Python has undoubtedly become the most preferred language in the data science space. Today, statisticians are recruited based on their expertise in python. It is pervasively being recognized as the language top-ranked in global data science surveys.
But What Makes Python So Special for Data Scientists?
Python is an integrated language that smoothly embraces comprehensive features like coding, object-oriented, and high-level language. Python encompasses various libraries that take care of various job functions like Math complex calculations, data mining, exploration of data sets, comparison, and visualization.
Various libraries constitute the strong foundation of the Python tool that further has strong roots for problem-solving. Here is a small list of python libraries that you should know about:
NumPy, as the name suggests, encompasses numerical problem solving with python. NumPy is used for scientific computing and complex computing like Machine learning and Deep Learning. The machine learning algorithms require complex problem solving that involves numerical and multidimensional array objects. In fact, NumPy has been recognized as the foundation library upon which Pandas, Matlotlib, and Scikit Learn have gradually evolved.
SciPy is also known as the library used for scientific computing of scientifically involved numerical. It involves problem-solving in the fields of mathematics and engineering.
It helps provide a user-friendly platform for efficient numerical routines of numerical integration and optimization.
Beautiful Soup is an advanced library that helps scrape data from the HTML and XML documents. It is known for automatically detecting encodings and analyzing HTML documents with special characters. It navigates the parsed documents for quick and painless data extraction from the web pages.
Pandas is an exclusive library that involves data exploration to visualization techniques. An open-source package helps perform the data analysis and manipulation easily in python language. It provides relational and structured data analysis techniques. It is one of the most powerful and easy to use data analysis and manipulation tools.
MatplotLib is an extensive library for exploration and data visualization. It has strong roots in the python ecosystem and forms the foundation for other libraries too.
If you wish to make histograms, scatterplots, and customizations with an array of colors, themes, and palettes, Matlplotlib is very useful. It is used for complete data exploration and machine learning projects.
Plotly is the most widely recognized open-source data visualization library. It helps develop high-quality, interactive, and print-ready publication charts. It is the smoothest data visualization library that works wonders with Boxplot, heat maps, and bubble charts. Plotly magnificently helps you convert your data into attractive looks that accelerate data visualization.
The jack of all trades or the SciKit-learn is the indispensable library that works wonders in solving complex statistical and numerical problem-solving. Scikit learn is used for making master machine learning models. Complex statistical operations require tools for predictive data analysis that work efficiently. It accurately provides statistical modeling including classification, regression, clustering, and dimensionality reduction.
Python is considered a powerful language that comes with a strong army of libraries that are capable of solving complex problems in the field of statistics and numerical problems. Our expert team at Sifars, a leading python development company, helps provide data evaluation, exploration, regression, and visualization an easy task.
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