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« **on:** July 06, 2019, 02:54:42 AM »
1. NumPy

NumPy is the first choice among developers and data scientists who are aware of the technologies which are dealing with data-oriented stuff. It is a Python package available for performing scientific computations. It is registered under the BSD license.

Through NumPy, you can leverage n-dimensional array objects, C, C++, Fortran program based integration tools, functions for performing complex mathematical operations like Fourier transformation, linear algebra, random number etc. One can also use NumPy as a multi-dimensional container to treat generic data. Thus, you can effectively integrate your database by choosing varieties of operations to perform with.

NumPy is installed under the TensorFlow and other complex machine learning platforms empowering their operations internally. Since it is an Array interface, it allows us multiple options to reshape large datasets. It can be used for treating images, sound waves representations, and other binary operations. If you have just marked your presence in this data science or ML field, you must have a great understanding of NumPy to process your real-world data sets.

2. Theano

Theano is another useful Python library assists data scientists in performing large multi-dimensional arrays related computing operations. It is more like TensorFlow but the only difference is, it is not that efficient.

It is getting used for distributed and parallel computing based tasks. Through it, you can optimize, express or evaluate you array-enabled mathematical operations. It is tightly coupled with NumPy powered by implemented numpy.ndarray function.

Due to GPU based infrastructure, it holds the capability to process operations in faster ways than CPU. It stands fit for speed and stability optimizations delivering us the expected outcomes.

For faster evaluation, its dynamic C code generator is popular among data scientists. Here, they can perform unit-testing to identify flaws in the whole model.

3. Keras

Keras is one of the most powerful Python libraries which allow high-level neural networks APIs for integration. Theses APIs execute over the top of TensorFlow, Theano and CNTK. Keras was created for reducing challenges faced in complex researches allowing them to compute faster. For one who is using deep learning libraries for their work, Keras is the best option.

It allows fast prototyping, supports recurrent and convolution networks individually and also their combination, execution over GPU and CPU.

Keras provides a user-friendly environment reducing your effort in cognitive load with simple APIs giving us the required results. Due to its modular nature, one can use varieties of modules from neural layers, optimizers, activation functions etc.., for developing a new model.

It is an open source library written in Python. For data scientists having trouble adding new modules, Keras is a good option where they can simply add a new module as classes and functions.

4. PyTorch

PyTorch is considered one of the largest machine learning libraries for data scientists and researchers. It helps them in dynamic computational graphs design, fast tensor computations accelerated through GPUs., and various other complex tasks. In neural network algorithms, PyTorch APIs plays an effective role.

The hybrid front-end PyTorch platform is very easy to use allows us transitioning in graph mode for optimizations. For achieving accurate results in asynchronous collective operations and establishing a peer to peer communication it provides a native supports to the users.

With native ONNX (Open Neural Network Exchange. support, one can export models to leverage visualizers, platforms, run-times, and various other resources. The best part of PyTorch it enables a cloud-based environment for easy scaling of resources used in deployment or testing.

It is developed on the concept of another ML library called as Torch. Since the past few years, PyTorch is getting more popular among data scientists due to trending data-centric demands.

5. SciPy

SciPy is another Python library for researchers, developers and data scientists. Do not get confused with the SciPy stack and library. It provides statistics, optimizations, integration and linear algebra packages for computation. It is based on NumPy concept to deal with complex mathematical problems.

It provides numerical routines for optimization and integration. It inherits varieties of sub-modules to choose from. If you have just started your data science career, SciPy can be very helpful to guide you throughout the whole numerical computations thing.

We can see how Python programming is assisting data scientists in crunching and analyzing large and unstructured data sets. Other libraries like TensorFlow, SciKit-Learn, Eli5 are also available to assist them throughout this journey.

6. PANDAS

PANDAS referred as Python Data Analysis Library. PANDAS is another open source Python library for availing high-performance data structures and analysis tools. It is developed over the Numpy package. It contains DataFrame as its main data structure.

With DataFrame you can store and manage data from tables by performing manipulation over rows and columns. Methods like square bracket notations reduce person’s effort in data analysis tasks like square bracket notations. Here, you will get tools for accessing data in-memory data structures performing read and write tasks even if they are in multiple formats such as CSV, SQL, HDFS or excel etc.

7. PyBrain

PyBrain is another powerful modular ML library available in Python. PyBrain stands for Python Based Reinforcement Learning, Artificial Intelligence, and Neural Network Library. For entry-level data scientists, it offers flexible modules and algorithms for advanced research. It has varieties for algorithms for evolution, neural networks, supervised and unsupervised learning. For real-life tasks, it has emerged as the best tool which is developed across the neural network in the kernel.

8. SciKit-Learn

Scikit-Learn is a simple tool for data analysis and mining-related tasks. It is open-source and licensed under the BSD. Anyone can access or reuse it in various contexts. SciKit is developed over the Numpy, Scipy, and Matplotlib. It is being used for classification, regression and clustering o manage spam, image recognition, drug response, stock pricing, customer segmentation etc. It also allows dimensionality reduction, model selection and pre-processing.

9. Matplotlib

This 2D plotting library of Python is very famous among data scientists for designing varieties of figures in multiple formats which is compatible across their respected platforms. One can easily use it in their Python code, IPython shells or Jupyter notebook, application servers. With Matplotlib, you can make histograms, plots, bar charts, scatter plots etc.

10. Tensorflow

This open source library was designed by Google to compute data low graphs with the empowered machine learning algorithms. It was designed to fulfill high demand for the training neural networks work. It is not just limited to the scientific computations performed by Google rater it is widely being used in the popular real-world application.

Due to its high performance and flexible architecture the deployment for all CPUs, GPUs or TPUs becomes easy task performing PC server clustering to the edge devices.

11. Seaborn

Seaborn was designed to visualize the complex statistical models. It has the potential to deliver accurate graphs such as heat maps. Seaborn was created on the concept of Matplotlib and somehow it is highly dependent on that. Minor to minor data distributions can be easily visualized through this library which is why it has become familiar among data scientists and developers.

12. Bokeh

Bokeh is one more visualization library for designing interactive plots. Just like the last one, it is also developed on matplotlib. Due to the used data-driven documents (D3.js. support it presents interactive designs in the web browser.

13. Plotly

Let’s talk about the Plotly which is one of the most famous web-based frameworks for data scientists. This toolbox offers designing of visualization models with varieties of APIs supported by multiple programing languages including Python. You can easily use interactive graphics and numerous robust accessible through its main website plot.ly. For using Plotly in your working model you need to set up available API keys properly. The accessible graphics are processed on the server side and once successfully executed they will appear on your browser screen.

14. NLTK

NLTK is pronounced as the Natural Language ToolKit. As per its name, this library is very helpful for accomplishing Natural language processing tasks. Initially, it was developed to promote the teaching models and other NLP enabled research such as the cognitive theory of artificial intelligence and linguistic models etc., which has become a successful resource in its field driving the real world innovations from artificial intelligence.

With NLTK one can perform operations like text tagging, stemming, classifications, regression, tokenization, corpus tree creation, name entities recognition, semantic reasoning, and various other complex AI tasks. Now challenging works requiring large building blocks like semantic analysis and automation or summarization has become an easier task which can be easily completed with NLTK.

15. Gensim

Gensim is an open source Python-based library which allows topic modeling and space vector computations with the implemented varieties of tools. It is compatible with the large texts making efficient operations and their in-memory processing. It uses the NumPy and SciPy modules for providing efficient and easy to handle the environment.

It uses the unstructured digital texts and processes them with the inbuilt algorithms like word2vec, hierarchical Dirichlet processes (HDP), latent Dirichlet allocation (LDA) and latent semantic analysis (LSA).

16. Scrapy

Scrapy is also pronounced as the spider bots. This library is responsible for crawling programs and retrieving of the structured data from the web applications. This open source library is written in Python. As per the name it was designed for scraping. It is the complete framework with the potential to collect data through APIs and act like a crawler.

Through it, one can write codes, reuse universal programs and create scalable crawlers for their application. Scrapy is created across the Spider class which contains the instructions for a crawler.

17. Statsmodels

This Python library is responsible for providing the data exploration modules with multiple methods to perform statistical analysis and assertions. The use of regression techniques, robust linear models, analysis models, time series and discrete choice model makes it popular among other data science libraries. It has the plotting function for statistical analysis to achieve high-performance outcomes while processing large statistical data sets.

18. Kivy

This open-source Python library provides a natural user interface which can be easily accessed over the Android, iOS, Linux or Windows. It is licensed open source under MIT. The library is very helpful in building mobile apps and multi-touch applications.

Initially, it was developed for Kivy iOS. It avails the elements like the graphics library, extensive support to hardware such as the mouse, keyboard and wide range of widgets. One can also use it as an intermediate language to create custom widgets.

19. PyQt

PyQt is a Python binding toolkit for cross-platform GUI. It is implemented as a Python plugin. PyQt is a free application which is licensed under the GNU General Public License. PyQt have almost 440 classes and more than 6000 functions to make a user’s journey easier. It includes classes for accessing SQL databases, an XML parser, active X controller classes, SVG support, and many more useful resources to reduce user’s challenges.

20. OpenCV

OpenCV is designed for driving growth of the real-time computing application development. It was created by Intel. This open-source platform is licensed under BSD and free to use for anyone. It includes 2D and 3D feature toolkits, object identification algorithms, mobile robotics, face recognition, gesture recognition, motion tracking, segmentation, SFM, AR, boosting, gradient boosting trees, Naive Bayes classifier and many other useful packages.