Dataframe apply does not accept axis argument

Question

I have two data frames: dataand 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 to the framework dataafter calculating the Levenshtein similarity between each seller and the rule. Therefore, in my data block, ideally there should be columns that look like this:

>>>data
  vendor   rule    similarity
0 googel   google    0.8

So far I am trying to execute a function applythat will return this structure to me, but the dataframe used does not accept an argument axis.

>>> 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'

Can someone please help me figure out what I'm doing wrong? Any changes I make are just making new mistakes. Thank.

Answer 1

Series apply, axis , .

:

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

df,

axis:

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

, , levenshtein .

:

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

, , .