Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

https://github.com/yungnickyoung/Python-3-Cheatsheet

Python 3 notes cheatsheet, focusing on fundamentals and useful interview tips
https://github.com/yungnickyoung/Python-3-Cheatsheet

cheatsheet python python-3

Last synced: 12 days ago
JSON representation

Python 3 notes cheatsheet, focusing on fundamentals and useful interview tips

Lists

README 

        
## Table of Contents

- [Built-in Types](#built-in-types)

  - [Boolean Types](#boolean-types)

  - [Numeric Types](#numeric-types)

  - [Sequence Types](#sequence-types)

    - [Mutable Sequences](#mutable-sequences)

    - [Immutable Sequences](#immutable-sequences)

  - [Set Types](#set-types)

  - [Mapping Types](#mapping-types)

- [Dictionaries](#dictionaries)

  - [Iteration](#dictionary-iteration)

  - [Sorting](#dictionary-sorting)

- [Lists](#lists)

  - [Comprehensions](#list-comprehensions)

  - [Initialization](#list-initialization)

  - [Reversal](#list-reversal)

  - [Sorting](#list-sorting)

- [Strings](#strings)

  - [From List](#from-list)

  - [Python String Constants](#string-constants)

  - [`isalnum()`](#isalnum)

  - [`split()`](#split)

  - [`strip()`](#strip)

  - [`str()` vs `repr()`](#str-vs-repr)

- [Iterators](#iterators)

  - [Iterator vs Iterable](#iterator-vs-iterable)

  - [How for loop actually works](#how-for-loop-actually-works)

  - [Creating an Iterator](#creating-an-iterator)

- [Functional Iteration](#functional-iteration)

  - [`map()`](#map)

  - [`filter()`](#filter)

  - [`reduce()`](#reduce)

- [Decorators](#decorators)

  - [`@classmethod`](#classmethod)

  - [`@staticmethod`](#staticmethod)

  - [`@property`](#property)

- [Generators](#generators)

  - [Using `yield`](#using-yield)

  - [Generator Expressions](#generator-expressions)

- [Other Useful Built-in Functions](#other-useful-built-in-functions)

  - [`abs()`](#abs)

  - [`any()`](#any)

  - [`all()`](#all)

  - [`chr()`](#chr)

  - [`enumerate()`](#enumerate)

  - [`input()`](#input)

  - [`isinstance()`](#isinstance)

  - [`len()`](#len)

  - [`max()`](#max)

  - [`min()`](#min)

  - [`ord()`](#ord)

  - [`pow()`](#pow)

  - [`type()`](#type) 

- [Common Gotchas](#common-gotchas)

  - [Nested List Initialization](#nested-list-initialization)

  - [Mutable Default Arguments](#mutable-default-arguments)



[▲ TOP](#table-of-contents)

## Built-in Types

In this section I have included information on the more basic built-in types. For information on more specialized built-in types, check out the [Python documentation](https://docs.python.org/3/library/stdtypes.html)



### Boolean Types

```python3

class 'bool'

```



By default, an object is considered `True` unless its class defines either a `__bool__()` method that returns `False` or a `__len__()` method that returns zero. Here are most of the built-in objects considered `False`:

- constants defined to be false: `None` and `False`

- zero of any numeric type: `0`, `0.0`, `0j`, `Decimal(0)`, `Fraction(0, 1)`

- empty sequences and collections: `''`, `()`, `[]`, `{}`, `set()`, `range(0)`



### Numeric Types

```python3

class 'int'

class 'float'

class 'complex'

```



Integers have unlimited precision. Floating point numbers are usually implemented using `double` in C, and are therefore system-dependent. Complex numbers have a real and imaginary part, which can be accessed using `z.real` and `z.imag`, respectively. Complex numbers must include `j` appended to a numeric literal (`0j` is acceptable for when you want a `complex` value with no imaginary part).



The standard libarary includes additional numeric types, [Fraction](https://docs.python.org/3/library/fractions.html#module-fractions)s which hold rationals, and [Decimal](https://docs.python.org/3/library/decimal.html#module-decimal)s which hold floating-point numbers with user-definable precision.



### Sequence Types

Immutable sequences have support for the `hash()` built-in, while mutable sequences do not. This means that immutable sequences can be used as `dict` keys or stored in `set` and `frozenset` instances, while mutable sequences cannot.



#### Mutable Sequences

```python3

class 'list'

class 'bytearray

```



`bytearray` objects are a mutable counterpart to `bytes` objects.



#### Immutable Sequences

```python3

class 'tuple'

class 'range'

class 'str'

class 'bytes'

```



`bytes` objects are sequences of single bytes. The syntax for `bytes` literals is largely the same as that for string literals, except that a `b` prefix is added:  

- Single quotes: `b'still allows embedded "double" quotes'`

- Double quotes: `b"still allows embedded 'single' quotes"`

- Triple quotes: `b'''3 single quotes'''`, `b"""3 double quotes"""`



Only ASCII chars are permitted in `bytes` literals.  

`bytes` objects actually behave like immutable sequences of integers, with each value restricted to `0 <= x < 256`.



`bytes` objects can be created in several ways:

- A zero-filled bytes object of a specific length: `bytes(10)`

- From an iterable of integers: `bytes(range(20))`

- Copying existing binary data via the buffer protocol: `bytes(obj)`



### Set Types

```python3

class 'set'

class 'frozenset'

```



`set` is mutable, while `frozenset` is immutable.  

Note that since `frozenset` is immutable, it must be entirely populated at the moment of construction. It cannot use the literal curly brace syntax that ordinary `set` uses, as that syntax is reserved for `set`.



Instead, use `frozenset([iterable])`.



### Mapping Types

```python3

class 'dict'

```



See the [Dictionaries](#dictionaries) section for more info.



[▲ TOP](#table-of-contents)

## Dictionaries

### Dictionary Iteration

Get w/ default value if key not in dict:

```python

my_dict[k] = my_dict.get(k, 0) + 1; # get retrieves value for k, or 0 if k not in dict

```



Iterating a dict iterates only the keys:

```python

for k in my_dict:  # k will be each key, not each key-value pair

    ...

```



Testing membership: `if k in dict: ...`



To get actual key-value pairs at the same time:

```python

for k,v in my_dict.items():

    ...

```

applies to comprehensions as well: `new_d = {k: v+1 for k,v in d.items()}`



### Dictionary Sorting

It is not possible to sort a dictionary, only to get a representation of a dictionary that is sorted. Dictionaries are inherently orderless, but other types, such as lists and tuples, are not. So you need an ordered data type to represent sorted values, which will be a list—probably a list of tuples.  

- `sorted(d.items())`

  - sorted list of key-value pairs by key

  - by value: `sorted(d.items(), key=lambda x: x[1]`

- `sorted(d)`

  - sorted list of keys only

  - sorted list of keys by value: `sorted(d, key=lambda x: d[x])`



[▲ TOP](#table-of-contents)

## Lists

### List Comprehensions

General Syntax:

```python

[ for item in list if conditional]

```

is equivalent to:

```python

for item in list:

    if conditional:

        

```



Note how the order of the `for` and `if` statements remains the same.

For example, 

```python

for row in grid:

    for x in row:

        

```

is the same as

```python

[ for row in grid for x in row]

```



### List Initialization

Can use comprehensions:

```python

my_list = [i for i in range(10)] # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

```

2-D list (list of lists):

```python

my_list = [[] for i in range(3)] # [[], [], []]

```

This is useful for a "visited" grid of some kind (common in Dynamic Programming problems):

```python

visited = [[0 for i in range(len(grid[0]))] for j in range(len(grid))]

```

*BE CAREFUL* when initializing a matrix.

Do this:

```python

my_list = [[None] * n for i in range(n)]

```

**NOT** this:

```python

my_list = [[None] * n] * n

```

The latter method makes copies of the **reference** to the original list, thus any modification to one row will change the other rows in the same way. The first method does not do this.



A list can be created from a string using `list(my_str)`

We can apply a filter as well:

```python

my_list = list(c for c in my_str if c not in ('a', 'c', 'e'))

```



### List Reversal

 - `my_list[::-1]`

   - returns copy of list in reverse

 - `reversed(my_list)`

   - returns an iterator on the list in reverse

   - can turn into a list via `list(reversed(my_list))`

 - `my_list.reverse()`

   - actually modifies the list

 

### List Sorting

 - `sorted(my_list)`

   - returns copy of sorted list

 - `my_list.sort()`

   - actually modifies the list

   

 By default, these methods will sort the list in ascending order.

 For descending order, we can supply the arg `reverse=True` to either of the aforementioned methods.

 

 We can also override the key for sorting by supplying the `key` arg.

 For example, if we have a list of tuples and we want to use the second item as the key:

 ```python

 list1 = [(1, 2), (3, 3), (4, 1)] 

 list1.sort(key=lambda x: x[1]) # list1 is now [(4, 1), (1, 2), (3, 3)]

 ```

 Additionally, if we want to sort in descending order:

 ```python

 list1.sort(key=lambda x: x[1], reverse=True) # list1 is now [(3, 3), (1, 2), (4, 1)]

 ```

 

 When using `sorted()` it works the same, except we supply the list as the first arg:

 ```python

 list2 = sorted(list1, key=lambda x: x[1], reverse=True)

 ```



[▲ TOP](#table-of-contents)

## Strings

### From List

```python

my_list = ['te', 's', 't', '1', '2', '3', '_']

s = ''.join(my_list) # "test123_"

s2 = ''.join(c for c in my_list if c.isalnum()) # "test123"

```



### String Constants

Python has a lot of useful string constants. A few of them are shown below.

For a complete list, see the [documentation](https://docs.python.org/3.7/library/string.html)

 - `string.ascii_letters`

 - `string.digits`

 - `string.whitespace`

 

 e.g. `if d in string.digits: ...`

 

### `isalnum()`

Returns `True` if a string consists only of alphanumeric characters.

```python

s = "test123"

s.isalnum() # True

```



### `split()`

Return a list of the words in the string, using `sep` as the delimiter string. If `maxsplit` is given, at most `maxsplit` splits are done (thus, the list will have at most `maxsplit + 1` elements). If `maxsplit` is not specified or `-1`, then there is no limit on the number of splits (all possible splits are made).  

Usage:

```python3

str.split(sep=None, maxsplit=-1)

```



```python3

'1,2,3'.split(',')             # ['1', '2', '3']

'1,2,3'.split(',', maxsplit=1) # ['1', '2,3']

'1,2,,3,'.split(',')           # ['1', '2', '', '3', '']

```



### `strip()`

Returns copy of string without surrounding whitespace, if any.

```python

s = "   test "

s.strip() # "test"

```



### `str()` vs `repr()`

See [this GeeksForGeeks article](https://www.geeksforgeeks.org/str-vs-repr-in-python/) for more info.



[▲ TOP](#table-of-contents)

## Iterators

In Python, an iterator is an object with a countable number of values that can be iterated upon.

An iterator is an object which implements the iterator protocol, consisting of `__iter__()` and `__next__()`.  

The `__iter__()` method returns an iterator on the object, and the `__next__()` method gets the next item using the iterator, or raises a `StopIteration` exception if the end of the iterable is reached.



### Iterator vs Iterable

Lists, tuples, dictionaries, and sets are all iterable objects. They are iterable *containers* which you can get an iterator from.

All these objects have a `__iter__()` method which is used to get an iterator:

```python3

mytuple = ("apple", "banana", "cherry")

myit = iter(mytuple)



print(next(myit)) # apple

print(next(myit)) # banana

print(next(myit)) # cherry

print(next(myit)) # raises StopIteration exception

```

Note -- `next(obj)` is the same as `obj.__next__()`.



### How for loop actually works

The `for` loop can iterate any iterable.  

The `for` loop in Python is actually implemented like so:

```python3

iter_obj = iter(iterable) # create iterator object from iterable



# infinite loop

while True:

    try:

        element = next(iter_obj) # get the next item

        # do something with element

    except StopIteration:

        break

```

So, internally, the `for` loop creates an iterator object by calling `iter()` on the iterable, and then repeatedly calling `next()` until a `StopIteration` exception is raised.



### Creating an Iterator

Here is an example of an iterator that will give us the next power of two in each iteration.

```python3

class PowTwo:

    """Class to implement an iterator of powers of two"""



    def __init__(self, max = 0):

        self.max = max



    def __iter__(self):

        self.n = 0

        return self



    def __next__(self):

        if self.n <= self.max:

            result = 2 ** self.n

            self.n += 1

            return result

        else:

            raise StopIteration

```



Now we can use it as follows:

```python3

>>> a = PowTwo(4)

>>> i = iter(a)

>>> next(i)

1

>>> next(i)

2

>>> next(i)

4

>>> next(i)

8

>>> next(i)

16

>>> next(i)

Traceback (most recent call last):

...

StopIteration

```

Or, alternatively, using a `for` loop:

```python3

>>> for i in PowTwo(5):

...     print(i)

...     

1

2

4

8

16

32

```



[▲ TOP](#table-of-contents)

## Functional Iteration

For some good explanations and examples for the following functions, see [here](http://book.pythontips.com/en/latest/map_filter.html).



Note that `map()` and `filter()` both return iterators, so if you want a list, you need to use `list()` on the output. However, this is typically better accomplished with list comprehensions or `for` loops for the sake of readability.

  

### `map()`

`map()` applies a function to all the items in a list.

```python

map(function_to_apply, list_of_inputs)

```

  

For example, the following code:

```python

items = [1, 2, 3, 4, 5]

squared = []

for i in items:

    squared.append(i**2)

```

can be accomplished more easily with `map()`:

```python3

items = [1, 2, 3, 4, 5]

squared = list(map(lambda x: x**2, items))

```



### `filter()`

`filter()` creates a list of elements for which a function returns `True`.

  

Here's an example:

```python3

number_list = range(-5, 5)

less_than_zero = list(filter(lambda x: x < 0, number_list))

print(less_than_zero) # [-5, -4, -3, -2, -1]

```



### `reduce()`

`reduce()` is used to perform a rolling computation on a list.



Here's an example:

```python3

from functools import reduce

number_list = [1, 2, 3, 4]

product = reduce((lambda x, y: x * y), number_list) # output: 24

```



Often times, an explicit `for` loop is more readable than using `reduce()`.

But if you're trying to flex in an interview, and the problem calls for it, it could be a nice way to subtly show your understanding of functional programming.



[▲ TOP](#table-of-contents)

## Decorators

A [decorator](https://www.scaler.com/topics/python/python-decorators/) is a function returning another function, usually applied as a function transformation using the `@wrapper` syntax. This syntax is merely syntactic sugar.



The following two function definitions are semantically equivalent:

```python3

def f(...):

    ...

f = staticmethod(f)



@staticmethod

def f(...):

    ...

```



### @classmethod

Transform a method into a class method. A class method receives the class as implicit first argument, just like how an instance method receives the instance. To declare a class method:

```python3

class C:

    @classmethod

    def f(cls, arg1, arg2, ...):

        ...

```



A class method can be called either on the class (like `C.f()`) or on an instance (like `C().f()`). The instance is ignored except for its class. If a class method is called for a derived class, the derived class object is passed as the implied first argument.



Note that class methods are not the same as C++ or Java static methods. If you want those, see [`@staticmethod`](#staticmethod).



### @staticmethod

Transform a method into a static method. A static method does not receive an implicit first argument. To declare a static method:

```python3

class C:

    @staticmethod

    def f(arg1, arg2, ...):

        ...

```



A static method can be called either on the class (like `C.f()`) or on an instance (like `C().f()`). Static methods in Python are similar to those found in Java or C++.



### @property

Return a property attribute.

Usage:

```python3

property(fget=None, fset=None, fdel=None, doc=None)

```

`fget` is a function for getting an attribute value. `fset` is a function for setting an attribute value. `fdel` is a function for deleting an attribute value. `doc` creates a docstring for the attribute.



The following is a typical use case for defining a managed attribute `x`:

```python3

class C:

    def __init__(self):

        self._x = None



    def getx(self):

        return self._x



    def setx(self, value):

        self._x = value



    def delx(self):

        del self._x



    x = property(getx, setx, delx, "I'm the 'x' property.")

```

Or, equivalently:

```python3

class C:

    def __init__(self):

        self._x = None



    @property

    def x(self):

        """I'm the 'x' property."""

        return self._x



    @x.setter

    def x(self, value):

        self._x = value



    @x.deleter

    def x(self):

        del self._x

```    



If `c` is an instance of `C`, then `c.x` will invoke the getter; `c.x = value` will invoke the setter; and `del c.x` the deleter.



If `doc` is not provided, the property will copy `fget`'s docstring, if it exists. Thus, it is straightforward to create read-only properties with the `@property` decorator:

```python3

class Parrot:

    def __init__(self):

        self._voltage = 100000



    @property

    def voltage(self):

        """Get the current voltage."""

        return self._voltage

```

The `@property` decorator turns the `voltage()` method into a “getter” for a read-only attribute with the same name, and it sets the docstring for `voltage` to “Get the current voltage.”



For more information, check out [the documentation](https://docs.python.org/3/library/functions.html#property) and [this Programiz article](https://www.programiz.com/python-programming/property).



[▲ TOP](#table-of-contents)

## Generators

Generators are simpler ways of creating [iterators](#iterators). The overhead of creating `__iter__()`, `__next__()`, raising `StopIteration`, and keeping track of state can all be handled internally by a generator.



A generator is a function that returns an object (iterator) which we can iterate over, one value at a time. 



### Using `yield`

To create a generator, simply define a function using a `yield` statement.



A function containing at least one `yield` statement (it may contain other `yield` and `return` statements) becomes a generator.



Both `yield` and `return` return some value from a function. The difference is that, while a `return` statement terminates a function entirely, `yield` pauses the function, saving its state and continuing from where it left off in successive calls.



Once a function yields, it is paused and control is transferred back to the caller. Local variables and their states are remembered between successive calls. When the function terminates, `StopIteration` is raised automatically on further calls.



Below is a simple generator example, for the sake of demonstrating how generators work.

```python3

def my_gen():

    n = 1

    print('This is printed first')

    yield n



    n += 1

    print('This is printed second')

    yield n

    

# Without for loop:

a = my_gen()

next(a) # 'This is printed first'

next(a) # 'This is printed second'

next(a) # Traceback ... StopIteration



# With for loop:

for item in my_gen():

    print(item)

````



Below is a more typical example. Generators often use loops with a suitable terminating condition.

```python3

def reverse(my_str):

    for i in range(len(my_str) - 1, -1, -1):

        yield my_str[i]

        

for char in reverse("hello"):

    print(char) # prints each char reverse on a new line

```

Note that the above example works not just with strings, but also other kinds of iterables.



### Generator Expressions

Generator expressions can be used to create an anonymous generator function. The syntax is similar to that of [list comprehensions](#list-comprehensions), but uses parentheses instead of square brackets. However, while a list comprehension produces the entire list, generator expressions produce one item at a time.



Generator expressions are kind of lazy, producing items only when asked for. For this reason, using a generator expression is much more memory efficient than an equivalent list comprehension.



```python3

items = [1, 3, 6]

item_squared = (item**2 for item in items)

print(next(item_squared)) # 1

print(next(item_squared)) # 9

print(next(item_squared)) # 36

next(item_squared) # StopIteration

```



Generator expressions can be used inside function calls. When used in such a way, the round parentheses can be dropped.

```python3

sum(x**2 for x in items) # 46

max(x**2 for x in items) # 36

```



[▲ TOP](#table-of-contents)

## Other Useful Built-in Functions

For a complete list of built-ins in Python 3, see [the documentation](https://docs.python.org/3/library/functions.html).

### `abs()`

Returns the absolute value of a number, either an integer or floating point number.

If the argument is a complex number, its magnitude is returned.



### `any()`

Usage:

```python

any(iterable)

```

`any()` takes any iterable as an argument and returns `True` if at least one element of the iterable is `True`.



```python

any([1, 3, 4, 0])   # True

any([0, False])     # False

any([0, False, 5])  # True

any([])             # False



any("This is good") # True

any("0")            # True

any("")             # False

```



See [here](https://www.programiz.com/python-programming/methods/built-in/any) for more info.



Check if any tuples contain a negative value:

```python

if any(x < 0 or y < 0 for (x, y) in list_ranges): ...

```



### `all()`

```python

all(iterable)

```

`all()` takes any iterable as an argument and returns `True` if all the elements of the iterable are `True`.



```python

all([1, 3, 4, 5])  # True

all([0, False])    # False

all([1, 3, 4, 0])  # False

all([0, False, 5]) # False

all([])            # True



all("This is good") # True

all("0")            # True

all("")             # True

```



See [here](https://www.programiz.com/python-programming/methods/built-in/all) for more info.



Check if all elements of a list are `x`: 

```python

if all(c == x for c in alst): ...

```



### `chr()`

Returns the string representing a character whose Unicode code point is the integer passed.



For example, `chr(97)` returns the string `a`, while `chr(8364)` returns the string `€`.



This is the inverse of [`ord()`](#ord).



### `enumerate()`

Usage:

```python3

enumerate(iterable, start=0)

```

Returns an enumerate object. `iterable` must be a sequence, iterator, or some object which suports iteration. The `__next__()` method of the iterator returned by `enumerate()` returns a tuple containing a count (from `start` which defaults to zero) and the values obtained from iterating over the iterable.  



Example:

```python3

seasons = ['Spring', 'Summer', 'Fall', 'Winter']

list(enumerate(seasons))              # [(0, 'Spring'), (1, 'Summer'), (2, 'Fall'), (3, 'Winter')]

list(enumerate(seasons, start=1))     # [(1, 'Spring'), (2, 'Summer'), (3, 'Fall'), (4, 'Winter')]

```

This is equivalent to:

```python3

def enumerate(sequence, start=0):

    n = start

    for elem in sequence:

        yield n, elem

        n += 1

```



### `input()`

Gets input from the user.

Usage:

```python3

input([prompt])

```



Example: 

```python3

>>> s = input('-> ')  

-> Monty Python's Flying Circus

>>> s  

"Monty Python's Flying Circus"

```

If the `prompt` arg is present, it is written to stdout without a trailing newline.



### `isinstance()`

Usage:

```python3

isinstance(object, classinfo)

```



Returns true if the `object` argument is an instance of the `classinfo` argument, or of a (direct, indirect, or virtual) subclass thereof. Returns false otherwise.



If `classinfo` is a tuple of type objects, return true if `object` is an instance of any of *any* of these types.



### `len()`

Return the length of an object. The argument may be a sequence (e.g. string, bytes, tuple, list, or range) or a collection (e.g. dictionary, set, frozen set).



### `max()`

Returns the max item in an iterable, or the max of multiple arguments passed.



### `min()`

Returns the min item in an iterable, or the min of multiple arguments passed.



### `ord()`

Given a string representing one Unicode character, return an integer representing the Unicode code point of that character.



For example, `ord('a')` returns the integer 97. `ord('€')` (Euro sign) return 8364. 



This is the inverse of [`chr()`](#chr).



### `pow()`

Usage:

```python3

pow(x, y[, z])

```

Return `x` to the power `y`; if `z` is present, return `x` to the power `y`, modulo `z` (computed more efficiently than `pow(x, y) % z`).  

`pow(x, y)` is equivalent to `x**y`.



### `type()`

Usage:

```python3

type(object)

type(name, bases, dict)

```



With one argument, return the type of `object`. The return value is a type object and generally the same object as returned by `object.__class__`.



E.g.

```python3

x = 5

type(x)  # class 'int'

```



The [`isinstance()`](#isinstance) function is recommended for testing the type of an object, since it accounts for subclasses.



[▲ TOP](#table-of-contents)

## Common Gotchas

### Nested List Initialization

When creating a list of lists, be sure to use the following structure:

```python

my_list = [[None] * n for i in range(n)]

```

Read the section on [list initialization](#list-initialization) to see why.



### Mutable Default Arguments

If we try to do something like `def f(x, arr=[])` this will most likely create undesirable behavior.

Default arguments are resolved *only once*, when the function is first defined. The same arg will be used in successive function calls. In the case of a mutable type like a list, this means that changes made to the list in one call will be carried over in successive calls.

Instead, consider doing:

```python

def f(x, arr=None):

    if not arr: arr = []

```