英文:
Get Closest match for a column in data frame
问题
我有一个包含不同呼叫类型的数据框，如下所示的值
CallType0 IN1 OUT2 a_in3 asms4 INCOMING5 OUTGOING6 A2P_SMSIN7 ain8 aout
我希望将其映射，以便输出如下
CallType0 IN1 OUT2 IN3 SMS4 IN5 OUT6 SMS7 IN8 OUT
我试图使用difflib.closestmatch，但它没有结果。以下是我的代码
CALL_TYPE=['IN','OUT','SMS','VOICE','SMT']def test1():final_file_data = pd.DataFrame({'CallType': ['IN', 'OUT', 'a_in','asms', 'INCOMING', 'OUTGOING','A2P_SMSIN','ain', 'aout']})print(final_file_data)final_file_data['CallType'] = final_file_data['CallType'].apply(lambda x: difflib.get_close_matches(x, CALL_TYPE, n=1))
我得到的输出如下，只有IN和OUT有结果
CallType0 [IN]1 [OUT]2 []3 []4 []5 []6 []7 []8 []
我不确定我哪里出错了。
英文:
I have a data Frame which contains different call types as below values
CallType0 IN1 OUT2 a_in3 asms4 INCOMING5 OUTGOING6 A2P_SMSIN7 ain8 aout
I want to map this in such a way the output would be
CallType0 IN1 OUT2 IN3 SMS4 IN5 OUT6 SMS7 IN8 OUT
I am trying to use difflib.closestmatch but it gives no result . Below is my code
CALL_TYPE=['IN','OUT','SMS','VOICE','SMT']def test1():final_file_data = pd.DataFrame({'CallType': ['IN', 'OUT', 'a_in','asms', 'INCOMING', 'OUTGOING','A2P_SMSIN','ain', 'aout']})print(final_file_data)final_file_data['CallType'] = final_file_data['CallType'].apply(lambda x: difflib.get_close_matches(x, CALL_TYPE, n=1))
The output I get is below which as results only for IN and OUT
CallType0 [IN]1 [OUT]2 []3 []4 []5 []6 []7 []8 []
I am not sure where I am going wrong .
答案1
得分: 1
这与 get_close_matches 是区分大小写的以及用于相似度得分的 cutoff 有关。您可以将字符串 x 转换为大写 (upper())，并将 cutoff 设置得更宽松。这就是我所做的：
final_file_data['CallType'] = final_file_data['CallType'].apply(lambda x: difflib.get_close_matches(x.upper(), CALL_TYPE, n=1, cutoff=0))
现在的 final_file_data 是：
CallType0 [IN]1 [OUT]2 [IN]3 [SMS]4 [IN]5 [OUT]6 [SMS]7 [IN]8 [OUT]
您可以在 这里 了解更多关于 get_close_matches 和 cutoff 参数的信息。
英文:
It has to do with get_close_matches being case-sensitive and the cutoff for the score that is gotten for similarity. You can manipulate the x string to upper() and change the cutoff to be less stringent. This is what I did:
final_file_data['CallType'] = final_file_data['CallType'].apply(lambda x: difflib.get_close_matches(x.upper(), CALL_TYPE, n=1, cutoff=0))
final_file_data is now:
CallType0 [IN]1 [OUT]2 [IN]3 [SMS]4 [IN]5 [OUT]6 [SMS]7 [IN]8 [OUT]
You can read more about the get_close_matches here to read more about the cutoff argument.
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