64)

Anyway, one route to a quick fix would be to push everything along the factorize branch, IIUC.

[sinhrks]

I can't track enough yet, but below part looks to break factorized labels.

• https://github.com/pydata/pandas/blob/master/pandas/core/groupby.py#L3768

@behzadnouri Any idea?

[pag]

Just a 'me too'. I'm running into this problem under python 2.7.10 now that I've upgraded to pandas 1.7. I can also reproduce using the example given in the stackoverflow post:

Before
   x  y
0  7  0
1  6  6
2  3  5
3  3  5
4  4  9
5  8  1
6  0  2

After:
   x  y
0  7  0
2  3  5
4  4  9
5  8  1
6  0  2

i.e. it's dropped the 3,5 duplicated (correct) but also the 6,6 row (incorrect).

[evanpw]

If all of the integers are non-negative, we can index directly with integers, but the shape should be the largest integer present + 1, rather than the number of unique integers.

[behzadnouri]

@jreback
@sinhrks

this is broken by pydata#10917
core/frame.py#L2997-L3002.

factorize cannot be replaced by unique1d.

>>> a
array([ 5,  8, 11])
>>> unique1d(a)
array([ 5,  8, 11])
>>> factorize(a)[0]
array([0, 1, 2])

[fridiculous]

👍 on duplicated issues with 0.17.0

i'm seeing a similar problems, as the following should return an empty data frame:

df[df.duplicated(['charstring','number'], keep=False)]

index   charstring                              number
7989    E1FDF0E7-DFBD-428F-A6E7-5D48EAD3A559    435
7990    B3308C59-B9CF-42CB-A2C6-A406CA36EF2B    0

[jreback]

closed by #11403