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In this assignment, you should work with books.csv file. This file contains the detailed information about books scraped via the Goodreads . The dataset is downloaded from Kaggle website: https://www.kaggle.com/jealousleopard/goodreadsbooks/downloads/goodreadsbooks.zip/6
Each row in the file includes ten columns. Detailed description for each column is provided in the following:
- bookID: A unique Identification number for each book.
- title: The name under which the book was published.
- authors: Names of the authors of the book. Multiple authors are delimited with -.
- average_rating: The average rating of the book received in total.
- isbn: Another unique number to identify the book, the International Standard Book Number.
- isbn13: A 13-digit ISBN to identify the book, instead of the standard 11-digit ISBN.
- language_code: Helps understand what is the primary language of the book.
- num_pages: Number of pages the book contains.
- ratings_count: Total number of ratings the book received.
- text_reviews_count: Total number of written text reviews the book received.
- Write the following codes:
- Use pandas to read the file as a dataframe (named as books). bookIDcolumn should be the index of the dataframe.
- Use books.head() to see the first 5 rows of the dataframe.
- Use book.shape to find the number of rows and columns in the dataframe.
- Use books.describe() to summarize the data.
- Use books[‘authors’].describe() to find about number of unique authors in the dataset and also most frequent author.
- Use OLS regression to test if average rating of a book is dependent to number of pages, number of ratings, and total number of written text reviews the book received.
- Summarize your findings in a Word file.
Please follow these directions carefully.
- Please type your codes in a Jupyter Network file and your summary in a word document named as follows: