This article explores the transformative power of Python and its integration with AI for modern workers, particularly those without a coding background. It highlights how learning Python, specifically the Pandas library, can streamline Excel tasks by automating repetitive processes, handling large datasets, and simplifying complex calculations. Readers are guided on how to get started with online platforms like Deepnote and Google Colab, which require no installation. The article emphasizes leveraging AI to assist in code generation and encourages learning through experimentation. Real-world examples demonstrate Python's efficiency in data analysis, showcasing the significant productivity gains achievable by transitioning from traditional Excel methods to Python programming.

Python + AI: The Essential Skill Combination for Modern Workers Without Coding Backgrounds

Discover how to quickly set up your NotionNext website with our step-by-step guide! Transform your Notion workspace into a fully functional website without coding knowledge. Learn to create a Notion page, fork the NotionNext source code from GitHub, and deploy your site on Vercel. With multiple theme options and a streamlined publishing process, NotionNext is perfect for showcasing your portfolio or monetizing your content. Start your journey today and join the community of creators simplifying their web presence!

How to Get Started with NotionNext Quickly

NotionNext is an open-source solution for building personal blogs directly from your Notion workspace, leveraging Next.js and the Notion API. This tool simplifies the blogging process by creating a seamless connection between your Notion content and your website, eliminating the need for traditional content management. With benefits like enhanced performance, improved SEO, and customizable themes, NotionNext is perfect for content creators looking to streamline their publishing workflow. Additionally, maintaining a blog can lead to monetization opportunities and enhance career prospects. Discover how NotionNext can transform your online presence and content creation experience.

NotionNext: The Ultimate Solution for Notion-Based Website Building

This article addresses the issue of URL path accumulation when building a multilingual blog using NotionNext. The problem arises when switching languages, leading to incorrect URL paths such as /zh/de instead of /de. The article analyzes the root causes, including Notion's menu configuration and Next.js routing behavior, and presents multiple attempted solutions. Ultimately, the author proposes a final solution involving hardcoded language menus that bypass Notion's system, offering better control and user experience. The implementation details are provided, including a new LanguageMenu component and modifications to existing menu components. This approach enhances navigation accuracy and simplifies maintenance, making it a viable recommendation for others facing similar multilingual functionality challenges in NotionNext.

Solving URL Path Accumulation in NotionNext Multilingual Menus

This article provides a comprehensive guide on how to get responses from local LLM models using Python. It covers essential steps including starting your local LLM system, listing available models through a RESTful API, and generating responses using different endpoints. Readers will learn how to send prompts to the model for both simple completions and interactive chat-like conversations, with detailed Python code examples for each use case. By following this guide, users can effectively integrate LLM capabilities into their applications, enhancing productivity and automation.

Getting Responses from Local LLM Models with Python

Local LLMs are models that run directly on personal devices. They offer unique advantages such as enhanced privacy, offline functionality, and customizable use cases. In 2025, I predict local LLMs will become a cornerstone of personal and professional productivity tools.

Local LLM Models and Game Changing Use Cases for Life Hackers

This article discusses the benefits of using local Large Language Models (LLMs) like LLaMA, emphasizing advantages such as enhanced privacy, speed, cost-effectiveness, and customization. It provides a step-by-step guide for Mac users on downloading and installing LM Studio, selecting and running the LLaMA model, and exploring various use cases, including integration with Obsidian and Python. The conclusion encourages developers and life hackers to embrace local LLMs as a more innovative alternative to traditional AI interactions, highlighting their potential for the future.

Why You Should Try a Local LLM Model—and How to Get Started

Data Manipulation Language (DML) commands enable you to add, modify, or remove rows in a database. In this article, we’ll explore these commands with practical examples. The INSERT command is used to add new rows to a table. The UPDATE command allows you to modify data in existing rows. The DELETE command removes rows from a table based on a condition.

A Quick Review of SQL Window Functions with Examples

Python script automates the publication of Markdown files directly to Medium. The script sends a request to Medium’s API to fetch your user information. The response includes details like your user ID, which is required to publish content.

How to Publish an Article to Medium Using Python and the Medium API

Tableau Desktop Public Edition is now free and packed with advanced features previously exclusive to Tableau Desktop Professional—excluding only enterprise database connectivity. Ideal for data enthusiasts, educators, and professionals, this powerful tool supports local file use, drag-and-drop visualizations, and cloud sharing for non-commercial purposes. Learn how to download, install, and get started with creating stunning data visualizations today.

Tableau Desktop Public: Try the Free Version for Data Visualization

Jupyter Notebook is a popular tool among data scientists and programmers for interactive data analysis, visualization, and machine learning. In this guide, we will walk you through the process of setting up Jupyer Notebook on a Windows server to enable remote access.

Setting Up Jupyter Notebook on a Windows Server: A Step-by-Step Guide

Combining data from multiple tables is a common requirement in Power BI. There are two primary methods to achieve this task. Using the DAX UNION function and using the Append Queries feature in Power Query are the best ways to do it. By following these two methods, you can effectively union two tables in power BI.

Power BI: Two ways to Union Tables - DAX and Power Query

Python provides built-in support for working with JSON files through the json module. You can read, write, and manipulate JSON data using Python's built- in functions. To read a JSON file in Python, you can follow these steps: Import the  json module, open the file, and load the contents as a Python dictionary.

How to read and write JSON files in Python

In Python, you can use the pandas library to work with tabular data. The core data type in pandas is the DataFrame. Sometimes, when working with DataFrame data, you may need to convert rows to columns or columns to rows. Here is a simple example demonstrating how to achieve this.

How to Convert Rows to Columns and Columns to Rows in Pandas DataFrame using Python?

Dynamic Show Hide Slicer Panel is a way to show or hide slicer panels in Power BI. This technique allows users to switch the visibility of slicer panel as needed, optimizing space and improving usability.

Power BI: How to Create Dynamic Show Hide Slicer Panel

In this article, we'll explore efficient methods for updating Pandas DataFrames. We'll look at updating specific columns or rows using dictionary, updating specific values on conditions, and appending new rows. With these techniques, we can streamlined our workflows.

Python: Updating and Appending pandas DataFrame using Dictionary

DAX Studio is an incredibly powerful tool that allows Power BI users to view and analyze their data model in great detail. One of the key features of DAX. Studio is its ability to extract all measures from a Power BI file. In this blog post, we will guide you through the simple steps to achieve this using DAX Studio.

How to Use DAX Studio to Retrieve All Measures in Power BI

The Logger class is a Python package that can be used to create and configure loggers in Python. The class uses two objects, one for general logging and one for error messages. The logger is set up to use the rotating file handler for logging messages.

Using the Logger Class in Python for Effective Logging

When dealing with databases, it's important to pay attention to data types to ensure that the table structure is preserved. The following is a basic code snippet to save a DataFrame to an Oracle database using SQLAlchemy and pandas.

Python: Setting data types when using to_sql

You can use the pandas library in combination with the sqlalchemy and cx_Oracle libraries to save a dataframe to an Oracle database in Python. The table structure should match the structure of the dataframe in terms of column names and data types.

Saving Dataframes into Oracle Database with Python

Pyinstaller is an incredibly useful tool for packaging your Python code into a standalone executable file. This makes it simple to distribute and run your code on various systems.

How to Use Pyinstaller to Generate an EXE File

Python can be a powerful tool for automating this process using Tableau's REST API. We will walk through the process of using Python to extract data from the Tableau Server. The following examples demonstrate how to retrieve information about views, projects, and workbooks.

Using Python to Retrieve Data Through Tableau REST-API

In this article, we explore how to preserve formatting when saving Excel files using Python, specifically with the xlsxwriter library. Data analysts often face the challenge of converting Excel files into plain text, losing important visual cues. This guide covers essential steps such as importing the xlsxwriter package, creating worksheets for multiple pandas DataFrames, and applying various styles and formats to ensure data types like dates, integers, and decimals are displayed correctly. By setting up header formats, using different cell formats for specific data types, and adjusting column widths, readers will learn how to create well-formatted Excel files effortlessly. Follow these straightforward steps to enhance your data presentation!

Specify data format when saving Excel via Python

This article provides a comprehensive guide on accessing Power BI data using Python through the Power BI Report Server REST API. It explains the capabilities of the API, including CRUD operations, and details the authentication process using NTLM. The article includes practical Python code snippets for retrieving Power BI reports, folders, refresh plans, user policies, and row-level security settings. Readers will gain insights into effectively managing and analyzing their Power BI Report Server data. Ideal for developers and data analysts looking to automate and integrate Power BI functionalities into their applications.

Accessing Power BI Data with Python Using Power BI REST-API

In Power BI, a date table is necessary because the DAX language does not support built-in date functions. In Tableau, no date tables are needed as it supports built-In date functions and features. To create a dynamic date table for the most recent three years, you can use the following code.

Calendar Table DAX in Power BI

Automating SAP with PyAutoGUI involves using the Python package to simulate mouse clicks and keyboard inputs. It offers the advantage of not requiring extra rights and permissions, but may be less efficient and require more maintenance. This method is best for situations where access to APIs or databases is restricted and there is a need to automate tasks.

Automate SAP Report Extraction with PyAutoGUI

Learn how to automate email sending with Python in this step-by-step guide! Designed for data analysts and Python developers, this tutorial covers the essentials—from setting up an SMTP connection to composing, sending, and closing the email connection. With Python’s smtplib and email.mime.text libraries, you can efficiently send scheduled reports, alerts, or notifications to streamline your daily workflow. Perfect for anyone seeking to integrate email automation into their data analysis or other Python-driven workflows. Includes practical code snippets to help you get started! 📧📊

Sending Emails with Python

This article provides a comprehensive guide on how to efficiently merge multiple Excel files using Python. It highlights the challenges faced when consolidating similarly formatted Excel files and presents a Python solution utilizing the pandas and os libraries to automate the process. The code snippet allows users to easily combine their Excel files into a single merged_excel.xlsx file. For those without Python installed, alternatives like Deepnote and Google Colab are suggested, complete with step-by-step instructions. The article emphasizes the importance of ensuring consistent file formats and provides pro tips for a smooth merging experience. Perfect for data analysts and anyone needing to handle multiple Excel files regularly.

Merge multiple Excel files using Python

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A self-taught coder who dreams of retiring early, working as a Data Analyst in Germany.

Setting Up Jupyter Notebook on a Windows Server: A Step-by-Step Guide

Introduction

Step 1: Install Jupyter Notebook

Step 2: Modify the Configuration File

Step 3: Set a Customized Password

Step 4: Remote Access

Conclusion

Luca Liu