Python

Python

Python is interpreted, object-oriented,high-level programming language with  dynamic semantics. It is high level build in data structures combined with dynamic typing and dynamic binding, make it very attractive for Rapid Action Devleopment, as well as for the use as a scripting  or glue language to connect existing components together. Python’s simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse. The Python interpreter and the extensive standard library are available in source or binary form without charge for all major platforms, and can be freely distributed.

Debugging Python programs is easy: a bug or bad input will never cause a segmentation fault. Instead, when the interpreter discovers an error, it raises an exception. When the program doesn’t catch the exception, the interpreter prints a stack trace. A source level debugger allows inspection of local and global variables, evaluation of arbitrary expressions, setting breakpoints, stepping through the code a line at a time, and so on. The debugger is written in Python itself, testifying to Python’s introspective power. On the other hand, often the quickest way to debug a program is to add a few print statements to the source: the fast edit-test-debug cycle makes this simple approach very effective.

What we mean by Quality Python Code

Rules & guidelines we follow

  • Style guide for Python code.
  • Commenting complex code parts.
  • Conducting regular unit tests.
  • Breaking up code into smaller logical units.
  • Documenting what code (as a whole) does and what its dependencies are in a final README doc.
  • Using version control.
  • Using source code management (SCM) systems.

    • Types of Applications Our Developers Build

    Machine learning

  • Demand forecasting systems
  • Customer segmentation and customer behavior prediction systems
  • Product/service recommendation engines, Financial risk evaluation, fraud detection systems
  • Predictive maintenance systems

    • Back-end Programming

    • Data-intensive web applications.
    • Database interactions.
    • APIs.

    Data Analysis

    • Custom-made statistical models..
    • Dashboards and reporting solutions.

    IOT Development

    • Data warehouse design and engineering.
    • Data analytics implementation.
    • Development of control apps.
    • Web and mobile application development.
    • API design and provisioning