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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

What are Frameworks in Python?

Ayushi Trivedi
Ayushi Trivedi Last Updated : 23 Aug, 2024
8 min read

Introduction

Imagine you’re building a new skyscraper. You wouldn’t start without a blueprint, right? Similarly, when developing software, having the right framework is crucial. In the vast world of Python, frameworks act as those blueprints, guiding developers through the construction process. They provide a structured foundation, so you can focus on creating impressive, functional applications. In this article, we’ll explore what Python frameworks are, why they matter, and dive into the top 5 that can elevate your coding projects from good to great.

Frameworks in Python

Overview

  • Understand the concept of frameworks and their role in Python development.
  • Identify the top five Python frameworks and their primary use cases.
  • Compare and contrast the features of these frameworks.
  • Learn about the strengths and weaknesses of each framework.
  • Gain insights into how to choose the right framework for different projects.

Table of contents

What are Frameworks?

Frameworks are reusable sets of libraries or modules which forms a structure by which certain kinds of software can be developed. They cut down the amount of work of the programmer, because there are a lot of pre-constructed blocks and a linear framework of work provides programmers with fewer difficulties to surmount and many tasks that can be solved in a rather similar manner can be addressed in similar ways.

In Python, frameworks can be categorized based on their primary purpose:

  • Web Development: Frameworks that help build web applications.
  • Data Analysis: Frameworks tailored for data manipulation and analysis.
  • Machine Learning: Frameworks designed to streamline machine learning tasks.

Key Characteristics of Python Frameworks

Understanding the characteristics of Python frameworks can help you make informed decisions about which one to use. Key characteristics include:

  • Modularity: Applications are normally segmented in to modules based on the architecture resulting to easy management and maintenance.
  • Scalability: They offer solutions for application scale; dealing with small workloads to large-scale enterprise applications.
  • Security: Some of the implementing frameworks that are available have inherent security solutions that guard applications against known threats.
  • Community Support: A strong community can provide large documentation, guidelines, and third-party plugins for the software.

Types of Python Frameworks

Python frameworks come in various types, each designed to address specific development needs. Understanding these types can help you choose the best framework for your project’s requirements.

Full-Stack Frameworks

Full-stack frameworks can be described as application frameworks that consist of many tools and libraries to support the front-end as well as the back-end development of websites. These frameworks comes with set of elements which provides almost all the needs of database management to authentication management, which makes these frameworks a package solution for the development of complex and sophisticated applications. They generally come with every tool you can imagine to develop an entire web application such as a templating engine, an Object relational mapping system, and user management and routing tools among others.

Use Cases

Full-stack frameworks are most appropriate to be used by developers who want to build enterprise-level web applications rapidly. They are most fitting for the projects where a great amount of functionality is expected, where speed of development and amount and versatility of features are paramount. Some of the examples of application are Content Management Systems, E-commerce solutions and Corporate web application.

Micro Frameworks

Microframeworks are as small and composed of separate components as possible in order to accommodate only the essential features for creating web applications. They do not have as many functionalities as full-stack frameworks, but they allow for the use of several extensions and libraries based on the project. Microframeworks leave much room for interpretation, to enable developers to implement only the barest essentials of the architecture.

Use Cases

Microframeworks are well-suited for smaller projects or applications where simplicity and flexibility are desired. They are also ideal for developers who prefer to build their application components from scratch or need a lightweight framework for rapid prototyping. Common use cases include simple web apps, APIs, and projects where developers want more control over the components they use.

Asynchronous Frameworks

Asynchronous frameworks are designed to handle concurrent operations efficiently, making them suitable for applications that require real-time processing and high performance. These frameworks use asynchronous programming techniques to manage multiple tasks simultaneously without blocking the execution of other tasks. This capability is crucial for applications that involve real-time data, such as chat applications, live updates, and streaming services.

Use Cases

Asynchronous frameworks are best for projects that need to handle a large number of simultaneous connections or tasks. They are particularly useful in scenarios requiring real-time communication, such as chat servers, gaming applications, or any application with high concurrency requirements. Their ability to manage numerous connections efficiently makes them a powerful tool for specific high-load scenarios.

Specialized Frameworks

Specialized frameworks focus on specific areas of development, offering tools and features tailored to particular tasks or domains. These frameworks are built to address niche requirements or enhance specific functionalities, such as data analysis, machine learning, or API development. By concentrating on particular aspects of development, specialized frameworks provide targeted solutions that can simplify complex tasks.

Use Cases

Specialized frameworks are ideal for projects with specific needs that align with the framework’s focus. For instance, frameworks designed for machine learning or data analysis provide specialized tools for handling large datasets and performing complex computations. They are valuable in fields where domain-specific functionality is crucial, such as scientific computing, data science, or artificial intelligence.

Benefits of Using Frameworks

Using frameworks in Python offers numerous advantages, including:

  • Increased Productivity: Libraries are pre-existing code structures that help in increasing the speed of development and reduce the time for which a programmer spends on conventional coding.
  • Consistency: Developing within the framework also ensures that the entire code that is written is within this framework’s benchmark ensuring that the work created is consistent and in the standard that is expected.
  • Enhanced Security: Most of the frameworks have inherent security mechanisms that mitigate various weakness and threats thus enhancing the security of your application.
  • Community Support: Frameworks with an active community offer additional materials like documentation, tutorials and third party plugins helping with problem solving and expansion of the offered features.
  • Scalability: Frameworks are built to accommodate this and will never fail to scale your application as the users and data loads rise.

Top 5 Python Frameworks

When diving into Python development, choosing the right framework can make all the difference. Here are the top 5 Python frameworks that stand out for their versatility and powerful features.

Top 5 Python Frameworks

Django

Django – is a high level web framework that allows to create rather complex database oriented sites with little effort. It has integrated admin panel, ORM (Object-Relational Mapping), and it ships with most of development tools developers might need. This make for quick prototyping and fairly elegant, practical architecture. The scalability and the security options are the aspects which make Django highly preferred for use in powering everything from simple blogs to complex applications.

Key Features:

  • Built-in Admin Interface: Affords a ready to use admin panel to manage whole data of the application.
  • ORM (Object-Relational Mapping): Eases data manipulation by providing the developer with ways of accessing databases by direct manipulation of Python objects rather than using SQL statements.
  • Scalability: Custom made to perform very well in handling large volumes of traffic.

Strengths:

  • Comprehensive documentation and a large community.
  • Built-in security features to protect against common web vulnerabilities.

Weaknesses:

  • Can be heavy and complex for simple applications.
  • The steep learning curve for beginners.

Flask

Flask is a lightweight framework focusing on giving a basic setup for a web app to start with out of the box that doesn’t impose or prescribe the directory structure or the dependencies on the application. Its basic design and the fact that it supports the use of third-party plugins by other components make it a preferred platform among developers who like to have control over who compiles their components. Flask is most suitable for small to medium applications and of great scalability; there exists a myriad of third party plugins and extensions.

Key Features:

  • Minimalistic: Comes with the basics, allowing developers to add extensions as needed.
  • Routing: Simple and intuitive routing system.
  • Jinja2 Templating: Powerful templating engine for rendering HTML.

Strengths:

  • Lightweight and easy to extend.
  • More control over components and libraries.

Weaknesses:

  • Requires more configuration and setup compared to Django.
  • Fewer built-in features, which may necessitate additional development work.

Pyramid

Pyramid is a flexible and modular framework that allows developers to start small with simple applications and scale up to more complex systems. It supports both small and large projects, offering a high degree of configurability. Pyramid’s design encourages developers to choose the components they need, whether they prefer to use SQLAlchemy for database interaction or the built-in Kotti CMS. It’s praised for its flexibility and fine-grained control over the application structure.

Key Features:

  • Flexible: Supports both small and large applications with minimal boilerplate code.
  • Security: Includes robust security features to protect applications.
  • Customization: Highly customizable with support for various database backends and authentication systems.

Strengths:

  • Flexibility in design and configuration.
  • Strong support for both small and enterprise-level applications.

Weaknesses:

  • Smaller community compared to Django and Flask.
  • More configuration needed for initial setup.

FastAPI

FastAPI is designed for building modern, high-performance APIs with Python 3.6+ based on standard Python type hints. It’s known for its speed and efficiency, boasting impressive performance benchmarks. FastAPI supports asynchronous programming and is built on Starlette for the web parts and Pydantic for data validation. Its automatic generation of interactive API documentation and support for type checking make it a great choice for developing APIs with minimal boilerplate.

Key Features:

  • Performance: Built on top of Starlette and Pydantic, offering high performance for API development.
  • Type Hints: Utilizes Python type hints to provide automatic documentation and validation.
  • Asynchronous Support: Handles asynchronous requests efficiently.

Strengths:

  • Excellent performance and automatic API documentation.
  • Easy to use with modern Python features.

Weaknesses:

  • Relatively new, so the community and ecosystem are still growing.
  • Limited built-in features compared to more mature frameworks.

TensorFlow

Although TensorFlow is viewed as a deep learning library it is at the same time considered a machine learning framework. TensorFlow is Google’s machine learning platform containing various tools, libraries and other resources which help in the creation and management of new machine learning models. It ranges from creating the most basic neural network to training vast models and running them in production. The TensorFlow has the harness both the first level API for high-level model creation and second level API for model control at the lower level.

Key Features:

  • Versatile: Supports a range of machine learning tasks from neural networks to complex algorithms.
  • Ecosystem: Includes tools like TensorBoard for visualization and TensorFlow Lite for mobile applications.
  • Scalability: Designed to scale across different platforms and devices.

Strengths:

  • Powerful and flexible for machine learning tasks.
  • Strong support from Google and a large community.

Weaknesses:

  • Can be complex and have a steep learning curve.
  • Performance may vary depending on the task and implementation.

Choosing the Right Framework

Selecting the right framework depends on your project requirements, team expertise, and long-term maintenance considerations. Factors to consider include:

  • Project Size and Scope: Larger projects might benefit from full-stack frameworks, while smaller projects may find microframeworks more suitable.
  • Performance Needs: For high-performance requirements, consider frameworks like FastAPI.
  • Community and Support: Opt for frameworks with active communities and ample resources.

Conclusion

Frameworks are very essential in the development of python since they act as structures to the applications being developed. Starting with the rich set of features that Django covers, to the unparalleled performance of FastAPI, each framework presents its advantages and where it should be used. Knowledge of these frameworks enables the developers to select the most appropriate tool that should be used in their projects; thus increasing productivity on the respective projects.

All in all, no matter a web application, an API, or a journey into machine learning, frameworks for Python are helpful and effective to amplify the process of development. When you use the right framework you know that you are constructing apps that conform to modern standards of reliability and efficiency.

Check out our comprehensive Python course that covers everything from the basics to advanced topics. It’s a great way to solidify your understanding of Python and get hands-on experience.

Frequently Asked Questions

Q1. What is a Python framework?

A. A Python framework is a pre-built set of libraries and tools that provides a structure for developing applications, simplifying tasks, and speeding up development.

Q2. Which framework is best for web development?

A. Django and Flask are both popular choices for web development, with Django offering a comprehensive feature set and Flask providing flexibility and simplicity.

Q3. What is the difference between Django and Flask?

A. Django is a full-featured framework with many built-in components, while Flask is a micro-framework that offers more flexibility and requires additional configuration for advanced features.

Q4. Is FastAPI suitable for beginners?

A. FastAPI is designed for ease of use and modern Python features but may be more complex for beginners compared to simpler frameworks like Flask.

Q5. How does TensorFlow differ from other frameworks?

A. TensorFlow is focused on machine learning and deep learning, offering tools and libraries for developing and deploying machine learning models, unlike general-purpose web development frameworks.

Ayushi Trivedi
Ayushi Trivedi

My name is Ayushi Trivedi. I am a B. Tech graduate. I have 3 years of experience working as an educator and content editor. I have worked with various python libraries, like numpy, pandas, seaborn, matplotlib, scikit, imblearn, linear regression and many more. I am also an author. My first book named #turning25 has been published and is available on amazon and flipkart. Here, I am technical content editor at Analytics Vidhya. I feel proud and happy to be AVian. I have a great team to work with. I love building the bridge between the technology and the learner.

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