Python  Library

Python Library

1639053725

Notebooks for Data Visualization in Python Libraries (Example Code)

This repository contains sample code scripts for creating awesome data visualizations from scratch using different python libraries (such as matplotlib, plotly, seaborn) with the help of example notebooks. For sample code with datasets, please check individual folder.

If you find these resources useful, give this repository a star ⭐️.

Python libraries for data visualization

  • altair - Declarative statistical visualizations, based on Vega-Lite.
  • bokeh - Interactive Web Plotting for Python.
  • bqplot - plotting library for IPython/Jupyter notebooks - front-end in d3
  • Chartify - Bokeh wrapper that makes it easy for data scientists to create charts.
  • dash - Dash is a Python framework for building analytical web applications
  • diagram - Text mode diagrams using UTF-8 characters
  • ggplot - plotting system based on R's ggplot2.
  • glumpy - OpenGL scientific visualizations library.
  • holoviews - Complex and declarative visualizations from annotated data.
  • mayai - interactive scientific data visualization and 3D plotting in Python.
  • matplotlib - 2D plotting library.
  • missingno - provides flexible toolset of data-visualization utilities that allows quick visual summary of the completeness of your dataset, based on matplotlib.
  • plotly - Interactive web based visualization built on top of plotly.js
  • PyQtGraph - Interactive and realtime 2D/3D/Image plotting and science/engineering widgets.
  • PyVista – 3D plotting and mesh analysis through a streamlined interface for the Visualization Toolkit (VTK)
  • seaborn - A library for making attractive and informative statistical graphics.
  • toyplot - The kid-sized plotting toolkit for Python with grownup-sized goals.
  • three.py - Easy to use 3D library based on PyOpenGL. Inspired by Three.js.
  • veusz - Python multiplatform GUI plotting tool and graphing library
  • VisPy - High-performance scientific visualization based on OpenGL.
  • vtk - 3D computer graphics, image processing, and visualization that includes a Python interface.

Download Details:
Author: javedali99
Source Code: https://github.com/javedali99/python-data-visualization
License: GPL-3.0 License

#python 

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Notebooks for Data Visualization in Python Libraries (Example Code)
Ray  Patel

Ray Patel

1623171540

Clash of Python Data Visualization Libraries

Seaborn, Altair, and Plotly

Data visualization is a fundamental ingredient of data science. It helps us understand the data better by providing insights. We also use data visualization to deliver the results or findings.

Python, being the predominant choice of programming language in the data science ecosystem, offers a rich selection of data visualization libraries. In this article, we will do a practical comparison of 3 popular ones.

The libraries we will cover are Seaborn, Altair, and Plotly. The examples will consist of 3 fundamental data visualization types which are scatter plot, histogram, and line plot.

We will do the comparison by creating the same visualizations with all 3 libraries. We will be using the Melbourne housing dataset available on Kaggle for the examples.

#data-visualization #python #data-science #programming #clash of python data visualization libraries #libraries

HI Python

HI Python

1623719849

Must-Know Data Science Libraries in Python

Python is the most widespread and popular programming language in data science, software development, and related fields. The simplicity of codes in Python, which helps learners avoid any confusion, is the key to this popularity. Python has constantly been developing, and it keeps getting updated for more ease in using. With 137,000 plus libraries and tools, Python has always provided its users with the solutions to problems of any complexity level. This reason makes Python the ideal language for Data Science operations. This article focuses on some of the essential and must-learn libraries in Python used heavily by Data Scientists. I have tried to cover different libraries used in various stages of a data science cycle, such as Data Mining, processing and modeling, Data Visualization.

Learn Data Science in Python from here!

#data-visualization #data #data-science #python-programming #python #must-know data science libraries in python

Ray  Patel

Ray Patel

1619510796

Lambda, Map, Filter functions in python

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:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

Ray  Patel

Ray Patel

1623077340

50+ Basic Python Code Examples

List, strings, score calculation and more…

1. How to print “Hello World” on Python?

2. How to print “Hello + Username” with the user’s name on Python?

3. How to add 2 numbers entered on Python?

4. How to find the Average of 2 Entered Numbers on Python?

5. How to calculate the Entered Visa and Final Grade Average on Python?

6. How to find the Average of 3 Written Grades entered on Python?

7. How to show the Class Pass Status (PASSED — FAILED) of the Student whose Written Average Has Been Entered on Python?

8. How to find out if the entered number is odd or even on Python?

9. How to find out if the entered number is Positive, Negative, or 0 on Python?

#programming #python #coding #50+ basic python code examples #python programming examples #python code

 iOS App Dev

iOS App Dev

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition