DataJam Justin Viz v1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'ellipsis' object is not iterable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-869d034d20fc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# #import altair as alt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0maltair\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0malt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mvega_datasets\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.5/site-packages/altair/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0m__version__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'2.1.0'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mvegalite\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mload_ipython_extension\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mipython\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.5/site-packages/altair/vegalite/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# flake8: noqa\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mv2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda/lib/python3.5/site-packages/altair/vegalite/v2/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# flake8: noqa\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mschema\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mapi\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mexamples\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.5/site-packages/altair/vegalite/v2/api.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdata_transformers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpipe\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m...\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrenderers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mVEGALITE_VERSION\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mVEGAEMBED_VERSION\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mVEGA_VERSION\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mtheme\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mthemes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.5/site-packages/altair/vegalite/v2/display.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m...\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmimebundle\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mspec_to_mimebundle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDisplayable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdefault_renderer_base\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mjson_renderer_base\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.5/site-packages/altair/vegalite/display.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDisplayable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdefault_renderer_base\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjson_renderer_base\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mRendererRegistry\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m __all__ = (\n",
      "\u001b[0;32m~/anaconda/lib/python3.5/site-packages/altair/utils/display.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0;32mclass\u001b[0m \u001b[0mRendererRegistry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPluginRegistry\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mRendererType\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m     entrypoint_err_messages = {\n\u001b[1;32m     20\u001b[0m         'notebook': textwrap.dedent(\n",
      "\u001b[0;32m~/anaconda/lib/python3.5/typing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, params)\u001b[0m\n\u001b[1;32m   1023\u001b[0m                     \u001b[0;34m\"Too %s parameters for %s; actual %s, expected %s\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1024\u001b[0m                     (\"many\" if alen > elen else \"few\", repr(self), alen, elen))\n\u001b[0;32m-> 1025\u001b[0;31m             \u001b[0mtvars\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_type_vars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1026\u001b[0m             \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1027\u001b[0m         return self.__class__(self.__name__,\n",
      "\u001b[0;32m~/anaconda/lib/python3.5/typing.py\u001b[0m in \u001b[0;36m_type_vars\u001b[0;34m(types)\u001b[0m\n\u001b[1;32m    282\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_type_vars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtypes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    283\u001b[0m     \u001b[0mtvars\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 284\u001b[0;31m     \u001b[0m_get_type_vars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtypes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtvars\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    285\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtvars\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.5/typing.py\u001b[0m in \u001b[0;36m_get_type_vars\u001b[0;34m(types, tvars)\u001b[0m\n\u001b[1;32m    277\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtypes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    278\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypingMeta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 279\u001b[0;31m             \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_type_vars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtvars\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    280\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    281\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.5/typing.py\u001b[0m in \u001b[0;36m_get_type_vars\u001b[0;34m(self, tvars)\u001b[0m\n\u001b[1;32m    784\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_type_vars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtvars\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    785\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__args__\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 786\u001b[0;31m             \u001b[0m_get_type_vars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__args__\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtvars\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    787\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    788\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_eval_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mglobalns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocalns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.5/typing.py\u001b[0m in \u001b[0;36m_get_type_vars\u001b[0;34m(types, tvars)\u001b[0m\n\u001b[1;32m    275\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    276\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_get_type_vars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtypes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtvars\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 277\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mt\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtypes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    278\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypingMeta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m             \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_type_vars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtvars\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'ellipsis' object is not iterable"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pandas import DataFrame\n",
    "# from altair import Chart, X, Y, Axis, SortField\n",
    "# #import altair as alt\n",
    "\n",
    "import altair as alt\n",
    "from vega_datasets import data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NotebookApp.iopub_data_rate_limit = 1000000000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_merged_onlySharedCols = pd.read_pickle('converted_files/pickles/'+'all8WaterQStationsButOnlySharedColumns.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 947020 entries, 0 to 148473\n",
      "Data columns (total 14 columns):\n",
      "agency           947020 non-null object\n",
      "site_no          947020 non-null object\n",
      "datetime         947020 non-null object\n",
      "tz_cd            947020 non-null object\n",
      "temp             947020 non-null object\n",
      "temp_q           947020 non-null object\n",
      "conductance      947020 non-null object\n",
      "conductance_q    947020 non-null object\n",
      "pH               947020 non-null object\n",
      "pH_q             947020 non-null object\n",
      "oxygen           947020 non-null object\n",
      "oxygen_q         947020 non-null object\n",
      "turbidity        947020 non-null object\n",
      "turbidity_q      947020 non-null object\n",
      "dtypes: object(14)\n",
      "memory usage: 108.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df_merged_onlySharedCols.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2014-02-06 00:15</td>\n",
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       "      <td>USGS</td>\n",
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       "      <td>2014-02-06 01:15</td>\n",
       "      <td>CST</td>\n",
       "      <td>10.5</td>\n",
       "      <td>A</td>\n",
       "      <td>649</td>\n",
       "      <td>A</td>\n",
       "      <td>7.6</td>\n",
       "      <td>A</td>\n",
       "      <td>10.4</td>\n",
       "      <td>A</td>\n",
       "      <td>9.5</td>\n",
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       "  agency   site_no          datetime tz_cd  temp temp_q conductance  \\\n",
       "0   USGS  08068000  2014-02-06 00:15   CST  10.7      A         650   \n",
       "1   USGS  08068000  2014-02-06 00:30   CST  10.7      A         648   \n",
       "2   USGS  08068000  2014-02-06 00:45   CST  10.7      A         648   \n",
       "3   USGS  08068000  2014-02-06 01:00   CST  10.6      A         648   \n",
       "4   USGS  08068000  2014-02-06 01:15   CST  10.5      A         649   \n",
       "\n",
       "  conductance_q   pH pH_q oxygen oxygen_q turbidity turbidity_q  \n",
       "0             A  7.6    A   10.5       A        8.8           A  \n",
       "1             A  7.6    A   10.5       A        9.3           A  \n",
       "2             A  7.6    A   10.4       A        8.6           A  \n",
       "3             A  7.6    A   10.4       A        9.1           A  \n",
       "4             A  7.6    A   10.4       A        9.5           A  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged_onlySharedCols.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_08068000 = df_merged_onlySharedCols[(df_merged_onlySharedCols['site_no'] == '08068000')]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>08068000</td>\n",
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       "      <td>CST</td>\n",
       "      <td>10.5</td>\n",
       "      <td>A</td>\n",
       "      <td>649</td>\n",
       "      <td>A</td>\n",
       "      <td>7.6</td>\n",
       "      <td>A</td>\n",
       "      <td>10.4</td>\n",
       "      <td>A</td>\n",
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      "text/plain": [
       "  agency   site_no          datetime tz_cd  temp temp_q conductance  \\\n",
       "0   USGS  08068000  2014-02-06 00:15   CST  10.7      A         650   \n",
       "1   USGS  08068000  2014-02-06 00:30   CST  10.7      A         648   \n",
       "2   USGS  08068000  2014-02-06 00:45   CST  10.7      A         648   \n",
       "3   USGS  08068000  2014-02-06 01:00   CST  10.6      A         648   \n",
       "4   USGS  08068000  2014-02-06 01:15   CST  10.5      A         649   \n",
       "\n",
       "  conductance_q   pH pH_q oxygen oxygen_q turbidity turbidity_q  \n",
       "0             A  7.6    A   10.5       A        8.8           A  \n",
       "1             A  7.6    A   10.5       A        9.3           A  \n",
       "2             A  7.6    A   10.4       A        8.6           A  \n",
       "3             A  7.6    A   10.4       A        9.1           A  \n",
       "4             A  7.6    A   10.4       A        9.5           A  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_08068000.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>203</th>\n",
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       "      <td>A</td>\n",
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       "      <th>204</th>\n",
       "      <td>USGS</td>\n",
       "      <td>08068000</td>\n",
       "      <td>2014-02-08 03:15</td>\n",
       "      <td>CST</td>\n",
       "      <td>9.7</td>\n",
       "      <td>A</td>\n",
       "      <td>765</td>\n",
       "      <td>A</td>\n",
       "      <td>7.5</td>\n",
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       "      <td>10.2</td>\n",
       "      <td>A</td>\n",
       "      <td>6.7</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    agency   site_no          datetime tz_cd temp temp_q conductance  \\\n",
       "200   USGS  08068000  2014-02-08 02:15   CST  9.7      A         763   \n",
       "201   USGS  08068000  2014-02-08 02:30   CST  9.7      A         763   \n",
       "202   USGS  08068000  2014-02-08 02:45   CST  9.7      A         764   \n",
       "203   USGS  08068000  2014-02-08 03:00   CST  9.7      A         764   \n",
       "204   USGS  08068000  2014-02-08 03:15   CST  9.7      A         765   \n",
       "\n",
       "    conductance_q   pH pH_q oxygen oxygen_q turbidity turbidity_q  \n",
       "200             A  7.5    A   10.3       A        6.9           A  \n",
       "201             A  7.5    A   10.2       A        6.3           A  \n",
       "202             A  7.5    A   10.2       A        6.4           A  \n",
       "203             A  7.5    A   10.2       A        7.3           A  \n",
       "204             A  7.5    A   10.2       A        6.7           A  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_08068000_subset = df_08068000[200:700]\n",
    "df_08068000_subset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'to_numeric'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-b811f965a3ce>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_08068000_subset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtemp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_08068000_subset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numeric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_08068000_subset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtemp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'coerce'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda/lib/python3.5/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   4370\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4371\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4372\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4373\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4374\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'to_numeric'"
     ]
    }
   ],
   "source": [
    "df_08068000_subset.temp = df_08068000_subset.to_numeric(df_08068000_subset.temp, errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "scatter requires x column to be numeric",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-878fa3f93769>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf_08068000_subset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'conductance'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'pH'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda/lib/python3.5/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(self, x, y, s, c, **kwds)\u001b[0m\n\u001b[1;32m   3459\u001b[0m             ...                       colormap='viridis')\n\u001b[1;32m   3460\u001b[0m         \"\"\"\n\u001b[0;32m-> 3461\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'scatter'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3462\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3463\u001b[0m     def hexbin(self, x, y, C=None, reduce_C_function=None, gridsize=None,\n",
      "\u001b[0;32m~/anaconda/lib/python3.5/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds)\u001b[0m\n\u001b[1;32m   2939\u001b[0m                           \u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfontsize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2940\u001b[0m                           \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2941\u001b[0;31m                           sort_columns=sort_columns, **kwds)\n\u001b[0m\u001b[1;32m   2942\u001b[0m     \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_frame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2943\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.5/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mplot_frame\u001b[0;34m(data, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds)\u001b[0m\n\u001b[1;32m   1975\u001b[0m                  \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1976\u001b[0m                  \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msort_columns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort_columns\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1977\u001b[0;31m                  **kwds)\n\u001b[0m\u001b[1;32m   1978\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1979\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.5/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[1;32m   1741\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1742\u001b[0m             plot_obj = klass(data, x=x, y=y, subplots=subplots, ax=ax,\n\u001b[0;32m-> 1743\u001b[0;31m                              kind=kind, **kwds)\n\u001b[0m\u001b[1;32m   1744\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1745\u001b[0m             raise ValueError(\"plot kind %r can only be used for data frames\"\n",
      "\u001b[0;32m~/anaconda/lib/python3.5/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, x, y, s, c, **kwargs)\u001b[0m\n\u001b[1;32m    843\u001b[0m             \u001b[0;31m# the handling of this argument later\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    844\u001b[0m             \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m20\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 845\u001b[0;31m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mScatterPlot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    846\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mholds_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    847\u001b[0m             \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/lib/python3.5/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, x, y, **kwargs)\u001b[0m\n\u001b[1;32m    818\u001b[0m             \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    819\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_numeric_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 820\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_kind\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' requires x column to be numeric'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    821\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_numeric_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    822\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_kind\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' requires y column to be numeric'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: scatter requires x column to be numeric"
     ]
    }
   ],
   "source": [
    "df_08068000_subset.plot.scatter('conductance', 'pH')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Chart' object has no attribute 'properties'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-56-fda096843a54>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0malt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'conductance'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mScale\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzero\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpadding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'temp_q'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'pH'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproperties\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0mwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m150\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'Chart' object has no attribute 'properties'"
     ]
    }
   ],
   "source": [
    "# import altair as alt\n",
    "\n",
    "alt.Chart(df_08068000_subset).mark_circle().encode(\n",
    "    alt.X('oxygen', scale=alt.Scale(zero=False)),\n",
    "    alt.Y('conductance', scale=alt.Scale(zero=False, padding=1)),\n",
    "    color='temp_q',\n",
    "    size='pH'\n",
    ").properties(\n",
    "    width=150,\n",
    "    height=150\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"vega-embed\" id=\"e1faceac-c634-4bab-87c3-1ed50fd550f1\"></div>\n",
       "\n",
       "<style>\n",
       ".vega-embed svg, .vega-embed canvas {\n",
       "  border: 1px dotted gray;\n",
       "}\n",
       "\n",
       ".vega-embed .vega-actions a {\n",
       "  margin-right: 6px;\n",
       "}\n",
       "</style>\n"
      ]
     },
     "metadata": {
      "jupyter-vega": "#e1faceac-c634-4bab-87c3-1ed50fd550f1"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
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       "var selector = \"#e1faceac-c634-4bab-87c3-1ed50fd550f1\";\n",
       "var type = \"vega-lite\";\n",
       "\n",
       "var output_area = this;\n",
       "require(['nbextensions/jupyter-vega/index'], function(vega) {\n",
       "  vega.render(selector, spec, type, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== 'scripterror') {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ]
     },
     "metadata": {
      "jupyter-vega": "#e1faceac-c634-4bab-87c3-1ed50fd550f1"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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"
     },
     "metadata": {
      "jupyter-vega": "#e1faceac-c634-4bab-87c3-1ed50fd550f1"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import altair as alt\n",
    "from vega_datasets import data\n",
    "\n",
    "iris = data.iris()\n",
    "\n",
    "alt.Chart(iris).mark_circle().encode(\n",
    "    alt.X('sepalLength', scale=alt.Scale(zero=False)),\n",
    "    alt.Y('sepalWidth', scale=alt.Scale(zero=False, padding=1)),\n",
    "    color='species',\n",
    "    size='petalWidth'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "alt.Chart(iris).mark_circle().encode(\n",
    "    alt.X('sepalLength', scale=alt.Scale(zero=False)),\n",
    "    alt.Y('sepalWidth', scale=alt.Scale(zero=False, padding=1)),\n",
    "    color='species',\n",
    "    size='petalWidth'\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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