Top 10 Data Science Frameworks for Python
The Python language has emerged as one of the best tools for data science applications in recent years. It’s backed by a vast community that provides support in various forms to help you ease your way into this ecosystem. You can check out some great resources on our site. First, let’s take a look at some of the remarkable data science frameworks in Python.
What Is Data Science?
Data science is a large field, and the tools used to perform data exploration, machine learning, visualization, statistical analysis, NLP techniques, or deep learning are constantly evolving. Nowadays, there’s a wide variety of tools to address every need.
Data science is the process of using an iterative approach to extract insights from raw data and turn them into actionable knowledge. Data scientists are focused on making this process more efficient, which requires them to know the whole spectrum of tools needed for this task.
What is a Data Science Framework?
A data science framework is a collection of libraries that provides data mining functionality, i.e., methods for exploring the data, cleaning it up, and transforming it into some more useful format that can be used for data processing or machine learning tasks. The whole process involves building visualizations to gain insights from your data.
Why Python For Machine Learning and Data Science?
Python is a prevalent programming language for data scientists. It allows you to perform rapid prototyping of statistical models and quantitative analysis tools.
It has a large data science community of users, which is one of the reasons why Python is considered one of today’s most prominent languages. Various libraries help you perform data analysis and machine learning on big datasets. The diversity of Python’s libraries makes it an excellent environment for data science, as you can use the right tool for your job.
Top 10 Data Science Frameworks
Here are 10 of the top data science frameworks for Python. The list is based on insights and experience from practicing data scientists and feedback from our readers.
1. Tensorflow and Keras
Tensorflow is a powerful machine learning framework based on Python. It can be used to do everything from simple calculations to building complicated neural networks. It’s backed by Google and has been around since 2007, though it only became open source in 2015. In 2017, Tensorflow released an add-on package called Keras that provides high-level APIs and building blocks (similar to Matlab) for creating Deep Learning models.
Numpy library is a package built on top of the Python language providing efficient numerical operations. It’s great for manipulating matrices and performing many other numerical calculations. It can be used on its own or with other frameworks like Tensorflow or Theano.
Pandas is a package providing high-level data structures and analysis tools for Python. It can be used to load CSV or excel files, manipulate the data, visualize it using graphs or charts, etc. The core concept of pandas is that everything you do with your data happens in a Series (1D array) or a DataFrame (2D Array). Pandas make working with DataFrames extremely easy.
Matplotlib is a python library to visualize data. It provides some of the most used data visualization libraries for scientific and numeric data in Python so that you can create graphs similar to R or Matlab. You can also choose from different back-ends like Qt, WX, etc., for your visualizations.
When working with machine learning, it is beneficial to visualize your data to see if outliers or other suspicious values are present. Matplotlib is the most widely used Python graphing library, but some alternatives like Bokeh and Seaborn provide more advanced visualizations.
Scikit-learn is a collection of Python modules for machine learning built on top of SciPy. It provides many standard machine learning algorithms for classification, regression, clustering, etc. It also supports pipelines—a set of steps consisting of transformers and estimators connected to form a model.
SpaCy is an excellent Natural Language Processing (NLP) library in Python. It provides tools and models to process text to compute the meaning of words, sentences, or entire texts. In addition, you can easily tokenize and parse natural language with SpaCy’s easy-to-use API.
7. Natural Language Toolkit
NLTK is another collection of Python modules for processing natural languages. Its features include part-of-speech tagging, parsing trees, named entity recognition, classification, etc. In addition, it can be used to process text to compute the meaning of words, sentences, or entire texts.
Theano is a Python library to efficiently define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays. As a result, it can be used for machine learning applications that work with computationally intensive calculations.
Pytorch, a machine learning framework based on Torch, provides high-level APIs and building blocks for creating Deep Learning models. It offers maximum flexibility and the ability to use Python code to define, load, transform, and manipulate data.
Caffe is a machine learning/deep learning framework created with speed and modularity in mind. caffe2 is a lightweight, modular, and scalable library built to provide easy-to-use, extensible building blocks for fast prototyping of machine intelligence algorithms such as neural networks. In addition, Caffe/Caffe2 can be used for machine vision, speech and audio processing, and reinforcement learning.
How To Choose the Right Framework?
There are many frameworks for machine learning available. However, it is difficult to choose the proper framework without learning its capabilities, limitations, and use cases. Some factors to consider while choosing a framework are:
Ease of Use
A good library should make it easy to get started with your dataset, whether images, text, or anything else. You should be able to load and save data in memory efficiently.
Some frameworks target hardware deployment, and they might provide a way to speed up your models by using GPUs, TPUs, etc.
Many libraries support Python, R, Scala, C++, etc., whereas some modules are restricted to one or two languages. If you want to research building prototypes for your startup, consider the multi-language support.
Some frameworks are more rigid, forcing you to use their pre-defined architectures for building models. However, the ability to define your model is vital if you want to expand beyond the present capabilities of the framework.
A sound library should have documentation, tutorials, examples, Stack Overflow questions, etc., available online. This lets you get started quickly and efficiently.
Which Tools Do Data Scientists Use?
In addition to the frameworks listed above, data scientists use several tools for different tasks. In this paragraph, we go through some tools that data scientists mostly use.
Anaconda is an open-source data science platform that includes the most popular packages for data scientists, such as NumPy, SciPy, Pandas, Scikit-Learn, and Jupyter Notebook. It provides a much simpler way to set up your workstation for data analysis than installing each tool manually. Anaconda can be installed using the official Anaconda installer, making it available on Linux, Windows, and Mac OS X. This environment can be used to analyze data with pandas or build web applications with Flask.
Jupiter provides an interactive computing experience for data scientists, developers, students, and anyone interested in analyzing, transforming, and visualization of data. It’s a very flexible application, allowing you to create notebooks for data analysis and exploration. These notebooks can be shared with colleagues and provide an excellent environment for interactive work.
Another interactive computing program for data scientists is the IPython Notebook. It provides a web-based interface to an application called iPython. You can use it to create notebooks with markdown cells, which are converted into HTML documents decorated with text and multimedia. These notebooks provide a simple way of sharing your code and process with others.
Frequently Asked Questions
Can a data science project be outsourced?
Yes, it can be outsourced to data science companies. If you need some help with your project, then feel free to contact us.
Which other languages than Python are popular for data science?
R is another language that data scientists use for data science projects. Julia and Scala are also used for building data science applications.
Is Python only used for data science?
No, Python is used for machine learning, web development (Django), web applications (Flask), app development, data science projects, scientific computing, etc.
Is SQL a data science framework?
No, SQL is not a data science framework. It’s a database query language for structured queries on relational databases. So what are the best Python frameworks for Big Data Analysis?
It can be challenging to choose the proper framework for your machine learning project. Consider the following factors while selecting a library: ease of use, hardware deployment, multi-language support, flexibility, and ecosystem support. It is also advisable to try out a few popular frameworks before making your decision. Good luck!
Topics: Data Science