Let get straight to the expression False == False in [True], what you think this expression evaluates to True or False. Fire up your terminal and try, if you get the answer that you have thought of, congratulations!. My guess was wrong :p. Let break down the expression to see how it is evaluated in Python.

# False == False in [True] 
>>> False == False
True
>>> False in [True]
False
>>> (False == False) in [True]
True
>>>False == (False in [True])
True
# but the weird part is when we run
>>> False == False in [True]
False

Isn't this a bit weird? But it is not so, When we look at the expression the way python interprets the expression, things start making sense.

>>> False == False and False in [True]
False

On a bit further looking. I parse the expression to get the Abstract Syntax Tree, which tell a lot about the expression evaluation.

>>> import ast, pprint
>>> pprint.pprint(ast.dump(ast.parse('False == False in [True]')), indent=4)
('Module(body=[Expr(value=Compare(left=Constant(value=False, kind=None), '
 'ops=[Eq(), In()], comparators=[Constant(value=False, kind=None), '
 'List(elts=[Constant(value=True, kind=None)], ctx=Load())]))], '
 'type_ignores=[])')

From the above output of AST you can see that [Eq(), In()] are compound operators and in python, the precedence of these operations is the same.

comparisons, including tests, which all have the same precedence chain from left to right

The below mentioned operators have the same precedence.

in, not in, is, is not, <, <=, >, >=, <>, !=, ==

So, when Python tries to evaluate the expression False == False in [True], it encounters the operators is and == which have the same precedence, so it performs chaining from left to right. – from stackoverflow[0]

# so above expression on evaluating from left to right is done like this.
>>> False == False and False in [True]

Cheers!

References – [0] https://stackoverflow.com/questions/31354429/why-is-true-is-false-false-false-in-python – [1] https://stackoverflow.com/questions/12658197/what-is-the-operator-precedence-when-writing-a-double-inequality-in-python-expl – [2] https://www.youtube.com/watch?v=mRPU3l54Z7I&list=PLWBKAf81pmOamJfoHz4oRdieWQysmUkaW

#100DaysToOffload #Python

Django provides the support of using multiple databases in your project. Let's see how we can do that, but first, let me put some use case where we might need multiple databases for our application. Why need multiple databases? In today's world, we are gathering a lot of data from user which is used for different purposes, some data is relational data and other is non-relational data. Let me put few use cases

• Suppose you need to record all the touchpoints of a web page in your web application, for this you need a non-relation database to store that data to run some analytical result on it.
• Read replicas, you need to set up read replicas of your default database to speed up the fetching of data from the database.
• Saving the email metadata like how many emails were sent, open rate, error rate, link clicked to see the engagement of the emails.

Lets us see how to set up multiple databases in the Django project.

1. Need to add the details of the databases in settings.py of Django project.

DATABASES = {
    "default": {
        "ENGINE": "django.db.backends.mysql",
        "NAME": config.get("database_default", "name"),
        "USER": config.get("database_default", "user"),
        "PASSWORD": config.get("database_default", "password"),
        "HOST": config.get("database_default", "host"),
        "PORT": "3306",
        "CONN_MAX_AGE": 0,
    },
    "replica1": {
        "ENGINE": "django.db.backends.mysql",
        "NAME": config.get("database_replica1", "name"),
        "USER": config.get("database_replica1", "user"),
        "PASSWORD": config.get("database_replica1", "password"),
        "HOST": config.get("database_replica1", "host"),
        "PORT": "3306",
        "CONN_MAX_AGE": 0,
    },
    "mongo": {
        "ENGINE": "djongo",
        "NAME": config.get("mongo_database", "name"),
        "HOST": config.get("mongo_database", "host"),
        "USER": config.get("mongo_database", "user"),
        "PASSWORD": config.get("mongo_database", "password"),
    },
}

Here you can see we define 2 databases other than the default databases mongo and replica1. After this, you need to tell the Django router in which app you want to use which connection of database. This is one of the ways to do it, you can manually decide which database you want to use while querying.

DATABASE_ROUTERS = ['path.to.replica1', 'path.to.mongo']

1. Now we need to define this router class to tell them which database to use, for that we need to write a class

class MongoRouter:
    """
    A router to control all database operations on models in the
    analytics and status applications.
    """
    route_app_labels = {'analytics', 'status'}

    def db_for_read(self, model, **hints):
        """
        Attempts to read analytics and status models go to mongo db.
        """
        if model._meta.app_label in self.route_app_labels:
            return 'mongo'
        return None

    def db_for_write(self, model, **hints):
        """
        Attempts to write analytics and status models go to auth_db.
        """
        if model._meta.app_label in self.route_app_labels:
            return 'mongo'
        return None

    def allow_relation(self, obj1, obj2, **hints):
        """
        Allow relations if a model in the analytics and status apps is
        involved.
        """
        if (
            obj1._meta.app_label in self.route_app_labels or
            obj2._meta.app_label in self.route_app_labels
        ):
           return True
        return None

    def allow_migrate(self, db, app_label, model_name=None, **hints):
        """
        Make sure the analytics and status apps only appear in the
        'mongo' database.
        """
        if app_label in self.route_app_labels:
            return db == 'mongo
        return None

similar goes for replica1 database

class ReplicaRouter:
    def db_for_read(self, model, **hints):
        """
        Reads go to replica1.
        """
        return 'replica1'

    def db_for_write(self, model, **hints):
        """
        Writes always go to default.
        """
        return 'default'

    def allow_relation(self, obj1, obj2, **hints):
        """
        Relations between objects are allowed if both objects are
        in the default/replica1 pool.
        """
        db_set = {'default', 'replica1'}
        if obj1._state.db in db_set and obj2._state.db in db_set:
            return True
        return None

    def allow_migrate(self, db, app_label, model_name=None, **hints):
        """
        All non-mongo models end up in this pool.
        """
        return True

That's it, now you read to use multiples database in your project, which we early handle by the routers class you have defined.

Cheers!

#100DaysToOffload #Django #Python

In Django, we can use the abstraction concept in defining the tables for the columns which are common. We can make any modal as an abstract model by adding this meta property abstract = true.

Suppose you have some column fields which are common in all the tables, which you can abstract and have to just inherit this abstract class to add the fields in the model which can help you to follow the Don't Repeat Yourself principle. Let see an example

class Base(models.Model):
  """
  Base parent class for all the models
  """
  timestamp = models.DateTimeField(blank=True, db_index=True)
  is_active = models.BooleanField(default=True, db_index=True)

  def __init__(self, *args, **kwargs):
    super(Base, self).__init__(*args, **kwargs)

  class Meta:
    abstract = True

class OttPlatform(Base):
  """
  """

  name = models.CharField(max_length=200)
  ott_type = models.CharField(max_length=50)

  def __str__(self):
    return self.name

So, this helps you to stop duplication of code, but there is one more issue we can handle here. The is_active column is used to mark the row as deleted. Mainly in our use case, we can't delete the data from the table to keep the track of changes. So is_active field helps us with that. But now we have to use the is_active filter in every query.

We can solve this by overriding the manager, let see how

# First, define the Manager subclass.
class AtiveOTTManager(models.Manager):
    def get_queryset(self):
        return super().get_queryset().filter(is_active=True)

class OttPlatform(Base):
  """
  """

  name = models.CharField(max_length=200)
  ott_type = models.CharField(max_length=50)
  
   # the order matters, first come default manager, then custom managers.
  objects = models.Manager() # The default manager.
  active_objects = AtiveOTTManager() # The active OTT manager.

  def __str__(self):
    return self.name

# Now you have to do OttPlatform.active_objects.all(), to get all the active OTT platform name.

So, with overriding the manager we don't have to write a filter for is_active in every query.

Cheers!

#100DaysToOffload #django #python

Django Q() object helps to define SQL condition on the database and can be combined with the &(AND) and |(or) operator. Q() helps in the flexibility of defining and reusing the conditions.

• Using Q() objects to make an AND conditions.
• Using Q() objects to make an OR conditions.
• Using Q() objects to make reusable conditions.

Using Q() objects to make an AND conditions We can use Q() objects to combine multiple filter conditions into one condition as filter conditions always perform AND operations.

from django.db.models import Q

# Without Q() object
document_obj = Document.objects.filter(created_by=1282).filter(doc_type='purchase_order').filter(edit=0).filter(cancelled=0)

#With Q() object
q_filter_document = Q(created_by=1282) & Q(doc_type='purchase_order') & Q(cancelled=0) &(edit=0)

# can also be written as
q_filter_document_another_way = Q(created_by=1282, doc_type='purchase_order', cancelled=0, edit=0)

document_obj = Document.objects.filter(q_filter_document)

Using Q() objects to make an OR conditions

from django.db.models import Q


#With Q() object
q_filter_document = Q(created_by=1282) | Q(created_by=1282)
document_obj = Document.objects.filter(q_filter_document)

Q() to make reusable filter condition The best use of Q() objects is reusability, we define the Q() once and can use them to combine with different Q() objects with help of &, |, and ~ operators.

Let's consider a use case, in which the user can generate a report based on certain filters. User can filter report based on these values documenttype, isdraft, createdby, documentstatus

def get_document_object(document_type, is_draft, created_by, document_status):
    base_query = Q(active=1, cancelled=0, document_tye=document_type, is_draft=is_draft)

    # based on condition we can different Q() objects to filter the tables
    if document_status = 'in_progress':
        base_query = base_query & Q(document_status=document_status, completed=0)
    else if document_status = 'completed':
        base_query = base_query & Q(document_status=document_status, completed=1)


   return Documents.objects.filter(base_query)

In Q() objects we can use the same conditional operator which we use in filter objects like in operator, startswith, endswith, etc.

Conclusion Q() objects contribute to clean code and reusability. It helps to define the condition with &, |, and ~ relation operator to simplify the complex queries.

Cheers!

#django #python #100DaysToOffload

In the previous blog post, we have discussed F() Expression, we will now explore more query expression in Django, to name few that we will discuss in this post are

• Func() Expression
• Subquery Expression
• Aggregation () Expression

Func() Expression Func () Expression is the base of all the expressions and can be used to create your custom expression for the database level function.

# The table that we using for our query is the *Student* which keeps records of the students for the whole school.

from django.db.models import F, Func
student_obj = Student.objects.annotate(full_name=Func(F('first_name') + F('last_name'), function='UPPER')

# This will give a student object with a new field that is *full_name* of the student in upper case.

Subquery Expression Subquery are like nested condition in the query filter which helps you to make a complex query into a clean concise query. But you need to know the order of the sequence the query will be executed to use effectively. While using a Subquery you will also need to know about the OuterRef, which is like an F() Expression but points to the parent query value, let see both Subquery and OuterRef in action

# you are given a task to get the name of the student whose name starts with *S* and whose fees are due.

from django.db.models import OuterRef, Subquery
fee_objects = Fees.objects.filter(payment_due_gt=0)
student_obj = Student.objects.filter(name__startswith='S').filter(id__in=Subquery(fee_objects.values('student_id')))

# Get the lastest remarks for the students
remark = Remark.objects.filter(student_id=OuterRef('pk')).order_by('-created_at')
student_obj = Student.objects.annotate(newest_remark=Subquery(remark.values('remark_strl')[:1]))

Aggregation () Expression

Aggregation Expression is the Func Expression with GroupBy clause in the query filter.

# get the total student enrolled in the *Blind Faith* subject.

student_obj = Student.objects.filter(subject_name='blind_faith').annotate(total_count=Count('id'))

Note: All queries mentioned above in the code are not tested. So if you see any typo, a query that does not make sense, feel free to reach out to me at sandeepchoudhary1507[at]gmail[DOT]com.

Cheers!

#100DaysToOffload #django #python

What is the F() Expression? First let me explain to you what are Query Expressions are, these expressions let you use value or computation to be used in the update, create and filters, order by, annotation, aggregation. F() object represent the value of the model fields or annotated columns. It lets you help to not load the value of the field into the python memory rather directly handles in the Database query.

How to use the F() Expression? To use the F expression you have to import them from the from django.db.models import F and have to pass the name of the field or annotated column as argument, and it will return the value of the field from the database, without letting know the python any value. Let some example.

from django.db.models import F

# Documents is the table which have the details of the document submitted by user from the registrey portal for GYM membership

# We need update the count of the document submitted by the user with pk=10091

# without using F Expression

document = Documents.objects.get(user_id=10091)
document.document_counts += 1
document.save()

# Using F expression
document = Documents.objects.get(user_id=10091)
document.document_counts = F('document_counts') + 1
document.save()

Benefits of the F() Expression.

• With the help of F expression we can make are query clean and concise.

    from django.db.models import F
  document = Documents.objects.get(user_id=10091)
  document.update(document_counts=F('document_counts') + 1)

   #Here we also have achieved some performance advantage
    #1. All the work is done at database level, rather than throwing the value from the database in the python memory to do the computation.
    #2. Save queries hit on the database.

• F Expression can save you from the race condition. Consider a scenario where multiple user access your database and when bother user access the Document object for the user 10091, the count value is two, when user updates the value and save it and other user does the same the value will be saved as three not Four because when both user fetches the value its two.

  # user A fetch the document object, and value of document_counts is two.
  document = Documents.objects.get(user_id=10091)
  document.document_counts += 1
  document.save()
  # after the operation value of document_counts is three

  # Code running prallerly, User B also fetch the object, and value of document_counts is two.
  document = Documents.objects.get(user_id=10091)
  document.document_counts  += 1
  document.save()
  # after the operation value of document_counts is three

  # But actually value should be Four, but this is not the case using F expression here will save use from this race condition.

• F Expression are persistent, which means the query persist after the save operation, so you have to use the refreshfromdb to avoid the persistence.

  document = Documents.objects.get(user_id=10091)
  document.document_counts = F('document_counts') + 1
  document.save()

  document.document_validation = 0
  document.save()

  # This will increase the value of *document_counts* by two rather then one as the query presists and calling save will trigger the increment again.

• More example of F Expression in action with filter, annotate query.

from django.db.models import F

# annotation example
annotate_document = Document.objects.filter(created_by=F('created_by_first_name') + F('created_by_last_name')


# filter example
filter_document = Documents.objects.filter(total_documents_count__gt=F('valid_documents_count'))

That's all about the F Expression, it is pretty handy and helps you improve the performance, by reducing value loading in the memory and query hit optimization, but you have to keep an eye over the persistent issue, which I assume will not be the case if you are writing your code in structured way.

Cheers!

#100DaysToOffload #django #python

In this post, we will talk about the standard python library decorators and one or more things about the decorators . If you haven't read the previous blog post about decorator, go check out that here. I will be waiting...

We will talk about functools.lru_cache Decorator from Python standard Library, where lru means Least Recently Used.

lru_cache as the name suggested, it saves the previous result of the function expression based on argument and uses that result if the same argument passed. To save expensive calculations.

functools.lru_cache(maxsize=128, typed=False)

maxsize means that numbers of cache result which can be cached, once the cache is full the older result is discarded. One should use maxsize value as a power of 2 for optimal performance.

type true means argument will be treated differently as int and float values as 1 and 1.0 are treated the same, but if type value is set to true it will be treated differently.

>> 1 == 1.0
>> True

lru_cache use dict to the save the argument as position and keyword-based so all the argument passed to the decorator should be hash-able.

Some point as notes to remember about the decorators

• Decorators are executed when the module is loaded by Python and decorated function only executed if explicitly invoked.
• Decorators have the power to return the entirely a different function.
• We can also have a parameterized decorator as we have seen in the lru_cache decorator.
• Stocked Decorators means when more then one decorator is applied to a function, then the order of execution, is from the decorator nearest to the function definition to outside. Let seen an example

@d2
@d1
def func:
    print('f')

func = d2(d1(func))

so that wrap from my side on the topic Decorators.

#python

Decorators attach additional responsibility to the object dynamically. A decorator takes other function as an argument which it processes and returns that function or any other callable object.

So how the decorator looks like

@clean_strings
def get_full_name(first_name, middle_name, last_name):
    return first_name + middle_name + last_name

In above code snippet we have a decorator clean_strings, this can also be written in this way.

def get_full_name(first_name, middle_name, last_name):
    return first_name + middle_name + last_name

get_full_name = clean_strings(get_full_name)

There are one two things we will talk about decorator after understanding.

• Variable Scope
• Closure

Variable Scope

In every language, the variable has a scope where they are accessible and where not. So here we will talk about the local scope and global scope lets jump right into the code to see

def show(a):
    print(a)
    print(b)
>>> show(10) 
10
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<stdin>", line 3, in show
NameError: name 'b' is not defined 

We got the error as b is not defined in the scope

b = 101
def show(a):
    print(a)
    print(b)
>>> show(10) 
10
101

here its work fine as b is defined in the global scope, which can be accessed from within the function. let see another code snippet.

b = 101
def show(a):
    print(a)
    print(b)
    b =190
>>> show(10) 
10
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<stdin>", line 3, in show
UnboundLocalError: local variable 'b' referenced before assignment

here we face error as code interpret b as local variable of the function which being accessed before declaring as it is defined in the scope of the function. To treat b as global variable despite the assignment in function we can use global declaration.

b = 101
def show(a):
    global b
    print(a)
    print(b)
    global b =190
>>> show(10) 
10
101
>>> b
190

Closure

Closure are the function which have access to the non global variables referenced in the body of function.

Figure from Fluent Python Book, chapter 7

In Python3 nonlocal was introduced which allows assigning the variable inside the scope.

Consider an avg function to compute the mean of an ever-increasing series of values; for example, the average closing price of a commodity over its entire history. Every day a new price is added, and the average is computed taking into account all prices so far.

def avg_series():
    count = 0
    total = 0
    def averager():
        nonlocal count, total
        count += 1
        total += new_value
        return total / count

    return averager

The need of using nonlocal here is that if we don't, Python assumes that count and total are the local variable of averager method, which will break our logic.

Code example is taken from Fluent Python Book.

Now lets build a decorator that can logs the runtime for the function.

import time
def log_time(func):
    def clocked(*args):
        start_time = time.time()
        result = func(*args)
        elapsed_time = time.time() - start_time
        print("Elapsed time: {}".format(elapsed_time))
        return result
    return clocked    

There are also built-in decorators in Python Standard Library which we will discuss in the next blog post, so stay tuned till then cheers.

#python

Function are the code block which contain a logic to process on certain set of given input and return an output. Functions in Python are the First Class Object which basically means function as entity can be

• Create at Run-time.
• Passed as argument to function.
• Return as result from the function.
• Assigned to the variables.

Some functions are also called as Higher Order Function which means that a function which take other function as an argument or return a function as result. Example of higher order function in Python are Map, Filter, Sorted ...

Let see in Python function are classes or not and try to prove above all points to show in Python Function are First Class Object.

Creating function at run-time in console.

def add(x, y):
    return x+y

add(2, 4)
6
type(add)
<class 'function'>

Assigned to the variable

sum = add
sum
<function add at 0x7f2199555b70>
// notice above sum variable pointing to the add function.
sum(3, 4)
7

Passing function as argument.

list(map(add, range(5), range(5))) // here we pass *add* function as argument to the *map* function.
[0, 2, 4, 6, 8]

Returning function as result.

def factorial(x):
    if x < 1:
        return 1
    else:
        return x * factorial(x-1) // here we are returning a function

Above code snippets clearly show that function in Python are First Class Object.

#python