Zero‑Code AI Data Analysis: From Raw CSV to Insightful Report and Content Generation

In this hands‑on session we demonstrate how to perform a full data‑analysis pipeline on a small‑red‑book (Xiaohongshu) dataset without writing a single line of Python code, using the Tongyi Lingma low‑code AI platform.

Step 1 – Import and Understand the Data

Upload the provided 通义灵码小红书数据.xlsx file, view its schema and preview the first rows. The dataset contains 86 notes with fields such as likes, comments, shares, user demographics, and timestamps.

Step 2 – Data Cleaning

Identify missing values across all columns.

Apply reasonable imputation (e.g., fill numeric gaps with median, categorical gaps with "unknown").

Standardize fields: convert timestamps to YYYY‑MM‑DD HH:MM format, ensure numeric columns are typed correctly.

The cleaned data is saved as 通义灵码小红书数据_cleaned.

Step 3 – Insight & Visualization

Distribution of likes – plotted as a histogram.

Top‑10 notes by likes – displayed in a bar chart.

Tag frequency – visualized with a word‑cloud (Chinese font 汇文仿宋v1.001.ttf applied).

Hourly engagement trend – line chart showing when likes peak during the day.

All charts are generated automatically by Tongyi Lingma and can be exported as PNG or embedded in HTML.

Step 4 – Automatic Report Generation

Using the AI model (e.g., Qwen‑plus on Alibaba Cloud Baichuan), we prompt the system to produce a structured markdown report that includes:

Analysis purpose and data summary.

Key findings (e.g., most popular tags, optimal posting times).

Embedded visualizations.

Operational recommendations for content strategy.

The report can be exported as HTML, PDF, or PPT.

Step 5 – Content Generation (Explosive‑Post Replication)

Based on the insights, we construct a “burst‑content template” that captures the structure, tone, and tag usage of high‑performing notes. The template is then fed to the AI model to generate new Xiaohongshu posts on a chosen topic, complete with title, body, and suggested hashtags.

Example prompt:

基于“通义灵码小红书数据_cleaned”中点赞最高的笔记，提取标题结构、段落逻辑和常用标签，生成一条关于“夏季通勤穿搭推荐”的新笔记，要求标题吸引、正文200字以内、结尾引导互动，并附上3‑5个相关标签。

The AI returns multiple variations, ready for publishing.

Key Takeaways

Zero‑code AI tools can replace traditional scripting for data wrangling, visualization, and reporting.

Natural‑language prompts drive the entire workflow, from loading data to generating marketing copy.

The same pipeline can be adapted to other business scenarios such as brand marketing, short‑video script writing, or headline optimization.

For quick reuse, a reference table of common prompts is provided (e.g., load CSV, explain fields, handle missing values, create visualizations, generate reports).