I am trying to use a dictionary
key
to replace strings
in a pandas
column with its values
. However, each column contains sentences. Therefore, I must first tokenize the sentences and detect whether a Word in the sentence corresponds with a key in my dictionary, then replace the string with the corresponding value.
However, the result that I continue to get it none. Is there a better pythonic way to approach this problem?
Here is my MVC for the moment. In the comments, I specified where the issue is happening.
import pandas as pd
data = {'Categories': ['animal','plant','object'],
'Type': ['tree','dog','rock'],
'Comment': ['The NYC tree is very big','The cat from the UK is small','The rock was found in LA.']
}
ids = {'Id':['NYC','LA','UK'],
'City':['New York City','Los Angeles','United Kingdom']}
df = pd.DataFrame(data)
ids = pd.DataFrame(ids)
def col2dict(ids):
data = ids[['Id', 'City']]
idDict = data.set_index('Id').to_dict()['City']
return idDict
def replaceIds(data,idDict):
ids = idDict.keys()
types = idDict.values()
data['commentTest'] = data['Comment']
words = data['commentTest'].apply(lambda x: x.split())
for (i,word) in enumerate(words):
#Here we can see that the words appear
print word
print ids
if word in ids:
#Here we can see that they are not being recognized. What happened?
print ids
print word
words[i] = idDict[word]
data['commentTest'] = ' '.apply(lambda x: ''.join(x))
return data
idDict = col2dict(ids)
results = replaceIds(df, idDict)
Results:
None
I am using python2.7
and when I am printing out the dict
, there are u'
of Unicode.
My expected outcome is:
Categories
Comment
Type
commentTest
Categories Comment Type commentTest
0 animal The NYC tree is very big tree The New York City tree is very big
1 plant The cat from the UK is small dog The cat from the United Kingdom is small
2 object The rock was found in LA. rock The rock was found in Los Angeles.
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