talagluck

> npm install nodejs-polars
> npm install @glaredb/glaredb

const pl = require("nodejs-polars");
const glaredb = require("@glaredb/glaredb");
const con = await glaredb.connect();
const df = await (
	await con.sql(
		"select * from delta_scan('path/to/delta/table')",
	)

talagluck

# The mapping method
def map_column_names(expectation_suite, mapping_dict):
    for expectation in expectation_suite.expectations:
        if 'column' in expectation.get_domain_kwargs():
            source_col = expectation.get_domain_kwargs()['column']
            if source_col in mapping_dict:
                target_col = mapping_dict[source_col]
                print('column:', source_col, ' mapping to:', target_col)
                expectation.patch('replace', '/column', target_col)
            else:

talagluck

import pandas as pd

from great_expectations.render.renderer import (
    ExpectationSuitePageRenderer
)
from great_expectations.render.types import (
    RenderedDocumentContent,
    RenderedSectionContent,
    RenderedHeaderContent,
    RenderedTableContent,

talagluck

import pandas as pd

from great_expectations.render.renderer import (
    ExpectationSuitePageRenderer
)
from great_expectations.render.types import (
    RenderedDocumentContent,
    RenderedSectionContent,
    RenderedHeaderContent,
    RenderedTableContent,

talagluck

def _render_data_dictionary(self, expectations):
        data_dictionary_df = pd.DataFrame()
        data_dictionary_df["Column Name"] = [i for i in self._get_table_columns(expectations)]
        data_dictionary_df["Description"] = [i["description"] for i in self._get_table_columns(expectations).values()]

        expectations_by_column = self._sort_expectations_by_column(expectations)
        data_dictionary_df["Data Type"] = [i for i in self._get_column_data_types(expectations_by_column).values()]

        return RenderedComponentContent(**{
            "content_block_type": "table",

talagluck

def _get_column_data_types(self,expectations_by_column):
        column_data_type_expectations = {}
        for k,v in expectations_by_column.items():
            expectation = [i for i in v if i["expectation_type"] == "expect_column_values_to_be_in_type_list"]
            if len(expectation)>0:
                type_list = expectation[0].get("kwargs").get("type_list")
                if len(type_list) > 0:
                    column_data_type_expectations[k] = type_list[0]
                else:
                    column_data_type_expectations[k] = type_list

talagluck

def _sort_expectations_by_column(self, expectations):
        expectations_by_column = {}
        expectations_dictionary = expectations.get("expectations")
        column_names = list(self._get_table_columns(expectations).keys())
        for column in column_names:
            expectations_by_column[column] = list(filter(
                lambda x: x.get("kwargs").get("column")==column,
                expectations_dictionary))
        return expectations_by_column

talagluck

from great_expectations.render.renderer import (
    ExpectationSuitePageRenderer
)
from great_expectations.render.types import (
    RenderedDocumentContent,
    RenderedSectionContent,
    RenderedComponentContent,
)

import pandas as pd

talagluck

    def _get_table_columns(self,expectations):
        return expectations.get("meta").get("columns")

talagluck

from great_expectations.render.renderer import (
    ExpectationSuitePageRenderer
)
from great_expectations.render.types import (
    RenderedDocumentContent,
    RenderedSectionContent,
    RenderedComponentContent,
)

class MyCustomExpectationSuitePageRenderer(ExpectationSuitePageRenderer):