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***PYTHON 3 PLEASE*** dying_countries: This function takes a formatted countries DataFrame that has a Years to Extinction column and returns a Series whose labels are the countries with negative growth rates and whose values are the number of years until they're dead in sorted order from first to last to die: Windows PowerShell Botswana Monaco Ukraine Latvia Bulgaria Lesotho Russia 2115.0 2602.0 3038.0 3148.0 3 3266.0 3485.0 3839.0 Windows PowerShell class_perfomance: This function takes a connection object and a table name with default value of "ISTA_131_F17" and returns a dictionary that maps the grades (capitalized) to their 1-decimal point precision percentages of the class that got that grade. You did this in hw3 with two queries, only use one this time. In fact, it's relatively straightforward to write this function as a one-liner. improved: This function takes a connection object and two table names and returns a sorted list of the last names of the students that did better in the class represented by the second table than they did in the first. Obviously, you are only interested in students who were in both classes. main: main creates a frame from countries_of_the_world.csv, formats the frame, adds Growth Rate and Years to Extinction columns to it, and prints the top 5 dying countries in this format:

***PYTHON 3 PLEASE*** dying_countries: This function takes a formatted countries DataFrame that has a Years to Extinction column and returns a Series whose labels are the countries with negative growth rates and whose values are the number of years until they're dead in sorted order from first to last to die: Windows PowerShell Botswana Monaco Ukraine Latvia Bulgaria Lesotho Russia 2115.0 2602.0 3038.0 3148.0 3 3266.0 3485.0 3839.0 Windows PowerShell class_perfomance: This function takes a connection object and a table name with default value of "ISTA_131_F17" and returns a dictionary that maps the grades (capitalized) to their 1-decimal point precision percentages of the class that got that grade. You did this in hw3 with two queries, only use one this time. In fact, it's relatively straightforward to write this function as a one-liner. improved: This function takes a connection object and two table names and returns a sorted list of the last names of the students that did better in the class represented by the second table than they did in the first. Obviously, you are only interested in students who were in both classes. main: main creates a frame from countries_of_the_world.csv, formats the frame, adds Growth Rate and Years to Extinction columns to it, and prints the top 5 dying countries in this format:

Database System Concepts
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
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***PYTHON 3 PLEASE*** dying_countries: This function takes a formatted countries DataFrame that has a Years to Extinction column and returns a Series whose labels are the countries with negative growth rates and whose values are the number of years until they're dead in sorted order from first to last to die: Windows PowerShell Botswana Monaco Ukraine Latvia Bulgaria Lesotho Russia 2115.0 2602.0 3038.0 3148.0 3266.0 3485.0 3839.0 DX class_perfomance: This function takes a connection object and a table name with default value of "ISTA_131_F17" and returns a dictionary that maps the grades (capitalized) to their 1-decimal point precision percentages of the class that got that grade. You did this in hw3 with two queries, only use one this time. In fact, it's relatively straightforward to write this function as a one-liner. improved: This function takes a connection object and two table names and returns a sorted list of the last names of the students that did better in the class represented by the second table than they did in the first. Obviously, you are only interested in students who were in both classes. main: main creates a frame from countries_of_the_world.csv, formats the frame, adds Growth Rate and Years to Extinction columns to it, and prints the top 5 dying countries in this format: Windows PowerShell Botswana: 2115.0 Years to Extinction Monaco: 2602.0 Years to Extinction Ukraine: 3038.0 Years to Extinction Latvia: 3148.0 Years to Extinction Bulgaria: 3266.0 Years to Extinction PS C:\Users\Rich\Documents\ISTA_131_F18\hw\hw4>
Transcribed Image Text:***PYTHON 3 PLEASE*** dying_countries: This function takes a formatted countries DataFrame that has a Years to Extinction column and returns a Series whose labels are the countries with negative growth rates and whose values are the number of years until they're dead in sorted order from first to last to die: Windows PowerShell Botswana Monaco Ukraine Latvia Bulgaria Lesotho Russia 2115.0 2602.0 3038.0 3148.0 3266.0 3485.0 3839.0 DX class_perfomance: This function takes a connection object and a table name with default value of "ISTA_131_F17" and returns a dictionary that maps the grades (capitalized) to their 1-decimal point precision percentages of the class that got that grade. You did this in hw3 with two queries, only use one this time. In fact, it's relatively straightforward to write this function as a one-liner. improved: This function takes a connection object and two table names and returns a sorted list of the last names of the students that did better in the class represented by the second table than they did in the first. Obviously, you are only interested in students who were in both classes. main: main creates a frame from countries_of_the_world.csv, formats the frame, adds Growth Rate and Years to Extinction columns to it, and prints the top 5 dying countries in this format: Windows PowerShell Botswana: 2115.0 Years to Extinction Monaco: 2602.0 Years to Extinction Ukraine: 3038.0 Years to Extinction Latvia: 3148.0 Years to Extinction Bulgaria: 3266.0 Years to Extinction PS C:\Users\Rich\Documents\ISTA_131_F18\hw\hw4>
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