Top 10 Python Libraries for Data Science

by | Feb 5, 2018 | Data Analytics

8 Min Read. |

I suppose all of us listen to new songs on the web and forget them in the long run. We might at the best remember the genre or the tune when we hear it after a long time. What if you had something that saves your browser history and lets you create a spreadsheet of your searches? Sounds interesting, right? Well, Python is quite similar. It is simple, flexible, easy to use, allows you to save your codes in a .csv file and work on it, as and when you want. Let’s learn top python libraries for data science.

Python has gained immense popularity as a general-purpose, high-level back-end programming language for the creation of the prototype and developing applications. Python’s readability, flexibility, and suitability to data science operations have made it one of the most preferred languages among developers. It is extensively used by developers who need to apply statistical techniques or data analysis in their work. Data scientists use Python to integrate their tasks with web apps or production environments.

Python libraries simplify complex jobs and make data integration much easier with fewer codes and in lesser time. In this article, I will discuss the salient features of some the top Python libraries for Data Science, and how to use them for work.

1. Numeric and Scientific Computation (NumPy and SciPy):

NumPy or Numeric and Scientific Computation lays the basic premises for scientific computing in Python ranks among the top 10 Python Libraries for Data Science. It provides you with fast precompiled functions for mathematical and numerical routines. In addition, NumPy optimizes Python programming with powerful data structures for efficient computation of multi-dimensional arrays and matrices.
Scientific Python also is known as SciPy is inextricably linked with NumPy. Using SciPy you can lend a competitive edge to NumPy, by enhancing useful functions for regression, minimization, Fourier-transformation, and many more. You need to install NumPy first, and then SciPy.

2. PANDAS (Python Data Analysis Library):

Pandas is an open source tool that provides high-performance, easy-to-use data structures and data analysis tools for Python programming. It can be used to add data structures and tools designed for practical data analysis in multiple streams such as finance, statistics, social sciences, and engineering. The best part of Pandas is its easy adaptability, which makes it one of the top Python Libraries for Data Science. It can work perfectly well with incomplete, unstructured, messy, and uncategorized data. It can, at the same time provide tools for shaping, merging, reshaping, and slicing of datasets.
Pandas, one of the Top Python Libraries for Data Science, come with several unique features such as:

• Pandas python can reshape the data structures
• Pandas can label series and tabular data to facilitate automatic alignment of data
• Heterogeneous indexing of data along with systematic labeling
• Capable of identifying and fixing missing data
• Ability to load and save data from multiple formats
• Easy Conversion from NumPy and Python data structures to Pandas objects

3. Matplotlib:

Matplotlib is a Python 2D plotting library, capable of producing publication quality figures in a wide variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, the jupyter notebook, web application servers, and four graphical user interface toolkits.
Matplotlib, which is also one of the top Python Libraries for Data Science, is used for generating plots, histograms, power spectra, bar charts, error charts, scatterplots, etc., with fewer codes. For examples, see the sample plots and thumbnail gallery.
For simple plotting, the pyplot module provides a MATLAB-like interface, particularly when combined with IPython. For the power user, you will have full control of line styles, font properties, axes properties, through an object-oriented interface or through a set of functions familiar to MATLAB users.

4. SciKit-Learn:

Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. It is one of the best-known machine-learning libraries for python. The Scikit-learn package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. The primary emphasis is upon ease of use, performance, documentation, and API consistency.

With minimal dependencies and easy distribution under the simplified BSD license, SciKit-Learn is widely used in academic and commercial settings. Scikit-learn exposes a concise and consistent interface to the common machine learning algorithms, making it simple to bring ML into production systems.

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5. Theano:

Theano, one of highly-rated Python Libraries for Data Science, allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation. Theano has a steep learning curve for most Python users as the framework for declaring variables and building functions differ greatly from the basic premises of Python.

However, with good tutorials and examples, new users can start in the right direction. If you can get past these hurdles, the performance benefits are incredible. Many great Python libraries (such as NumpPy or Pandas) perform operations faster than analogous operations in base Python by taking advantage of optimized methods ran in C or FORTRAN. In addition, Theano code can be executed on GPUs (which a typical PC may have hundreds of versus a small handful of CPUs), increasing speed by an order of magnitude by taking advantage of additional parallel processing.

6. Tensorflow:

Tensor, one of the top Python Libraries for Data Science for a job, is Google Brain’s second-generation system. Written mostly written in C++, it includes the Python bindings, performance is not a matter of worry. One of my favorite features is the flexible architecture, which allows me to deploy it to one or more CPUs or GPUs in a desktop, server, or mobile device all with the same API. Not many, if any, libraries can make that claim. It was developed for the Google Brain project and is now extensively used. However, you must dedicate some time to learn its API, but the time spent is worth it. Within the first few minutes of playing around with the core features, I could already tell TensorFlow would allow me to spend more time implementing my network designs and not fighting through the API.
If you want to learn more about TensorFlow and neural networks, try taking a course like Deep Learning with TensorFlow, which will not only teach you about TensorFlow but the many deep learning techniques as well.

7. Keras:

Keras is an open-source library for building Neural Networks at a high-level of the interface, and it is written in Python. It is minimalistic and straightforward with a high-level of extensibility. It uses Theano or TensorFlow and CNTK as its backends. Keras is an API designed for human beings, not machines. It puts user experience front and center.

Keras follows best practices for reducing cognitive load: it offers consistent and simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear and actionable feedback upon user error. A model is understood as a sequence or a graph of standalone, fully-configurable modules that can be plugged together with as little restrictions as possible. Neural layers, cost functions, optimizers, initialization schemes, activation functions, regularization schemes are all standalone modules that you can combine to create new models. New modules are simple to add (as new classes and functions), and existing modules provide ample examples. To be able to easily create new modules allows for total expressiveness, making Keras suitable for advanced research.

8. Pybrain:

PyBrain, another top Python Library for Data Science, aims at offering flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test and compare your algorithms. While there are a few machine learning libraries out there, PyBrain aims to be a very easy-to-use modular library that can be used by entry-level students. It is popular because of the flexibility and algorithms for state-of-the-art research.

We are constantly working on more and faster algorithms, developing new environments and improving usability. PyBrain, as its written-out name already suggests, contains algorithms for neural networks, for reinforcement learning (and the combination of the two), for unsupervised learning, and evolution. Since most of the current problems deal with continuous state and action spaces, function approximators (like neural networks) must be used to cope with the large dimensionality. Our library is built around neural networks in the kernel and all the training methods accept a neural network as the to-be-trained instance. This makes PyBrain a powerful tool for real-life tasks as well.

9. Shogun:

Shogun one of the top Python Libraries for Data Science focused on large-scale kernel methods. Shogun was initiated by Soeren Sonnenburg and Gunnar Raetsch in 1999 and is currently under rapid development by a large team of programmers. This free and open-source toolbox written in C++ provides algorithms and data structures for machine learning problems. Shogun Toolbox provides the use of a toolbox via a unified interface from C++, Python, Octave, R, Java, Lua and C++; and can run on Windows, Linux, and even MacOS.
Shogun is designed for unified large-scale learning for a broad range of feature types and learning settings, like classification, regression, dimensionality reduction, clustering, etc. It contains many exclusive state-of-art algorithms, such as a wealth of efficient SVM implementations, multiple kernel learning, kernel hypothesis testing, Krylov methods, etc.
Shogun supports bindings to other machine learning libraries like LibSVM, LibLinear, SVMLight, LibOCAS, libqp, VowpalWabbit, Tapkee, SLEP, GPML and many more.

Some of the most well-known features include one-time classification, multi-class classification, regression, structured output learning, pre-processing, built-in model selection strategies, visualization and test frameworks; and semi-supervised, multi-task and large-scale learning.

10. Caffe:

Caffe, ranked among the top 10 Python Libraries for Data Science, is a library for machine learning in vision applications. It is a very useful machine learning library when used for computer vision. You might use it to create deep neural networks that recognize objects in images or even to recognize a visual style.

Caffe offers seamless integration with GPU training is offered, highly recommended when training on images. Although preferred for academics and research, it has plenty of scope for training models for production use as well. Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices.

Extensible code fosters active development. In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back. Thanks to these contributors the framework tracks the state-of-the-art in both code and models. Speed makes Caffe perfect for research experiments and industry deployment. Caffe can process over 60M images per day with a single NVIDIA K40 GPU. That is 1 ms/image for inference and 4 ms/image for learning and more recent library versions and hardware are faster still. Caffe already powers academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia. Join our community of brewers on the Caffe-users group and Github.

Here we have discussed a few of the popular Python libraries. Several others may be explored. Build the right team with knowledgeable resources for success. Find developers with a good knowledge of tools and techniques of statistical analysis. Your team should also have an experienced data scientist with the development skills to work in a production environment.

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