SampleNotebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sample notebook starter\n",
    "\n",
    "This notebook provides some scaffolding to get you started querying\n",
    "an Aircloak Insights instance.\n",
    "\n",
    "It is meant to be used in a classroom setting where you are working\n",
    "with and exploring a dataset on an Aircloak demo instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "username=\"YOUR USERNAME\"\n",
    "password=\"YOUR PASSWORD\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import psycopg2\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn = psycopg2.connect(host=\"demo.aircloak.com\", port=8432, user=username, password=password, database=\"BBank\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial exploration\n",
    "\n",
    "With the setup out of the way we can now start exploring the dataset \n",
    "contained in Aircloak using Pandas.\n",
    "\n",
    "Crucially the following SQL queries allow you to explore the table structure:\n",
    "\n",
    "- `SHOW tables` yields a list of tables\n",
    "- `SHOW column FROM <tableName>` gives you a list of the columns within a table along with additional metadata about the columns\n",
    "\n",
    "Particularly useful are the `key type` information associated with columns. It provides you information about whether a column is a `user-id` (remember each anonymizing query needs to include a table that has a `user-id` column), or another key or just a normal column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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>name</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>accounts</td>\n",
       "      <td>personal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>clients</td>\n",
       "      <td>personal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>credit_cards</td>\n",
       "      <td>personal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dispositions</td>\n",
       "      <td>personal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>loans</td>\n",
       "      <td>personal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>transactions</td>\n",
       "      <td>personal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name      type\n",
       "0      accounts  personal\n",
       "1       clients  personal\n",
       "2  credit_cards  personal\n",
       "3  dispositions  personal\n",
       "4         loans  personal\n",
       "5  transactions  personal"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql_query(\"SHOW tables\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>data type</th>\n",
       "      <th>isolator?</th>\n",
       "      <th>key type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>disp_id</td>\n",
       "      <td>integer</td>\n",
       "      <td>true</td>\n",
       "      <td>disp_id</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>client_id</td>\n",
       "      <td>integer</td>\n",
       "      <td>true</td>\n",
       "      <td>user_id</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>account_id</td>\n",
       "      <td>integer</td>\n",
       "      <td>true</td>\n",
       "      <td>account_id</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>type</td>\n",
       "      <td>text</td>\n",
       "      <td>false</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         name data type isolator?    key type\n",
       "0     disp_id   integer      true     disp_id\n",
       "1   client_id   integer      true     user_id\n",
       "2  account_id   integer      true  account_id\n",
       "3        type      text     false        None"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql_query(\"SHOW columns FROM dispositions\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>data type</th>\n",
       "      <th>isolator?</th>\n",
       "      <th>key type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>trans_id</td>\n",
       "      <td>integer</td>\n",
       "      <td>true</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>account_id</td>\n",
       "      <td>integer</td>\n",
       "      <td>true</td>\n",
       "      <td>account_id</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>date</td>\n",
       "      <td>text</td>\n",
       "      <td>false</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>type</td>\n",
       "      <td>text</td>\n",
       "      <td>false</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>operation</td>\n",
       "      <td>text</td>\n",
       "      <td>false</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>amount</td>\n",
       "      <td>real</td>\n",
       "      <td>false</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>balance</td>\n",
       "      <td>real</td>\n",
       "      <td>false</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>k_symbol</td>\n",
       "      <td>text</td>\n",
       "      <td>false</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>bank</td>\n",
       "      <td>text</td>\n",
       "      <td>false</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>account</td>\n",
       "      <td>text</td>\n",
       "      <td>false</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         name data type isolator?    key type\n",
       "0    trans_id   integer      true        None\n",
       "1  account_id   integer      true  account_id\n",
       "2        date      text     false        None\n",
       "3        type      text     false        None\n",
       "4   operation      text     false        None\n",
       "5      amount      real     false        None\n",
       "6     balance      real     false        None\n",
       "7    k_symbol      text     false        None\n",
       "8        bank      text     false        None\n",
       "9     account      text     false        None"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql_query(\"SHOW columns FROM transactions\", conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Keys in practise\n",
    "\n",
    "Let's assume we wanted to run a query over the `transactions` table.\n",
    "It does not contain any column with a type of `user-id`. It does however contain a key of type `account_id`, and so does the `dispositions`-table too. The `dispositions` table does in turn contain a `user_id` column. To query the `transactions` table we will need to join the `dispositions` table into the query."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"\"\"\n",
    "SELECT\n",
    "  bucket(t.amount by 10) as bucketizedTransactionAmount,\n",
    "  count(*) as numTransactions\n",
    "FROM transactions t INNER JOIN dispositions d -- JOIN required to get a user-id\n",
    "  ON t.account_id = d.account_id\n",
    "GROUP BY bucketizedTransactionAmount\n",
    "\"\"\"\n",
    "\n",
    "transactions = pd.read_sql_query(query, conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bucketizedTransactionAmount</th>\n",
       "      <th>numTransactions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5103.000000</td>\n",
       "      <td>5104.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>25834.003527</td>\n",
       "      <td>246.052312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15299.958333</td>\n",
       "      <td>2603.885840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12755.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>25510.000000</td>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>38425.000000</td>\n",
       "      <td>121.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80400.000000</td>\n",
       "      <td>176453.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       bucketizedTransactionAmount  numTransactions\n",
       "count                  5103.000000      5104.000000\n",
       "mean                  25834.003527       246.052312\n",
       "std                   15299.958333      2603.885840\n",
       "min                       0.000000         2.000000\n",
       "25%                   12755.000000        13.000000\n",
       "50%                   25510.000000        32.000000\n",
       "75%                   38425.000000       121.250000\n",
       "max                   80400.000000    176453.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x110201710>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "transactions.plot(kind=\"scatter\", \n",
    "                  x=\"bucketizedTransactionAmount\", y=\"numTransactions\",\n",
    "                  ylim=(0, 4000), xlim=(0, 60000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11ff10eb8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "transactions.plot(kind=\"hist\", bins=1000,\n",
    "                  x=\"bucketizedTransactionAmount\", y=\"numTransactions\",\n",
    "                  ylim=(0, 20), xlim=(0, 60000))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now it's up to you"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}