Facets - Data Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <link rel=\"import\" href=\"/nbextensions/facets-dist/facets-jupyter.html\">\n",
       "        <facets-dive id=\"elem_id\" height=\"600\"></facets-dive>\n",
       "        <script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.datasets import load_boston\n",
    "boston_data = load_boston()\n",
    "\n",
    "df = pd.DataFrame(boston_data['data'], columns=boston_data['feature_names'])\n",
    "jsonstr = df.to_json(orient='records')\n",
    "\n",
    "HTML_TEMPLATE = \\\n",
    "    \"\"\"\n",
    "        <link rel=\"import\" href=\"/nbextensions/facets-dist/facets-jupyter.html\">\n",
    "        <facets-dive id=\"elem_id\" height=\"600\"></facets-dive>\n",
    "        <script>\n",
    "    \"\"\"\n",
    "    \n",
    "# Load the json dataset and the sprite_size into the template\n",
    "html = HTML_TEMPLATE.format(jsonstr=jsonstr)\n",
    "\n",
    "# Display the template\n",
    "display(HTML(html))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "IncompleteRead",
     "evalue": "IncompleteRead(1605632 bytes read, 397521 more expected)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIncompleteRead\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-c53ed7f41515>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     11\u001b[0m     \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'python'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m     \u001b[0mskiprows\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;31m# skip first row without data\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m     na_values=\"?\") # add ? where data is missing\n\u001b[0m\u001b[0;32m     14\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[1;31m# set the sprite_size based on the number of records in dataset,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\product\\python36\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    707\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[0;32m    708\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 709\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    710\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    711\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\product\\python36\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    431\u001b[0m     \u001b[0mcompression\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_infer_compression\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompression\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    432\u001b[0m     filepath_or_buffer, _, compression = get_filepath_or_buffer(\n\u001b[1;32m--> 433\u001b[1;33m         filepath_or_buffer, encoding, compression)\n\u001b[0m\u001b[0;32m    434\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'compression'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompression\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    435\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\product\\python36\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36mget_filepath_or_buffer\u001b[1;34m(filepath_or_buffer, encoding, compression)\u001b[0m\n\u001b[0;32m    193\u001b[0m             \u001b[1;31m# Override compression based on Content-Encoding header\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    194\u001b[0m             \u001b[0mcompression\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'gzip'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 195\u001b[1;33m         \u001b[0mreader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBytesIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    196\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mreader\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompression\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    197\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\product\\python36\\lib\\http\\client.py\u001b[0m in \u001b[0;36mread\u001b[1;34m(self, amt)\u001b[0m\n\u001b[0;32m    460\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    461\u001b[0m                 \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 462\u001b[1;33m                     \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_safe_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlength\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    463\u001b[0m                 \u001b[1;32mexcept\u001b[0m \u001b[0mIncompleteRead\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    464\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_close_conn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32md:\\product\\python36\\lib\\http\\client.py\u001b[0m in \u001b[0;36m_safe_read\u001b[1;34m(self, amt)\u001b[0m\n\u001b[0;32m    612\u001b[0m             \u001b[0mchunk\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mamt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mMAXAMOUNT\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    613\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mchunk\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 614\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mIncompleteRead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mb''\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mamt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    615\u001b[0m             \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchunk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    616\u001b[0m             \u001b[0mamt\u001b[0m \u001b[1;33m-=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchunk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIncompleteRead\u001b[0m: IncompleteRead(1605632 bytes read, 397521 more expected)"
     ]
    }
   ],
   "source": [
    "# Load UCI census and convert to json for sending to the visualization\n",
    "import pandas as pd\n",
    "features = [\"Age\", \"Workclass\", \"fnlwgt\", \"Education\", \"Education-Num\", \"Marital Status\",\n",
    "            \"Occupation\", \"Relationship\", \"Race\", \"Sex\", \"Capital Gain\", \"Capital Loss\",\n",
    "            \"Hours per week\", \"Country\", \"Target\"]\n",
    "\n",
    "# Load dataframe from external CSV and add header information\n",
    "df = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test\",\n",
    "    names=features, # name features for header row\n",
    "    sep=r'\\s*,\\s*', # separator used in this dataset\n",
    "    engine='python',\n",
    "    skiprows=[0], # skip first row without data \n",
    "    na_values=\"?\") # add ? where data is missing\n",
    "\n",
    "# set the sprite_size based on the number of records in dataset,\n",
    "# larger datasets can crash the browser if the size is too large (>50000)\n",
    "sprite_size = 32 if len(df.index)>50000 else 64\n",
    "\n",
    "jsonstr = df.to_json(orient='records')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<link rel=\"import\" href=\"/nbextensions/facets-dist/facets-jupyter.html\">\n",
       "        <facets-dive sprite-image-width=\"32\" sprite-image-height=\"32\" id=\"elem\" height=\"600\"></facets-dive>\n",
       "        <script>\n",
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       "        </script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the Dive visualization for this data\n",
    "from IPython.core.display import display, HTML\n",
    "\n",
    "# Create Facets template  \n",
    "HTML_TEMPLATE = \"\"\"<link rel=\"import\" href=\"/nbextensions/facets-dist/facets-jupyter.html\">\n",
    "        <facets-dive sprite-image-width=\"{sprite_size}\" sprite-image-height=\"{sprite_size}\" id=\"elem\" height=\"600\"></facets-dive>\n",
    "        <script>\n",
    "          document.querySelector(\"#elem\").data = {jsonstr};\n",
    "        </script>\"\"\"\n",
    "\n",
    "# Load the json dataset and the sprite_size into the template\n",
    "html = HTML_TEMPLATE.format(jsonstr=jsonstr, sprite_size=32)\n",
    "\n",
    "# Display the template\n",
    "display(HTML(html))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.6.5"
  }
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 "nbformat": 4,
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