From 1f05ad4ef0b44d408eee037bf10d5dd1a73f69b0 Mon Sep 17 00:00:00 2001
From: Chuong Ho <chuongpqvn@gmail.com>
Date: Wed, 6 Mar 2024 17:52:14 +0800
Subject: [PATCH] add docs

---
 .../07. Explore Big Data Format Storage.ipynb | 882 ++++++++++++++++++
 Readme.md                                     |   1 +
 2 files changed, 883 insertions(+)
 create mode 100644 APSToolkitPython/Tutorials/07. Explore Big Data Format Storage.ipynb

diff --git a/APSToolkitPython/Tutorials/07. Explore Big Data Format Storage.ipynb b/APSToolkitPython/Tutorials/07. Explore Big Data Format Storage.ipynb
new file mode 100644
index 0000000..d98cc5d
--- /dev/null
+++ b/APSToolkitPython/Tutorials/07. Explore Big Data Format Storage.ipynb	
@@ -0,0 +1,882 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "dotnet_interactive": {
+     "language": "csharp"
+    },
+    "polyglot_notebook": {
+     "kernelName": "csharp"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: aps-toolkit in c:\\users\\vho2\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (0.2.9)\n",
+      "Requirement already satisfied: requests in c:\\users\\vho2\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from aps-toolkit) (2.31.0)\n",
+      "Requirement already satisfied: pandas in c:\\users\\vho2\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from aps-toolkit) (2.2.0)\n",
+      "Requirement already satisfied: numpy<2,>=1.23.2 in c:\\users\\vho2\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from pandas->aps-toolkit) (1.23.5)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\vho2\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from pandas->aps-toolkit) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\vho2\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from pandas->aps-toolkit) (2023.3.post1)\n",
+      "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\vho2\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from pandas->aps-toolkit) (2023.3)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\vho2\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from requests->aps-toolkit) (3.1.0)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\vho2\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from requests->aps-toolkit) (3.4)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\vho2\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from requests->aps-toolkit) (1.26.18)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\vho2\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from requests->aps-toolkit) (2023.5.7)\n",
+      "Requirement already satisfied: six>=1.5 in c:\\users\\vho2\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from python-dateutil>=2.8.2->pandas->aps-toolkit) (1.16.0)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "[notice] A new release of pip is available: 23.3.2 -> 24.0\n",
+      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install aps-toolkit --upgrade"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-03-02T14:12:22.214171Z",
+     "start_time": "2024-03-02T14:12:22.211115Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from aps_toolkit import Auth\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-03-02T14:12:23.004384Z",
+     "start_time": "2024-03-02T14:12:22.215209Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "token = Auth().auth2leg()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-03-02T14:12:23.009480Z",
+     "start_time": "2024-03-02T14:12:23.005633Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "urn = \"dXJuOmFkc2sud2lwcHJvZDpmcy5maWxlOnZmLk54SW12TFQyVDB5aUZpMHlaV19jS3c_dmVyc2lvbj05\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-03-02T14:12:31.994676Z",
+     "start_time": "2024-03-02T14:12:23.010491Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from aps_toolkit import PropDbReaderRevit\n",
+    "prop_reader = PropDbReaderRevit(urn, token)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "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>dbId</th>\n",
+       "      <th>external_id</th>\n",
+       "      <th>ElementId</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Category</th>\n",
+       "      <th>CategoryId</th>\n",
+       "      <th>Image</th>\n",
+       "      <th>Keynote</th>\n",
+       "      <th>Export to IFC</th>\n",
+       "      <th>Export to IFC As</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Filter</th>\n",
+       "      <th>Sorting/Grouping</th>\n",
+       "      <th>Formatting</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Embedded Schedule</th>\n",
+       "      <th>ScheduleType_ARC</th>\n",
+       "      <th>Swatch Width</th>\n",
+       "      <th>Swatch Height</th>\n",
+       "      <th>Values Displayed</th>\n",
+       "      <th>Font</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>254</td>\n",
+       "      <td>e3e052f9-0156-11d5-9301-0000863f27ad-00000017</td>\n",
+       "      <td>23</td>\n",
+       "      <td>Default</td>\n",
+       "      <td>Revit Materials</td>\n",
+       "      <td>-2000700.0</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>By Type</td>\n",
+       "      <td></td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>255</td>\n",
+       "      <td>e3e052f9-0156-11d5-9301-0000863f27ad-00000018</td>\n",
+       "      <td>24</td>\n",
+       "      <td>Default Wall</td>\n",
+       "      <td>Revit Materials</td>\n",
+       "      <td>-2000700.0</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>By Type</td>\n",
+       "      <td></td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>256</td>\n",
+       "      <td>e3e052f9-0156-11d5-9301-0000863f27ad-00000019</td>\n",
+       "      <td>25</td>\n",
+       "      <td>Default Roof</td>\n",
+       "      <td>Revit Materials</td>\n",
+       "      <td>-2000700.0</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>By Type</td>\n",
+       "      <td></td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>257</td>\n",
+       "      <td>e3e052f9-0156-11d5-9301-0000863f27ad-0000001a</td>\n",
+       "      <td>26</td>\n",
+       "      <td>Glass</td>\n",
+       "      <td>Revit Materials</td>\n",
+       "      <td>-2000700.0</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>By Type</td>\n",
+       "      <td></td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>258</td>\n",
+       "      <td>e3e052f9-0156-11d5-9301-0000863f27ad-0000001d</td>\n",
+       "      <td>29</td>\n",
+       "      <td>Phase - Temporary</td>\n",
+       "      <td>Revit Materials</td>\n",
+       "      <td>-2000700.0</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>By Type</td>\n",
+       "      <td></td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 1464 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   dbId                                    external_id ElementId  \\\n",
+       "0   254  e3e052f9-0156-11d5-9301-0000863f27ad-00000017        23   \n",
+       "1   255  e3e052f9-0156-11d5-9301-0000863f27ad-00000018        24   \n",
+       "2   256  e3e052f9-0156-11d5-9301-0000863f27ad-00000019        25   \n",
+       "3   257  e3e052f9-0156-11d5-9301-0000863f27ad-0000001a        26   \n",
+       "4   258  e3e052f9-0156-11d5-9301-0000863f27ad-0000001d        29   \n",
+       "\n",
+       "                Name         Category  CategoryId Image Keynote Export to IFC  \\\n",
+       "0            Default  Revit Materials  -2000700.0                     By Type   \n",
+       "1       Default Wall  Revit Materials  -2000700.0                     By Type   \n",
+       "2       Default Roof  Revit Materials  -2000700.0                     By Type   \n",
+       "3              Glass  Revit Materials  -2000700.0                     By Type   \n",
+       "4  Phase - Temporary  Revit Materials  -2000700.0                     By Type   \n",
+       "\n",
+       "  Export to IFC As  ... Filter Sorting/Grouping Formatting Appearance  \\\n",
+       "0                   ...    NaN              NaN        NaN        NaN   \n",
+       "1                   ...    NaN              NaN        NaN        NaN   \n",
+       "2                   ...    NaN              NaN        NaN        NaN   \n",
+       "3                   ...    NaN              NaN        NaN        NaN   \n",
+       "4                   ...    NaN              NaN        NaN        NaN   \n",
+       "\n",
+       "  Embedded Schedule ScheduleType_ARC Swatch Width  Swatch Height  \\\n",
+       "0               NaN              NaN          NaN            NaN   \n",
+       "1               NaN              NaN          NaN            NaN   \n",
+       "2               NaN              NaN          NaN            NaN   \n",
+       "3               NaN              NaN          NaN            NaN   \n",
+       "4               NaN              NaN          NaN            NaN   \n",
+       "\n",
+       "  Values Displayed  Font  \n",
+       "0              NaN   NaN  \n",
+       "1              NaN   NaN  \n",
+       "2              NaN   NaN  \n",
+       "3              NaN   NaN  \n",
+       "4              NaN   NaN  \n",
+       "\n",
+       "[5 rows x 1464 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df =prop_reader.get_all_data()\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dir = \"./output\"\n",
+    "import os\n",
+    "if not os.path.exists(dir):\n",
+    "    os.makedirs(dir)\n",
+    "file_path = os.path.join(dir, \"Model.parquet\")\n",
+    "df.astype(str).to_parquet(file_path, index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "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>dbId</th>\n",
+       "      <th>external_id</th>\n",
+       "      <th>ElementId</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Category</th>\n",
+       "      <th>CategoryId</th>\n",
+       "      <th>Image</th>\n",
+       "      <th>Keynote</th>\n",
+       "      <th>Export to IFC</th>\n",
+       "      <th>Export to IFC As</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Filter</th>\n",
+       "      <th>Sorting/Grouping</th>\n",
+       "      <th>Formatting</th>\n",
+       "      <th>Appearance</th>\n",
+       "      <th>Embedded Schedule</th>\n",
+       "      <th>ScheduleType_ARC</th>\n",
+       "      <th>Swatch Width</th>\n",
+       "      <th>Swatch Height</th>\n",
+       "      <th>Values Displayed</th>\n",
+       "      <th>Font</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>254</td>\n",
+       "      <td>e3e052f9-0156-11d5-9301-0000863f27ad-00000017</td>\n",
+       "      <td>23</td>\n",
+       "      <td>Default</td>\n",
+       "      <td>Revit Materials</td>\n",
+       "      <td>-2000700.0</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>By Type</td>\n",
+       "      <td></td>\n",
+       "      <td>...</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>255</td>\n",
+       "      <td>e3e052f9-0156-11d5-9301-0000863f27ad-00000018</td>\n",
+       "      <td>24</td>\n",
+       "      <td>Default Wall</td>\n",
+       "      <td>Revit Materials</td>\n",
+       "      <td>-2000700.0</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>By Type</td>\n",
+       "      <td></td>\n",
+       "      <td>...</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>256</td>\n",
+       "      <td>e3e052f9-0156-11d5-9301-0000863f27ad-00000019</td>\n",
+       "      <td>25</td>\n",
+       "      <td>Default Roof</td>\n",
+       "      <td>Revit Materials</td>\n",
+       "      <td>-2000700.0</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>By Type</td>\n",
+       "      <td></td>\n",
+       "      <td>...</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>257</td>\n",
+       "      <td>e3e052f9-0156-11d5-9301-0000863f27ad-0000001a</td>\n",
+       "      <td>26</td>\n",
+       "      <td>Glass</td>\n",
+       "      <td>Revit Materials</td>\n",
+       "      <td>-2000700.0</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>By Type</td>\n",
+       "      <td></td>\n",
+       "      <td>...</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>258</td>\n",
+       "      <td>e3e052f9-0156-11d5-9301-0000863f27ad-0000001d</td>\n",
+       "      <td>29</td>\n",
+       "      <td>Phase - Temporary</td>\n",
+       "      <td>Revit Materials</td>\n",
+       "      <td>-2000700.0</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>By Type</td>\n",
+       "      <td></td>\n",
+       "      <td>...</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "      <td>nan</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 1464 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  dbId                                    external_id ElementId  \\\n",
+       "0  254  e3e052f9-0156-11d5-9301-0000863f27ad-00000017        23   \n",
+       "1  255  e3e052f9-0156-11d5-9301-0000863f27ad-00000018        24   \n",
+       "2  256  e3e052f9-0156-11d5-9301-0000863f27ad-00000019        25   \n",
+       "3  257  e3e052f9-0156-11d5-9301-0000863f27ad-0000001a        26   \n",
+       "4  258  e3e052f9-0156-11d5-9301-0000863f27ad-0000001d        29   \n",
+       "\n",
+       "                Name         Category  CategoryId Image Keynote Export to IFC  \\\n",
+       "0            Default  Revit Materials  -2000700.0                     By Type   \n",
+       "1       Default Wall  Revit Materials  -2000700.0                     By Type   \n",
+       "2       Default Roof  Revit Materials  -2000700.0                     By Type   \n",
+       "3              Glass  Revit Materials  -2000700.0                     By Type   \n",
+       "4  Phase - Temporary  Revit Materials  -2000700.0                     By Type   \n",
+       "\n",
+       "  Export to IFC As  ... Filter Sorting/Grouping Formatting Appearance  \\\n",
+       "0                   ...    nan              nan        nan        nan   \n",
+       "1                   ...    nan              nan        nan        nan   \n",
+       "2                   ...    nan              nan        nan        nan   \n",
+       "3                   ...    nan              nan        nan        nan   \n",
+       "4                   ...    nan              nan        nan        nan   \n",
+       "\n",
+       "  Embedded Schedule ScheduleType_ARC Swatch Width Swatch Height  \\\n",
+       "0               nan              nan          nan           nan   \n",
+       "1               nan              nan          nan           nan   \n",
+       "2               nan              nan          nan           nan   \n",
+       "3               nan              nan          nan           nan   \n",
+       "4               nan              nan          nan           nan   \n",
+       "\n",
+       "  Values Displayed Font  \n",
+       "0              nan  nan  \n",
+       "1              nan  nan  \n",
+       "2              nan  nan  \n",
+       "3              nan  nan  \n",
+       "4              nan  nan  \n",
+       "\n",
+       "[5 rows x 1464 columns]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# read parquet file\n",
+    "df = pd.read_parquet(file_path)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# clear data null\n",
+    "df = df.dropna()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Review Categories"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Revit Materials', 'Revit Primary Contours', 'Revit Area Schemes',\n",
+       "       'Revit Phases', 'Revit Shared Site', 'Revit Work Plane Grid',\n",
+       "       'Revit Project Information', 'Revit Structural Load Cases',\n",
+       "       'Revit Sun Path', 'Revit Internal Origin',\n",
+       "       'Revit Color Fill Schema', 'Revit HVAC Zones',\n",
+       "       'Revit HVAC Load Schedules', 'Revit Building Type Settings',\n",
+       "       'Revit Space Type Settings', 'Revit Survey Point',\n",
+       "       'Revit Project Base Point',\n",
+       "       'Revit Electrical Demand Factor Definitions',\n",
+       "       'Revit Electrical Load Classifications',\n",
+       "       'Revit Panel Schedule Templates - Branch Panel',\n",
+       "       'Revit Panel Schedule Templates - Data Panel',\n",
+       "       'Revit Panel Schedule Templates - Switchboard',\n",
+       "       'Revit Electrical Load Classification Parameter Element',\n",
+       "       'Revit Pipe Segments', 'Revit Material Assets',\n",
+       "       'Revit Legend Components', 'Revit Views', 'Revit Text Notes',\n",
+       "       'Revit Lines', 'Revit Detail Items', 'Revit <Sketch>',\n",
+       "       'Revit Automatic Sketch Dimensions', 'Revit Constraints',\n",
+       "       'Revit Dimensions', 'Revit Generic Annotations',\n",
+       "       'Revit Revision Numbering Sequences', 'Revit Areas',\n",
+       "       'Revit <Area Boundary>', 'Revit Guide Grid', 'Revit Title Blocks',\n",
+       "       'Revit Elevations', 'Revit Viewports', 'Revit RVT Links',\n",
+       "       'Revit Grids', 'Revit Spot Elevations', 'Revit Cameras',\n",
+       "       'Revit Section Boxes', 'Revit Walls', 'Revit Curtain Panels',\n",
+       "       'Revit Curtain Wall Mullions', 'Revit Doors',\n",
+       "       'Revit Plumbing Fixtures', 'n/a', 'Revit Specialty Equipment',\n",
+       "       'Revit Furniture', 'Revit Casework', 'Revit Ceilings',\n",
+       "       'Revit Rooms', 'Revit Stairs', 'Revit Runs', 'Revit Supports',\n",
+       "       'Revit Railings', 'Revit Top Rails', 'Revit Reference Planes',\n",
+       "       'Revit Railing Rail Path Extension Lines', 'Revit Landings',\n",
+       "       'Revit <Stair/Ramp Sketch: Boundary>', 'Revit Balusters',\n",
+       "       'Revit <Room Separation>', 'Revit Room Tags', 'Revit Wall Sweeps',\n",
+       "       'Revit Roofs', 'Revit Windows', 'Revit Floors',\n",
+       "       'Revit Stair Paths', 'Revit Generic Models',\n",
+       "       'Revit <Stair/Ramp Sketch: Stair Path>',\n",
+       "       'Revit Curtain Wall Grids', 'Revit Matchline', 'Revit Revision',\n",
+       "       'Revit Revision Clouds', 'Revit Schedules', 'Revit ',\n",
+       "       'Revit Shaft Openings', 'Revit Color Fill Legends',\n",
+       "       'Revit Schedule Graphics'], dtype=object)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# get all categories by df.Category\n",
+    "df.Category.unique()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Review Elements "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "18355"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# how many elements\n",
+    "df.ElementId.count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ElementId\n",
+       "23         1\n",
+       "6362799    1\n",
+       "6362821    1\n",
+       "6362820    1\n",
+       "6362819    1\n",
+       "          ..\n",
+       "6367336    1\n",
+       "5929880    1\n",
+       "6060120    1\n",
+       "6060128    1\n",
+       "6386022    1\n",
+       "Name: count, Length: 18355, dtype: int64"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.ElementId.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Category\n",
+       "Revit <Sketch>                                  4043\n",
+       "Revit Lines                                     3603\n",
+       "Revit Automatic Sketch Dimensions               2215\n",
+       "Revit Room Tags                                  696\n",
+       "Revit Rooms                                      691\n",
+       "                                                ... \n",
+       "Revit HVAC Zones                                   1\n",
+       "Revit Panel Schedule Templates - Switchboard       1\n",
+       "Revit Project Base Point                           1\n",
+       "Revit Project Information                          1\n",
+       "Revit Panel Schedule Templates - Data Panel        1\n",
+       "Name: ElementId, Length: 86, dtype: int64"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# see how many ElementId group by Category\n",
+    "df_category_group = df.groupby('Category').ElementId.count()\n",
+    "# sort by value count \n",
+    "df_category_group = df_category_group.sort_values(ascending=False)\n",
+    "df_category_group"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Category\n",
+       "Revit <Sketch>                             4043\n",
+       "Revit Lines                                3603\n",
+       "Revit Automatic Sketch Dimensions          2215\n",
+       "Revit Room Tags                             696\n",
+       "Revit Rooms                                 691\n",
+       "Revit Text Notes                            594\n",
+       "Revit Railing Rail Path Extension Lines     537\n",
+       "Revit Materials                             518\n",
+       "Revit Reference Planes                      491\n",
+       "Revit Legend Components                     429\n",
+       "Revit Detail Items                          413\n",
+       "Revit <Room Separation>                     345\n",
+       "Revit Walls                                 289\n",
+       "Revit <Area Boundary>                       275\n",
+       "Revit <Stair/Ramp Sketch: Boundary>         274\n",
+       "Revit Dimensions                            203\n",
+       "Revit Balusters                             194\n",
+       "Revit Spot Elevations                       182\n",
+       "Revit Sun Path                              172\n",
+       "Revit Doors                                 145\n",
+       "Revit Ceilings                              141\n",
+       "Revit Railings                              136\n",
+       "Revit Top Rails                             135\n",
+       "Revit Space Type Settings                   125\n",
+       "Revit Constraints                           117\n",
+       "Name: ElementId, dtype: int64"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# remove all df_category_group have values <=1\n",
+    "df_category_group = df_category_group[df_category_group > 100]\n",
+    "df_category_group"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Visualize the result\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "plt.figure(figsize=(15, 5))\n",
+    "sns.barplot(x=df_category_group.index, y=df_category_group.values)\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.title('Number of Elements by Category')\n",
+    "plt.xlabel('Category')\n",
+    "plt.ylabel('Number of Elements')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Issues with model\n",
+    "- 2D sketech"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Revit <Sketch>', 'Revit <Area Boundary>',\n",
+       "       'Revit <Stair/Ramp Sketch: Boundary>', 'Revit <Room Separation>',\n",
+       "       'Revit <Stair/Ramp Sketch: Stair Path>'], dtype=object)"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# see all Category have < and < inside string \n",
+    "df[df.Category.str.contains('<')].Category.unique()"
+   ]
+  }
+ ],
+ "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": "3.11.7"
+  },
+  "polyglot_notebook": {
+   "kernelInfo": {
+    "defaultKernelName": "csharp",
+    "items": [
+     {
+      "aliases": [],
+      "name": "csharp"
+     }
+    ]
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Readme.md b/Readme.md
index 8b4f22d..40021bf 100644
--- a/Readme.md
+++ b/Readme.md
@@ -125,6 +125,7 @@ All Tutorials are available under Jupyter Notebook at [Python Tutorials](./APSTo
   - [04.02. Explore Compare Revit Data Versions](./APSToolkitPython/Tutorials/04.02.%20Explore%20Compare%20Revit%20Data%20Versions.ipynb)
 - [05. Explore Access Database And Query Items](./APSToolkitPython/Tutorials/05.%20Explore%20Access%20Database%20And%20Query%20Items.ipynb)
 - [06. Data Visualization - Analyst BIM Model](./APSToolkitPython/Tutorials/06.%20Data%20Visualization%20-%20Analyst%20BIM%20Model.ipynb)
+- [07. Explore Big Data Format Storage](./APSToolkitPython/Tutorials/07.%20Explore%20Big%20Data%20Format%20Storage.ipynb)
 
 ## License
 Thís project is licensed under the terms of the [MIT](LICENSE).