takuti/pandas-td-sample.ipynb

## pandas-td-sample.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas_td as td"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "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>symbol</th>\n",
       "      <th>open</th>\n",
       "      <th>volume</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>VLGEA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2812</td>\n",
       "      <td>252691200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ZION</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24800</td>\n",
       "      <td>1.6250</td>\n",
       "      <td>1.5664</td>\n",
       "      <td>1.5664</td>\n",
       "      <td>252691200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WSCI</td>\n",
       "      <td>0.0</td>\n",
       "      <td>400</td>\n",
       "      <td>0.9063</td>\n",
       "      <td>0.7188</td>\n",
       "      <td>0.7188</td>\n",
       "      <td>252691200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>YRCW</td>\n",
       "      <td>0.0</td>\n",
       "      <td>233</td>\n",
       "      <td>4200.0000</td>\n",
       "      <td>4087.5000</td>\n",
       "      <td>4087.5000</td>\n",
       "      <td>252691200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AMIC</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300</td>\n",
       "      <td>4.1250</td>\n",
       "      <td>4.1250</td>\n",
       "      <td>4.1250</td>\n",
       "      <td>252691200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  symbol  open  volume       high        low      close       time\n",
       "0  VLGEA   0.0       0     0.0000     0.0000     0.2812  252691200\n",
       "1   ZION   0.0   24800     1.6250     1.5664     1.5664  252691200\n",
       "2   WSCI   0.0     400     0.9063     0.7188     0.7188  252691200\n",
       "3   YRCW   0.0     233  4200.0000  4087.5000  4087.5000  252691200\n",
       "4   AMIC   0.0     300     4.1250     4.1250     4.1250  252691200"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con = td.connect(apikey=os.environ['TD_API_KEY'], endpoint=os.environ['TD_API_SERVER'])\n",
    "presto = td.create_engine('presto:sample_datasets', con=con)\n",
    "df = td.read_td_table('nasdaq', presto)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10ab5dcc0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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U3VS2LmfsTrIVaVxc1lsaVG0Jj3YkGeHjQoOnqunUDaU1FK5mgrzkSvocV/UVlyxnZhvx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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ab2d828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "df['high'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"import os\n",
	"import pandas_td as td"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 2,
	"metadata": {
	"collapsed": false
	},
	"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>symbol</th>\n",
	" <th>open</th>\n",
	" <th>volume</th>\n",
	" <th>high</th>\n",
	" <th>low</th>\n",
	" <th>close</th>\n",
	" <th>time</th>\n",
	" </tr>\n",
	" </thead>\n",
	" <tbody>\n",
	" <tr>\n",
	" <th>0</th>\n",
	" <td>VLGEA</td>\n",
	" <td>0.0</td>\n",
	" <td>0</td>\n",
	" <td>0.0000</td>\n",
	" <td>0.0000</td>\n",
	" <td>0.2812</td>\n",
	" <td>252691200</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>1</th>\n",
	" <td>ZION</td>\n",
	" <td>0.0</td>\n",
	" <td>24800</td>\n",
	" <td>1.6250</td>\n",
	" <td>1.5664</td>\n",
	" <td>1.5664</td>\n",
	" <td>252691200</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>2</th>\n",
	" <td>WSCI</td>\n",
	" <td>0.0</td>\n",
	" <td>400</td>\n",
	" <td>0.9063</td>\n",
	" <td>0.7188</td>\n",
	" <td>0.7188</td>\n",
	" <td>252691200</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>3</th>\n",
	" <td>YRCW</td>\n",
	" <td>0.0</td>\n",
	" <td>233</td>\n",
	" <td>4200.0000</td>\n",
	" <td>4087.5000</td>\n",
	" <td>4087.5000</td>\n",
	" <td>252691200</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>4</th>\n",
	" <td>AMIC</td>\n",
	" <td>0.0</td>\n",
	" <td>300</td>\n",
	" <td>4.1250</td>\n",
	" <td>4.1250</td>\n",
	" <td>4.1250</td>\n",
	" <td>252691200</td>\n",
	" </tr>\n",
	" </tbody>\n",
	"</table>\n",
	"</div>"
	],
	"text/plain": [
	" symbol open volume high low close time\n",
	"0 VLGEA 0.0 0 0.0000 0.0000 0.2812 252691200\n",
	"1 ZION 0.0 24800 1.6250 1.5664 1.5664 252691200\n",
	"2 WSCI 0.0 400 0.9063 0.7188 0.7188 252691200\n",
	"3 YRCW 0.0 233 4200.0000 4087.5000 4087.5000 252691200\n",
	"4 AMIC 0.0 300 4.1250 4.1250 4.1250 252691200"
	]
	},
	"execution_count": 2,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"con = td.connect(apikey=os.environ['TD_API_KEY'], endpoint=os.environ['TD_API_SERVER'])\n",
	"presto = td.create_engine('presto:sample_datasets', con=con)\n",
	"df = td.read_td_table('nasdaq', presto)\n",
	"df.head()"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 3,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"<matplotlib.axes._subplots.AxesSubplot at 0x10ab5dcc0>"
	]
	},
	"execution_count": 3,
	"metadata": {},
	"output_type": "execute_result"
	},
	{
	"data": {
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U3VS2LmfsTrIVaVxc1lsaVG0Jj3YkGeHjQoOnqunUDaU1FK5mgrzkSvocV/UVlyxnZhvx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\nRU5ErkJggg==\n",
	"text/plain": [
	"<matplotlib.figure.Figure at 0x10ab2d828>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"%matplotlib inline\n",
	"df['high'].plot()"
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": []
	}
	],
	"metadata": {
	"anaconda-cloud": {},
	"kernelspec": {
	"display_name": "Python [conda root]",
	"language": "python",
	"name": "conda-root-py"
	},
	"language_info": {
	"codemirror_mode": {
	"name": "ipython",
	"version": 3
	},
	"file_extension": ".py",
	"mimetype": "text/x-python",
	"name": "python",
	"nbconvert_exporter": "python",
	"pygments_lexer": "ipython3",
	"version": "3.5.2"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 1
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