1 R A triangle of characters

Write a program characters_triangle.py that gets a strictly positive integer N as input and outputs a triangle of height N, following this kind of interaction:

$ python3 characters_triangle.py

Enter strictly positive number: 13

A

BCB

DEFED

GHIJIHG

KLMNONMLK

PQRSTUTSRQP

VWXYZABAZYXWV

CDEFGHIJIHGFEDC

KLMNOPQRSRQPONMLK

TUVWXYZABCBAZYXWVUT

DEFGHIJKLMNMLKJIHGFED

OPQRSTUVWXYZYXWVUTSRQPO

ABCDEFGHIJKLMLKJIHGFEDCBA

Two built-in functions are useful for this exercise:

• ord() returns the integer that encodes the character provided as argument;
• chr() returns the character encoded by the integer provided as argument.

For instance:

>>> ord(A)

65

>>> chr(65) A

Consecutive uppercase letters are encoded by consecutive integers. For instance:

>>> ord(A), ord(B), ord(C)

(65, 66, 67)

2 R Pascal triangle

Write a program pascal_triangle.py that prompts the user for a number N and prints out the first N +1 lines of Pascal triangle, making sure the numbers are nicely aligned, following this kind of interaction.

$ python3 pascal_triangle.py

Enter a nonnegative integer: 3

1

1 1

1 2 1

1 3 3 1

$ python3 pascal_triangle.py

Enter a nonnegative integer: 7

1

1 1

1 2 1

1 3 3 1

1 4 6 4 1

1 5 10 10 5 1

1 6 15 20 15 6 1

1 7 21 35 35 21 7 1

$ python3 pascal_triangle.py

Enter a nonnegative integer: 11

1

1 1

1 2 1

1 3 3 1

1 4 6 4 1

1 5 10 10 5 1

1 6 15 20 15 6 1

1 7 21 35 35 21 7 1

1 8 28 56 70 56 28 8 1

1 9 36 84 126 126 84 36 9 1

1 10 45 120 210 252 210 120 45 10 1

1 11 55 165 330 462 462 330 165 55 11 1

3 R Computing statistics on the characters in a text

Write a program text_statistics.py that prompts the user for the name of a file and outputs how many times each digit occurs in this file, provided it does occur, following this kind of interaction:

$ cat test_1.txt

The Kiwis were the tournaments gallants, but this day were overwhelmed, perhaps by the occasion, certainly by Australias brand of cricket forte. Plan B sans McCullums salvo had worked against other attacks, but not the Australians lair of limber lefties.

Beforehand, speculation centred on how the Kiwis, playing away from their compact homelands for the first time in the tournament, would deal with the vastness of the MCG. Now, though, the problem was not that the boundaries were too far away, but the bowlers too close. Starc, and after him Mitch Johnson, and must have looked like fishtailing trucks coming towards them, with Josh Hazlewood swerving from the other direction in the next lane. Our bowlers won us the World Cup, Clarke would aver later. After six weeks of batting hit-and-giggle, bowlers had the last laugh. They always do.

$ python3 text_statistics.py Enter the name of a file: test_1.txt There is no digit in this file.

$ cat test_2.txt

Chevrons decision to sell its 50 per cent stake in Caltex Australia will make it easier for the local fuel supplier to release franking credits to shareholders, Caltex chief financial officer Simon Hepworth says.

Speaking after the $4.6 billion block sale, Caltex management sought to assure investors that the companys broader business strategy would be unchanged, despite the departure of its US-domiciled major shareholder, which has held its stake for 40 years.

But Mr Hepworth conceded the deployment of the companys $1.1 billion franking credit balance could be made easier by the transaction, given that as a US-based shareholder, the return of franking credits was not available to Chevron.

$ python3 text_statistics.py

Enter the name of a file: test_2.txt

Digits: 0 1 4 5 6

Count: 2 2 2 1 1

4 Map of CO₂ emissions (optional, needs a module not installed on CSE computers)

Write a program that extracts from the file API_EN.ATM.CO2E.KT_DS2_en_csv_v2.csv, stored in the subdirectory API_EN of the working directory, the country CO₂ emissions for the year 2011. Some data in this file are for entities different to countries, or for countries which are not values of the COUNTRIES dictionary of the pygal.maps.world module. The program will produce an output of the form

Leaving out Aruba

Leaving out Arab World

Leaving out American Samoa

Leaving out Antigua and Barbuda

Leaving out Bahamas, The

Leaving out Latin America & Caribbean (all income levels)

Leaving out Least developed countries: UN classification

Leaving out Low income

Leaving out Lower middle income

Leaving out Low & middle income

Leaving out Virgin Islands (U.S.)

Leaving out Vanuatu

Leaving out West Bank and Gaza

Leaving out World Leaving out Samoa

to let the user know of all those entities and countries, which will be ignored. Some countries are described differently in the dictionary and in the file; these countries will not be ignored. The data will be shown interactively on a map, created as an object of class World of the pygal.maps.world module, that can be displayed in a browser by opening a file named CO2_emissions.svgcheck out render_to_file(). To create the World object from a dictionary having as keys the keys of COUNTRIES, check out add(). The map should havecheck out the Style class from the pygal.style module:

• as title for the map, CO2 emissions in 2011;
• one group of data with Known data as legend and with #B22222 as colour, another group of data with No data as legend and with #A9A9A9 as colour, both with a font size of 10pt;
• tooltips providing standard display for the first group, but with the amount of CO₂ emissions replaced by ? for the second group, both with a font size of 8pt.

Here is the map with the cursor hovering over Australia, for which the CO₂ emissions are known.

Here is the map with the cursor hovering over Puerto Rico, for which the CO₂ emissions are not known.