Python | Category | Luca Liu Blog

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.

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pandas

productivity

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

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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.

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.

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.

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.

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.

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.

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!

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! 📧📊