Resource Information

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:

1. bookID: A unique Identification number for each book.
2. title: The name under which the book was published.
3. authors: Names of the authors of the book. Multiple authors are delimited with -.
4. average_rating: The average rating of the book received in total.
5. isbn: Another unique number to identify the book, the International Standard Book Number.
6. isbn13: A 13-digit ISBN to identify the book, instead of the standard 11-digit ISBN.
7. language_code: Helps understand what is the primary language of the book.
8. num_pages: Number of pages the book contains.
9. ratings_count: Total number of ratings the book received.
10. text_reviews_count: Total number of written text reviews the book received.

Task

1. Write the following codes:
   1. Use pandas to read the file as a dataframe (named as books). bookIDcolumn should be the index of the dataframe.
   2. Use books.head() to see the first 5 rows of the dataframe.
   3. Use book.shape to find the number of rows and columns in the dataframe.
   4. Use books.describe() to summarize the data.
   5. Use books[‘authors’].describe() to find about number of unique authors in the dataset and also most frequent author.
   6. 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.
2. Summarize your findings in a Word file.

Instructions

 Please follow these directions carefully.

1. Please type your codes in a Jupyter Network file and your summary in a word document named as follows:
   HW6YourFirstNameYourLastName.