Django - Basics

Django is a high-level Python web framework that encourages rapid development and clean, pragmatic design. It's known for its simplicity and robustness. Here's a basic overview:

 1. MVC Architecture: 

Django follows the Model-View-Controller (MVC) architecture, though it refers to it as Model-Template-View (MTV).

    - Model: Defines the data structure. It's a Python class that maps to a database table.

    - View: Handles the business logic and interacts with the model to carry data and renders the template.

    - Template: The presentation layer which handles the User Interface part entirely.

 2. URL Dispatcher: 

URL patterns are defined in Django to help direct incoming web requests to the appropriate view, based on the request URL.

 3. Admin Interface: 

One of Django's most powerful features is its automatic admin interface. It reads metadata from your models to provide a quick, model-centric interface where trusted users can manage content on your site.

 4. ORM (Object-Relational Mapping): 

Django comes with a built-in ORM that abstracts the database operations and provides a Pythonic way of interacting with the database.

 5. Development Server: 

Django includes a lightweight web server for development and testing purposes.

 6. Forms: 

Django provides a powerful form library that handles rendering forms as HTML, validating user-submitted data, and converting that data to native Python types.

 7. Security: 

Django emphasizes security and helps developers avoid common security mistakes, like SQL injection, cross-site scripting, cross-site request forgery, and clickjacking.

 8. Extensibility: 

It offers numerous extras for working with tasks like user authentication, content administration, RSS feeds, and more.

 9. Scalability: 

Django is designed to help developers take applications from concept to completion as quickly as possible and scales to handle increasing traffic.

 To get started with Django, you typically set up a project with one or more apps - modular components that can be reused across different projects. Each app has its models, views, templates, and URLs.


Comments

Post a Comment

Popular posts from this blog

Data Preprocessing 1 - Key Steps

  Data preprocessing is a critical step in the data analysis and machine learning pipeline. It involves transforming raw data into an understandable and usable format . Raw data, as it's collected, is often incomplete, inconsistent, or lacking in certain behaviors or trends, and may contain many errors. Data preprocessing is used to clean and organize the data so that it can be analyzed and used effectively.   Key steps in data preprocessing include: 1. Data Cleaning:  This involves handling missing data, smoothing noisy data, identifying or removing outliers, and resolving inconsistencies. Missing data can be filled in using data imputation techniques, noise can be smoothed out using various methods like binning, clustering, or regression, and outliers can be detected and dealt with appropriately. 2. Data Integration:  Combining data from different sources and ensuring that the data is consistent. For example, if you are combining data from different databases...
Read more

Python Libraries for Time-Series Forecasting

  Several Python libraries excel at time series forecasting, each with its own strengths and weaknesses. Here are some of the most popular options: 1. Prophet: Focus: Univariate time series forecasting. Strengths: Easy to use, good for seasonal and trend forecasting, interpretable results. Weaknesses: Limited to univariate forecasting, not suitable for complex models. Libraries: Prophet (standalone package). 2. Darts: Focus: General-purpose time series forecasting. Strengths: Wide range of forecasting models (ARIMA, ETS, Prophet, LSTMs), flexible and customizable. Weaknesses: Can be complex to get started with, requires more knowledge of forecasting techniques. Libraries: Darts. 3. Sktime: Focus: Time series analysis and forecasting. Strengths: Comprehensive set of tools for time series analysis, integrates with other scikit-learn libraries. Weaknesses: Not as user-friendly as Prophet or Darts, requires some background in statistics and mac...
Read more

Data Preprocessing 2 - Data Imputation

Data imputation is a process used in statistics and data science to handle missing values in datasets . When a dataset has missing or null values, it can lead to inaccurate results during analysis or model training. To address this, data imputation techniques are used to estimate and fill in these missing values.   When to do it Use it only when 5-15% of data has this.  This is surely not right data.  Ensure statistical properties of data are maintained even after imputation. Consider the following column in a dataset: [5, nan, nan, 8, 10] The key approaches and methods in data imputation include: 1. Mean Imputation:  Replacing the missing values with the mean value of the entire feature (column). This is best used when the data distribution is normal and the missing values are not too many. Eg. Finding mean we get (5+8+10)/3 = 7.67 Result : [5, 7.67, 7.67, 8, 10] 2. Median Imputation:  Similar to mean imputation, but using the median value. This is more r...
Read more