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savedata101/repair_data

Repair Data (Cleaned Preview) This dataset publishes the contents of the repair_data folder, with the dataset UI preview focused on cleaned.csv. The cleaned file is produced with csv_repair.py to standardize types, trim whitespace, harmonize null-like tokens, and optionally split a location column into city and country. City names can be made country-specific using a real city catalog built from GeoNames. Files cleaned.csv — primary data file targeted for… See the full description on the dataset page: https://huggingface.co/datasets/savedata101/repair_data.

sourceHugging Faceotherupdated 1y agoView on Hugging Face
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Dataset Card

# Repair Data (Cleaned Preview)

This dataset publishes the contents of the repairdata folder, with the dataset UI preview focused on cleaned.csv. The cleaned file is produced with csvrepair.py to standardize types, trim whitespace, harmonize null-like tokens, and optionally split a location column into city and country. City names can be made country-specific using a real city catalog built from GeoNames.

## Files

  • —cleaned.csv — primary data file targeted for preview.
  • —Other helper scripts and reports are included for reproducibility (e.g., csvrepair.py, extractcountries.py, geonames_fetch.py, JSON mappings under report/).

If cleaned.csv lives in a subfolder, update the front‑matter viewer.default_path to that path (e.g., report/cleaned.csv).

## How It Was Built

  • —Analysis and cleaning: csv_repair.py (trims strings, standardizes boolean‑like values, parses dates where feasible, detects outliers, suggests column fixes).
  • —Location repair (optional): splits location into city and country.
  • —Real city mode: --location-mode real --cities-json report/citiesbycountry.json.
  • —GeoNames data: geonamesfetch.py builds countries.json, provincesbycountry.json, and citiesby_country.json.

Example command to generate cleaned.csv:

python csvrepair.py \ -i HRDataClean20202025.csv \ -o report \ --cleaned-csv cleaned.csv \ --fix-location \ --location-mode real \ --cities-json report/citiesby_country.json

## Load Examples

  • —With datasets:

from datasets import loaddataset ds = loaddataset("savedata101/repairdata", datafiles={"train": "cleaned.csv"}) print(ds["train"])

  • —With pandas (direct URL to main branch):

import pandas as pd url = "https://huggingface.co/datasets/savedata101/repairdata/resolve/main/cleaned.csv" df = pd.readcsv(url) print(df.head())

## Notes

  • —Preview focuses on cleaned.csv via the dataset card front‑matter.
  • —If your cleaned file path changes, keep viewer.default_path in sync.
  • —Large original CSVs may be excluded from preview but remain accessible in the repo.

## License

Data license is set to other as a placeholder. Please update to the appropriate license for your data.