CS21 Lab 8: Searching Data

As always, run update21, to create the cs21/labs/08 directory. Then cd into your cs21/labs/08 directory and create the program for lab 8 in this directory

• practice reading data from a file
• use a list of lists to store the data
• understand when linear and binary searches should/can be used

This week's lab involves climate data for different countries. The file /usr/local/doc/climatedata.csv contains data from the CAIT Climate Data Explorer. Here are the first few lines from climatedata.csv:

Afghanistan|30682500|19731.34|0|15.21|44.42
Albania|2896652|12542.47|2319|11.84|7.95
Algeria|38186135|176324.90|47580|91.71|186.84
Angola|23448202|96261.43|15356|160.32|270.19
Antigua & Barbuda|89985|1155.65|0|0.40|1.11
Argentina|42538304|457639.09|80594|303.46|431.64

Each line contains data for a different country. The lines in the file are in alphabetical order based on the name of the country. Here's a handy chart for the position and meaning of each piece of data:

0. Name -- name of country
1. Population -- population of country (as of 2013)
2. GDP -- country's GDP in US Dollars
3. EnergyUse -- country's energy use in Thousand tonnes oil equivalent (ktoe)
4. GHG1990 -- greenhouse gas emissions in 1990 (MtCO2e, Including Land-Use Change and Forestry)
5. GHG2013 -- greenhouse gas emissions in 2013 (MtCO2e, Including Land-Use Change and Forestry)

Your job this week is to write a program (ghg.py) that reads in the climate data and displays the following information:

• total number of entries in the file
• the names and populations of each entry with a population greater than 500 million (e.g., India: 1279498874)
• the names and GHG emissions of each entry with a GHG2013 greater than 1000 (e.g., United States: 6213.27)
• the names of all entries with an average change/year in emissions (1990 to 2013) greater than 30 (e.g., Indonesia: 36)
• all information about a user-specified country:

  Country: Brazil
           Name:    Brazil
     Population: 204259377
        GDP USD:   2409739
     Energy Use:    293683
       GHG 1990:    1447.7
       GHG 2013:    1317.2
    Change/year:      -5.7
  

To get the information required above, you will (obviously) have to search through the climate data. For many of the searches, since the data is only sorted alphabetically by country, you will have to use a linear search. For the last one (search for data about a specific country) you can/should use a binary search.

Here are the specifications for this program and a few helpful "tips". Make sure your program meets all of these specifications.

• You should use top-down design for this program! Design your main() function and any helper functions. Your main() function should be relatively short and easy to read. Implement and test the program one function at a time. A good program design and good use of functions is part of the grade.
• read in the data for all countries and store all of the data in a list-of-lists, like this:

  [[data for country 1], [data for country 2], [data for country 3], ...]
  

  Here's what the inner lists for each country should contain:

  [Name,Pop,GDP,EnergyUse,GHG1990,GHG2013,AvgChangePerYear]
  

• The last item in the above list, AvgChangePerYear, is not stored in the file, but you can calculate it for each country as follows: (GHG2013 - GHG1990)/(2013-1990)
• For a list-of-lists, accessing individual items in the inner lists can either be done with assignment or multiple square brackets:

  >>> print(LOL)
  [['Afghanistan',30682500,19731.34], ['Albania',2896652,12542.47], ['Algeria',38186135,176324.9]]
  >>> cdata = LOL[0]
  >>> print(cdata)
  ['Afghanistan', 30682500, 19731.34]
  >>> print(cdata[0])
  Afghanistan
  >>>
  >>> print(LOL[0])
  ['Afghanistan', 30682500, 19731.34]
  >>> print(LOL[0][0])
  Afghanistan
  

• the data for each country should be stored in the correct format. For example, the name should be a string, but the population should be an integer, and the GDP should be a float. Here's a real example for one of the countries (Germany's GHG emissions are down an average of 10.7 per year since 1990!):

  ['Germany', 82132753, 3577227.56, 317658, 1103.55, 856.69, -10.733043478260866]
  

• The climate data file uses a pipe symbol ("|") as a delimiter. In python, you can easily split a string using the split() str method:

  >>> S = "a|b|c|d"
  >>> data = S.split("|")
  >>> print(data)
  ['a', 'b', 'c', 'd']
  

• You are free to format the output of your program any way you want, but it should be clear and concise and easy to read.
• When asking the user for a country, your program should display all information for the user-chosen country. This should work independent of case (i.e., Germany or germany). If the country is not listed in the climate data file, your program should say that. Also, keep asking for a country until the user enters "quit" or a blank line ("").

  Country: genovia
  genovia not found...
  Country: germany
           Name:   Germany
     Population:  82132753
        GDP USD:   3577227
     Energy Use:    317658
       GHG 1990:    1103.5
       GHG 2013:     856.7
    Change/year:     -10.7
  

• See our pages on File I/O and str and list methods for more help with those topics.

Once you are confident that your program works, fill out the questionnaire in README-08.txt.

Then run handin21 a final time to make sure we have access to the most recent versions of the files required for this lab.