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Python (noob notes)
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python.md

🤷‍♂️ Python (noob notes)

Context: These are noob notes on Python (mostly notes-to-self). They are incomplete by default.

Formatting and style

Indentation

Closing brace/bracket/parenthesis on multiline constructs:

# correct
my_list = [
    1, 2, 3,
    4, 5, 6,
    ]
result = some_function_that_takes_arguments(
    'a', 'b', 'c',
    'd', 'e', 'f',
    )

# also correct
my_list = [
    1, 2, 3,
    4, 5, 6,
]
result = some_function_that_takes_arguments(
    'a', 'b', 'c',
    'd', 'e', 'f',
)

Source: PEP 8: Indentation

Built-in

Strings

f-strings (formatted string literals)

Source: RealPython (for all notes on formatted strings below)

Syntax: {[<name>][!<conversion>][:<format_spec>]}
Component Meaning
<name> Specifies the source of the value to be formatted
!<conversion> Indicates which standard Python function to use to perform the conversion
:<format_spec> Specifies more detail about how the value should be converted
<name>:
  • indicates which arguments are passed (e.g. a, b, c in example below)
# with variables
a = 1
b = 2
c = 3
f'{a} {b} {c}'

# with list
L = ['foo', 'bar', 'baz']
f'{L[0]} {L[1]} {L[3]}'

# with dictionary
d = {'key1': 'foo', 'key2': 'bar'}
f'{d[key1]} {d[key2]}'

# with arbitrary object attribute
f'{obj.attr}'
!<conversion>:
  • format an object as a string
Value Meaning
!s Convert with str()
!r Convert with repr()
!a Convert with ascii()
:<format_spec>:
  • represents the .format() functionality
  • :[[<fill>]<align>][<sign>][#][0][<width>][<group>][.<prec>][<type>]
Subcomponent Effect
: Separates the <format_spec> from the rest of the replacement field
<fill> Specifies how to pad values that don’t occupy the entire field width
<align> Specifies how to justify values that don’t occupy the entire field width
<sign> Controls whether a leading sign is included for numeric values
# Selects an alternate output form for certain presentation types
0 Causes values to be padded on the left with zeros instead of ASCII space characters
<width> Specifies the minimum width of the output
<group> Specifies a grouping character for numeric output
.<prec> Specifies the number of digits after the decimal point for floating-point presentation types, and the maximum output width for string presentations types
<type> Specifies the presentation type, which is the type of conversion performed on the corresponding argument
<type>:
  • specifies the presentation
Value Presentation Type
b Binary integer
c Single character
d Decimal integer
e or E Exponential
f or F Floating point
g or G Floating point or Exponential
o Octal integer
s String
x or X Hexadecimal integer
% Percentage 
# ':b' binary
>>> f'{1:b}'
'1'
>>> f'{5:b}'
'101'
>>> f'{10:b}'
'1010'

# '%' percentage
>>> f'{0.01:%}'
'1.000000%'
>>> f'{0.01:.1%}'
'1.0%'
>>> f'{0.01:.0%}'
'1%'
[[<fill>]<align>]:
  • controls where output is positioned within the specified field width
  • <width> has to be specified, else <fill> and <align> are ignored
<align>:
Option Action
< left-justifies
> right-justifies
^ centers
= left-aligns sign 
# '<'
>>> f'{1:=+8}'
'+      1'
>>> f'{1:+8}' # comparison without '='
'      +1'
<fill>:
  • specifies how to fill in extra space when the formatted value doesn’t completely fill the output width
>>> f'{1:+>8}'
'+++++++1'
>>> f'{1:+<8}'
'1+++++++'
# '.f'
>>> f'{123.456789:.2f}'
'123.46'
>>> f'{123.456789:.3f}'
'123.457'
>>> f'{123.456789:.4f}'
'123.4568'
>>> f'{123.456789:.5f}'
'123.45679'
<sign>:
  • controls whether a sign appears in numeric output
Sign Action
+ incl. leading sign for positive and negative values
- incl. leading sign for negative values 
# '+' pos and neg values
>>> f'{1:+}' 
'+1'
>>> f'{-1:+}'
'-1'

# '-' only neg values
>>> f'{1:-}'
'1'
>>> f'{-1:-}'
'-1'
<width>:
  • specifies the minimum width of the output field
  • if output is longer, minimum is ignored
>>> f'{1:8}'
'       1'
>>> f'{1:4}'
'   1'
>>> f'{1:2}'
' 1'
>>> f'{1000:2}' # ignored if longer
'1000'
<group>:
  • add a separator character in numeric output (either a comma character , or an underscore character _)
>>> f'{1000:,}'
'1,000'
>>> f'{1000:_}'
'1_000'
>>> f'{1000000:,}'
'1,000,000'
>>> f'{1000000:_}'
'1_000_000'
.<prec>:
  • decimal digits for floating point
# decimals
>>> f'{1:.0f}'
'1'
>>> f'{1:.1f}'
'1.0'
>>> f'{1:.2f}'
'1.00'
>>> f'{1:.3f}'
'1.000'

# with width
>>> f'{1:4.0f}'
'   1'
>>> f'{1:4.1f}'
' 1.0'
>>> f'{1:4.2f}'
'1.00'
>>> f'{1:4.3f}'
'1.000'

ord(c) and chr(i)

>>> ord('a')
97
>>> chr(97)
'a'

ord(c): 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 and ord('€') (Euro sign) returns 8364. This is the inverse of chr().

chr(i): Return the string representing a character whose Unicode code point is the integer i. For example, chr(97) returns the string 'a', while chr(8364) returns the string '€'. This is the inverse of ord().

List of Unicode characters (wikipedia):

Code Glyph Decimal Octal Description #
U+0061 a 97 0141 Latin Small Letter A 0066
U+0062 b 98 0142 Latin Small Letter B 0067
U+0063 c 99 0143 Latin Small Letter C 0068
... ... ... ... ... ...
U+007A z 122 0172 Latin Small Letter Z 0091

Lists

Comparing lists with .sort() or set(list) and ==

Lists are not == if elements are at different indices.

Instead sort lists in-situ using .sort() or compare sets using set(list).

# Example
>>> [1, 2] == [2, 1]
False

>>> [1, 2].sort() == [2, 1].sort()
True

>>> set([1, 2]) == set([2, 1])
True

Checks if element(s) exists in list

Using in operator:

if i in lst:
  print("Found")
  
if "a" in "arthur":
  print("Found")

Using list.count() function:

list.count(elem) == 1

Source: thispointer.com

Using all() function:

'''
    check if list1 contains all elements in list2
'''
result =  all(elem in list1  for elem in list2)

Source: thispointer.com

all(iterable): Return True if all elements of the iterable are true (or if the iterable is empty). Equivalent to:

def all(iterable):
    for element in iterable:
        if not element:
            return False
    return True

Using any() function:

'''
    check if list1 contains any elements of list2
'''
result =  any(elem in list1  for elem in list2)

Source: thispointer.com

any(iterable): Return True if any element of the iterable is true. If the iterable is empty, return False. Equivalent to:

def any(iterable):
    for element in iterable:
        if element:
            return True
    return False

list.index(x)

Return zero-based index in the list of the first item whose value is equal to x. Raises a ValueError if there is no such item.

list.index(x[, start[, end]]): The optional arguments start and end are interpreted as in the slice notation and are used to limit the search to a particular subsequence of the list. The returned index is computed relative to the beginning of the full sequence rather than the start argument.

Sets

The difference_update() method removes the items that exist in both sets.

The difference_update() method is different from the difference() method, because the difference() method returns a new set, without the unwanted items, and the difference_update() method removes the unwanted items from the original set.

# difference = new set (return type = set)
new = x.difference(s)
# difference_update = update set (return type = None)
x.difference_update(s)

Source: W3School

The update() method updates the current set, by adding items from another set (or any other iterable).

The add() method adds an element to the set.

# update = same set (return type = None)
x.update(s)
# add = same set (return type = None)
x.add("a")

Source: W3School

Dictionaries

Initialise dictionary

# 2 ways (equal result)
d : dict = dict()
d : dict = {}

Source: Geek for Geeks

Key-value types

  • Keys are unique within a dictionary and must be of an immutable data type such as strings, numbers, or tuples.
  • Values must not be unique and can be of any type

Source: TutorialPoint

Add elements

Dict[0] = 'Geeks'
Dict[2] = 'For'
Dict[3] = 1

Source: Geek for Geeks

d.items() and ErrorValue: too many values to unpack (expected 2)

Source: Career Karma

Objects

Constructor (__init__)

Constructors initialise variables when an object is created. Constructors have a reserved name __init__, e.g.: We never really call the __init__ method itself e.g. CashRegister.__init__. It's only really used to initialise variables when an object is instantiated.

class CashRegister:
  def __init__(self):
    self._itemCount = 0
    self._totalPrice = 0.0
# instantiate using registerABC = CashRegister()

# or with argument
class CashRegister:
  def __init__(self, initial_balance):
    self._itemCount = 0
    self._totalPrice = initial_balance
# instantiate using registerABC = CashRegister(50)

self

By default, we always pass self as an argument when defining a method, e.g.:

def methodName(self, argument_1, argument_1, ...)

But we don't pass self when calling the method, e.g.:

object.methodName(argument_1, argument_1, ...)

You can also use self inside methods of a class to refer to the state of the object, e.g.:

class CashRegister: 
  def clear(self):
    self._itemCount = 0

Similarly you can reference other objects to access their state inside methods, e.g.:

class CashRegister: 
  def copy(self, other):
    self._itemCount = other._itemCount
    self._totalPrice = other._totalPrice

# call inside main function using registerABC.copy(registerDEF)

is operator

is and is not are the identity operators. They are used to check if two values (or variables) are located on the same part of the memory.

id(object) (memory-like address)

id(object): Return the “identity” of an object. This is an integer which is guaranteed to be unique and constant for this object during its lifetime.

hex(x) Convert an integer number to a lowercase hexadecimal string prefixed with “0x”

# Example
obj = [1, 2]

>>> id(obj)
4466657600

>>> hex(id(obj))
'0x10a3bc940'

Attributes of an object

object.__dict__: A dictionary or other mapping object used to store an object’s (writable) attributes.

Alternatively, vars(an_obj): return the __dict__ attribute for a module, class, instance, or any other object with a __dict__ attribute.

class Parent:
  name : str
  age : int
  def __init__(self, name : str, age : int):
    self.name = name
    self.age = age

p = Parent("John Doe", 40)
vars(p)
>>> {'name': 'John Doe', 'age': 40}
p.__dict__
>>> {'name': 'John Doe', 'age': 40}

Rare use of global

We can only access global variables in local scopes but cannot modify them from local scopes.

The solution is to use the global keyword.

The basic rules for global keyword in Python are:

  • When we create a variable inside a function, it is local by default.
  • When we define a variable outside of a function, it is global by default. You don't have to use global keyword.
  • We use global keyword to read and write a global variable inside a function.
  • Use of global keyword outside a function has no effect.
# Example 1: **Accessing** global Variable From Inside a Function
c = 1 # global variable

def add():
    print(c)

add()
>>> 1
# Example 2: **Modifying** Global Variable From Inside the Function
c = 1 # global variable
    
def add():
    c = c + 2 # increment c by 2
    print(c)

add()
>>> UnboundLocalError: local variable 'c' referenced before assignment
# Example 3: Changing Global Variable From Inside a Function using global
c = 0 # global variable

def add():
    global c
    c = c + 2 # increment by 2
    print("Inside add():", c)

add()
print("In main:", c)
>>> Inside add(): 2
>>> In main: 2

Source: Programmiz

Inheritance

  • Inheritance syntax
    • Pass parent class as argument in child class definition, e.g. class Child_class(Parent_class):
    • Use super() to pass arguments into parent class method
class Parent:
  name : str
  age : int
  def __init__(self, name : str, age : int):
    self.name = name
    self.age = age
 
class Mum(Parent):
  profession : str
  def __init__(self, name : str, age : int, profession : str):
    super().__init__(name, age)
    self.profession = profession
    
class Dad(Parent):
  hobby : str
  def __init__(self, name : str, age : int, hobby : str):
    super().__init__(name, age)
    self.hobby = hobby

Example:

class Employee:
'''
Attributes: 
- name
- payroll number
- salary
'''
  def __init__(self, nm, prnum):
    self._name = nm
    self._payrollNum = prnum
    self._salary = 0

  def setSalary(self, sal):
    self._salary = sal

  def statusReport(self):
    s = "%s:%s,%s." % (self._name, self._payrollNum, self._salary)
    return s

class AcademicEmployee(Employee):
'''
New attribute(s):
- department

Inherited attributes (from Employee): 
- name
- payroll number
- salary
'''
  def __init__(self, nm, prnum):
    super().__init__(nm, prnum)
    self._department = "N/A"
  
  def setDepartment(self, dept):
    self._department = dept

class TeachingEmployee(AcademicEmployee):
'''
New attribute(s):
- courses

Inherited attributes (from AcademicEmployee): 
- name
- payroll number
- salary
- department
'''
  def __init__(self, nm, prnum):
    super().__init__(nm, prnum)
    self._courses = "N/A"
  
  def setCourses(self, crss):
    self._courses = crss

__repr__ and __str__

Almost every object you implement should have a functional __repr__ that’s usable for understanding the object. Implementing str is optional: do that if you need a “pretty print” functionality (for example, used by a report generator).

Source: Stack Overflow

Example:

class Person:

    def __init__(self, person_name, person_age):
        self.name = person_name
        self.age = person_age

    def __str__(self):
        return f'Person name is {self.name} and age is {self.age}'

    def __repr__(self):
        return f'Person(name={self.name}, age={self.age})'


p = Person('Pankaj', 34)

print(p.__str__())
>>> Person name is Pankaj and age is 34
print(p.__repr__())
>>> Person(name=Pankaj, age=34)

Source: JournalDev

Name of a class using type().__name__

definition.name: The name of the class, type, function, method, descriptor, or generator instance.

class Parent:
  name : str
  age : int
  def __init__(self, name : str, age : int):
    self.name = name
    self.age = age

def __repr__(self) -> str: # visualises Parent object in CLI
  return f'{type(self).__name__}(name: {self.name}, age: {self.name})'
  

>>> john = Parent("John Doe", 54)
>>> print(john)
Parent(name: John Doe, age: 54)

Source: DelftStack

Reading Files

open()

Returns a file object, and is most commonly used with two arguments: open(filename, mode).

You can also specify the exact path that the file is located at (Source)

f = open('workfile.txt', 'r')

It is good practice to use the with keyword when dealing with file objects. The advantage is that the file is properly closed after its suite finishes, even if an exception is raised at some point. Using with is also much shorter than writing equivalent try-finally blocks:

>>> with open('workfile.txt', 'r') as f:
...     read_data = f.read()

>>> # We can check that the file has been automatically closed.
>>> f.closed
True

After a file object is closed, either by a with statement or by calling f.close(), attempts to use the file object will automatically fail.

Character Meaning
'r' open for reading (default)
'w' open for writing, truncating the file first
'x' open for exclusive creation, failing if the file already exists
'a' open for writing, appending to the end of file if it exists
'b' binary mode
't' text mode (default)
'+' open for updating (reading and writing)

Source: Python docs open(file, mode)

f.read()

  • Reads data and returns it as a string (in text mode) or bytes object (in binary mode).
  • Optional argument size (f.read(size)): at most size characters (in text mode) or size bytes (in binary mode) are read and returned.
  • Without size argument, f.read() reads the entire contents of the file (your problem if the file is twice as large as your machine’s memory)
  • if the end of the file has been reached, f.read() will return an empty string ('').
>>> f.read()
'This is the entire file.\n'
>>> f.read() # reached end of file
''

f.readline()

  • Reads a single line from the file
  • A newline character (\n) is left at the end of the string, and is only omitted on the last line of the file (if the file doesn’t end in a newline)
  • If f.readline() returns an empty string (''), the end of the file has been reached
  • you can loop over the file object
# manual
>>> f.readline()
'This is the first line of the file.\n'
>>> f.readline()
'Second line of the file\n'
>>> f.readline() # reached end of file
''

# looping (preferred)
>>> for line in f:
...     print(line, end='')
...
This is the first line of the file.
Second line of the file

f.readlines() or list(f)

  • Reads all the lines of a file in a list
  • Each element in the list is a string representing a line from the file
# Example
>>> lines = f.readlines()
>>> print(lines)
['This is the first line of the file.\n', 'Second line of the file\n']

str.split(): Used to split string into separate words

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

  • 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).
  • If sep is given, consecutive delimiters are not grouped together and are deemed to delimit empty strings (for example, '1,,2'.split(',') returns ['1', '', '2'])
  • If sep is not specified or is None, consecutive whitespace are regarded as a single separator, and the result will contain no empty strings at the start or end if the string has leading or trailing whitespace (for example, ' 1 2 3 '.split() returns ['1', '2', '3'])
line = 'Hello how are you doing'
words = line.split()
print(words)
>>> ['Hello', 'how', 'are', 'you', 'doing']

str.lstrip() str.rstrip() str.strip(): Used to remove whitespace

str.lstrip([chars]): Return a copy of the string with leading characters removed.

str.rstrip([chars]): Return a copy of the string with trailing characters removed.

str.strip([chars]): Return a copy of the string with the leading and trailing characters removed.

If chars argument is omitted or None, defaults to removing whitespace.

>>> '   spacious   '.lstrip()
'spacious   '
>>> '   spacious   '.rstrip()
'   spacious'
>>> '   spacious   '.strip()
'spacious'

f.write(string)

  • Writes the contents of string to the file, returning the number of characters written.
# "w" for writing to file
>>> outfile = open("text.txt", "w")
>>> outfile.write('This is a test\n')
15

Type hinting

Forward type hinting: Used Class is hinted but not yet defined

When a type hint contains names that have not been defined yet, that definition may be expressed as a string literal, to be resolved later.

Source: PEP484 on Type Hints

# Example
class Tree:
    def __init__(self, left: 'Tree', right: 'Tree'):
        self.left = left
        self.right = right

typing.Callable

Requires import:

from typing import Callable

# code
  • Syntax: Callable[[Arg1Type, Arg2Type], ReturnType]
  • Requires exactly two values (e.g. Callable[[int], str]):
  1. argument list: must be a list of types or an ellipsis (e.g. [int])
  2. return type: must be a single type (e.g. str)

There is no syntax to indicate optional or keyword arguments

# Example
from typing import Callable

foo : Callable[[int], str] # function of (int) -> str.

def foo(int: i) -> str:
  return str(i)

Exiting a Python program

The most preferred method is sys.exit(), because exit() and quit() functions cannot be used in the operational and production codes. They can only be implemented if the site module is imported.

sys.exit([arg]):

  • This is implemented by raising the SystemExit exception, so cleanup actions specified by finally clauses of try statements are honored, and it is possible to intercept the exit attempt at an outer level.
  • The optional argument arg can be an integer giving the exit status (defaulting to zero), or another type of object. If it is an integer, zero is considered “successful termination” and any nonzero value is considered “abnormal termination” by shells and the like.
import sys 
 
x = 50
 
if x != 100: 
    sys.exit("Values do not match")  
else: 
    print("Validation of values completed!!") 
>>> Values do not match

Using quit():

  • As soon as the system encounters the quit() function, it terminates the execution of the program completely.
  • The exit() function can be considered as an alternative to the quit() function, which enables us to terminate the execution of the program.
for x in range(1,10):
    print(x*10)
    quit()
# no output

Source: askpython.com

Exceptions (errors)

Exception types

exception TypeError Raised when an operation or function is applied to an object of inappropriate type.

Passing arguments of the wrong type (e.g. passing a list when an int is expected) should result in a TypeError, but passing arguments with the wrong value (e.g. a number outside expected boundaries) should result in a ValueError.

exception ValueError: Raised when an operation or function receives an argument that has the right type but an inappropriate value, and the situation is not described by a more precise exception such as IndexError.

exception IndexError: Raised when a sequence subscript is out of range. (Slice indices are silently truncated to fall in the allowed range; if an index is not an integer, TypeError is raised.)

Handling exceptions

while True:
    try:
        x = int(input("Please enter a number: "))
        break
    except ValueError:
        print("Oops!  That was no valid number.  Try again...")

The try statement works as follows.

  • First, the try clause (the statement(s) between the try and except keywords) is executed.
  • If no exception occurs, the except clause is skipped and execution of the try statement is finished.
  • If an exception occurs during execution of the try clause, the rest of the clause is skipped. Then, if its type matches the exception named after the except keyword, the except clause is executed, and then execution continues after the try/except block.
  • If an exception occurs which does not match the exception named in the except clause, it is passed on to outer try statements; if no handler is found, it is an unhandled exception and execution stops with a message as shown above.

Source: 8.3. Handling Exceptions

Raising exceptions

The raise statement allows the programmer to force a specified exception to occur.

raise NameError('HiThere')

pytest (testing)

To run a specific test within a module:

$ pytest test_mod.py::test_func

Source: How to invoke pytest in command line

Assertions about objects (e.g. correct instantion)

# foo.py
class MyClass:
    def __init__(self, public_attr_1, public_attr_2):
        self.public_attr_1 = public_attr_1
        self.public_attr_2 = public_attr_2
# test_foo.py
import pytest
from foo import MyClass
 
def test_initial_value():
    obj_1 = MyClass(1, 2)     
    assert obj_1.public_attr_1 == 1
    assert obj_1.public_attr_2 == 2
 
def test_no_value():
    with pytest.raises(Exception) as e_info:
        obj = MyClass()

Source: Python-forum.io

Assertions about exceptions

In order to write assertions about raised exceptions, you can use pytest.raises().

# Module example
import pytest

def myfunc():
    raise ValueError("Exception 123 raised")
# Test example
def test_myfunc():
    with pytest.raises(ValueError):
        myfunc()

Source: Assertions about exceptions

Pip (package management)

Check installed pip packages:

$ python3 -m pip list

Source: Pip documenation > Pip list

Example output:

Package    Version
---------- -------
attrs      21.2.0
iniconfig  1.1.1
packaging  21.3
pip        21.3.1
pluggy     1.0.0
py         1.11.0
pyparsing  3.0.6
pytest     6.2.5
setuptools 59.0.1
toml       0.10.2
wheel      0.37.0

Libraries

import copy

copy.deepcopy()

import copy

copy.copy(x) # Return a shallow copy of x.
copy.deepcopy(x) # Return a deep copy of x.

copy() is a shallow copy function. If the given argument is a compound data structure, for instance a list, then Python will create another object of the same type (in this case, a new list) but for everything inside the old list, only their reference is copied. Think of it like:

newList = [elem for elem in oldlist]

Source: Stack Overflow

import random

random.choice(seq): Return a random element from the non-empty sequence seq. If seq is empty, raises IndexError.

The sequence can be a string, a range, a list, a tuple or any other kind of sequence. Source: W3School

random.randint(a, b): Return a random integer N such that a <= N <= b. Alias for randrange(a, b+1).

import os.path: Used to check if file exists

os.path.exists(path): Return True if file exists.

  • If the file is in the same folder as the program, the path is just simply the file name.
  • Else you need to pass the full file path of the file
  • You should use the forward-slash (/) to separate the path. It’ll work across Windows, macOS, and Linux.
import os.path

file_exists = os.path.exists('readme.txt')
print(file_exists)
>>> True

Source: Pythontutorial

import json

To handle the data flow in a file, the JSON library in Python uses dump() or dumps() function to convert the Python objects into their respective JSON object, so it makes it easy to write data to files.

PYTHON OBJECT JSON OBJECT
Dict object
list, tuple array
str string
int, long, float numbers
True true
False false
None null

Method 1: Writing JSON to a file in Python using json.dumps()

json.dumps() takes two parameters:

  • dictionary: the name of a dictionary which should be converted to a JSON object.
  • indent: defines the number of units for indentation
import json
 
# Data to be written
dictionary = {
  "name": "sathiyajith",
  "rollno": 56,
  "cgpa": 8.6,
  "phonenumber": "9976770500"
}
 
# Serializing json
json_object = json.dumps(dictionary, indent=4)
 
# Writing to sample.json
with open("sample.json", "w") as outfile:
  outfile.write(json_object)

Source: Reading and Writing JSON to a File in Python - Geek for Geeks

Method 2: Writing JSON to a file in Python using json.dump()

json.dump() directly writes the dictionary to a file and takes 2 parameters:

  • dictionary: the name of a dictionary which should be converted to a JSON object.
  • file pointer: pointer of the file opened in write or append mode.
import json
 
# Data to be written
dictionary = {
  "name": "sathiyajith",
  "rollno": 56,
  "cgpa": 8.6,
  "phonenumber": "9976770500"
}
 
with open("sample.json", "w") as outfile:
  json.dump(dictionary, outfile)

Reading JSON from a file using json.load()

json.load() loads into a dictionary and takes one parameter:

  • File pointer: A file pointer that points to a JSON file.
import json

with open('sample.json', 'r') as openfile:
 
  # Reading from json file
  json_object = json.load(openfile)

Reading JSON from string using json.loads()

json.loads() parses a valid JSON string and converts it into a Python Dictionary. It is mainly used for deserializing native string, byte, or byte array which consists of JSON data into Python Dictionary.

Source: Geek for Geeks

  • Syntax : json.loads(s)
  • Argument: it takes a string, bytes, or byte array instance which contains the JSON document as a parameter s.
  • Return: It returns a Python object.

import requests

Simple HTTP GET, POST, DETGEL

import requests

r = requests.get('https://api.github.com/events')
# we have a Response object called `r`. We can get all the information we need from this object.

print(r.text)
'[{"repository":{"open_issues":0,"url":"https://github.com/...
# Requests will automatically decode content from the server. Most unicode charsets are seamlessly decoded.

Source: requests docs

Parse JSON response as dictionary

Method 1: Using json library

import json
import requests

r = requests.get(...)
json_data = json.loads(r.text) # import json

Source: Stack Overflow

Method 2: Using requests built-in JSON decoder

import json
import requests

r = requests.get(...)
json_data = r.json() # built-in
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