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Lab 0: Python Fundamentals

The purpose of this lab is to make sure you are sufficiently acquainted with Python to succeed in the rest of the labs. If Python is one of your primary language, this should be smooth sailing; otherwise, please make sure you complete these tasks before moving on to the next labs.

This lab assumes that you have Python 2.6+ installed on your system. If you're using the instructor-provided appliance, you're all set. Otherwise, please make sure that Python is installed and is in the path, so you can type python to launch it from a terminal window.

If you're installing Python yourself, please install Python 2.x and not 3.x. Even though everything in these labs is supposed to work just fine with Python 3, a lot of libraries and frameworks still don't support it.

Task 1: Experimenting with the Python REPL

Open a terminal window and run python. An interactive prompt similar to the following should appear:

Python 2.7.6 (default, Jun 22 2015, 17:58:13) 
[GCC 4.8.2] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>>

This is the Python REPL -- Read, Eval, Print Loop environment. Try some basic commands to make sure everything works (do not type the >>> prompt):

>>> 2 + 2
4
>>> print("Hello, REPL")
Hello, REPL
>>> exit()

Instead of exit(), you can also type Ctrl+D to leave the REPL environment.

Task 2: Implementing Python Functions

Create a new file called functions.py. Use the following template so that when the file is executed directly, the run function will be called:

def run():
    print("Hey there!")

if __name__ == "__main__":
    run()

Which editor should you use in the appliance? If you want to get into the spirit of the course, you could use vim, but if you're looking for something more user-friendly, use nano or the built-in web-based editor.

To make sure everything's fine so far, run your Python program from a terminal window:

python functions.py

You should see "Hey there!" printed out.

Next, implement a function called wordcount that takes a list of strings, and produces a dict with the number of times each string appears. Here is an example of its invocation and expected output:

print(wordcount(["the", "fox", "jumped", "over", "the", "dog"]))
# Expecting { 'the': 2, 'fox': 1 }, and so on

You might find dict's setdefault method useful. To find out how it works, run help(dict.setdefault) from the Python REPL. Alternatively, to test whether a key is present in a dictionary, use if key in dict ....

Solution:

def wordcount(words):
    freqs = {}
    for word in words:
        freqs[word] = freqs.setdefault(word, 0) + 1
    return freqs

Task 3: Using Collection Pipelines

Given a collection of items, the map, filter, reduce and other functions we learned are very useful for transforming the collection into your desired dataset. Implement the following functions according to the instructions provided, and do not use loops in your implementation:

  • Given a list of numbers, use filter to filter out only the even numbers.

  • Given a list of numbers, use map to raise each number to the power of 2.

  • Given a list of words, use reduce to find the average word length.

  • Use map and reduce to solve problem 6 from Project Euler, which states:

Find the difference between the sum of the squares of the first one hundred natural numbers and the square of the sum.

Solution:

def evens(numbers):
    return filter(lambda n: n % 2 == 0, numbers)

def squares(numbers):
    return map(lambda n: n * n, numbers)

def avg_length(words):
    return reduce(lambda sum, word: sum + len(word), words, 0) / \
           float(len(words))

def problem6():
    def _sum(numbers):
        return reduce(lambda a, b: a + b, numbers)      # or use built-in sum()
    def square(n):
        return n * n
    return _sum(squares(xrange(1, 100))) - square(_sum(xrange(1, 100)))

Discussion

Why do you think Python is so successful in the data science, data analysis, machine learning, and scientific computing fields?

Compare the solutions above to your favorite programming language (or at least the one you're using in your day job). Do you feel the lack of strong typing makes Python code harder to read or write?