Simmer your data science recipe with Python (Part -2)
By: Shubhangi Gupta
Tags: Python, Data Science, Data Visualization, Data Analysis

Python, an open-source high-level programming language. The fact that you don’t know is Python was named after the comedy television show Monty Python’s Flying Circus. It was not named after the Python, the snake species. A great programming toolbox for professionals of backend web development, data analysis, artificial intelligence, and scientific computing. It has been proved not less than a blessing for beginners as it comprises of a lot of libraries inbuilt for coding with ease. Also, is a high-level and scripting programming language which has readable and easily maintainable. It is the fastest-growing programming language in the world because of the explosion of artificial intelligence (AI) productivity and data science.

33% of the data scientist practice python for their data.

Why learn Python?

A data science practitioner combines statistical and machine learning techniques with python programming to analyze and interpret complex data. Python not just being a highly functional programming language but it can do almost what other languages can do with comparable speed. It is used to make data analysis, create GUIs and websites. Python is simple enough for things to happen quicker than it seems and powerful enough to allow the implementation of the most complex ideas.

Why choose Python?

Not just it covering the pitfalls of advance programming that R language has, also available in various platforms of operating systems like Mac, Windows, Linux, and Unix. Python supports exception handling that would help you make your code less error one. Also, used for scripting (A small code used for automating a small task in a specific environment for sending automated response emails, FTP, etc.) Python could be used for GUI (Graphical User Interface). Most commonly used is Tkinter outputs the fastest and easiest way to create GUI applications. Then PyQT, Kivy, PyGUI, etc. frameworks are used and popular amongst coders now. Game development and Web development is also possible with the same platform. Web development frameworks like Django and Flask made a sensation nowadays.

Where to use which Python libraries?

Numpy provides a high-performance multidimensional array and basic tools to compute with and manipulate these arrays. This library provides fundamental scientific computing. It uses less memory to store data. Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It used for plotting and visualization. Pandas is applied for data manipulation and analysis. Scikit-learn is a library designed for machine learning and data mining. Scikit is a library that provides many unsupervised and supervised for machine learning algorithms. StatsModels is packed with statistical modeling, testing, and analysis. The library is used for the statistical function of the data. SciPy is a bunch of mathematical algorithms and convenience functions built on the Numpy extension of Python. It helps as to do the mathematical and scientific operation and used extensively in data science. Plotly is a web-based toolbox for constructing visualizations. It is famous amongst the generations for plotting the visualizing the various types of data.

Conclusion

Pros:

Python is commonly used as a high-level interpreted language. Many developers use Python to build productivity tools, games as it is easy to use, powerful, versatile, making it a great choice for beginners and experts. A huge variety of statistical packages present in python is widely used amongst data science practitioner. Seaborn and Theano are statistical libraries that are followed by the data analyst nowadays.

Cons:

Python programs are generally expected to run slower than Java programs. The drawback of run-time typing, Python’s run time work harder than Java’s.

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