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Changelog

v0.0.12

Fixed:

  • using na_pct_below
  • from_df now includes metadata
  • from_df now generates correct na_pct_below (0.01) for full datasets #63

Changed:

  • bumped minimum python version to 3.8
  • Support for Python 3.11 #54
  • Pydantic migrated to v2
  • Allows use of Pandas v2

v0.0.11:

  • Version in metadata
    • adds dfschema and pandas version in metadata upon generation (Later will worn if Schema is initialized from json, generated by later version)
  • Renamed na_limit to na_pct_below to make it unambiguous (with backward support) #64
  • Added optional=True flag for columns. If true, does not raise exception if column is not present
  • added dfschema update {existing_schema} {output_schema} command to upgrade schemas

v0.0.10:

  • relaxed Pydantic requirement to >=1.9

v0.0.9:

  • Pydantic bumped to 1.10
  • Bug Fix: Categorical constraints (exact_set, oneof, include) now can keeo int and float values. That expands to legacy schemas as well.

v0.0.8:

Legacy Schema Aliases (support for legacy schemas):

  • min_value now also supports min alias
  • max_value now also supports max alias
  • oneof now also supports one_of alias
  • version is now correctly moved to metadata from root on migration
  • If column schema has both oneof and includes and they are identical, will replace with exact_set

Testing:

  • conftest code improved to showcase bad json on Exception
  • multiple v1 schemas were added for testing
  • pre-commit setup was updated

v0.0.7:

  • rename DfSchema.validate_df to DfSchema.validate (UNDONE: validate is reserved by Pydantic object)
  • updated documentation

v0.0.6:

- `DfSchema.to_file`, `DfSchema.from_file` proper testing
- CLI command help texts
- added pre-commit install to the repo
- Some benchmarking
- renamed `dfs.validate_df` to `dfs.validate`

v0.0.5

  • fix column dtype generation/validation bug

Pre-Publication

v1.3.0

  • renamed strict_column_set to additionalColumns
  • renamed strict_column_order to exactColumnOrder

v1.2.0

  • Metadata SubObject
  • Summary Exception is now collected for specific DfSchema, not via Borg State
  • Supports SubSets
  • Support reading and writing schemas as yaml
  • added validate_sql method (based on pd.read_sql for everything including dtype mapping)
  • added cli support for schema generation or validation
  • support for subsets in from_df
  • support for str_patterns (string columns are matched against string prefix / regex patterns )

v1.1.0

  • added support for "exact_set" (exact match of categorical values)
  • better structure of tests and code
  • added summary argument. If True, all tests will be ran and errors will be summarized in DataFrameSummaryError exception.
  • re-enabled schema generation