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Python for Data Science
  • Last Updated : 16 Jan, 2019

Python is open source, interpreted, high level language and provides great approach for object-oriented programming. It is one of the best language used by data scientist for various data science projects/application. Python provide great functionality to deal with mathematics, statistics and scientific function. It provides great libraries to deals with data science application.

One of the main reasons why Python is widely used in the scientific and research communities is because of its ease of use and simple syntax which makes it easy to adapt for people who do not have an engineering background. It is also more suited for quick prototyping.

According to engineers coming from academia and industry, deep learning frameworks available with Python APIs, in addition to the scientific packages have made Python incredibly productive and versatile. There has been a lot of evolution in deep learning Python frameworks and it’s rapidly upgrading.

In terms of application areas, ML scientists prefer Python as well. When it comes to areas like building fraud detection algorithms and network security, developers leaned towards Java, while for applications like natural language processing (NLP) and sentiment analysis, developers opted for Python, because it provides large collection of libraries that help to solve complex business problem easily, build strong system and data application.

Following are some useful features of Python language:

  • It uses the elegant syntax, hence the programs are easier to read.
  • It is a simple to access language, which makes it easy to achieve the program working.
  • The large standard library and community support.
  • The interactive mode of Python makes its simple to test codes.
  • In Python, it is also simple to extend the code by appending new modules that are implemented in other compiled language like C++ or C.
  • Python is an expressive language which is possible to embed into applications to offer a programmable interface.
  • Allows developer to run the code anywhere, including Windows, Mac OS X, UNIX, and Linux.
  • It is free software in a couple of categories. It does not cost anything to use or download Pythons or to add it to the application.

Most Commonly used libraries for data science :

  • Numpy: Numpy is Python library that provides mathematical function to handle large dimension array. It provides various method/function for Array, Metrics, and linear algebra.
    NumPy stands for Numerical Python. It provides lots of useful features for operations on n-arrays and matrices in Python. The library provides vectorization of mathematical operations on the NumPy array type, which enhance performance and speeds up the execution. It’s very easy to work with large multidimensional arrays and matrices using NumPy.
  • Pandas: Pandas is one of the most popular Python library for data manipulation and analysis. Pandas provide useful functions to manipulate large amount of structured data. Pandas provide easiest method to perform analysis. It provide large data structures and manipulating numerical tables and time series data. Pandas is a perfect tool for data wrangling. Pandas is designed for quick and easy data manipulation, aggregation, and visualization. There two data structures in Pandas –
    Series – It Handle and store data in one-dimensional data.
    DataFrame – It Handle and store Two dimensional data.
  • Matplotlib: Matplolib is another useful Python library for Data Visualization. Descriptive analysis and visualizing data is very important for any organization. Matplotlib provides various method to Visualize data in more effective way. Matplotlib allows to quickly make line graphs, pie charts, histograms, and other professional grade figures. Using Matplotlib, one can customize every aspect of a figure. Matplotlib has interactive features like zooming and planning and saving the Graph in graphics format.
  • Scipy: Scipy is another popular Python library for data science and scientific computing. Scipy provides great functionality to scientific mathematics and computing programming. SciPy contains sub-modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers, Statmodel and other tasks common in science and engineering.
  • Scikit – learn: Sklearn is Python library for machine learning. Sklearn provides various algorithms and functions that are used in machine learning. Sklearn is built on NumPy, SciPy, and matplotlib. Sklearn provides easy and simple tools for data mining and data analysis. It provides a set of common machine learning algorithms to users through a consistent interface. Scikit-Learn helps to quickly implement popular algorithms on datasets and solve real-world problems.

Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course.

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