Lately, Python has gained a lot of popularity due 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 places more emphasis on code readability and a syntax that allows programmers to express concepts in fewer lines of code that might be used in languages ​​like C++ or Java. Since its release in 1991, the language has provided constructs to allow clear programs to be written on both small and large scales. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essay Python has not gained 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 process financial data. The Theoretical Physics Division at Los Alamos National Laboratory chose Python not only to control simulations, but also to analyze and visualize data. Social media giant Facebook also turns to the Python Pandas library for data analytics because it sees the benefit of using one programming language across multiple applications. In the words of Facebook's Burc Arpat, “One of the reasons we like to use Pandas is because we like to stay in the Python ecosystem.” One of the most hotly debated topics today is the battle between Python and R: which one to use for Data Science. The growing use of Python in data science applications has placed it in opposition to R, a programming language and software environment specifically designed to perform the type of data analysis tasks that Python can now handle. Recent speculation is whether one of languages ​​will eventually replace each other in the data science sphere, individuals need to decide which language to learn or which to use for a specific project One of the main advantages of Python is the huge number of libraries that help you make the most of the data science. While there are many libraries available for performing data analysis in Python, some of the most popular are: NumPy - Considered foundational for scientific computing with Python, it supports large, multidimensional arrays and matrices and includes an assortment of high quality libraries. level mathematical functions to operate on these arrays. SciPy: Works alongside NumPy arrays and provides quite efficient routines for numerical integration and optimization. Please note: this is just an example. Get a custom paper from our expert writers now. Get Custom EssayPandas: It is also built on top of NumPy and offers data structures and operations to manipulate number tables and time series. Matplotlib: is a 2D plot library that can generate data visualizations such as histograms, power spectra, bar graphs, and scatterplots with just a few lines of code. Scikit-learn: This machine learning library implements classification, regression, and 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 a problem and it is possible to find a suitable and robust solution that works reliably.