Top 10 Python Libraries For Data Science
With the advent of digitization, the business space has been critically revolutionized and with the introduction of data analytics, it has become easier to tap prospects and convert them by understanding their psychology by the insights derived from the same. In today’s scenario, Python language has proven to be the big boon for developers in order to create websites, applications as well as computer games. Also, with its 137000 libraries, it has helped greatly in the world of data analysis where the business platforms ardently require relevant information derived from big data that can prove conducive for critical decision making.
Let us discuss some important names of Python Libraries that can greatly benefit the data analytics space.
Theono
Theono is similar to Tensorflow that helps data scientists in performing multi-dimensional arrays relevant to computing operations. With Theono you can optimize, express and array enabled mathematical operations. It is popular amongst data scientists because of its C code generator that helps in faster evaluation.
NumPy
NumPy is undoubtedly one of the first choices amongst data scientists who are well informed about the technologies and work with data-oriented stuff. It comes with a registered BSD license and it is useful for performing scientific computations. It can also be used as a multi-dimensional container that can treat generic data. If you are at a nascent stage of data science, then it is key for you to have a good comprehension of NumPy in order to process real-world data sets. NumPy is the foundational scientific-computational library in Data Science. Its precompiled numerical and mathematical routines combined with its ability to optimize data-structures make it ideal for computations with complex matrices and data arrays.
Keras
One of the most powerful libraries on the list that allows high-level neural networks APIs for integration is Keras. It was primarily created to help with the growing challenges in complex research, thus helping to compute faster. Keras is one of the best options if you use deep learning libraries in your work. It creates a user-friendly environment to reduce efforts in cognitive load with facile API’s giving the results we want. Keras written in Python is used with building interfaces for Neural Networks. The Keras API is for humans and emphasizes user experience. It is supported at the backend by CNTK, TensorFlow or Theano. It is useful for advanced and research apps because it can use individual stand-alone components like optimizers, neural layers, initialization sequences, cost functions, regularization and activation sequences for newer expressions and combinations.
SciPy
A number of people get confused between SciPy stack and library. SciPy is widely preferred by data scientists, researchers, and developers as it provides statistics, integration, optimization and linear algebra packages for computation. SciPy is a linked library which aids NumPy and makes it applicable to functions like Fourier series and transformation, regression and minimization. SciPy follows the installation of NumPy.
NLKT
NLKT is basically national language tool kit. And as its name suggests, it is very useful for accomplishing national language tasks. With its help, you can perform operations like text tagging, stemming, classifications, regression, tokenization, corpus tree creation, name entities recognition, semantic reasoning, and various other complex AI tasks.
Tensorflow
Tensorflow is an open source library designed by Google that helps in computing data low graphs with empowered machine learning algorithms. It was created to cater to the high demand for training neural networks work. It is known for its high performance and flexible architecture deployment for all GPUs, CPUs, and TPUs. Tensor has a flexible architecture written in C and has features for binding while being deployed on GPUs, CPUs used for deep learning in neural networks. Being a second generation language its enhanced speed, performance and flexibility are excellent.
Bokeh
Bokeh is a visualization library for designing that helps in designing interactive plots. It is developed on Matplotib and supports interactive designs in the web browser.
Plotly
Plotly is one of the most popular and talked about web-based frameworks for data scientists. If you want to employ Plotly in your web-based model is to be employed properly with setting up API keys.
SciKit-Learn
SciKit learn is typically used for simple data related and mining work. Licensed under BSD, it is an open source. It is mostly used for classification, regression and clustering manage spam, image recognition, and a lot more. The Scikit-learn module in Python integrates ML algorithms for both unsupervised and supervised medium-scale problems. Its API consistency, performance, documentation, and emphasis are on bringing ML to non-specialists in a ready simple high-level language. It is easy to adapt in production, commercial and academic enterprises because of its interface to the ML algorithms library.
Pandas:
The open-source library of Pandas has the ability to reshape structures in data and label tabular and series data for alignment automatically. It can find and fix missing data, work and save multiple formats of data, and provides labelling of heterogeneous data indexing. It is compatible with NumPy and can be used in various streams like statistics, engineering, social sciences, and finance.
Theano:
Theano is used to define arrays in Data Science which allows optimization, definition, and evaluation of mathematical expressions and differentiation of symbols using GPUs. It is initially difficult to learn and differs from Python libraries running on Fortran and C. Theano can also run on GPUs thereby increasing speed and performance using parallel processing.
PyBrain
PyBrain is one of the best in class ML libraries and it stands for Python Based Reinforcement Learning, Artificial Intelligence. If you are an entry-level data scientist, it will provide you with flexible modules and algorithms for advanced research. PyBrain is stacked with neural network algorithms that can deal with large dimensionality and continuous states. Its flexible algorithms are popular in research and since the algorithms are in the kernel they can be adapted using deep learning neural networks to any real-life tasks using reinforcement learning.
Shogun:
Shogun like the other Python libraries has the best features of semi-supervised, multi-task and large-scale learning, visualization and test frameworks; multi-class classification, one-time classification, regression, pre-processing, structured output learning, and built-in model selection strategies. It can be deployed on most OSs, is written in C and uses multiple kernel learning, testing and even supports binding to other ML libraries.
Comprehensively, if you are a budding data analyst or an established data scientist, you can use the above-mentioned tools as per your requirement depending on the kind of work you're doing. This is why it is very important to understand the various libraries available that can make your work much easier for you to accomplish your task much effectively and faster. Python has been traversing the data universe for a long time with its ever-evolving tools and it is key to know them if you want to make a mark in the data analytics field. For more details, in brief, you can also search for - Imarticus Learning and can drop your query by filling up a simple form from the site or can contact us through the Live Chat Support system or can even visit one of our training centers based in - Mumbai, Thane, Pune, Chennai, Bangalore, Hyderabad, Delhi and Gurgaon.
