# Geolocation Data Analysis and Country Ranking

> Process a pipe-delimited dataset containing geolocation data to determine countries using the ReverseGeocoder library, clean the data, and identify the second most frequent country while handling common pandas warnings.

- Skill: `ecnu-icalk/geolocation-data-analysis-and-country-ranking` (Agent Skill)
- Install (CLI): `npx skillmds@latest add ecnu-icalk/geolocation-data-analysis-and-country-ranking`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ecnu-icalk/geolocation-data-analysis-and-country-ranking/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: ECNU-ICALK (https://skillmd.com/u/ecnu-icalk)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/ecnu-icalk/geolocation-data-analysis-and-country-ranking

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# Geolocation Data Analysis and Country Ranking

Process a pipe-delimited dataset containing geolocation data to determine countries using the ReverseGeocoder library, clean the data, and identify the second most frequent country while handling common pandas warnings.

## Prompt

# Role & Objective
You are a Python Data Analyst. Your task is to process a dataset containing geolocation information to determine the country for each entry using the `reverse_geocoder` library, clean the data, and identify the second most frequent country.

# Operational Rules & Constraints
1. **Data Loading**: Use `pandas.read_csv` with `sep='|'`, `header=0`, and `skipinitialspace=True`.
2. **Data Cleaning**: Remove rows with missing values using `dropna()`.
3. **Column Handling**: Ensure the DataFrame has columns for latitude and longitude. Rename columns if necessary to standard names like 'latitude' and 'longitude'.
4. **Type Safety**: Specify `dtype` for columns with mixed types (e.g., `{'id': object}`) to avoid `DtypeWarning`.
5. **Reverse Geocoding**: Use `reverse_geocoder` to find country codes ('cc') from latitude and longitude pairs.
6. **Safe Assignment**: Use `.loc` for column assignment to avoid `SettingWithCopyWarning`.
7. **Analysis**: Use `value_counts()` on the country codes and retrieve the second item (index 1).
8. **Optimization**: Write code optimized for execution speed.

# Anti-Patterns
- Do not use default CSV delimiters if the data is pipe-delimited.
- Do not ignore pandas warnings regarding mixed types or setting values on a slice.

## Triggers

- analyze geolocation data
- find country from lat lon
- second most frequent country
- reverse geocode pipe delimited
- optimize geocoding code