Top Python Projects

     Top Python Projects service offers amazing challengeable research strategies to students and researchers to travel among the challengeable scientific environment. Our major goal is to serve the world of research scholars and students with innovations in leading edge technologies and ultramodern scientific achievements. Today, our highly talented technical team of brilliants extends their life towards current trends. Scholars you can call us today to build win-win achievements with the aim of support for your future profession. Do you have any queries? You can approach us today?

Top Python Projects

     Top Python Projects service is initiated by our dedicative team of experts for the focus of train students and researchers to successfully conquer the best position in their life. Our technology driven research center is working with the advanced and fast growing technologies on Python. As a part of projects and research development phase, we provide IEEE standard projects in the view of advanced emerging technologies and trends under parallel research development process. Today, Python programming language is being used in many IT industries which support both structured programming and object oriented programming.

Top GUI Python Frameworks

  • Kivy (based on OpenGL ES2)
  • Pyforms (based on OpenGL and PyQt)
  • PyGObject (cross platform library)
  • PyQt (multi-licensed cross platform)
  • PyGUI (native GUI platform)
  • libavg (third-party library)
  • wxPython (based on wxWidgets)

Top Python OpenSource IDEs

  • Eric
  • PyCharm
  • PyDev with Eclipse

Python Projects in Advanced Level

  • Leveraging Python on .NET platform in IronPython implementation
  • Java Virtual Machine(JVM) applications with Python in Jython Implementation
  • Web2py framework
  • Flask framework (loosely coupled and modern framework)

Top Python Methodologies and Technologies:

  • HTML/HTML 5, CSS
  • AWS, EC2, RDS, S3, Windows and GAE
  • Apache, Gunicorn, Ngnix, Linux
  • Jquery and JavaScript
  • Tastypie, Celery, XML, SOAP, Ajax, REST, JSON
  • MSSQL, GIT, SVN, HG, PostgresSQL, MySQL
  • Pylint, Fabric, Sentry, Redis, Gearman, RabbitMQ
  • Flask, Django, Pinx, Tornado, Zope, Satchmo, Web2py

We practically show the following each of the things for students in both beginners level and advanced level,

  • Standard hosting or Google Application Engine
  • PostgreSQL or MySQL in RDBMS
  • Eclipse, PyCharm IDE Tools Implementation
  • MongoDB NoSQl Database

Here we describes the installation procedure to develop Top Python Projects,

  1. Download the latest version for Python (Python 3.6.2) and MacOS X (python-3.6.2-macosx10.6pkg or later)
  2. Install Python 3.6 directory

/Library/Frameworks/Python.framework/Versions/3.6

  1. Run the Python executable using

/user/local/bin/python3.6

  1. /Library/Frameworks/Python.framework/Versions/3.6/bin/python 3.6
  2. Run the Python 3.6 on Shell using

>python3.6

  1. Python 3.6 support the Basic IDE called IDLE.app

Here’s some of the new code, we have partially written for students to understand Python 3.6,

x=10

y=int(input(“Enter x integer number.”))

z=x+y

print(“The value of c is:”, c)

print(Value,…..,sep=,`  `, end=’\n’, file=sys.stdout, flush=False) // Python 3.6

Top Research Areas using Python

  • Natural Language Processing (using nltk library)
  • Machine Learning (using sklearn library)
  • Data Analysis (using Pandas)
  • Signal Processing and Linear Algebra (SciPy library)
  • Internet of Things (using ibmiotf library)
  • Fog Computing (using tlsv1.2 library [Python3] and coap library [Python 2.7.9])
  • Green Network (green 2.11.0 Python test runner)

Other Research Areas:

  • Econometrics (statsmodels)
  • Graphing (ggplot and seaborn)
  • Network Analysis (iGraph)
  • Geo-spatial analysis (geopandas/arcpy)
  • Big Data (pyspark, and dask)
  • Text-Analysis (NLTK)

       Learning Python is a quite challenging. For this very reason, we provide sample program to those who are interested in getting up and running on Python for academic research.

/*Sample Program in the field of Green Network*/

# Import Required Packages

from shortestPath import *

from networkFillStandby import *

from greenerNetwork import *

# DiGraph stores nodes and edges with optional data, or attributes

G = nx.DiGraph();

# deviceProfile = [energyConsumption, (renewableEnergyUsage * energyConsumption), wattPerMb]

redDevicesMetrics = [50, (0.5*50), 1]

blueDevicesMetrics = [60, (0.8*60), 0.5]

blackDevicesMetrics = [10, (0.05*10), 2]

G.add_node(0, energyConsumption=redDevicesMetrics[0],

renewableEnergyUsage=redDevicesMetrics[1], wattPerMb=redDevicesMetrics[2], on=True)

G.add_node(1, energyConsumption=redDevicesMetrics[0],

renewableEnergyUsage=redDevicesMetrics[1], wattPerMb=redDevicesMetrics[2], on=True)

G.add_node(2, energyConsumption=redDevicesMetrics[0],

renewableEnergyUsage=redDevicesMetrics[1], wattPerMb=redDevicesMetrics[2], on=True)

G.add_node(3, energyConsumption=redDevicesMetrics[0],

renewableEnergyUsage=redDevicesMetrics[1], wattPerMb=redDevicesMetrics[2], on=True)

# Edges

G.add_edge(0, 3, capacity=100, flow = 0)

G.add_edge(0, 2, capacity=100, flow = 0)

G.add_edge(0, 1, capacity=100, flow = 0)

G.add_edge(1, 3, capacity=100, flow = 0)

G.add_edge(2, 3, capacity=100, flow = 0)

     In the above code, we have used “green 2.11.0” for Green Network Optimization. This is a colorful, simple and clean Python test runner. To install this, we need Python 2.7 and 3.4+. In addition to this, we also supported networkx and Pypy for writing Python code. Install using:

$ pip install green

To run the code use:

$ green proj

Here, we provide the most important Green Network Topics for your best understanding,

  • An effective mechanism for Green Spectrum Assignment of Secure Cloud Radio Network by Cluster Formation
  • An efficient performance for Green Data Center Placement based on Optical Cloud Networks
  • A new mechanism for Distributed Energy-Spectrum Trading by Green Cognitive Radio Cellular Networks
  • The new process of Hotspot-oriented Green Frameworks in Ultra-small Cell Cloud by Radio Access Networks
  • A new mechanism for Joint Cell Selection and Activation on Green Communications by Ultra-Dense Heterogeneous Networks
  • A new methodology of Circuit modeling for Green Fluorescent Protein’s hydrogen by bonding network on cadence
  • The new process of WiFi offloading for enhanced interaction in Smart Grid via green mobile networks
  • An effective mechanism for improving interaction of green mobile network based on by smart grid
  • An efficient performance for Cost-effective vehicular network planning used by cache-enabled green roadside units
  • A new mechanism for Throughput aware and green energy aware user association in heterogeneous networks

 

      Our service is like an ocean. We provide customized documentation for IEEE and application projects and also offer tailored support for project development, PhD research guidance, real time projects, review paper/research paper writing and revising, and journal publication with affordable price. You can make communication with us for your great future. We are always near to help you.

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