CODEWITHSUNDEEP
pythonpandasjupyter-notebookdata-science
wahmulyadi
wahmulyadi

Reputation: 5

How to labeling with Pandas (.map)?

label with pandas (.map) in the following table

m2m_similarity.columns = ['MoviId 1','MoviId 2','similarity_score']
m2m_similarity.head(3)

I have tried to get the label slightly-similar, similar, and exacly

m2m_similarity['analysis'] = m2m_similarity['similarity_score'].map({
    0.1: 'slightly-similar', 0.2: 'slightly-similar', 0.3: 'slightly-similar', 0.4: 'slightly-similar',
    0.5: 'similar', 0.6: 'similar', 0.7: 'similar', 0.8: 'similar',0.9: 'similar', 
    1.0: 'Exacly'
})
m2m_similarity.head(3)

and the result is Nan

enter image description here

enter image description here

Upvotes: 0

Views: 249

Answers (2)

Aryerez
Aryerez

Reputation: 3495

A better way would be:

m2m_similarity['analysis'] = m2m_similarity['similarity_score'].map(lambda s: 'Exacly' if round(s, 2) == 1 else ('similar' if round(s, 2) >= 0.5 else 'slightly-similar'))

As it would cover all the options in between. And anyway make sure that in similarity_score you have numbers and not strings, and if they aren't actually high-precision floats that you only show the first digit.

Upvotes: 1

abhi
abhi

Reputation: 377

Try using:

m2m_similarity['analysis'] = m2m_similarity['similarity_score'].replace({
    0.1: 'slightly-similar', 0.2: 'slightly-similar', 0.3: 'slightly-similar', 0.4: 'slightly-similar',
    0.5: 'similar', 0.6: 'similar', 0.7: 'similar', 0.8: 'similar',0.9: 'similar', 
    1.0: 'Exacly'
})

Upvotes: 0

Related Questions