Data Science from Scratch: First Principles with Python by Joel Grus

By Joel Grus

Information technology libraries, frameworks, modules, and toolkits are nice for doing information technology, yet they're additionally that will dive into the self-discipline with no really knowing information technology. during this booklet, you'll find out how a few of the so much basic facts technology instruments and algorithms paintings via imposing them from scratch.

If you've got a flair for arithmetic and a few programming talents, writer Joel Grus can help you get happy with the mathematics and statistics on the middle of knowledge technological know-how, and with hacking abilities you must start as an information scientist. Today's messy glut of information holds solutions to questions no one's even proposal to invite. This ebook offers you the information to dig these solutions out.
•Get a crash path in Python
•Learn the fundamentals of linear algebra, information, and probability—and know the way and while they're utilized in info science
•Collect, discover, fresh, munge, and control data
•Dive into the basics of computing device learning
•Implement types comparable to k-nearest friends, Naive Bayes, linear and logistic regression, determination timber, neural networks, and clustering
•Explore recommender structures, common language processing, community research, MapReduce, and databases

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In Python, we typically define functions using def: def double(x): """this is where you put an optional docstring that explains what the function does. for example, this function multiplies its input by 2""" return x * 2 Python functions are first-class, which means that we can assign them to variables and pass them into functions just like any other arguments: def apply_to_one(f): """calls the function f with 1 as its argument""" return f(1) my_double = double x = apply_to_one(my_double) # refers to the previously defined function # equals 2 It is also easy to create short anonymous functions, or lambdas: y = apply_to_one(lambda x: x + 4) # equals 5 You can assign lambdas to variables, although most people will tell you that you should just use def instead: another_double = lambda x: 2 * x def another_double(x): return 2 * x # don't do this # do this instead Function parameters can also be given default arguments, which only need to be specified when you want a value other than the default: def my_print(message="my default message"): print message 18 | Chapter 2: A Crash Course in Python my_print("hello") my_print() # prints 'hello' # prints 'my default message' It is sometimes useful to specify arguments by name: def subtract(a=0, b=0): return a - b subtract(10, 5) # returns 5 subtract(0, 5) # returns -5 subtract(b=5) # same as previous We will be creating many, many functions.

It is similar to other lan‐ guages’ null: x = None print x == None print x is None # prints True, but is not Pythonic # prints True, and is Pythonic Python lets you use any value where it expects a Boolean. 0 Pretty much anything else gets treated as True. This allows you to easily use if state‐ ments to test for empty lists or empty strings or empty dictionaries or so on. It also sometimes causes tricky bugs if you’re not expecting this behavior: s = some_function_that_returns_a_string() if s: first_char = s[0] else: first_char = "" A simpler way of doing the same is: first_char = s and s[0] since and returns its second value when the first is “truthy,” the first value when it’s not.

What behavior should our class have? Given an instance of Set, we’ll need to be able to add items to it, remove items from it, and check whether it contains a certain value. We’ll create all of these as member functions, which means we’ll access them with a dot after a Set object: # by convention, we give classes PascalCase names class Set: # these are the member functions # every one takes a first parameter "self" (another convention) # that refers to the particular Set object being used def __init__(self, values=None): """This is the constructor.

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