Loading...

Messages

Proposals

Stuck in your homework and missing deadline? Get urgent help in $10/Page with 24 hours deadline

Get Urgent Writing Help In Your Essays, Assignments, Homeworks, Dissertation, Thesis Or Coursework & Achieve A+ Grades.

Privacy Guaranteed - 100% Plagiarism Free Writing - Free Turnitin Report - Professional And Experienced Writers - 24/7 Online Support

The late mattia pascal sparknotes

26/10/2021 Client: muhammad11 Deadline: 2 Day

Write A Python Code On The Anaconda Navigator

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:

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:
HW6YourFirstNameYourLastName.

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Python Basics (Instructor: Dr. Milad Baghersad)\n", "## Module 6: Data Analysis with Python Part 1\n", "\n", "- Reference: McKinney, Wes (2018) Python for data analysis: Data wrangling with Pandas, NumPy, and IPython, Second Edition, O'Reilly Media, Inc. ISBN-13: 978-1491957660 ISBN-10: 1491957662\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "___\n", "___\n", "___\n", "___\n", "### review: Numpy (https://www.numpy.org/)\n", "NumPy, short for Numerical Python, is one of the most important foundational packages for numerical computing in Python.\n", "\n", "One of the key features of NumPy is its N-dimensional array object, or ndarray, which is a fast, flexible container for large datasets in Python. Arrays enable you to perform mathematical operations on whole blocks of data using similar syntax to the\n", "equivalent operations between scalar elements." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "b = np.array([[ 0, 1, 2, 3, 4],\n", " [ 5, 6, 7, 8, 9],\n", " [10, 11, 12, 13, 14]])\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(b)\n", "type(b)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(b.sum(axis=0)) # sum of each column" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.ones( (5,4) )" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Create an array of the given shape and populate it with random samples from a uniform distribution over [0, 1)\n", "np.random.rand(4,2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "---\n", "---\n", "# pandas (https://pandas.pydata.org/)\n", "\n", "- Developed by Wes McKinney.\n", "- pandas contains data structures and data manipulation tools designed to make data cleaning and analysis fast and easy in Python.\n", "- While pandas adopts many coding idioms from NumPy, the biggest difference is that pandas is designed for working with tabular or heterogeneous data. \n", "- NumPy, by contrast, is best suited for working with homogeneous numerical array data.\n", "- Can be used to collect data from different sources such as Yahoo Finance!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_data = np.random.rand(4,2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "type(my_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### change the array to a pandas dataframe:\n", "A DataFrame represents a rectangular table of data and contains an ordered collection of columns, each of which can be a different value type (numeric, string, boolean, etc.)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_data_df = pd.DataFrame(my_data)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_data_df" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "type(my_data_df)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_data_df.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#assign columns name\n", "my_data_df = pd.DataFrame(my_data,columns=[\"first column\", \"Second column\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_data_df" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#assign rows name\n", "my_data_df = pd.DataFrame(my_data,columns=[\"first column\", \"Second column\"],index=['a', 'b', 'c', 'd'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_data_df" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#There are many ways to construct a DataFrame, though one of the most common is\n", "# from a dict of equal-length lists or NumPy arrays:\n", "data = {'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada', 'Nevada'],\n", " 'year': [2000, 2001, 2002, 2001, 2002, 2003],\n", " 'pop': [1.5, 1.7, 3.6, 2.4, 2.9, 3.2]}" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_t = pd.DataFrame(data)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_t" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#For large DataFrames, the head method selects only the first five rows:\n", "data_t.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_t.tail()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_t.columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#If you specify a sequence of columns, the DataFrame’s columns will be arranged in that order:\n", "pd.DataFrame(data, columns=['year', 'state', 'pop'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df2 = pd.DataFrame(data, columns=['year', 'state', 'pop'])\n", "df2" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df2.set_index('year',inplace=True)\n", "df2" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#If you pass a column that isn’t contained in the dict, it will appear with missing values in the result:\n", "data_t2 = pd.DataFrame(data, columns=['year', 'state', 'pop', 'debt'],\n", " index=['one', 'two', 'three', 'four', 'five', 'six'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_t2" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_t2.columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#retrieving a column by dict-like notation \n", "data_t2[\"state\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# or by attribute:\n", "data_t2.state" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Rows can be retrieved by position or name with the special loc attribute:\n", "data_t2.loc['three']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Columns can be modified by assignment. \n", "data_t2['debt'] = 16.5" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_t2" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_t2['debt'] = np.arange(6.)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_t2" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "val = pd.DataFrame([2, 4, 5],index=['two', 'four', 'five'])\n", "val" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_t2['debt'] = val" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_t2" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_t2['state'] == 'Ohio'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_t2['eastern'] = data_t2['state'] == 'Ohio'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_t2" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#The del method can then be used to remove this column:\n", "del data_t2['eastern']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_t2" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Index objects are immutable and thus can’t be modified by the user:\n", "data_t2.index" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data_t2.index[0]" ] }, { "cell_type": "code",

Homework is Completed By:

Writer Writer Name Amount Client Comments & Rating
Instant Homework Helper

ONLINE

Instant Homework Helper

$36

She helped me in last minute in a very reasonable price. She is a lifesaver, I got A+ grade in my homework, I will surely hire her again for my next assignments, Thumbs Up!

Order & Get This Solution Within 3 Hours in $25/Page

Custom Original Solution And Get A+ Grades

  • 100% Plagiarism Free
  • Proper APA/MLA/Harvard Referencing
  • Delivery in 3 Hours After Placing Order
  • Free Turnitin Report
  • Unlimited Revisions
  • Privacy Guaranteed

Order & Get This Solution Within 6 Hours in $20/Page

Custom Original Solution And Get A+ Grades

  • 100% Plagiarism Free
  • Proper APA/MLA/Harvard Referencing
  • Delivery in 6 Hours After Placing Order
  • Free Turnitin Report
  • Unlimited Revisions
  • Privacy Guaranteed

Order & Get This Solution Within 12 Hours in $15/Page

Custom Original Solution And Get A+ Grades

  • 100% Plagiarism Free
  • Proper APA/MLA/Harvard Referencing
  • Delivery in 12 Hours After Placing Order
  • Free Turnitin Report
  • Unlimited Revisions
  • Privacy Guaranteed

6 writers have sent their proposals to do this homework:

Helping Engineer
Custom Coursework Service
Financial Assignments
Top Class Engineers
Top Grade Tutor
Innovative Writer
Writer Writer Name Offer Chat
Helping Engineer

ONLINE

Helping Engineer

I am an experienced researcher here with master education. After reading your posting, I feel, you need an expert research writer to complete your project.Thank You

$31 Chat With Writer
Custom Coursework Service

ONLINE

Custom Coursework Service

I am an experienced researcher here with master education. After reading your posting, I feel, you need an expert research writer to complete your project.Thank You

$48 Chat With Writer
Financial Assignments

ONLINE

Financial Assignments

I am an academic and research writer with having an MBA degree in business and finance. I have written many business reports on several topics and am well aware of all academic referencing styles.

$19 Chat With Writer
Top Class Engineers

ONLINE

Top Class Engineers

I am a PhD writer with 10 years of experience. I will be delivering high-quality, plagiarism-free work to you in the minimum amount of time. Waiting for your message.

$15 Chat With Writer
Top Grade Tutor

ONLINE

Top Grade Tutor

Being a Ph.D. in the Business field, I have been doing academic writing for the past 7 years and have a good command over writing research papers, essay, dissertations and all kinds of academic writing and proofreading.

$27 Chat With Writer
Innovative Writer

ONLINE

Innovative Writer

I will be delighted to work on your project. As an experienced writer, I can provide you top quality, well researched, concise and error-free work within your provided deadline at very reasonable prices.

$47 Chat With Writer

Let our expert academic writers to help you in achieving a+ grades in your homework, assignment, quiz or exam.

Similar Homework Questions

Sap srm integration with s 4 hana - The gangster we are all looking for chapter 2 summary - Identifying claims of fact value and policy - During 2014 raines umbrella corp - Wizard of oz story summary - 338 woodpark road smithfield - 8 parts of speech song - Unit 7 management accounting - A company's resources and capabilities represent - Stock in daenerys industries has a beta of - Finance and accounting for nonfinancial managers finkler pdf - The artichoke version of the self - Ashford university sci 207 lab kit 1 - Cj hauser wedding - Hs341 enterprise investment scheme - First act lola ce140 - NURS-6003N-39/NURS-6003C-39/NRSE-6003C-39-Foundations for Graduate Study - Cjt202 assignment - Examples of perception checking statements - Snow removal cost per month - Harpephyllum caffrum root system - Julian opie famous portraits - CPO 2100 European Democracies discussion Board about chapter 3 - Howard publishing company songs of faith and praise - Select appropriate communication techniques for managing change initiatives - Assignment # 3 - The pedestrian figurative language packet answers - Www businessmodelgeneration com downloads - Boys and girls alice munro questions and answers - Ethics Essay - Acid base titration lab vernier answers - Text to world connections - Montefiore Medical Case Study - 25 hp air cooled diesel engine - Legal en - The innovative team unleashing creative potential for breakthrough results - Ce cet cette ces - Www federalreserve gov releases h6 current - Service journal entry - I need 300 words in Systematic risk, also known as "market risk" or "un-diversifiable risk" - Cisco cucm 10.5 eol - Human/sex trafficking - Legal studies preliminary syllabus notes - De escalation strategies in the classroom - Ap style time of day - Ccnpv7 tshoot skills based assessment answers - Homework - Visio 2010 uml stencil - Report for experiment 15 forming and naming ionic compounds answers - Buzz marketing has become a pervasive persuasion strategy because - Sodium benzoate and hcl - Does mcdonalds serve lunch all day 2015 - Oco and the juice - Mitcham medical centre tooting - Bbc weather cockermouth cumbria - Accounting rules - Dr lash trials in tainted space - Problem One: Graphs and Logic, Problem Two: Relations and Functions and Problem Three: Functions - Thermochemistry the heat of reaction lab report answers - Benefits of using powerpoint as a tool in the classroom - Jupiter serial number chart - Discuss the four developmental components of an authentic leader - Departure procedure in front office - TLDQ8-1 - How to charge a capacitor with a light bulb - Sky rider ferris wheel - What happens when you mix sodium thiosulphate and hydrochloric acid - Andrew parsons dentist nelson bay - Kanji with 6 strokes - Provide a 50-75 word discussion reply to the following post below. Finding opportunities in the Energy Industry - Certificate of classification brisbane city council - Management samson and daft 6th edition pdf - Chemical and mechanical digestion - BUSN 299 - Assignment - 1 oz to 5 gallon ratio - Bupa change of details form - Capstone Project Topic Selection and Approval - The product design group of iyengar electric supplies - Tupperware parts catalog 2016 - What does ph paper do - Probate application form pa3 - 54 pounds to euro - Organizational Theory - Distancemath com answers - Module 02 Written Assignment - Outline and Annotated Bibliography - Late unit enrolment mq - Need Response 2 to below discussion-cost - City and guilds english courses in sri lanka - Christy v row 1808 - SIMPLE AND EASY ASSIGNMENT ONE PAGE OR LESS - Standing wave ratio animation - 45 stonier road ross creek - Implementing the nist cybersecurity framework using cobit 2019 pdf - Module 07 Pre-Lab Assignment - Healthy eating informative speech outline - Interest expense on an interest-bearing note is: - Gs gene expression system - Toucon collections - What does rcd mean in penn foster