Top 20 Python libraries for Data Science

Data science libraries

 Top Data science libraries introduction of The Python programming language is assisting the developers in creating standalone PC games, mobiles, and other similar enterprise applications. Python has in excess of 1, 37,000 libraries which help in many ways. In this data-centric world, most consumers demand relevant information during their buying process. The companies also need data scientists for achieving deep insights by processing the big data. 

This info will guide the data scientists while making critical decisions regarding streamlining business operations and several other related tasks that need valuable information for accomplishment efficiently. Therefore, with the rise in demand for data scientists, beginners and pros are looking to reach resources for learning this art of analysis and representation of data. There are some certifications programs available online which can be helpful for training. You can find blogs, videos, and other resources online as well. 

Let’s have a look at some of the Python Data science libraries that are helpful for you.

1. NumPy: 

NumPy is among the first choice for data scientists and developers who know their technologies dealing with data-related things. This is a Python package and is available for performing scientific computations.  By using NumPy, you may leverage the n-dimensional array objects, C, C++, FORTRAN programs based on integration tools, functions for difficult mathematical operations such as Fourier transformations, linear algebra, and random numbers. Therefore you may effectively integrate the DB by selecting a variety of operations for performing. 

NumPy gets installed under TensorFlow and other such machine learning platforms, thereby internally providing strength to their operations. As this is an array interface, it will allow multiple options for reshaping large data sets. NumPy may be used for treating images, sound wave representations, and other binary operations. In case you have just arrived in the field of data science and machine learning, you must acquire a good understanding of NumPy for processing the real-world data sets.

 2. Theano:

 Another useful Python library is Theano, which assists data scientists to create big multi-dimensional arrays which are related to computing operations. This is similar to TensorFlow; however, the only difference being it is not very efficient. It involves getting used to parallel and distributed computing-related tasks. By using this, you may optimize, evaluate, or express the data-enabled mathematical operations.

Due to its GPU based infrastructure, the library has the capability of processing the operations in quicker ways than compared to the CPU. The library stands fit for stability and speed optimization and delivering you the expected outcome. For quicker evaluation, the C code generator used is dynamic and is extremely popular among data scientists. They can do unit testing here for identifying the flaws in the model. 

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3. Keras:

 One of the most powerful Python libraries is Keras that permits higher-level neural network APIs for integration. The APIs will execute over the top of TensorFlow, CNTK, and Theano. Keras was developed for decreasing the challenges faced in difficult researches permitting them to compute quicker. For someone using the deep learning libraries for their work, Keras will be their best option. Keras permits quicker prototyping and supports recurrent and convoluted networks independently. It also allows various blends and execution over CPU and GPU.

 Keras give you a user-friendly environment, thereby decreasing the efforts required for cognitive loads by using simple APIs and so providing necessary results. Because of the modular nature of Keras, you may use a range of modules from optimizers, neural layers, and activation functions, etc. for preparing newer models. Keras is an open source library and is written in Python. It is a particularly good option for the data scientists who are having trouble in adding newer models as they may easily add newer modules as functions and classes.

4. PyTorch:

 It is one of the largest machine learning libraries available for data scientists and researchers. The library aids them with dynamic computational graph designs; quick tensor computation accelerated via GPU and other complicated tasks. In the case of neural network algorithms, the PyTorch APIs will play an effective role. 

This crossbreed front-end platform is simple to use and allows transitioning into a graphical mode for optimization. In order to get precise results in the asynchronous collective operations and for the establishment of peer to peer communication, the library gives native support to its users. By using ONNX (Open Neural Network Exchange), you may export models for leveraging the visualizers, run times, platforms, and many other resources. The greatest part of PyTorch is that it enables a cloud-based environment for simple scaling of resources utilized for deployment testing. 

PyTorch is developed on a similar concept to another machine learning library called Torch. During the last few years, Python has gradually become more popular with the data scientists because of the trending data-centric demands. 

5. SciPy: 

This is a Python data science library used by researchers, data scientists, and developers alike. However, do not confuse the SciPy stack with the library. SciPy gives you optimizations, integration, statistics, and linear algebra packages for the computations. The SciPy is based on the NumPy concept for dealing with difficult mathematical problems. SciPy gives numerical routines that can be used for integration and optimization. SciPy will inherit a range of sub-modules to select from. In the event that you have recently started your career in data science, SciPy will be quite helpful for guiding you through the whole numerical computation.

 We have seen thus far how the Python programming can assist the data scientists in analyzing and crunching big and unstructured data sets. There are other libraries such as Scikit-Learn, TensorFlow, and Eli5 available for assistance through this journey.

 6. Pandas

The Python Data Analysis Library is called PANDAS. It is an open source library in Python for availing the analysis tools and high-performance data structures. PANDAS is developed on the NumPy package, and its main data structure is DataFrame. By using DataFrame, you can manage and store data from the tables by doing manipulation of rows and columns.

 Methods such as square bracket notation decrease the personal effort involved in data analysis tasks such as square bracket notation. In this case, you will have the tools for accessing the data in the memory data structures and perform read and write tasks even though they are in multiple formats like SQL, CSV, Excel, or HDFS, etc.

 7. PyBrain: 

This is a powerful modular machine learning library which is available in Python. The long form of PyBrain goes like Python Based Reinforcement Learning ArtifiArtificial Intelligence and Neural Network Library. For the entry-level data scientists, this offers flexible algorithms and modules for advanced research. It has a range of algorithms available for evolution, supervised and unsupervised learning, and neural network. For the real-life tasks PyBrain has emerged as a great tool, and it is developed across a neural network in the kernel.

 8. SciKit-Learn:

This is a simple tool used for data analysis and data mining-related tasks. It is licensed under BSD and is an open-source tool. It can be reused or accessed by anyone in different contexts. The SciKit is developed over NumPy, Matplottlib, and SciPy. The tool is utilized for regression, classification, and clustering or managing spam, image recognition, stock pricing, drug response, and customer segmentation, etc. SciKit-Learn allows for dimensionality reduction, pre-processing, and model selection.

 9. Matplotlib: 

This library of Python is used for 2D plotting and is quite popular among data scientists for designing different figures in multiple formats across the respective platforms. It can be easily used in the Python code, Jupyter notebook, or IPython shells application servers. By using the Matplotlib, you will be able to make histograms, bar charts, plots, and scatter plots, etc. 

10. TensorFlow: 

TensorFlow is an open source library designed by Google for computing the data low graphs by using empowered ML algorithms. The library was designated for fulfill fulfilling the high demands for training for neural network work. TensorFlow is not only limited to scientific computations conducted by a Google rater. It is used extensively for the popular real-world applications. Because of the flexible and high-performance architecture, you can easily deploy it for all GPUs, CPUs, or TPUs and you can perform the PC server clustering for the edge devices. 

11. Seaborn: 

It was designed for visualizing complex statistical models. Seaborn comes with the potential of delivering accurate graphs like heat maps. Seaborn is developed on the Matplotlib concept, and as a result, it is highly dependent on it. Even the minor data distributions can be seen by using this library, which is the reason why the library has become popular with the developers and data scientists.

 12. Bokeh: 

It is one of the more visualization purpose libraries used for the design of interactive plots. Similar to the library described above, this one is also developed on Matplotlib. Because of the support of used data-driven components such as D3.js this library can present interactive designs in your web browser.

 13. Plotly:

 Now, let’s see the description of Plotly, which happens to be one of the most popular web-based frameworks used by the data scientists. The toolbox offers the design of visualization models by using a range of API varieties supported by the multiple programming languages which include Python. InterInteractive graphics can be easily used along with numerous robust accessories via the main site For utilizing Plotly in the working model, you will have to set up the available API keys correctly. The graphics are processed on the server side, and once they are successfully executed, they will start appearing on the browser screen.

 14. NLTK: 

The long form of NLTK is Natural Language ToolKit. As indicated by its name, the library is useful in accomplishing natural language processing tasks. In the beginning, it was created for promoting teaching models along with other NLP enabled research like the cognitive theory used in AI and linguistic models. It has been a successful resource in its area and drives real-world innovations of artificial intelligence. By using NLTK you can perform operations such as stemming, text tagging, regression, corpus tree creation, semantic reasoning, named entities recognition, tokenization, classifications and a range of other difficult AI related tasks. Now challenging work will need large building blocks such as semantic analysis, summarization, and automation. But this work has become easier and can be easily accomplished by using NLTK. 

15. Gensim:

It is a Python-based open-source library that permits topic modeling and space vector computation by using an implemented range of tools. compatible with the big test and makes for efficient operation and in-memory processing. It utilizes SciPy and NumPy modules to provide easy and efficient handling of the environment. Gensim utilizes unstructured digital text and processes it by using in-built algorithms such as word2vec, Latent Dirichlet Allocation (LDA), Hierarchical Dirichlet Processes (HDP), and Latent Semantic Analysis (LSA). 

16. Scrapy:

Scrapy is also known as spider bots. Scrapy is a Data science library responsible for crawling the programs and retrieving structured data out of web applications. Scrapy is a Python-written open-source library. This happens to be a complete framework having the potential to collect data via APIs and acts as a crawler. You can write codes by using Scrapy, re-utilize universal programs, and develop scalable crawlers for the applications. it is created across a spider class that contains instructions for the crawler. 

17. Statsmodels:

Statsmodels is another Python library, and it is responsible for giving exploration modules by using multiple methods for performing assertions and statistical analysis. It uses robust linear models, time series, analysis models, regression techniques, and discrete choice models, thereby making it prominent among similar data science libraries. It comes with a plotting function for the statistical analysis for achieving high-performance outcomes during the processing of the large statistical data sets.

 18. Kivy:

This is another open-source Python library providing a natural user interface that may be accessed easily over Linux, Windows, or Android. The open-source library is licensed under MIT, and it is quite helpful in the building of mobile apps along with multi-touch applications. In the beginning, the library was developed for the Kivy iOS and came with features such as a graphics library. Extensive support is provided to the hardware with a keyboard, mouse, and a range of widgets. You can also use Kivy for creating custom widgets by applying it as an intermediate language. 

19. PyQt:

Another Python binding toolkit for being used as a cross-platform GUI is PyQt. PyQt is being implemented as the Python plugin. It is a free application licensed under the General Public License (GNU). It comes with around 440 classes and in excess of 6000 functions in order to make the user experience simpler. PyQt has classes to access SQL databases, active X controller classes, an XML parser, SVG support, and several other useful resources for reducing user challenges.

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20. OpenCV:

This library is designed for driving the growth of real-time computation application development. The library is created by Intel, and the open-source platform is licensed with BSD. It is free for use by anyone. OpenCV comes with 2D and 3D feature toolkits, mobile robotics, gesture recognition, SFM, Naive Bayes classifier, gradient boosting trees, AR boosting, motion tracking, segmentation, face recognition and object identification algorithms. Although OpenCV is written by using C++, it will provide binding with Python, Octave, and Java.