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Of late, python has earned a lot of popularity owing to its simple and easy to understand syntax. It is a widely used high-level programming language for general-purpose programming, created by Guido van Rossum. An interpreted language, Python has a design philosophy that emphasizes more on code readability and a syntax that allows programmers to express concepts in fewer lines of code that might be used in languages such as C++ or Java. Ever since its release in 1991 the language has provided constructs to enable writing clear programs on both a small and large scale.
Python did not gain much popularity in the field of Data Science until recently. Nowadays, tools for almost every aspect of scientific computing are readily available in Python. For example, Bank of America uses Python to crunch financial data. The Theoretical Physics Division of Los Alamos National Laboratory chose Python to not only control simulations, but also analyse and visualize data. Even the social media giant Facebook turns to the Python library Pandas for its data analysis because it sees the benefit of using one programming language across multiple applications.
In the words of Burc Arpat from Facebook, “One of the reasons we like to use Pandas is because we like to stay in the Python ecosystem. ”One of the most debatable topics of today is the battle between Python and R: Which one to use for Data Science. Python’s increased use in data science applications has situated it in opposition to R, a programming language and software environment specifically designed to execute the kind of data analysis tasks Python can now handle. The recent speculation is about whether one of the languages will eventually replace the other in the data science sphere, individuals have to decide which language to learn or which to use for a specific project.
One of the major advantages of Python is the huge number of libraries that help you make the best with Data Science. While there are many libraries available to perform data analysis in Python, some of the most popular ones are: NumPy- Regarded as a fundamental for scientific computing with Python, it supports large, multi-dimensional arrays and matrices and includes an assortment of high-level mathematical functions to operate on these arrays. SciPy – It works in conjunction with NumPy arrays and provides quite efficient routines for numerical integration as well as optimization.
Pandas- It is also built on top of NumPy and offers data structures and operations for manipulating numerical tables and time series. Matplotlib- It is a 2D plotting library that can generate data visualizations as histograms, power spectra, bar charts, and scatterplots with just a few lines of code. Scikit-learn – This machine learning library implements classification, regression, clustering algorithms including support vector machines, logistic regression, naive Bayes, random forests and gradient boosting. Constraints (in optimization methods/functions) that were missing a year ago are no longer an issue, and you can find a proper robust solution that works reliably.
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