Dataframe apply doesn't accept axis argument

Question

I have two dataframes: data and rules .

>>>data                            >>>rules
   vendor                             rule
0  googel                           0 google
1  google                           1 dell
2  googly                           2 macbook

I am trying to add two new columns into the data dataframe after computing the Levenshtein similarity between each vendor and rule. So my dataframe should ideally contain columns looking like this:

>>>data
  vendor   rule    similarity
0 googel   google    0.8

So far I am trying to perform an apply function that will return me this structure, but the dataframe apply is not accepting the axis argument.

>>> for index,r in rules.iterrows():
...     data[['rule','similarity']]=data['vendor'].apply(lambda row:[r[0],ratio(row[0],r[0])],axis=1)
...
Traceback (most recent call last):

File "<stdin>", line 2, in <module>

File "/home/mnnr/test/env/test-1.0/runtime/lib/python3.4/site-packages/pandas/core/series.py", line 2220, in apply
mapped = lib.map_infer(values, f, convert=convert_dtype)
File "pandas/src/inference.pyx", line 1088, in pandas.lib.map_infer (pandas/lib.c:62658)
File "/home/mnnr/test/env/test-1.0/runtime/lib/python3.4/site-packages/pandas/core/series.py", line 2209, in <lambda>
f = lambda x: func(x, *args, **kwds)

TypeError: <lambda>() got an unexpected keyword argument 'axis'

Could someone please help me figure out what I am doing wrong? Any change I make is just creating new errors.Thank you

Solution

You're calling the Series version of apply for which it doesn't make sense to have an axis arg hence the error.

If you did:

data[['rule','similarity']]=data[['vendor']].apply(lambda row:[r[0],ratio(row[0],r[0])],axis=1)

then this makes a single column df for which this would work

Or just remove the axis arg:

data[['rule','similarity']]=data['vendor'].apply(lambda row:[r[0],ratio(row[0],r[0])])

update

Looking at what you're doing, you need to calculate the levenshtein ratio for each rule against every vendor.

You can do this by:

data['vendor'].apply(lambda row: rules['rule'].apply(lambda x: ratio(x, row))

this I think should calculate the ratio for each vendor against every rule.