3 (Contacts Data set) Study the books.sql script gave in the ch17 models organizer's sql subfolder. Save the content as addressbook.sql and it to make a solitary table named contacts. The table ought to contain an auto-increased id segment and message sections for an individual memorable name, last name and telephone number. make a Python script illustrating: 1. embed contacts into the information base, 2. question information base to list all the endlessly contacts with explicit last name, 3. update a contact and 4. erase a contact. books.sql code: import panda as pd mport sqlite3 connection sqlite3.connect('books.db') pd.options.display.max_columns = 10 pd.read_sql("SELECT * FROM authors", connection) df = pd. read_sql( "SELECT * FROM authors", connection) DROP TABLE IF EXISTS author_ISBN; DROP TABLE IF EXISTS titles; DROP TABLE IF EXISTS authors; CREATE TABLE authors ( id INTEGER NOT NULL PRIMARY KEY AUTOINCREMENT, first TEXT NOT NULL, last TEXT NOT NULL CREATE TABLE titles ( isbn TEXT NOT NULL, title TEXT NOT NULL, edition INTEGER NOT NULL, copyright TEXT NOT NULL, PRIMARY KEY (1sbn) 0; CREATE TABLE author_ISBN ( id INTEGER NOT NULL, isbn TEXT NOT NULL, PRIMARY KEY (td, isbn),

3 (Contacts Data set) Study the books.sql script gave in the ch17 models organizer's sql subfolder. Save the content as addressbook.sql and it to make a solitary table named contacts. The table ought to contain an auto-increased id segment and message sections for an individual memorable name, last name and telephone number. make a Python script illustrating: 1. embed contacts into the information base, 2. question information base to list all the endlessly contacts with explicit last name, 3. update a contact and 4. erase a contact. books.sql code: import panda as pd mport sqlite3 connection sqlite3.connect('books.db') pd.options.display.max_columns = 10 pd.read_sql("SELECT * FROM authors", connection) df = pd. read_sql( "SELECT * FROM authors", connection) DROP TABLE IF EXISTS author_ISBN; DROP TABLE IF EXISTS titles; DROP TABLE IF EXISTS authors; CREATE TABLE authors ( id INTEGER NOT NULL PRIMARY KEY AUTOINCREMENT, first TEXT NOT NULL, last TEXT NOT NULL CREATE TABLE titles ( isbn TEXT NOT NULL, title TEXT NOT NULL, edition INTEGER NOT NULL, copyright TEXT NOT NULL, PRIMARY KEY (1sbn) 0; CREATE TABLE author_ISBN ( id INTEGER NOT NULL, isbn TEXT NOT NULL, PRIMARY KEY (td, isbn),

Systems Architecture

7th Edition

ISBN:9781305080195

Author:Stephen D. Burd

Publisher:Stephen D. Burd

Chapter3: Data Representation

Section: Chapter Questions

Problem 3RP

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For this assignment, you will create a glossary (dictionary) of technical terms and definitions. It will be set up as a Python dictionary structure. The file glossary_starter.py is a complete starter framework for the assignment. It includes some initial values for the dictionary. It is long because most of the code has already been written for you.Your task is to complete the five individual functions for adding and deleting terms, looking up terms, listing them, and printing out both the terms and definitions. These functions are all short, just a couple of lines, and use basic dictionary methods and techniques. """ Program framework for module 9 graded program Creating a dictionary of technical terms and basic definitions key - the word to define value - the definition of the word Note: This isn't a usable application, as new data added is just temporary.""" tech_terms = { "dict": "stores a key/value pair", "list": "stores a value at each index",…
Numpy and Pandas libraries are said to be similar in functionality. Briefly describe the similarities and differences between the two libraries. Use the editor to format your answer
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7-2 Discussion: Interpreting Multiple Regression Models Previous Next In this discussion, you will apply the statistical concepts and techniques covered in this week's reading about multiple regression. You will not be completing work in Jupyter Notebook this week. Instead, you will be interpreting output from your Python scripts for the Module Six discussion. If you did not complete the Module Six discussion, please complete that before working on this assignment. Last week's discussion involved development of a multiple regression model that used miles per gallon as a response variable. Weight and horsepower were predictor variables. You performed an overall F-test to evaluate the significance of your model. This week, you will evaluate the significance of individual predictors. You will use output of Python script from Module Six to perform individual t-tests for each predictor variable. Specifically, you will look at Step 5 of the Python script to answer…
Explain term encapsulation.
Write, test, and debug (if necessary) PHP scripts for the specifications that follow write functions and the code to test them. 1. Parameter: An array of strings. Return value: A list of the unique strings in the parameter array. 2. Parameter: An array of numbers. Return value: The average and median of the parameter array. 3. Parameter: An array of strings. Return value: A list of the three strings that occur most frequently in the parameter array
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Using C++ create a class Parent with Name & CNIC number as a private data members. Create a getinput & showinput functions & set values through getter & setters. Create another class Student with section as private data member & examscore as a protected data members. Class Student is a relationship with class Parent. Create a getdata & displaydata functions & set values through getter & setters. Create a third class Resultcard with remarksbyteacher as a private data members. Create a function getgrade which calculate the grade of students according to their marks and then display the report card of student. Class Resultcard is a relation with class Student.