10 Ways You Can Use Python for Big Data Analytics

Python is one of the most popular languages thousands of programmers use worldwide for data analytics, app development and website designing. It is chosen as the most popular language for data analytics because of its power of statistical readability and easy analysis. All the top online companies like Facebook, Quora, Mozilla, Google, etc., use Python extensively for their data analytics projects. Sometimes students have to complete assignments on big data analytics for their projects. If you want to complete your data analytics assignment successfully without external assignment help, you must know the following benefits of using Python.

  1. It’s an open-source language

Python is widely believed to be one of the easiest languages to learn. It is an open-source language, meaning you can use it without spending a penny. Their official website is called python.org. If you want to use Python for any project, download it free. And it is extremely easy to learn with common logic. Both experienced web developers and budding students love to use Python because of its convenience and flexibility. It has one of the most easy-to-read syntaxes that invite more developers to learn and write codes in it. Python is also famous because computer science engineers and data science professionals need not spend much of their time and energy learning the intricacies of language and take more important decisions instead.

  1. It is scalable and flexible

The second most valuable reason for Python’s use in big data is its scalability. It can handle large volumes of data with relative ease. Many other languages can handle big data. Such as R and Java. But they fall short of Python in maintaining scalability and flexibility. The data processing speed of Python increases equally with the increase in the volume of data. No other language like R or Java can increase the data processing speed as fast as Python. Besides being the most flexible and scalable language, it is highly efficient. Developers use fewer lines to write codes in Python than in any other language.

  1. It has various libraries

Because of its rising popularity and convenience, Python started producing many libraries. Developers can use these frameworks anytime, anywhere, as per their requirements. Many Python libraries are useful in machine learning and big data analytics. Here is a list of Python libraries for big data analytics.

  • Pandas

It is a free tool and library for handling and analytics of big data. This library will help you to manipulate operations and structures in time series and numerical tables.

  • SciPy

It is another free library and tool for the technical and scientific computation of big data. SciPy is best for interpolation, integration, modification and optimisation of data. It carries out these operations with the help of special functions and linear algebra.

Other popular Python libraries include Scikit and NumPy.

  1. High-speed data processing

Another reason for the extensive use of Python is its high-speed data processing. That’s why developers consider Python the optimal option for managing big data. Data processing speed has many real-time benefits. For instance, once you write a code in Python, it will take half the time to execute the code as it will take in other languages. There is a software called Anaconda, which makes Python’s data processing speed so high. Before, Python was thought to be much slower compared to Scala or Java. However, the Anaconda software introduced one after another version of Python, making it the most popular language in managing big data and software industries.

  1. You can extend it with greater portability

The fifth most important reason for Python being used by big data is its portability and extensibility. You can perform a plethora of cross-language operations in Python because of its extensible and portable character. In most machine learning models, data scientists use GPU s or Graphics Processing Units. Python’s portability and extending nature allow them to do that more conveniently. Other platforms such as Linux, Macintosh, Solaris and Windows use Python for its extensive portability. In addition, you can easily integrate Python with components of .NET and Java or C++ and C libraries.

  1. Inbuilt processing of data

Python has an inbuilt support system that helps complete data processing. This is probably why Python is so popular among companies that deal with big data. Python also identifies and processes data which are not structured. These data exist in different JSON, HTML, XML and CSV files. Python can process these files even when the processing format is different for different files. One can use free libraries exclusively created for Python usage for data processing. Some popular libraries are SciPy, Pandas, NumPy etc.

  1. Compatible with Hadoop

Both Hadoop and Python are two famous open-source languages. That’s why you can be securely compatible using one with the other. Most users prefer using Python with Hadoop and not Scala or Java. The reason is simple. The number of libraries in Python is huge compared to Scala and Java. Those who want to develop Hadoop to Python can use a package called Pydoop. If you want to write and read different files of global data systems of files, you can use the HDFS API access that Pydoop gives you. You can use Pydoop MapReduce API to resolve complex problems of data science in Python. The best part of using Python is that it allows you to program the minimal amount necessary for the design. That’s why you generally choose Python for big data over other languages.

  1. Supported by a big community

Python was created in 1990. From then to today, it has built a global supportive community. This support leads the learners of Python to excel in data analytics and big data problems. Global companies such as Netflix, Google, Instagram, Quora and Facebook use Python for their products. Google alone has created many popular Python libraries like TensorFlow, Keras etc. Data scientists and developers can easily access all these resources as long as they use Python as a programming language.

  1. It has the support of data visualisation

You can use Python programming in many data visualisation software than other languages. We use data visualisation to understand the covered layers and patterns within the daIDEs. The only big competitor of Python in this regard is R., But R does not provide as much access to the hidden layers and patterns of data. You can use the following Python libraries to work on data visualisation.

  • Seaborn
  • Plotly
  • Matplotit
  • NetworkX
  • Altair
  • ggplot
  • Pyga etc.
  1. You can use various IDEs

The greatest strength of Python is its different IDEs. These IDEs allow you to do machine learning, data visualisation, processing of natural language, data analysis etc. All these components make it a perfect fit for data science. Here are some examples of Python IDEs.

  • Rodeo

It is an open-source IDE especially developed for managing data in Python. It has many cheat sheets and tutorials for ready reference while using Python. Some specific features of Rodeo are automatic completion, highlighting of syntax, convenient interactions with plots and data frames, inbuilt support of IPython etc.

  • Spyder

You can integrate this IDE with multiple Python libraries such as Pandas, SymPy, NumPy, IPython etc. Spyder is an open-source IDE that carries out introspection of codes, completion, highlighting of syntax and vertical or horizontal splitting of syntaxes.

  • Pycharm

This IDE was created by the organisation called JetBrains. It has several unique features making it highly compatible to use with Python. For example, it has a debugger support integrated tester for units.

These are the ten reasons you can use Python in machine learning and data science.

Author bio: Selena Fernandez is a freelance writer who works as  pay for assignment  at Esaayassignmenthelp.com.

Post Author: mindmingles

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