8 Python Frameworks For Data Science
Create better design patterns and avoid duplicate or insecure code with Data Science Frameworks.
The swiftly changing global marketplace requires companies to take a more sophisticated approach to market dominance. Innovate companies now use data science to attract new clients, recommend products, increase sales, and improve customer satisfaction, ultimately helping them gain a competitive advantage.
Data Science
Data Science is simply the study of data. It leverages domain expertise from mathematics, statistics, and programming to extract, analyze, visualize, and manage data to find unseen patterns, create insights and make powerful data-driven decisions.
According to Wiki, a data scientist is someone who combines programming code with statistical knowledge to create insights from data. Being a mixture of many fields, it is difficult to master each field and equivalently be an expert in all of them.
However, data science frameworks help data scientists get the best out of data by letting them focus on business problems rather than getting entangled in the coding process.
What is a Framework?
Originally, a framework means a structure that supports or frames something, like in a building.
In software, a framework is a cohesive set of individual components (with reusable functionalities) that are developed and used by developers to build well-structured, reliable software and systems. Frameworks are available in code form (called libraries) and are designed to run independently or together to easily achieve a complicated task.
With frameworks, data scientists could create projects easily and faster since they don’t need to start development from scratch; the framework takes care of the low-level functionality and helps developers to avoid reinventing the wheel. You only need to learn how to customize the frameworks to your specific technical/business needs.
Data Science Frameworks
Here are some popular frameworks data scientists use to speed up the machine learning project lifecycle. A major advantage of these frameworks is their vast community and detailed documentation.
- Numpy
NumPy, short for Numerical Python, lies at the core of a rich ecosystem of data science libraries. It is a linear algebra library in Python that provides a simple yet powerful data structure (the n-dimensional array) for storing and manipulating extensive data.
In addition to being a general-purpose library for working with large arrays and matrices, NumPy is very important because almost all libraries in PyData Ecosystem rely on it as the main building block.
It is useful for number-crunching applications like Quantum Computing, Statistical computing, signal processing, image processing, graphs and networks, cognitive psychology, and more.
2. Pandas
A game-changer for data science and analytics, Pandas is a high-level Python library that offers data structures and operations for data analysis and manipulation. Built on top of the NumPy package, it provides many functions and methods to speed up the data analysis process.
As the most advanced and fastest-growing tool for data processing in the PyData Ecosystem, it is perfect for data preparation and wrangling and dealing with messy, unstructured, and unlabeled data.
Data scientists use it to normalize incomplete and messy data with features of shaping, slicing, dicing, and merging datasets.
3. Scikit-learn
Scikit-Learn is a fast machine learning library for predictive modeling — which is a pillar of modern data science. Built on NumPy, SciPy, and Matplotlib, it is best used to build machine learning models and not efficient for reading, manipulating, or summarizing data.
Data scientists leverage its simple and efficient tools to model algorithms for both supervised (classification, regression) and unsupervised learning (clustering, dimensionality reduction, and anomaly detection).
Probably the most useful library for machine learning in Python, it is an easy and ready-to-use tool for various data science projects for research and industrial systems that use classical algorithms.
4. Matplotlib
Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. As a cross-platform, data visualization and graphical plotting library, it is important because it provides an easy way to get visual feedback and present our findings.
Data scientists use it to generate high-quality, publication-ready graphs with minimal effort and for elaborate graphs. It supports various types of graphical representations like Bar Graphs, Histograms, Line Graph, Scatter Plot, Stem Plots, etc.
5. Plotly
Plotly is a user-friendly data visualization graphing library that is used to make interactive, publication-quality graphs. Offering advanced visualization tools, it provides beautiful interactive web-based visualizations that can be displayed in Jupyter notebooks, saved to standalone HTML files, or served as part of pure Python-built web applications using Dash.
Data scientists use it for exploratory data analysis and to make interactive plots in less time than Matplotlib, often with one line of code. In addition to being free and open-source, its advantages include ease of use, advanced analytics, reduced costs, scalability, and total customization.
While Matplotlib is also a great place for beginners to start their data visualization journey, Plotly is a more sophisticated data visualization tool that is better suited for creating elaborate plots more efficiently.
6. Keras
Keras is a deep learning API written in Python, running on top of the machine learning platform TensorFlow. Keras offers consistent & simple APIs and has the advantage of minimizing the number of user actions required for common use cases while providing clear and actionable feedback upon user error.
Designed for human beings and not machines, Keras makes it easier to run new experiments and try more ideas. Both Keras and Tensorflow provide high-level APIs used for easily building and training models, but Keras is more user-friendly because it is built-in Python
7. Tensorflow
TensorFlow is an end-to-end open-source platform used extensively for building, training, and deploying machine learning and deep learning models.
Fast and efficient, data scientists, software developers, and educators use it to develop models for various tasks, including natural language processing, image recognition, handwriting recognition, and different computational-based simulations such as partial differential equations.
It also comes with many supporting features like TensorBoard, which allows users to visually monitor the training process, underlying computational graphs, and metrics for purposes of debugging runs and evaluating model performance.
Airplane manufacturing giant Airbus is using TensorFlow to extract and analyze information from satellite images to deliver valuable real-time information to clients.
8. spaCy
spaCy is an industrial-grade, efficient NLP Python library for implementing Natural Language Processing (NLP) applications using the Python ecosystem.
With several pre-trained models and ready-to-use features, it is designed specifically for production use and can perform most common NLP tasks, such as tokenization, parts of speech (POS) tagging, named entity recognition (NER), lemmatization, transforming to word vectors, etc.
Data scientists use it to build information extraction or natural language understanding systems, and models that can deliver document analysis, chatbot capabilities, and all other forms of text analysis.
Conclusion
Both big and small corporations use these data science frameworks to create cutting-edge projects. These are only some of the frameworks that data scientists use to build amazing solutions to meet business challenges. The Python ecosystem has a lot of other tools for working with sophisticated models and complex calculations.
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