Description

Introduction

The file energy.csv (attached) contains the values of energy generated for each country in Terawatt Hours for different years (from OECD Factbook 2011: Economic, Environmental and Social Statistics). The data contains missing values (denoted by ..) and also aggregate rows for the EU, OECD, and World countries.

 

Requirements

You are to create a program in Python that performs the following:

1. Loads the energy.csv file (assume it’s in the current directory) and create a DataFrame object from it.
2. Replaces the missing values with the mean energy production for that country.
3. Removes the data for the aggregate values (EU27, OECD, World)
4. Adds a new column called ‘Continent’ and fills it in with the continent that corresponds to each country.
5. Creates a DataFrame that contains the continent name as the index and the following columns:
   1. ‘num_countries’ = number of countries for this continent
   2. ‘mean’ = mean energy production of the countries in this continent
   3. ‘small_production’ = 1 if continent mean is less than the mean production of all countries minus one standard deviation; 0 otherwise
   4. ‘avg_production’ = 1 if continent mean is greater than the mean production of all countries minus one standard deviation, but less than the mean production of all countries plus one standard deviation; 0 otherwise
   5. ‘large_production’ = 1 if continent mean is greater than the mean production of all countries plus one standard deviation; 0 otherwise
6. Display the new DataFrame to the screen

 

 

                 

Additional Requirements

1. The name of your source code file should be py. All your code should be within a single file.
2. You need to use the pandas DataFrame object for storing data.
3. Your code should follow good coding practices, including good use of whitespace and use of both inline and block comments.
4. You need to use meaningful identifier names that conform to standard naming conventions.
5. At the top of each file, you need to put in a block comment with the following information: your name, date, course name, semester, and assignment name.

 

What to Turn In

You will turn in the single DataPrep.py file using BlackBoard.

                 

Sample Program Output 

70-511, [semester] [year]

NAME: [put your name here]

PROGRAMMING ASSIGNMENT #5

 

num_countries         mean  small  avg  large Europe                    25   161.572326      0    1      0

Asia                       7   607.983830      0    1      0

North America              3  1560.438095      0    0      1

South America              2   199.148901      0    1      0

Australia                  1   218.021429      0    1      0

Africa                     1   213.846154      0    1      0

Oceania                    1    39.378571      0    1      0