Python For Big Data : Explore Top 13 Reasons
Python - As by its definition, it is an interpreted and general purpose programming language. So with python we can develop advanced desktop applications, web applications, websites, mobile apps and more. Mr. Guido Van Rossum has invented python to overcome the flaws of farmer programming language ABC developed by CWI(Centrum Wiskunde & Informatica), Netherlands. Python has several specialties like dynamic typing, dynamic binding in order to proceed with Rapid Application Development.
Why Python For Big Data?
Python brings higher efficiency and provides an easy way to migrate any big data or data science projects into the desired programming language at any time. Many developers and experts point out that the Python is a most suitable programming language for technology projects like AI, IOT and more.
Python is not only favoring the developers alone, but also favoring business in terms of fulfilling the project goals on time. Likewise, we can list out N number of powerful use cases and benefits of python in big data. Let us discuss the top 13 benefits while using python for big data in detail below.
1. Open Source Language
Python is a completely open source programming language which has been developed as a community-based model, so the developers are connected under one roof. Python can be run on various platform including Windows, Linux and more. Since it supports various platform, we can easily interchange it to any platform at any time. You can download the recent version of python directly from their official website. python.org
2. Multiple Library Support
Python is widely used in computing in various industry fields, so in order to fulfill the computing process python have been inbuilt with various analytics libraries and packages.
include,
i) Numerical computing Packages.
ii) Data Analysis Packages.
iii) Statistical Analysis of Libraries Packages.
iv) Visualization Packages.
v) Machine Learning Packages.
3. Lesser codes
The beauty of python is we can make programs and applications with least line of codes. Python has been made with an inbuilt nature of automatically identifying data types and follows nesting structures to increase readability. Python can make a program in just 20 lines, whereas in Java, we used to write 200 lines. So the development drastically decreases while using python for big data.
4. Unbelievable Speed of Processing
Every developer should expect a programming language to be faster while writing and executing the codes. Python meets developer expectation with ultra speed data processing characteristics. As Python makes a program in simple codes, it increases the execution of data in a fraction of time.
The acceleration of code development has been fulfilled as it enables prototyping ideas during the code writing which makes the execution of codes faster. The transparency between code and its execution makes code maintenance easy in a multi-user development environment.
5. Data Processing Support
Python provides increased support for big data analytics to identify and process unstructured data. Python has an inbuilt feature of identifying voice, text and image data so it can be very useful in big data analytics while processing social media data.
6. Scope
Scope in programming: Pythons comes under OOP's Concept, which is created to support various data structure concepts like Linked Lists, sets, tuples, dictionaries, Matrix, data frames and more. This is also another factor of increased data processing.
Scope in platforms: As said earlier, python is a general-purpose language, so it supports the development of various GUI applications, Data processing applications, web applications, website development, and mobile app development.
7. Powerful Scientific Packages
Python is the best fit for big data, as it has many robust scientific library packages. Let us have a look at some of those library packages
Pandas:
It helps in data analysis. Provides various operations like data manipulation on time series and numeric tables also some functions to deal with different data structures
NumPy :
NumPy is the primary package of python which is scientific computing on data. It supports linear algebra, Fourier transforms, random number crunchings. Also, support a multi-dimensional array of generic data to easily integrate with many different databases.
SciPy :
Used for scientific and technical computing. It contains various modules for data science and data engineering tasks like.
- linear algebra,
- interpolation,
- signals and image processing,
- ODE solvers
- FFT
and other tasks common in data science and data engineering.
MlPy- It is a machine learning library which runs on top of both NumPy and SciPy.
Scikit-learn: Also a machine learning library runs on NumPy and SciPy.
SymPy - Libray for symbolic computation
Thenao - Library for numerical computation
TensorFlow - An open source software library based on machine learning which is capable of building and also manipulating neural networks. Tensor flow is used to detect patterns, decipher the patterns and correlations.
These are the primary libraries which are packed with python.
Other libraries are..
- Dmelt
- Dask
- NetworkX
- Matplotlib
Explore the Other reasons in original article : 13 reasons why you should choose python for big data