New To Data Science? Start With These 10 Python Libraries

August 29, 2018

Data Analysis has become the forefront of every organisation. Companies combine Big Data and cutting-edge data analytics to arrive at actionable insights that benefit business performances.

We know that Data Science has been dubbed as one of the sexiest jobs of the 21st century. If you’ve always wanted to learn Data Science, then R and Python are your bread and butter. To get started, here are the top ten Python Libraries you should sink your teeth into:

  • NumPy

NumPy stands out as a beginner-friendly Python library. It features sophisticated broadcasting functions, powerful multidimensional array objects, and matrices. It doesn’t use loops and lets you transfer data to external libraries that are written in C, C++ or Fortran Code.

  • SciPy

SciPy is NumPy’s best friend and relies on its speedy N-Dimensional array manipulation. SciPy offers users various numerical routines such as numerical integration and optimisation. SciPy, when coupled with NumPy, is used to solve multiple tasks related to integral calculus, linear algebra, probability theory, and others. The latest editions of SciPy involve significant build improvements and bundle the new BLAS and LAPACK functions.

  • Pandas

Pandas is a Python Library that lets you translate complex operations with data in just a few commands. It includes built-in features like grouping, time-series functionality, filtering, and lets you combine data sets. Its numerous bug fixes and API improvements make it a must-use library for Data Science enthusiasts. Additionally, Pandas lets you perform custom operations.

  • Matplotlib

Matplotlib is a low-level Python library used for data visualisation in interactive environments and hardcopy formats. It lets you create graphs, histograms, pie charts, scatterplots, and more. There’s a colourblind-friendly colour cycle feature, and the latest versions include support different GUI backends on operating systems and lets you export graphics/images in various formats like PDF, SVG, GIF, JPG, BMP, etc. The legends and graph axes are automatically aligned, and when you use it with the iPython Notebook, it becomes your visualisation playground, literally.

  • Scikit-Learn

Scikit-Learn lets you quickly implement various Machine Learning Algorithms on your datasets. It gives you apply algorithms on tasks related to logistic regression, classification, clustering, etc. It’s a popular module that’s built on top of the SciPy library and is perfect for beginner and advanced Data Scientists.

  • Theano

Theano is a Python library explicitly used for mathematical computations. It lets you optimise and evaluate mathematical expressions to your liking and uses multi-dimensional arrays for blazing fast calculations. It also works as a core computational component in libraries like the PyLearn 2.

  • Statsmodels

Statsmodels lets you statistically explore data and includes various classes and functions that help you estimate statistical models. Its ‘estimator’ brings a list of ‘result statistics’ that let you test your analyses against existing statistical packages which are released under an open-source license.

  • Plotly

Plotly lets you create complex visualisations, maps, financial charts and various graphical presentations that meet publication quality online. It works with interactive web applications and bundles features such as ternary plots, 3D charts, contour graphics, etc. Crosstalk integration, “multiple linked views” and animation generation make it one of the hottest visualisation tools in Data Science.

  • Bokeh

Bokeh lets you create scalable and interactive visualisations using JavaScript widgets. It includes a small zoom tool, customizable tooltip field enhancements, linking plots, and many versatile (but interactive) styling and graphing features.

  • Gensim

Gensim is a free Python library used for building scalable semantic statistics. Its retrieves structurally similar documents and speedily implements Machine Learning algorithms for useful statistical analysis. Perfect for topic modelling with large data-sets and is used popularly in text mining projects.

Conclusion

Use these libraries to kickstart your ML projects and avoid writing algorithms from scratch. They save time, are ideal for beginners and advanced Data Scientists, and are highly recommended in the Data Science community worldwide.

References:

https://activewizards.com/blog/top-20-python-libraries-for-data-science-in-2018/

http://bigdata-madesimple.com/ten-handy-python-libraries-for-aspiring-data-scientists/

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