Step by Step Guides

We provide a number of Jupyter notebooks examples to walk through various openclean functionalities. All notebooks and datasets are available in the GitHub repository.

• Downloading master data from Reference Data Repository
  • restcountries.eu
  • Encyclopaedia Britannica
  • Cleanup
• Downloading DOB Job Application Filings from Socrata
• Misspellings in Country Names
  • Download Country Names Masterdata
  • Identify Country Name Outliers in ITU ICT Development Index (IDI)
  • Repair Country Name Outliers in ITU ICT Development Index (IDI)
• Statistical Outliers in City names
• Misspellings of Brooklyn
• Profiling - DOHMH New York City Restaurant Inspection Results
  • Data Profiling
• Wrangling - DOHMH New York City Restaurant Inspection Results
  • Data Cleaning
  • Extract Relevant Records
• Features
  • Data Profiling
  • Data Cleaning & Wrangling
  • Data Enrichment
  • Data Provenance
• Setting up
• Loading data
• Profiling
• Transformations
  • Date Conversion
  • Standardizing Spellings
• kNN Clustering - DOHMH New York City Restaurant Inspection Results
  • Extract Relevant Records
• Functional Dependency Violations
• Token Signature Outliers for Street Names
• Standardization of Street Names
• User-defined Functions
• Engine - Datastore
  • Notebook Spreadsheet UI
  • Rollback Changes in Persistent Archive