By Joel Grus

Facts technological know-how libraries, frameworks, modules, and toolkits are nice for doing information technological know-how, yet they're additionally with a purpose to dive into the self-discipline with out really figuring out facts technological know-how. during this booklet, you'll find out how a number of the such a lot primary info technological know-how instruments and algorithms paintings via imposing them from scratch.

If you could have an inherent ability for arithmetic and a few programming abilities, writer Joel Grus might help you get happy with the maths and information on the middle of information technological know-how, and with hacking talents you want to start as a knowledge scientist. Today's messy glut of information holds solutions to questions no one's even proposal to invite. This publication provide you with the knowledge to dig these solutions out.
•Get a crash direction in Python
•Learn the fundamentals of linear algebra, facts, and probability—and know how and while they're utilized in info science
•Collect, discover, fresh, munge, and manage data
•Dive into the basics of desktop learning
•Implement types akin to k-nearest buddies, Naive Bayes, linear and logistic regression, determination timber, neural networks, and clustering
•Explore recommender platforms, typical language processing, community research, MapReduce, and databases

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Partial: The Not-So-Basics | 31 from functools import partial two_to_the = partial(exp, 2) print two_to_the(3) # is now a function of one variable # 8 You can also use partial to fill in later arguments if you specify their names: square_of = partial(exp, power=2) print square_of(3) # 9 It starts to get messy if you curry arguments in the middle of the function, so we’ll try to avoid doing that. We will also occasionally use map, reduce, and filter, which provide functional alternatives to list comprehensions: def double(x): return 2 * x xs = [1, 2, 3, 4] twice_xs = [double(x) for x in xs] twice_xs = map(double, xs) list_doubler = partial(map, double) twice_xs = list_doubler(xs) # # # # [2, 4, 6, 8] same as above *function* that doubles a list again [2, 4, 6, 8] You can use map with multiple-argument functions if you provide multiple lists: def multiply(x, y): return x * y products = map(multiply, [1, 2], [4, 5]) # [1 * 4, 2 * 5] = [4, 10] Similarly, filter does the work of a list-comprehension if: def is_even(x): """True if x is even, False if x is odd""" return x % 2 == 0 x_evens = [x for x in xs if is_even(x)] x_evens = filter(is_even, xs) list_evener = partial(filter, is_even) x_evens = list_evener(xs) # # # # [2, 4] same as above *function* that filters a list again [2, 4] And reduce combines the first two elements of a list, then that result with the third, that result with the fourth, and so on, producing a single result: x_product = reduce(multiply, xs) list_product = partial(reduce, multiply) x_product = list_product(xs) # = 1 * 2 * 3 * 4 = 24 # *function* that reduces a list # again = 24 enumerate Not infrequently, you’ll want to iterate over a list and use both its elements and their indexes: 32 | Chapter 2: A Crash Course in Python # not Pythonic for i in range(len(documents)): document = documents[i] do_something(i, document) # also not Pythonic i = 0 for document in documents: do_something(i, document) i += 1 The Pythonic solution is enumerate, which produces tuples (index, element): for i, document in enumerate(documents): do_something(i, document) Similarly, if we just want the indexes: for i in range(len(documents)): do_something(i) for i, _ in enumerate(documents): do_something(i) # not Pythonic # Pythonic We’ll use this a lot.

If you need a multipart key, you should use a tuple or figure out a way to turn the key into a string. defaultdict Imagine that you’re trying to count the words in a document. An obvious approach is to create a dictionary in which the keys are words and the values are counts. get(word, 0) word_counts[word] = previous_count + 1 Every one of these is slightly unwieldy, which is why defaultdict is useful. A defaultdict is like a regular dictionary, except that when you try to look up a key it doesn’t contain, it first adds a value for it using a zero-argument function you pro‐ vided when you created it.

Motivating Hypothetical: DataSciencester | 13 CHAPTER 2 A Crash Course in Python People are still crazy about Python after twenty-five years, which I find hard to believe. —Michael Palin All new employees at DataSciencester are required to go through new employee ori‐ entation, the most interesting part of which is a crash course in Python. This is not a comprehensive Python tutorial but instead is intended to highlight the parts of the language that will be most important to us (some of which are often not the focus of Python tutorials).

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