Excel takes it to the next level: Seamless Python integration in latest update-Python Tutorial-php.cn

Home

Backend Development

Python Tutorial

Excel takes it to the next level: Seamless Python integration in latest update

WBOYWBOYWBOYWBOYWBOYWBOYWBOYWBOYWBOYWBOYWBOYWBOYWB

Sep 19, 2023 pm 09:57 PM

Excel 将其提升到新的水平：最新更新中的无缝 Python 集成

Getting Started with Python in Excel

Built-in integration: No additional download required. Users can start coding directly by clicking the "Insert Python" button under the "Formulas" section.
Powered by Anaconda: Microsoft partners with Anaconda to ensure users have access to premium libraries and unparalleled support.

Security and Collaboration

Cloud Execution: Python scripts in Excel run on the Microsoft cloud, providing a seamless and secure experience.
Enterprise-Grade Security: As part of the M365 connected experience, users can rest assured that their data and processes are hardened with best-in-class security measures.
Share and co-author: Just like any other Excel file, Python-enhanced workbooks can be shared. Collaborators can easily refresh and interact with Python scripts.

Read more: Artificial Intelligence Game Changer: Every time you play, it’s a new adventure!

Beta testing and availability

Current Phase: Currently, this feature is in public preview, available exclusively to members of the Microsoft 365 Insiders Beta channel. Excel for Windows version 16818.
Upcoming Features: Microsoft promises to enhance the user experience through:
- Syntax Highlighting
- Autocomplete
- Improved error feedback
- Comprehensive documentation
Cost Impact: After preview, some features may require a license. Details will be provided closer to General Availability (GA).

In an unprecedented move, Microsoft Excel will now integrate the highly regarded Python programming language, heralding a new era of data analysis. With the release of public preview, the impact is huge: Power users can now embed Python code directly into Excel, bridging the gap between spreadsheet utility and programming capabilities.

Combining the best features of Excel and Python

Steffan Kinnestrand, General Manager of Modern Work at Microsoft, elaborated on the groundbreaking synergy: "Combining Python's powerful data visualization and analysis library with the typical capabilities of Excel paves the way for enhanced data exploration." Users can use Python's libraries drill down into your data, then seamlessly switch to Excel's formulas, pivot tables, and charts for further insights.

Availability and licensing details

First Rollout: As of now, this feature is available to Microsoft 365 Insiders in the Beta channel. Its availability is currently limited to Windows users.
Future Expansion: We are planning to expand this functionality to other platforms in subsequent phases.
Subscription Details: While Python in Excel will be available under a Microsoft 365 subscription in public preview, it’s worth noting that after this preview period, some features may require a paid license.

Enhance data visualization capabilities

Excel is known for its data processing and visualization capabilities, and it will benefit greatly from Python's visualization library. Users can:

Create complex formulas, pivot tables, and charts based on Python data.
Combine powerful charting capabilities like Matplotlib and Seaborn to create visually compelling heat map visualizations, violin plots, and more.

Microsoft’s move to inject Python capabilities into Excel holds great promise. The combination of Excel's analytical capabilities and Python's versatile libraries can revolutionize the way professionals perform data analysis.

The merger of Python and Excel represents a transformative leap for data enthusiasts and professionals alike. As Excel continues to evolve to take advantage of the power of Python, users can expect a more dynamic, insightful, and comprehensive data analysis experience.

The above is the detailed content of Excel takes it to the next level: Seamless Python integration in latest update. For more information, please follow other related articles on the PHP Chinese website!

Statement

The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn

How to Use Python to Find the Zipf Distribution of a Text FileMar 05, 2025 am 09:58 AM

This tutorial demonstrates how to use Python to process the statistical concept of Zipf's law and demonstrates the efficiency of Python's reading and sorting large text files when processing the law. You may be wondering what the term Zipf distribution means. To understand this term, we first need to define Zipf's law. Don't worry, I'll try to simplify the instructions. Zipf's Law Zipf's law simply means: in a large natural language corpus, the most frequently occurring words appear about twice as frequently as the second frequent words, three times as the third frequent words, four times as the fourth frequent words, and so on. Let's look at an example. If you look at the Brown corpus in American English, you will notice that the most frequent word is "th

Image Filtering in PythonMar 03, 2025 am 09:44 AM

Dealing with noisy images is a common problem, especially with mobile phone or low-resolution camera photos. This tutorial explores image filtering techniques in Python using OpenCV to tackle this issue. Image Filtering: A Powerful Tool Image filter

How Do I Use Beautiful Soup to Parse HTML?Mar 10, 2025 pm 06:54 PM

This article explains how to use Beautiful Soup, a Python library, to parse HTML. It details common methods like find(), find_all(), select(), and get_text() for data extraction, handling of diverse HTML structures and errors, and alternatives (Sel

How to Perform Deep Learning with TensorFlow or PyTorch?Mar 10, 2025 pm 06:52 PM

This article compares TensorFlow and PyTorch for deep learning. It details the steps involved: data preparation, model building, training, evaluation, and deployment. Key differences between the frameworks, particularly regarding computational grap

Introduction to Parallel and Concurrent Programming in PythonMar 03, 2025 am 10:32 AM

Python, a favorite for data science and processing, offers a rich ecosystem for high-performance computing. However, parallel programming in Python presents unique challenges. This tutorial explores these challenges, focusing on the Global Interprete

How to Implement Your Own Data Structure in PythonMar 03, 2025 am 09:28 AM

This tutorial demonstrates creating a custom pipeline data structure in Python 3, leveraging classes and operator overloading for enhanced functionality. The pipeline's flexibility lies in its ability to apply a series of functions to a data set, ge

Serialization and Deserialization of Python Objects: Part 1Mar 08, 2025 am 09:39 AM

Serialization and deserialization of Python objects are key aspects of any non-trivial program. If you save something to a Python file, you do object serialization and deserialization if you read the configuration file, or if you respond to an HTTP request. In a sense, serialization and deserialization are the most boring things in the world. Who cares about all these formats and protocols? You want to persist or stream some Python objects and retrieve them in full at a later time. This is a great way to see the world on a conceptual level. However, on a practical level, the serialization scheme, format or protocol you choose may determine the speed, security, freedom of maintenance status, and other aspects of the program

Mathematical Modules in Python: StatisticsMar 09, 2025 am 11:40 AM

Python's statistics module provides powerful data statistical analysis capabilities to help us quickly understand the overall characteristics of data, such as biostatistics and business analysis. Instead of looking at data points one by one, just look at statistics such as mean or variance to discover trends and features in the original data that may be ignored, and compare large datasets more easily and effectively. This tutorial will explain how to calculate the mean and measure the degree of dispersion of the dataset. Unless otherwise stated, all functions in this module support the calculation of the mean() function instead of simply summing the average. Floating point numbers can also be used. import random import statistics from fracti

See all articles