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valkryhx
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数据分析专家

Analyze CSV data with Pandas and Matplotlib for statistics and charts.

6|2|Updated Jan 23, 2026
Search Tags:#charts#data analysis#visualization#pandas#statistics#query#csv
One-click install
npx skills add https://github.com/valkryhx/google_adk_agent --skill -valkryhx
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: 数据分析专家
Source: https://github.com/valkryhx/google_adk_agent/tree/main/skills/data_analyst
Command: npx skills add https://github.com/valkryhx/google_adk_agent --skill -valkryhx

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, matplotlib, numpy, and includes scripts (resource) components.

What problem does it solve?

This Skill automates the process of analyzing CSV data, providing statistical insights and generating visualizations to help users understand their data quickly.

Core Features & Use Cases

  • Data Loading & Preview: Loads CSV files and displays a preview along with column names and data types.
  • Statistical Analysis: Calculates descriptive statistics for numerical columns and value counts for categorical columns.
  • Chart Generation: Creates various types of charts (line, bar, scatter, histogram) to visualize data relationships.
  • Data Querying: Allows users to query data using pandas' query syntax.
  • Use Case: Analyze a sales CSV file to understand sales trends over time, identify top-performing products, and visualize the distribution of customer demographics.

Quick Start

Analyze the file 'sales_data.csv' and print the statistical summary.

Frequently Asked Questions about 数据分析专家

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I perform statistical analysis on CSV data and generate charts?▼

To perform statistical analysis on CSV data and generate charts, this Skill uses pandas and matplotlib to calculate descriptive statistics for numerical columns and render visualizations like line, bar, scatter, and histogram charts.

Can I create data visualizations directly from a CSV file?▼

Yes, you can create data visualizations directly from a CSV file by loading the dataset with pandas, which automatically previews column names and data types before generating matplotlib charts for data exploration.

How do I query CSV data using pandas syntax for business intelligence?▼

You can query CSV data using pandas syntax for business intelligence by loading the file and applying pandas' query syntax to filter rows, identify sales trends, and extract specific data subsets for reporting.

Does this data analysis tool require numpy and matplotlib dependencies?▼

Yes, this data analysis tool requires numpy and matplotlib dependencies, along with pandas, to execute its data manipulation, statistical computation, and chart rendering processes for CSV files.

What is the best way to analyze categorical and numerical columns in a CSV?▼

The best way to analyze categorical and numerical columns in a CSV is using pandas to calculate value counts for categorical fields and descriptive statistics for numerical fields, providing quick data insights.

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