Difference between revisions of "Python"
Line 16: | Line 16: | ||
If it tells you a version of Python (like "2.7.1"), then you're good to go. If not, you need to do a quick package install to get it up and running. Apt and Yum both call a functional Python package '''python'''. | If it tells you a version of Python (like "2.7.1"), then you're good to go. If not, you need to do a quick package install to get it up and running. Apt and Yum both call a functional Python package '''python'''. | ||
− | == Pip == | + | === Pip === |
Linux distributions have package managers like ''Apt'', ''Yum'', and ''YaST''. PHP has a package manager named ''PEAR''. It's now time to introduce Python's leading package manager: '''pip'''. | Linux distributions have package managers like ''Apt'', ''Yum'', and ''YaST''. PHP has a package manager named ''PEAR''. It's now time to introduce Python's leading package manager: '''pip'''. | ||
Line 59: | Line 59: | ||
fruits = ["apple", "banana", "cherry", "date"] | fruits = ["apple", "banana", "cherry", "date"] | ||
− | for | + | for fruit in fruits: |
print "I always love to eat a fresh %s." % fruit | print "I always love to eat a fresh %s." % fruit | ||
Line 77: | Line 77: | ||
* When we compute the average fruit size, we need to cast ''len(fruit_size)'', which returns an int, to a float in order to prevent integer truncation. Note that ''sum(fruit_size)'' returns a float, even though the list contains only ints. | * When we compute the average fruit size, we need to cast ''len(fruit_size)'', which returns an int, to a float in order to prevent integer truncation. Note that ''sum(fruit_size)'' returns a float, even though the list contains only ints. | ||
− | === | + | For some more examples, see [http://wiki.python.org/moin/SimplePrograms the Python wiki]. |
+ | |||
+ | === Functions === | ||
Define functions using the '''def''' keyword: | Define functions using the '''def''' keyword: | ||
Line 89: | Line 91: | ||
</source> | </source> | ||
− | Python has a special datatype called '''tuples''', which are essentially short arrays useful for storing data that is associated with each other: | + | === Tuples === |
+ | |||
+ | Python has a special datatype called '''tuples''', which are essentially short arrays useful for storing data that is associated with each other. They can also serve as convenient ways to assign multiple variables at once: | ||
<source lang="python"> | <source lang="python"> | ||
+ | first_name, last_name = ("John", "Smith") # this is our tuple | ||
+ | </source> | ||
+ | Tuples also enable you to have multiple return values from a function: | ||
+ | |||
+ | <source lang="python"> | ||
+ | def compute_length(string): | ||
+ | str_len = len(string) | ||
+ | if str_len < 5: | ||
+ | return (str_len, "short") | ||
+ | elif str_len < 40: | ||
+ | return (str_len, "medium") | ||
+ | else: | ||
+ | return (str_len, "long") | ||
+ | |||
+ | length, description = compute_length("Four score and seven years ago") | ||
+ | print "The %s string is %d characters long." % (description, length) | ||
</source> | </source> | ||
+ | |||
+ | The above example also demonstrates Python's if...elif...else conditional structure. | ||
+ | |||
+ | === Dictionaries === | ||
Python has another datatype called '''dictionaries''', which are like ''maps'' in Java, ''associative arrays'' in PHP, and ''object literals'' in JavaScript (coming up soon in Module 6). Essentially, they enable you to use strings as the keys in your data structure. | Python has another datatype called '''dictionaries''', which are like ''maps'' in Java, ''associative arrays'' in PHP, and ''object literals'' in JavaScript (coming up soon in Module 6). Essentially, they enable you to use strings as the keys in your data structure. | ||
− | === Object-Oriented Programming | + | <source lang="python"> |
+ | fruits_in_bowl = { | ||
+ | 'apple': 4, | ||
+ | 'banana': 2, | ||
+ | 'cherry': 0, | ||
+ | 'date': 12 | ||
+ | } | ||
+ | |||
+ | for fruit, num in fruits_in_bowl.items(): | ||
+ | print "There are %d %s(s) in the bowl." % (num, fruit) | ||
+ | </source> | ||
+ | |||
+ | === Import === | ||
+ | |||
+ | If you want to use functions from other libraries (including ones that you install using pip), use '''import''': | ||
+ | |||
+ | <source lang="python"> | ||
+ | import time | ||
+ | |||
+ | current_time = time.localtime() | ||
+ | print time.strftime('%a, %d %b %Y %H:%M:%S', current_time) | ||
+ | </source> | ||
+ | |||
+ | If you want to pull the functions out of their namespace, you can use '''from ___ import ___''' syntax: | ||
+ | |||
+ | <source lang="python"> | ||
+ | from time import localtime, strftime | ||
+ | |||
+ | current_time = localtime() | ||
+ | print strftime('%a, %d %b %Y %H:%M:%S', current_time) | ||
+ | |||
+ | # Be aware that this technique, although convenient, may cause unexpected behavior if the function names that you're pulling out of the namespace are already used for other purposes in Python. | ||
+ | </source> | ||
+ | |||
+ | === File I/O === | ||
+ | |||
+ | You can read an entire file into a variable like this: | ||
+ | |||
+ | <source lang="python"> | ||
+ | f = open("example.txt") | ||
+ | file_contents = f.read() | ||
+ | |||
+ | f.close() # free up memory when we're finished with the file | ||
+ | </source> | ||
+ | |||
+ | You can read a file line-by-line like this: | ||
+ | |||
+ | <source lang="python"> | ||
+ | f = open("example.txt") | ||
+ | for line in f: | ||
+ | print "Read line: %s" % line.rstrip() | ||
+ | |||
+ | f.close() | ||
+ | </source> | ||
+ | |||
+ | You can write to a file like this: | ||
+ | |||
+ | <source lang="python"> | ||
+ | # the `with` block will automatically call f.close() at the end so you don't forget: | ||
+ | |||
+ | with open("example.txt", "w") as f: | ||
+ | f.write("Hello\nWorld\n") | ||
+ | </source> | ||
+ | |||
+ | === Object-Oriented Programming === | ||
+ | |||
+ | You can define and use a class like this: | ||
+ | |||
+ | <source lang="python"> | ||
+ | class Food: | ||
+ | # constructor: | ||
+ | def __init__(self, name): | ||
+ | self.name = name | ||
+ | |||
+ | @staticmethod | ||
+ | def get_definition(): | ||
+ | return "Food is nourishment for carbon-based lifeforms." | ||
+ | |||
+ | def format_name(self): | ||
+ | return "Gotta love to eat " + self.name | ||
+ | |||
+ | class Fruit(Food): | ||
+ | def format_name(self): | ||
+ | return Food.format_name(self) + " (fruit)" | ||
+ | |||
+ | fruit = Fruit("Cherry") | ||
+ | print fruit.format_name() | ||
+ | print Food.get_definition() | ||
+ | </source> | ||
+ | |||
+ | This is the same example as in [[PHP#Object-Oriented Programming|the PHP guide]]. | ||
+ | |||
+ | Some things to notice: | ||
+ | * Static methods require the '''@staticmethod''' decorator | ||
+ | * Non-static methods ''always'' take ''self'' as their first argument, followed by any number of additional parameters. This can be misleading for programmers familiar with other languages, because the number of arguments you feed to the method is actually ''one less than'' the number of declared parameters. Whenever you call a method on a class instance, that instance is implicitly fed into the explicitly-declared ''self'' parameter of the method. | ||
+ | *: Take home message is that you need to add an additional parameter, ''self'', at the beginning of any instance method. | ||
+ | * There is no need for a ''new'' keyword in Python. | ||
[[Category:Module 4]] | [[Category:Module 4]] |
Revision as of 08:18, 23 March 2013
Languages like Java and C++ have lots of rules regarding variable types, syntax, return values, and so on. Although these restrictions help make the compiled program run quickly, they are cumbersome when you are trying to write short, quick scripts to perform tasks. This is where a scripting language comes into play.
Python is a language well-suited to rapid prototype development. It is an interpreted language, which means that you do not need to compile the code when you run it. The syntax is clean, and it is usually clear at first glance what is going on when you write in Python.
Contents
Installation
Python may already be installed on your system. To see whether or not it is, enter the command
$ python --version
If it tells you a version of Python (like "2.7.1"), then you're good to go. If not, you need to do a quick package install to get it up and running. Apt and Yum both call a functional Python package python.
Pip
Linux distributions have package managers like Apt, Yum, and YaST. PHP has a package manager named PEAR. It's now time to introduce Python's leading package manager: pip.
You need to install Pip from Apt or Yum before you can use it. Both call the package python-pip.
Once you have pip installed, you can use it to install Python packages. Use the pip-python (RHEL) or pip (Debian) command:
$ pip-python install package_name # RHEL
$ pip install package_name # Debian
Running Python
There are two common ways to run Python code: via the console, and via a Python script file.
The Python Console
The Python console enables you to experiment with code without opening a text editor. To enter the Python console, simply type the python command at the terminal:
$ python
To leave the interactive console, either type "quit()" or press Ctrl-D (on both Mac and Windows).
Python Script Files
You can also save Python script files for later use. The extension for Python scripts is *.py. To run a script file, simply feed its path as an argument to the python command in Terminal:
$ python my_script.py
Syntax
This section contains a very brief overview of Python syntax. For a more comprehensive introduction, see the Python docs.
An Example Python Script
In my mind, there's no better way to learn Python than to be immersed in a simple example script.
print "Hello World"
fruits = ["apple", "banana", "cherry", "date"]
for fruit in fruits:
print "I always love to eat a fresh %s." % fruit
# Map the fruits list over to a new list containing the length of the fruit strings:
fruit_size = [len(fruit) for fruit in fruits]
avg_fruit_size = sum(fruit_size) / float(len(fruit_size))
print "The average fruit string length is %4.2f." % avg_fruit_size
Some things to notice:
- Printing is achieved using the print command
- A colon starts a block, similar to a curly brace { in many other languages. The corresponding code block must be indented. The end of the code block is signified by when the indentation ends.
- Strings can be printf-style formatted using the % operator
- Inline comments start with a pound symbol #
- We can map a list to a new list in just one line. (Beat that, Java!)
- When we compute the average fruit size, we need to cast len(fruit_size), which returns an int, to a float in order to prevent integer truncation. Note that sum(fruit_size) returns a float, even though the list contains only ints.
For some more examples, see the Python wiki.
Functions
Define functions using the def keyword:
def my_function(name):
print "Hello, %s!" % name
my_function("Batman")
my_function("Superman")
Tuples
Python has a special datatype called tuples, which are essentially short arrays useful for storing data that is associated with each other. They can also serve as convenient ways to assign multiple variables at once:
first_name, last_name = ("John", "Smith") # this is our tuple
Tuples also enable you to have multiple return values from a function:
def compute_length(string):
str_len = len(string)
if str_len < 5:
return (str_len, "short")
elif str_len < 40:
return (str_len, "medium")
else:
return (str_len, "long")
length, description = compute_length("Four score and seven years ago")
print "The %s string is %d characters long." % (description, length)
The above example also demonstrates Python's if...elif...else conditional structure.
Dictionaries
Python has another datatype called dictionaries, which are like maps in Java, associative arrays in PHP, and object literals in JavaScript (coming up soon in Module 6). Essentially, they enable you to use strings as the keys in your data structure.
fruits_in_bowl = {
'apple': 4,
'banana': 2,
'cherry': 0,
'date': 12
}
for fruit, num in fruits_in_bowl.items():
print "There are %d %s(s) in the bowl." % (num, fruit)
Import
If you want to use functions from other libraries (including ones that you install using pip), use import:
import time
current_time = time.localtime()
print time.strftime('%a, %d %b %Y %H:%M:%S', current_time)
If you want to pull the functions out of their namespace, you can use from ___ import ___ syntax:
from time import localtime, strftime
current_time = localtime()
print strftime('%a, %d %b %Y %H:%M:%S', current_time)
# Be aware that this technique, although convenient, may cause unexpected behavior if the function names that you're pulling out of the namespace are already used for other purposes in Python.
File I/O
You can read an entire file into a variable like this:
f = open("example.txt")
file_contents = f.read()
f.close() # free up memory when we're finished with the file
You can read a file line-by-line like this:
f = open("example.txt")
for line in f:
print "Read line: %s" % line.rstrip()
f.close()
You can write to a file like this:
# the `with` block will automatically call f.close() at the end so you don't forget:
with open("example.txt", "w") as f:
f.write("Hello\nWorld\n")
Object-Oriented Programming
You can define and use a class like this:
class Food:
# constructor:
def __init__(self, name):
self.name = name
@staticmethod
def get_definition():
return "Food is nourishment for carbon-based lifeforms."
def format_name(self):
return "Gotta love to eat " + self.name
class Fruit(Food):
def format_name(self):
return Food.format_name(self) + " (fruit)"
fruit = Fruit("Cherry")
print fruit.format_name()
print Food.get_definition()
This is the same example as in the PHP guide.
Some things to notice:
- Static methods require the @staticmethod decorator
- Non-static methods always take self as their first argument, followed by any number of additional parameters. This can be misleading for programmers familiar with other languages, because the number of arguments you feed to the method is actually one less than the number of declared parameters. Whenever you call a method on a class instance, that instance is implicitly fed into the explicitly-declared self parameter of the method.
- Take home message is that you need to add an additional parameter, self, at the beginning of any instance method.
- There is no need for a new keyword in Python.