Jupyter Notebook is a popular open-source tool for development and exploration in a data world. It has evolved into four web applications: Classic Jupyter Notebook, Jupyter Lab, Jupyter RetroLab, and Jupyter Lite, each with unique features.
Matplotlib is a Python visualization package widely used in commercial and academic applications. This article demonstrates nine methods for customizing colors in Matplotlib plots, including predefined styles for aesthetically pleasing visuals.
Pandas, a popular data manipulation library. It offers three methods to obtain row counts from a DataFrame. The article explores these approaches.
Jupyter Notebooks use the .ipynb format, a JSON structure containing code, Markdown, and outputs. Converting to a Python script is useful for repository storage or creating standalone packages; three methods for export are explained.
Explore the pyTZ Python package, a implementation of the tz database, to list 594 available timezones. Learn how to access and utilize timezones beyond the tz database.
Discover how to save Matplotlib plots in Python using 14 different formats. This guide helps you save plots in various file types for Python scripts and Jupyter Notebooks.
Learn 2 methods to save and load machine learning models in scikit-learn. The first, using the pickle package, is faster but takes more storage. The second, employing joblib, conserves disk space but is slower.
Convert your Jupyter Notebook presentation to an interactive presentation using the nbconvert tool and Reveal.js library.
Learn three methods to change the figure size in Matplotlib for your Python plots. These approaches, applicable in Python scripts or Jupyter Notebooks, provide flexibility for creating visually appealing plots.
Securely share your Jupyter Notebook analysis with selected individuals by learning three methods to add password protection. This ensures access only for authorized users to your valuable content.
Convert Jupyter Notebook presentations, created with Reveal.js for interactivity, into PDF slides. Explore two methods to easily export and share your data-rich presentations in a portable format.
Discover various approaches to run Jupyter Notebooks from the command line. Tailor your method based on specific needs, such as sharing output as PDF/HTML, uploading to cloud storage, or adding parameters.
Effortlessly share Jupyter Notebooks as web applications, making it simpler for multiple users to interact without code changes. Use user-defined widgets for flexible control and eliminate the need for individual installations.
Learn three simple ways to hide code in Jupyter Notebook presentations, making it easy to share data-rich results without intimidating non-technical stakeholders with code.
Enhance your Jupyter Notebook presentations with interactive widgets using the open-source framework Mercury. Easily parameterize your slides, allowing viewers to interactively recompute charts and values during the presentation.
Effortlessly create data-rich presentations directly from Jupyter Notebook, saving time on manual copying and updating. Hide code to cater to non-technical audiences, and easily share as a website or PDF.
Learn to schedule your Jupyter Notebook to run daily with the open-source Mercury framework. Automate tasks like sending executed notebooks as PDFs by email, serving updated HTML views, and parameterizing executions.
Learn five ways to schedule Jupyter Notebooks for regular updates, enabling easy sharing with your team. Explore options like local/cloud execution, UI, export to HTML/PDF, email sending, and more.
Share Jupyter Notebooks with non-technical users using HTML. Serve as a static website or send via email. Explore three simple ways to convert Jupyter Notebook to HTML.
Explore building an automated reporting system in Python using Jupyter Notebook and the Mercury framework. Fetch stock market data, display news, price chart, and analysis. Schedule daily execution, convert the notebook to PDF, and send it via email.
Learn to detect data drift in Machine Learning models using Python. Utilize the Evidently package for drift detection and build a dashboard in Jupyter Notebook, published as a web app with the Mercury framework.
Build a data dashboard in Python with just 9 lines of code using Jupyter Notebook. Display stock information for a selected ticker with a data table and chart. Publish it as a web app with the open-source Mercury framework.
Easily send emails from Python using the send_email() function, simplifying the process with built-in packages smtplib and email. Ideal for automation and notification applications.
Jupyter Notebook, a powerful tool for code and Markdown mixing, offers fast feedback for data analysis. This article explores various ways to install Jupyter Notebook on your local machine.
Create a Python dashboard to monitor and act on data from multiple sources. This guide demonstrates building a Python dashboard in Jupyter Notebook, monitoring stock and cryptocurrency exchanges, and weather data. Deployed on AWS using the open-source Mercury framework.
Hide the Python code in your Jupyter Notebook to share results with non-technical users. Learn various approaches in this guide to make your analysis more accessible and less intimidating.
Enhance your Jupyter Notebook with visual appeal by learning various approaches to insert images. This post guides you on making your notebook more informative and attractive with inserted images.
Export your Jupyter Notebook to a PDF file using four different methods. PDF, a versatile format, allows easy sharing across operating systems. This post guides you through the exporting process.
Share your Jupyter Notebook with non-programmers efficiently using the Mercury framework. This guide takes you through creating an example notebook, adding a YAML header, and deploying it to the cloud with Heroku for an interactive experience.
Style your Jupyter Notebook text effortlessly with Markdown or HTML syntax. Enhance your computational documents by blending code with formatted text.
Explore the Jupyter Notebook beyond its traditional use. Discover 8 alternative ways to utilize this versatile tool, making it even more powerful for coding, experimentation, and collaboration.
Explore convenient alternatives for running Jupyter Notebooks online, including popular services and new approaches, to create, edit, and run Python notebooks in the cloud without the hassle of local setup.
Learn essential Jupyter Notebook keyboard shortcuts to enhance your productivity. Speed up your data analysis and problem-solving using shortcuts tailored for Mac, Windows, and Linux users. Download the ready-to-print PDF guide provided at the end of the article.
Learn how to create a private fork of the Mercury framework repository for personalized web app deployments. Explore the options available with the open-source and commercial-friendly licenses.
The *.ipynb file extension is used for Jupyter Notebooks, supporting interactive computing in Python. Originating from IPython, it offers rich media, shell syntax, tab completion, and history for Python programming.
The Jupyter notebooks provide great flexibility for developing Python programs. By adding interactive widgets, notebooks become accessible to non-programmers. Two approaches presented are ipywidgets+Voila and Mercury.
Natural Language Processing (NLP) involves computer-human written language interactions. This post shows how to develop a sentiment analysis web app using Jupyter, TextBlob, SpaCy, and the Mercury framework deployed on Heroku.
Computer Vision methods offer flexibility in image processing. This post demonstrates how to develop a web app for converting images into sketches using Jupyter, OpenCV, Mercury, and deploying.
Enable file uploads in your Jupyter Notebook and share it as a web app with Mercury. Enhance collaboration by allowing coworkers to upload files for processing.
Easily share your Jupyter notebook as an interactive web app with the Mercury framework. Provide a user-friendly interface and hide code to make it accessible for non-programmers.
