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Jupyter for Data Science

You're reading from   Jupyter for Data Science Exploratory analysis, statistical modeling, machine learning, and data visualization with Jupyter

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781785880070
Length 242 pages
Edition 1st Edition
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Author (1):
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Dan Toomey Dan Toomey
Author Profile Icon Dan Toomey
Dan Toomey
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Table of Contents (11) Chapters Close

Preface 1. Jupyter and Data Science FREE CHAPTER 2. Working with Analytical Data on Jupyter 3. Data Visualization and Prediction 4. Data Mining and SQL Queries 5. R with Jupyter 6. Data Wrangling 7. Jupyter Dashboards 8. Statistical Modeling 9. Machine Learning Using Jupyter 10. Optimizing Jupyter Notebooks

Determining relationships between number of ratings and ratings

Given the preceding results it appears that people mostly only vote in a positive manner. We can look to see if there is a relationship between how many votes a company has received and their rating.

First, we accumulate the dataset using the following script, extracting the number of votes and rating for each firm:

#determine relationship between number of reviews and star rating
import pandas as pd
from pandas import DataFrame as df  
import numpy as np  

dfr2 = pd.DataFrame(columns=['reviews', 'rating'])
mynparray = dfr2.values

for line in lines:
    line = unicode(line, errors='ignore')
    obj = json.loads(line)
    reviews = int(obj['review_count'])
    rating = float(obj['stars'])
    arow = [reviews,rating]
    mynparray = np.vstack((mynparray,arow)) 

dfr2...
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