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+{
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+ "cells": [
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+ {
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+ "cell_type": "code",
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+ "execution_count": 66,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "import pandas as pd\n",
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+ "import numpy as np\n",
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+ "from google.cloud import bigquery as bq\n",
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+ "\n",
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+ "client = bq.Client.from_service_account_json('/home/steve/dev/google-cloud-sdk/accounts/vumc-test.json')"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 33,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "xo = ['person_id','date_of_birth','race']\n",
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+ "xi = ['person_id','value_as_number','value_source_value']"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 181,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "def get_tables(client,id,fields=[]):\n",
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+ " \"\"\"\n",
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+ " getting table lists from google\n",
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+ " \"\"\"\n",
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+ " r = []\n",
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+ " ref = client.dataset(id)\n",
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+ " tables = list(client.list_tables(ref))\n",
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+ " for table in tables :\n",
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+ " ref = table.reference\n",
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+ " schema = client.get_table(ref).schema\n",
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+ " names = [f.name for f in schema]\n",
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+ " x = list(set(names) & set(fields))\n",
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+ " if x :\n",
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+ " r.append({\"name\":table.table_id,\"fields\":names})\n",
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+ " return r\n",
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+ " \n",
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+ "def get_fields(**args):\n",
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+ " \"\"\"\n",
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+ " This function will generate a random set of fields from two tables. Tables are structured as follows \n",
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+ " {name,fields:[],\"y\":}, with \n",
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+ " name table name (needed to generate sql query)\n",
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+ " fields list of field names, used in the projection\n",
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+ " y name of the field to be joined.\n",
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+ " @param xo candidate table in the join\n",
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+ " @param xi candidate table in the join\n",
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+ " @param join field by which the tables can be joined.\n",
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+ " \"\"\"\n",
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+ " # The set operation will remove redundancies in the field names (not sure it's a good idea)\n",
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+ "# xo = args['xo']['fields']\n",
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+ "# xi = args['xi']['fields']\n",
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+ "# zi = args['xi']['name']\n",
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+ "# return list(set([ \".\".join([args['xo']['name'],name]) for name in xo]) | set(['.'.join([args['xi']['name'],name]) for name in xi if name != args['join']]) )\n",
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+ " xo = args['xo']\n",
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+ " fields = [\".\".join([args['xo']['name'],name]) for name in args['xo']['fields']]\n",
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+ " if not isinstance(args['xi'],list) :\n",
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+ " x_ = [args['xi']]\n",
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+ " else:\n",
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+ " x_ = args['xi']\n",
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+ " for xi in x_ :\n",
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+ " fields += (['.'.join([xi['name'],name]) for name in xi['fields'] if name != args['join']])\n",
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+ " return fields\n",
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+ "def generate_sql(**args):\n",
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+ " \"\"\"\n",
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+ " This function will generate the SQL query for the resulting join\n",
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+ " \"\"\"\n",
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+ " \n",
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+ " xo = args['xo']\n",
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+ " x_ = args['xi']\n",
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+ " xo_name = \".\".join([args['prefix'],xo['name'] ]) if 'prefix' in args else xo['name']\n",
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+ " SQL = \"SELECT :fields FROM :xo.name \".replace(\":xo.name\",xo_name)\n",
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+ " if not isinstance(x_,list):\n",
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+ " x_ = [x_]\n",
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+ " f = []#[\".\".join([args['xo']['name'],args['join']] )] \n",
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+ " INNER_JOINS = []\n",
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+ " for xi in x_ :\n",
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+ " xi_name = \".\".join([args['prefix'],xi['name'] ]) if 'prefix' in args else xi['name']\n",
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+ " JOIN_SQL = \"INNER JOIN :xi.name ON \".replace(':xi.name',xi_name)\n",
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+ " value = \".\".join([xi['name'],args['join']])\n",
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+ " f.append(value) \n",
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+ " \n",
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+ " ON_SQL = \"\"\n",
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+ " tmp = []\n",
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+ " for term in f :\n",
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+ " ON_SQL = \":xi.name.:ofield = :xo.name.:ofield\".replace(\":xo.name\",xo['name'])\n",
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+ " ON_SQL = ON_SQL.replace(\":xi.name.:ofield\",term).replace(\":ofield\",args['join'])\n",
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+ " tmp.append(ON_SQL)\n",
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+ " INNER_JOINS += [JOIN_SQL + \" AND \".join(tmp)]\n",
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+ " return SQL + \" \".join(INNER_JOINS)\n",
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+ " \n",
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+ "# sql = \"SELECT :fields FROM :xo.name INNER JOIN :xi.name ON :xi.name.:xi.y = :xo.y \"\n",
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+ "# fields = \",\".join(get_fields(xo=xi,xi=xi,join=xi['y']))\n",
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+ " \n",
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+ " \n",
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+ "# sql = sql.replace(\":fields\",fields).replace(\":xo.name\",xo['name']).replace(\":xi.name\",xi['name'])\n",
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+ "# sql = sql.replace(\":xi.y\",xi['y']).replace(\":xo.y\",xo['y'])\n",
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+ "# return sql\n",
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+ " \n",
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+ " "
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 183,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/plain": [
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+ "'SELECT :fields FROM raw.person INNER JOIN raw.measurement ON measurement.person_id = person.person_id'"
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+ ]
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+ },
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+ "execution_count": 183,
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+ "metadata": {},
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+ "output_type": "execute_result"
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+ }
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+ ],
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+ "source": [
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+ "xo = {\"name\":\"person\",\"fields\":['person_id','date_of_birth','race']}\n",
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+ "xi = [{\"name\":\"measurement\",\"fields\":['person_id','value_as_number','value_source_value']}] #,{\"name\":\"observation\",\"fields\":[\"person_id\",\"value_as_string\",\"observation_source_value\"]}]\n",
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+ "generate_sql(xo=xo,xi=xi,join=\"person_id\",prefix='raw')"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 55,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/plain": [
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+ "'SELECT person_id,value_as_number,measurements.value_source_value,measurements.value_as_number,value_source_value FROM person INNER JOIN measurements ON measurements.person_id = person_id '"
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+ ]
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+ },
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+ "execution_count": 55,
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+ "metadata": {},
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+ "output_type": "execute_result"
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+ }
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+ ],
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+ "source": [
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+ "xo = {\"name\":\"person\",\"fields\":['person_id','date_of_birth','race'],\"y\":\"person_id\"}\n",
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+ "xi = {\"name\":\"measurements\",\"fields\":['person_id','value_as_number','value_source_value'],\"y\":\"person_id\"}\n",
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+ "generate_sql(xo=xo,xi=xi)"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 59,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/plain": [
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+ "[('a', 'b'), ('a', 'c'), ('b', 'c')]"
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+ ]
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+ },
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+ "execution_count": 59,
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+ "metadata": {},
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+ "output_type": "execute_result"
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+ }
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+ ],
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+ "source": [
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+ "\"\"\"\n",
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+ " We are designing a process that will take two tables that will generate \n",
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+ "\"\"\"\n",
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+ "import itertools\n",
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+ "list(itertools.combinations(['a','b','c'],2))"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 111,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/plain": [
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+ "[u'condition_occurrence.condition_occurrence_id',\n",
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+ " u'condition_occurrence.person_id',\n",
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+ " u'condition_occurrence.condition_concept_id',\n",
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+ " u'condition_occurrence.condition_start_date',\n",
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+ " u'condition_occurrence.condition_start_datetime',\n",
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+ " u'condition_occurrence.condition_end_date',\n",
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+ " u'condition_occurrence.condition_end_datetime',\n",
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+ " u'condition_occurrence.condition_type_concept_id',\n",
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+ " u'condition_occurrence.stop_reason',\n",
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+ " u'condition_occurrence.provider_id',\n",
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+ " u'condition_occurrence.visit_occurrence_id',\n",
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+ " u'condition_occurrence.condition_source_value',\n",
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+ " u'condition_occurrence.condition_source_concept_id',\n",
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+ " u'death.death_date',\n",
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+ " u'death.death_datetime',\n",
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+ " u'death.death_type_concept_id',\n",
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+ " u'death.cause_concept_id',\n",
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+ " u'death.cause_source_value',\n",
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+ " u'death.cause_source_concept_id',\n",
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+ " u'device_exposure.device_exposure_id',\n",
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+ " u'device_exposure.device_concept_id',\n",
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+ " u'device_exposure.device_exposure_start_date',\n",
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+ " u'device_exposure.device_exposure_start_datetime',\n",
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+ " u'device_exposure.device_exposure_end_date',\n",
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+ " u'device_exposure.device_exposure_end_datetime',\n",
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+ " u'device_exposure.device_type_concept_id',\n",
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+ " u'device_exposure.unique_device_id',\n",
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+ " u'device_exposure.quantity',\n",
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+ " u'device_exposure.provider_id',\n",
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+ " u'device_exposure.visit_occurrence_id',\n",
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+ " u'device_exposure.device_source_value',\n",
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+ " u'device_exposure.device_source_concept_id',\n",
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+ " u'drug_exposure.drug_exposure_id',\n",
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+ " u'drug_exposure.drug_concept_id',\n",
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+ " u'drug_exposure.drug_exposure_start_date',\n",
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+ " u'drug_exposure.drug_exposure_start_datetime',\n",
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+ " u'drug_exposure.drug_exposure_end_date',\n",
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+ " u'drug_exposure.drug_exposure_end_datetime',\n",
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+ " u'drug_exposure.drug_type_concept_id',\n",
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+ " u'drug_exposure.stop_reason',\n",
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+ " u'drug_exposure.refills',\n",
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+ " u'drug_exposure.quantity',\n",
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+ " u'drug_exposure.days_supply',\n",
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+ " u'drug_exposure.sig',\n",
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+ " u'drug_exposure.route_concept_id',\n",
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+ " u'drug_exposure.effective_drug_dose',\n",
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+ " u'drug_exposure.dose_unit_concept_id',\n",
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+ " u'drug_exposure.lot_number',\n",
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+ " u'drug_exposure.provider_id',\n",
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+ " u'drug_exposure.visit_occurrence_id',\n",
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+ " u'drug_exposure.drug_source_value',\n",
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+ " u'drug_exposure.drug_source_concept_id',\n",
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+ " u'drug_exposure.route_source_value',\n",
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+ " u'drug_exposure.dose_unit_source_value']"
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+ ]
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+ },
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+ "execution_count": 111,
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+ "metadata": {},
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+ "output_type": "execute_result"
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+ }
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+ ],
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+ "source": [
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+ "#\n",
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+ "# find every table with person id at the very least or a subset of fields\n",
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+ "#\n",
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+ "info = get_tables(client,'raw',['person_id'])\n",
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+ "# get_fields(xo=names[0],xi=names[1:4],join='person_id')\n",
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+ "\n",
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+ "# q = ['person_id']\n",
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+ "# pairs = list(itertools.combinations(names,len(names)))\n",
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+ "# pairs[0]"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 90,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/plain": [
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+ "['a']"
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+ ]
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+ },
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+ "execution_count": 90,
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+ "metadata": {},
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+ "output_type": "execute_result"
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+ }
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+ ],
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+ "source": [
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+ "list(set(['a','b']) & set(['a']))"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 120,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "x_ = 1"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": null,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": []
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+ }
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+ ],
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+ "metadata": {
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+ "kernelspec": {
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+ "display_name": "Python 2",
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+ "language": "python",
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+ "name": "python2"
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+ },
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+ "language_info": {
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+ "codemirror_mode": {
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+ "name": "ipython",
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+ "version": 2
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+ },
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+ "file_extension": ".py",
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+ "mimetype": "text/x-python",
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+ "name": "python",
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+ "nbconvert_exporter": "python",
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+ "pygments_lexer": "ipython2",
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+ "version": "2.7.15rc1"
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+ }
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+ },
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+ "nbformat": 4,
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+ "nbformat_minor": 2
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+}
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