|
@@ -2,294 +2,209 @@
|
|
|
"cells": [
|
|
|
{
|
|
|
"cell_type": "code",
|
|
|
- "execution_count": 1,
|
|
|
+ "execution_count": 4,
|
|
|
"metadata": {},
|
|
|
- "outputs": [
|
|
|
- {
|
|
|
- "name": "stdout",
|
|
|
- "output_type": "stream",
|
|
|
- "text": [
|
|
|
- "dev-deid-600@aou-res-deid-vumc-test.iam.gserviceaccount.com df0ac049-d5b6-416f-ab3c-6321eda919d6 2018-09-25 08:18:34.829000+00:00 DONE\n"
|
|
|
- ]
|
|
|
- }
|
|
|
- ],
|
|
|
+ "outputs": [],
|
|
|
"source": [
|
|
|
+ "\"\"\"\n",
|
|
|
+ " This notebook is intended to show how to use the risk framework:\n",
|
|
|
+ " There are two basic usages:\n",
|
|
|
+ " 1. Experiment\n",
|
|
|
+ " \n",
|
|
|
+ " Here the framework will select a number of random fields other than the patient id and compute risk for the selection.\n",
|
|
|
+ " This will repeat over a designated number of runs.\n",
|
|
|
+ " \n",
|
|
|
+ " The parameters to pass to enable this mode are id=<patient id>,nun_runs=<number of runs>\n",
|
|
|
+ " 2. Assessment\n",
|
|
|
+ " \n",
|
|
|
+ " Here the framework assumes you are only interested in a list of quasi identifiers and will run the evaluation once for a given list of quasi identifiers.\n",
|
|
|
+ " The parameters to enable this mode are id=<patient id>,quasi_id=<list of quasi ids>\n",
|
|
|
+ "\"\"\"\n",
|
|
|
+ "import os\n",
|
|
|
"import pandas as pd\n",
|
|
|
"import numpy as np\n",
|
|
|
- "from google.cloud import bigquery as bq\n",
|
|
|
"\n",
|
|
|
- "client = bq.Client.from_service_account_json('/home/steve/dev/google-cloud-sdk/accounts/vumc-test.json')\n",
|
|
|
- "# pd.read_gbq(query=\"select * from raw.observation limit 10\",private_key='/home/steve/dev/google-cloud-sdk/accounts/vumc-test.json')\n",
|
|
|
- "jobs = client.list_jobs()\n",
|
|
|
- "for job in jobs :\n",
|
|
|
- "# print dir(job)\n",
|
|
|
- " print job.user_email,job.job_id,job.started, job.state\n",
|
|
|
- " break"
|
|
|
- ]
|
|
|
- },
|
|
|
- {
|
|
|
- "cell_type": "code",
|
|
|
- "execution_count": 33,
|
|
|
- "metadata": {},
|
|
|
- "outputs": [],
|
|
|
- "source": [
|
|
|
- "xo = ['person_id','date_of_birth','race']\n",
|
|
|
- "xi = ['person_id','value_as_number','value_source_value']"
|
|
|
+ "\n",
|
|
|
+ "#\n",
|
|
|
+ "#-- Loading a template file\n",
|
|
|
+ "# The example taken a de-identification white-paper\n",
|
|
|
+ "# http://www.ehealthinformation.ca/wp-content/uploads/2014/08/2009-De-identification-PA-whitepaper1.pdf\n",
|
|
|
+ "#\n",
|
|
|
+ "\n",
|
|
|
+ "import pandas as pd\n",
|
|
|
+ "import numpy as np\n",
|
|
|
+ "from io import StringIO\n",
|
|
|
+ "csv = \"\"\"\n",
|
|
|
+ "id,sex,age,profession,drug_test\n",
|
|
|
+ "1,M,37,doctor,-\n",
|
|
|
+ "2,F,28,doctor,+\n",
|
|
|
+ "3,M,37,doctor,-\n",
|
|
|
+ "4,M,28,doctor,+\n",
|
|
|
+ "5,M,28,doctor,-\n",
|
|
|
+ "6,M,37,doctor,-\n",
|
|
|
+ "\"\"\"\n",
|
|
|
+ "f = StringIO()\n",
|
|
|
+ "f.write(unicode(csv))\n",
|
|
|
+ "f.seek(0)\n",
|
|
|
+ "MY_DATAFRAME = pd.read_csv(f) "
|
|
|
]
|
|
|
},
|
|
|
{
|
|
|
"cell_type": "code",
|
|
|
- "execution_count": 10,
|
|
|
+ "execution_count": 2,
|
|
|
"metadata": {},
|
|
|
"outputs": [],
|
|
|
"source": [
|
|
|
- "def get_tables(client,id,fields=[]):\n",
|
|
|
- " \"\"\"\n",
|
|
|
- " getting table lists from google\n",
|
|
|
- " \"\"\"\n",
|
|
|
- " r = []\n",
|
|
|
- " ref = client.dataset(id)\n",
|
|
|
- " tables = list(client.list_tables(ref))\n",
|
|
|
- " for table in tables :\n",
|
|
|
- " ref = table.reference\n",
|
|
|
- " schema = client.get_table(ref).schema\n",
|
|
|
- " names = [f.name for f in schema]\n",
|
|
|
- " x = list(set(names) & set(fields))\n",
|
|
|
- " if x :\n",
|
|
|
- " r.append({\"name\":table.table_id,\"fields\":names})\n",
|
|
|
- " return r\n",
|
|
|
- " \n",
|
|
|
- "def get_fields(**args):\n",
|
|
|
- " \"\"\"\n",
|
|
|
- " This function will generate a random set of fields from two tables. Tables are structured as follows \n",
|
|
|
- " {name,fields:[],\"y\":}, with \n",
|
|
|
- " name table name (needed to generate sql query)\n",
|
|
|
- " fields list of field names, used in the projection\n",
|
|
|
- " y name of the field to be joined.\n",
|
|
|
- " @param xo candidate table in the join\n",
|
|
|
- " @param xi candidate table in the join\n",
|
|
|
- " @param join field by which the tables can be joined.\n",
|
|
|
- " \"\"\"\n",
|
|
|
- " # The set operation will remove redundancies in the field names (not sure it's a good idea)\n",
|
|
|
- "# xo = args['xo']['fields']\n",
|
|
|
- "# xi = args['xi']['fields']\n",
|
|
|
- "# zi = args['xi']['name']\n",
|
|
|
- "# 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",
|
|
|
- " xo = args['xo']\n",
|
|
|
- " fields = [\".\".join([args['xo']['name'],name]) for name in args['xo']['fields']]\n",
|
|
|
- " if not isinstance(args['xi'],list) :\n",
|
|
|
- " x_ = [args['xi']]\n",
|
|
|
- " else:\n",
|
|
|
- " x_ = args['xi']\n",
|
|
|
- " for xi in x_ :\n",
|
|
|
- " fields += (['.'.join([xi['name'], name]) for name in xi['fields'] if name != args['join']])\n",
|
|
|
- " return fields\n",
|
|
|
- "def generate_sql(**args):\n",
|
|
|
+ "\"\"\"\n",
|
|
|
+ " Here's the pandas_risk code verbatim. \n",
|
|
|
+ " NOTE: \n",
|
|
|
+ "\"\"\"\n",
|
|
|
+ "@pd.api.extensions.register_dataframe_accessor(\"deid\")\n",
|
|
|
+ "class deid :\n",
|
|
|
" \"\"\"\n",
|
|
|
- " This function will generate the SQL query for the resulting join\n",
|
|
|
+ " This class is a deidentification class that will compute risk (marketer, prosecutor) given a pandas dataframe\n",
|
|
|
" \"\"\"\n",
|
|
|
+ " def __init__(self,df):\n",
|
|
|
+ " self._df = df\n",
|
|
|
" \n",
|
|
|
- " xo = args['xo']\n",
|
|
|
- " x_ = args['xi']\n",
|
|
|
- " xo_name = \".\".join([args['prefix'],xo['name'] ]) if 'prefix' in args else xo['name']\n",
|
|
|
- " SQL = \"SELECT :fields FROM :xo.name \".replace(\":xo.name\",xo_name)\n",
|
|
|
- " if not isinstance(x_,list):\n",
|
|
|
- " x_ = [x_]\n",
|
|
|
- " f = []#[\".\".join([args['xo']['name'],args['join']] )] \n",
|
|
|
- " INNER_JOINS = []\n",
|
|
|
- " for xi in x_ :\n",
|
|
|
- " xi_name = \".\".join([args['prefix'],xi['name'] ]) if 'prefix' in args else xi['name']\n",
|
|
|
- " JOIN_SQL = \"INNER JOIN :xi.name ON \".replace(':xi.name',xi_name)\n",
|
|
|
- " value = \".\".join([xi['name'],args['join']])\n",
|
|
|
- " f.append(value) \n",
|
|
|
+ " def risk(self,**args):\n",
|
|
|
+ " \"\"\"\n",
|
|
|
+ " @param id name of patient field \n",
|
|
|
+ " @params num_runs number of runs (default will be 100)\n",
|
|
|
+ " @params quasi_id \tlist of quasi identifiers to be used (this will only perform a single run)\n",
|
|
|
+ " \"\"\"\n",
|
|
|
" \n",
|
|
|
- " ON_SQL = \"\"\n",
|
|
|
- " tmp = []\n",
|
|
|
- " for term in f :\n",
|
|
|
- " ON_SQL = \":xi.name.:ofield = :xo.name.:ofield\".replace(\":xo.name\",xo['name'])\n",
|
|
|
- " ON_SQL = ON_SQL.replace(\":xi.name.:ofield\",term).replace(\":ofield\",args['join'])\n",
|
|
|
- " tmp.append(ON_SQL)\n",
|
|
|
- " INNER_JOINS += [JOIN_SQL + \" AND \".join(tmp)]\n",
|
|
|
- " return SQL + \" \".join(INNER_JOINS)\n",
|
|
|
- "def get_final_sql(**args):\n",
|
|
|
- " xo = args['xo']\n",
|
|
|
- " xi = args['xi']\n",
|
|
|
- " join=args['join']\n",
|
|
|
- " prefix = args['prefix'] if 'prefix' in args else ''\n",
|
|
|
- " fields = get_fields (xo=xo,xi=xi,join=join)\n",
|
|
|
- " k = len(fields)\n",
|
|
|
- " n = np.random.randint(2,k) #-- number of fields to select\n",
|
|
|
- " i = np.random.randint(0,k,size=n)\n",
|
|
|
- " fields = [name for name in fields if fields.index(name) in i]\n",
|
|
|
- " base_sql = generate_sql(xo=xo,xi=xi,prefix)\n",
|
|
|
- " SQL = \"\"\"\n",
|
|
|
- " SELECT AVERAGE(count),size,n as selected_features,k as total_features\n",
|
|
|
- " FROM(\n",
|
|
|
- " SELECT COUNT(*) as count,count(:join) as pop,sum(:n) as N,sum(:k) as k,:fields\n",
|
|
|
- " FROM (:sql)\n",
|
|
|
- " GROUP BY :fields\n",
|
|
|
- " ) \n",
|
|
|
- " order by 1\n",
|
|
|
- " \n",
|
|
|
- " \"\"\".replace(\":sql\",base_sql)\n",
|
|
|
- "# sql = \"SELECT :fields FROM :xo.name INNER JOIN :xi.name ON :xi.name.:xi.y = :xo.y \"\n",
|
|
|
- "# fields = \",\".join(get_fields(xo=xi,xi=xi,join=xi['y']))\n",
|
|
|
- " \n",
|
|
|
- " \n",
|
|
|
- "# sql = sql.replace(\":fields\",fields).replace(\":xo.name\",xo['name']).replace(\":xi.name\",xi['name'])\n",
|
|
|
- "# sql = sql.replace(\":xi.y\",xi['y']).replace(\":xo.y\",xo['y'])\n",
|
|
|
- "# return sql\n",
|
|
|
- " \n",
|
|
|
- " "
|
|
|
- ]
|
|
|
- },
|
|
|
- {
|
|
|
- "cell_type": "code",
|
|
|
- "execution_count": 33,
|
|
|
- "metadata": {},
|
|
|
- "outputs": [],
|
|
|
- "source": [
|
|
|
- "xo = {\"name\":\"person\",\"fields\":['person_id','date_of_birth','race','value_as_number']}\n",
|
|
|
- "xi = [{\"name\":\"measurement\",\"fields\":['person_id','value_as_number','value_source_value']}] #,{\"name\":\"observation\",\"fields\":[\"person_id\",\"value_as_string\",\"observation_source_value\"]}]\n",
|
|
|
- "# generate_sql(xo=xo,xi=xi,join=\"person_id\",prefix='raw')\n",
|
|
|
- "fields = get_fields(xo=xo,xi=xi,join='person_id')\n",
|
|
|
- "ofields = list(fields)\n",
|
|
|
- "k = len(fields)\n",
|
|
|
- "n = np.random.randint(2,k) #-- number of fields to select\n",
|
|
|
- "i = np.random.randint(0,k,size=n)\n",
|
|
|
- "fields = [name for name in fields if fields.index(name) in i]"
|
|
|
- ]
|
|
|
- },
|
|
|
- {
|
|
|
- "cell_type": "code",
|
|
|
- "execution_count": 34,
|
|
|
- "metadata": {},
|
|
|
- "outputs": [
|
|
|
- {
|
|
|
- "data": {
|
|
|
- "text/plain": [
|
|
|
- "['person.race', 'person.value_as_number', 'measurement.value_source_value']"
|
|
|
- ]
|
|
|
- },
|
|
|
- "execution_count": 34,
|
|
|
- "metadata": {},
|
|
|
- "output_type": "execute_result"
|
|
|
- }
|
|
|
- ],
|
|
|
- "source": [
|
|
|
- "fields\n"
|
|
|
- ]
|
|
|
- },
|
|
|
- {
|
|
|
- "cell_type": "code",
|
|
|
- "execution_count": 55,
|
|
|
- "metadata": {},
|
|
|
- "outputs": [
|
|
|
- {
|
|
|
- "data": {
|
|
|
- "text/plain": [
|
|
|
- "'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 '"
|
|
|
- ]
|
|
|
- },
|
|
|
- "execution_count": 55,
|
|
|
- "metadata": {},
|
|
|
- "output_type": "execute_result"
|
|
|
- }
|
|
|
- ],
|
|
|
- "source": [
|
|
|
- "xo = {\"name\":\"person\",\"fields\":['person_id','date_of_birth','race'],\"y\":\"person_id\"}\n",
|
|
|
- "xi = {\"name\":\"measurements\",\"fields\":['person_id','value_as_number','value_source_value'],\"y\":\"person_id\"}\n",
|
|
|
- "generate_sql(xo=xo,xi=xi)"
|
|
|
- ]
|
|
|
- },
|
|
|
- {
|
|
|
- "cell_type": "code",
|
|
|
- "execution_count": 59,
|
|
|
- "metadata": {},
|
|
|
- "outputs": [
|
|
|
- {
|
|
|
- "data": {
|
|
|
- "text/plain": [
|
|
|
- "[('a', 'b'), ('a', 'c'), ('b', 'c')]"
|
|
|
- ]
|
|
|
- },
|
|
|
- "execution_count": 59,
|
|
|
- "metadata": {},
|
|
|
- "output_type": "execute_result"
|
|
|
- }
|
|
|
- ],
|
|
|
- "source": [
|
|
|
- "\"\"\"\n",
|
|
|
- " We are designing a process that will take two tables that will generate \n",
|
|
|
- "\"\"\"\n",
|
|
|
- "import itertools\n",
|
|
|
- "list(itertools.combinations(['a','b','c'],2))"
|
|
|
+ " id = args['id']\n",
|
|
|
+ " if 'quasi_id' in args :\n",
|
|
|
+ " num_runs = 1\n",
|
|
|
+ " columns = list(set(args['quasi_id'])- set(id) )\n",
|
|
|
+ " else :\n",
|
|
|
+ " num_runs = args['num_runs'] if 'num_runs' in args else 100\n",
|
|
|
+ " columns = list(set(self._df.columns) - set([id]))\n",
|
|
|
+ " r = pd.DataFrame() \n",
|
|
|
+ " k = len(columns)\n",
|
|
|
+ " for i in range(0,num_runs) :\n",
|
|
|
+ " #\n",
|
|
|
+ " # let's chose a random number of columns and compute marketer and prosecutor risk\n",
|
|
|
+ " # Once the fields are selected we run a groupby clause\n",
|
|
|
+ " #\n",
|
|
|
+ " if 'quasi_id' not in args :\n",
|
|
|
+ " n = np.random.randint(2,k) #-- number of random fields we are picking\n",
|
|
|
+ " ii = np.random.choice(k,n,replace=False)\n",
|
|
|
+ " cols = np.array(columns)[ii].tolist()\n",
|
|
|
+ " else:\n",
|
|
|
+ " cols \t= columns\n",
|
|
|
+ " n \t= len(cols)\n",
|
|
|
+ " x_ = self._df.groupby(cols).count()[id].values\n",
|
|
|
+ " r = r.append(\n",
|
|
|
+ " pd.DataFrame(\n",
|
|
|
+ " [\n",
|
|
|
+ " {\n",
|
|
|
+ " \"selected\":n,\n",
|
|
|
+ " \"marketer\": x_.size / np.float64(np.sum(x_)),\n",
|
|
|
+ " \"prosecutor\":1 / np.float64(np.min(x_))\n",
|
|
|
+ "\n",
|
|
|
+ " }\n",
|
|
|
+ " ]\n",
|
|
|
+ " )\n",
|
|
|
+ " )\n",
|
|
|
+ " g_size = x_.size\n",
|
|
|
+ " n_ids = np.float64(np.sum(x_))\n",
|
|
|
+ "\n",
|
|
|
+ " return r"
|
|
|
]
|
|
|
},
|
|
|
{
|
|
|
"cell_type": "code",
|
|
|
- "execution_count": 6,
|
|
|
+ "execution_count": 7,
|
|
|
"metadata": {},
|
|
|
"outputs": [
|
|
|
{
|
|
|
"data": {
|
|
|
+ "text/html": [
|
|
|
+ "<div>\n",
|
|
|
+ "<style scoped>\n",
|
|
|
+ " .dataframe tbody tr th:only-of-type {\n",
|
|
|
+ " vertical-align: middle;\n",
|
|
|
+ " }\n",
|
|
|
+ "\n",
|
|
|
+ " .dataframe tbody tr th {\n",
|
|
|
+ " vertical-align: top;\n",
|
|
|
+ " }\n",
|
|
|
+ "\n",
|
|
|
+ " .dataframe thead th {\n",
|
|
|
+ " text-align: right;\n",
|
|
|
+ " }\n",
|
|
|
+ "</style>\n",
|
|
|
+ "<table border=\"1\" class=\"dataframe\">\n",
|
|
|
+ " <thead>\n",
|
|
|
+ " <tr style=\"text-align: right;\">\n",
|
|
|
+ " <th></th>\n",
|
|
|
+ " <th>marketer</th>\n",
|
|
|
+ " <th>prosecutor</th>\n",
|
|
|
+ " <th>selected</th>\n",
|
|
|
+ " </tr>\n",
|
|
|
+ " </thead>\n",
|
|
|
+ " <tbody>\n",
|
|
|
+ " <tr>\n",
|
|
|
+ " <th>0</th>\n",
|
|
|
+ " <td>0.500000</td>\n",
|
|
|
+ " <td>1.0</td>\n",
|
|
|
+ " <td>2</td>\n",
|
|
|
+ " </tr>\n",
|
|
|
+ " <tr>\n",
|
|
|
+ " <th>0</th>\n",
|
|
|
+ " <td>0.500000</td>\n",
|
|
|
+ " <td>1.0</td>\n",
|
|
|
+ " <td>3</td>\n",
|
|
|
+ " </tr>\n",
|
|
|
+ " <tr>\n",
|
|
|
+ " <th>0</th>\n",
|
|
|
+ " <td>0.500000</td>\n",
|
|
|
+ " <td>1.0</td>\n",
|
|
|
+ " <td>3</td>\n",
|
|
|
+ " </tr>\n",
|
|
|
+ " <tr>\n",
|
|
|
+ " <th>0</th>\n",
|
|
|
+ " <td>0.333333</td>\n",
|
|
|
+ " <td>1.0</td>\n",
|
|
|
+ " <td>2</td>\n",
|
|
|
+ " </tr>\n",
|
|
|
+ " <tr>\n",
|
|
|
+ " <th>0</th>\n",
|
|
|
+ " <td>0.333333</td>\n",
|
|
|
+ " <td>0.5</td>\n",
|
|
|
+ " <td>2</td>\n",
|
|
|
+ " </tr>\n",
|
|
|
+ " </tbody>\n",
|
|
|
+ "</table>\n",
|
|
|
+ "</div>"
|
|
|
+ ],
|
|
|
"text/plain": [
|
|
|
- "array([1, 3, 0, 0])"
|
|
|
+ " marketer prosecutor selected\n",
|
|
|
+ "0 0.500000 1.0 2\n",
|
|
|
+ "0 0.500000 1.0 3\n",
|
|
|
+ "0 0.500000 1.0 3\n",
|
|
|
+ "0 0.333333 1.0 2\n",
|
|
|
+ "0 0.333333 0.5 2"
|
|
|
]
|
|
|
},
|
|
|
- "execution_count": 6,
|
|
|
+ "execution_count": 7,
|
|
|
"metadata": {},
|
|
|
"output_type": "execute_result"
|
|
|
}
|
|
|
],
|
|
|
"source": [
|
|
|
"#\n",
|
|
|
- "# find every table with person id at the very least or a subset of fields\n",
|
|
|
+ "# Lets us compute risk here for a random any random selection of quasi identifiers\n",
|
|
|
+ "# We will run this experiment 5 times\n",
|
|
|
"#\n",
|
|
|
- "np.random.randint(0,4,size=4)"
|
|
|
- ]
|
|
|
- },
|
|
|
- {
|
|
|
- "cell_type": "code",
|
|
|
- "execution_count": 90,
|
|
|
- "metadata": {},
|
|
|
- "outputs": [
|
|
|
- {
|
|
|
- "data": {
|
|
|
- "text/plain": [
|
|
|
- "['a']"
|
|
|
- ]
|
|
|
- },
|
|
|
- "execution_count": 90,
|
|
|
- "metadata": {},
|
|
|
- "output_type": "execute_result"
|
|
|
- }
|
|
|
- ],
|
|
|
- "source": [
|
|
|
- "list(set(['a','b']) & set(['a']))"
|
|
|
- ]
|
|
|
- },
|
|
|
- {
|
|
|
- "cell_type": "code",
|
|
|
- "execution_count": 120,
|
|
|
- "metadata": {},
|
|
|
- "outputs": [],
|
|
|
- "source": [
|
|
|
- "x_ = 1"
|
|
|
- ]
|
|
|
- },
|
|
|
- {
|
|
|
- "cell_type": "code",
|
|
|
- "execution_count": 10,
|
|
|
- "metadata": {},
|
|
|
- "outputs": [],
|
|
|
- "source": [
|
|
|
- "x_ = pd.DataFrame({\"group\":[1,1,1,1,1], \"size\":[2,1,1,1,1]})"
|
|
|
+ "MY_DATAFRAME.deid.risk(id='id',num_runs=5)"
|
|
|
]
|
|
|
},
|
|
|
{
|
|
|
"cell_type": "code",
|
|
|
- "execution_count": 12,
|
|
|
+ "execution_count": 8,
|
|
|
"metadata": {},
|
|
|
"outputs": [
|
|
|
{
|
|
@@ -313,35 +228,37 @@
|
|
|
" <thead>\n",
|
|
|
" <tr style=\"text-align: right;\">\n",
|
|
|
" <th></th>\n",
|
|
|
- " <th>size</th>\n",
|
|
|
- " </tr>\n",
|
|
|
- " <tr>\n",
|
|
|
- " <th>group</th>\n",
|
|
|
- " <th></th>\n",
|
|
|
+ " <th>marketer</th>\n",
|
|
|
+ " <th>prosecutor</th>\n",
|
|
|
+ " <th>selected</th>\n",
|
|
|
" </tr>\n",
|
|
|
" </thead>\n",
|
|
|
" <tbody>\n",
|
|
|
" <tr>\n",
|
|
|
- " <th>1</th>\n",
|
|
|
- " <td>1.2</td>\n",
|
|
|
+ " <th>0</th>\n",
|
|
|
+ " <td>0.5</td>\n",
|
|
|
+ " <td>1.0</td>\n",
|
|
|
+ " <td>3</td>\n",
|
|
|
" </tr>\n",
|
|
|
" </tbody>\n",
|
|
|
"</table>\n",
|
|
|
"</div>"
|
|
|
],
|
|
|
"text/plain": [
|
|
|
- " size\n",
|
|
|
- "group \n",
|
|
|
- "1 1.2"
|
|
|
+ " marketer prosecutor selected\n",
|
|
|
+ "0 0.5 1.0 3"
|
|
|
]
|
|
|
},
|
|
|
- "execution_count": 12,
|
|
|
+ "execution_count": 8,
|
|
|
"metadata": {},
|
|
|
"output_type": "execute_result"
|
|
|
}
|
|
|
],
|
|
|
"source": [
|
|
|
- "x_.groupby(['group']).mean()\n"
|
|
|
+ "#\n",
|
|
|
+ "# In this scenario we are just interested in sex,profession,age\n",
|
|
|
+ "#\n",
|
|
|
+ "MY_DATAFRAME.deid.risk(id='id',quasi_id=['age','sex','profession'])"
|
|
|
]
|
|
|
},
|
|
|
{
|