问题
I am trying to perform a fuzzywuzzy command comparing two columns in a dataframe. I want to know if a character string from one column ('Relationship') exists in another ('CUST_NAME'), even partially. Then repeat the process for a second column ('Dealer_Name'), on the same column as prior ('CUST_NAME'). I am currently trying to run the following code:
Here is my dataframe:
RapDF1 = RapDF[['APP_KEY','Relationship','Dealer_Name','CUST_NAME']]
Here is the fuzzy matching:
from fuzzywuzzy import process, fuzz
RapDF1.assign(dealer_compare=[process.extract(i, RapDF1['Dealer_Name'], limit=3) for i in RapDF1['CUST_NAME']])
RapDF1.assign(broker_compare=[process.extract(i, RapDF1['Relationship'], limit=3) for i in RapDF1['CUST_NAME']])
However, I receive the following python error:
TypeError Traceback (most recent call last)
<ipython-input-76-2faf28514c26> in <module>()
52 # Attempt 7
53
---> 54 RapDF1.assign(dealer_compare=[process.extract(i, RapDF1['Dealer_Name'], limit=3) for i in RapDF1['CUST_NAME']])
55 RapDF1.assign(broker_compare=[process.extract(i, RapDF1['Relationship'], limit=3) for i in RapDF1['CUST_NAME']])
56
<ipython-input-76-2faf28514c26> in <listcomp>(.0)
52 # Attempt 7
53
---> 54 RapDF1.assign(dealer_compare=[process.extract(i, RapDF1['Dealer_Name'], limit=3) for i in RapDF1['CUST_NAME']])
55 RapDF1.assign(broker_compare=[process.extract(i, RapDF1['Relationship'], limit=3) for i in RapDF1['CUST_NAME']])
56
C:\ProgramData\Anaconda3\lib\site-packages\fuzzywuzzy\process.py in extract(query, choices, processor, scorer, limit)
166 """
167 sl = extractWithoutOrder(query, choices, processor, scorer)
--> 168 return heapq.nlargest(limit, sl, key=lambda i: i[1]) if limit is not None else \
169 sorted(sl, key=lambda i: i[1], reverse=True)
170
C:\ProgramData\Anaconda3\lib\heapq.py in nlargest(n, iterable, key)
567 # General case, slowest method
568 it = iter(iterable)
--> 569 result = [(key(elem), i, elem) for i, elem in zip(range(0, -n, -1), it)]
570 if not result:
571 return result
C:\ProgramData\Anaconda3\lib\heapq.py in <listcomp>(.0)
567 # General case, slowest method
568 it = iter(iterable)
--> 569 result = [(key(elem), i, elem) for i, elem in zip(range(0, -n, -1), it)]
570 if not result:
571 return result
C:\ProgramData\Anaconda3\lib\site-packages\fuzzywuzzy\process.py in extractWithoutOrder(query, choices, processor, scorer, score_cutoff)
76
77 # Run the processor on the input query.
---> 78 processed_query = processor(query)
79
80 if len(processed_query) == 0:
C:\ProgramData\Anaconda3\lib\site-packages\fuzzywuzzy\utils.py in full_process(s, force_ascii)
93 s = asciidammit(s)
94 # Keep only Letters and Numbers (see Unicode docs).
---> 95 string_out = StringProcessor.replace_non_letters_non_numbers_with_whitespace(s)
96 # Force into lowercase.
97 string_out = StringProcessor.to_lower_case(string_out)
C:\ProgramData\Anaconda3\lib\site-packages\fuzzywuzzy\string_processing.py in replace_non_letters_non_numbers_with_whitespace(cls, a_string)
24 numbers with a single white space.
25 """
---> 26 return cls.regex.sub(" ", a_string)
27
28 strip = staticmethod(string.strip)
TypeError: expected string or bytes-like object
回答1:
Propably there are nan
values in the dataframe, nan
has a type float and causes an error:
from fuzzywuzzy import process, fuzz
import pandas as pd
import numpy as np
df_nan = pd.DataFrame({'text1': ["quick", "brown", "fox"], "text2": ["hello", np.NaN, "world"]})
df_nan
Out:
text1 text2
0 quick hello
1 brown NaN
2 fox world
Just an example of code which causes the same error:
[process.extract(i, df_nan['text1'], limit=3) for i in df_nan['text2']]
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
...
/usr/local/lib/python3.6/dist-packages/fuzzywuzzy/string_processing.py in replace_non_letters_non_numbers_with_whitespace(cls, a_string)
24 numbers with a single white space.
25 """
---> 26 return cls.regex.sub(" ", a_string)
27
28 strip = staticmethod(string.strip)
TypeError: expected string or bytes-like object
Replcace nan
's with some token (choose correct token will be hard and data-dependent task, probably empty string is a bad choice):
df = df_nan.fillna('##SOME_TOKEN##')
[process.extract(i, df['text1'], limit=3) for i in df['text2']]
Out:
[[('fox', 36, 2), ('brown', 20, 1), ('quick', 0, 0)],
[('brown', 36, 1), ('fox', 30, 2), ('quick', 18, 0)],
[('fox', 30, 2), ('brown', 20, 1), ('quick', 0, 0)]]
I guess replace or drop all non-string values will help.
来源:https://stackoverflow.com/questions/54228572/trying-to-perform-fuzzy-matching-in-python