Cheatsheet-v2
The Zen of Python
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Python Cheatsheet
The Zen of Python
Python Basics
Math Operators
Data Types
String Concatenation and Replication
Variables
Comments
The print() Function
The input() Function
The len() Function
The str(), int(), and float() Functions
Flow Control
Comparison Operators
Boolean evaluation
Boolean Operators
Mixing Boolean and Comparison Operators
if Statements
else Statements
elif Statements
while Loop Statements
break Statements
continue Statements
for Loops and the range() Function
For else statement
Importing Modules
Ending a Program Early with sys.exit()
Functions
Return Values and return Statements
The None Value
Keyword Arguments and print()
Local and Global Scope
The global Statement
Exception Handling
Basic exception handling
Final code in exception handling
Lists
Getting Individual Values in a List with Indexes
Negative Indexes
Getting Sublists with Slices
Getting a List’s Length with len()
Changing Values in a List with Indexes
List Concatenation and List Replication
Removing Values from Lists with del Statements
Using for Loops with Lists
Looping Through Multiple Lists with zip()
The in and not in Operators
The Multiple Assignment Trick
Augmented Assignment Operators
Finding a Value in a List with the index() Method
Adding Values to Lists with the append() and insert() Methods
Removing Values from Lists with remove()
Removing Values from Lists with pop()
Sorting the Values in a List with the sort() Method
Tuple Data Type
Converting Types with the list() and tuple() Functions
Dictionaries and Structuring Data
The keys(), values(), and items() Methods
Checking Whether a Key or Value Exists in a Dictionary
The get() Method
The setdefault() Method
Pretty Printing
Merge two dictionaries
sets
Initializing a set
sets: unordered collections of unique elements
set add() and update()
set remove() and discard()
set union()
set intersection
set difference
set symetric_difference
itertools Module
accumulate()
combinations()
combinations_with_replacement()
count()
cycle()
chain()
compress()
dropwhile()
filterfalse()
groupby()
islice()
permutations()
product()
repeat()
starmap()
takewhile()
tee()
zip_longest()
Comprehensions
List comprehension
Set comprehension
Dict comprehension
Manipulating Strings
Escape Characters
Raw Strings
Multiline Strings with Triple Quotes
Indexing and Slicing Strings
The in and not in Operators with Strings
The in and not in Operators with list
The upper(), lower(), isupper(), and islower() String Methods
The isX String Methods
The startswith() and endswith() String Methods
The join() and split() String Methods
Justifying Text with rjust(), ljust(), and center()
Removing Whitespace with strip(), rstrip(), and lstrip()
Copying and Pasting Strings with the pyperclip Module (need pip install)
String Formatting
% operator
String Formatting (str.format)
Lazy string formatting
Formatted String Literals or f-strings (Python 3.6+)
Template Strings
Regular Expressions
Matching Regex Objects
Grouping with Parentheses
Matching Multiple Groups with the Pipe
Optional Matching with the Question Mark
Matching Zero or More with the Star
Matching One or More with the Plus
Matching Specific Repetitions with Curly Brackets
Greedy and Nongreedy Matching
The findall() Method
Making Your Own Character Classes
The Caret and Dollar Sign Characters
The Wildcard Character
Matching Everything with Dot-Star
Matching Newlines with the Dot Character
Review of Regex Symbols
Case-Insensitive Matching
Substituting Strings with the sub() Method
Managing Complex Regexes
Handling File and Directory Paths
Backslash on Windows and Forward Slash on OS X and Linux
The Current Working Directory
Creating New Folders
Absolute vs. Relative Paths
Handling Absolute and Relative Paths
Checking Path Validity
Finding File Sizes and Folder Contents
Copying Files and Folders
Moving and Renaming Files and Folders
Permanently Deleting Files and Folders
Safe Deletes with the send2trash Module
Walking a Directory Tree
Reading and Writing Files
The File Reading/Writing Process
Opening and reading files with the open() function
Writing to Files
Saving Variables with the shelve Module
Saving Variables with the pprint.pformat() Function
Reading ZIP Files
Extracting from ZIP Files
Creating and Adding to ZIP Files
JSON, YAML and configuration files
JSON
YAML
Anyconfig
Debugging
Raising Exceptions
Getting the Traceback as a String
Assertions
Logging
Logging Levels
Disabling Logging
Logging to a File
Lambda Functions
Ternary Conditional Operator
args and kwargs
Things to Remember(args)
Things to Remember(kwargs)
Context Manager
with statement
Writing your own contextmanager using generator syntax
__main__Top-level script environmentAdvantages
setup.py
Dataclasses
Features
Default values
Type hints
Virtual Environment
virtualenv
poetry
pipenv
anaconda
The Zen of Python
From the PEP 20 -- The Zen of Python:
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.
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Python Basics
Math Operators
From Highest to Lowest precedence:
**
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:
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Data Types
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:
Note: Avoid + operator for string concatenation. Prefer string formatting.
String Replication:
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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 should not be used again in the code.
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Comments
Inline comment:
Multiline comment:
Code with a comment:
Please note the two spaces in front of the comment.
Function docstring:
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The print() Function
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The input() Function
Example Code:
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The len() Function
Evaluates to the integer value of the number of characters in a string:
Note: test of emptiness of strings, lists, dictionary, etc, should not use len, but prefer direct boolean evaluation.
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The str(), int(), and float() Functions
Integer to String or Float:
Float to Integer:
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Flow Control
Comparison Operators
==
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:
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):
YES (even if they are valid Python):
These statements are equivalent:
And these as well:
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Boolean Operators
There are three Boolean operators: and, or, and not.
The and Operator’s Truth Table:
True and True
True
True and False
False
False and True
False
False and False
False
The or Operator’s Truth Table:
True or True
True
True or False
True
False or True
True
False or False
False
The not Operator’s Truth Table:
not True
False
not False
True
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Mixing Boolean and Comparison Operators
You can also use multiple Boolean operators in an expression, along with the comparison operators:
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if Statements
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else Statements
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elif Statements
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while Loop Statements
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break Statements
If the execution reaches a break statement, it immediately exits the while loop’s clause:
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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.
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for Loops and the range() Function
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.
You can even use a negative number for the step argument to make the for loop count down instead of up.
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:
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Importing Modules
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Ending a Program Early with sys.exit()
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Functions
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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.
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The None Value
Note: never compare to None with the == operator. Always use is.
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Keyword Arguments and print()
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Local and Global Scope
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:
There are four rules to tell whether a variable is in a local scope or global scope:
If a variable is being used in the global scope (that is, outside of all functions), then it is always a global variable.
If there is a global statement for that variable in a function, it is a global variable.
Otherwise, if the variable is used in an assignment statement in the function, it is a local variable.
But if the variable is not used in an assignment statement, it is a global variable.
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Exception Handling
Basic exception handling
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Final code in exception handling
Code inside the finally section is always executed, no matter if an exception has been raised or not, and even if an exception is not caught.
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Lists
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Getting Individual Values in a List with Indexes
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Negative Indexes
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Getting Sublists with Slices
Slicing the complete list will perform a copy:
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Getting a List’s Length with len()
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Changing Values in a List with Indexes
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List Concatenation and List Replication
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Removing Values from Lists with del Statements
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Using for Loops with Lists
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Looping Through Multiple Lists with zip()
The in and not in Operators
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The Multiple Assignment Trick
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:
You could type this line of code:
The multiple assignment trick can also be used to swap the values in two variables:
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Augmented Assignment Operators
spam += 1
spam = spam + 1
spam -= 1
spam = spam - 1
spam *= 1
spam = spam * 1
spam /= 1
spam = spam / 1
spam %= 1
spam = spam % 1
Examples:
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Finding a Value in a List with the index() Method
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Adding Values to Lists with the append() and insert() Methods
append():
insert():
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Removing Values from Lists with remove()
If the value appears multiple times in the list, only the first instance of the value will be removed.
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Removing Values from Lists with pop()
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Sorting the Values in a List with the sort() Method
You can also pass True for the reverse keyword argument to have sort() sort the values in reverse order:
If you need to sort the values in regular alphabetical order, pass str. lower for the key keyword argument in the sort() method call:
You can use the built-in function sorted to return a new list:
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Tuple Data Type
The main way that tuples are different from lists is that tuples, like strings, are immutable.
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Converting Types with the list() and tuple() Functions
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Dictionaries and Structuring Data
Example Dictionary:
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The keys(), values(), and items() Methods
values():
keys():
items():
Using the keys(), values(), and items() methods, a for loop can iterate over the keys, values, or key-value pairs in a dictionary, respectively.
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Checking Whether a Key or Value Exists in a Dictionary
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The get() Method
Get has two parameters: key and default value if the key did not exist
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The setdefault() Method
Let's consider this code:
Using setdefault we could write the same code more succinctly:
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Pretty Printing
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Merge two dictionaries
sets
From the Python 3 documentation
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()
When creating an empty set, be sure to not use the curly braces {} or you will get an empty dictionary instead.
sets: unordered collections of unique elements
A set automatically remove all the duplicate values.
And as an unordered data type, they can't be indexed.
set add() and update()
Using the add() method we can add a single element to the set.
And with update(), multiple ones .
set remove() and discard()
Both methods will remove an element from the set, but remove() will raise a key error if the value doesn't exist.
discard() won't raise any errors.
set union()
union() or | will create a new set that contains all the elements from the sets provided.
set intersection
intersection or & will return a set containing only the elements that are common to all of them.
set difference
difference or - will return only the elements that are unique to the first set (invoked set).
set symetric_difference
symetric_difference or ^ will return all the elements that are not common between them.
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itertools Module
The itertools module is a collection of tools intended to be fast and use memory efficiently when handling iterators (like lists or dictionaries).
From the official Python 3.x documentation:
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.
Example:
The operator.mul takes two numbers and multiplies them:
Passing a function is optional:
If no function is designated the items will be summed:
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combinations()
Takes an iterable and a integer. This will create all the unique combination that have r members.
Example:
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combinations_with_replacement()
Just like combinations(), but allows individual elements to be repeated more than once.
Example:
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count()
Makes an iterator that returns evenly spaced values starting with number start.
Example:
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cycle()
This function cycles through an iterator endlessly.
Example:
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.
Example:
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compress()
Filters one iterable with another.
Example:
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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.
Example:
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filterfalse()
Makes an iterator that filters elements from iterable returning only those for which the predicate is False.
Example:
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groupby()
Simply put, this function groups things together.
Example:
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islice()
This function is very much like slices. This allows you to cut out a piece of an iterable.
Example:
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permutations()
Example:
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product()
Creates the cartesian products from a series of iterables.
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repeat()
This function will repeat an object over and over again. Unless, there is a times argument.
Example:
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starmap()
Makes an iterator that computes the function using arguments obtained from the iterable.
Example:
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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.
Example:
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tee()
Return n independent iterators from a single iterable.
Example:
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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.
Example:
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Comprehensions
List comprehension
Set comprehension
Dict comprehension
A List comprehension can be generated from a dictionary:
Manipulating Strings
Escape Characters
\'
Single quote
\"
Double quote
Tab
Newline (line break)
\\
Backslash
Example:
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Raw Strings
A raw string completely ignores all escape characters and prints any backslash that appears in the string.
Note: mostly used for regular expression definition (see re package)
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Multiline Strings with Triple Quotes
To keep a nicer flow in your code, you can use the dedent function from the textwrap standard package.
This generates the same string than before.
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Indexing and Slicing Strings
Slicing:
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The in and not in Operators with Strings
The in and not in Operators with list
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The upper(), lower(), isupper(), and islower() String Methods
upper() and lower():
isupper() and islower():
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The isX String Methods
isalpha() returns True if the string consists only of letters and is not blank.
isalnum() returns True if the string consists only of letters and numbers and is not blank.
isdecimal() returns True if the string consists only of numeric characters and is not blank.
isspace() returns True if the string consists only of spaces,tabs, and new-lines and is not blank.
istitle() returns True if the string consists only of words that begin with an uppercase letter followed by only lowercase letters.
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The startswith() and endswith() String Methods
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The join() and split() String Methods
join():
split():
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Justifying Text with rjust(), ljust(), and center()
rjust() and ljust():
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:
center():
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Removing Whitespace with strip(), rstrip(), and lstrip()
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Copying and Pasting Strings with the pyperclip Module (need pip install)
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String Formatting
% operator
We can use the %x format specifier to convert an int value to a string:
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.
The official Python 3.x documentation recommend str.format over the % operator:
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:
Over:
Or:
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Formatted String Literals or f-strings (Python 3.6+)
It is even possible to do inline arithmetic with it:
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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.
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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 aMatchobject.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:
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Matching Regex Objects
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Grouping with Parentheses
To retrieve all the groups at once: use the groups() method—note the plural form for the name.
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Matching Multiple Groups with the Pipe
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'.
You can also use the pipe to match one of several patterns as part of your regex:
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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.
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Matching Zero or More with the Star
The * (called the star or asterisk) means “match zero or more”—the group that precedes the star can occur any number of times in the text.
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Matching One or More with the Plus
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:
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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'.
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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.
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The findall() Method
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.
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:
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The Caret and Dollar Sign Characters
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':
The r'\d$' regular expression string matches strings that end with a numeric character from 0 to 9:
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The Wildcard Character
The . (or dot) character in a regular expression is called a wildcard and will match any character except for a newline:
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Matching Everything with Dot-Star
The dot-star uses greedy mode: It will always try to match as much text as possible. To match any and all text in a nongreedy fashion, use the dot, star, and question mark (.*?). The question mark tells Python to match in a nongreedy way:
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Matching Newlines with the Dot Character
The dot-star will match everything except a newline. By passing re.DOTALL as the second argument to re.compile(), you can make the dot character match all characters, including the newline character:
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Review of Regex Symbols
?
zero or one of the preceding group.
*
zero or more of the preceding group.
+
one or more of the preceding group.
{n}
exactly n of the preceding group.
{n,}
n or more of the preceding group.
{,m}
0 to m of the preceding group.
{n,m}
at least n and at most m of the preceding p.
{n,m}? or *? or +?
performs a nongreedy match of the preceding p.
^spam
means the string must begin with spam.
spam$
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():
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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:
Another example:
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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:
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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:
And using pathlib on *nix:
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:
Using pathlib on *nix:
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The Current Working Directory
Using os on Windows:
Using pathlib on *nix:
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Creating New Folders
Using os on Windows:
Using pathlib on *nix:
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:
And all is good :)
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Absolute vs. Relative Paths
There are two ways to specify a file path.
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:
Using pathlib on *nix:
You can extract an absolute path with both os.path and pathlib
Using os.path on *nix:
Using pathlib on *nix:
You can get a relative path from a starting path to another path.
Using os.path on *nix:
Using pathlib on *nix:
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Checking Path Validity
Checking if a file/directory exists:
Using os.path on *nix:
Using pathlib on *nix:
Checking if a path is a file:
Using os.path on *nix:
Using pathlib on *nix:
Checking if a path is a directory:
Using os.path on *nix:
Using pathlib on *nix:
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Finding File Sizes and Folder Contents
Getting a file's size in bytes:
Using os.path on Windows:
Using pathlib on *nix:
Listing directory contents using os.listdir on Windows:
Listing directory contents using pathlib on *nix:
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:
Using pathlib on *nix:
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Copying Files and Folders
The shutil module provides functions for copying files, as well as entire folders.
While shutil.copy() will copy a single file, shutil.copytree() will copy an entire folder and every folder and file contained in it:
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Moving and Renaming Files and Folders
The destination path can also specify a filename. In the following example, the source file is moved and renamed:
If there is no eggs folder, then move() will rename bacon.txt to a file named eggs.
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Permanently Deleting Files and 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.
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Walking a Directory Tree
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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
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Writing to Files
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Saving Variables with the shelve Module
To save variables:
To open and read variables:
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.
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Saving Variables with the pprint.pformat() Function
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Reading ZIP Files
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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.
The extract() method for ZipFile objects will extract a single file from the ZIP file. Continue the interactive shell example:
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Creating and Adding to ZIP Files
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:
Write a JSON file with:
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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:
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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.
Install it with:
Usage:
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Debugging
Raising Exceptions
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
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.
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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.
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.
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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
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.
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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.
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.
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Logging to a File
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:
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Lambda Functions
This function:
Is equivalent to the lambda function:
It's not even need to bind it to a name like add before:
Like regular nested functions, lambdas also work as lexical closures:
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.
Example:
Ternary operators can be chained:
The code above is equivalent to:
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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.
Similarly, you use **kwargs when you have an indefinite number of keyword arguments.
Things to Remember(args)
Functions can accept a variable number of positional arguments by using
*argsin 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
*argscan 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:
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:
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__main__ Top-level script environment
__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:
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:
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:
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:
Find more information visit http://docs.python.org/install/index.html.
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Dataclasses
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
with dataclass
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Default values
It is easy to add default values to the fields of your data class.
Type hints
It is mandatory to define the data type in dataclass. However, If you don't want specify the datatype then, use typing.Any.
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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.
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virtualenv
Install virtualenv
Install virtualenvwrapper-win (Windows)
Usage:
Make a Virtual Environment
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:
Deactivate
To move onto something else in the command line type ‘deactivate’ to deactivate your environment.
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
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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.
Install Poetry
Create a new project
This will create a my-project directory:
The pyproject.toml file will orchestrate your project and its dependencies:
Packages
To add dependencies to your project, you can specify them in the tool.poetry.dependencies section:
Also, instead of modifying the pyproject.toml file by hand, you can use the add command and it will automatically find a suitable version constraint.
To install the dependencies listed in the pyproject.toml:
To remove dependencies:
For more information, check the documentation.
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pipenv
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
Enter your Project directory and install the Packages for your project
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
Activate the Virtual Environment associated with your Python project
Exit the Virtual Environment
Find more information and a video in docs.pipenv.org.
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anaconda
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
To use the Virtual Environment, activate it by:
Anything installed now will be specific to the project HelloWorld
Exit the Virtual Environment
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Python Basics
Math Operators
From Highest to Lowest precedence:
**
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:
Data Types
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'
String Concatenation and Replication
String concatenation:
Note: Avoid + operator for string concatenation. Prefer string formatting.
String Replication:
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 should not be used again in the code.
Comments
Inline comment:
Multiline comment:
Code with a comment:
Please note the two spaces in front of the comment.
Function docstring:
The print() Function
The input() Function
Example Code:
The len() Function
Evaluates to the integer value of the number of characters in a string:
Note: test of emptiness of strings, lists, dictionary, etc, should not use len, but prefer direct boolean evaluation.
The str(), int(), and float() Functions
Integer to String or Float:
Float to Integer:
Flow Control
Comparison Operators
==
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:
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):
YES (even if they are valid Python):
These statements are equivalent:
And these as well:
Boolean Operators
There are three Boolean operators: and, or, and not.
The and Operator’s Truth Table:
True and True
True
True and False
False
False and True
False
False and False
False
The or Operator’s Truth Table:
True or True
True
True or False
True
False or True
True
False or False
False
The not Operator’s Truth Table:
not True
False
not False
True
Mixing Boolean and Comparison Operators
You can also use multiple Boolean operators in an expression, along with the comparison operators:
if Statements
else Statements
elif Statements
while Loop Statements
break Statements
If the execution reaches a break statement, it immediately exits the while loop’s clause:
continue Statements
When the program execution reaches a continue statement, the program execution immediately jumps back to the start of the loop.
for Loops and the range() Function
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.
You can even use a negative number for the step argument to make the for loop count down instead of up.
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:
Importing Modules
Ending a Program Early with sys.exit()
Functions
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.
The None Value
Note: never compare to None with the == operator. Always use is.
Keyword Arguments and print()
Local and Global Scope
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.
The global Statement
If you need to modify a global variable from within a function, use the global statement:
There are four rules to tell whether a variable is in a local scope or global scope:
If a variable is being used in the global scope (that is, outside of all functions), then it is always a global variable.
If there is a global statement for that variable in a function, it is a global variable.
Otherwise, if the variable is used in an assignment statement in the function, it is a local variable.
But if the variable is not used in an assignment statement, it is a global variable.
Exception Handling
Basic exception handling
Final code in exception handling
Code inside the finally section is always executed, no matter if an exception has been raised or not, and even if an exception is not caught.
Lists
Getting Individual Values in a List with Indexes
Negative Indexes
Getting Sublists with Slices
Slicing the complete list will perform a copy:
Getting a List’s Length with len()
Changing Values in a List with Indexes
List Concatenation and List Replication
Removing Values from Lists with del Statements
Using for Loops with Lists
Looping Through Multiple Lists with zip()
The in and not in Operators
The Multiple Assignment Trick
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:
You could type this line of code:
The multiple assignment trick can also be used to swap the values in two variables:
Augmented Assignment Operators
spam += 1
spam = spam + 1
spam -= 1
spam = spam - 1
spam *= 1
spam = spam * 1
spam /= 1
spam = spam / 1
spam %= 1
spam = spam % 1
Examples:
Finding a Value in a List with the index() Method
Adding Values to Lists with the append() and insert() Methods
append():
insert():
Removing Values from Lists with remove()
If the value appears multiple times in the list, only the first instance of the value will be removed.
Removing Values from Lists with pop()
Sorting the Values in a List with the sort() Method
You can also pass True for the reverse keyword argument to have sort() sort the values in reverse order:
If you need to sort the values in regular alphabetical order, pass str. lower for the key keyword argument in the sort() method call:
You can use the built-in function sorted to return a new list:
Tuple Data Type
The main way that tuples are different from lists is that tuples, like strings, are immutable.
Converting Types with the list() and tuple() Functions
Dictionaries and Structuring Data
Example Dictionary:
The keys(), values(), and items() Methods
values():
keys():
items():
Using the keys(), values(), and items() methods, a for loop can iterate over the keys, values, or key-value pairs in a dictionary, respectively.
Checking Whether a Key or Value Exists in a Dictionary
The get() Method
Get has two parameters: key and default value if the key did not exist
The setdefault() Method
Let's consider this code:
Using setdefault we could write the same code more succinctly:
Pretty Printing
Merge two dictionaries
sets
From the Python 3 documentation
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()
When creating an empty set, be sure to not use the curly braces {} or you will get an empty dictionary instead.
sets: unordered collections of unique elements
A set automatically remove all the duplicate values.
And as an unordered data type, they can't be indexed.
set add() and update()
Using the add() method we can add a single element to the set.
And with update(), multiple ones .
set remove() and discard()
Both methods will remove an element from the set, but remove() will raise a key error if the value doesn't exist.
discard() won't raise any errors.
set union()
union() or | will create a new set that contains all the elements from the sets provided.
set intersection
intersection or & will return a set containing only the elements that are common to all of them.
set difference
difference or - will return only the elements that are unique to the first set (invoked set).
set symetric_difference
symetric_difference or ^ will return all the elements that are not common between them.
itertools Module
The itertools module is a collection of tools intended to be fast and use memory efficiently when handling iterators (like lists or dictionaries).
From the official Python 3.x documentation:
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.
accumulate()
Makes an iterator that returns the results of a function.
Example:
The operator.mul takes two numbers and multiplies them:
Passing a function is optional:
If no function is designated the items will be summed:
combinations()
Takes an iterable and a integer. This will create all the unique combination that have r members.
Example:
combinations_with_replacement()
Just like combinations(), but allows individual elements to be repeated more than once.
Example:
count()
Makes an iterator that returns evenly spaced values starting with number start.
Example:
cycle()
This function cycles through an iterator endlessly.
Example:
When reached the end of the iterable it start over again from the beginning.
chain()
Take a series of iterables and return them as one long iterable.
Example:
compress()
Filters one iterable with another.
Example:
dropwhile()
Make an iterator that drops elements from the iterable as long as the predicate is true; afterwards, returns every element.
Example:
filterfalse()
Makes an iterator that filters elements from iterable returning only those for which the predicate is False.
Example:
groupby()
Simply put, this function groups things together.
Example:
islice()
This function is very much like slices. This allows you to cut out a piece of an iterable.
Example:
permutations()
Example:
product()
Creates the cartesian products from a series of iterables.
repeat()
This function will repeat an object over and over again. Unless, there is a times argument.
Example:
starmap()
Makes an iterator that computes the function using arguments obtained from the iterable.
Example:
takewhile()
The opposite of dropwhile(). Makes an iterator and returns elements from the iterable as long as the predicate is true.
Example:
tee()
Return n independent iterators from a single iterable.
Example:
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.
Example:
Comprehensions
List comprehension
Set comprehension
Dict comprehension
A List comprehension can be generated from a dictionary:
Manipulating Strings
Escape Characters
\'
Single quote
\"
Double quote
Tab
Newline (line break)
\\
Backslash
Example:
Raw Strings
A raw string completely ignores all escape characters and prints any backslash that appears in the string.
Note: mostly used for regular expression definition (see re package)
Multiline Strings with Triple Quotes
To keep a nicer flow in your code, you can use the dedent function from the textwrap standard package.
This generates the same string than before.
Indexing and Slicing Strings
Slicing:
The in and not in Operators with Strings
The in and not in Operators with list
The upper(), lower(), isupper(), and islower() String Methods
upper() and lower():
isupper() and islower():
The isX String Methods
isalpha() returns True if the string consists only of letters and is not blank.
isalnum() returns True if the string consists only of letters and numbers and is not blank.
isdecimal() returns True if the string consists only of numeric characters and is not blank.
isspace() returns True if the string consists only of spaces,tabs, and new-lines and is not blank.
istitle() returns True if the string consists only of words that begin with an uppercase letter followed by only lowercase letters.
The startswith() and endswith() String Methods
The join() and split() String Methods
join():
split():
Justifying Text with rjust(), ljust(), and center()
rjust() and ljust():
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:
center():
Removing Whitespace with strip(), rstrip(), and lstrip()
Copying and Pasting Strings with the pyperclip Module (need pip install)
String Formatting
% operator
We can use the %x format specifier to convert an int value to a string:
Note: For new code, using str.format or f-strings (Python 3.6+) is strongly recommended over the % operator.
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.
The official Python 3.x documentation recommend str.format over the % operator:
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.
Lazy string formatting
You would only use %s string formatting on functions that can do lazy parameters evaluation, the most common being logging:
Prefer:
Over:
Or:
Formatted String Literals or f-strings (Python 3.6+)
It is even possible to do inline arithmetic with it:
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.
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 aMatchobject.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:
Matching Regex Objects
Grouping with Parentheses
To retrieve all the groups at once: use the groups() method—note the plural form for the name.
Matching Multiple Groups with the Pipe
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'.
You can also use the pipe to match one of several patterns as part of your regex:
Optional Matching with the Question Mark
The ? character flags the group that precedes it as an optional part of the pattern.
Matching Zero or More with the Star
The * (called the star or asterisk) means “match zero or more”—the group that precedes the star can occur any number of times in the text.
Matching One or More with the Plus
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:
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'.
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.
The findall() Method
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.
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')].
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:
The Caret and Dollar Sign Characters
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':
The r'\d$' regular expression string matches strings that end with a numeric character from 0 to 9:
The Wildcard Character
The . (or dot) character in a regular expression is called a wildcard and will match any character except for a newline:
Matching Everything with Dot-Star
The dot-star uses greedy mode: It will always try to match as much text as possible. To match any and all text in a nongreedy fashion, use the dot, star, and question mark (.*?). The question mark tells Python to match in a nongreedy way:
Matching Newlines with the Dot Character
The dot-star will match everything except a newline. By passing re.DOTALL as the second argument to re.compile(), you can make the dot character match all characters, including the newline character:
Review of Regex Symbols
?
zero or one of the preceding group.
*
zero or more of the preceding group.
+
one or more of the preceding group.
{n}
exactly n of the preceding group.
{n,}
n or more of the preceding group.
{,m}
0 to m of the preceding group.
{n,m}
at least n and at most m of the preceding p.
{n,m}? or *? or +?
performs a nongreedy match of the preceding p.
^spam
means the string must begin with spam.
spam$
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.
Case-Insensitive Matching
To make your regex case-insensitive, you can pass re.IGNORECASE or re.I as a second argument to re.compile():
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:
Another example:
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:
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.
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:
And using pathlib on *nix:
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:
Using pathlib on *nix:
The Current Working Directory
Using os on Windows:
Using pathlib on *nix:
Creating New Folders
Using os on Windows:
Using pathlib on *nix:
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:
And all is good :)
Absolute vs. Relative Paths
There are two ways to specify a file path.
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.”
Handling Absolute and Relative Paths
To see if a path is an absolute path:
Using os.path on *nix:
Using pathlib on *nix:
You can extract an absolute path with both os.path and pathlib
Using os.path on *nix:
Using pathlib on *nix:
You can get a relative path from a starting path to another path.
Using os.path on *nix:
Using pathlib on *nix:
Checking Path Validity
Checking if a file/directory exists:
Using os.path on *nix:
Using pathlib on *nix:
Checking if a path is a file:
Using os.path on *nix:
Using pathlib on *nix:
Checking if a path is a directory:
Using os.path on *nix:
Using pathlib on *nix:
Finding File Sizes and Folder Contents
Getting a file's size in bytes:
Using os.path on Windows:
Using pathlib on *nix:
Listing directory contents using os.listdir on Windows:
Listing directory contents using pathlib on *nix:
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:
Using pathlib on *nix:
Copying Files and Folders
The shutil module provides functions for copying files, as well as entire folders.
While shutil.copy() will copy a single file, shutil.copytree() will copy an entire folder and every folder and file contained in it:
Moving and Renaming Files and Folders
The destination path can also specify a filename. In the following example, the source file is moved and renamed:
If there is no eggs folder, then move() will rename bacon.txt to a file named eggs.
Permanently Deleting Files and 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.
Safe Deletes with the send2trash Module
You can install this module by running pip install send2trash from a Terminal window.
Walking a Directory Tree
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
Writing to Files
Saving Variables with the shelve Module
To save variables:
To open and read variables:
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.
Saving Variables with the pprint.pformat() Function
Reading ZIP Files
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.
The extract() method for ZipFile objects will extract a single file from the ZIP file. Continue the interactive shell example:
Creating and Adding to ZIP Files
This code will create a new ZIP file named new.zip that has the compressed contents of spam.txt.
JSON, YAML and configuration files
JSON
Open a JSON file with:
Write a JSON file with:
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:
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.
Install it with:
Usage:
Debugging
Raising Exceptions
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
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.
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.
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.
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
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.
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.
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.
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.
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.
Logging to a File
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:
Lambda Functions
This function:
Is equivalent to the lambda function:
It's not even need to bind it to a name like add before:
Like regular nested functions, lambdas also work as lexical closures:
Note: lambda can only evaluate an expression, like a single line of code.
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.
Example:
Ternary operators can be chained:
The code above is equivalent to:
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.
Similarly, you use **kwargs when you have an indefinite number of keyword arguments.
Things to Remember(args)
Functions can accept a variable number of positional arguments by using
*argsin 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
*argscan 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.
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:
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:
__main__ Top-level script environment
__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:
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:
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.
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:
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:
Find more information visit http://docs.python.org/install/index.html.
Dataclasses
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
with dataclass
Default values
It is easy to add default values to the fields of your data class.
Type hints
It is mandatory to define the data type in dataclass. However, If you don't want specify the datatype then, use typing.Any.
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.
virtualenv
Install virtualenv
Install virtualenvwrapper-win (Windows)
Usage:
Make a Virtual Environment
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:
Deactivate
To move onto something else in the command line type ‘deactivate’ to deactivate your environment.
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
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.
Install Poetry
Create a new project
This will create a my-project directory:
The pyproject.toml file will orchestrate your project and its dependencies:
Packages
To add dependencies to your project, you can specify them in the tool.poetry.dependencies section:
Also, instead of modifying the pyproject.toml file by hand, you can use the add command and it will automatically find a suitable version constraint.
To install the dependencies listed in the pyproject.toml:
To remove dependencies:
For more information, check the documentation.
pipenv
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
Enter your Project directory and install the Packages for your project
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
Activate the Virtual Environment associated with your Python project
Exit the Virtual Environment
Find more information and a video in docs.pipenv.org.
anaconda
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
To use the Virtual Environment, activate it by:
Anything installed now will be specific to the project HelloWorld
Exit the Virtual Environment
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