Data Analysis on Organisational Metadata obtained from API.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysing the Metadata of an Organization (a Publisher) - Ordnance Survey Ireland:\n",
    "\n",
    "#### This shows how to extract information about datasets/publishers (CKAN refers to publishers internally as organizations):\n",
    "\n",
    "This gives an overview of how to analyse the data that belongs to the particular organization. The find organization function helps to find_organization_id you are looking for and organization_show helps to extract all the packages/datasets id that belongs to the organization (only 1000 ids can be extracted once). In addition to packages ids, these function also returns the meta information of the organization and the datasets.\n",
    "\n",
    "This shows how to do a analysis on the available meta information about the publishers/organizations. Here for the demo purpose Ordance Survey Ireland is taken as an example for organization, however we can do analysis on meta information vailable on any organization."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Import all requirements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: requests in /anaconda3/lib/python3.7/site-packages (2.19.1)\n",
      "Requirement already satisfied: fuzzywuzzy in /anaconda3/lib/python3.7/site-packages (0.17.0)\n",
      "Requirement already satisfied: pandas in /anaconda3/lib/python3.7/site-packages (0.23.4)\n",
      "Requirement already satisfied: pyjstat in /anaconda3/lib/python3.7/site-packages (1.0.1)\n",
      "Requirement already satisfied: numpy in /anaconda3/lib/python3.7/site-packages (1.15.1)\n",
      "Requirement already satisfied: plotly in /anaconda3/lib/python3.7/site-packages (3.4.2)\n",
      "Requirement already satisfied: matplotlib in /anaconda3/lib/python3.7/site-packages (2.2.3)\n",
      "Requirement already satisfied: seaborn in /anaconda3/lib/python3.7/site-packages (0.9.0)\n",
      "Requirement already satisfied: wordcloud in /anaconda3/lib/python3.7/site-packages (1.5.0)\n",
      "Requirement already satisfied: urllib3<1.24,>=1.21.1 in /anaconda3/lib/python3.7/site-packages (from requests) (1.23)\n",
      "Requirement already satisfied: idna<2.8,>=2.5 in /anaconda3/lib/python3.7/site-packages (from requests) (2.7)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /anaconda3/lib/python3.7/site-packages (from requests) (2018.8.24)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /anaconda3/lib/python3.7/site-packages (from requests) (3.0.4)\n",
      "Requirement already satisfied: python-dateutil>=2.5.0 in /anaconda3/lib/python3.7/site-packages (from pandas) (2.7.3)\n",
      "Requirement already satisfied: pytz>=2011k in /anaconda3/lib/python3.7/site-packages (from pandas) (2018.5)\n",
      "Requirement already satisfied: decorator>=4.0.6 in /anaconda3/lib/python3.7/site-packages (from plotly) (4.3.0)\n",
      "Requirement already satisfied: nbformat>=4.2 in /anaconda3/lib/python3.7/site-packages (from plotly) (4.4.0)\n",
      "Requirement already satisfied: retrying>=1.3.3 in /anaconda3/lib/python3.7/site-packages (from plotly) (1.3.3)\n",
      "Requirement already satisfied: six in /anaconda3/lib/python3.7/site-packages (from plotly) (1.11.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /anaconda3/lib/python3.7/site-packages (from matplotlib) (0.10.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /anaconda3/lib/python3.7/site-packages (from matplotlib) (2.2.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /anaconda3/lib/python3.7/site-packages (from matplotlib) (1.0.1)\n",
      "Requirement already satisfied: scipy>=0.14.0 in /anaconda3/lib/python3.7/site-packages (from seaborn) (1.1.0)\n",
      "Requirement already satisfied: pillow in /anaconda3/lib/python3.7/site-packages (from wordcloud) (5.2.0)\n",
      "Requirement already satisfied: jupyter-core in /anaconda3/lib/python3.7/site-packages (from nbformat>=4.2->plotly) (4.4.0)\n",
      "Requirement already satisfied: traitlets>=4.1 in /anaconda3/lib/python3.7/site-packages (from nbformat>=4.2->plotly) (4.3.2)\n",
      "Requirement already satisfied: ipython-genutils in /anaconda3/lib/python3.7/site-packages (from nbformat>=4.2->plotly) (0.2.0)\n",
      "Requirement already satisfied: jsonschema!=2.5.0,>=2.4 in /anaconda3/lib/python3.7/site-packages (from nbformat>=4.2->plotly) (2.6.0)\n",
      "Requirement already satisfied: setuptools in /anaconda3/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib) (40.2.0)\n",
      "\u001b[33mYou are using pip version 10.0.1, however version 18.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install requests fuzzywuzzy pandas pyjstat numpy plotly matplotlib seaborn wordcloud"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Import all necessary libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from fuzzywuzzy import fuzz\n",
    "from fuzzywuzzy import process\n",
    "import sys\n",
    "import pandas as pd\n",
    "from pyjstat import pyjstat\n",
    "import numpy as np\n",
    "from pyjstat import pyjstat\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import collections\n",
    "from IPython.display import display, HTML\n",
    "import plotly.tools as tls\n",
    "from IPython.display import Image\n",
    "from IPython.core.display import HTML \n",
    "import matplotlib.image as mpimg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find organization id by fuzzy matching search term: Ordance Survey Ireland\n",
    "\n",
    "This helps users to fetch the organization id given name of the organization. For example, for given name Ordancd Survey Ireland, this functions fetches the id of the corresponding organization - "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_organization_id(url, org_name, param):\n",
    "    \n",
    "    try:\n",
    "\n",
    "        response = requests.get(url, param)\n",
    "        results = response.json()\n",
    "\n",
    "        if not results['success']:\n",
    "            raise SystemError\n",
    "\n",
    "        if len(results['result']) == 0:\n",
    "\n",
    "            return (\"No matching Organization found\")\n",
    "\n",
    "        else:\n",
    "\n",
    "            ratio = {}\n",
    "\n",
    "            for index, x in enumerate(results['result']):\n",
    "                ratio[index] = fuzz.ratio(org_name, x['display_name'])\n",
    "\n",
    "            mtch_rtr = sorted(ratio.values())[-1]\n",
    "\n",
    "            if mtch_rtr >= 75:\n",
    "\n",
    "                lKey = [k for k,v in ratio.items() if v == mtch_rtr]\n",
    "\n",
    "                return results['result'][int(lKey[0])]\n",
    "\n",
    "            else:\n",
    "                return (\"No matching Organization found\")\n",
    "\n",
    "    except SystemError:\n",
    "        print(\"Failure in request - bad action/url\")\n",
    "        sys.exit(1)\n",
    "        \n",
    "    except Exception as e:\n",
    "        print(e)\n",
    "        sys.exit(1)\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Meta Information for the Organization Ordnance Survey Ireland:\n",
    "This function fetches the datasets id and corresponding meta information like, url, star rating, tags, name, number of resources, formats available and its corresponding analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_all_datasets_for_org(url, org_name):\n",
    "    \n",
    "    try: \n",
    "        \n",
    "        param_find_org = {'all_fields': True}\n",
    "        \n",
    "        org_id = find_organization_id(url+'api/3/action/organization_list', org_name, param_find_org)['id']\n",
    "        \n",
    "        param_pkgs = {'id': org_id, 'include_users': True, 'include_dataset_count': True, \n",
    "                      'include_users': True, 'include_groups': True, 'include_tags': True, 'include_datasets': True}\n",
    "        \n",
    "        response = requests.get(url+'api/3/action/organization_show', param_pkgs)\n",
    "        results_org = response.json()\n",
    "        \n",
    "        if not results_org['success']:\n",
    "            raise SystemError\n",
    "\n",
    "        if len(results_org['result']) == 0:\n",
    "            raise Exception\n",
    "            \n",
    "        pkg_cnt = results_org['result']['package_count']\n",
    "        frmts_cnt = {}\n",
    "        star_cnt = {}\n",
    "        tags_cnt = {}\n",
    "        \n",
    "        df_ids_details = []\n",
    "        \n",
    "        for x in results_org['result']['packages']:\n",
    "            \n",
    "            \n",
    "            param_pkg_show = {'id': x['id']}\n",
    "            response = requests.get(url+'api/3/action/package_show', param_pkg_show)\n",
    "            results_pkg = response.json()\n",
    "            \n",
    "            if results_pkg['result']['qa']['openness_score'] in list(star_cnt.keys()):\n",
    "                    \n",
    "                star_cnt[results_pkg['result']['qa']['openness_score']] += 1\n",
    "                    \n",
    "            else:\n",
    "                    \n",
    "                star_cnt[results_pkg['result']['qa']['openness_score']] = 1\n",
    "                    \n",
    "            \n",
    "            \n",
    "            frmt = []\n",
    "            tag_nm = []\n",
    "            \n",
    "            for tag in results_pkg['result']['tags']:\n",
    "                \n",
    "                tag_nm.append(tag['display_name'].strip())\n",
    "                    \n",
    "                if tag['display_name'].strip() in list(tags_cnt.keys()):\n",
    "                    \n",
    "                    tags_cnt[tag['display_name'].strip()] += 1\n",
    "                    \n",
    "                else:\n",
    "                    \n",
    "                    tags_cnt[tag['display_name'].strip()] = 1\n",
    "        \n",
    "            \n",
    "            for y in results_pkg['result']['resources']:\n",
    "                \n",
    "                frmt.append(y['format'])\n",
    "                \n",
    "                if y['format'] in list(frmts_cnt.keys()):\n",
    "                    \n",
    "                    frmts_cnt[y['format']] += 1\n",
    "                    \n",
    "                else:\n",
    "                    \n",
    "                    frmts_cnt[y['format']] = 1\n",
    "                    \n",
    "            \n",
    "            df_ids_details.append({'id': x['id'], 'num_tags':x['num_tags'], 'num_resources':x['num_resources'],\n",
    "                                  'name': x['name'], 'url':x['url'], 'formats': \", \".join(frmt), \n",
    "                                  'star_rating': results_pkg['result']['qa']['openness_score'],\n",
    "                                  'tags': \", \".join(tag_nm)})\n",
    "            \n",
    "        \n",
    "            \n",
    "        return pd.DataFrame(sorted(df_ids_details, key=lambda k: k['star_rating'], reverse=True)), frmts_cnt, star_cnt, tags_cnt\n",
    "        \n",
    "        \n",
    "    except SystemError:\n",
    "        print(\"Failure in request - bad organization name\"\\\n",
    "              \"Hint: No matching organization found - please check the name of the organization\")\n",
    "        sys.exit(1)\n",
    "        \n",
    "    except Exception as e:\n",
    "        \n",
    "        print(e)\n",
    "        sys.exit(1)\n",
    "        \n",
    "\n",
    "        \n",
    "hg_score_df, frmts_cnt, star_cnt, tags_cnt = find_all_datasets_for_org('https://data.gov.ie/', 'Ordancd Survey Ireland')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metadata of the publisher: Ordnance Survey Ireland"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>formats</th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>num_resources</th>\n",
       "      <th>num_tags</th>\n",
       "      <th>star_rating</th>\n",
       "      <th>tags</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ttl, rdf-xml</td>\n",
       "      <td>bf293fc8-cfe5-42a0-a31b-f47b75285f6d</td>\n",
       "      <td>osi-national-statutory-boundary-linked-data</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>adapt, boundary, geospatial, linked data, osi,...</td>\n",
       "      <td>http://data.geohive.ie/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HTML, Esri REST, GeoJSON, CSV, KML, ZIP</td>\n",
       "      <td>3054fb53-b1b4-4148-b2ac-11c99524327d</td>\n",
       "      <td>baronies-osi-national-placenames-gazetteer</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>Baronies, OSi National Placenames Gazetteer, O...</td>\n",
       "      <td>http://data-osi.opendata.arcgis.com/datasets/2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HTML, Esri REST, GeoJSON, CSV, KML, ZIP</td>\n",
       "      <td>5029a660-de34-41ed-9484-6526d5794a34</td>\n",
       "      <td>centres-of-population-osi-national-placenames-...</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>Centres of Population, OSi National Placenames...</td>\n",
       "      <td>http://data-osi.opendata.arcgis.com/datasets/a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HTML, Esri REST, GeoJSON, CSV, KML, ZIP</td>\n",
       "      <td>aa7fe29c-d353-427f-9d88-d8e29699a101</td>\n",
       "      <td>civil-parishes-osi-national-placenames-gazetteer</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>Civil Parishes, OSi National Placenames Gazett...</td>\n",
       "      <td>http://data-osi.opendata.arcgis.com/datasets/d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HTML, Esri REST, GeoJSON, CSV, KML, ZIP</td>\n",
       "      <td>c5385677-c7ea-4c1e-bd73-eb7b265250b2</td>\n",
       "      <td>counties-osi-national-placenames-gazetteer</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>Admin, Counties, OSi National Placenames Gazet...</td>\n",
       "      <td>http://data-osi.opendata.arcgis.com/datasets/8...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(HTML(hg_score_df.head().to_html()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Formats</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HTML</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Esri REST</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GeoJSON</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CSV</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KML</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Formats  Count\n",
       "0       HTML    154\n",
       "1  Esri REST    154\n",
       "2    GeoJSON    154\n",
       "3        CSV    154\n",
       "4        KML    154"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\"Formats\": list(frmts_cnt.keys()), 'Count': list(frmts_cnt.values())}).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{3: 154, 5: 1}\n",
      "{1: 0, 2: 0, 3: 154, 4: 0, 5: 1}\n"
     ]
    }
   ],
   "source": [
    "print(star_cnt)\n",
    "star_cnt[1] = 0\n",
    "star_cnt[2] = 0\n",
    "star_cnt[4] = 0\n",
    "star_cnt = dict(collections.OrderedDict(sorted(star_cnt.items())))\n",
    "print(star_cnt)"
   ]
  },
  {
   "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>Tags</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Baronies</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OSi National Placenames Gazetteer</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OpenData</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>administrative</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>placenames</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Tags  Count\n",
       "0                           Baronies      1\n",
       "1  OSi National Placenames Gazetteer      7\n",
       "2                           OpenData     48\n",
       "3                     administrative     16\n",
       "4                         placenames      8"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\"Tags\": list(tags_cnt.keys()), 'Count': list(tags_cnt.values())}).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting Top 5 Datasets of Ordnance Survey Ireland:\n",
    "\n",
    "This selection is based on the star rating of the resources:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
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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>formats</th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>num_resources</th>\n",
       "      <th>num_tags</th>\n",
       "      <th>star_rating</th>\n",
       "      <th>tags</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ttl, rdf-xml</td>\n",
       "      <td>bf293fc8-cfe5-42a0-a31b-f47b75285f6d</td>\n",
       "      <td>osi-national-statutory-boundary-linked-data</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>adapt, boundary, geospatial, linked data, osi,...</td>\n",
       "      <td>http://data.geohive.ie/</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HTML, Esri REST, GeoJSON, CSV, KML, ZIP</td>\n",
       "      <td>3054fb53-b1b4-4148-b2ac-11c99524327d</td>\n",
       "      <td>baronies-osi-national-placenames-gazetteer</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>Baronies, OSi National Placenames Gazetteer, O...</td>\n",
       "      <td>http://data-osi.opendata.arcgis.com/datasets/2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HTML, Esri REST, GeoJSON, CSV, KML, ZIP</td>\n",
       "      <td>5029a660-de34-41ed-9484-6526d5794a34</td>\n",
       "      <td>centres-of-population-osi-national-placenames-...</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>Centres of Population, OSi National Placenames...</td>\n",
       "      <td>http://data-osi.opendata.arcgis.com/datasets/a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>HTML, Esri REST, GeoJSON, CSV, KML, ZIP</td>\n",
       "      <td>aa7fe29c-d353-427f-9d88-d8e29699a101</td>\n",
       "      <td>civil-parishes-osi-national-placenames-gazetteer</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>Civil Parishes, OSi National Placenames Gazett...</td>\n",
       "      <td>http://data-osi.opendata.arcgis.com/datasets/d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HTML, Esri REST, GeoJSON, CSV, KML, ZIP</td>\n",
       "      <td>c5385677-c7ea-4c1e-bd73-eb7b265250b2</td>\n",
       "      <td>counties-osi-national-placenames-gazetteer</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>Admin, Counties, OSi National Placenames Gazet...</td>\n",
       "      <td>http://data-osi.opendata.arcgis.com/datasets/8...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   formats  \\\n",
       "0                             ttl, rdf-xml   \n",
       "1  HTML, Esri REST, GeoJSON, CSV, KML, ZIP   \n",
       "2  HTML, Esri REST, GeoJSON, CSV, KML, ZIP   \n",
       "3  HTML, Esri REST, GeoJSON, CSV, KML, ZIP   \n",
       "4  HTML, Esri REST, GeoJSON, CSV, KML, ZIP   \n",
       "\n",
       "                                     id  \\\n",
       "0  bf293fc8-cfe5-42a0-a31b-f47b75285f6d   \n",
       "1  3054fb53-b1b4-4148-b2ac-11c99524327d   \n",
       "2  5029a660-de34-41ed-9484-6526d5794a34   \n",
       "3  aa7fe29c-d353-427f-9d88-d8e29699a101   \n",
       "4  c5385677-c7ea-4c1e-bd73-eb7b265250b2   \n",
       "\n",
       "                                                name  num_resources  num_tags  \\\n",
       "0        osi-national-statutory-boundary-linked-data              2         8   \n",
       "1         baronies-osi-national-placenames-gazetteer              6         5   \n",
       "2  centres-of-population-osi-national-placenames-...              6         5   \n",
       "3   civil-parishes-osi-national-placenames-gazetteer              6         5   \n",
       "4         counties-osi-national-placenames-gazetteer              6         6   \n",
       "\n",
       "   star_rating                                               tags  \\\n",
       "0            5  adapt, boundary, geospatial, linked data, osi,...   \n",
       "1            3  Baronies, OSi National Placenames Gazetteer, O...   \n",
       "2            3  Centres of Population, OSi National Placenames...   \n",
       "3            3  Civil Parishes, OSi National Placenames Gazett...   \n",
       "4            3  Admin, Counties, OSi National Placenames Gazet...   \n",
       "\n",
       "                                                 url  \n",
       "0                            http://data.geohive.ie/  \n",
       "1  http://data-osi.opendata.arcgis.com/datasets/2...  \n",
       "2  http://data-osi.opendata.arcgis.com/datasets/a...  \n",
       "3  http://data-osi.opendata.arcgis.com/datasets/d...  \n",
       "4  http://data-osi.opendata.arcgis.com/datasets/8...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hg_score_df.to_csv('Top05_org_OSI.csv')\n",
    "hg_score_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualising the metadata\n",
    "\n",
    "#### Viz 1: Star Rating and its count - Ordnance Survey Ireland:"
   ]
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       "<div id=\"9abed13d-334b-443b-9bde-96bb41866bcf\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"9abed13d-334b-443b-9bde-96bb41866bcf\", [{\"marker\": {\"color\": \"rgba(50, 171, 96, 0.6)\", \"line\": {\"color\": \"rgba(50, 171, 96, 1.0)\", \"width\": 1}}, \"name\": \"Percentage of packages/datasets in a given star rating\", \"orientation\": \"h\", \"x\": [0.0, 0.0, 0.9935483870967742, 0.0, 0.0064516129032258064], \"y\": [\"1-star\", \"2-star\", \"3-star\", \"4-star\", \"5-star\"], \"type\": \"bar\", \"uid\": \"f72f6639-1231-47a7-a05d-ff0971451a7b\", \"xaxis\": \"x\", \"yaxis\": \"y\"}, {\"line\": {\"color\": \"rgb(128, 0, 128)\"}, \"mode\": \"lines+markers\", \"name\": \"Number of resources in a given star rating\", \"x\": [0, 0, 154, 0, 1], \"y\": [\"1-star\", \"2-star\", \"3-star\", \"4-star\", \"5-star\"], \"type\": \"scatter\", \"uid\": \"1f800729-55e8-4b19-bf1f-b5e1f0416ccb\", \"xaxis\": \"x2\", \"yaxis\": \"y2\"}], {\"xaxis\": {\"anchor\": \"y\", \"domain\": [0, 0.42], \"zeroline\": false, \"showline\": false, \"showticklabels\": true, \"showgrid\": true}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0, 0.85], \"showgrid\": false, \"showline\": false, \"showticklabels\": true}, \"xaxis2\": {\"anchor\": \"y2\", \"domain\": [0.47, 1], \"zeroline\": false, \"showline\": false, \"showticklabels\": true, \"showgrid\": true, \"side\": \"top\", \"dtick\": 25000}, \"yaxis2\": {\"anchor\": \"x2\", \"domain\": [0, 0.85], \"showgrid\": false, \"showline\": true, \"showticklabels\": false, \"linecolor\": \"rgba(102, 102, 102, 0.8)\", \"linewidth\": 2}, \"legend\": {\"font\": {\"size\": 10}, \"x\": 0.029, \"y\": 1.038}, \"margin\": {\"l\": 100, \"r\": 20, \"t\": 70, \"b\": 70}, \"title\": \"Analysis of Star Rating vs Datasets for the publisher Ordnance Survey Ireland\", \"paper_bgcolor\": \"rgb(248, 248, 255)\", \"plot_bgcolor\": \"rgb(248, 248, 255)\", \"annotations\": [{\"font\": {\"color\": \"rgb(128, 0, 128)\", \"family\": \"Arial\", \"size\": 12}, \"showarrow\": false, \"text\": \"Count: 0.0\", \"x\": -20000.0, \"xref\": \"x2\", \"y\": \"1-star\", \"yref\": \"y2\"}, {\"font\": {\"color\": \"rgb(50, 171, 96)\", \"family\": \"Arial\", \"size\": 12}, \"showarrow\": false, \"text\": \"0.0%\", \"x\": 3.0, \"xref\": \"x\", \"y\": \"1-star\", \"yref\": \"y\"}, {\"font\": {\"color\": \"rgb(128, 0, 128)\", \"family\": \"Arial\", \"size\": 12}, \"showarrow\": false, \"text\": \"Count: 0.0\", \"x\": -20000.0, \"xref\": \"x2\", \"y\": \"2-star\", \"yref\": \"y2\"}, {\"font\": {\"color\": \"rgb(50, 171, 96)\", \"family\": \"Arial\", \"size\": 12}, \"showarrow\": false, \"text\": \"0.0%\", \"x\": 3.0, \"xref\": \"x\", \"y\": \"2-star\", \"yref\": \"y\"}, {\"font\": {\"color\": \"rgb(128, 0, 128)\", \"family\": \"Arial\", \"size\": 12}, \"showarrow\": false, \"text\": \"Count: 154.0\", \"x\": -19846.0, \"xref\": \"x2\", \"y\": \"3-star\", \"yref\": \"y2\"}, {\"font\": {\"color\": \"rgb(50, 171, 96)\", \"family\": \"Arial\", \"size\": 12}, \"showarrow\": false, \"text\": \"0.99%\", \"x\": 3.99, \"xref\": \"x\", \"y\": \"3-star\", \"yref\": \"y\"}, {\"font\": {\"color\": \"rgb(128, 0, 128)\", \"family\": \"Arial\", \"size\": 12}, \"showarrow\": false, \"text\": \"Count: 0.0\", \"x\": -20000.0, \"xref\": \"x2\", \"y\": \"4-star\", \"yref\": \"y2\"}, {\"font\": {\"color\": \"rgb(50, 171, 96)\", \"family\": \"Arial\", \"size\": 12}, \"showarrow\": false, \"text\": \"0.0%\", \"x\": 3.0, \"xref\": \"x\", \"y\": \"4-star\", \"yref\": \"y\"}, {\"font\": {\"color\": \"rgb(128, 0, 128)\", \"family\": \"Arial\", \"size\": 12}, \"showarrow\": false, \"text\": \"Count: 1.0\", \"x\": -19999.0, \"xref\": \"x2\", \"y\": \"5-star\", \"yref\": \"y2\"}, {\"font\": {\"color\": \"rgb(50, 171, 96)\", \"family\": \"Arial\", \"size\": 12}, \"showarrow\": false, \"text\": \"0.01%\", \"x\": 3.01, \"xref\": \"x\", \"y\": \"5-star\", \"yref\": \"y\"}, {\"font\": {\"color\": \"rgb(150,150,150)\", \"family\": \"Arial\", \"size\": 10}, \"showarrow\": false, \"text\": \"Star Rating Analysis of Ordnance Survey Ireland\", \"x\": -0.2, \"xref\": \"paper\", \"y\": -0.109, \"yref\": \"paper\"}]}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){window._Plotly.Plots.resize(document.getElementById(\"9abed13d-334b-443b-9bde-96bb41866bcf\"));});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\" Please note, ploty dosent show image in colab hence using static image. \n",
    "However, you can also use ploty object in imshow() in your local machine\"\"\"\n",
    "\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "from plotly import tools\n",
    "import numpy as np\n",
    "from plotly.offline import init_notebook_mode, iplot\n",
    "import plotly.offline as po\n",
    "\n",
    "init_notebook_mode(connected=True)\n",
    "\n",
    "y_saving = list(np.asarray(list(star_cnt.values()))/sum(star_cnt.values()))\n",
    "y_net_worth = list(star_cnt.values())\n",
    "                \n",
    "x_saving = ['1-star', '2-star', '3-star', '4-star', '5-star']\n",
    "x_net_worth = ['1-star', '2-star', '3-star', '4-star', '5-star']\n",
    "\n",
    "trace0 = go.Bar(\n",
    "    x=y_saving,\n",
    "    y=x_saving,\n",
    "    marker=dict(\n",
    "        color='rgba(50, 171, 96, 0.6)',\n",
    "        line=dict(\n",
    "            color='rgba(50, 171, 96, 1.0)',\n",
    "            width=1),\n",
    "    ),\n",
    "    name='Percentage of packages/datasets in a given star rating',\n",
    "    orientation='h',\n",
    ")\n",
    "trace1 = go.Scatter(\n",
    "    x=y_net_worth,\n",
    "    y=x_net_worth,\n",
    "    mode='lines+markers',\n",
    "    line=dict(\n",
    "        color='rgb(128, 0, 128)'),\n",
    "    name='Number of resources in a given star rating',\n",
    ")\n",
    "layout = dict(\n",
    "    title='Analysis of Star Rating vs Datasets for the publisher Ordnance Survey Ireland',\n",
    "    yaxis=dict(\n",
    "        showgrid=False,\n",
    "        showline=False,\n",
    "        showticklabels=True,\n",
    "        domain=[0, 0.85],\n",
    "    ),\n",
    "    yaxis2=dict(\n",
    "        showgrid=False,\n",
    "        showline=True,\n",
    "        showticklabels=False,\n",
    "        linecolor='rgba(102, 102, 102, 0.8)',\n",
    "        linewidth=2,\n",
    "        domain=[0, 0.85],\n",
    "    ),\n",
    "    xaxis=dict(\n",
    "        zeroline=False,\n",
    "        showline=False,\n",
    "        showticklabels=True,\n",
    "        showgrid=True,\n",
    "        domain=[0, 0.42],\n",
    "    ),\n",
    "    xaxis2=dict(\n",
    "        zeroline=False,\n",
    "        showline=False,\n",
    "        showticklabels=True,\n",
    "        showgrid=True,\n",
    "        domain=[0.47, 1],\n",
    "        side='top',\n",
    "        dtick=25000,\n",
    "    ),\n",
    "    legend=dict(\n",
    "        x=0.029,\n",
    "        y=1.038,\n",
    "        font=dict(\n",
    "            size=10,\n",
    "        ),\n",
    "    ),\n",
    "    margin=dict(\n",
    "        l=100,\n",
    "        r=20,\n",
    "        t=70,\n",
    "        b=70,\n",
    "    ),\n",
    "    paper_bgcolor='rgb(248, 248, 255)',\n",
    "    plot_bgcolor='rgb(248, 248, 255)',\n",
    ")\n",
    "\n",
    "annotations = []\n",
    "\n",
    "y_s = np.round(y_saving, decimals=2)\n",
    "y_nw = np.rint(y_net_worth)\n",
    "\n",
    "# Adding labels\n",
    "for ydn, yd, xd in zip(y_nw, y_s, x_saving):\n",
    "    # labeling the scatter savings\n",
    "    annotations.append(dict(xref='x2', yref='y2',\n",
    "                            y=xd, x=ydn - 20000,\n",
    "                            text='Count: '+'{:,}'.format(ydn),\n",
    "                            font=dict(family='Arial', size=12,\n",
    "                                      color='rgb(128, 0, 128)'),\n",
    "                            showarrow=False))\n",
    "    # labeling the bar net worth\n",
    "    annotations.append(dict(xref='x1', yref='y1',\n",
    "                            y=xd, x=yd + 3,\n",
    "                            text=str(yd) + '%',\n",
    "                            font=dict(family='Arial', size=12,\n",
    "                                      color='rgb(50, 171, 96)'),\n",
    "                            showarrow=False))\n",
    "# Source\n",
    "annotations.append(dict(xref='paper', yref='paper',\n",
    "                        x=-0.2, y=-0.109,\n",
    "                        text='Star Rating Analysis of Ordnance Survey Ireland',\n",
    "                        font=dict(family='Arial', size=10,\n",
    "                                  color='rgb(150,150,150)'),\n",
    "                        showarrow=False))\n",
    "\n",
    "layout['annotations'] = annotations\n",
    "\n",
    "# Creating two subplots\n",
    "fig = tools.make_subplots(rows=1, cols=2, specs=[[{}, {}]], shared_xaxes=True,\n",
    "                          shared_yaxes=False, vertical_spacing=0.001)\n",
    "\n",
    "fig.append_trace(trace0, 1, 1)\n",
    "fig.append_trace(trace1, 1, 2)\n",
    "\n",
    "fig['layout'].update(layout)\n",
    "#iplot(fig, filename='oecd-networth-saving-bar-line')\n",
    "Image(url= \"https://github.com/derilinx/Developer_Corner_DGI/blob/master/star_rating.png?raw=true\", width=900, height=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Viz 2 - Available data formats in Ordnance Survey Ireland:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": true
       },
       "data": [
        {
         "fill": "toself",
         "name": "Datasets formats and its counts",
         "r": [
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          0.16630669546436286,
          0.16630669546436286,
          0.0010799136069114472,
          0.0010799136069114472
         ],
         "theta": [
          "HTML",
          "Esri REST",
          "GeoJSON",
          "CSV",
          "KML",
          "ZIP",
          "ttl",
          "rdf-xml"
         ],
         "type": "scatterpolar",
         "uid": "0c7a175b-af7a-4a8f-8602-a607f5f1e763"
        }
       ],
       "layout": {
        "polar": {
         "radialaxis": {
          "range": [
           0,
           0.5
          ],
          "visible": true
         }
        },
        "showlegend": true,
        "title": "Available Data Formats (%) in Ordance Survey Ireland"
       }
      },
      "text/html": [
       "<div id=\"f642c7a1-60ec-401a-95fe-c02da51aa2bb\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"f642c7a1-60ec-401a-95fe-c02da51aa2bb\", [{\"fill\": \"toself\", \"name\": \"Datasets formats and its counts\", \"r\": [0.16630669546436286, 0.16630669546436286, 0.16630669546436286, 0.16630669546436286, 0.16630669546436286, 0.16630669546436286, 0.0010799136069114472, 0.0010799136069114472], \"theta\": [\"HTML\", \"Esri REST\", \"GeoJSON\", \"CSV\", \"KML\", \"ZIP\", \"ttl\", \"rdf-xml\"], \"type\": \"scatterpolar\", \"uid\": \"0c7a175b-af7a-4a8f-8602-a607f5f1e763\"}], {\"polar\": {\"radialaxis\": {\"range\": [0, 0.5], \"visible\": true}}, \"showlegend\": true, \"title\": \"Available Data Formats (%) in Ordance Survey Ireland\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){window._Plotly.Plots.resize(document.getElementById(\"f642c7a1-60ec-401a-95fe-c02da51aa2bb\"));});</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<div id=\"f642c7a1-60ec-401a-95fe-c02da51aa2bb\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"f642c7a1-60ec-401a-95fe-c02da51aa2bb\", [{\"fill\": \"toself\", \"name\": \"Datasets formats and its counts\", \"r\": [0.16630669546436286, 0.16630669546436286, 0.16630669546436286, 0.16630669546436286, 0.16630669546436286, 0.16630669546436286, 0.0010799136069114472, 0.0010799136069114472], \"theta\": [\"HTML\", \"Esri REST\", \"GeoJSON\", \"CSV\", \"KML\", \"ZIP\", \"ttl\", \"rdf-xml\"], \"type\": \"scatterpolar\", \"uid\": \"0c7a175b-af7a-4a8f-8602-a607f5f1e763\"}], {\"polar\": {\"radialaxis\": {\"range\": [0, 0.5], \"visible\": true}}, \"showlegend\": true, \"title\": \"Available Data Formats (%) in Ordance Survey Ireland\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){window._Plotly.Plots.resize(document.getElementById(\"f642c7a1-60ec-401a-95fe-c02da51aa2bb\"));});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\" Please note, ploty dosent show image in colab hence using static image. \n",
    "However, you can also use ploty object in imshow() in your local machine\"\"\"\n",
    "\n",
    "frmt_df = pd.DataFrame({'Dataset Format': list(frmts_cnt.keys()), 'Count': list(frmts_cnt.values())})\n",
    "frmt_df_cpy = frmt_df.copy()\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "data = [\n",
    "    go.Scatterpolar(\n",
    "      r = list(np.asarray(list(frmts_cnt.values()))/sum(list(frmts_cnt.values()))),\n",
    "      theta = list(frmts_cnt.keys()),\n",
    "      fill = 'toself',\n",
    "      name = 'Datasets formats and its counts'\n",
    "    )\n",
    "]\n",
    "\n",
    "layout = go.Layout(\n",
    "    title = \"Available Data Formats (%) in Ordnance Survey Ireland\",\n",
    "  polar = dict(\n",
    "    radialaxis = dict(\n",
    "      visible = True,\n",
    "      range = [0, 0.5]\n",
    "    )\n",
    "  ),\n",
    "  showlegend = True\n",
    ")\n",
    "\n",
    "fig1 = go.Figure(data=data, layout=layout)\n",
    "#iplot(fig1, filename = 'radar', image='png')\n",
    "#plotly.offline.plot(fig1, filename='radar', image='png')\n",
    "Image(url= \"https://github.com/derilinx/Developer_Corner_DGI/blob/master/radar_format.png?raw=true\", width=900, height=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Vis 3 - Percentage of data fromats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": true
       },
       "data": [
        {
         "hole": 0.4,
         "hoverinfo": "label+percent+name",
         "labels": [
          "CSV",
          "Esri REST",
          "GeoJSON",
          "HTML",
          "KML",
          "ZIP",
          "rdf-xml",
          "ttl"
         ],
         "name": "% of count",
         "type": "pie",
         "uid": "572bdebb-f04a-4bef-9780-9fb5ab651282",
         "values": [
          0.16630669546436286,
          0.16630669546436286,
          0.16630669546436286,
          0.16630669546436286,
          0.16630669546436286,
          0.16630669546436286,
          0.0010799136069114472,
          0.0010799136069114472
         ]
        }
       ],
       "layout": {
        "annotations": [
         {
          "font": {
           "size": 11
          },
          "showarrow": false,
          "text": "Formats",
          "x": 0.16,
          "y": 0.5
         }
        ],
        "title": "Distribution of datsets formats"
       }
      },
      "text/html": [
       "<div id=\"d25c9b11-bdb3-4b76-b7ed-a29aafef4265\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"d25c9b11-bdb3-4b76-b7ed-a29aafef4265\", [{\"hole\": 0.4, \"hoverinfo\": \"label+percent+name\", \"labels\": [\"CSV\", \"Esri REST\", \"GeoJSON\", \"HTML\", \"KML\", \"ZIP\", \"rdf-xml\", \"ttl\"], \"name\": \"% of count\", \"values\": [0.16630669546436286, 0.16630669546436286, 0.16630669546436286, 0.16630669546436286, 0.16630669546436286, 0.16630669546436286, 0.0010799136069114472, 0.0010799136069114472], \"type\": \"pie\", \"uid\": \"7a0452e2-b0c8-40a4-9d5e-f42327cf8185\"}], {\"annotations\": [{\"font\": {\"size\": 11}, \"showarrow\": false, \"text\": \"Formats\", \"x\": 0.16, \"y\": 0.5}], \"title\": \"Distribution of datsets formats\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){window._Plotly.Plots.resize(document.getElementById(\"d25c9b11-bdb3-4b76-b7ed-a29aafef4265\"));});</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<div id=\"d25c9b11-bdb3-4b76-b7ed-a29aafef4265\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"d25c9b11-bdb3-4b76-b7ed-a29aafef4265\", [{\"hole\": 0.4, \"hoverinfo\": \"label+percent+name\", \"labels\": [\"CSV\", \"Esri REST\", \"GeoJSON\", \"HTML\", \"KML\", \"ZIP\", \"rdf-xml\", \"ttl\"], \"name\": \"% of count\", \"values\": [0.16630669546436286, 0.16630669546436286, 0.16630669546436286, 0.16630669546436286, 0.16630669546436286, 0.16630669546436286, 0.0010799136069114472, 0.0010799136069114472], \"type\": \"pie\", \"uid\": \"7a0452e2-b0c8-40a4-9d5e-f42327cf8185\"}], {\"annotations\": [{\"font\": {\"size\": 11}, \"showarrow\": false, \"text\": \"Formats\", \"x\": 0.16, \"y\": 0.5}], \"title\": \"Distribution of datsets formats\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){window._Plotly.Plots.resize(document.getElementById(\"d25c9b11-bdb3-4b76-b7ed-a29aafef4265\"));});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\" Please note, ploty dosent show image in colab hence using static image. \n",
    "However, you can also use ploty object in imshow() in your local machine\"\"\"\n",
    "\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "fig2 = {\n",
    "  \"data\": [\n",
    "    {\n",
    "      \"values\": list(np.asarray(list(dict(frmt_df_cpy.groupby('Dataset Format')['Count'].agg('sum')).values()))/sum(list(dict(frmt_df_cpy.groupby('Dataset Format')['Count'].agg('sum')).values()))),\n",
    "      \"labels\": list(dict(frmt_df_cpy.groupby('Dataset Format')['Count'].agg('sum')).keys()),\n",
    "      \"name\": '% of count',\n",
    "      \"hoverinfo\":\"label+percent+name\",\n",
    "      \"hole\": .4,\n",
    "      \"type\": \"pie\"\n",
    "    }],\n",
    "  \"layout\": {\n",
    "        \"title\":\"Distribution of datsets formats\",\n",
    "        \"annotations\": [\n",
    "            {\n",
    "                \"font\": {\n",
    "                    \"size\": 11\n",
    "                },\n",
    "                \"showarrow\": False,\n",
    "                \"text\": \"Formats\",\n",
    "                \"x\": 0.16,\n",
    "                \"y\": 0.5\n",
    "            }\n",
    "        ]\n",
    "    }\n",
    "}\n",
    "\n",
    "#iplot(fig2, filename='donut', image='png')\n",
    "Image(url= \"https://github.com/derilinx/Developer_Corner_DGI/blob/master/pie_format.png?raw=true\", width=900, height=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Viz 4 (word cloud) - Most frequent Tags in Ordance Survey Ireland:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "wc = WordCloud(background_color=\"white\", max_words=200, relative_scaling=0.5, normalize_plurals=False).generate_from_frequencies(tags_cnt)\n",
    "\n",
    "plt.figure()\n",
    "plt.title(\"Tags in Organization Survey of Ireland\")\n",
    "plt.imshow(wc, interpolation=\"bilinear\")\n",
    "plt.axis(\"off\")\n",
    "plt.savefig('tag_wordcloud', dpi=500)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Viz 4: Top 40 tags in Ordnance Survey Ireland"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": true
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        {
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         "type": "bar",
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         "x": [
          "boundaries",
          "open data",
          "osi",
          "dgigovernment",
          "National",
          "OpenData",
          "generalised",
          "Administrative",
          "OSi National Administrative Boundaries",
          "OSi National 250k Map of Ireland",
          "cso",
          "OSi National Statistical Boundaries",
          "Statistical",
          "built environment",
          "environment",
          "Admin",
          "20m",
          "administrative",
          "ungeneralised",
          "50m",
          "100m",
          "transportation",
          "water",
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          "lea",
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          "Statutory",
          "placenames",
          "Gaeltacht",
          "Náisiúnta",
          "Riaracháin",
          "Teorainneacha Riaracháin Náisiúnta OSi",
          "sonraí oscailte",
          "teorainneacha",
          "NUTS",
          "provinces",
          "areas",
          "nuts",
          "nuts2"
         ],
         "y": [
          94,
          76,
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          74,
          66,
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          20,
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          8
         ]
        }
       ],
       "layout": {
        "title": "Top 40 tags in organization - Ordance Survey Ireland",
        "xaxis": {
         "tickangle": -45,
         "title": "Tag Words",
         "titlefont": {
          "color": "#7f7f7f",
          "family": "Courier New, monospace",
          "size": 18
         }
        },
        "yaxis": {
         "title": "Tag Count",
         "titlefont": {
          "color": "#7f7f7f",
          "family": "Courier New, monospace",
          "size": 18
         }
        }
       }
      },
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       "<div id=\"eac1a630-d5b1-43c0-973a-6459a2bfde65\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"eac1a630-d5b1-43c0-973a-6459a2bfde65\", [{\"name\": \"Count: \", \"x\": [\"boundaries\", \"open data\", \"osi\", \"dgigovernment\", \"National\", \"OpenData\", \"generalised\", \"Administrative\", \"OSi National Administrative Boundaries\", \"OSi National 250k Map of Ireland\", \"cso\", \"OSi National Statistical Boundaries\", \"Statistical\", \"built environment\", \"environment\", \"Admin\", \"20m\", \"administrative\", \"ungeneralised\", \"50m\", \"100m\", \"transportation\", \"water\", \"electoral\", \"OSi National 1m Map Of Ireland\", \"lea\", \"OSi National Statutory Boundaries\", \"Statutory\", \"placenames\", \"Gaeltacht\", \"N\\u00e1isi\\u00fanta\", \"Riarach\\u00e1in\", \"Teorainneacha Riarach\\u00e1in N\\u00e1isi\\u00fanta OSi\", \"sonra\\u00ed oscailte\", \"teorainneacha\", \"NUTS\", \"provinces\", \"areas\", \"nuts\", \"nuts2\"], \"y\": [94, 76, 76, 74, 66, 48, 36, 33, 32, 32, 26, 20, 20, 20, 20, 17, 17, 16, 16, 15, 12, 12, 12, 11, 9, 9, 9, 9, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], \"type\": \"bar\", \"uid\": \"af2f9733-3df8-4ffe-b12a-2c1bd5539f24\"}], {\"title\": \"Top 40 tags in organization - Ordance Survey Ireland\", \"xaxis\": {\"tickangle\": -45, \"title\": \"Tag Words\", \"titlefont\": {\"color\": \"#7f7f7f\", \"family\": \"Courier New, monospace\", \"size\": 18}}, \"yaxis\": {\"title\": \"Tag Count\", \"titlefont\": {\"color\": \"#7f7f7f\", \"family\": \"Courier New, monospace\", \"size\": 18}}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){window._Plotly.Plots.resize(document.getElementById(\"eac1a630-d5b1-43c0-973a-6459a2bfde65\"));});</script>"
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       "<div id=\"eac1a630-d5b1-43c0-973a-6459a2bfde65\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"eac1a630-d5b1-43c0-973a-6459a2bfde65\", [{\"name\": \"Count: \", \"x\": [\"boundaries\", \"open data\", \"osi\", \"dgigovernment\", \"National\", \"OpenData\", \"generalised\", \"Administrative\", \"OSi National Administrative Boundaries\", \"OSi National 250k Map of Ireland\", \"cso\", \"OSi National Statistical Boundaries\", \"Statistical\", \"built environment\", \"environment\", \"Admin\", \"20m\", \"administrative\", \"ungeneralised\", \"50m\", \"100m\", \"transportation\", \"water\", \"electoral\", \"OSi National 1m Map Of Ireland\", \"lea\", \"OSi National Statutory Boundaries\", \"Statutory\", \"placenames\", \"Gaeltacht\", \"N\\u00e1isi\\u00fanta\", \"Riarach\\u00e1in\", \"Teorainneacha Riarach\\u00e1in N\\u00e1isi\\u00fanta OSi\", \"sonra\\u00ed oscailte\", \"teorainneacha\", \"NUTS\", \"provinces\", \"areas\", \"nuts\", \"nuts2\"], \"y\": [94, 76, 76, 74, 66, 48, 36, 33, 32, 32, 26, 20, 20, 20, 20, 17, 17, 16, 16, 15, 12, 12, 12, 11, 9, 9, 9, 9, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], \"type\": \"bar\", \"uid\": \"af2f9733-3df8-4ffe-b12a-2c1bd5539f24\"}], {\"title\": \"Top 40 tags in organization - Ordance Survey Ireland\", \"xaxis\": {\"tickangle\": -45, \"title\": \"Tag Words\", \"titlefont\": {\"color\": \"#7f7f7f\", \"family\": \"Courier New, monospace\", \"size\": 18}}, \"yaxis\": {\"title\": \"Tag Count\", \"titlefont\": {\"color\": \"#7f7f7f\", \"family\": \"Courier New, monospace\", \"size\": 18}}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"})});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){window._Plotly.Plots.resize(document.getElementById(\"eac1a630-d5b1-43c0-973a-6459a2bfde65\"));});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<script>function downloadimage(format, height, width, filename) {var p = document.getElementById('eac1a630-d5b1-43c0-973a-6459a2bfde65');Plotly.downloadImage(p, {format: format, height: height, width: width, filename: filename});};if(document.readyState == 'complete') {{downloadimage('png', 600, 800, 'horizontal-bar');}}</script>"
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     "metadata": {},
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   ],
   "source": [
    "\"\"\" Please note, ploty dosent show image in colab hence using static image. \n",
    "However, you can also use ploty object in imshow() in your local machine\"\"\"\n",
    "\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "tags_cnt_ordr = dict(sorted(tags_cnt.items(), key=lambda x: x[1], reverse=True))\n",
    "\n",
    "data = [go.Bar(\n",
    "            x=list(tags_cnt_ordr.keys())[:40],\n",
    "            y=list(tags_cnt_ordr.values())[:40],\n",
    "            name = \"Count: \"\n",
    "    )]\n",
    "\n",
    "\n",
    "layout = go.Layout(\n",
    "    title='Top 40 tags in organization - Ordance Survey Ireland',\n",
    "    xaxis=dict(\n",
    "        title='Tag Words',\n",
    "        tickangle=-45,\n",
    "        titlefont=dict(\n",
    "            family='Courier New, monospace',\n",
    "            size=18,\n",
    "            color='#7f7f7f'\n",
    "        )\n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        title='Tag Count',\n",
    "        titlefont=dict(\n",
    "            family='Courier New, monospace',\n",
    "            size=18,\n",
    "            color='#7f7f7f'\n",
    "        )\n",
    "    )\n",
    ")\n",
    "\n",
    "fig3 = go.Figure(data=data, layout=layout)\n",
    "Image(url= \"https://github.com/derilinx/Developer_Corner_DGI/blob/master/tag_bar.png?raw=true\", width=900, height=500)\n",
    "#iplot(fig3, filename='horizontal-bar', image='png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Publisher dashboard: Ordnance Survey Ireland"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1512 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\" Please note, ploty dosent show image in colab hence using static image. \n",
    "However, you can also use ploty object in imshow() in your local machine\"\"\"\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pylab as pl\n",
    "import matplotlib.gridspec as gridspec\n",
    "from matplotlib import rcParams\n",
    "rcParams['figure.figsize'] = 16 ,21\n",
    "\n",
    "# Create 2x2 sub plots\n",
    "gs = gridspec.GridSpec(3, 2)\n",
    "gs.update(wspace=0, hspace=0)\n",
    "\n",
    "pl.figure()\n",
    "ax = pl.subplot(gs[0, :])\n",
    "ax.axis('off')\n",
    "pl.imshow(mpimg.imread(\"https://github.com/derilinx/Developer_Corner_DGI/blob/master/star_rating.png?raw=true\"))\n",
    "\n",
    "ax = pl.subplot(gs[1, 0]) \n",
    "ax.axis('off')\n",
    "pl.imshow(mpimg.imread(\"https://github.com/derilinx/Developer_Corner_DGI/blob/master/pie_format.png?raw=true\"))\n",
    "\n",
    "ax = pl.subplot(gs[1, 1])\n",
    "ax.axis('off')\n",
    "pl.imshow(mpimg.imread(\"https://github.com/derilinx/Developer_Corner_DGI/blob/master/tag_bar.png?raw=true\"))\n",
    "\n",
    "ax = pl.subplot(gs[2, 0])\n",
    "ax.axis('off')\n",
    "pl.imshow(mpimg.imread(\"https://github.com/derilinx/Developer_Corner_DGI/blob/master/tag_wordcloud.png?raw=true\"))\n",
    "\n",
    "ax = pl.subplot(gs[2, 1])\n",
    "ax.axis('off')\n",
    "pl.imshow(mpimg.imread(\"https://github.com/derilinx/Developer_Corner_DGI/blob/master/radar_format.png?raw=true\"))\n",
    "pl.suptitle(\"DGI - Ordance Survey Ireland Dashboard\", fontsize=14, fontweight=1)\n",
    "pl.subplots_adjust(top=0.95)\n",
    "pl.savefig('dashboard_dgi', dpi=500)"
   ]
  },
  {
   "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}