Code Sample, a copy-pastable example if possible
# Note: This is not real Python code. I can't reproduce these problems with toy data, but they
# show up consistently with certain sets of real data.
from pandas import Series
vocabulary = Series(data = <a few thousand integers>, index = <a few thousand words/tokens, including u"null">)
new_vocab = Series(data = <a smaller list of integers>, index = <a smaller list of words, mostly overlapping with the first one, also including u"null">)
new_words = new_vocab.index.difference(vocabulary.index)
print u"null" is in vocabulary.index
print u"null" is in new_vocab.index
print u"null" is in new_words
> True
> True
> True
from pandas import DataFrame
matrix1 = DataFrame(data = <a bunch of numbers>, index = <list of topic names>, columns <list of words/tokens, _not_ including NaN or u"nan">)
matrix2 = DataFrame(data = <a bunch of numbers>, index = <list of topic names>, columns <list of words/tokens, _not_ including NaN or u"nan">)
matrix3 = matrix1.multiply(matrix2)
from numpy import NaN
print NaN in matrix1.columns
print u"nan" in matrix1.columns
print NaN in matrix2.columns
print u"nan" in matrix2.columns
print NaN in matrix3.columns
print u"nan" in matrix3.columns
> False
> False
> False
> False
> True
> True
Problem description
This may be related to #18988.
I feel bad about reporting these bugs, because I can't duplicate them outside actual scripts with actual (rather voluminous) data. The first common denominator to all the issues is that when I perform operations involving two objects, I end up with extra columns (in DataFrames) or elements (in Series or Indexes): sometimes duplicates of existing columns (but the second, duplicate column will contain all null values), sometimes entirely new columns/elements.
The second common denominator is that every case seems to involve "null-like" index values: NaN, u"nan", u"null", u"n/a", and u"null.1.1" are the ones I've actually observed. In one case, u"null" creates a problem; in another u"null" works just fine, while u"n/a" and u"null.1.1" create problems (but u"null.1" doesn't). In a third case, the null-like values don't exist in the original objects, but show up after an operation, as new columns (interestingly, in this case, under pandas 20.3 I got two columns, NaN, and u"nan", while in pandas 22.0, I get those two plus u"n/a"--or actually, the "len" function is telling me there are four extra columns, but I using a "difference" method only gives me a list of three)--and they show up with some sets of data but not others.
In each case where errors have shown up, I've added code to check for them and remove the spurious columns/elements, but it's been very frustrating to run down different problem that can occur.
Output of pd.show_versions()
Comment From: chris-b1
Shot in the dark - can you try with python 3?
Comment From: MTKnife
I'm not familiar with Python 3, but I know there's a conversion tool, so maybe I can try that when I get a chance.
I can report that I got rid of most (maybe all?) of the problems by changing two arguments in read_csv: I set "na_values" to "''" and "keep_default_na" to False. Apparently one factor leading to the issues in this particular case was that the "na" settings in read_csv affect not only the values in a csv but also the index/column labels. When doing natural language processing and using tokens (words) as DataFrame and Series labels, it's pretty much inevitable that the default null values will come up, unless you explicitly exclude them from your vocabulary (and there are good reasons not do do so). The current behavior of read_csv is not nonsensical, but it could be better documented, and I think there's a good case for having different args for the labels and the values.