Solutions: You can find the file with solutions for all questions here.

In the previous lab, you went through a crash course going through the wonders of list comprehension, conditionals and iteration! In this week’s lab, we will begin to explore the world of higher order functions!

## Functions as Arguments (Funargs)

So far we have used several type of data - ints, floats, booleans, strings, lists, tuples, and numpy.arrays. We perform operations on them in constructing expressions; we assign them to variables; we pass them to functions and return them as results. So what about functions themselves? So far we have called them, that is we applied them to arguments. Sometimes we compose them - just like in math; apply a function to the result of applying a function. You did that several times above.

In modern programming languages like Python, functions are first class citizens; we can pass them around and put them in data structures. Take a look at the following and try it out for various functions that you have available in the `.py` file for this lab.

``````>>> square(square(3))
81
>>> square
<function square at 0x102033d90>
>>> x = square
>>> x(3)
9
>>> x(x(2))
16
>>> ``````

## Introduction to 'Map'

Higher order functions fit into a domain of programming known as "functional" or "functional form" programming, centered around this idea of passing and returning functions as parameters and arguments. In class, you learned the command `map` that is a fundamental example of higher order functions.

Let's take a closer look at how `map` works. At its core, `map` applies a function to all items in an input list. It takes in a function as the first parameter and a series of inputs as the second parameter.

``map(function_to_apply, list_of_inputs)``

A potentially easier way to think about `map` is to draw an equivalent with a list comprehension! Given the `func` (function to apply) and `inputs` (list of inputs), a map is similar to this:

``[func(x) for x in inputs]``

Keep in mind that the `map` function actually returns a `map` object, not a list, by default. Consequently, in order to make the result actionable, it's important for us to adjust the return value of map to a `list` by passing the map object into the `list()` function.

Let's do a Python Tutor example to understand how map works.

Open Python Tutor in a new tab.

Paste this code into the interpreter:

``````INCR = 2
def inc(x):
return x+INCR

def mymap(fun, seq):
return [fun(x) for x in seq]

result = mymap(inc, [5, 6, 7])
print(result)``````

So what's happening here? In the first 3 lines, we're defining a function `inc` which increments an input `x` by a certain amount, `INCR`.

Notice that `INCR` is defined once in the Global frame. This is a nice review of how Python resolves references when there are both local and global variables. When the `inc` method executes, remember that since the `INCR` variable isn't declared locally within the `inc` function, the Python compiler will look at the parent frame, the frame in which `inc` was declared, for the value of `INCR`. In this case, since the `inc` function was declared in the Global frame, the global `INC` variable value will apply.

The second function, `mymap`, is an example of how map works in the form of a list comprehension! Notice that `mymap` takes in a function as its first argument and a sequence as its second. Just like `map`, this list comprehension runs each element of `seq` through the `fun` method.

As you run through the program in Python Tutor, notice how the list comprehension in `mymap` will repeatedly call the `inc` function. The functional anatomy of how `map` works is exactly encapsulated by the `mymap` function.

### Question 1: Data Cleaning

Given a list of float numbers, round each number in the list down to the nearest tens place. (i.e. 17 -> 10, 23 -> 20)

We recommend writing a nested function that performs the rounding, then use `map` to perform the rounding on each element of the list.

You can assume that none of the values in the problem's tests will be floats or negative.

``````def data_clean(a):
"""Write a function that rounds each element of the list down to the nearest tens place.

>>> a = [12, 23, 34]
>>> data_clean(a)
[10, 20, 30]
>>> b = [238, 193, 928]
>>> data_clean(b)
[230, 190, 920]
>>> c = [10, 20, 30]
>>> data_clean(c)
[10, 20, 30]
>>> d = [9, 9, 9]
>>> data_clean(d)
[0, 0, 0]
"""
return _____
def truncateNumber(x):
return x - (x%10)
return list(map(truncateNumber, a))``````

Use OK to test your code:

``python3 ok -q data_clean --local``

## Introduction to 'Filter'

The `filter` keyword is similar in nature to the `map` with a very important distinction. In `map`, the function we pass in is being applied to every item in our sequence. In `filter`, the function we pass in filters the elements for which the function returns true. For example, if I wanted to remove all negative numbers from a list, I could use the `filter` function to identify values that satisfy the greater than or equal to 0 criterion.

``````def isPositive(number):
return number >= 0

numbers = [-1, 1, -2, 2, -3, 3, -4, 4]
positive_nums = list(filter(isPositive, numbers))``````

Again, similar to `map`, the output of the `filter` function is a `filter` object, not a list, so casting is required. In addition, continuing off the above example, the equivalent for filter in the form of a list comprehension would look something along the lines of this:

``positive_nums = [x for x in numbers if isPositive(numbers)]``

## Introduction to 'Reduce'

One of the most useful functional functions we'll encounter in this class is the `reduce` keyword. Before diving into the inner workings, it's best to start off with a iterative equivalent that will helps us better appreciate the benefits of using `reduce`.

Let's say I wanted to calculate the product of the square roots of a list of numbers. The non-`reduce` version of this code would look something along the lines of this:

``````product = 1
numbers = [4, 9, 16, 25, 36]

for num in numbers:
product = product * sqrt(num)``````

`Reduce` can be broken down into three different parameters: A function, a sequence, and an identity. The function and sequence are the same parameters as before. The identity can be thought of as the value through which function outputs are aggregated. In the above case, the identity would be the `product` variable.

`Reduce` is very useful for performing computations on lists that involve every element in the list. Computations are performed in a rolling fashion, where the function acts upon each element one at a time.

### Question 2: reduce

Write the higher order function `reduce` which takes

• reducer - a two-argument function that reduces elements to a single value
• s - a sequence of values
• base - the starting value in the reduction. This is usually the identity of the reducer

If you're feeling stuck, think about the parameters of `reduce`. This is meant to be a simple problem that provides hands-on experience of understanding what `reduce` does.

``````from operator import add, mul

def reduce(reducer, s, base):
"""Reduce a sequence under a two-argument function starting from a base value.

...     return x + y
>>> def mul(x, y):
...     return x*y
10
>>> reduce(mul, [1,2,3,4], 0)
0
>>> reduce(mul, [1,2,3,4], 1)
24
"""
return _____
result = base
for x in s:
result = reducer(result, x)
return result``````

Use OK to test your code:

``python3 ok -q reduce --local``

## Higher Order Functions

Thus far, in Python Tutor, we’ve visualized Python programs in the form of environment diagrams that display which variables are tied to which values within different frames. However, as we noted when introducing Python, values are not necessarily just primitive expressions or types like float, string, integer, and boolean.

In a nutshell, a higher order function is any function that takes a function as a parameter or provides a function has a return value. We will be exploring many applications of higher order functions.

Let's think about a more practical use of higher order functions. Pretend you’re a math teacher, and you want to teach your students how coefficients affect the shape of a parabola.

Open Python Tutor in a new tab

PythonTutor

Paste this code into the interpreter:

``````def define_parabola(a, b, c):
def parabola(x):
return a*(x**2) + b*x + c
return parabola

parabola = define_parabola(-2, 3, -4)
y1 = parabola(1)
y2 = parabola(10)
print(y1, y2)``````

Now step through the code. In the `define_parabola` function, the coefficient values of 'a', 'b', and 'c' are taken in, and in return, a parabolic function with those coefficient values is returned.

As you step through the second half of the code, notice how the value of `parabola` points at a function object! The `define_parabola` higher order nature comes from the fact that its return value is a function.

Another thing noting is where the pointer moves after the `parabola` function is called. Notice that the pointer goes to line 2, where `parabola` was originally defined. In a nutshell, this example is meant to show how a closure is returned from the `define_parabola` function.

### Question 3: Piecewise

Implement `piecewise`, which takes two one-argument functions, `f` and `g`, along with a number `b`. It returns a new function that takes a number `x` and returns either `f(x)` if `x` is less than `b`, or `g(x)` if `x` is greater than or equal to `b`.

``````def piecewise(f, g, b):
"""Returns the piecewise function h where:

h(x) = f(x) if x < b,
g(x) otherwise

>>> def negate(x):
...     return -x
>>> def identity(x):
...     return x
>>> abs_value = piecewise(negate, identity, 0)
>>> abs_value(6)
6
>>> abs_value(-1)
1
"""
return _____
def h(x):
if x < b:
return f(x)
return g(x)
return h``````

Use OK to test your code:

``python3 ok -q piecewise --local``

### Question 4: Flight of the Bumblebee

Write a function that takes in a number `n` and returns a function that takes in a number `m` which will print all numbers from `0` to `m - 1` (including `0` but excluding `m`) but print `Buzz!` instead for all the numbers that are divisible by `n`.

``````def make_buzzer(n):
""" Returns a function that prints numbers in a specified
range except those divisible by n.

>>> i_hate_fives = make_buzzer(5)
>>> i_hate_fives(10)
Buzz!
1
2
3
4
Buzz!
6
7
8
9
"""
return _____
def buzz(m):
i = 0
while i < m:
if i % n == 0:
print('Buzz!')
else:
print(i)
i += 1
return buzz``````

Use OK to test your code:

``python3 ok -q make_buzzer --local``

### Question 5: Intersect

Two functions intersect at an argument `x` if they return equal values. Implement `intersects`, which takes a one-argument functions `f` and a value `x`. It returns a function that takes another function `g` and returns whether `f` and `g` intersect at `x`.

``````def intersects(f, x):
"""Returns a function that returns whether f intersects g at x.

>>> def square(x):
...     return x * x
>>> def triple(x):
...     return x * 3
>>> def increment(x):
...     return x + 1
>>> def identity(x):
...     return x
>>> at_three = intersects(square, 3)
>>> at_three(triple) # triple(3) == square(3)
True
>>> at_three(increment)
False
>>> at_one = intersects(identity, 1)
>>> at_one(square)
True
>>> at_one(triple)
False
"""
return _____
def at_x(g):
return f(x) == g(x)
return at_x``````

Use OK to test your code:

``python3 ok -q intersects --local``

## Tools Installation

Congrats on finishing the lab this week! Before you leave, we're going to introduce two new Python libraries that we'd like to add to your development toolkit this week. These two kits are the "datascience" module developed locally here at Berkeley for Data 8, and Anaconda, one of the most popular data science platforms today for Python developers!

To install the above libraries, please do the following:

1. 'datascience' module: Please open a new Terminal / Git-bash locally on your computer. Then, type in `pip3 install datascience` and click enter. If that does not work, try `pip install datascience`.

`pip3` is a package management system for Python. To put it simply, it helps you install and manage software packages written in Python. Your machine should come pre-installed with pip3 (or pip). After running the above line, you'll see some output indication that the `datascience` module and its related dependencies are being installed.

The module installed successfully if the last line of the output looks something along the lines of `Successfully installed coveralls-0.5 datascience-0.10.6`.

The `datascience` module was written by Professors John Denero and David Culler (your instructor!) along with the help of several undergraduate students. It was originally written for the Data 8 class as a friendly introduction to analytical tools used by data science developers. To learn more about this library, you can follow this link.

Anaconda is essentially the industry standard when it comes to developing data science and machine learning related applications using Python. The Anaconda installation comes chock full with useful data science libraries such as `numpy` and `pandas` that will become increasingly useful as you pursue and study data science. Installing Anaconda will equip you with the appropriate tools that we'll be using later on in this course.