How model queries work in Django

How model queries work in Django

Welcome to my blog, hey everyone in this article we are going to be working with queries in Django so for any web app that you build your going to want to write a query so you can retrieve information from your database so in this article I’ll be showing you all the different ways that you can write queries and it should cover about 90% of the cases that you’ll have when you’re writing your code the other 10% depend on your specific use case you may have to get more complicated but for the most part what I cover in this article should be able to help you so let’s start with the model that I have I’ve already created it.

**Read More : **How to make Chatbot in Python.

Read More : Django Admin Full Customization step by step

let’s just get into this diagram that I made so in here:

Describe each parameter in Django querset

we’re making a simple query for the myModel table so we want to pull out all the information in the database so we have this variable which is gonna hold a return value and we have our myModel models so this is simply the myModel model name so whatever you named your model just make sure you specify that and we’re gonna access the objects attribute once we get that object’s attribute we can simply use the all method and this will return all the information in the database so we’re gonna start with all and then we will go into getting single items filtering that data and go to our command prompt.

Here and we’ll actually start making our queries from here to do this let’s just go ahead and run** Python manage.py shell** and I am in my project file so make sure you’re in there when you start and what this does is it gives us an interactive shell to actually start working with our data so this is a lot like the Python shell but because we did manage.py it allows us to do things a Django way and actually query our database now open up the command prompt and let’s go ahead and start making our first queries.

#django #django model queries #django orm #django queries #django query #model django query #model query #query with django

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How model queries work in Django

How model queries work in Django

Welcome to my blog, hey everyone in this article we are going to be working with queries in Django so for any web app that you build your going to want to write a query so you can retrieve information from your database so in this article I’ll be showing you all the different ways that you can write queries and it should cover about 90% of the cases that you’ll have when you’re writing your code the other 10% depend on your specific use case you may have to get more complicated but for the most part what I cover in this article should be able to help you so let’s start with the model that I have I’ve already created it.

**Read More : **How to make Chatbot in Python.

Read More : Django Admin Full Customization step by step

let’s just get into this diagram that I made so in here:

Describe each parameter in Django querset

we’re making a simple query for the myModel table so we want to pull out all the information in the database so we have this variable which is gonna hold a return value and we have our myModel models so this is simply the myModel model name so whatever you named your model just make sure you specify that and we’re gonna access the objects attribute once we get that object’s attribute we can simply use the all method and this will return all the information in the database so we’re gonna start with all and then we will go into getting single items filtering that data and go to our command prompt.

Here and we’ll actually start making our queries from here to do this let’s just go ahead and run** Python manage.py shell** and I am in my project file so make sure you’re in there when you start and what this does is it gives us an interactive shell to actually start working with our data so this is a lot like the Python shell but because we did manage.py it allows us to do things a Django way and actually query our database now open up the command prompt and let’s go ahead and start making our first queries.

#django #django model queries #django orm #django queries #django query #model django query #model query #query with django

Django admin full Customization step by step

Welcome to my blog , hey everyone in this article you learn how to customize the Django app and view in the article you will know how to register  and unregister  models from the admin view how to add filtering how to add a custom input field, and a button that triggers an action on all objects and even how to change the look of your app and page using the Django suit package let’s get started.

Database

Custom Titles of Django Admin

Exclude in Django Admin

Fields in Django Admin

#django #create super user django #customize django admin dashboard #django admin #django admin custom field display #django admin customization #django admin full customization #django admin interface #django admin register all models #django customization

How Django Middleware Works?

How Django Middleware Works?

 April 25, 2021  Deepak@321  0 Comments

Welcome to my Blog, in this article we learn about How Django Middleware Works?

Django Middleware is a lightweight, low-level plugin system that modifies Django’s input and output. It is a framework that integrates Django for the processing of queries and answers. You can use middleware if you want to change the request object.

Django maintains a list of middleware for each project. Middleware allows you to edit requests from the browser before they reach Django, and to view the response from the view before they reach the browser. The middleware is applied in the same order as it is added to the list in the Django settings. If a new Django project has added a number of middlewares, in most cases they cannot be removed. Middleware is a checkmark that modifies the Django query and response objects.

In order for middleware to play a role, it is dependent on other middleware. For example, AuthenticationMiddleware stores the authenticated user session and executes the SessionMiddleware.

#django #django middleware #django middleware works #how django middleware works #structure of middleware in django

Main Pros and Cons of Django As A Web Framework for Python Developers

Django depicts itself as “the web system for fussbudgets with cutoff times”. It was intended to help Python engineers take applications from idea to consummation as fast as could be expected under the circumstances.

It permits fast turn of events on the off chance that you need to make a CRUD application with batteries included. With Django, you won’t need to rehash an already solved problem. It just works and lets you center around your business rationale and making something clients can utilize.

Pros of Django

“Batteries included” theory

The standard behind batteries-included methods normal usefulness for building web applications accompanies the system, not as isolated libraries.

Django incorporates much usefulness you can use to deal with normal web advancement undertakings. Here are some significant level functionalities that Django gives you, which else you need to stay together if you somehow happened to utilize a small scale structure:

ORM

Database relocations

Client validation

Administrator board

Structures

Normalized structure

Django as a system proposes the right structure of an undertaking. That structure helps designers in making sense of how and where to execute any new component.

With a generally acknowledged venture structure that is like numerous tasks, it is a lot simpler to discover online good arrangements or approach the network for help. There are numerous energetic Python designers who will assist you with comprehending any issue you may experience.

Django applications

Django applications (or applications for short) permit designers to separate a task into numerous applications. An application is whatever is introduced by putting in settings.INSTALLED_APPS. This makes it simpler for engineers to add usefulness to the web application by coordinating outer Django applications into the venture.

There are many reusable modules and applications to accelerate your turn of events learn through Online Django Class and Check the Django website.

Secure of course

Django gives great security assurance out of the crate and incorporates avoidance components for basic assaults like SQL Injection (XSS) and Cross-site Request Forgery (CSRF). You can discover more subtleties in the official security diagram control.

REST structure for building APIs

Django REST Framework, commonly condensed “DRF”, is a Python library for building APIs. It has secluded and adaptable engineering that functions admirably for both straightforward and complex web APIs.

DRF gives a lot of verification and authorization strategies out of the case. It is an adaptable, full-included library with measured and adjustable engineering. It accompanies nonexclusive classes for CRUD tasks and an implicit API program for testing API endpoints.

GraphQL structure for building APIs

Huge REST APIs regularly require a lot of solicitations to various endpoints to recover every single required datum. GraphQL it’s a question language that permits us to share related information in a lot simpler design. For a prologue to GraphQL and an outline of its ideas, if it’s not too much trouble allude to the authority GraphQL documentation.

Graphene-Django gives reflections that make it simple to add GraphQL usefulness to your Django venture. Ordinary Django models, structures, validation, consent arrangements, and different functionalities can be reused to manufacture GraphQL blueprint. It additionally gives an implicit API program for testing API endpoints.

Cons of Django

Django ORM

Django ORM, made before SQLAlchemy existed, is currently much sub-par compared to SQLAlchemy. It depends on the Active Record design which is more regrettable than the Unit of Work design embraced by SQLAlchemy. This implies, in Django, models can “spare” themselves and exchanges are off as a matter of course, they are a bit of hindsight. Peruse more in Why I kind of aversion Django.

Django advances course popularity increses day by day:

Django is huge and is viewed as strong bit of programming. This permits the network to create several reusable modules and applications yet has additionally restricted the speed of advancement of the Django. On head of that Django needs to keep up in reverse similarity, so it advances gradually.

Rundown - Should I use Django as a Python designer?

While Django ORM isn’t as adaptable as SQLAlchemy and the enormous environment of reusable modules and applications hinders structure advancement - plainly Django ought to be the best option web system for Python engineers.

Elective, light systems, similar to Flask, while offering a retreat from Django huge biological system and designs, in the long haul can require substantially more additional libraries and usefulness, in the end making many experienced Python engineers winding up wishing they’d began with Django.

Django undertaking’s security and network have become enormously over the previous decade since the system’s creation. Official documentation and instructional exercises are probably the best anyplace in programming advancement. With each delivery, Django keeps on including huge new usefulness.

#django online training #django online course #online django course #django course #django training #django certification course

Django Pandas: Tools for working with pandas in your Django projects

Django Pandas

Tools for working with pandas in your Django projects

What's New

This is release facilitates running of test with Python 3.10 and automates the publishing of the package to PYPI as per PR #146 (again much thanks @graingert). As usual we have attempted support legacy versions of Python/Django/Pandas and this sometimes results in deperation errors being displayed in when test are run. To avoid use python -Werror runtests.py

Dependencies

django-pandas supports Django (>=1.4.5) or later and requires django-model-utils (>= 1.4.0) and Pandas (>= 0.12.0). Note because of problems with the requires directive of setuptools you probably need to install numpy in your virtualenv before you install this package or if you want to run the test suite

pip install numpy
pip install -e .[test]
python runtests.py

Some pandas functionality requires parts of the Scipy stack. You may wish to consult http://www.scipy.org/install.html for more information on installing the Scipy stack.

You need to install your preferred version of Django. as that Django 2 does not support Python 2.

Contributing

Please file bugs and send pull requests to the GitHub repository and issue tracker.

Installation

Start by creating a new virtualenv for your project

mkvirtualenv myproject

Next install numpy and pandas and optionally scipy

pip install numpy
pip install pandas

You may want to consult the scipy documentation for more information on installing the Scipy stack.

Finally, install django-pandas using pip:

pip install django-pandas

or install the development version from github

pip install https://github.com/chrisdev/django-pandas/tarball/master

Usage

IO Module

The django-pandas.io module provides some convenience methods to facilitate the creation of DataFrames from Django QuerySets.

read_frame

Parameters

• qs: A Django QuerySet.

column_names: If not None, use to override the column names in the

DateFrame

verbose: If this is True then populate the DataFrame with the

human readable versions of any foreign key or choice fields else use the actual values set in the model.

coerce_float : Boolean, defaults to True

Attempt to convert values to non-string, non-numeric objects (like decimal.Decimal) to floating point.

index_col: Use specify the field name to use for the DataFrame index.

If the index field is not in the field list it will be appended

fieldnames: A list of model field names to use in creating the DataFrame.

You can span a relationship in the usual Django way by using double underscores to specify a related field in another model

Examples

Assume that this is your model:

class MyModel(models.Model):

    full_name = models.CharField(max_length=25)
    age = models.IntegerField()
    department = models.CharField(max_length=3)
    wage = models.FloatField()

First create a query set:

from django_pandas.io import read_frame
qs = MyModel.objects.all()

To create a dataframe using all the fields in the underlying model

df = read_frame(qs)

The df will contain human readable column values for foreign key and choice fields. The DataFrame will include all the fields in the underlying model including the primary key. To create a DataFrame using specified field names:

df = read_frame(qs, fieldnames=['age', 'wage', 'full_name'])

To set full_name as the DataFrame index

qs.to_dataframe(['age', 'wage'], index_col='full_name'])

You can use filters and excludes

qs.filter(age__gt=20, department='IT').to_dataframe(index_col='full_name')

DataFrameManager

django-pandas provides a custom manager to use with models that you want to render as Pandas Dataframes. The DataFrameManager manager provides the to_dataframe method that returns your models queryset as a Pandas DataFrame. To use the DataFrameManager, first override the default manager (objects) in your model's definition as shown in the example below

#models.py

from django_pandas.managers import DataFrameManager

class MyModel(models.Model):

    full_name = models.CharField(max_length=25)
    age = models.IntegerField()
    department = models.CharField(max_length=3)
    wage = models.FloatField()

    objects = DataFrameManager()

This will give you access to the following QuerySet methods:

• to_dataframe
• to_timeseries
• to_pivot_table

to_dataframe

Returns a DataFrame from the QuerySet

Parameters

verbose: If this is True then populate the DataFrame with the

human readable versions of any foreign key or choice fields else use the actual value set in the model.

coerce_float: Attempt to convert the numeric non-string data

like object, decimal etc. to float if possible

index: specify the field to use for the index. If the index

field is not in the field list it will be appended

fieldnames: The model field names to utilise in creating the frame.

to span a relationship, use the field name of related fields across models, separated by double underscores,

Examples

Create a dataframe using all the fields in your model as follows

qs = MyModel.objects.all()

df = qs.to_dataframe()

This will include your primary key. To create a DataFrame using specified field names:

df = qs.to_dataframe(fieldnames=['age', 'department', 'wage'])

To set full_name as the index

qs.to_dataframe(['age', 'department', 'wage'], index='full_name'])

You can use filters and excludes

qs.filter(age__gt=20, department='IT').to_dataframe(index='full_name')

to_timeseries

A convenience method for creating a time series i.e the DataFrame index is instance of a DateTime or PeriodIndex

Parameters

• freq: the offset string or object representing a target conversion
• rs_kwargs: Arguments based on pandas.DataFrame.resample

verbose: If this is True then populate the DataFrame with the

human readable versions of any foreign key or choice fields else use the actual value set in the model.

values: Also required if you utilize the long storage the

values column name is use for populating new frame values

pivot_columns: Required once the you specify long format

storage. This could either be a list or string identifying the field name or combination of field. If the pivot_column is a single column then the unique values in this column become a new columns in the DataFrame If the pivot column is a list the values in these columns are concatenated (using the '-' as a separator) and these values are used for the new timeseries columns

storage: Specify if the queryset uses the wide or long format

for data.

index: specify the field to use for the index. If the index

field is not in the field list it will be appended. This is mandatory.

fieldnames: The model field names to utilise in creating the frame.

to span a relationship, just use the field name of related fields across models, separated by double underscores,

Examples

Using a long storage format

#models.py

class LongTimeSeries(models.Model):
    date_ix = models.DateTimeField()
    series_name = models.CharField(max_length=100)
    value = models.FloatField()

    objects = DataFrameManager()

Some sample data::

========   =====       =====
date_ix    series_name value
========   =====       ======
2010-01-01  gdp        204699

2010-01-01  inflation  2.0

2010-01-01  wages      100.7

2010-02-01  gdp        204704

2010-02-01  inflation  2.4

2010-03-01  wages      100.4

2010-02-01  gdp        205966

2010-02-01  inflation  2.5

2010-03-01  wages      100.5
==========  ========== ======

Create a QuerySet

qs = LongTimeSeries.objects.filter(date_ix__year__gte=2010)

Create a timeseries dataframe

df = qs.to_timeseries(index='date_ix',
                      pivot_columns='series_name',
                      values='value',
                      storage='long')
df.head()

date_ix      gdp     inflation     wages

2010-01-01   204966     2.0       100.7

2010-02-01   204704      2.4       100.4

2010-03-01   205966      2.5       100.5

Using a wide storage format

class WideTimeSeries(models.Model):
    date_ix = models.DateTimeField()
    col1 = models.FloatField()
    col2 = models.FloatField()
    col3 = models.FloatField()
    col4 = models.FloatField()

    objects = DataFrameManager()

qs = WideTimeSeries.objects.all()

rs_kwargs = {'how': 'sum', 'kind': 'period'}
df = qs.to_timeseries(index='date_ix', pivot_columns='series_name',
                      values='value', storage='long',
                      freq='M', rs_kwargs=rs_kwargs)

to_pivot_table

A convenience method for creating a pivot table from a QuerySet

Parameters

• fieldnames: The model field names to utilise in creating the frame.

to span a relationship, just use the field name of related fields across models, separated by double underscores,

• values : column to aggregate, optional
• rows : list of column names or arrays to group on

Keys to group on the x-axis of the pivot table

• dropna : boolean, default True
• margins : boolean, default False

Add all row / columns (e.g. for subtotal / grand totals)

• fill_value : scalar, default None

Value to replace missing values with

• aggfunc : function, default numpy.mean, or list of functions

If list of functions passed, the resulting pivot table will have hierarchical columns whose top level are the function names (inferred from the function objects themselves)

• cols : list of column names or arrays to group on

Keys to group on the y-axis of the pivot table

Example

# models.py
class PivotData(models.Model):
    row_col_a = models.CharField(max_length=15)
    row_col_b = models.CharField(max_length=15)
    row_col_c = models.CharField(max_length=15)
    value_col_d = models.FloatField()
    value_col_e = models.FloatField()
    value_col_f = models.FloatField()

    objects = DataFrameManager()

Usage

rows = ['row_col_a', 'row_col_b']
cols = ['row_col_c']

pt = qs.to_pivot_table(values='value_col_d', rows=rows, cols=cols)

Download Details:
Author: chrisdev
Source Code: https://github.com/chrisdev/django-pandas
License: BSD-3-Clause License

#django  #python