notebooks/prototype_demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/root/rolling-pin/python\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<module 'rolling_pin.blob_etl' from '/root/rolling-pin/python/rolling_pin/blob_etl.py'>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from importlib import reload\n",
    "\n",
    "from pandas import DataFrame\n",
    "\n",
    "%cd /root/rolling-pin/python\n",
    "from rolling_pin import tools, utils, blob_etl\n",
    "from rolling_pin.blob_etl import BlobETL\n",
    "reload(tools)\n",
    "reload(blob_etl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BLOB DATA WITH A REPEATING STRUCTURE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "blob = dict(\n",
    "    a=dict(\n",
    "        b=[\n",
    "            dict(            # <--|\n",
    "                c='foo',     #    |   repeated structure\n",
    "                d=dict(      #    |__   of 2 branches\n",
    "                    e='taco' #    |   a/b/list/c and a/b/list/d/e  \n",
    "                )            #    |\n",
    "            ),               # <--|\n",
    "            dict(\n",
    "                c='foo',\n",
    "                d=dict(\n",
    "                    e='pizza'\n",
    "                )\n",
    "            ),\n",
    "            dict(\n",
    "                c='bar',\n",
    "                d=dict(\n",
    "                    e='taco'\n",
    "                )\n",
    "            ),\n",
    "            dict(\n",
    "                c='foo',\n",
    "                d=dict(\n",
    "                    e='kiwi'\n",
    "                )\n",
    "            ),\n",
    "            dict(\n",
    "                c='bar',\n",
    "                d=dict(\n",
    "                    e='pizza'\n",
    "                )\n",
    "            )\n",
    "        ]\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ETL OF BLOB DATA\n",
    "* ### Notice the repetative topology"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "etl = BlobETL(blob)\n",
    "etl.to_html(orient='lr', as_png=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PROTOTYPE OF BLOB DATA\n",
    "* ### Notice that the key value pairs at the end of the branches\n",
    "* ### They constitute the unique value and count thereof for the two prototypical branches of the blob data (ie histograms)\n",
    "* ### For instance, under the a/b/list/d/e branch the word \"pizza\" appears 2 times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proto = etl.to_prototype()\n",
    "proto.to_html(orient='lr', as_png=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PROTOTYPICAL TOPOLOGY (ie protype without values)\n",
    "* ### The fundamental structure that is repeated throughout the original blob data\n",
    "* ### Think of this as the union of every unique topology within the blob data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proto.set(value_setter=lambda k, v: '').to_html(orient='lr', as_png=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PROTOTYPICAL FLAT DICTIONARY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'^a/b/<list_[0-9]+>/c$': Counter({'foo': 3, 'bar': 2}),\n",
       " '^a/b/<list_[0-9]+>/d/e$': Counter({'taco': 2, 'pizza': 2, 'kiwi': 1})}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proto.to_flat_dict()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DATAFRAME OF BLOB DATA"
   ]
  },
  {
   "cell_type": "code",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>b</td>\n",
       "      <td>&lt;list_0&gt;</td>\n",
       "      <td>c</td>\n",
       "      <td>NaN</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>b</td>\n",
       "      <td>&lt;list_0&gt;</td>\n",
       "      <td>d</td>\n",
       "      <td>e</td>\n",
       "      <td>taco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>b</td>\n",
       "      <td>&lt;list_1&gt;</td>\n",
       "      <td>c</td>\n",
       "      <td>NaN</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a</td>\n",
       "      <td>b</td>\n",
       "      <td>&lt;list_1&gt;</td>\n",
       "      <td>d</td>\n",
       "      <td>e</td>\n",
       "      <td>pizza</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a</td>\n",
       "      <td>b</td>\n",
       "      <td>&lt;list_2&gt;</td>\n",
       "      <td>c</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>a</td>\n",
       "      <td>b</td>\n",
       "      <td>&lt;list_2&gt;</td>\n",
       "      <td>d</td>\n",
       "      <td>e</td>\n",
       "      <td>taco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>a</td>\n",
       "      <td>b</td>\n",
       "      <td>&lt;list_3&gt;</td>\n",
       "      <td>c</td>\n",
       "      <td>NaN</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>a</td>\n",
       "      <td>b</td>\n",
       "      <td>&lt;list_3&gt;</td>\n",
       "      <td>d</td>\n",
       "      <td>e</td>\n",
       "      <td>kiwi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>a</td>\n",
       "      <td>b</td>\n",
       "      <td>&lt;list_4&gt;</td>\n",
       "      <td>c</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>a</td>\n",
       "      <td>b</td>\n",
       "      <td>&lt;list_4&gt;</td>\n",
       "      <td>d</td>\n",
       "      <td>e</td>\n",
       "      <td>pizza</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0  1         2  3    4  value\n",
       "0  a  b  <list_0>  c  NaN    foo\n",
       "1  a  b  <list_0>  d    e   taco\n",
       "2  a  b  <list_1>  c  NaN    foo\n",
       "3  a  b  <list_1>  d    e  pizza\n",
       "4  a  b  <list_2>  c  NaN    bar\n",
       "5  a  b  <list_2>  d    e   taco\n",
       "6  a  b  <list_3>  c  NaN    foo\n",
       "7  a  b  <list_3>  d    e   kiwi\n",
       "8  a  b  <list_4>  c  NaN    bar\n",
       "9  a  b  <list_4>  d    e  pizza"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "etl.to_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DATAFRAME OF PROTOTYPE"
   ]
  },
  {
   "cell_type": "code",
   "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>0</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>^a/b/&lt;list_[0-9]+&gt;/c$</td>\n",
       "      <td>{'foo': 3, 'bar': 2}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>^a/b/&lt;list_[0-9]+&gt;/d/e$</td>\n",
       "      <td>{'taco': 2, 'pizza': 2, 'kiwi': 1}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         0                               value\n",
       "0    ^a/b/<list_[0-9]+>/c$                {'foo': 3, 'bar': 2}\n",
       "1  ^a/b/<list_[0-9]+>/d/e$  {'taco': 2, 'pizza': 2, 'kiwi': 1}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = BlobETL(proto.to_flat_dict(), separator='#').to_dataframe()\n",
    "temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PROTOTYPE DATAFRAME CONVERTED TO 1 PDF PER KEY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>bar</th>\n",
       "      <th>foo</th>\n",
       "      <th>kiwi</th>\n",
       "      <th>pizza</th>\n",
       "      <th>taco</th>\n",
       "      <th>key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>^a/b/&lt;list_[0-9]+&gt;/c$</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.4</td>\n",
       "      <td>^a/b/&lt;list_[0-9]+&gt;/d/e$</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bar  foo  kiwi  pizza  taco                      key\n",
       "0  0.4  0.6   NaN    NaN   NaN    ^a/b/<list_[0-9]+>/c$\n",
       "1  NaN  NaN   0.2    0.4   0.4  ^a/b/<list_[0-9]+>/d/e$"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = DataFrame(temp.value.tolist())\n",
    "data = data.apply(lambda x: x / x.sum(), axis=1)\n",
    "data['key'] = temp[0]\n",
    "data"
   ]
  }
 ],
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