Long time Pythoneer Tim Peters succinctly channels the BDFL's guiding principles for Python's design into 20 aphorisms, only 19 of which have been written down.
>>>import thisThe Zen of Python, by Tim PetersBeautiful is better than ugly.Explicit is better than implicit.Simple is better than complex.Complex is better than complicated.Flat is better than nested.Sparse is better than dense.Readability counts.Special cases aren't special enough to break the rules.Although practicality beats purity.Errors should never pass silently.Unless explicitly silenced.In the face of ambiguity, refuse the temptation to guess.There should be one-- and preferably only one --obvious way to do it.Although that way may not be obvious at first unless you're Dutch.Now is better than never.Although never is often better than *right* now.If the implementation is hard to explain, it's a bad idea.If the implementation is easy to explain, it may be a good idea.Namespaces are one honking great idea -- let's do more of those!
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Python Basics
Math Operators
From Highest to Lowest precedence:
Operators
Operation
Example
**
Exponent
2 ** 3 = 8
%
Modulus/Remainder
22 % 8 = 6
//
Integer division
22 // 8 = 2
/
Division
22 / 8 = 2.75
*
Multiplication
3 * 3 = 9
-
Subtraction
5 - 2 = 3
+
Addition
2 + 2 = 4
Examples of expressions in the interactive shell:
>>>2+3*620
>>> (2+3) *630
>>>2**8256
>>>23//73
>>>23%72
>>> (5-1) * ((7+1) / (3-1))16.0
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Data Types
Data Type
Examples
Integers
-2, -1, 0, 1, 2, 3, 4, 5
Floating-point numbers
-1.25, -1.0, --0.5, 0.0, 0.5, 1.0, 1.25
Strings
'a', 'aa', 'aaa', 'Hello!', '11 cats'
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String Concatenation and Replication
String concatenation:
>>>'Alice''Bob''AliceBob'
Note: Avoid + operator for string concatenation. Prefer string formatting.
String Replication:
>>>'Alice'*5'AliceAliceAliceAliceAlice'
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Variables
You can name a variable anything as long as it obeys the following rules:
It can be only one word.
It can use only letters, numbers, and the underscore (_) character.
It can't begin with a number.
Variable name starting with an underscore (_) are considered as "unuseful`.
Example:
>>> spam ='Hello'>>> spam'Hello'
>>> _spam ='Hello'
_spam should not be used again in the code.
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Comments
Inline comment:
# This is a comment
Multiline comment:
# This is a# multiline comment
Code with a comment:
a =1# initialization
Please note the two spaces in front of the comment.
Function docstring:
deffoo():""" This is a function docstring You can also use: ''' Function Docstring ''' """
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The print() Function
>>>print('Hello world!')Hello world!
>>> a =1>>>print('Hello world!', a)Hello world! 1
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The input() Function
Example Code:
>>>print('What is your name?')# ask for their name>>> myName =input()>>>print('It is good to meet you, {}'.format(myName))What is your name?AlIt is good to meet you, Al
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The len() Function
Evaluates to the integer value of the number of characters in a string:
>>>len('hello')5
Note: test of emptiness of strings, lists, dictionary, etc, should not use len, but prefer direct boolean evaluation.
>>> a = [1,2,3]>>>if a:>>>print("the list is not empty!")
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The str(), int(), and float() Functions
Integer to String or Float:
>>>str(29)'29'
>>>print('I am {} years old.'.format(str(29)))I am 29 years old.
>>>str(-3.14)'-3.14'
Float to Integer:
>>>int(7.7)7
>>>int(7.7)+18
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Flow Control
Comparison Operators
Operator
Meaning
==
Equal to
!=
Not equal to
<
Less than
>
Greater Than
<=
Less than or Equal to
>=
Greater than or Equal to
These operators evaluate to True or False depending on the values you give them.
Examples:
>>>42==42True
>>>40==42False
>>>'hello'=='hello'True
>>>'hello'=='Hello'False
>>>'dog'!='cat'True
>>>42==42.0True
>>>42=='42'False
Boolean evaluation
Never use == or != operator to evaluate boolean operation. Use the is or is not operators, or use implicit boolean evaluation.
NO (even if they are valid Python):
>>>True==TrueTrue
>>>True!=FalseTrue
YES (even if they are valid Python):
>>>TrueisTrueTrue
>>>TrueisnotFalseTrue
These statements are equivalent:
>>>if a isTrue:>>>pass>>>if a isnotFalse:>>>pass>>>if a:>>>pass
And these as well:
>>>if a isFalse:>>>pass>>>if a isnotTrue:>>>pass>>>ifnot a:>>>pass
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Boolean Operators
There are three Boolean operators: and, or, and not.
The and Operator's Truth Table:
Expression
Evaluates to
True and True
True
True and False
False
False and True
False
False and False
False
The or Operator's Truth Table:
Expression
Evaluates to
True or True
True
True or False
True
False or True
True
False or False
False
The not Operator's Truth Table:
Expression
Evaluates to
not True
False
not False
True
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Mixing Boolean and Comparison Operators
>>> (4<5) and (5<6)True
>>> (4<5) and (9<6)False
>>> (1==2) or (2==2)True
You can also use multiple Boolean operators in an expression, along with the comparison operators:
>>>2+2==4andnot2+2==5and2*2==2+2True
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if Statements
if name =='Alice':print('Hi, Alice.')
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else Statements
name ='Bob'if name =='Alice':print('Hi, Alice.')else:print('Hello, stranger.')
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elif Statements
name ='Bob'age =5if name =='Alice':print('Hi, Alice.')elif age <12:print('You are not Alice, kiddo.')
name ='Bob'age =30if name =='Alice':print('Hi, Alice.')elif age <12:print('You are not Alice, kiddo.')else:print('You are neither Alice nor a little kid.')
If the execution reaches a break statement, it immediately exits the while loop's clause:
whileTrue:print('Please type your name.') name =input()if name =='your name':breakprint('Thank you!')
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continue Statements
When the program execution reaches a continue statement, the program execution immediately jumps back to the start of the loop.
whileTrue:print('Who are you?') name =input()if name !='Joe':continueprint('Hello, Joe. What is the password? (It is a fish.)') password =input()if password =='swordfish':breakprint('Access granted.')
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for Loops and the range() Function
>>> print('My name is')
>>> for i in range(5):
>>> print('Jimmy Five Times ({})'.format(str(i)))
My name is
Jimmy Five Times (0)
Jimmy Five Times (1)
Jimmy Five Times (2)
Jimmy Five Times (3)
Jimmy Five Times (4)
The range() function can also be called with three arguments. The first two arguments will be the start and stop values, and the third will be the step argument. The step is the amount that the variable is increased by after each iteration.
>>> for i in range(0, 10, 2):
>>> print(i)
0
2
4
6
8
You can even use a negative number for the step argument to make the for loop count down instead of up.
>>> for i in range(5, -1, -1):
>>> print(i)
5
4
3
2
1
0
For else statement
This allows to specify a statement to execute in case of the full loop has been executed. Only useful when a break condition can occur in the loop:
>>> for i in [1, 2, 3, 4, 5]:
>>> if i == 3:
>>> break
>>> else:
>>> print("only executed when no item of the list is equal to 3")
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Importing Modules
import random
for i in range(5):
print(random.randint(1, 10))
import random, sys, os, math
from random import *
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Ending a Program Early with sys.exit()
import sys
while True:
print('Type exit to exit.')
response = input()
if response == 'exit':
sys.exit()
print('You typed {}.'.format(response))
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Functions
>>> def hello(name):
>>> print('Hello {}'.format(name))
>>>
>>> hello('Alice')
>>> hello('Bob')
Hello Alice
Hello Bob
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Return Values and return Statements
When creating a function using the def statement, you can specify what the return value should be with a return statement. A return statement consists of the following:
The return keyword.
The value or expression that the function should return.
import random
def getAnswer(answerNumber):
if answerNumber == 1:
return 'It is certain'
elif answerNumber == 2:
return 'It is decidedly so'
elif answerNumber == 3:
return 'Yes'
elif answerNumber == 4:
return 'Reply hazy try again'
elif answerNumber == 5:
return 'Ask again later'
elif answerNumber == 6:
return 'Concentrate and ask again'
elif answerNumber == 7:
return 'My reply is no'
elif answerNumber == 8:
return 'Outlook not so good'
elif answerNumber == 9:
return 'Very doubtful'
r = random.randint(1, 9)
fortune = getAnswer(r)
print(fortune)
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The None Value
>>> spam = print('Hello!')
Hello!
>>> spam is None
True
Note: never compare to None with the == operator. Always use is.
Code in the global scope cannot use any local variables.
However, a local scope can access global variables.
Code in a function's local scope cannot use variables in any other local scope.
You can use the same name for different variables if they are in different scopes. That is, there can be a local variable named spam and a global variable also named spam.
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The global Statement
If you need to modify a global variable from within a function, use the global statement:
>>> supplies = ['pens', 'staplers', 'flame-throwers', 'binders']
>>> for i, supply in enumerate(supplies):
>>> print('Index {} in supplies is: {}'.format(str(i), supply))
Index 0 in supplies is: pens
Index 1 in supplies is: staplers
Index 2 in supplies is: flame-throwers
Index 3 in supplies is: binders
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Looping Through Multiple Lists with zip()
>>> name = ['Pete', 'John', 'Elizabeth']
>>> age = [6, 23, 44]
>>> for n, a in zip(name, age):
>>> print('{} is {} years old'.format(n, a))
Pete is 6 years old
John is 23 years old
Elizabeth is 44 years old
The in and not in Operators
>>> 'howdy' in ['hello', 'hi', 'howdy', 'heyas']
True
The multiple assignment trick is a shortcut that lets you assign multiple variables with the values in a list in one line of code. So instead of doing this:
>>> spam = {'color': 'red', 'age': 42}
>>> for v in spam.values():
>>> print(v)
red
42
keys():
>>> for k in spam.keys():
>>> print(k)
color
age
items():
>>> for i in spam.items():
>>> print(i)
('color', 'red')
('age', 42)
Using the keys(), values(), and items() methods, a for loop can iterate over the keys, values, or key-value pairs in a dictionary, respectively.
>>> spam = {'color': 'red', 'age': 42}
>>>
>>> for k, v in spam.items():
>>> print('Key: {} Value: {}'.format(k, str(v)))
Key: age Value: 42
Key: color Value: red
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Checking Whether a Key or Value Exists in a Dictionary
>>> spam = {'name': 'Zophie', 'age': 7}
>>> 'name' in spam.keys()
True
>>> 'Zophie' in spam.values()
True
>>> # You can omit the call to keys() when checking for a key
>>> 'color' in spam
False
>>> 'color' not in spam
True
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The get() Method
Get has two parameters: key and default value if the key did not exist
>>> picnic_items = {'apples': 5, 'cups': 2}
>>> 'I am bringing {} cups.'.format(str(picnic_items.get('cups', 0)))
'I am bringing 2 cups.'
>>> 'I am bringing {} eggs.'.format(str(picnic_items.get('eggs', 0)))
'I am bringing 0 eggs.'
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The setdefault() Method
Let's consider this code:
spam = {'name': 'Pooka', 'age': 5}
if 'color' not in spam:
spam['color'] = 'black'
Using setdefault we could write the same code more succinctly:
A set is an unordered collection with no duplicate elements. Basic uses include membership testing and eliminating duplicate entries. Set objects also support mathematical operations like union, intersection, difference, and symmetric difference.
Initializing a set
There are two ways to create sets: using curly braces {} and the built-in function set()
>>> s = {1, 2, 3}
>>> s = set([1, 2, 3])
When creating an empty set, be sure to not use the curly braces {} or you will get an empty dictionary instead.
>>> s = {}
>>> type(s)
<class 'dict'>
sets: unordered collections of unique elements
A set automatically remove all the duplicate values.
>>> s = {1, 2, 3, 2, 3, 4}
>>> s
{1, 2, 3, 4}
And as an unordered data type, they can't be indexed.
>>> s = {1, 2, 3}
>>> s[0]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: 'set' object does not support indexing
>>>
set add() and update()
Using the add() method we can add a single element to the set.
The module standardizes a core set of fast, memory efficient tools that are useful by themselves or in combination. Together, they form an "iterator algebra” making it possible to construct specialized tools succinctly and efficiently in pure Python.
The itertools module comes in the standard library and must be imported.
The operator module will also be used. This module is not necessary when using itertools, but needed for some of the examples below.
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accumulate()
Makes an iterator that returns the results of a function.
itertools.accumulate(iterable[, func])
Example:
>>> data = [1, 2, 3, 4, 5]
>>> result = itertools.accumulate(data, operator.mul)
>>> for each in result:
>>> print(each)
1
2
6
24
120
The operator.mul takes two numbers and multiplies them:
>>> shapes = ['circle', 'triangle', 'square']
>>> result = itertools.combinations_with_replacement(shapes, 2)
>>> for each in result:
>>> print(each)
('circle', 'circle')
('circle', 'triangle')
('circle', 'square')
('triangle', 'triangle')
('triangle', 'square')
('square', 'square')
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count()
Makes an iterator that returns evenly spaced values starting with number start.
itertools.count(start=0, step=1)
Example:
>>> for i in itertools.count(10,3):
>>> print(i)
>>> if i > 20:
>>> break
10
13
16
19
22
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cycle()
This function cycles through an iterator endlessly.
itertools.cycle(iterable)
Example:
>>> colors = ['red', 'orange', 'yellow', 'green', 'blue', 'violet']
>>> for color in itertools.cycle(colors):
>>> print(color)
red
orange
yellow
green
blue
violet
red
orange
When reached the end of the iterable it start over again from the beginning.
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chain()
Take a series of iterables and return them as one long iterable.
itertools.chain(*iterables)
Example:
>>> colors = ['red', 'orange', 'yellow', 'green', 'blue']
>>> shapes = ['circle', 'triangle', 'square', 'pentagon']
>>> result = itertools.chain(colors, shapes)
>>> for each in result:
>>> print(each)
red
orange
yellow
green
blue
circle
triangle
square
pentagon
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compress()
Filters one iterable with another.
itertools.compress(data, selectors)
Example:
>>> shapes = ['circle', 'triangle', 'square', 'pentagon']
>>> selections = [True, False, True, False]
>>> result = itertools.compress(shapes, selections)
>>> for each in result:
>>> print(each)
circle
square
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dropwhile()
Make an iterator that drops elements from the iterable as long as the predicate is true; afterwards, returns every element.
itertools.dropwhile(predicate, iterable)
Example:
>>> data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1]
>>> result = itertools.dropwhile(lambda x: x<5, data)
>>> for each in result:
>>> print(each)
5
6
7
8
9
10
1
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filterfalse()
Makes an iterator that filters elements from iterable returning only those for which the predicate is False.
itertools.filterfalse(predicate, iterable)
Example:
>>> data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1]
>>> result = itertools.filterfalse(lambda x: x<5, data)
>>> for each in result:
>>> print(each)
5
6
7
8
9
10
This function will repeat an object over and over again. Unless, there is a times argument.
itertools.repeat(object[, times])
Example:
>>> for i in itertools.repeat("spam", 3):
print(i)
spam
spam
spam
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starmap()
Makes an iterator that computes the function using arguments obtained from the iterable.
itertools.starmap(function, iterable)
Example:
>>> data = [(2, 6), (8, 4), (7, 3)]
>>> result = itertools.starmap(operator.mul, data)
>>> for each in result:
>>> print(each)
12
32
21
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takewhile()
The opposite of dropwhile(). Makes an iterator and returns elements from the iterable as long as the predicate is true.
itertools.takewhile(predicate, iterable)
Example:
>>> data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1]
>>> result = itertools.takewhile(lambda x: x<5, data)
>>> for each in result:
>>> print(each)
1
2
3
4
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tee()
Return n independent iterators from a single iterable.
itertools.tee(iterable, n=2)
Example:
>>> colors = ['red', 'orange', 'yellow', 'green', 'blue']
>>> alpha_colors, beta_colors = itertools.tee(colors)
>>> for each in alpha_colors:
>>> print(each)
red
orange
yellow
green
blue
>>> colors = ['red', 'orange', 'yellow', 'green', 'blue']
>>> alpha_colors, beta_colors = itertools.tee(colors)
>>> for each in beta_colors:
>>> print(each)
red
orange
yellow
green
blue
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zip_longest()
Makes an iterator that aggregates elements from each of the iterables. If the iterables are of uneven length, missing values are filled-in with fillvalue. Iteration continues until the longest iterable is exhausted.
>>> a = [1, 3, 5, 7, 9, 11]
>>> [i - 1 for i in a]
[0, 2, 4, 6, 8, 10]
Set comprehension
>>> b = {"abc", "def"}
>>> {s.upper() for s in b}
{"ABC", "DEF"}
Dict comprehension
>>> c = {'name': 'Pooka', 'age': 5}
>>> {v: k for k, v in c.items()}
{'Pooka': 'name', 5: 'age'}
A List comprehension can be generated from a dictionary:
>>> c = {'name': 'Pooka', 'first_name': 'Oooka'}
>>> ["{}:{}".format(k.upper(), v.upper()) for k, v in c.items()]
['NAME:POOKA', 'FIRST_NAME:OOOKA']
Manipulating Strings
Escape Characters
Escape character
Prints as
\'
Single quote
\"
Double quote
Tab
Newline (line break)
\\
Backslash
Example:
>>> print("Hello there!\nHow are you?\nI\'m doing fine.")
Hello there!
How are you?
I'm doing fine.
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Raw Strings
A raw string completely ignores all escape characters and prints any backslash that appears in the string.
>>> print(r'That is Carol\'s cat.')
That is Carol\'s cat.
Note: mostly used for regular expression definition (see re package)
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Multiline Strings with Triple Quotes
>>> print('''Dear Alice,
>>>
>>> Eve's cat has been arrested for catnapping, cat burglary, and extortion.
>>>
>>> Sincerely,
>>> Bob''')
Dear Alice,
Eve's cat has been arrested for catnapping, cat burglary, and extortion.
Sincerely,
Bob
To keep a nicer flow in your code, you can use the dedent function from the textwrap standard package.
>>> from textwrap import dedent
>>>
>>> def my_function():
>>> print('''
>>> Dear Alice,
>>>
>>> Eve's cat has been arrested for catnapping, cat burglary, and extortion.
>>>
>>> Sincerely,
>>> Bob
>>> ''').strip()
>>> 'My name is Simon'.split('m')
['My na', 'e is Si', 'on']
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Justifying Text with rjust(), ljust(), and center()
rjust() and ljust():
>>> 'Hello'.rjust(10)
' Hello'
>>> 'Hello'.rjust(20)
' Hello'
>>> 'Hello World'.rjust(20)
' Hello World'
>>> 'Hello'.ljust(10)
'Hello '
An optional second argument to rjust() and ljust() will specify a fill character other than a space character. Enter the following into the interactive shell:
>>> name = 'Pete'
>>> 'Hello %s' % name
"Hello Pete"
We can use the %x format specifier to convert an int value to a string:
>>> num = 5
>>> 'I have %x apples' % num
"I have 5 apples"
Note: For new code, using str.format or f-strings (Python 3.6+) is strongly recommended over the % operator.
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String Formatting (str.format)
Python 3 introduced a new way to do string formatting that was later back-ported to Python 2.7. This makes the syntax for string formatting more regular.
>>> name = 'John'
>>> age = 20'
>>> "Hello I'm {}, my age is {}".format(name, age)
"Hello I'm John, my age is 20"
>>> "Hello I'm {0}, my age is {1}".format(name, age)
"Hello I'm John, my age is 20"
The formatting operations described here exhibit a variety of quirks that lead to a number of common errors (such as failing to display tuples and dictionaries correctly). Using the newer formatted string literals or the str.format() interface helps avoid these errors. These alternatives also provide more powerful, flexible and extensible approaches to formatting text.
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Lazy string formatting
You would only use %s string formatting on functions that can do lazy parameters evaluation, the most common being logging:
Prefer:
>>> name = "alice"
>>> logging.debug("User name: %s", name)
Over:
>>> logging.debug("User name: {}".format(name))
Or:
>>> logging.debug("User name: " + name)
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Formatted String Literals or f-strings (Python 3.6+)
>>> name = 'Elizabeth'
>>> f'Hello {name}!'
'Hello Elizabeth!
It is even possible to do inline arithmetic with it:
>>> a = 5
>>> b = 10
>>> f'Five plus ten is {a + b} and not {2 * (a + b)}.'
'Five plus ten is 15 and not 30.'
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Template Strings
A simpler and less powerful mechanism, but it is recommended when handling format strings generated by users. Due to their reduced complexity template strings are a safer choice.
>>> from string import Template
>>> name = 'Elizabeth'
>>> t = Template('Hey $name!')
>>> t.substitute(name=name)
'Hey Elizabeth!'
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Regular Expressions
Import the regex module with import re.
Create a Regex object with the re.compile() function. (Remember to use a raw string.)
Pass the string you want to search into the Regex object's search() method. This returns a Match object.
Call the Match object's group() method to return a string of the actual matched text.
All the regex functions in Python are in the re module:
>>> import re
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Matching Regex Objects
>>> phone_num_regex = re.compile(r'\d\d\d-\d\d\d-\d\d\d\d')
>>> mo = phone_num_regex.search('My number is 415-555-4242.')
>>> print('Phone number found: {}'.format(mo.group()))
Phone number found: 415-555-4242
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Grouping with Parentheses
>>> phone_num_regex = re.compile(r'(\d\d\d)-(\d\d\d-\d\d\d\d)')
>>> mo = phone_num_regex.search('My number is 415-555-4242.')
>>> mo.group(1)
'415'
>>> mo.group(2)
'555-4242'
>>> mo.group(0)
'415-555-4242'
>>> mo.group()
'415-555-4242'
To retrieve all the groups at once: use the groups() method—note the plural form for the name.
The | character is called a pipe. You can use it anywhere you want to match one of many expressions. For example, the regular expression r'Batman|Tina Fey' will match either 'Batman' or 'Tina Fey'.
>>> hero_regex = re.compile (r'Batman|Tina Fey')
>>> mo1 = hero_regex.search('Batman and Tina Fey.')
>>> mo1.group()
'Batman'
>>> mo2 = hero_regex.search('Tina Fey and Batman.')
>>> mo2.group()
'Tina Fey'
You can also use the pipe to match one of several patterns as part of your regex:
>>> bat_regex = re.compile(r'Bat(man|mobile|copter|bat)')
>>> mo = bat_regex.search('Batmobile lost a wheel')
>>> mo.group()
'Batmobile'
>>> mo.group(1)
'mobile'
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Optional Matching with the Question Mark
The ? character flags the group that precedes it as an optional part of the pattern.
While * means "match zero or more,” the + (or plus) means "match one or more”. The group preceding a plus must appear at least once. It is not optional:
>>> mo2 = bat_regex.search('The Adventures of Batwowowowoman')
>>> mo2.group()
'Batwowowowoman'
>>> mo3 = bat_regex.search('The Adventures of Batman')
>>> mo3 is None
True
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Matching Specific Repetitions with Curly Brackets
If you have a group that you want to repeat a specific number of times, follow the group in your regex with a number in curly brackets. For example, the regex (Ha){3} will match the string 'HaHaHa', but it will not match 'HaHa', since the latter has only two repeats of the (Ha) group.
Instead of one number, you can specify a range by writing a minimum, a comma, and a maximum in between the curly brackets. For example, the regex (Ha){3,5} will match 'HaHaHa', 'HaHaHaHa', and 'HaHaHaHaHa'.
>>> mo2 = ha_regex.search('Ha')
>>> mo2 is None
True
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Greedy and Nongreedy Matching
Python's regular expressions are greedy by default, which means that in ambiguous situations they will match the longest string possible. The non-greedy version of the curly brackets, which matches the shortest string possible, has the closing curly bracket followed by a question mark.
In addition to the search() method, Regex objects also have a findall() method. While search() will return a Match object of the first matched text in the searched string, the findall() method will return the strings of every match in the searched string.
>>> phone_num_regex = re.compile(r'\d\d\d-\d\d\d-\d\d\d\d') # has no groups
>>> phone_num_regex.findall('Cell: 415-555-9999 Work: 212-555-0000')
['415-555-9999', '212-555-0000']
To summarize what the findall() method returns, remember the following:
When called on a regex with no groups, such as \d-\d\d\d-\d\d\d\d, the method findall() returns a list of ng matches, such as ['415-555-9999', '212-555-0000'].
When called on a regex that has groups, such as (\d\d\d)-(d\d)-(\d\d\d\d), the method findall() returns a list of es of strings (one string for each group), such as [('415', '555', '9999'), ('212', '555', '0000')].
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Making Your Own Character Classes
There are times when you want to match a set of characters but the shorthand character classes (\d, \w, \s, and so on) are too broad. You can define your own character class using square brackets. For example, the character class [aeiouAEIOU] will match any vowel, both lowercase and uppercase.
You can also include ranges of letters or numbers by using a hyphen. For example, the character class [a-zA-Z0-9] will match all lowercase letters, uppercase letters, and numbers.
By placing a caret character (^) just after the character class's opening bracket, you can make a negative character class. A negative character class will match all the characters that are not in the character class. For example, enter the following into the interactive shell:
You can also use the caret symbol (^) at the start of a regex to indicate that a match must occur at the beginning of the searched text.
Likewise, you can put a dollar sign ($) at the end of the regex to indicate the string must end with this regex pattern.
And you can use the ^ and $ together to indicate that the entire string must match the regex—that is, it's not enough for a match to be made on some subset of the string.
The r'^Hello' regular expression string matches strings that begin with 'Hello':
>>> begins_with_hello = re.compile(r'^Hello')
>>> begins_with_hello.search('Hello world!')
<_sre.SRE_Match object; span=(0, 5), match='Hello'>
>>> begins_with_hello.search('He said hello.') is None
True
The r'\d$' regular expression string matches strings that end with a numeric character from 0 to 9:
The . (or dot) character in a regular expression is called a wildcard and will match any character except for a newline:
>>> at_regex = re.compile(r'.at')
>>> at_regex.findall('The cat in the hat sat on the flat mat.')
['cat', 'hat', 'sat', 'lat', 'mat']
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Matching Everything with Dot-Star
>>> name_regex = re.compile(r'First Name: (.*) Last Name: (.*)')
>>> mo = name_regex.search('First Name: Al Last Name: Sweigart')
>>> mo.group(1)
'Al'
>>> mo.group(2)
'Sweigart'
The dot-star uses greedy mode: It will always try to match as much text as possible. To match any and all text in a nongreedy fashion, use the dot, star, and question mark (.*?). The question mark tells Python to match in a nongreedy way:
>>> nongreedy_regex = re.compile(r'<.*?>')
>>> mo = nongreedy_regex.search('<To serve man> for dinner.>')
>>> mo.group()
'<To serve man>'
>>> greedy_regex = re.compile(r'<.*>')
>>> mo = greedy_regex.search('<To serve man> for dinner.>')
>>> mo.group()
'<To serve man> for dinner.>'
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Matching Newlines with the Dot Character
The dot-star will match everything except a newline. By passing re.DOTALL as the second argument to re.compile(), you can make the dot character match all characters, including the newline character:
>>> no_newline_regex = re.compile('.*')
>>> no_newline_regex.search('Serve the public trust.\nProtect the innocent.\nUphold the law.').group()
'Serve the public trust.'
>>> newline_regex = re.compile('.*', re.DOTALL)
>>> newline_regex.search('Serve the public trust.\nProtect the innocent.\nUphold the law.').group()
'Serve the public trust.\nProtect the innocent.\nUphold the law.'
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Review of Regex Symbols
Symbol
Matches
?
zero or one of the preceding group.
*
zero or more of the preceding group.
+
one or more of the preceding group.
{n}
exactly n of the preceding group.
{n,}
n or more of the preceding group.
{,m}
0 to m of the preceding group.
{n,m}
at least n and at most m of the preceding p.
{n,m}? or *? or +?
performs a nongreedy match of the preceding p.
^spam
means the string must begin with spam.
spam$
means the string must end with spam.
.
any character, except newline characters.
\d, \w, and \s
a digit, word, or space character, respectively.
\D, \W, and \S
anything except a digit, word, or space, respectively.
[abc]
any character between the brackets (such as a, b, ).
[^abc]
any character that isn't between the brackets.
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Case-Insensitive Matching
To make your regex case-insensitive, you can pass re.IGNORECASE or re.I as a second argument to re.compile():
>>> robocop = re.compile(r'robocop', re.I)
>>> robocop.search('Robocop is part man, part machine, all cop.').group()
'Robocop'
>>> robocop.search('ROBOCOP protects the innocent.').group()
'ROBOCOP'
>>> robocop.search('Al, why does your programming book talk about robocop so much?').group()
'robocop'
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Substituting Strings with the sub() Method
The sub() method for Regex objects is passed two arguments:
The first argument is a string to replace any matches.
The second is the string for the regular expression.
The sub() method returns a string with the substitutions applied:
>>> names_regex = re.compile(r'Agent \w+')
>>> names_regex.sub('CENSORED', 'Agent Alice gave the secret documents to Agent Bob.')
'CENSORED gave the secret documents to CENSORED.'
Another example:
>>> agent_names_regex = re.compile(r'Agent (\w)\w*')
>>> agent_names_regex.sub(r'\1****', 'Agent Alice told Agent Carol that Agent Eve knew Agent Bob was a double agent.')
A**** told C**** that E**** knew B**** was a double agent.'
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Managing Complex Regexes
To tell the re.compile() function to ignore whitespace and comments inside the regular expression string, "verbose mode” can be enabled by passing the variable re.VERBOSE as the second argument to re.compile().
Now instead of a hard-to-read regular expression like this:
you can spread the regular expression over multiple lines with comments like this:
phone_regex = re.compile(r'''(
(\d{3}|\(\d{3}\))? # area code
(\s|-|\.)? # separator
\d{3} # first 3 digits
(\s|-|\.) # separator
\d{4} # last 4 digits
(\s*(ext|x|ext.)\s*\d{2,5})? # extension
)''', re.VERBOSE)
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Handling File and Directory Paths
There are two main modules in Python that deals with path manipulation. One is the os.path module and the other is the pathlib module. The pathlib module was added in Python 3.4, offering an object-oriented way to handle file system paths.
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Backslash on Windows and Forward Slash on OS X and Linux
On Windows, paths are written using backslashes (\) as the separator between folder names. On Unix based operating system such as macOS, Linux, and BSDs, the forward slash (/) is used as the path separator. Joining paths can be a headache if your code needs to work on different platforms.
Fortunately, Python provides easy ways to handle this. We will showcase how to deal with this with both os.path.join and pathlib.Path.joinpath
Using os.path.join on Windows:
>>> import os
>>> os.path.join('usr', 'bin', 'spam')
'usr\\bin\\spam'
And using pathlib on *nix:
>>> from pathlib import Path
>>> print(Path('usr').joinpath('bin').joinpath('spam'))
usr/bin/spam
pathlib also provides a shortcut to joinpath using the / operator:
Notice the path separator is different between Windows and Unix based operating system, that's why you want to use one of the above methods instead of adding strings together to join paths together.
Joining paths is helpful if you need to create different file paths under the same directory.
Using os.path.join on Windows:
>>> my_files = ['accounts.txt', 'details.csv', 'invite.docx']
>>> for filename in my_files:
>>> print(os.path.join('C:\\Users\\asweigart', filename))
C:\Users\asweigart\accounts.txt
C:\Users\asweigart\details.csv
C:\Users\asweigart\invite.docx
Using pathlib on *nix:
>>> my_files = ['accounts.txt', 'details.csv', 'invite.docx']
>>> home = Path.home()
>>> for filename in my_files:
>>> print(home / filename)
/home/asweigart/accounts.txt
/home/asweigart/details.csv
/home/asweigart/invite.docx
>>> from pathlib import Path
>>> from os import chdir
>>> print(Path.cwd())
/home/asweigart
>>> chdir('/usr/lib/python3.6')
>>> print(Path.cwd())
/usr/lib/python3.6
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Creating New Folders
Using os on Windows:
>>> import os
>>> os.makedirs('C:\\delicious\\walnut\\waffles')
Using pathlib on *nix:
>>> from pathlib import Path
>>> cwd = Path.cwd()
>>> (cwd / 'delicious' / 'walnut' / 'waffles').mkdir()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python3.6/pathlib.py", line 1226, in mkdir
self._accessor.mkdir(self, mode)
File "/usr/lib/python3.6/pathlib.py", line 387, in wrapped
return strfunc(str(pathobj), *args)
FileNotFoundError: [Errno 2] No such file or directory: '/home/asweigart/delicious/walnut/waffles'
Oh no, we got a nasty error! The reason is that the 'delicious' directory does not exist, so we cannot make the 'walnut' and the 'waffles' directories under it. To fix this, do:
An absolute path, which always begins with the root folder
A relative path, which is relative to the program's current working directory
There are also the dot (.) and dot-dot (..) folders. These are not real folders but special names that can be used in a path. A single period ("dot”) for a folder name is shorthand for "this directory.” Two periods ("dot-dot”) means "the parent folder.”
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Handling Absolute and Relative Paths
To see if a path is an absolute path:
Using os.path on *nix:
>>> import os
>>> os.path.isabs('/')
True
>>> os.path.isabs('..')
False
>>> import os
>>> os.path.getsize('C:\\Windows\\System32\\calc.exe')
776192
Using pathlib on *nix:
>>> from pathlib import Path
>>> stat = Path('/bin/python3.6').stat()
>>> print(stat) # stat contains some other information about the file as well
os.stat_result(st_mode=33261, st_ino=141087, st_dev=2051, st_nlink=2, st_uid=0,
--snip--
st_gid=0, st_size=10024, st_atime=1517725562, st_mtime=1515119809, st_ctime=1517261276)
>>> print(stat.st_size) # size in bytes
10024
Listing directory contents using os.listdir on Windows:
>>> from pathlib import Path
>>> for f in Path('/usr/bin').iterdir():
>>> print(f)
...
/usr/bin/tiff2rgba
/usr/bin/iconv
/usr/bin/ldd
/usr/bin/cache_restore
/usr/bin/udiskie
/usr/bin/unix2dos
/usr/bin/t1reencode
/usr/bin/epstopdf
/usr/bin/idle3
...
To find the total size of all the files in this directory:
WARNING: Directories themselves also have a size! So you might want to check for whether a path is a file or directory using the methods in the methods discussed in the above section!
Using os.path.getsize() and os.listdir() together on Windows:
>>> import os
>>> total_size = 0
>>> for filename in os.listdir('C:\\Windows\\System32'):
total_size = total_size + os.path.getsize(os.path.join('C:\\Windows\\System32', filename))
>>> print(total_size)
1117846456
Using pathlib on *nix:
>>> from pathlib import Path
>>> total_size = 0
>>> for sub_path in Path('/usr/bin').iterdir():
... total_size += sub_path.stat().st_size
>>>
>>> print(total_size)
1903178911
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Copying Files and Folders
The shutil module provides functions for copying files, as well as entire folders.
Calling os.unlink(path) or Path.unlink() will delete the file at path.
Calling os.rmdir(path) or Path.rmdir() will delete the folder at path. This folder must be empty of any files or folders.
Calling shutil.rmtree(path) will remove the folder at path, and all files and folders it contains will also be deleted.
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Safe Deletes with the send2trash Module
You can install this module by running pip install send2trash from a Terminal window.
>>> import send2trash
>>> with open('bacon.txt', 'a') as bacon_file: # creates the file
... bacon_file.write('Bacon is not a vegetable.')
25
>>> send2trash.send2trash('bacon.txt')
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Walking a Directory Tree
>>> import os
>>>
>>> for folder_name, subfolders, filenames in os.walk('C:\\delicious'):
>>> print('The current folder is {}'.format(folder_name))
>>>
>>> for subfolder in subfolders:
>>> print('SUBFOLDER OF {}: {}'.format(folder_name, subfolder))
>>> for filename in filenames:
>>> print('FILE INSIDE {}: {}'.format(folder_name, filename))
>>>
>>> print('')
The current folder is C:\delicious
SUBFOLDER OF C:\delicious: cats
SUBFOLDER OF C:\delicious: walnut
FILE INSIDE C:\delicious: spam.txt
The current folder is C:\delicious\cats
FILE INSIDE C:\delicious\cats: catnames.txt
FILE INSIDE C:\delicious\cats: zophie.jpg
The current folder is C:\delicious\walnut
SUBFOLDER OF C:\delicious\walnut: waffles
The current folder is C:\delicious\walnut\waffles
FILE INSIDE C:\delicious\walnut\waffles: butter.txt
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pathlib provides a lot more functionality than the ones listed above, like getting file name, getting file extension, reading/writing a file without manually opening it, etc. Check out the official documentation if you want to know more!
Reading and Writing Files
The File Reading/Writing Process
To read/write to a file in Python, you will want to use the with statement, which will close the file for you after you are done.
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Opening and reading files with the open() function
>>> with open('C:\\Users\\your_home_folder\\hello.txt') as hello_file:
... hello_content = hello_file.read()
>>> hello_content
'Hello World!'
>>> # Alternatively, you can use the *readlines()* method to get a list of string values from the file, one string for each line of text:
>>> with open('sonnet29.txt') as sonnet_file:
... sonnet_file.readlines()
[When, in disgrace with fortune and men's eyes,\n', ' I all alone beweep my
outcast state,\n', And trouble deaf heaven with my bootless cries,\n', And
look upon myself and curse my fate,']
>>> # You can also iterate through the file line by line:
>>> with open('sonnet29.txt') as sonnet_file:
... for line in sonnet_file: # note the new line character will be included in the line
... print(line, end='')
When, in disgrace with fortune and men's eyes,
I all alone beweep my outcast state,
And trouble deaf heaven with my bootless cries,
And look upon myself and curse my fate,
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Writing to Files
>>> with open('bacon.txt', 'w') as bacon_file:
... bacon_file.write('Hello world!\n')
13
>>> with open('bacon.txt', 'a') as bacon_file:
... bacon_file.write('Bacon is not a vegetable.')
25
>>> with open('bacon.txt') as bacon_file:
... content = bacon_file.read()
>>> print(content)
Hello world!
Bacon is not a vegetable.
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Saving Variables with the shelve Module
To save variables:
>>> import shelve
>>> cats = ['Zophie', 'Pooka', 'Simon']
>>> with shelve.open('mydata') as shelf_file:
... shelf_file['cats'] = cats
To open and read variables:
>>> with shelve.open('mydata') as shelf_file:
... print(type(shelf_file))
... print(shelf_file['cats'])
<class 'shelve.DbfilenameShelf'>
['Zophie', 'Pooka', 'Simon']
Just like dictionaries, shelf values have keys() and values() methods that will return list-like values of the keys and values in the shelf. Since these methods return list-like values instead of true lists, you should pass them to the list() function to get them in list form.
>>> with shelve.open('mydata') as shelf_file:
... print(list(shelf_file.keys()))
... print(list(shelf_file.values()))
['cats']
[['Zophie', 'Pooka', 'Simon']]
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Saving Variables with the pprint.pformat() Function
>>> import zipfile, os
>>> os.chdir('C:\\') # move to the folder with example.zip
>>> with zipfile.ZipFile('example.zip') as example_zip:
... print(example_zip.namelist())
... spam_info = example_zip.getinfo('spam.txt')
... print(spam_info.file_size)
... print(spam_info.compress_size)
... print('Compressed file is %sx smaller!' % (round(spam_info.file_size / spam_info.compress_size, 2)))
['spam.txt', 'cats/', 'cats/catnames.txt', 'cats/zophie.jpg']
13908
3828
'Compressed file is 3.63x smaller!'
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Extracting from ZIP Files
The extractall() method for ZipFile objects extracts all the files and folders from a ZIP file into the current working directory.
>>> import zipfile, os
>>> os.chdir('C:\\') # move to the folder with example.zip
>>> with zipfile.ZipFile('example.zip') as example_zip:
... example_zip.extractall()
The extract() method for ZipFile objects will extract a single file from the ZIP file. Continue the interactive shell example:
>>> with zipfile.ZipFile('example.zip') as example_zip:
... print(example_zip.extract('spam.txt'))
... print(example_zip.extract('spam.txt', 'C:\\some\\new\\folders'))
'C:\\spam.txt'
'C:\\some\\new\\folders\\spam.txt'
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Creating and Adding to ZIP Files
>>> import zipfile
>>> with zipfile.ZipFile('new.zip', 'w') as new_zip:
... new_zip.write('spam.txt', compress_type=zipfile.ZIP_DEFLATED)
This code will create a new ZIP file named new.zip that has the compressed contents of spam.txt.
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JSON, YAML and configuration files
JSON
Open a JSON file with:
import json
with open("filename.json", "r") as f:
content = json.loads(f.read())
Write a JSON file with:
import json
content = {"name": "Joe", "age": 20}
with open("filename.json", "w") as f:
f.write(json.dumps(content, indent=2))
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YAML
Compared to JSON, YAML allows for much better human maintainability and gives you the option to add comments. It is a convenient choice for configuration files where humans will have to edit it.
There are two main libraries allowing to access to YAML files:
Install them using pip install in your virtual environment.
The first one it easier to use but the second one, Ruamel, implements much better the YAML specification, and allow for example to modify a YAML content without altering comments.
Open a YAML file with:
from ruamel.yaml import YAML
with open("filename.yaml") as f:
yaml=YAML()
yaml.load(f)
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Anyconfig
Anyconfig is a very handy package allowing to abstract completely the underlying configuration file format. It allows to load a Python dictionary from JSON, YAML, TOML, and so on.
Exceptions are raised with a raise statement. In code, a raise statement consists of the following:
The raise keyword
A call to the Exception() function
A string with a helpful error message passed to the Exception() function
>>> raise Exception('This is the error message.')
Traceback (most recent call last):
File "<pyshell#191>", line 1, in <module>
raise Exception('This is the error message.')
Exception: This is the error message.
Often it's the code that calls the function, not the function itself, that knows how to handle an exception. So you will commonly see a raise statement inside a function and the try and except statements in the code calling the function.
def box_print(symbol, width, height):
if len(symbol) != 1:
raise Exception('Symbol must be a single character string.')
if width <= 2:
raise Exception('Width must be greater than 2.')
if height <= 2:
raise Exception('Height must be greater than 2.')
print(symbol * width)
for i in range(height - 2):
print(symbol + (' ' * (width - 2)) + symbol)
print(symbol * width)
for sym, w, h in (('*', 4, 4), ('O', 20, 5), ('x', 1, 3), ('ZZ', 3, 3)):
try:
box_print(sym, w, h)
except Exception as err:
print('An exception happened: ' + str(err))
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Getting the Traceback as a String
The traceback is displayed by Python whenever a raised exception goes unhandled. But can also obtain it as a string by calling traceback.format_exc(). This function is useful if you want the information from an exception's traceback but also want an except statement to gracefully handle the exception. You will need to import Python's traceback module before calling this function.
>>> import traceback
>>> try:
>>> raise Exception('This is the error message.')
>>> except:
>>> with open('errorInfo.txt', 'w') as error_file:
>>> error_file.write(traceback.format_exc())
>>> print('The traceback info was written to errorInfo.txt.')
116
The traceback info was written to errorInfo.txt.
The 116 is the return value from the write() method, since 116 characters were written to the file. The traceback text was written to errorInfo.txt.
Traceback (most recent call last):
File "<pyshell#28>", line 2, in <module>
Exception: This is the error message.
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Assertions
An assertion is a sanity check to make sure your code isn't doing something obviously wrong. These sanity checks are performed by assert statements. If the sanity check fails, then an AssertionError exception is raised. In code, an assert statement consists of the following:
The assert keyword
A condition (that is, an expression that evaluates to True or False)
A comma
A string to display when the condition is False
>>> pod_bay_door_status = 'open'
>>> assert pod_bay_door_status == 'open', 'The pod bay doors need to be "open".'
>>> pod_bay_door_status = 'I\'m sorry, Dave. I\'m afraid I can\'t do that.'
>>> assert pod_bay_door_status == 'open', 'The pod bay doors need to be "open".'
Traceback (most recent call last):
File "<pyshell#10>", line 1, in <module>
assert pod_bay_door_status == 'open', 'The pod bay doors need to be "open".'
AssertionError: The pod bay doors need to be "open".
In plain English, an assert statement says, "I assert that this condition holds true, and if not, there is a bug somewhere in the program.” Unlike exceptions, your code should not handle assert statements with try and except; if an assert fails, your program should crash. By failing fast like this, you shorten the time between the original cause of the bug and when you first notice the bug. This will reduce the amount of code you will have to check before finding the code that's causing the bug.
Disabling Assertions
Assertions can be disabled by passing the -O option when running Python.
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Logging
To enable the logging module to display log messages on your screen as your program runs, copy the following to the top of your program (but under the #! python shebang line):
Say you wrote a function to calculate the factorial of a number. In mathematics, factorial 4 is 1 × 2 × 3 × 4, or 24. Factorial 7 is 1 × 2 × 3 × 4 × 5 × 6 × 7, or 5,040. Open a new file editor window and enter the following code. It has a bug in it, but you will also enter several log messages to help yourself figure out what is going wrong. Save the program as factorialLog.py.
>>> import logging
>>>
>>> logging.basicConfig(level=logging.DEBUG, format=' %(asctime)s - %(levelname)s- %(message)s')
>>>
>>> logging.debug('Start of program')
>>>
>>> def factorial(n):
>>>
>>> logging.debug('Start of factorial(%s)' % (n))
>>> total = 1
>>>
>>> for i in range(1, n + 1):
>>> total *= i
>>> logging.debug('i is ' + str(i) + ', total is ' + str(total))
>>>
>>> logging.debug('End of factorial(%s)' % (n))
>>>
>>> return total
>>>
>>> print(factorial(5))
>>> logging.debug('End of program')
2015-05-23 16:20:12,664 - DEBUG - Start of program
2015-05-23 16:20:12,664 - DEBUG - Start of factorial(5)
2015-05-23 16:20:12,665 - DEBUG - i is 0, total is 0
2015-05-23 16:20:12,668 - DEBUG - i is 1, total is 0
2015-05-23 16:20:12,670 - DEBUG - i is 2, total is 0
2015-05-23 16:20:12,673 - DEBUG - i is 3, total is 0
2015-05-23 16:20:12,675 - DEBUG - i is 4, total is 0
2015-05-23 16:20:12,678 - DEBUG - i is 5, total is 0
2015-05-23 16:20:12,680 - DEBUG - End of factorial(5)
0
2015-05-23 16:20:12,684 - DEBUG - End of program
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Logging Levels
Logging levels provide a way to categorize your log messages by importance. There are five logging levels, described in Table 10-1 from least to most important. Messages can be logged at each level using a different logging function.
Level
Logging Function
Description
DEBUG
logging.debug()
The lowest level. Used for small details. Usually you care about these messages only when diagnosing problems.
INFO
logging.info()
Used to record information on general events in your program or confirm that things are working at their point in the program.
WARNING
logging.warning()
Used to indicate a potential problem that doesn't prevent the program from working but might do so in the future.
ERROR
logging.error()
Used to record an error that caused the program to fail to do something.
CRITICAL
logging.critical()
The highest level. Used to indicate a fatal error that has caused or is about to cause the program to stop running entirely.
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Disabling Logging
After you've debugged your program, you probably don't want all these log messages cluttering the screen. The logging.disable() function disables these so that you don't have to go into your program and remove all the logging calls by hand.
Instead of displaying the log messages to the screen, you can write them to a text file. The logging.basicConfig() function takes a filename keyword argument, like so:
Note: lambda can only evaluate an expression, like a single line of code.
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Ternary Conditional Operator
Many programming languages have a ternary operator, which define a conditional expression. The most common usage is to make a terse simple conditional assignment statement. In other words, it offers one-line code to evaluate the first expression if the condition is true, otherwise it evaluates the second expression.
<expression1> if <condition> else <expression2>
Example:
>>> age = 15
>>> print('kid' if age < 18 else 'adult')
kid
Ternary operators can be chained:
>>> age = 15
>>> print('kid' if age < 13 else 'teenager' if age < 18 else 'adult')
teenager
The code above is equivalent to:
if age < 18:
if age < 13:
print('kid')
else:
print('teenager')
else:
print('adult')
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args and kwargs
The names args and kwargs are arbitrary - the important thing are the * and ** operators. They can mean:
In a function declaration, * means "pack all remaining positional arguments into a tuple named <name>”, while ** is the same for keyword arguments (except it uses a dictionary, not a tuple).
In a function call, * means "unpack tuple or list named <name> to positional arguments at this position”, while ** is the same for keyword arguments.
For example you can make a function that you can use to call any other function, no matter what parameters it has:
Inside forward, args is a tuple (of all positional arguments except the first one, because we specified it - the f), kwargs is a dict. Then we call f and unpack them so they become normal arguments to f.
You use *args when you have an indefinite amount of positional arguments.
>>> def fruits(*args):
>>> for fruit in args:
>>> print(fruit)
>>> fruits("apples", "bananas", "grapes")
"apples"
"bananas"
"grapes"
Similarly, you use **kwargs when you have an indefinite number of keyword arguments.
>>> def fruit(**kwargs):
>>> for key, value in kwargs.items():
>>> print("{0}: {1}".format(key, value))
>>> fruit(name = "apple", color = "red")
name: apple
color: red
Functions can accept a variable number of positional arguments by using *args in the def statement.
You can use the items from a sequence as the positional arguments for a function with the * operator.
Using the * operator with a generator may cause your program to run out of memory and crash.
Adding new positional parameters to functions that accept *args can introduce hard-to-find bugs.
Things to Remember(kwargs)
Function arguments can be specified by position or by keyword.
Keywords make it clear what the purpose of each argument is when it would be confusing with only positional arguments.
Keyword arguments with default values make it easy to add new behaviors to a function, especially when the function has existing callers.
Optional keyword arguments should always be passed by keyword instead of by position.
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Context Manager
While Python's context managers are widely used, few understand the purpose behind their use. These statements, commonly used with reading and writing files, assist the application in conserving system memory and improve resource management by ensuring specific resources are only in use for certain processes.
with statement
A context manager is an object that is notified when a context (a block of code) starts and ends. You commonly use one with the with statement. It takes care of the notifying.
For example, file objects are context managers. When a context ends, the file object is closed automatically:
>>> with open(filename) as f:
>>> file_contents = f.read()
# the open_file object has automatically been closed.
Anything that ends execution of the block causes the context manager's exit method to be called. This includes exceptions, and can be useful when an error causes you to prematurely exit from an open file or connection. Exiting a script without properly closing files/connections is a bad idea, that may cause data loss or other problems. By using a context manager you can ensure that precautions are always taken to prevent damage or loss in this way.
Writing your own contextmanager using generator syntax
It is also possible to write a context manager using generator syntax thanks to the contextlib.contextmanager decorator:
>>> import contextlib
>>> @contextlib.contextmanager
... def context_manager(num):
... print('Enter')
... yield num + 1
... print('Exit')
>>> with context_manager(2) as cm:
... # the following instructions are run when the 'yield' point of the context
... # manager is reached.
... # 'cm' will have the value that was yielded
... print('Right in the middle with cm = {}'.format(cm))
Enter
Right in the middle with cm = 3
Exit
>>>
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__main__ Top-level script environment
__main__ is the name of the scope in which top-level code executes. A module's name is set equal to __main__ when read from standard input, a script, or from an interactive prompt.
A module can discover whether or not it is running in the main scope by checking its own __name__, which allows a common idiom for conditionally executing code in a module when it is run as a script or with python -m but not when it is imported:
>>> if __name__ == "__main__":
... # execute only if run as a script
... main()
For a package, the same effect can be achieved by including a main.py module, the contents of which will be executed when the module is run with -m
For example we are developing script which is designed to be used as module, we should do:
>>> # Python program to execute function directly
>>> def add(a, b):
... return a+b
...
>>> add(10, 20) # we can test it by calling the function save it as calculate.py
30
>>> # Now if we want to use that module by importing we have to comment out our call,
>>> # Instead we can write like this in calculate.py
>>> if __name__ == "__main__":
... add(3, 5)
...
>>> import calculate
>>> calculate.add(3, 5)
8
Advantages
Every Python module has it's __name__ defined and if this is __main__, it implies that the module is being run standalone by the user and we can do corresponding appropriate actions.
If you import this script as a module in another script, the name is set to the name of the script/module.
Python files can act as either reusable modules, or as standalone programs.
if __name__ == "main”: is used to execute some code only if the file was run directly, and not imported.
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setup.py
The setup script is the centre of all activity in building, distributing, and installing modules using the Distutils. The main purpose of the setup script is to describe your module distribution to the Distutils, so that the various commands that operate on your modules do the right thing.
The setup.py file is at the heart of a Python project. It describes all of the metadata about your project. There a quite a few fields you can add to a project to give it a rich set of metadata describing the project. However, there are only three required fields: name, version, and packages. The name field must be unique if you wish to publish your package on the Python Package Index (PyPI). The version field keeps track of different releases of the project. The packages field describes where you've put the Python source code within your project.
This allows you to easily install Python packages. Often it's enough to write:
python setup.py install
and module will install itself.
Our initial setup.py will also include information about the license and will re-use the README.txt file for the long_description field. This will look like:
Dataclasses are python classes but are suited for storing data objects. This module provides a decorator and functions for automatically adding generated special methods such as __init__() and __repr__() to user-defined classes.
Features
They store data and represent a certain data type. Ex: A number. For people familiar with ORMs, a model instance is a data object. It represents a specific kind of entity. It holds attributes that define or represent the entity.
They can be compared to other objects of the same type. Ex: A number can be greater than, less than, or equal to another number.
Python 3.7 provides a decorator dataclass that is used to convert a class into a dataclass.
python 2.7
>>> class Number:
... def __init__(self, val):
... self.val = val
...
>>> obj = Number(2)
>>> obj.val
2
with dataclass
>>> @dataclass
... class Number:
... val: int
...
>>> obj = Number(2)
>>> obj.val
2
Return to the Top
Default values
It is easy to add default values to the fields of your data class.
It is mandatory to define the data type in dataclass. However, If you don't want specify the datatype then, use typing.Any.
>>> from dataclasses import dataclass
>>> from typing import Any
>>> @dataclass
... class WithoutExplicitTypes:
... name: Any
... value: Any = 42
...
Return to the Top
Virtual Environment
The use of a Virtual Environment is to test python code in encapsulated environments and to also avoid filling the base Python installation with libraries we might use for only one project.
Return to the Top
virtualenv
Install virtualenv
pip install virtualenv
Install virtualenvwrapper-win (Windows)
pip install virtualenvwrapper-win
Usage:
Make a Virtual Environment
mkvirtualenv HelloWold
Anything we install now will be specific to this project. And available to the projects we connect to this environment.
Set Project Directory
To bind our virtualenv with our current working directory we simply enter:
setprojectdir .
Deactivate
To move onto something else in the command line type ‘deactivate' to deactivate your environment.
deactivate
Notice how the parenthesis disappear.
Workon
Open up the command prompt and type ‘workon HelloWold' to activate the environment and move into your root project folder
workon HelloWold
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poetry
Poetry is a tool for dependency management and packaging in Python. It allows you to declare the libraries your project depends on and it will manage (install/update) them for you.
Pipenv is a tool that aims to bring the best of all packaging worlds (bundler, composer, npm, cargo, yarn, etc.) to the Python world. Windows is a first-class citizen, in our world.
Install pipenv
pip install pipenv
Enter your Project directory and install the Packages for your project
cd my_project
pipenv install <package>
Pipenv will install your package and create a Pipfile for you in your project's directory. The Pipfile is used to track which dependencies your project needs in case you need to re-install them.
Uninstall Packages
pipenv uninstall <package>
Activate the Virtual Environment associated with your Python project
Anaconda is another popular tool to manage python packages.
Where packages, notebooks, projects and environments are shared. Your place for free public conda package hosting.
Usage:
Make a Virtual Environment
conda create -n HelloWorld
To use the Virtual Environment, activate it by:
conda activate HelloWorld
Anything installed now will be specific to the project HelloWorld
Exit the Virtual Environment
conda deactivate
Return to the Top
## Python Cheatsheet
- [About](#about)
- [Contribute](#contribute)
- [Read It](#read-it)
- [Python Cheatsheet](#python-cheatsheet)
- [The Zen of Python](#the-zen-of-python)
- [Python Basics](#python-basics)
- [Math Operators](#math-operators)
- [Data Types](#data-types)
- [String Concatenation and Replication](#string-concatenation-and-replication)
- [Variables](#variables)
- [Comments](#comments)
- [The print() Function](#the-print-function)
- [The input() Function](#the-input-function)
- [The len() Function](#the-len-function)
- [The str(), int(), and float() Functions](#the-str-int-and-float-functions)
- [Flow Control](#flow-control)
- [Comparison Operators](#comparison-operators)
- [Boolean evaluation](#boolean-evaluation)
- [Boolean Operators](#boolean-operators)
- [Mixing Boolean and Comparison Operators](#mixing-boolean-and-comparison-operators)
- [if Statements](#if-statements)
- [else Statements](#else-statements)
- [elif Statements](#elif-statements)
- [while Loop Statements](#while-loop-statements)
- [break Statements](#break-statements)
- [continue Statements](#continue-statements)
- [for Loops and the range() Function](#for-loops-and-the-range-function)
- [For else statement](#for-else-statement)
- [Importing Modules](#importing-modules)
- [Ending a Program Early with sys.exit()](#ending-a-program-early-with-sysexit)
- [Functions](#functions)
- [Return Values and return Statements](#return-values-and-return-statements)
- [The None Value](#the-none-value)
- [Keyword Arguments and print()](#keyword-arguments-and-print)
- [Local and Global Scope](#local-and-global-scope)
- [The global Statement](#the-global-statement)
- [Exception Handling](#exception-handling)
- [Basic exception handling](#basic-exception-handling)
- [Final code in exception handling](#final-code-in-exception-handling)
- [Lists](#lists)
- [Getting Individual Values in a List with Indexes](#getting-individual-values-in-a-list-with-indexes)
- [Negative Indexes](#negative-indexes)
- [Getting Sublists with Slices](#getting-sublists-with-slices)
- [Getting a List's Length with len()](#getting-a-lists-length-with-len)
- [Changing Values in a List with Indexes](#changing-values-in-a-list-with-indexes)
- [List Concatenation and List Replication](#list-concatenation-and-list-replication)
- [Removing Values from Lists with del Statements](#removing-values-from-lists-with-del-statements)
- [Using for Loops with Lists](#using-for-loops-with-lists)
- [Looping Through Multiple Lists with zip()](#looping-through-multiple-lists-with-zip)
- [The in and not in Operators](#the-in-and-not-in-operators)
- [The Multiple Assignment Trick](#the-multiple-assignment-trick)
- [Augmented Assignment Operators](#augmented-assignment-operators)
- [Finding a Value in a List with the index() Method](#finding-a-value-in-a-list-with-the-index-method)
- [Adding Values to Lists with the append() and insert() Methods](#adding-values-to-lists-with-the-append-and-insert-methods)
- [Removing Values from Lists with remove()](#removing-values-from-lists-with-remove)
- [Removing Values from Lists with pop()](#removing-values-from-lists-with-pop)
- [Sorting the Values in a List with the sort() Method](#sorting-the-values-in-a-list-with-the-sort-method)
- [Tuple Data Type](#tuple-data-type)
- [Converting Types with the list() and tuple() Functions](#converting-types-with-the-list-and-tuple-functions)
- [Dictionaries and Structuring Data](#dictionaries-and-structuring-data)
- [The keys(), values(), and items() Methods](#the-keys-values-and-items-methods)
- [Checking Whether a Key or Value Exists in a Dictionary](#checking-whether-a-key-or-value-exists-in-a-dictionary)
- [The get() Method](#the-get-method)
- [The setdefault() Method](#the-setdefault-method)
- [Pretty Printing](#pretty-printing)
- [Merge two dictionaries](#merge-two-dictionaries)
- [sets](#sets)
- [Initializing a set](#initializing-a-set)
- [sets: unordered collections of unique elements](#sets-unordered-collections-of-unique-elements)
- [set add() and update()](#set-add-and-update)
- [set remove() and discard()](#set-remove-and-discard)
- [set union()](#set-union)
- [set intersection](#set-intersection)
- [set difference](#set-difference)
- [set symetric_difference](#set-symetric_difference)
- [itertools Module](#itertools-module)
- [accumulate()](#accumulate)
- [combinations()](#combinations)
- [combinations_with_replacement()](#combinations_with_replacement)
- [count()](#count)
- [cycle()](#cycle)
- [chain()](#chain)
- [compress()](#compress)
- [dropwhile()](#dropwhile)
- [filterfalse()](#filterfalse)
- [groupby()](#groupby)
- [islice()](#islice)
- [permutations()](#permutations)
- [product()](#product)
- [repeat()](#repeat)
- [starmap()](#starmap)
- [takewhile()](#takewhile)
- [tee()](#tee)
- [zip_longest()](#zip_longest)
- [Comprehensions](#comprehensions)
- [List comprehension](#list-comprehension)
- [Set comprehension](#set-comprehension)
- [Dict comprehension](#dict-comprehension)
- [Manipulating Strings](#manipulating-strings)
- [Escape Characters](#escape-characters)
- [Raw Strings](#raw-strings)
- [Multiline Strings with Triple Quotes](#multiline-strings-with-triple-quotes)
- [Indexing and Slicing Strings](#indexing-and-slicing-strings)
- [The in and not in Operators with Strings](#the-in-and-not-in-operators-with-strings)
- [The in and not in Operators with list](#the-in-and-not-in-operators-with-list)
- [The upper(), lower(), isupper(), and islower() String Methods](#the-upper-lower-isupper-and-islower-string-methods)
- [The isX String Methods](#the-isx-string-methods)
- [The startswith() and endswith() String Methods](#the-startswith-and-endswith-string-methods)
- [The join() and split() String Methods](#the-join-and-split-string-methods)
- [Justifying Text with rjust(), ljust(), and center()](#justifying-text-with-rjust-ljust-and-center)
- [Removing Whitespace with strip(), rstrip(), and lstrip()](#removing-whitespace-with-strip-rstrip-and-lstrip)
- [Copying and Pasting Strings with the pyperclip Module (need pip install)](#copying-and-pasting-strings-with-the-pyperclip-module-need-pip-install)
- [String Formatting](#string-formatting)
- [% operator](#-operator)
- [String Formatting (str.format)](#string-formatting-strformat)
- [Lazy string formatting](#lazy-string-formatting)
- [Formatted String Literals or f-strings (Python 3.6+)](#formatted-string-literals-or-f-strings-python-36)
- [Template Strings](#template-strings)
- [Regular Expressions](#regular-expressions)
- [Matching Regex Objects](#matching-regex-objects)
- [Grouping with Parentheses](#grouping-with-parentheses)
- [Matching Multiple Groups with the Pipe](#matching-multiple-groups-with-the-pipe)
- [Optional Matching with the Question Mark](#optional-matching-with-the-question-mark)
- [Matching Zero or More with the Star](#matching-zero-or-more-with-the-star)
- [Matching One or More with the Plus](#matching-one-or-more-with-the-plus)
- [Matching Specific Repetitions with Curly Brackets](#matching-specific-repetitions-with-curly-brackets)
- [Greedy and Nongreedy Matching](#greedy-and-nongreedy-matching)
- [The findall() Method](#the-findall-method)
- [Making Your Own Character Classes](#making-your-own-character-classes)
- [The Caret and Dollar Sign Characters](#the-caret-and-dollar-sign-characters)
- [The Wildcard Character](#the-wildcard-character)
- [Matching Everything with Dot-Star](#matching-everything-with-dot-star)
- [Matching Newlines with the Dot Character](#matching-newlines-with-the-dot-character)
- [Review of Regex Symbols](#review-of-regex-symbols)
- [Case-Insensitive Matching](#case-insensitive-matching)
- [Substituting Strings with the sub() Method](#substituting-strings-with-the-sub-method)
- [Managing Complex Regexes](#managing-complex-regexes)
- [Handling File and Directory Paths](#handling-file-and-directory-paths)
- [Backslash on Windows and Forward Slash on OS X and Linux](#backslash-on-windows-and-forward-slash-on-os-x-and-linux)
- [The Current Working Directory](#the-current-working-directory)
- [Creating New Folders](#creating-new-folders)
- [Absolute vs. Relative Paths](#absolute-vs-relative-paths)
- [Handling Absolute and Relative Paths](#handling-absolute-and-relative-paths)
- [Checking Path Validity](#checking-path-validity)
- [Finding File Sizes and Folder Contents](#finding-file-sizes-and-folder-contents)
- [Copying Files and Folders](#copying-files-and-folders)
- [Moving and Renaming Files and Folders](#moving-and-renaming-files-and-folders)
- [Permanently Deleting Files and Folders](#permanently-deleting-files-and-folders)
- [Safe Deletes with the send2trash Module](#safe-deletes-with-the-send2trash-module)
- [Walking a Directory Tree](#walking-a-directory-tree)
- [Reading and Writing Files](#reading-and-writing-files)
- [The File Reading/Writing Process](#the-file-readingwriting-process)
- [Opening and reading files with the open() function](#opening-and-reading-files-with-the-open-function)
- [Writing to Files](#writing-to-files)
- [Saving Variables with the shelve Module](#saving-variables-with-the-shelve-module)
- [Saving Variables with the pprint.pformat() Function](#saving-variables-with-the-pprintpformat-function)
- [Reading ZIP Files](#reading-zip-files)
- [Extracting from ZIP Files](#extracting-from-zip-files)
- [Creating and Adding to ZIP Files](#creating-and-adding-to-zip-files)
- [JSON, YAML and configuration files](#json-yaml-and-configuration-files)
- [JSON](#json)
- [YAML](#yaml)
- [Anyconfig](#anyconfig)
- [Debugging](#debugging)
- [Raising Exceptions](#raising-exceptions)
- [Getting the Traceback as a String](#getting-the-traceback-as-a-string)
- [Assertions](#assertions)
- [Logging](#logging)
- [Logging Levels](#logging-levels)
- [Disabling Logging](#disabling-logging)
- [Logging to a File](#logging-to-a-file)
- [Lambda Functions](#lambda-functions)
- [Ternary Conditional Operator](#ternary-conditional-operator)
- [args and kwargs](#args-and-kwargs)
- [Things to Remember(args)](#things-to-rememberargs)
- [Things to Remember(kwargs)](#things-to-rememberkwargs)
- [Context Manager](#context-manager)
- [with statement](#with-statement)
- [Writing your own contextmanager using generator syntax](#writing-your-own-contextmanager-using-generator-syntax)
- [`__main__` Top-level script environment](#__main__-top-level-script-environment)
- [Advantages](#advantages)
- [setup.py](#setuppy)
- [Dataclasses](#dataclasses)
- [Features](#features)
- [Default values](#default-values)
- [Type hints](#type-hints)
- [Virtual Environment](#virtual-environment)
- [virtualenv](#virtualenv)
- [poetry](#poetry)
- [pipenv](#pipenv)
- [anaconda](#anaconda)
## The Zen of Python
From the [PEP 20 -- The Zen of Python](https://www.python.org/dev/peps/pep-0020/):
> Long time Pythoneer Tim Peters succinctly channels the BDFL's guiding principles for Python's design into 20 aphorisms, only 19 of which have been written down.
```python
>>> import this
The Zen of Python, by Tim Peters
Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren't special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you're Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it's a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea -- let's do more of those!
>>> print('What is your name?') # ask for their name
>>> myName = input()
>>> print('It is good to meet you, {}'.format(myName))
What is your name?
Al
It is good to meet you, Al
name = 'Bob'
age = 5
if name == 'Alice':
print('Hi, Alice.')
elif age < 12:
print('You are not Alice, kiddo.')
name = 'Bob'
age = 30
if name == 'Alice':
print('Hi, Alice.')
elif age < 12:
print('You are not Alice, kiddo.')
else:
print('You are neither Alice nor a little kid.')
When the program execution reaches a continue statement, the program execution immediately jumps back to the start of the loop.
while True:
print('Who are you?')
name = input()
if name != 'Joe':
continue
print('Hello, Joe. What is the password? (It is a fish.)')
password = input()
if password == 'swordfish':
break
print('Access granted.')
>>> print('My name is')
>>> for i in range(5):
>>> print('Jimmy Five Times ({})'.format(str(i)))
My name is
Jimmy Five Times (0)
Jimmy Five Times (1)
Jimmy Five Times (2)
Jimmy Five Times (3)
Jimmy Five Times (4)
The range() function can also be called with three arguments. The first two arguments will be the start and stop values, and the third will be the step argument. The step is the amount that the variable is increased by after each iteration.
>>> for i in range(0, 10, 2):
>>> print(i)
0
2
4
6
8
You can even use a negative number for the step argument to make the for loop count down instead of up.
>>> for i in range(5, -1, -1):
>>> print(i)
5
4
3
2
1
0
For else statement
This allows to specify a statement to execute in case of the full loop has been executed. Only useful when a break condition can occur in the loop:
>>> for i in [1, 2, 3, 4, 5]:
>>> if i == 3:
>>> break
>>> else:
>>> print("only executed when no item of the list is equal to 3")
When creating a function using the def statement, you can specify what the return value should be with a return statement. A return statement consists of the following:
The return keyword.
The value or expression that the function should return.
import random
def getAnswer(answerNumber):
if answerNumber == 1:
return 'It is certain'
elif answerNumber == 2:
return 'It is decidedly so'
elif answerNumber == 3:
return 'Yes'
elif answerNumber == 4:
return 'Reply hazy try again'
elif answerNumber == 5:
return 'Ask again later'
elif answerNumber == 6:
return 'Concentrate and ask again'
elif answerNumber == 7:
return 'My reply is no'
elif answerNumber == 8:
return 'Outlook not so good'
elif answerNumber == 9:
return 'Very doubtful'
r = random.randint(1, 9)
fortune = getAnswer(r)
print(fortune)
Code in the global scope cannot use any local variables.
However, a local scope can access global variables.
Code in a function's local scope cannot use variables in any other local scope.
You can use the same name for different variables if they are in different scopes. That is, there can be a local variable named spam and a global variable also named spam.
>>> supplies = ['pens', 'staplers', 'flame-throwers', 'binders']
>>> for i, supply in enumerate(supplies):
>>> print('Index {} in supplies is: {}'.format(str(i), supply))
Index 0 in supplies is: pens
Index 1 in supplies is: staplers
Index 2 in supplies is: flame-throwers
Index 3 in supplies is: binders
>>> name = ['Pete', 'John', 'Elizabeth']
>>> age = [6, 23, 44]
>>> for n, a in zip(name, age):
>>> print('{} is {} years old'.format(n, a))
Pete is 6 years old
John is 23 years old
Elizabeth is 44 years old
The in and not in Operators
>>> 'howdy' in ['hello', 'hi', 'howdy', 'heyas']
True
The multiple assignment trick is a shortcut that lets you assign multiple variables with the values in a list in one line of code. So instead of doing this:
>>> spam = {'color': 'red', 'age': 42}
>>> for v in spam.values():
>>> print(v)
red
42
keys():
>>> for k in spam.keys():
>>> print(k)
color
age
items():
>>> for i in spam.items():
>>> print(i)
('color', 'red')
('age', 42)
Using the keys(), values(), and items() methods, a for loop can iterate over the keys, values, or key-value pairs in a dictionary, respectively.
>>> spam = {'color': 'red', 'age': 42}
>>>
>>> for k, v in spam.items():
>>> print('Key: {} Value: {}'.format(k, str(v)))
Key: age Value: 42
Key: color Value: red
A set is an unordered collection with no duplicate elements. Basic uses include membership testing and eliminating duplicate entries. Set objects also support mathematical operations like union, intersection, difference, and symmetric difference.
Initializing a set
There are two ways to create sets: using curly braces {} and the built-in function set()
>>> s = {1, 2, 3}
>>> s = set([1, 2, 3])
When creating an empty set, be sure to not use the curly braces {} or you will get an empty dictionary instead.
>>> s = {}
>>> type(s)
<class 'dict'>
sets: unordered collections of unique elements
A set automatically remove all the duplicate values.
>>> s = {1, 2, 3, 2, 3, 4}
>>> s
{1, 2, 3, 4}
And as an unordered data type, they can't be indexed.
>>> s = {1, 2, 3}
>>> s[0]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: 'set' object does not support indexing
>>>
set add() and update()
Using the add() method we can add a single element to the set.
The module standardizes a core set of fast, memory efficient tools that are useful by themselves or in combination. Together, they form an "iterator algebra” making it possible to construct specialized tools succinctly and efficiently in pure Python.
The itertools module comes in the standard library and must be imported.
The operator module will also be used. This module is not necessary when using itertools, but needed for some of the examples below.
This function cycles through an iterator endlessly.
itertools.cycle(iterable)
Example:
>>> colors = ['red', 'orange', 'yellow', 'green', 'blue', 'violet']
>>> for color in itertools.cycle(colors):
>>> print(color)
red
orange
yellow
green
blue
violet
red
orange
When reached the end of the iterable it start over again from the beginning.
Take a series of iterables and return them as one long iterable.
itertools.chain(*iterables)
Example:
>>> colors = ['red', 'orange', 'yellow', 'green', 'blue']
>>> shapes = ['circle', 'triangle', 'square', 'pentagon']
>>> result = itertools.chain(colors, shapes)
>>> for each in result:
>>> print(each)
red
orange
yellow
green
blue
circle
triangle
square
pentagon
Return n independent iterators from a single iterable.
itertools.tee(iterable, n=2)
Example:
>>> colors = ['red', 'orange', 'yellow', 'green', 'blue']
>>> alpha_colors, beta_colors = itertools.tee(colors)
>>> for each in alpha_colors:
>>> print(each)
red
orange
yellow
green
blue
>>> colors = ['red', 'orange', 'yellow', 'green', 'blue']
>>> alpha_colors, beta_colors = itertools.tee(colors)
>>> for each in beta_colors:
>>> print(each)
red
orange
yellow
green
blue
Makes an iterator that aggregates elements from each of the iterables. If the iterables are of uneven length, missing values are filled-in with fillvalue. Iteration continues until the longest iterable is exhausted.
>>> print('''Dear Alice,
>>>
>>> Eve's cat has been arrested for catnapping, cat burglary, and extortion.
>>>
>>> Sincerely,
>>> Bob''')
Dear Alice,
Eve's cat has been arrested for catnapping, cat burglary, and extortion.
Sincerely,
Bob
To keep a nicer flow in your code, you can use the dedent function from the textwrap standard package.
>>> from textwrap import dedent
>>>
>>> def my_function():
>>> print('''
>>> Dear Alice,
>>>
>>> Eve's cat has been arrested for catnapping, cat burglary, and extortion.
>>>
>>> Sincerely,
>>> Bob
>>> ''').strip()
Justifying Text with rjust(), ljust(), and center()
rjust() and ljust():
>>> 'Hello'.rjust(10)
' Hello'
>>> 'Hello'.rjust(20)
' Hello'
>>> 'Hello World'.rjust(20)
' Hello World'
>>> 'Hello'.ljust(10)
'Hello '
An optional second argument to rjust() and ljust() will specify a fill character other than a space character. Enter the following into the interactive shell:
Python 3 introduced a new way to do string formatting that was later back-ported to Python 2.7. This makes the syntax for string formatting more regular.
>>> name = 'John'
>>> age = 20'
>>> "Hello I'm {}, my age is {}".format(name, age)
"Hello I'm John, my age is 20"
>>> "Hello I'm {0}, my age is {1}".format(name, age)
"Hello I'm John, my age is 20"
The formatting operations described here exhibit a variety of quirks that lead to a number of common errors (such as failing to display tuples and dictionaries correctly). Using the newer formatted string literals or the str.format() interface helps avoid these errors. These alternatives also provide more powerful, flexible and extensible approaches to formatting text.
A simpler and less powerful mechanism, but it is recommended when handling format strings generated by users. Due to their reduced complexity template strings are a safer choice.
>>> from string import Template
>>> name = 'Elizabeth'
>>> t = Template('Hey $name!')
>>> t.substitute(name=name)
'Hey Elizabeth!'
>>> phone_num_regex = re.compile(r'\d\d\d-\d\d\d-\d\d\d\d')
>>> mo = phone_num_regex.search('My number is 415-555-4242.')
>>> print('Phone number found: {}'.format(mo.group()))
Phone number found: 415-555-4242
The | character is called a pipe. You can use it anywhere you want to match one of many expressions. For example, the regular expression r'Batman|Tina Fey' will match either 'Batman' or 'Tina Fey'.
>>> hero_regex = re.compile (r'Batman|Tina Fey')
>>> mo1 = hero_regex.search('Batman and Tina Fey.')
>>> mo1.group()
'Batman'
>>> mo2 = hero_regex.search('Tina Fey and Batman.')
>>> mo2.group()
'Tina Fey'
You can also use the pipe to match one of several patterns as part of your regex:
>>> bat_regex = re.compile(r'Bat(man|mobile|copter|bat)')
>>> mo = bat_regex.search('Batmobile lost a wheel')
>>> mo.group()
'Batmobile'
>>> mo.group(1)
'mobile'
While * means "match zero or more,” the + (or plus) means "match one or more”. The group preceding a plus must appear at least once. It is not optional:
If you have a group that you want to repeat a specific number of times, follow the group in your regex with a number in curly brackets. For example, the regex (Ha){3} will match the string 'HaHaHa', but it will not match 'HaHa', since the latter has only two repeats of the (Ha) group.
Instead of one number, you can specify a range by writing a minimum, a comma, and a maximum in between the curly brackets. For example, the regex (Ha){3,5} will match 'HaHaHa', 'HaHaHaHa', and 'HaHaHaHaHa'.
Python's regular expressions are greedy by default, which means that in ambiguous situations they will match the longest string possible. The non-greedy version of the curly brackets, which matches the shortest string possible, has the closing curly bracket followed by a question mark.
In addition to the search() method, Regex objects also have a findall() method. While search() will return a Match object of the first matched text in the searched string, the findall() method will return the strings of every match in the searched string.
>>> phone_num_regex = re.compile(r'\d\d\d-\d\d\d-\d\d\d\d') # has no groups
>>> phone_num_regex.findall('Cell: 415-555-9999 Work: 212-555-0000')
['415-555-9999', '212-555-0000']
To summarize what the findall() method returns, remember the following:
When called on a regex with no groups, such as \d-\d\d\d-\d\d\d\d, the method findall() returns a list of ng matches, such as ['415-555-9999', '212-555-0000'].
When called on a regex that has groups, such as (\d\d\d)-(d\d)-(\d\d\d\d), the method findall() returns a list of es of strings (one string for each group), such as [('415', '555', '9999'), ('212', '555', '0000')].
There are times when you want to match a set of characters but the shorthand character classes (\d, \w, \s, and so on) are too broad. You can define your own character class using square brackets. For example, the character class [aeiouAEIOU] will match any vowel, both lowercase and uppercase.
You can also include ranges of letters or numbers by using a hyphen. For example, the character class [a-zA-Z0-9] will match all lowercase letters, uppercase letters, and numbers.
By placing a caret character (^) just after the character class's opening bracket, you can make a negative character class. A negative character class will match all the characters that are not in the character class. For example, enter the following into the interactive shell:
You can also use the caret symbol (^) at the start of a regex to indicate that a match must occur at the beginning of the searched text.
Likewise, you can put a dollar sign ($) at the end of the regex to indicate the string must end with this regex pattern.
And you can use the ^ and $ together to indicate that the entire string must match the regex—that is, it's not enough for a match to be made on some subset of the string.
The r'^Hello' regular expression string matches strings that begin with 'Hello':
>>> begins_with_hello = re.compile(r'^Hello')
>>> begins_with_hello.search('Hello world!')
<_sre.SRE_Match object; span=(0, 5), match='Hello'>
>>> begins_with_hello.search('He said hello.') is None
True
The r'\d$' regular expression string matches strings that end with a numeric character from 0 to 9:
>>> name_regex = re.compile(r'First Name: (.*) Last Name: (.*)')
>>> mo = name_regex.search('First Name: Al Last Name: Sweigart')
>>> mo.group(1)
'Al'
>>> mo.group(2)
'Sweigart'
The dot-star uses greedy mode: It will always try to match as much text as possible. To match any and all text in a nongreedy fashion, use the dot, star, and question mark (.*?). The question mark tells Python to match in a nongreedy way:
>>> nongreedy_regex = re.compile(r'<.*?>')
>>> mo = nongreedy_regex.search('<To serve man> for dinner.>')
>>> mo.group()
'<To serve man>'
>>> greedy_regex = re.compile(r'<.*>')
>>> mo = greedy_regex.search('<To serve man> for dinner.>')
>>> mo.group()
'<To serve man> for dinner.>'
The dot-star will match everything except a newline. By passing re.DOTALL as the second argument to re.compile(), you can make the dot character match all characters, including the newline character:
>>> no_newline_regex = re.compile('.*')
>>> no_newline_regex.search('Serve the public trust.\nProtect the innocent.\nUphold the law.').group()
'Serve the public trust.'
>>> newline_regex = re.compile('.*', re.DOTALL)
>>> newline_regex.search('Serve the public trust.\nProtect the innocent.\nUphold the law.').group()
'Serve the public trust.\nProtect the innocent.\nUphold the law.'
The sub() method for Regex objects is passed two arguments:
The first argument is a string to replace any matches.
The second is the string for the regular expression.
The sub() method returns a string with the substitutions applied:
>>> names_regex = re.compile(r'Agent \w+')
>>> names_regex.sub('CENSORED', 'Agent Alice gave the secret documents to Agent Bob.')
'CENSORED gave the secret documents to CENSORED.'
Another example:
>>> agent_names_regex = re.compile(r'Agent (\w)\w*')
>>> agent_names_regex.sub(r'\1****', 'Agent Alice told Agent Carol that Agent Eve knew Agent Bob was a double agent.')
A**** told C**** that E**** knew B**** was a double agent.'
To tell the re.compile() function to ignore whitespace and comments inside the regular expression string, "verbose mode” can be enabled by passing the variable re.VERBOSE as the second argument to re.compile().
Now instead of a hard-to-read regular expression like this:
There are two main modules in Python that deals with path manipulation. One is the os.path module and the other is the pathlib module. The pathlib module was added in Python 3.4, offering an object-oriented way to handle file system paths.
Backslash on Windows and Forward Slash on OS X and Linux
On Windows, paths are written using backslashes (\) as the separator between folder names. On Unix based operating system such as macOS, Linux, and BSDs, the forward slash (/) is used as the path separator. Joining paths can be a headache if your code needs to work on different platforms.
Fortunately, Python provides easy ways to handle this. We will showcase how to deal with this with both os.path.join and pathlib.Path.joinpath
Using os.path.join on Windows:
>>> import os
>>> os.path.join('usr', 'bin', 'spam')
'usr\\bin\\spam'
And using pathlib on *nix:
>>> from pathlib import Path
>>> print(Path('usr').joinpath('bin').joinpath('spam'))
usr/bin/spam
pathlib also provides a shortcut to joinpath using the / operator:
Notice the path separator is different between Windows and Unix based operating system, that's why you want to use one of the above methods instead of adding strings together to join paths together.
Joining paths is helpful if you need to create different file paths under the same directory.
Using os.path.join on Windows:
>>> my_files = ['accounts.txt', 'details.csv', 'invite.docx']
>>> for filename in my_files:
>>> print(os.path.join('C:\\Users\\asweigart', filename))
C:\Users\asweigart\accounts.txt
C:\Users\asweigart\details.csv
C:\Users\asweigart\invite.docx
Using pathlib on *nix:
>>> my_files = ['accounts.txt', 'details.csv', 'invite.docx']
>>> home = Path.home()
>>> for filename in my_files:
>>> print(home / filename)
/home/asweigart/accounts.txt
/home/asweigart/details.csv
/home/asweigart/invite.docx
>>> import os
>>> os.makedirs('C:\\delicious\\walnut\\waffles')
Using pathlib on *nix:
>>> from pathlib import Path
>>> cwd = Path.cwd()
>>> (cwd / 'delicious' / 'walnut' / 'waffles').mkdir()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python3.6/pathlib.py", line 1226, in mkdir
self._accessor.mkdir(self, mode)
File "/usr/lib/python3.6/pathlib.py", line 387, in wrapped
return strfunc(str(pathobj), *args)
FileNotFoundError: [Errno 2] No such file or directory: '/home/asweigart/delicious/walnut/waffles'
Oh no, we got a nasty error! The reason is that the 'delicious' directory does not exist, so we cannot make the 'walnut' and the 'waffles' directories under it. To fix this, do:
An absolute path, which always begins with the root folder
A relative path, which is relative to the program's current working directory
There are also the dot (.) and dot-dot (..) folders. These are not real folders but special names that can be used in a path. A single period ("dot”) for a folder name is shorthand for "this directory.” Two periods ("dot-dot”) means "the parent folder.”
>>> import os
>>> os.path.getsize('C:\\Windows\\System32\\calc.exe')
776192
Using pathlib on *nix:
>>> from pathlib import Path
>>> stat = Path('/bin/python3.6').stat()
>>> print(stat) # stat contains some other information about the file as well
os.stat_result(st_mode=33261, st_ino=141087, st_dev=2051, st_nlink=2, st_uid=0,
--snip--
st_gid=0, st_size=10024, st_atime=1517725562, st_mtime=1515119809, st_ctime=1517261276)
>>> print(stat.st_size) # size in bytes
10024
Listing directory contents using os.listdir on Windows:
>>> from pathlib import Path
>>> for f in Path('/usr/bin').iterdir():
>>> print(f)
...
/usr/bin/tiff2rgba
/usr/bin/iconv
/usr/bin/ldd
/usr/bin/cache_restore
/usr/bin/udiskie
/usr/bin/unix2dos
/usr/bin/t1reencode
/usr/bin/epstopdf
/usr/bin/idle3
...
To find the total size of all the files in this directory:
WARNING: Directories themselves also have a size! So you might want to check for whether a path is a file or directory using the methods in the methods discussed in the above section!
Using os.path.getsize() and os.listdir() together on Windows:
>>> import os
>>> total_size = 0
>>> for filename in os.listdir('C:\\Windows\\System32'):
total_size = total_size + os.path.getsize(os.path.join('C:\\Windows\\System32', filename))
>>> print(total_size)
1117846456
Using pathlib on *nix:
>>> from pathlib import Path
>>> total_size = 0
>>> for sub_path in Path('/usr/bin').iterdir():
... total_size += sub_path.stat().st_size
>>>
>>> print(total_size)
1903178911
You can install this module by running pip install send2trash from a Terminal window.
>>> import send2trash
>>> with open('bacon.txt', 'a') as bacon_file: # creates the file
... bacon_file.write('Bacon is not a vegetable.')
25
>>> send2trash.send2trash('bacon.txt')
>>> import os
>>>
>>> for folder_name, subfolders, filenames in os.walk('C:\\delicious'):
>>> print('The current folder is {}'.format(folder_name))
>>>
>>> for subfolder in subfolders:
>>> print('SUBFOLDER OF {}: {}'.format(folder_name, subfolder))
>>> for filename in filenames:
>>> print('FILE INSIDE {}: {}'.format(folder_name, filename))
>>>
>>> print('')
The current folder is C:\delicious
SUBFOLDER OF C:\delicious: cats
SUBFOLDER OF C:\delicious: walnut
FILE INSIDE C:\delicious: spam.txt
The current folder is C:\delicious\cats
FILE INSIDE C:\delicious\cats: catnames.txt
FILE INSIDE C:\delicious\cats: zophie.jpg
The current folder is C:\delicious\walnut
SUBFOLDER OF C:\delicious\walnut: waffles
The current folder is C:\delicious\walnut\waffles
FILE INSIDE C:\delicious\walnut\waffles: butter.txt
pathlib provides a lot more functionality than the ones listed above, like getting file name, getting file extension, reading/writing a file without manually opening it, etc. Check out the official documentation if you want to know more!
Reading and Writing Files
The File Reading/Writing Process
To read/write to a file in Python, you will want to use the with statement, which will close the file for you after you are done.
Opening and reading files with the open() function
>>> with open('C:\\Users\\your_home_folder\\hello.txt') as hello_file:
... hello_content = hello_file.read()
>>> hello_content
'Hello World!'
>>> # Alternatively, you can use the *readlines()* method to get a list of string values from the file, one string for each line of text:
>>> with open('sonnet29.txt') as sonnet_file:
... sonnet_file.readlines()
[When, in disgrace with fortune and men's eyes,\n', ' I all alone beweep my
outcast state,\n', And trouble deaf heaven with my bootless cries,\n', And
look upon myself and curse my fate,']
>>> # You can also iterate through the file line by line:
>>> with open('sonnet29.txt') as sonnet_file:
... for line in sonnet_file: # note the new line character will be included in the line
... print(line, end='')
When, in disgrace with fortune and men's eyes,
I all alone beweep my outcast state,
And trouble deaf heaven with my bootless cries,
And look upon myself and curse my fate,
>>> with open('bacon.txt', 'w') as bacon_file:
... bacon_file.write('Hello world!\n')
13
>>> with open('bacon.txt', 'a') as bacon_file:
... bacon_file.write('Bacon is not a vegetable.')
25
>>> with open('bacon.txt') as bacon_file:
... content = bacon_file.read()
>>> print(content)
Hello world!
Bacon is not a vegetable.
>>> import shelve
>>> cats = ['Zophie', 'Pooka', 'Simon']
>>> with shelve.open('mydata') as shelf_file:
... shelf_file['cats'] = cats
To open and read variables:
>>> with shelve.open('mydata') as shelf_file:
... print(type(shelf_file))
... print(shelf_file['cats'])
<class 'shelve.DbfilenameShelf'>
['Zophie', 'Pooka', 'Simon']
Just like dictionaries, shelf values have keys() and values() methods that will return list-like values of the keys and values in the shelf. Since these methods return list-like values instead of true lists, you should pass them to the list() function to get them in list form.
>>> with shelve.open('mydata') as shelf_file:
... print(list(shelf_file.keys()))
... print(list(shelf_file.values()))
['cats']
[['Zophie', 'Pooka', 'Simon']]
The extractall() method for ZipFile objects extracts all the files and folders from a ZIP file into the current working directory.
>>> import zipfile, os
>>> os.chdir('C:\\') # move to the folder with example.zip
>>> with zipfile.ZipFile('example.zip') as example_zip:
... example_zip.extractall()
The extract() method for ZipFile objects will extract a single file from the ZIP file. Continue the interactive shell example:
>>> with zipfile.ZipFile('example.zip') as example_zip:
... print(example_zip.extract('spam.txt'))
... print(example_zip.extract('spam.txt', 'C:\\some\\new\\folders'))
'C:\\spam.txt'
'C:\\some\\new\\folders\\spam.txt'
Compared to JSON, YAML allows for much better human maintainability and gives you the option to add comments. It is a convenient choice for configuration files where humans will have to edit it.
There are two main libraries allowing to access to YAML files:
Install them using pip install in your virtual environment.
The first one it easier to use but the second one, Ruamel, implements much better the YAML specification, and allow for example to modify a YAML content without altering comments.
Open a YAML file with:
from ruamel.yaml import YAML
with open("filename.yaml") as f:
yaml=YAML()
yaml.load(f)
Anyconfig is a very handy package allowing to abstract completely the underlying configuration file format. It allows to load a Python dictionary from JSON, YAML, TOML, and so on.
Exceptions are raised with a raise statement. In code, a raise statement consists of the following:
The raise keyword
A call to the Exception() function
A string with a helpful error message passed to the Exception() function
>>> raise Exception('This is the error message.')
Traceback (most recent call last):
File "<pyshell#191>", line 1, in <module>
raise Exception('This is the error message.')
Exception: This is the error message.
Often it's the code that calls the function, not the function itself, that knows how to handle an exception. So you will commonly see a raise statement inside a function and the try and except statements in the code calling the function.
def box_print(symbol, width, height):
if len(symbol) != 1:
raise Exception('Symbol must be a single character string.')
if width <= 2:
raise Exception('Width must be greater than 2.')
if height <= 2:
raise Exception('Height must be greater than 2.')
print(symbol * width)
for i in range(height - 2):
print(symbol + (' ' * (width - 2)) + symbol)
print(symbol * width)
for sym, w, h in (('*', 4, 4), ('O', 20, 5), ('x', 1, 3), ('ZZ', 3, 3)):
try:
box_print(sym, w, h)
except Exception as err:
print('An exception happened: ' + str(err))
The traceback is displayed by Python whenever a raised exception goes unhandled. But can also obtain it as a string by calling traceback.format_exc(). This function is useful if you want the information from an exception's traceback but also want an except statement to gracefully handle the exception. You will need to import Python's traceback module before calling this function.
>>> import traceback
>>> try:
>>> raise Exception('This is the error message.')
>>> except:
>>> with open('errorInfo.txt', 'w') as error_file:
>>> error_file.write(traceback.format_exc())
>>> print('The traceback info was written to errorInfo.txt.')
116
The traceback info was written to errorInfo.txt.
The 116 is the return value from the write() method, since 116 characters were written to the file. The traceback text was written to errorInfo.txt.
Traceback (most recent call last):
File "<pyshell#28>", line 2, in <module>
Exception: This is the error message.
An assertion is a sanity check to make sure your code isn't doing something obviously wrong. These sanity checks are performed by assert statements. If the sanity check fails, then an AssertionError exception is raised. In code, an assert statement consists of the following:
The assert keyword
A condition (that is, an expression that evaluates to True or False)
A comma
A string to display when the condition is False
>>> pod_bay_door_status = 'open'
>>> assert pod_bay_door_status == 'open', 'The pod bay doors need to be "open".'
>>> pod_bay_door_status = 'I\'m sorry, Dave. I\'m afraid I can\'t do that.'
>>> assert pod_bay_door_status == 'open', 'The pod bay doors need to be "open".'
Traceback (most recent call last):
File "<pyshell#10>", line 1, in <module>
assert pod_bay_door_status == 'open', 'The pod bay doors need to be "open".'
AssertionError: The pod bay doors need to be "open".
In plain English, an assert statement says, "I assert that this condition holds true, and if not, there is a bug somewhere in the program.” Unlike exceptions, your code should not handle assert statements with try and except; if an assert fails, your program should crash. By failing fast like this, you shorten the time between the original cause of the bug and when you first notice the bug. This will reduce the amount of code you will have to check before finding the code that's causing the bug.
Disabling Assertions
Assertions can be disabled by passing the -O option when running Python.
To enable the logging module to display log messages on your screen as your program runs, copy the following to the top of your program (but under the #! python shebang line):
Say you wrote a function to calculate the factorial of a number. In mathematics, factorial 4 is 1 × 2 × 3 × 4, or 24. Factorial 7 is 1 × 2 × 3 × 4 × 5 × 6 × 7, or 5,040. Open a new file editor window and enter the following code. It has a bug in it, but you will also enter several log messages to help yourself figure out what is going wrong. Save the program as factorialLog.py.
>>> import logging
>>>
>>> logging.basicConfig(level=logging.DEBUG, format=' %(asctime)s - %(levelname)s- %(message)s')
>>>
>>> logging.debug('Start of program')
>>>
>>> def factorial(n):
>>>
>>> logging.debug('Start of factorial(%s)' % (n))
>>> total = 1
>>>
>>> for i in range(1, n + 1):
>>> total *= i
>>> logging.debug('i is ' + str(i) + ', total is ' + str(total))
>>>
>>> logging.debug('End of factorial(%s)' % (n))
>>>
>>> return total
>>>
>>> print(factorial(5))
>>> logging.debug('End of program')
2015-05-23 16:20:12,664 - DEBUG - Start of program
2015-05-23 16:20:12,664 - DEBUG - Start of factorial(5)
2015-05-23 16:20:12,665 - DEBUG - i is 0, total is 0
2015-05-23 16:20:12,668 - DEBUG - i is 1, total is 0
2015-05-23 16:20:12,670 - DEBUG - i is 2, total is 0
2015-05-23 16:20:12,673 - DEBUG - i is 3, total is 0
2015-05-23 16:20:12,675 - DEBUG - i is 4, total is 0
2015-05-23 16:20:12,678 - DEBUG - i is 5, total is 0
2015-05-23 16:20:12,680 - DEBUG - End of factorial(5)
0
2015-05-23 16:20:12,684 - DEBUG - End of program
Logging levels provide a way to categorize your log messages by importance. There are five logging levels, described in Table 10-1 from least to most important. Messages can be logged at each level using a different logging function.
Level
Logging Function
Description
DEBUG
logging.debug()
The lowest level. Used for small details. Usually you care about these messages only when diagnosing problems.
INFO
logging.info()
Used to record information on general events in your program or confirm that things are working at their point in the program.
WARNING
logging.warning()
Used to indicate a potential problem that doesn't prevent the program from working but might do so in the future.
ERROR
logging.error()
Used to record an error that caused the program to fail to do something.
CRITICAL
logging.critical()
The highest level. Used to indicate a fatal error that has caused or is about to cause the program to stop running entirely.
After you've debugged your program, you probably don't want all these log messages cluttering the screen. The logging.disable() function disables these so that you don't have to go into your program and remove all the logging calls by hand.
Instead of displaying the log messages to the screen, you can write them to a text file. The logging.basicConfig() function takes a filename keyword argument, like so:
Many programming languages have a ternary operator, which define a conditional expression. The most common usage is to make a terse simple conditional assignment statement. In other words, it offers one-line code to evaluate the first expression if the condition is true, otherwise it evaluates the second expression.
<expression1> if <condition> else <expression2>
Example:
>>> age = 15
>>> print('kid' if age < 18 else 'adult')
kid
Ternary operators can be chained:
>>> age = 15
>>> print('kid' if age < 13 else 'teenager' if age < 18 else 'adult')
teenager
The code above is equivalent to:
if age < 18:
if age < 13:
print('kid')
else:
print('teenager')
else:
print('adult')
The names args and kwargs are arbitrary - the important thing are the * and ** operators. They can mean:
In a function declaration, * means "pack all remaining positional arguments into a tuple named <name>”, while ** is the same for keyword arguments (except it uses a dictionary, not a tuple).
In a function call, * means "unpack tuple or list named <name> to positional arguments at this position”, while ** is the same for keyword arguments.
For example you can make a function that you can use to call any other function, no matter what parameters it has:
Inside forward, args is a tuple (of all positional arguments except the first one, because we specified it - the f), kwargs is a dict. Then we call f and unpack them so they become normal arguments to f.
You use *args when you have an indefinite amount of positional arguments.
>>> def fruits(*args):
>>> for fruit in args:
>>> print(fruit)
>>> fruits("apples", "bananas", "grapes")
"apples"
"bananas"
"grapes"
Similarly, you use **kwargs when you have an indefinite number of keyword arguments.
>>> def fruit(**kwargs):
>>> for key, value in kwargs.items():
>>> print("{0}: {1}".format(key, value))
>>> fruit(name = "apple", color = "red")
name: apple
color: red
While Python's context managers are widely used, few understand the purpose behind their use. These statements, commonly used with reading and writing files, assist the application in conserving system memory and improve resource management by ensuring specific resources are only in use for certain processes.
with statement
A context manager is an object that is notified when a context (a block of code) starts and ends. You commonly use one with the with statement. It takes care of the notifying.
For example, file objects are context managers. When a context ends, the file object is closed automatically:
>>> with open(filename) as f:
>>> file_contents = f.read()
# the open_file object has automatically been closed.
Anything that ends execution of the block causes the context manager's exit method to be called. This includes exceptions, and can be useful when an error causes you to prematurely exit from an open file or connection. Exiting a script without properly closing files/connections is a bad idea, that may cause data loss or other problems. By using a context manager you can ensure that precautions are always taken to prevent damage or loss in this way.
Writing your own contextmanager using generator syntax
It is also possible to write a context manager using generator syntax thanks to the contextlib.contextmanager decorator:
>>> import contextlib
>>> @contextlib.contextmanager
... def context_manager(num):
... print('Enter')
... yield num + 1
... print('Exit')
>>> with context_manager(2) as cm:
... # the following instructions are run when the 'yield' point of the context
... # manager is reached.
... # 'cm' will have the value that was yielded
... print('Right in the middle with cm = {}'.format(cm))
Enter
Right in the middle with cm = 3
Exit
>>>
__main__ is the name of the scope in which top-level code executes. A module's name is set equal to __main__ when read from standard input, a script, or from an interactive prompt.
A module can discover whether or not it is running in the main scope by checking its own __name__, which allows a common idiom for conditionally executing code in a module when it is run as a script or with python -m but not when it is imported:
>>> if __name__ == "__main__":
... # execute only if run as a script
... main()
For a package, the same effect can be achieved by including a main.py module, the contents of which will be executed when the module is run with -m
For example we are developing script which is designed to be used as module, we should do:
>>> # Python program to execute function directly
>>> def add(a, b):
... return a+b
...
>>> add(10, 20) # we can test it by calling the function save it as calculate.py
30
>>> # Now if we want to use that module by importing we have to comment out our call,
>>> # Instead we can write like this in calculate.py
>>> if __name__ == "__main__":
... add(3, 5)
...
>>> import calculate
>>> calculate.add(3, 5)
8
Advantages
Every Python module has it's __name__ defined and if this is __main__, it implies that the module is being run standalone by the user and we can do corresponding appropriate actions.
If you import this script as a module in another script, the name is set to the name of the script/module.
Python files can act as either reusable modules, or as standalone programs.
if __name__ == "main”: is used to execute some code only if the file was run directly, and not imported.
The setup script is the centre of all activity in building, distributing, and installing modules using the Distutils. The main purpose of the setup script is to describe your module distribution to the Distutils, so that the various commands that operate on your modules do the right thing.
The setup.py file is at the heart of a Python project. It describes all of the metadata about your project. There a quite a few fields you can add to a project to give it a rich set of metadata describing the project. However, there are only three required fields: name, version, and packages. The name field must be unique if you wish to publish your package on the Python Package Index (PyPI). The version field keeps track of different releases of the project. The packages field describes where you've put the Python source code within your project.
This allows you to easily install Python packages. Often it's enough to write:
python setup.py install
and module will install itself.
Our initial setup.py will also include information about the license and will re-use the README.txt file for the long_description field. This will look like:
Dataclasses are python classes but are suited for storing data objects. This module provides a decorator and functions for automatically adding generated special methods such as __init__() and __repr__() to user-defined classes.
Features
They store data and represent a certain data type. Ex: A number. For people familiar with ORMs, a model instance is a data object. It represents a specific kind of entity. It holds attributes that define or represent the entity.
They can be compared to other objects of the same type. Ex: A number can be greater than, less than, or equal to another number.
Python 3.7 provides a decorator dataclass that is used to convert a class into a dataclass.
python 2.7
>>> class Number:
... def __init__(self, val):
... self.val = val
...
>>> obj = Number(2)
>>> obj.val
2
with dataclass
>>> @dataclass
... class Number:
... val: int
...
>>> obj = Number(2)
>>> obj.val
2
The use of a Virtual Environment is to test python code in encapsulated environments and to also avoid filling the base Python installation with libraries we might use for only one project.
Poetry is a tool for dependency management and packaging in Python. It allows you to declare the libraries your project depends on and it will manage (install/update) them for you.
Pipenv is a tool that aims to bring the best of all packaging worlds (bundler, composer, npm, cargo, yarn, etc.) to the Python world. Windows is a first-class citizen, in our world.
Install pipenv
pip install pipenv
Enter your Project directory and install the Packages for your project
cd my_project
pipenv install <package>
Pipenv will install your package and create a Pipfile for you in your project's directory. The Pipfile is used to track which dependencies your project needs in case you need to re-install them.
Uninstall Packages
pipenv uninstall <package>
Activate the Virtual Environment associated with your Python project
