Python for Data Science:

Python is an open source, high level, and interpreted language that provides a great approach for object-oriented programming. Python is widely there for data science projects or applications. It provides functionality for dealing with mathematics, statistics, and scientific functions. It provides us with libraries for dealing with data science applications.

One of the main reason for using Python iPython for Data Sciencen scientific and research community is because of its ease of use and simple coding. Also, it suits better for quick prototyping.

The deep learning frameworks which are available with Python APIs in addition to scientific packages has made Python very productive and versatile.

When we want to work with applications like natural language processing (NLP) and sentimental analysis, then Python is best suited. This is because it provides a large collection of libraries for solving complex business problems easily, build strong system and data application.

Below are some useful features of Python language:
  • Elegant syntax is there, and the programs are easier to read.
  • It is a programming language that can be accessed simply and makes it easy to achieve the working program.
  • It has a large standard library and community support.
  • Python’s interactive mode makes it simple to test codes.
  • In Python, we can simply extend the code by appending new modules which are implemente in other compile languages such as C++ or C.
  • It is an expressive language, and we can embed it into applications for offering a programmable interface.
  • It allows the developer to run the code in any OS such as Windows, Mac OS, UNIX, and Linux.
  • Python is free, and we don’t have to pay for using or downloading it.
Most commonly use libraries in data science are as follows:
  • NumPy: It is a Python library that provides mathematical functions for handling large dimensional arrays. So, provides us with methods or functions for array, metrics, and linear algebra.

It will provide useful features for operation on n-arrays and matrices in Python. NumPy library provides vectorization of mathematical operations on NumPy array type, which enhances the performance and speeds up execution. It makes work easy with multidimensional arrays and matrices.

  • Pandas: It is there mainly for data manipulation and analysis. Also, it provides useful functions for the manipulation of large amount of structure data. Pandas is a perfect tool made for data wrangling. Is design for easy and quick data manipulation, aggregation, and visualization. The data structures in Pandas are as follows:

Series: It handles and stores data in one-dimensional data.

DataFrame:

It handles and stores two-dimensional data.

  • Matplotlib: Is a Python library for data visualization. The descriptive analysis and data visualization is very important for all organizations. It provides various methods for visualizing data in an effective way. Matplotlib will allow us to make line graphs, histograms, pie charts, and other grade figures. Matplotlib provides interactive features such as zooming and planning and then saving it in graphics format.
  • Scipy: It is another Python library for data science and scientific computing. So, it contains sub modules for integration, optimization, linear algebra, interpolation, special functions, FFT, image processing and signal and similar scientific tasks.
  • Scikit-learn: It is a Python library for machine learning. So, provides algorithms and functions which are there in machine learning. Sklearn has its base built on SciPy, NumPy, and matplotlib. It provides simple and easy tools for data mining and data analysis. It provides users with a common set of machine learning algorithms through a consistent interface. Also it helps in quickly implementing popular algorithms on datasets and for solving real-life problems.

So, to learn more about python for data science, you can check this and this as well.

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