In this guide, you will find the best Python frameworks to use for Data Science, one of the biggest growth areas for IT professionals in recent years.
In a world where data is more valuable than oil, the demand for Data Scientists and Analysts is skyrocketing. So what are the best tools for tapping into these data reserves? Hands down, Python is the clear choice for any aspiring developer trying to break into the field of Data Analysis.
With its relatively simple code structure and a plethora of libraries and frameworks, Python is an invaluable tool for coders of all stripes. Per Stack Overflow’s most recent 2020 Developer Survey, Python ranks in the top 5 in both most used and most beloved programming languages, with Python developers commanding an average global salary of $59,000. If you are looking for Python software engineers, you may be interested in our guide to best practices when hiring a data scientist.
In terms of tools for Data Science, Python contains multitudes; to help sift through the options, we’ve listed the top ten frameworks we consider the most useful for those trying to join in on the exciting new data-based era!
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Data Science projects need to have a large and stable environment of technologies. This is not an easy task if you don’t have strong organization and monitoring of your libraries and their versions. Thankfully, Anaconda—a distribution of the Python and R programming languages for scientific computing—can simplify both package deployment and management.
It comes with over 250 base packages automatically installed with an additional 7,500+ open-source ones available from PyPI, the repository of Python software. Additionally, it comes with Anaconda Navigator, a GUI that is included as a graphical alternative to the command-line interface.
Available on any SO distribution (Linux, Windows or IOS), Anaconda’s open-source version is more than sufficient to perform professional work. The default installation of Anaconda2 includes Python 2.7 while Anaconda3 does so with Python 3.7. However, it is possible to create new environments that include any version of the Python package with Anaconda, its package manager.
If you want to break into the profession of Data Science, it is absolutely fundamental to work with Jupyter. More than just a framework or library, this platform allows you to create and share reports for the client while you develop your machine learning models, analyze data, draw your graphs, or whatever other coding you might need.
The open-source application enables the creation of documents containing live code, equations, visualizations, or narrative text. When you finish your document, you can download it as a PDF, HTML web Page, DOC, or most formats needed to send it to clients. You can also extract the code on it to create a script or application and use it in production.
Originally released in notebook format, a new version has been created that is more powerful and has a much better interface: the JupyterLab. It is more flexible so that the interface can be configured and arranged to support a wide range of workflows in data science, scientific computing, and machine learning. JupyterLab is extensible and modular in that you are able to write plugins that add new components and integrate with existing ones.
Pandas is a fundamental, high-level building block for achieving practical and real-world data analysis in Python. It has been said to be the most powerful and flexible available tool for open-source data analysis and manipulation in any programming language. Python has been used with pandas in an array of academic and commercial domains, including Finance, Neuroscience, Economics, Statistics, Advertising, and Web Analytics.
Through this library, you will be able to import any kind of data source (CSV, Text files, Microsoft Excel, SQL database, and HDF5) to a data frame object, with various useful properties and functionalities such as:
Plus, all of this can be efficiently implemented through C!
Working with arrays is a very common task during a data science project. Fortunately, Numpy comes to help us make this work much easier. It delivers a multidimensional array object, various derived objects, such as masked arrays and matrices, as well as an assortment of routines for quick operations on arrays, including logical, mathematical, selecting, I/O, shape manipulation, discrete Fourier transform, sorting, basic linear algebra, basic statistical operations, random simulation and an abundance of other operations.
Python already has tools to work with arrays, but there are several important differences between NumPy arrays and the standard Python sequences that make NumPy more useful, for example:
An increasing number of scientific and mathematical Python-based packages are utilizing NumPy arrays. Though they often support Python-sequence input, such input is converted to NumPy arrays prior to processing, and frequently output NumPy arrays. Therefore, to efficiently use much of current scientific or mathematical Python-based software, applying only Python’s built-in sequence types can be insufficient. It is highly valuable to be able to employ NumPy arrays.
Having meaningful and attractive graphs is fundamental to both understanding data and explaining results and analyses to clients or colleagues. Matplot has been created for just this purpose, possessing a comprehensive set of tools for creating static, animated, and interactive visualizations.
It is likely the most used Python package for 2D-graphics as it supplies publication-quality figures in numerous formats and quick ways to create data visualization from Python. With only a few lines of code, you can draw many graph types, such as histograms, power spectra, bar charts, error charts, scatterplots, pie charts, heatmaps, line plots, and many many more.
It is so customizable that it is possible to be overwhelmed with all available options. Thankfully, it maintains an immense community, generating a plethora of examples, tutorials, and documentation for every kind of graph that you could need.
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One of the reasons for the great popularization of machine learning among software developers in the last year is Scikit Learn. With three lines of code (instantiate, train and predict) you can create quite sophisticated mathematical models able to make predictions even better than humans. It was started in 2007 as a Google Summer of Code project by David Cournapeau, later released as an open-source library to be used by anyone.
It features various classification, regression, and clustering classical algorithms, including support vector machines, k-nearest neighborhood, decision trees, random forests, gradient boosting, k-means, and DBSCAN, and is designed to interoperate with NumPy.
Much scientific and academic research is accomplished thanks to this library and most of the Kaggle competition winners use their algorithms’ implementation. Furthermore, most of the new AI companies build their products using the features available in sklearn.
Do you want to create AI models? Do you wish to create a robot more intelligent than any human being? Have you dreamed of making science fiction real? If so, then the invaluable Scikit Learn framework is right for you!
When you read or hear talk about Artificial Intelligence, you almost certainly will hear the term 'neural network’. One of the most extraordinary concepts in AI, neural networks are a series of algorithms that emulate the workings of a brain. Indeed, the universal approximation theorem states that a neural network is theoretically capable of modeling any kind of problem.
Tensorflow is the open-source library that builds these amazing networks. It is applicable across a range of tasks with a particular focus on training and inference of deep neural networks. Developed by the Google Brain team for internal Google use, it is now used for both research and production at many other research centers and companies. It can be run over GPU, which reduces the time requirements of testing and training a deep network, which can take days or months on a home processor.
However, one notable drawback with Tensorflow is that it is not easy to use and has a considerable learning curve in order to comprehend and become familiar with all configurable options, error messages and mathematics needed to construct the network.
Keras is an API designed for regular human beings, not machines or specially trained scientists. It follows best practices for reducing cognitive load, offering consistent and simple APIs. It minimizes the number of required user actions for common use cases while providing clear and actionable error messages. It also has extensive documentation and developer guides.
This tool is the most-used deep learning framework, among the top-5 winning teams on Kaggle. Because Keras makes it easier to run new experiments, it empowers users to more quickly explore and test a greater number of ideas than competitors.
It is an industry-strength framework that can scale to large clusters of GPUs or an entire TPU pod, built on top of TensorFlow. So, if you just started working with neural network models this will be highly useful for you!
Nowadays, one of the most popular areas of Machine Learning is Natural Language Processing (NLP). Last year, OpenAI, one of the top AI companies, released GPT-3, an NLP model that is capable of amazing things. For example, it is able to generate news articles almost indistinguishable from articles written by humans. Moreover, it can program websites, discuss topics like politics and economics with humans, and draw amazing pictures through text orders.
GPT-3 is a very complex model and is not accessible for all. However, if you want to dive into the NLP world you have the option to begin with the Natural Language Toolkit (NLTK). It provides intuitive interfaces to more than 50 corpora and lexical resources, such as WordNet, along with a suite of many text-related methods, namely text processing libraries for classification, tokenization, stemming, tagging, parsing, semantic reasoning, and wrappers for industrial-strength NLP libraries. It also contains an active discussion forum. In summary, it provides a practical introduction to programming for language processing.
If you want to progress in this area, there are more advanced and powerful libraries. One of them is Fasttext, a simple neural network that is implemented so efficiently that you can train language models over millions of words in seconds. Moreover, it exhibits better performance in classification text (i.e. sentiment analysis) than many deep neural networks.
Fasttext was developed by the Facebook Research Lab. It is implemented in C++ and it has a Python wrapper for easier utilization. In addition, pre-trained vectors in 156 languages have been uploaded to Fasttext. This allows you to load a language model trained overall Wikipedia articles instantly and shortly afterward over the network for use in a specific task.
This library has high-level functionalities, so you will be able to implement it even with a basic knowledge of NLP!
Technologies are changing and improving constantly, so it is essential to remain up to date with state-of-the-art techniques and libraries. For this reason, we have listed the best libraries for any Data Science professionals to use. Through them, you will be able to accomplish any professional Data Science work and function as part of this amazing tech transformation. There are many other helpful Python libraries that deserve to be recognised, in addition to these top ten for data science. If you found this information useful, you may be curious about how the future of data science is shaping out.
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