CODEWITHSUNDEEP
python-3.xpandasdictionarystartswithends-with
sharp
sharp

Reputation: 2158

Python get first and last value from string using dictionary key values

I have gotten a very strange data. I have dictionary with keys and values where I want to use this dictionary to search if these keywords are ONLY starting and/or end of the text not middle of the sentence. I tried to create simple data frame below to show the problem case and python codes that I have tried so far. How do I get it go search for only starting or ending of the sentence? This one searches whole text sub-strings.

Code:

d = {'apple corp':'Company','app':'Application'} #dictionary
l1 = [1, 2, 3,4]
l2 = [
    "The word Apple is commonly confused with Apple Corp which is a business",
    "Apple Corp is a business they make computers",
    "Apple Corp also writes App",
    "The Apple Corp also writes App"
]
df = pd.DataFrame({'id':l1,'text':l2})
df['text'] = df['text'].str.lower()
df

Original Dataframe:

id   text 
1    The word Apple is commonly confused with Apple Corp which is a business         
2    Apple Corp is a business they make computers                                    
3    Apple Corp also writes App                                                      
4    The Apple Corp also writes App

Code Tried out:

def matcher(k):
    x = (i for i in d if i in k)
    # i.startswith(k) getting error
    return ';'.join(map(d.get, x))
df['text_value'] = df['text'].map(matcher)
df

Error: TypeError: 'in <string>' requires string as left operand, not bool when I use this x = (i for i in d if i.startswith(k) in k)

Empty values if i tried this x = (i for i in d if i.startswith(k) == True in k)

TypeError: sequence item 0: expected str instance, NoneType found when i use this x = (i.startswith(k) for i in d if i in k)

Results from Code above ... Create new field 'text_value':

id   text                                                                            text_value
1    The word Apple is commonly confused with Apple Corp which is a business         Company;Application
2    Apple Corp is a business they make computers                                    Company;Application
3    Apple Corp also writes App                                                      Company;Application
4    The Apple Corp also writes App                                                  Company;Application

Trying to get an FINAL output like this:

id   text                                                                            text_value
1    The word Apple is commonly confused with Apple Corp which is a business         NaN
2    Apple Corp is a business they make computers                                    Company
3    Apple Corp also writes App                                                      Company;Application
4    The Apple Corp also writes App                                                  Application

Upvotes: 0

Views: 193

Answers (2)

Onyambu
Onyambu

Reputation: 79288

joined = "|".join(d.keys())

pat = '(?i)^(?:the\\s*)?(' + joined + ')\\b.*?|.*\\b(' + joined + ')$'+'|.*'

get = lambda x: d.get(x.group(1),"") + (';' +d.get(x.group(2),"") if x.group(2) else '')

df.text.str.replace(pat,get)


0                       
1                Company
2    Company;Application
3    Company;Application
Name: text, dtype: object

Upvotes: 0

harpan
harpan

Reputation: 8631

You need a matcher function which can accept flag and then call that twice to get the results for startswith and endswith. 

def matcher(s, flag="start"):
    if flag=="start":
        for i in d:
            if s.startswith(i):
                return d[i]
    else:
        for i in d:
            if s.endswith(i):
                return d[i]
    return None

df['st'] = df['text'].apply(matcher)
df['ed'] = df['text'].apply(matcher, flag="end")
df['text_value'] = df[['st', 'ed']].apply(lambda x: ';'.join(x.dropna()),1)
df = df[['id','text', 'text_value']]

The text_value column looks like:

0                       
1                Company
2    Company;Application
3            Application
Name: text_value, dtype: object

Upvotes: 1

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