PDX daily rainfall

kpdx.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/paul/miniconda3/envs/metar/lib/python3.4/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n",
      "  warnings.warn(self.msg_depr % (key, alt_key))\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn\n",
    "seaborn.set_style('ticks')\n",
    "seaborn.set_color_codes()\n",
    "\n",
    "import metar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10921ed30>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gBAAAgAeECQAAYAphAgAAmEKYAAAAphAmAACAKYQJAABgCmECAACYQpgAAACm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\ntVSguozpwgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1091e2c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kpdx = metar.WeatherStation('KPDX')\n",
    "kpdx.getWundergroundData('2015-11-07', '2015-12-07')\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "(\n",
    "    kpdx.data['wunder'][['Precip']]\n",
    "        .resample('1D', 'sum')\n",
    "        .reset_index()\n",
    "        .pipe((ax.stem, 'data'), 'Date', 'Precip',  markerfmt='_')\n",
    ")\n",
    "ax.invert_yaxis()\n",
    "ax.set_ylabel('Daily Precip Totals at PDX (in)')"
   ]
  }
 ],
 "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}