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provides *named* data frames, not *typed* data frames. \n\n\n## Installation\n\n```bash\npip install namedframes\n```\n\n## Usage\n\n### Pandas\n\n```python\nimport pandas as pd\nfrom namedframes import PandasNamedFrame\n\nclass InputDF(PandasNamedFrame):\n    x: float\n\nclass OutputDF(InputDF):\n    blah: bool\n\n\ndef transform(input_data: InputDF) -\u003e OutputDF:\n    return OutputDF(input_data.assign(blah = True))\n\ninput_df = InputDF(pd.DataFrame({\"x\": [1.1, 2.2]}))\n\noutput = transform(input_df)\n\nisinstance(input_df, InputDF)\nTrue\n\nisinstance(output, OutputDF)\nTrue\n```\n\nIf a column is missing, a validation error occurs,\n\n```python\nOutputDF(input_df)\n\nValueError: missing columns: [('blah', \u003cclass 'bool'\u003e)]\n```\n\n### Spark\n\n`namedframes` includes an option for pyspark dataframes. \nUsing it requires installation of `pyspark`. You can install this\nseparately or with the `[pyspark]` flag to `namedframes`, i.e., \n\n```bash\npip install namedframes[pyspark]\n```\n\nExample usage:\n\n```\nimport pandas as pd \nfrom pyspark.sql import SparkSession\nfrom namedframes import SparkNamedFrame \n\nclass InputDF(SparkNamedFrame): \n    x: float \n\nspark = SparkSession.builder.getOrCreate()\nspark_df = spark.createDataFrame(pd.DataFrame({\"x\": [1.1, 2.2]}))                                                      \ninput_df = InputDF(spark_df)\n```","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbdilday%2Fnamedframes","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbdilday%2Fnamedframes","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbdilday%2Fnamedframes/lists"}