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Exploring Pandas GUI [List of Best Features You Should Be Aware Of]
Pandas is the favourite library for any Data Science enthusiast. It caters to all the needs of processing the Data via the structured tabular format, date-time formats, and providing the matplotlib API to instantly perform plotting within the pandas chaining operations. You can load Data from websites directly into data frames. This library also comes in very handy while performing exploratory data analysis that reveals insights about the dataset and various distributions it aligns with.
As more and more tools are built to enhance Data exploration, Pandas GUI is one of them that uses pandas as the core component and displays a windowed GUI with a lot of additional functions that are usually performed manually.
Let’s explore this utility and look at some of the best features.
Best Features of Python GUI
#data science #python gui #exploring pandas gui #pandas #list of best features you should be aware of #exploring
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Complete Tutorial on DearPyGui - GPU Accelerated Python GUI Framework
Graphical User Interface provides interaction between the user and the application. With the help of different widgets and functions, we can create a GUI of an application. Different indicators like buttons, text boxes, checkboxes can be used to build the GUI of an application.
DearPyGUI is a powerful python GUI framework which is easy to use and is a wrapper for Dearlmgui. Dearpygui is not an ordinary python GUI framework as it does not use the native widgets but instead draws the widgets using the system’s GPU. It is an all-included GUI framework which means that it can perform and create each and every widget that is used to make GUI interfaces.
In this article, we will explore what are the different types of widgets that we can create using Dearpygui also we will see how we can leverage visualizations using Dearpygui.
Implementation:
Like any other python library, we will install Dearpygui using pip install dearpygui.
- Importing Required Libraries
We will start by importing dearpygui to look at the creation of the different widgets. We will import any other library required as and when required.
from dearpygui.dearpygui import *
- Creating Different Widgets
As dearpygui supports a large variety of we will now explore some of the most used widgets and how we can create them. While creating an interface we need to end the python script with start_dearpygui() to launch the interface.
- Text Box and clickable Buttons
We can create different types of widgets using different parameters. Let us start by creating a text box and a button. We will also define and fix the Main Window Size.
#developers corner #gpu acceleration #gui frameworks #gui interface #interface #python gui #python gui toolkit
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Complete Guide to Develop an Interface Using Tkinter Python GUI Toolkit
A Graphical User Interface allows the user to interact with the application created on different platforms.
GUI interfaces use different indicators like audio indicators, graphical icons, different widgets which makes it highly interactive and user friendly rather than Command-Line applications which are not visually appealing and are text-based interactions.
Tkinter provides a GUI look to the standard python interface. It comes pre-installed with the standard versions of Python on Windows, Linux, and macOS. Tkinter is a Python binding to the Tk GUI toolkit which is why it is named Tkinter. It is the most commonly used python GUI toolkit due to a large variety of widgets it supports and its ease of use.
Tkinter provides powerful GUI based widgets and functions which create a visually appealing and highly creative application in just a few lines of codes. Tkinter is famous for creating a GUI application because it opens up in a new window where the user can interact with the application.
In this article, we will explore how we can create a GUI application with a variety of widgets that are available in Tkinter.
Implementation of Tkinter Python GUI Toolkit
As Tkinter comes pre-installed with standard python installation so we will not be installing it although if you don’t have it installed you can install it using pip install tkinter.
- Importing required libraries
We will create a form using Tkinter and the widgets it provides. So we will import Tkinter. Also, we will create a window that will initiate the Tk class.
import tkinter as tk
window = tk.Tk()
- Creating a form step by step
Now we will create the form using different widgets and wrapping them in a single loop.
- Setting the Title
We will start by setting the turtle of the window that will run our form. As I already mentioned that we need everything in a single loop so that everything displays at one go we will create the main loop and define all our widgets and functions before that.
window.title('Article Submission Form')
window.mainloop() #this will be the end of our form to wrap everything
This is the basic layout of the window we created with the title as we mentioned in the code.
- Adding Label and Textbox
We will start by adding Labels for different sections and adding a text box to those labels to the user input. We will also make the application window a bit large in size so that we can see everything clearly without maximizing. Here we will use the ‘place’ function which takes the X and Y coordinate values and displays the widgets accordingly.
#developers corner #complete guide #gui frameworks #gui interface #gui toolkit #python gui toolkit #python programming #tkinter
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3 Lessons from 7 Years of Building a GUI for MongoDB
Search for “mongodb gui” and you’ll see a full search results page of ads, listicles, and tool options.
That wasn’t the case in 2013.
We were SQL users back then who liked what we saw in MongoDB, the new NoSQL database launched in 2007.
We liked the ease and flexibility of the database itself, but the mongo shell can be pretty time-consuming and the tools available at the time were not up to scratch.
A not-so-pretty mongo shell (Photo: w3resource)
And so we built 3T Data Manager, which, along with Schema Explorer and Data Compare & Sync we then combined into MongoChef.
Then in 2017, MongoChef turned into what we are today: Studio 3T.
We’ve seen the third-party tooling space grow alongside MongoDB. The growth of this independent software vendor (ISV) ecosystem is one of the reasons MongoDB has grown to be a multi-billion dollar company.
In a similarly organic way, the Studio 3T feature set has also grown wider and stronger.
How did we decide what to build and improve? We listened to our pain points and to our customers. Then sweated the small stuff.
Lesson 1: Scratch your own itch
“If you are not embarrassed by the first version of your product, you’ve launched too late.”
Reid Garrett Hoffman, co-founder of LinkedIn
We spent the first months releasing our software publicly but quietly, honing core features born out of our own pain points:
- We needed a multi-platform tool
- Viewing data was a nightmare, MongoDB lacked a proper GUI
- Comparing two collections wasn’t possible
- There was no way to debug a collection and spot mistakes in the schema
- The mongo shell could be much better
Finally in May 2014, we announced 3T Data Manager to the world.
Studio 3T (then 3T Data Manager) in 2014
As an “in-place data browser and editor GUI for MongoDB”, it already had the key core features which remain in Studio 3T to this day:
- In-Place Data Editing — Double-click on a cell to edit
- Visual Query Builder — Drag-and-drop MongoDB query generation
- Table, Tree, and JSON views — Flexible ways to view collections
- Schema Explorer — Easy schema analysis
- Data Compare & Sync — Compare two MongoDB collections side-by-side
By September 2014, we started gathering customer feedback through Uservoice, which we still use.
#gui #gui