mayo/demo.ipnb

## demo.ipnb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-01-01 00:00:00', '2015-01-01 01:00:00',\n",
       "               '2015-01-01 02:00:00', '2015-01-01 03:00:00',\n",
       "               '2015-01-01 04:00:00', '2015-01-01 05:00:00',\n",
       "               '2015-01-01 06:00:00', '2015-01-01 07:00:00',\n",
       "               '2015-01-01 08:00:00', '2015-01-01 09:00:00',\n",
       "               '2015-01-01 10:00:00', '2015-01-01 11:00:00'],\n",
       "              dtype='datetime64[ns]', freq='H')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "rng = pd.date_range('2015-01-01', periods=12, freq='H')\n",
    "rng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015-01-01 00:00:00    0.142135\n",
       "2015-01-01 01:00:00   -0.165542\n",
       "2015-01-01 02:00:00    1.197356\n",
       "2015-01-01 03:00:00    0.563595\n",
       "2015-01-01 04:00:00    0.084052\n",
       "2015-01-01 05:00:00    0.879332\n",
       "2015-01-01 06:00:00   -1.265971\n",
       "2015-01-01 07:00:00    0.386967\n",
       "2015-01-01 08:00:00    0.643613\n",
       "2015-01-01 09:00:00    0.924646\n",
       "2015-01-01 10:00:00   -0.467987\n",
       "2015-01-01 11:00:00   -0.934062\n",
       "Freq: H, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = pd.Series(np.random.randn(len(rng)), index=rng)\n",
    "\n",
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc45648c750>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc458cd5c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.figure()\n",
    "ts.plot()\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015-01-01 00:00:00,0.142134797458\n",
      "2015-01-01 01:00:00,-0.165541923985\n",
      "2015-01-01 02:00:00,1.19735634208\n",
      "2015-01-01 03:00:00,0.563595257066\n",
      "2015-01-01 04:00:00,0.0840523379199\n",
      "2015-01-01 05:00:00,0.879332394951\n",
      "2015-01-01 06:00:00,-1.2659707659\n",
      "2015-01-01 07:00:00,0.386967053407\n",
      "2015-01-01 08:00:00,0.64361292236\n",
      "2015-01-01 09:00:00,0.924646255364\n",
      "2015-01-01 10:00:00,-0.467987037176\n",
      "2015-01-01 11:00:00,-0.93406210889\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "ts.to_csv(sys.stdout)\n",
    "\n",
    "# ts.to_csv(\"test.csv\") would save a test.csv file on your drive\n",
    "# ts.to_excel(\"test.xls\") would save an Excel file on your drive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 3,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"DatetimeIndex(['2015-01-01 00:00:00', '2015-01-01 01:00:00',\n",
	" '2015-01-01 02:00:00', '2015-01-01 03:00:00',\n",
	" '2015-01-01 04:00:00', '2015-01-01 05:00:00',\n",
	" '2015-01-01 06:00:00', '2015-01-01 07:00:00',\n",
	" '2015-01-01 08:00:00', '2015-01-01 09:00:00',\n",
	" '2015-01-01 10:00:00', '2015-01-01 11:00:00'],\n",
	" dtype='datetime64[ns]', freq='H')"
	]
	},
	"execution_count": 3,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"import pandas as pd\n",
	"import numpy as np\n",
	"\n",
	"\n",
	"rng = pd.date_range('2015-01-01', periods=12, freq='H')\n",
	"rng"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 4,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"2015-01-01 00:00:00 0.142135\n",
	"2015-01-01 01:00:00 -0.165542\n",
	"2015-01-01 02:00:00 1.197356\n",
	"2015-01-01 03:00:00 0.563595\n",
	"2015-01-01 04:00:00 0.084052\n",
	"2015-01-01 05:00:00 0.879332\n",
	"2015-01-01 06:00:00 -1.265971\n",
	"2015-01-01 07:00:00 0.386967\n",
	"2015-01-01 08:00:00 0.643613\n",
	"2015-01-01 09:00:00 0.924646\n",
	"2015-01-01 10:00:00 -0.467987\n",
	"2015-01-01 11:00:00 -0.934062\n",
	"Freq: H, dtype: float64"
	]
	},
	"execution_count": 4,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"ts = pd.Series(np.random.randn(len(rng)), index=rng)\n",
	"\n",
	"ts"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 5,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"<matplotlib.legend.Legend at 0x7fc45648c750>"
	]
	},
	"execution_count": 5,
	"metadata": {},
	"output_type": "execute_result"
	},
	{
	"data": {
	"image/png": 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	"text/plain": [
	"<matplotlib.figure.Figure at 0x7fc458cd5c10>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"%matplotlib inline\n",
	"\n",
	"import matplotlib\n",
	"from matplotlib import pyplot as plt\n",
	"\n",
	"plt.figure()\n",
	"ts.plot()\n",
	"plt.legend(loc='best')"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 10,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"2015-01-01 00:00:00,0.142134797458\n",
	"2015-01-01 01:00:00,-0.165541923985\n",
	"2015-01-01 02:00:00,1.19735634208\n",
	"2015-01-01 03:00:00,0.563595257066\n",
	"2015-01-01 04:00:00,0.0840523379199\n",
	"2015-01-01 05:00:00,0.879332394951\n",
	"2015-01-01 06:00:00,-1.2659707659\n",
	"2015-01-01 07:00:00,0.386967053407\n",
	"2015-01-01 08:00:00,0.64361292236\n",
	"2015-01-01 09:00:00,0.924646255364\n",
	"2015-01-01 10:00:00,-0.467987037176\n",
	"2015-01-01 11:00:00,-0.93406210889\n"
	]
	}
	],
	"source": [
	"import sys\n",
	"ts.to_csv(sys.stdout)\n",
	"\n",
	"# ts.to_csv(\"test.csv\") would save a test.csv file on your drive\n",
	"# ts.to_excel(\"test.xls\") would save an Excel file on your drive"
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": []
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": []
	}
	],
	"metadata": {
	"kernelspec": {
	"display_name": "Python 2",
	"language": "python",
	"name": "python2"
	},
	"language_info": {
	"codemirror_mode": {
	"name": "ipython",
	"version": 2
	},
	"file_extension": ".py",
	"mimetype": "text/x-python",
	"name": "python",
	"nbconvert_exporter": "python",
	"pygments_lexer": "ipython2",
	"version": "2.7.9"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 0
	}