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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": {},