Python is an easy language to fall in love with, not just because of its simplicity and elegance, but also because it is a handy ‘glue language’ that can incorporate rich and powerful third-party libraries. Supported by a large and vibrant scientific computing community, tripartite libraries such as NumPy, SciPy, and Pandas have emerged and iterated to bring Python closer to and better than other open source and commercial tools in data analysis and interaction, exploratory computing, and data visualization. Python is the language of scientific computing, making it easy to implement algorithms such as linear algebra, optimization, integration, fast Fourier transform, and other data science algorithms.
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For example, in just a few lines of code, we can solve the slope and intercept of a linear equation of the form y = mx + c using the least square fitting data and make predictions. Import numpy as NP x= Np.array ([243,314,625,1024]) y= Np.array ([75,103,155,269]) x= Np.vstack ([x, Np.ones (len(x))]).t #model LSTSQ (A,y)[0] print(m,c) #predict newX=2000 print(m*newX+c) Applications in Python data science are not only the hottest career in Python right now (far more than in the WEB), but they are also a must for future advances in machine learning. So how do Python programmers and traditional data analysts get into Python data analysis?