Write A Python Code On The Anaconda Navigator

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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. Task 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. Instructions Please follow these directions carefully. Please type your codes in a Jupyter Network file and your summary in a word document named as follows: