20191004_dropping_evaluation_parameters.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.7.11__develop'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import great_expectations as ge\n",
    "ge.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = ge.dataset.PandasDataset({\"a\": [1,2,3]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data_asset_name': None,\n",
       " 'expectation_suite_name': 'default',\n",
       " 'meta': {'great_expectations.__version__': '0.7.11__develop'},\n",
       " 'expectations': [],\n",
       " 'data_asset_type': 'Dataset'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.get_expectation_suite()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'success': True}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.expect_column_to_exist({\"$PARAMETER\": \"col_name\", \"$PARAMETER.col_name\": \"a\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data_asset_name': None,\n",
       " 'expectation_suite_name': 'default',\n",
       " 'meta': {'great_expectations.__version__': '0.7.11__develop'},\n",
       " 'expectations': [{'expectation_type': 'expect_column_to_exist',\n",
       "   'kwargs': {'column': {'$PARAMETER': 'col_name'}}}],\n",
       " 'data_asset_type': 'Dataset'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.get_expectation_suite()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'dict'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-5a915a03152f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalidate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluation_parameters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"col_name\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"a\"\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/dev/support/great_expectations/great_expectations/data_asset/data_asset.py\u001b[0m in \u001b[0;36mvalidate\u001b[0;34m(self, expectation_suite, run_id, data_context, evaluation_parameters, catch_exceptions, result_format, only_return_failures)\u001b[0m\n\u001b[1;32m    977\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    978\u001b[0m                 \u001b[0mcolumn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"_nocolumn\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 979\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    980\u001b[0m                 \u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    981\u001b[0m             \u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpectation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'dict'"
     ]
    }
   ],
   "source": [
    "df.validate(evaluation_parameters={\"col_name\": \"a\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "## SEE ABOVE BUG!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = ge.dataset.PandasDataset({\"a\": [1,2,3]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'success': True,\n",
       " 'result': {'element_count': 3,\n",
       "  'missing_count': 0,\n",
       "  'missing_percent': 0.0,\n",
       "  'unexpected_count': 0,\n",
       "  'unexpected_percent': 0.0,\n",
       "  'unexpected_percent_nonmissing': 0.0,\n",
       "  'partial_unexpected_list': []}}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.expect_column_values_to_be_in_set(\"a\", value_set={\"$PARAMETER\": \"runtime_set\", \"$PARAMETER.runtime_set\": [1,2,3]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data_asset_name': None,\n",
       " 'expectation_suite_name': 'default',\n",
       " 'meta': {'great_expectations.__version__': '0.7.11__develop'},\n",
       " 'expectations': [{'expectation_type': 'expect_column_values_to_be_in_set',\n",
       "   'kwargs': {'column': 'a', 'value_set': {'$PARAMETER': 'runtime_set'}}}],\n",
       " 'data_asset_type': 'Dataset'}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.get_expectation_suite()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'results': [{'success': True,\n",
       "   'result': {'element_count': 3,\n",
       "    'missing_count': 0,\n",
       "    'missing_percent': 0.0,\n",
       "    'unexpected_count': 0,\n",
       "    'unexpected_percent': 0.0,\n",
       "    'unexpected_percent_nonmissing': 0.0,\n",
       "    'partial_unexpected_list': []},\n",
       "   'exception_info': {'raised_exception': False,\n",
       "    'exception_message': None,\n",
       "    'exception_traceback': None},\n",
       "   'expectation_config': {'expectation_type': 'expect_column_values_to_be_in_set',\n",
       "    'kwargs': {'column': 'a',\n",
       "     'value_set': {'$PARAMETER': 'runtime_set'},\n",
       "     'result_format': 'BASIC'}}}],\n",
       " 'success': True,\n",
       " 'statistics': {'evaluated_expectations': 1,\n",
       "  'successful_expectations': 1,\n",
       "  'unsuccessful_expectations': 0,\n",
       "  'success_percent': 100.0},\n",
       " 'meta': {'great_expectations.__version__': '0.7.11__develop',\n",
       "  'data_asset_name': None,\n",
       "  'expectation_suite_name': 'default',\n",
       "  'run_id': '2019-10-04T173707.713140Z'},\n",
       " 'evaluation_parameters': {'runtime_set': [1, 2, 3]}}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.validate(evaluation_parameters={\"runtime_set\": [1,2,3]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data_asset_name': None,\n",
       " 'expectation_suite_name': 'default',\n",
       " 'meta': {'great_expectations.__version__': '0.7.11__develop'},\n",
       " 'expectations': [{'expectation_type': 'expect_column_values_to_be_in_set',\n",
       "   'kwargs': {'column': 'a', 'value_set': [1, 2, 3]}}],\n",
       " 'data_asset_type': 'Dataset'}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.get_expectation_suite()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## SEE BROKEN VALUE_SET"
   ]
  }
 ],
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