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Overview

This assignment is based on the material covered in Lab 14, Lab 16, and Lab 17.

The goal of the assignment is to develop a simple query language that lets the user select rows and columns from a CSV File, in effect treating it like database.

General Instructions

Every non-test function should have a docstring
Feel free to add docstrings for tests if you think they need explanation
Use list and dictionary comprehensions as much as reasonable.
Your code should pass all of the given tests, plus some of your own with different data. If you want, you can use some of the sample data from the US Government College Scorecard. I've selected some of the data into smaller files:

2013-100.csv.gz
Fri 16 Nov 2018 06:58:06 AM AST

2013-1000.csv.gz
Fri 16 Nov 2018 06:58:06 AM AST

2014-100.csv.gz
Fri 16 Nov 2018 06:58:06 AM AST

2015-100.csv.gz
Fri 16 Nov 2018 06:58:06 AM AST

2014-1000.csv.gz
Fri 16 Nov 2018 06:58:06 AM AST

2015-1000.csv.gz
Fri 16 Nov 2018 06:58:06 AM AST

Reading CSV Files

We will use the builtin Python CSV module to read CSV files.

def read_csv(filename):
    """Read a CSV file, return list of rows

Save the following as "~/fcshome/assignments/A5/test1.csv"; we will use it several tests. You should also construct your own example CSV files and corresponding tests.

name,   age,    eye colour
Bob,    5,      blue
Mary,   27,     brown
Vij,    54,     green

Here is a test to give you the idea of the returned data structure from read_csv.

def test_read_csv():
    assert read_csv('test1.csv') == [['name', 'age', 'eye colour'],
                                     ['Bob', '5', 'blue'],
                                     ['Mary', '27', 'brown'],
                                     ['Vij', '54', 'green']]

Parsing Headers

The first row most in most CSV files consists of column labels. We will use this to help the user access columns by name rather than by counting columns.

Write a function header_map that builds a dictionary from labels to column numbers.

table = read_csv('test1.csv')

def test_header_map_1():
    hmap = header_map(table[0])
    assert hmap == { 'name': 0, 'age': 1, 'eye colour': 2 }

Selecting columns

Use your implimentation of header_map to write a function select that creates a new table with some of the columns of the given table.

def test_select_1():
    assert select(table,{'name','eye colour'}) == [['name', 'eye colour'],
                                                   ['Bob',  'blue'],
                                                   ['Mary', 'brown'],
                                                   ['Vij',  'green']]

Transforming rows into dictionaries

Sometimes it's more convenient to work with rows of the table as dictionaries, rather than passing around the map of column labels everwhere. Write a function row2dict that takes the output from header_map, and a row, and returns a dictionary representing that row (column order is lost here, but that will be ok in our application).

def test_row2dict():
    hmap = header_map(table[0])
    assert row2dict(hmap, table[1]) == {'name': 'Bob', 'age': '5', 'eye colour': 'blue'}

Matching rows

We are going to write a simple query languge where each query is a 3-tuple (left, op, right), and op is one of ==, <=, and >=. In the initial version, left and right are numbers or strings. Strings are interpreted as follows: if they are column labels, retrieve the value in that column; otherwise treat it as a literal string. With this in mind, write a function check_row that takes a row in dictionary form, and checks if it matches a query tuple.

def test_check_row():
    row = {'name': 'Bob', 'age': '5', 'eye colour': 'blue'}
    assert check_row(row, ('age', '==', 5))
    assert not check_row(row, ('eye colour', '==', 5))
    assert check_row(row, ('eye colour', '==', 'blue'))
    assert check_row(row, ('age', '>=', 4))
    assert check_row(row, ('age', '<=', 1000))

Extending the query language

Extend check_row so that it supports operations AND and OR. For these cases both left and right operands must be queries. Hint: this should only be a few more lines of code.

def test_check_row_logical():
    row = {'name': 'Bob', 'age': '5', 'eye colour': 'blue'}
    assert check_row(row, (('age', '==', 5),'OR',('eye colour', '==', 5)))
    assert not check_row(row, (('age', '==', 5),'AND',('eye colour', '==', 5)))

Filtering tables

Use you previously developed functions to impliment a function filter_table that selects certain rows of the table according to a query.

def test_filter_table1():
    assert filter_table(table,('age', '>=', 0)) == [['name', 'age', 'eye colour'],
                                                    ['Bob', '5', 'blue'],
                                                    ['Mary', '27', 'brown'],
                                                    ['Vij', '54', 'green']]

    assert filter_table(table,('age', '<=', 27)) == [['name', 'age', 'eye colour'],
                                                     ['Bob', '5', 'blue'],
                                                     ['Mary', '27', 'brown']]

    assert filter_table(table,('eye colour', '==', 'brown')) == [['name', 'age', 'eye colour'],
                                                                 ['Mary', '27', 'brown']]

    assert filter_table(table,('name', '==', 'Vij')) == [['name', 'age', 'eye colour'],
                                                         ['Vij', '54', 'green']]

def test_filter_table2():
    assert filter_table(table,(('age', '>=', 0),'AND',('age','>=','27'))) == [['name', 'age', 'eye colour'],
                                                                              ['Mary', '27', 'brown'],
                                                                              ['Vij', '54', 'green']]


    assert filter_table(table,(('age', '<=', 27),'AND',('age','>=','27'))) == [['name', 'age', 'eye colour'],
                                                                               ['Mary', '27', 'brown']]

    assert filter_table(table,(('eye colour', '==', 'brown'),
                               'OR',
                               ('name','==','Vij'))) == [['name', 'age', 'eye colour'],
                                                        ['Mary', '27', 'brown'],
                                                        ['Vij', '54', 'green']]

Posted Fri 23 Nov 2018 05:00:00 PM AST Tags: /tags/assignment /tags/cs2613 /tags/python

operation	replacement
+	a+b
-	a-b
/	⌊a / b⌋
*	a*b
^	a^b

dup	copy the top element of the stack
print	print the top element of the stack
pop	remove the top element of the stack
swap	interchange the top two elements on the stack
quit	Stop reading and processing input

Overview

General Instructions

Reading CSV Files

Parsing Headers

Selecting columns

Transforming rows into dictionaries

Matching rows

Extending the query language

Filtering tables

Background

Counter, generators, and classes

Fibonacci, again

Object copying and equality

Arithmetic

Background

Splitting strings

Regular Expressions

Stripping Quotes

Handling quoted commas

Parsing more columns

Background

Globbing and List comprehensions

Dictionary Comprehensions

Filtered List Comprehensions

Using format

Background

Problem Description

Input

Output

Design for test

Numbers

Arithmetic

Improving robustness of the tests

Stack manipulation

Output

Processing lists of operations

Working with files

Background

A first example

Pytest

Modules

More testing, docstrings

Indentation

Exceptions

Introduction

Hack

Conclusions

Using `format`