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data-cleaning-standards

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Automate vehicle insurance data cleaning & prep.

Data & Analytics#data quality#standardization#deduplication#data cleaning#Pandas#insurance data#missing values
Authoralongor666
Version1.0.0
Installs0

System Documentation

What problem does it solve?

This Skill addresses the common challenges of messy, inconsistent vehicle insurance data, such as missing values, duplicate records, and non-standardized formats. It ensures data quality and reliability for accurate analysis.

Core Features & Use Cases

  • Missing Value Handling: Implements strategies for filling or removing nulls based on field type (e.g., fill '手续费含税' with 0, delete rows for '签单/批改保费').
  • Duplicate Removal: Deduplicates records using a composite key (保单号 + 投保确认时间), keeping the most recent entry.
  • Format Standardization: Converts dates to datetime64[ns], numerics to float64, and strings (stripping whitespace, mapping 'Y'/'N' to '是'/'否').
  • Use Case: A user receives a monthly Excel report with raw vehicle insurance data. This skill can automatically clean, standardize, and deduplicate the data, making it ready for immediate analysis and reporting.

Quick Start

Clean and standardize the attached vehicle insurance data file 'monthly_report.csv'.

Dependency Matrix

Required Modules

pandas

Components

Standard package

💻 Claude Code Installation

Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.

Please help me install this Skill:
Name: data-cleaning-standards
Download link: https://github.com/alongor666/daylyreport/archive/main.zip#data-cleaning-standards

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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