CODEWITHSUNDEEP
pythonlistnlp
Qianru Song
Qianru Song

Reputation: 341

Argument 'string' has incorrect type (expected str, got list) Spacy NLP

I want to calculate cosine similarity, but I got an error message after converting the dataframe column to its list: Argument 'string' has incorrect type (expected str, got list).

import pandas as pd
import spacy
nlp = spacy.load("en_core_web_sm")

df= [['24, Single, Consultant, Canada, I am interested in visiting Isreal again'], ['18, Single, Student, I want to go back Costa Rica again'], ['45,Married, Unemployed, I want to take my family to Florida for the summer vacation']] 
df = pd.DataFrame(df, columns = ['Free Text'])
df["N_Application"]=range(0, len(df))

# convert datafram to list
data=df['Free Text'].tolist()
df_spacy=nlp(data)

I appreciate someone help me fix it, Thank you.

Upvotes: 1

Views: 535

Answers (1)

mechanical_meat
mechanical_meat

Reputation: 169494

The way you get a function to operate across an entire pd.Series is to use .apply(). And you can chain .apply() calls.

Example:

# changing to strings instead of nested list
l = ['24, Single, Consultant, Canada, I am interested in visiting Isreal again', 
     '18, Single, Student, I want to go back Costa Rica again', 
     '45,Married, Unemployed, I want to take my family to Florida for the summer vacation']

# remove stop words and punctuation for later similarity calculations
df_spacy = df['Free Text'].apply(nlp)\
                          .apply(lambda doc: nlp(' '.join(str(t) 
                                                 for t in doc 
                                                 if not t.is_stop 
                                                 and not t.is_punct)))

Edit: per your comment, here is a similarity calculation between each row and all other rows:

df_spacy.apply(lambda row: df_spacy\
        .apply(lambda doc: row.similarity(doc) if row != doc else None))

Resulting similarity matrix:

          0         1         2
0       NaN  0.776098  0.716560
1  0.776098       NaN  0.705024
2  0.716560  0.705024       NaN

Upvotes: 1

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