Trying to work out a python code

Question

I am new to Machine Learning and python. Recently i have been working with Amazon fine food review data from kaggle and its code. What i don't understand is how is the 'partiton' method used here ? Moreover, What actually does last 3 lines of code do ?

    %matplotlib inline
    import sqlite3
    import pandas as pd
    import numpy as np
    import nltk
    import string
    import matplotlib.pyplot as plt
    import seaborn as sns
    from sklearn.feature_extraction.text import TfidfTransformer
    from sklearn.feature_extraction.text import TfidfVectorizer

    from sklearn.feature_extraction.text import CountVectorizer
    from sklearn.metrics import confusion_matrix
    from sklearn import metrics
    from sklearn.metrics import roc_curve, auc
    from nltk.stem.porter import PorterStemmer



    # using the SQLite Table to read data.
    con = sqlite3.connect('./amazon-fine-food-reviews/database.sqlite') 




    #filtering only positive and negative reviews i.e. 
    # not taking into consideration those reviews with Score=3
    filtered_data = pd.read_sql_query("""
    SELECT *
    FROM Reviews
    WHERE Score != 3
    """, con) 




    # Give reviews with Score>3 a positive rating, and reviews with a 
    score<3 a negative rating.
    def partition(x):
    if x < 3:
        return 'negative'
    return 'positive'

    #changing reviews with score less than 3 to be positive vice-versa
    actualScore = filtered_data['Score']
    positiveNegative = actualScore.map(partition) 
    filtered_data['Score'] = positiveNegative

parition method here used to make a label or classify based on the review. for example if rating value is 4 it label as postitve suppose rating is 2 it label as negative. — Mohamed Thasin ah, Mar 06 '18 at 07:27
Thats fine. But how does the actualscore.map(partition) is working internally here ? — Akash Dubey, Mar 06 '18 at 07:30
Yes. I know how it works in general. i am confused about how is it working here ? — Akash Dubey, Mar 06 '18 at 07:40
you could refer vince miller's answer. It gives idea about what you are asking. — Mohamed Thasin ah, Mar 06 '18 at 07:40
you can get more details here : https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.map.html — Mohamed Thasin ah, Mar 06 '18 at 07:42

score 0 · Answer 1 · answered Mar 06 '18 at 07:32

0

creates an array called actualScore using the column Score from filtered_data

actualScore = filtered_data['Score']

creates array positiveNegative coding negative for values <3 and positive for >3

positiveNegative = actualScore.map(partition)

overwrites old column score with new coded values

filtered_data['Score'] = positiveNegative

answered Mar 06 '18 at 07:32

Vince Miller

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applying the function partition to every row of actualScore – Vince Miller Mar 06 '18 at 07:40
can you please explain it a bit more ? – Akash Dubey Mar 06 '18 at 07:44
This statement new_list = list(map(function, old_list)) creates new_list i.e. a list that the designated function is used on every item in old_list. The syntax in your question is another way to write it. – Vince Miller Mar 06 '18 at 07:51
Okay. Got it. Thanks a lot. – Akash Dubey Mar 06 '18 at 07:55
1

@AkashDubey if this answer or comment was helpful please accept or upvote. – Vince Miller Mar 06 '18 at 07:59
I already upvoted it. But i am not yet eligible to upvote. Have less then 15 reps. – Akash Dubey Mar 06 '18 at 08:07

score 0 · Answer 2 · answered Nov 03 '19 at 08:56

I think Actually to replace Score column in table with positve or negetive, we use method called partition.Get the Score column as dataframe actualScore, then map the dataframe with replacing values of whether it is positive or negetive. Then replace values in score column with positiveNegative.

Trying to work out a python code

2 Answers2