I don't understand this error... I've already turned df into lowercase before turning it into a list
dataframe:
all_cols
0 who is your hero and why
1 what do you do to relax
2 this is a hero
4 how many hours of sleep do you get a night
5 describe the last time you were relax
Code:
from sklearn.cluster import MeanShift
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import FunctionTransformer
from sklearn.feature_extraction.text import TfidfVectorizer
df['all_cols'] = df['all_cols'].str.lower()
df_list = df.values.tolist()
pipeline = Pipeline(steps=[
('tfidf', TfidfVectorizer()),
('trans', FunctionTransformer(lambda x: x.todense(), accept_sparse=True)),
('clust', MeanShift())])
pipeline.fit(df_list)
pipeline.named_steps['clust'].labels_
result = [(label,doc) for doc,label in zip(df_list, pipeline.named_steps['clust'].labels_)]
for label,doc in sorted(result):
print(label, doc)
But I have an error in this line:
AttributeError Traceback (most recent call last) in
----> 1 pipeline.fit(df_list)
2 pipeline.named_steps['clust'].labels_AttributeError: 'list' object has no attribute 'lower'
But why is the program returning a lowercase error if I've already passed the lowercase dataframe before?