Learning Python - Mark Lutz [62]
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[12] In this book, the term literal simply means an expression whose syntax generates an object—sometimes also called a constant. Note that the term “constant” does not imply objects or variables that can never be changed (i.e., this term is unrelated to C++’s const or Python’s “immutable”—a topic explored in the section Immutability).
Numbers
If you’ve done any programming or scripting in the past, some of the object types in Table 4-1 will probably seem familiar. Even if you haven’t, numbers are fairly straightforward. Python’s core objects set includes the usual suspects: integers (numbers without a fractional part), floating-point numbers (roughly, numbers with a decimal point in them), and more exotic numeric types (complex numbers with imaginary parts, fixed-precision decimals, rational fractions with numerator and denominator, and full-featured sets).
Although it offers some fancier options, Python’s basic number types are, well, basic. Numbers in Python support the normal mathematical operations. For instance, the plus sign (+) performs addition, a star (*) is used for multiplication, and two stars (**) are used for exponentiation:
>>> 123 + 222 # Integer addition
345
>>> 1.5 * 4 # Floating-point multiplication
6.0
>>> 2 ** 100 # 2 to the power 100
1267650600228229401496703205376
Notice the last result here: Python 3.0’s integer type automatically provides extra precision for large numbers like this when needed (in 2.6, a separate long integer type handles numbers too large for the normal integer type in similar ways). You can, for instance, compute 2 to the power 1,000,000 as an integer in Python, but you probably shouldn’t try to print the result—with more than 300,000 digits, you may be waiting awhile!
>>> len(str(2 ** 1000000)) # How many digits in a really BIG number?
301030
Once you start experimenting with floating-point numbers, you’re likely to stumble across something that may look a bit odd on first glance:
>>> 3.1415 * 2 # repr: as code
6.2830000000000004
>>> print(3.1415 * 2) # str: user-friendly
6.283
The first result isn’t a bug; it’s a display issue. It turns out that there are two ways to print every object: with full precision (as in the first result shown here), and in a user-friendly form (as in the second). Formally, the first form is known as an object’s as-code repr, and the second is its user-friendly str. The difference can matter when we step up to using classes; for now, if something looks odd, try showing it with a print built-in call statement.
Besides expressions, there are a handful of useful numeric modules that ship with Python—modules are just packages of additional tools that we import to use:
>>> import math
>>> math.pi
3.1415926535897931
>>> math.sqrt(85)
9.2195444572928871
The math module contains more advanced numeric tools as functions, while the random module performs random number generation and random selections (here, from a Python list, introduced later in this chapter):
>>> import random
>>> random.random()
0.59268735266273953
>>> random.choice([1, 2, 3, 4])
1
Python also includes more exotic numeric objects—such as complex, fixed-precision, and rational numbers, as well as sets and Booleans—and the third-party open source extension domain has even more (e.g., matrixes and vectors). We’ll defer discussion of these types until later in the book.
So far, we’ve been using Python much like a simple calculator; to do better justice to its built-in types, let’s move on to explore strings.
Strings
Strings are used to record textual information as well as arbitrary collections of bytes. They are our first example of what we call a sequence in Python—that is, a positionally