Problem Description

You need to do some text processing and decide to write a few utility functions to get started.

Solution Description

Write a module named hw1 with the functions specified below.

doctest

The specification for each function is given as a docstring which you should include in your code and the types of arguments and return values are given using type hints documented in PEP 484 Type Hints.

Because each function will have a docstring that includes usage examples formatted as Python interactive sessions, you can use doctest to test your code using this command line invocation:

python -m doctest -v hw1.py  

Ensure that ALL tests pass.

Required Functions

IMPORTANT: Do not modify the provided docstrings!

def long_words(text, n): """Return the words in text that are longer than n. Parameters: text: str -- the text of interest Return: Sequence[int] -- words in text that are longer than n Usage Examples: >>> long_words('I do not write good', 3) ['write', 'good'] """ def num_long_words(text, n): """Return the number of words in text that are longer than n. Parameters: text: str -- the text of interest Return: int -- the number of words in text that are longer than n Usage Examples: >>> num_long_words('I do not write good', 3) 2 """ def count_if(sequence, predicate): """Count the number of elements in sequence that satisfy predicate function. Parameters: sequence: Sequence -- a sequence of elements of any type predicate: Function: Any -> bool -- a function that takes a single parameter and returns a bool Usage Examples: >>> count_if([0, 1, 2, 3, 4], lambda x: x % 2 == 0) 3 >>> count_if(['fe', 'fi', 'fo', 'fum'], lambda s: len(s) > 2) 1 """ def word_counts(sentence_list): """Return a dictionary mapping words in normalized text to their counts in the text. Parameters: sentence_list: List[str] - a list of sentences Return: a Dict[str, int] whose keys are words and associated values are the number of times the word appears in the sentences in sentence_list Usage Examples: (Note technique for testing dict equality.) >>> word_counts(["i dont even have any skills", "i have numchuk skills \ bow hunting skills computer hacking skills"]) == {'have': 2, 'numchuk': 1, \ 'hacking': 1, 'i': 2, 'even': 1, 'computer': 1, 'any': 1, 'bow': 1,\ 'hunting': 1, 'dont': 1, 'skills': 4} True """ def make_tups(seq1, seq2): """Convert seq1 and seq2 to a list of tuples with corresponding elements of seq1 and seq2. Parameters: seq1: Sequence[Any] -- a sequence of elements of any type seq2: Sequence[Any] -- a sequence of elements of any type Return: Sequence[(Any, Any)] - a sequence of 2-tuples where the ith tuples contain the ith elements of seq1 and seq2. Length of returned list is length of shortest input seq Usage Examples: >>> make_tups(['a', 'b', 'c', 'd'], [1, 2, 3]) [('a', 1), ('b', 2), ('c', 3)] """ 

Testing Your Module Interactively

In addition to running the doctests as described above, you can import your hw1 module in the Python REPL to test your functions as you write and modify them. For example, ssuming youre in the same directory as your hw1.py file:

Python 3.6.1 |Continuum Analytics, Inc.| (default, Jul 2 2016, 17:53:06) [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux Type "help", "copyright", "credits" or "license" for more information. >>> import hw1 >>> hw1.palindrome("a but tuba") True 

After you modify your module youll need to restart your Python REPL, or reload it using the importlib module:

>>> import importlib as imp >>> imp.reload(hw1) 

50 points for having all required functions that can be called with the right parameters l returning values of the correct type but not necessarily correct values

10 points for each correctly implemented function named exactly as listed above with docstrings as provided (40 points total)