In the recent year, Python has got a lot of traction in data science industry. Here I wanted to summarize some of its most useful libraries for data scientist and engineer, based on recent experience. And We summarize all of the libraries are open source.
NumPy :
One inevitably comes for help to python’s Scipy stack when starting to deal with the scientific task in python. Numpy library is specially designed for scientific computing in python. The most fundamental package, around which the scientific computation stack build, Numpy.
Scipy:
Scipy is an open source python library for engineering and science task. First, we need to understand the difference between scipy stack and scipy library. Scipy contains modules for linear algebra, interpolation, optimization, integration, and statistics. The main functionality of scipy library builds on the Numpy array objects
Pandas:
Pandas is a Python library designed for data manipulation and analysis. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python.
Matplotlib:
Matplotlib another python 2D plotting library that is tailored for the generation of simple and powerful visualizations with ease is matplotlib. You can generate plots, histograms, power spectra, bar, charts, scatterplots etc with just a few line code
Seaborn:
Seaborn is a Python visualization library for making attractive and informative statistical graphic in Python. Seaborn is based on matplotlib and highly dependent on that
Bokeh:
Bokeh, An another Python interactive visualization library, enables beautiful and meaningful visual presentation of data in a modern web browser. With help of bokeh, you can quickly and easily create interactive plots, dashboard and data application as well provide elegant, concise construction of novel graphic in D3JS
Tensor flow:
Coming from developers at google, it is an open source library of data flow graphics computations, which are sharpened for machine learning. Tensor flow isn’t strictly for scientific use in borders of google. It is general enough to use it in a variety of real time application
Conclusion :
These are the libraries that are considered to be the top of the list by many data scientists and engineer. This is not the fully exhaustive list and there are many other libraries and framework that are also worthy and deserve proper attention for a particular task.
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