Dashboards are collections of bars, charts, and graphs that help us visualize different attributes of a dataset. A dashboard works as a graphical user interface which helps us identify the key performance indicators relevant to the dataset or the particular business model. Python provides different open-source libraries that can help you create your own dashboard with your dataset. Today we will be talking about Bokeh which is an open-source python library for interactive visualizations for the modern web browsers.
Bokeh provides elegant, concise construction of versatile graphics, and affords high-performance interactivity over large or streaming datasets. It can be used for different purposes like creating interactive plots, dashboards, and even data-driven applications.
In this article we will discuss:
Like any other library, we need to install Bokeh for exploring it by pip install bokeh
We will import pandas for loading the dataset and will import different functions of bokeh as and when required.
import pandas as pd
from bokeh.plotting import figure, output_file, show
We will create a sales dashboard for which we need sales data of a company, here I will use a dataset which contains Sales of a company and different attributes on which it depends.
df = pd.read_csv(‘Advertising.csv’)
#developers corner #bokeh #data analysis #data analytics #sales analytics tools #data analysis
Hey there everybody,
Early in 2020 I started a journey as a Content Creator, where I’ve been writing technical content on Software Development — mostly focused on the Python stack.
In the past 5 months, I began a new journey as an open-source contributor, and to date, I’ve contributed to 8 different python packages.
I’m going to share my story as 5-month old contributor with you here, along with my thoughts on why it’s beneficial to contribute to open source.
This article originally started as a thread on Twitter and then decided to turn it into an article regarding my journey as an open-source contributor. I hope you’ll enjoy it, and take your time!
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Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.
Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is
Syntax: x = lambda arguments : expression
Now i will show you some python lambda function examples:
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Python is among the most popular programming languages on the planet, and there are many reasons behind this fame. One of those reasons is a large number of open-source projects and libraries available for this language. From machine learning to animation, there’s a Python project for nearly everything. If you want to become a proficient Python developer, you should be familiar with some of these projects (if not all).
That’s why in this article, we’ll discuss different Python projects with source code Github. Because Python has applications in various industries, you might find many projects to help you complete your tasks. You should choose projects according to your interests and your experience. You can bookmark this article for future reference. Let’s get started.
Here are a few of the Python Open Source Project Ideas –
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No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas.
By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities.
Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly.
Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.
Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions.
Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events.
Simple to read and compose
Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building.
The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties.
Utilized by the best
Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player.
Massive community support
Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions.
Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking.
Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.
The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.
Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential.
The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.
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Welcome to my blog, In this article, we will learn the top 20 most useful python modules or packages and these modules every Python developer should know.
Hello everybody and welcome back so in this article I’m going to be sharing with you 20 Python modules you need to know. Now I’ve split these python modules into four different categories to make little bit easier for us and the categories are:
Near the end of the article, I also share my personal favorite Python module so make sure you stay tuned to see what that is also make sure to share with me in the comments down below your favorite Python module.
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