Learn how to list installed packages in JupyterLab without using the terminal. Use a simple command or the Jupyter Package Manager for a quick, no-code solution. Save time and code smarter!
Learn how to easily delete packages in JupyterLab without using the terminal. Use a simple command or the Jupyter Package Manager for a quick, no-code solution. Save time and code smarter!
Discover two simple ways to install packages in JupyterLab without using the terminal. Learn how to use a notebook command or the Jupyter Package Manager to save time and avoid errors.
Learn the top programming languages for Data Science! Discover how Python, R, and SQL help with data analysis, visualization, and machine learning. Find out which one suits your needs best!
In this post, I will explore the evolution of no-code data science, discuss its benefits and pitfalls, and introduce a solution I am building to simplify data analysis for everyone.
Learn how to install and manage Python packages using pip. This article covers setting up virtual environments, installing packages, updating them, and managing packages in Jupyter Notebooks to make your projects easier!
Compare Python packages for data visualization, including Matplotlib, Seaborn, Plotly, and more. Learn their features, strengths, and best use cases to find the ideal tool for your data projects.
"LightGBM is a fast, efficient gradient boosting framework. In this article I show you how to visualize its decision trees using tools like plot_tree for basic plots, dtreeviz for enhanced visuals, graphviz for detailed rendering, and SuperTree for interactive exploration of complex tree structures."
"A Random Forest is a supervised machine learning algorithm used for classification and regression. This article demonstrates four ways to visualize Random Forests in Python, including feature importance plots, individual tree visualization using plot_tree, and SuperTree."
"XGBoost is a supervised machine learning algorithm used for both classification and regression tasks. This article demonstrates four ways to visualize XGBoost models in Python, including feature importance plots, individual tree visualization using plot_tree, dtreeviz, graphviz, and SuperTree."
We make Python notebooks accessible with REST API. Custom ChatGPT can use actions to execute Python notebooks. You can easily turn any Python code into API available in the ChatGPT
Let us introduce you Invoice Generator, which was created only with Mercury's widgets in Python.
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.
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.
Learn how Python virtual environments work to manage dependencies, avoid conflicts, and ensure smooth execution of scripts. Avoid broken dependencies and enhance your Python development experience.
