krischer/anna.ipynb

## anna.ipynb
{
 "metadata": {
  "name": "Anna Daten"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from datetime import datetime\n",
      "import pandas as pd\n",
      "\n",
      "filename = \"/Users/lion/Desktop/Anni.csv\"\n",
      "\n",
      "def date_parser(date_string):\n",
      "    \"\"\"\n",
      "    Parses weird datetime string.\n",
      "    \"\"\"\n",
      "    return datetime.strptime(date_string, \"%d/%m/%y %H:%M\")\n",
      "    \n",
      "\n",
      "data = pd.io.parsers.read_csv(filename, delimiter=\",\", quotechar='\"',\n",
      "    parse_dates=True, index_col=0, date_parser=date_parser)\n",
      "\n",
      "# Delete empty columns\n",
      "del data[\"Unnamed: 19\"]\n",
      "del data[\"Unnamed: 20\"]\n",
      "del data[\"Unnamed: 21\"]\n",
      "del data[\"Unnamed: 22\"]\n",
      "\n",
      "# Remove rows who's 'Fehler' is not zero\n",
      "data = data[data[\"Index\"] == 0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(18, 5))\n",
      "data[\"Bodentemp 05 cm \u00b0C\"].plot(color=\"red\", label=\"Bodentemp 05 cm \u00b0C\")\n",
      "data[\"Bodentemp 10 cm \u00b0C\"].plot(color=\"blue\", label=\"Bodentemp 10 cm \u00b0C\")\n",
      "data[\"Lufttmp 2 m Pst \u00b0C\"].plot(color=\"green\", label=\"Lufttmp 2 m Pst \u00b0C\")\n",
      "plt.ylabel(\"Temperature in degree Celcius\")\n",
      "plt.legend()\n",
      "plt.show()\n",
      "\n",
      "plt.figure(figsize=(18, 5))\n",
      "data[\"Bodenfeuchte TDR01 in 25 cm Tiefe\"].plot(color=\"red\", label=\"Bodenfeuchte TDR01 in 25 cm Tiefe \")\n",
      "data[\"Bodenfeuchte TDR02 in 10 cm Tiefe\"].plot(color=\"blue\", label=\"Bodenfeuchte TDR02 in 10 cm Tiefe\")\n",
      "plt.ylabel(\"Bodenfeuchte in ?\")\n",
      "plt.legend()\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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Xjlw6onjM8lDm756vjF0z1u4x/nfmf8qDKx5U2i9pr+w6v8tpY5N4ug6Jpeuo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w1UOev+p5zN8wH1aHtVkdwpvDs5mmP6y8tLyhXLhrSghRtjRjGk5UnhAeh1ppIU5aROtdSlpd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\n6f8BJk5B6ctTRKgAAAAASUVORK5CYII=\n"
      },
      {
       "output_type": "display_data",
       "png": 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PP/20cdmoUaOIiYnh4sWL7Nmzh5UrV7Jy5UoAlixZwqZNmzh69Ci7d+9m+fLl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\nBRERERERERFZJTYtiIiIiIiIiMgq/R/DZvkYb99lCAAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Daily min and max values. Might not be the most efficient way.\n",
      "boden_5_daily_max = data.groupby(lambda x: x.date())[\"Bodentemp 05 cm \u00b0C\"].max()\n",
      "boden_5_daily_min = data.groupby(lambda x: x.date())[\"Bodentemp 05 cm \u00b0C\"].min()\n",
      "boden_10_daily_max = data.groupby(lambda x: x.date())[\"Bodentemp 10 cm \u00b0C\"].max()\n",
      "boden_10_daily_min = data.groupby(lambda x: x.date())[\"Bodentemp 10 cm \u00b0C\"].min()\n",
      "luft_2_daily_max = data.groupby(lambda x: x.date())[\"Lufttmp 2 m Pst \u00b0C\"].max()\n",
      "luft_2_daily_min = data.groupby(lambda x: x.date())[\"Lufttmp 2 m Pst \u00b0C\"].min()\n",
      "bodenfeuchte_10_daily_max = data.groupby(lambda x: x.date())[\"Bodenfeuchte TDR02 in 10 cm Tiefe\"].max()\n",
      "bodenfeuchte_10_daily_min = data.groupby(lambda x: x.date())[\"Bodenfeuchte TDR02 in 10 cm Tiefe\"].min()\n",
      "bodenfeuchte_25_daily_max = data.groupby(lambda x: x.date())[\"Bodenfeuchte TDR01 in 25 cm Tiefe\"].max()\n",
      "bodenfeuchte_25_daily_min = data.groupby(lambda x: x.date())[\"Bodenfeuchte TDR01 in 25 cm Tiefe\"].min()\n",
      "\n",
      "# Rename stuff\n",
      "boden_5_daily_max.name = \"Bodentemp 05 cm C daily max\"\n",
      "boden_5_daily_min.name = \"Bodentemp 05 cm C daily min\"\n",
      "boden_10_daily_max.name = \"Bodentemp 10 cm C daily max\"\n",
      "boden_10_daily_min.name = \"Bodentemp 10 cm C daily min\"\n",
      "luft_2_daily_max.name = \"Lufttemperatur 2 m C daily max\"\n",
      "luft_2_daily_min.name = \"Lufttemperatur 2 m C daily min\"\n",
      "bodenfeuchte_10_daily_max.name = \"Bodenfeuchte in 10 cm Tiefe daily max\"\n",
      "bodenfeuchte_10_daily_min.name = \"Bodenfeuchte in 10 cm Tiefe daily min\"\n",
      "bodenfeuchte_25_daily_max.name = \"Bodenfeuchte in 25 cm Tiefe daily max\"\n",
      "bodenfeuchte_25_daily_min.name = \"Bodenfeuchte in 25 cm Tiefe daily min\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Weekly mean and media. ISO Week number\n",
      "boden_5_weekly_mean = data.groupby(lambda x: x.isocalendar()[1])[\"Bodentemp 05 cm \u00b0C\"].mean()\n",
      "boden_5_weekly_median = data.groupby(lambda x: x.isocalendar()[1])[\"Bodentemp 05 cm \u00b0C\"].median()\n",
      "boden_10_weekly_mean = data.groupby(lambda x: x.isocalendar()[1])[\"Bodentemp 10 cm \u00b0C\"].mean()\n",
      "boden_10_weekly_median = data.groupby(lambda x: x.isocalendar()[1])[\"Bodentemp 10 cm \u00b0C\"].median()\n",
      "luft_2_weekly_mean = data.groupby(lambda x: x.isocalendar()[1])[\"Lufttmp 2 m Pst \u00b0C\"].mean()\n",
      "luft_2_weekly_median = data.groupby(lambda x: x.isocalendar()[1])[\"Lufttmp 2 m Pst \u00b0C\"].median()\n",
      "bodenfeuchte_10_weekly_mean = data.groupby(lambda x: x.isocalendar()[1])[\"Bodenfeuchte TDR02 in 10 cm Tiefe\"].mean()\n",
      "bodenfeuchte_10_weekly_median = data.groupby(lambda x: x.isocalendar()[1])[\"Bodenfeuchte TDR02 in 10 cm Tiefe\"].median()\n",
      "bodenfeuchte_25_weekly_mean = data.groupby(lambda x: x.isocalendar()[1])[\"Bodenfeuchte TDR01 in 25 cm Tiefe\"].mean()\n",
      "bodenfeuchte_25_weekly_median = data.groupby(lambda x: x.isocalendar()[1])[\"Bodenfeuchte TDR01 in 25 cm Tiefe\"].median()\n",
      "\n",
      "# Rename stuff\n",
      "boden_5_weekly_mean.name = \"Bodentemp 05 cm C weekly mean\"\n",
      "boden_5_weekly_median.name = \"Bodentemp 05 cm C weekly median\"\n",
      "boden_10_weekly_mean.name = \"Bodentemp 10 cm C weekly mean\"\n",
      "boden_10_weekly_median.name = \"Bodentemp 10 cm C weekly median\"\n",
      "luft_2_weekly_mean.name = \"Lufttemperatur 2 m C weekly mean\"\n",
      "luft_2_weekly_median.name = \"Lufttemperatur 2 m C weekly median\"\n",
      "bodenfeuchte_10_weekly_mean.name = \"Bodenfeuchte in 10 cm Tiefe weekly mean\"\n",
      "bodenfeuchte_10_weekly_median.name = \"Bodenfeuchte in 10 cm Tiefe weekly median\"\n",
      "bodenfeuchte_25_weekly_mean.name = \"Bodenfeuchte in 25 cm Tiefe weekly mean\"\n",
      "bodenfeuchte_25_weekly_median.name = \"Bodenfeuchte in 25 cm Tiefe weekly median\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plots for sanity check\n",
      "plt.figure(figsize=(18, 1))\n",
      "boden_10_daily_max.plot(color=\"red\", drawstyle=\"steps\")\n",
      "boden_10_daily_min.plot(color=\"blue\", drawstyle=\"steps\")\n",
      "plt.title(\"Bodentemp 10 cm C daily min and max\")\n",
      "plt.show()\n",
      "\n",
      "plt.figure(figsize=(18, 1))\n",
      "plt.title(\"Bodentemp 5 cm C dialy min and max\")\n",
      "boden_5_daily_max.plot(color=\"red\", drawstyle=\"steps\")\n",
      "boden_5_daily_min.plot(color=\"blue\", drawstyle=\"steps\")\n",
      "plt.show()\n",
      "\n",
      "plt.figure(figsize=(18, 1))\n",
      "plt.title(\"Lufttmp 2 m Pst C daily min and max\")\n",
      "luft_2_daily_max.plot(color=\"red\", drawstyle=\"steps\")\n",
      "luft_2_daily_min.plot(color=\"blue\", drawstyle=\"steps\")\n",
      "plt.show()\n",
      "\n",
      "plt.figure(figsize=(18, 1))\n",
      "plt.title(\"Bodenfeuchte 10 cm Tiefe daily min and max\")\n",
      "bodenfeuchte_10_daily_max.plot(color=\"red\", drawstyle=\"steps\")\n",
      "bodenfeuchte_10_daily_min.plot(color=\"blue\", drawstyle=\"steps\")\n",
      "plt.show()\n",
      "\n",
      "plt.figure(figsize=(18, 1))\n",
      "plt.title(\"Bodenfeuchte 25 cm Tiefe daily min and max\")\n",
      "bodenfeuchte_25_daily_max.plot(color=\"red\", drawstyle=\"steps\")\n",
      "bodenfeuchte_25_daily_min.plot(color=\"blue\", drawstyle=\"steps\")\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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PTjxVrU5ERNREXX/99UhNTUVSUlJDF0WTtm/fjkmTJiEnJ6ehi0JERKR5LrckeOuttxAZ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\nH9Z943IFta76OzX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      },
      {
       "output_type": "display_data",
       "png": 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      },
      {
       "output_type": "display_data",
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JJ/HKK6+goKAAkydPxvPPP4/Zs2fb3U9qairCwsIAAAEBAbBYLLCWThRU1msn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\nmZmJvLw8LFmyRPZaW/6cKP8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      },
      {
       "output_type": "display_data",
       "png": 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8HQvj6VgYT8fDmDoWxtOxMJ4WGgoKCwvRp08faLVa9O3bF0uXLgUAvPbaa/Dz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\n4Yc1Km9DdLtjCgBZWVk4duw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      },
      {
       "output_type": "display_data",
       "png": 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ho48+sl5trS011fLH7Tk7G78eHjC6DmcngQbG/5xhYWMFERERERERkRWYbChI\nTExEZGQkxo8fr7Q2bNy4ESEhIQCAkJAQ/Prrr2VSuXK5N8TUTfFmNASYuiLBVFuFue0VasD7fdSF\neaoL81QX5qk+zFRdmKe6ME91YZ5m9FEwffp0zJs3D+np6cqwGzduwNXVFQDg6uqKGzdulF0Ny5ql\ntx3AvLsbTClor7BkGURERERERESWMtpQsHnzZri4uMDf399gq4pGo1FuSdBn9OjR8PDwAAA4OjpC\nq9UqnSIWLNNQuWCY0emDg6HLzMwvP5ynYO4YALC3/7tsYn2lLaem6iBi2fJSUsqufpWtXKA089vb\nAxqNrmAJD/8tWnbSpCBFozEw1ozyw/4iKsv2KnU5f6CR4ysGMTEVmyfLla9coLLUh2XLygUqS31Y\nZplllllmmeWKK4eFhSE2NlY5PzfGaGeGoaGhCA8PR/Xq1ZGVlYX09HQMGjQI//vf/xATE4OGDRsi\nKSkJPXr0wNmzZ0su3NLODM3pJNDCzgjLoR9CKmeWZuqkSUWKmHgOY1UInZ0ZEhERERGRAaXuzHDO\nnDm4cuUK4uLisHr1agQFBSE8PBzPP/88VqxYAQBYsWIFBg4caP1aA4gx5+Z+EydrFvZDaNZfZT9f\nrEwKWrXKUkqKZXnC0clk55Oa1BSj+5WzJsV03xdGF2CioaKSKI88qfwwT3VhnurDTNWFeaoL81QX\n5mlGHwWFFdxi8O6772LkyJHw9fVVHo9YUczpH4C/mtKjsEbDj7OzMzSpFux4qQCM9FlhDidNCp+l\nQUREREREj8zorQcWL9zSWw/MuDaal08T6WdOIxqvhiEiIiIiejyV+taDrKwsdOzYEVqtFp06dcLC\nhQsBAKdPn0b//v2h1WoRHByMM2fOWL/WRGQRU7dgsJGAiIiIiIj0MdpQUKtWLURHRyM2Nha7d+/G\n0qVLcf78ecyePRujRo1CbGwsXnrpJcyePdvwQiy4T9sBG01OxscGVi2830ddmKe6ME91YZ7qw0zV\nhXmqC/NUF+ZpoqEAAGrXrg0AyMzMRE5ODmrWrIm6desiOTkZeXl5SE5OhpOxs3UjP2k6OxnvMA72\nDuxIkIiIiIiIiKgcmeyjIC8vD/7+/jh16hTCwsIwZcoUpKeno0OHDkhKSkLjxo1x+PBhODg4lFy4\niT4K2L8AERERERERUfkrdR8FAGBjY4MTJ07gwoUL+Prrr3H8+HGMHTsWU6dORXJyMiZOnIhx48ZZ\nvdJERETJIml6AAAUFElEQVREREREVP4e6akHM2bMgJubG+bOnYu4uDjY2dkhMzMTLVu2xPXr10su\nXKNBSEgIPDw8AACOjo7QarXQ6XQPx8cgOhpKueBekIJyWFhYkemLj2e56pVjY2Mxbdq0SlMflpkn\ny3+Xmae6ysxTfeWCYZWlPixbVi4YVlnqw7Jl5YJhlaU+LFtWLhhWWepjrXJYWBhiY2OV8/MPPvjA\n4BUFRhsKbt++jerVq8PR0RHJycno1q0bFi1ahKVLl2LgwIEYOnQoVq1ahS1btiA8PLzkwi289SAm\nJkZ5UaQOzFRdmKe6ME91YZ7qw0zVhXmqC/NUl8clT2Pn60YbCv744w+EhIQgNzcXDRs2xJAhQzBu\n3DicOnUKH330EU6fPg0fHx+8++67aN269SOtOH88+yggIiIiIiIiKm+lbigoyxXnj2dDARERERER\nEVF5s6gzw4pU+B4RUgdmqi7MU12Yp7owT/VhpurCPNWFeaoL8zTRUJCVlYWOHTtCq9WiU6dOWLhw\noTJu2bJlaNeuHXx8fPD222+XSeViY2PLZLlUcZipujBPdWGe6sI81YeZqgvzVBfmqS7ME6hubGSt\nWrUQHR2N2rVrIzs7G4GBgejfvz8SExPx448/4sCBA7C1tcWtW7fKpHJ//fVXmSyXKg4zVRfmqS7M\nU12Yp/owU3VhnurCPNWFeZpoKACA2rVrAwAyMzORk5ODmjVrYsmSJXjnnXdga2sLAGjQoIHB+TUa\nw8t2cnrE2hIRERERERFRmTLZR0FeXh78/Pzg6uqKKVOmwN3dHefPn8eePXsQEBCA7t2749ixYwbn\nFzH8l5JifN2XL19+1NdDlRwzVRfmqS7MU12Yp/owU3VhnurCPNWFeT7CUw8uX76Mvn37YtWqVRg1\nahT8/f3x7bffYseOHVi4cCF27dpVcuHGLicgIiIiIiIiogpjlccjzpgxA25ubti6dSumTp2Kvn37\nAgAaN26MS5cuoVatWtapLRERERERERFVCKO3Hty+fVvpyCE5ORlRUVFo27YtBg4ciMjISIgIDh06\nhBYtWrCRgIiIiIiIiEgFjHZmmJSUhJCQEOTm5qJhw4Z444030LNnT+h0OkyePBmtW7eGra0tvvnm\nm/KqLxERERERERGVIaNXFLRt2xbHjh3DH3/8gbZt22LcuHEAgIULF6JRo0Y4d+4cTp48iS5dupRq\n5Q8ePMC4cePg6+uLnj174vr16wCA+Ph4BAYGQqvVomvXroiIiNA7/8yZM9GmTRsEBARg2rRpSEtL\nU8Z98cUX8PX1RUBAAPbt26cMnzVrFtzd3eHg4FBkWZ9//jm8vb2h1WoxYcIEXLlypVSvqSqwsbHB\njBkzlPL8+fPxwQcfWLzcgg4ubW1tsW7dOmV4dHQ0/P39lT87Ozts3LixxPzMs3SqVasGf39/tGvX\nDp06dcIPP/xg8F6jR2Ho+ASAPn36wMnJCcHBwQbnZ56lU1Z5Gjo+C6/T398fAwcO1Ds/8yydssrT\n2DYMDQ2Fp6cnfHx8sHnzZr3zM0/L2dvbW7wMQ8dlbGwsOnfujLZt26J37956PzMB6+b4zTffwNfX\nF1qtFi+++CIuXrxo8euraqyRqbHj4ebNmxgwYAC0Wi28vb0RHx9fYn5marnCn2n+/v5ISEgwOK1O\np8PRo0dNLtPQtl+7di1atWqFNm3a4MMPP9Q776pVq+Dn5wc/Pz+89NJLOHnypDKu4D3A19cXX375\npTL8559/hre3N6pVq1ak0/jDhw/D398fWq0WPXv2xJYtW0zWvaqzdp7bt29Hu3bt4Ovri4EDBxbJ\ns0+fPtBqtQgMDMQ777yjd/7HKk8xQ82aNcXT01Nu374tIiLz58+Xf//73+bMatTixYtl0qRJIiKy\nevVqGTp0qIiI3L9/X+7fvy8iIikpKeLh4SFpaWkl5t+2bZvk5uZKbm6ujB8/Xt5++20RETl16pT4\n+fnJ/fv3JS4uTlq0aCF5eXkiInLo0CFJSkoSe3v7IsuKjo6We/fuiYjIBx98oNRFjcoqz8uXL8vv\nv/8uo0aNkrVr1+qdJiUlRZydnZVtXRjzLJ3Cr/3mzZvy7LPPysKFCy1erqHjU0Rk586dsmnTJunf\nv7/B+Zln6ZRVnsaOz+LbWx/mWTpllaehbXju3Dnp0KGD3LlzR27fvi1PPvmkpKenl5ifeVrOnOPG\nFEPH5Z9//ikXLlwQEZHz58+Lg4NDmX8PKryfLF++XEaOHGnx66tqrJGpseOhe/fusmPHDhERuXPn\njty9e7fE/MzUco+So06nkyNHjhidxtC2z83NlVatWklCQoJkZ2fL888/Lzt37iwx/2+//SZ//fWX\niOTn0LFjRxERycnJkRYtWkhcXJzcv39f/Pz85PTp0yIicubMGTl37pzodDo5evSosqy7d+9Kbm6u\niOS/N7Rq1UrZD9TK2nkeP35ckpKSRERk9+7d0rBhQ2VcRkaGiORn06tXr8c+T5OPRwQAW1tbvPzy\ny1i4cGGJcQkJCdDpdGjWrBmCgoJw5coVpKWlwcPDQ5nmzp07cHd3R25ubpF5N27ciJCQEADACy+8\ngJ07dyrrs7W1BQCkp6cjLy8PNWvWLLHuXr16wcbGBjY2NujduzcSExMBABs2bMDw4cNha2sLDw8P\ntGzZEocOHQIAdOjQAQ0bNiyxLJ1Op/Sz0LdvX2VZalRWeTZr1gxt27aFjY3h3ernn39G37599fZp\nwTwt16BBA3z33Xf47LPPAAC5ubmYOXMmvLy80LJlS/zwww/KtJ9++ik8PT3h5+ent9XU0PEJAEFB\nQSZ/eWGelrNmnuYcn8YwT8tZM09D23Dnzp0ICgpC7dq1Ua9ePWi1Wr2/UDBP67hz5w569uyJgIAA\n9O3bV3mfvHz5Mry8vDB58mR4eXlh4sSJePDgQYn5DR2XrVq1QosWLQAALVu2RLNmzXDr1q0S81sz\nx4Jfo/Py8pCRkfHY9j1laaaGjofTp08jNzcXPXv2BADUrl0bdnZ2JeZnpmXj6NGj6NSpE1q3bo3n\nnnsOt2/fVsatW7cObdu2xVNPPYUjR46UmNfQtj937hzq1auHpk2bokaNGujdu3eJK/YA4Omnn0bd\nunUBAP369VMyPXz4MFq2bAkPDw/Y2tpi2LBh2LBhAwCgdevWePLJJ0ssy87OTnm/yMjIQLVq1R7L\np8xZkqdWq1WOl65duyIrK0s5lgu+2967dw/379/Xe/75OOVp9jfGV199FatWrUJ6enqR4WFhYejX\nrx/i4+PRq1cvvPbaa6hbty60Wi1iYmIAAJs3b0afPn1QrVq1IvNevXoVTZs2BQBUr14ddevWRUpK\nCgAgMTER3t7e8PT0xJdffqk3qMK+//57DBgwAABw7do1uLm5KePc3Nxw9epVc18qvvvuO2VZalUW\neZpj9erVGD58uMnpmGfpNW/eHPfu3UNGRgaWLl2KvLw8nDp1CidOnMBXX32F+/fvIyoqCj/++CP2\n7duHEydO4K233iqxHGPH56NinqVnrTyNycrKglarRadOnZQPNWOYZ+mVRZ6Ft2GfPn0QFRWF69ev\n4+LFi4iJiTF54s48S8/Ozg6//vorjh07hm+++QajR49Wxp09exaDBg3CyZMncfnyZRw4cKBU69i3\nbx+qVaumNBwYYo0cFy9eDDc3NyxatAjz5s0rVX2rOmtmWvh4+PPPP+Ho6Ih//OMfaNu2rVnv08y0\ndO7du6dcpv7CCy8gJycHY8aMwdq1a3H27FkMHDgQS5cuBZD/WLiLFy/i6NGj+PDDD/Xeiqtv21+7\ndg1t2rRBcnIyYmNjkZqainXr1pl8vy28TxT+nlWwXHMyPXz4MFq1aoXOnTsjPDzcrG1SlVk7z8J+\n+ukndO7cWfmBGgB69+6N+vXro127dnjmmWeMzq/2PI12ZliYg4MDRo0ahS+++KJIC2hUVJRyb8fY\nsWOxYMECAMDQoUPx3//+FzqdDqtXr8aUKVMeqWJubm44deoUTp06hWeffRZPP/00GjRooHfajz/+\nGA4ODhg8eLDB5ZnbOhMREYFTp05h8eLFj1Tfqqa88wTyO8c8efIkevfubXQ65mkdGo0G27Ztwx9/\n/IFdu3YByL9C58CBA9i5cyeGDx+Oxo0bAwCcnJzKrB7M0zrKMs+EhAQ0atQI//vf/9C7d28cOXIE\nnp6eeqdlntZhrTyLb8PmzZtj+vTpeOmllwD8/eukIczTMjY2Nli0aBEiIyNx584dpKSk4ObNmwCA\nJk2aKL8ed+/eHQcOHEC3bt0eaflJSUkYP368yS+P1spx8uTJePnll/HFF19g9OjR+OWXXx6pvmpg\nrUyLHw85OTnYunUrDh48CE9PT7z44ovYs2ePwfmZaenZ2dnh+PHjSvnkyZOIj49X+lXKzc1VrpTV\naDQYNmwYatSogRdeeAHTp0/HgwcPipw4GrN06VJ89NFHSEhIQLdu3Yz2AxEdHY2IiAj89ttvyrpL\no0OHDjh//jz27duH4OBgJCYmlurHu6qirPI8deoU3nvvPWzfvr3I8K1bt+LGjRsYMmQINmzYYLDx\n+3HI0+yGAgCYNm0aAgICMGbMmCLD5WHHTIU3UHBwMEJDQ5Gamopjx44hKCioxPKaNGmChIQENG7c\nGDk5OUhLS4Ozs3ORaby9vfHMM89g//79ejvZWr58OSIjI4tcFt2kSZMincckJiaiSZMmJl/fjh07\n8Mknn2DPnj1mv0FUZdbOszB9B8uaNWswaNAgozs/87TcpUuXULt2beXyqVmzZmHUqFFFptm0aZPJ\nDtVMHZ/mvCEyT8tZK8/CimfXqFEjAED79u2h0+lw/PhxvQ0FzNNy1szT0DYMCQlRbhv65z//iaee\nekrv/MzTcrt378bevXuxdetW1KlTBy4uLsjKygIAODo6KtPVqFEDmZmZRpdV/LhMT09H//79MWfO\nHLRv397gfNbMEci/PXHChAn4+OOPzZpebayRqb7joWnTpvDz80NAQACA/Fv6oqKi9DYUMFPrEhE4\nOzsXOdksPr6Avu82xrZ9165d0bVrVwD5vy5Xr67/1Or333/Hyy+/jKioKGU/Kr7cK1euFLlywZQu\nXbqgSZMmOHnyJPz8/Myer6qzNE8gP8NBgwYhPDwczZs3LzHe1dUVgwcPRnR0tN6GgscmT3M6Mijc\nicRbb70l7u7u8sEHH4iIyBtvvCHz58+X3Nxc+eSTT2TQoEHKtIMHD5YRI0bI5MmT9S538eLFMnHi\nRBER+emnn5QOXxITE5UOXq5cuSINGjSQc+fOlZg/KipKvLy8lE75ChR0OpKdnS2XLl0ST0/PEh1D\nFO8Y49ixY9KiRQu5ePGiOZukSiurPAuEhITo7cywY8eOEhMTY3A+5lk6xTtLe+655yQsLExERL77\n7jsJCgqSmzdvikh+R2d37tyRLVu2iK+vryQmJopIfieTxRk6PgtER0cb7cyQeZZOWeVZoPjxmZqa\nKllZWSKS37FavXr15MyZMyXmY56lU1Z5GtqGubm5SkYnTpwQHx8fpaOkwpin5ezt7SUiIkLGjBkj\nIiKrVq0SjUYj8fHxEhcXJz4+Psq0pjoNLn5cZmdnS1BQkCxatMhoHayZ4/nz50VEJC8vT5YsWSJd\nunQxum41skamho6Hgo7O4uPjJTs7WwYMGKB0bFgYM7Vc8e2QnZ0tHh4esnbtWsnLy5P79+/LqVOn\nRCS/g8mhQ4dKdna2/PzzzxIcHFxieca2/Y0bN0Qk/31aq9Uq27yw+Ph4admypRw8eLDI8AcPHoin\np6fExcVJdnZ2kc7vChTvnC8uLk4ePHggIvnv8XXr1tXb0amaWDvP1NRU8fX1lV9++aXI8MzMTLl2\n7ZqI5HcE2rNnT/nPf/5TYv7HKU+zGgocHByU/9+4cUNq166tnFgmJCSITqcTd3d3CQoKkitXrijT\nrl27VmxsbGTPnj16l3v//n0ZM2aM+Pj4SI8ePZQeKLdv3y6+vr7i5+cnwcHBsn79er3zt2zZUtzd\n3UWr1YpWq1V6aBcRCQsLEx8fH9FqtUXWP3PmTHFzc5Nq1aqJm5ub8jqeffZZadiwobKsAQMGmLNp\nqqSyyvPw4cPi5uYmderUkXr16hX5QI2LixM3Nzej9WKepVOtWjXRarUSGBgoHTt2lKVLlyofYHl5\neRIaGioeHh7i4+MjQUFBSi/Ic+fOFXd3d/Hz85NZs2aVWK6h41NEpEuXLtKgQQOxs7MTNzc32bZt\nW4n5mWfplFWeho7P/fv3S9u2bcXPz0/69u0ra9as0Vsv5lk6ZZWnoW1479498fLyEl9fX+nZs6fe\nL60izNNSd+/elXr16smdO3fkH//4h7Ru3VomTZok3t7eykll27Ztlennz5+vbK/CDB2X4eHhYmtr\nq2xTrVYrJ06cKDG/NXN8/fXXxdvbWwICAmTy5MmPXcOPtTI1djzs379ffH19xdPTU8aOHau3d3Nm\narnC33MLxMbGSufOnaVVq1bi7e2tnADqdDoJDQ0VHx8feeqppwz2mG9o2w8fPly8vb2lffv2snnz\nZr3zjh8/XpydnZVM27dvr4yLiYkRrVYrPj4+RRoG169fL25ublKrVi1xdXWVPn36iIjIypUrxdvb\nW7RarQwdOlSio6MfeftUNdbO88MPP5Q6deoUeX+9deuW3LhxQ9q3by++vr7SvXt35YfT4h6nPDUi\nVnigMxEREdFjIjo6Gu+88w4OHjxY0VUhK2GmRERFPVIfBURERESPsyVLlmDdunVKZ79U9TFTIqKS\neEUBERERERERESkMPzOJiIiIiIiIiB47bCggIiIiIiIiIgUbCoiIiIiIiIhIwYYCIiIiIiIiIlKw\noYCIiIiIiIiIFGwoICIiIiIiIiLF/wMTe0vqFIEeLQAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Plots for sanity check\n",
      "plt.figure(figsize=(18, 1))\n",
      "boden_5_weekly_mean.plot(color=\"green\", drawstyle=\"steps\")\n",
      "boden_5_weekly_median.plot(color=\"yellow\", drawstyle=\"steps\")\n",
      "plt.title(\"Bodentemp 10 cm C weekly mean and median\")\n",
      "plt.show()\n",
      "\n",
      "plt.figure(figsize=(18, 1))\n",
      "plt.title(\"Bodentemp 5 cm C weekly mean and median\")\n",
      "boden_10_weekly_mean.plot(color=\"green\", drawstyle=\"steps\")\n",
      "boden_10_weekly_median.plot(color=\"yellow\", drawstyle=\"steps\")\n",
      "plt.show()\n",
      "\n",
      "plt.figure(figsize=(18, 1))\n",
      "plt.title(\"Lufttmp 2 m Pst C weekly mean and median\")\n",
      "luft_2_weekly_mean.plot(color=\"green\", drawstyle=\"steps\")\n",
      "luft_2_weekly_median.plot(color=\"yellow\", drawstyle=\"steps\")\n",
      "plt.show()\n",
      "\n",
      "plt.figure(figsize=(18, 1))\n",
      "plt.title(\"Bodenfeuchte 10 cm Tiefe weekly mean and median\")\n",
      "bodenfeuchte_10_weekly_mean.plot(color=\"green\", drawstyle=\"steps\")\n",
      "bodenfeuchte_10_weekly_median.plot(color=\"yellow\", drawstyle=\"steps\")\n",
      "plt.show()\n",
      "\n",
      "\n",
      "plt.figure(figsize=(18, 1))\n",
      "plt.title(\"Bodenfeuchte 25 cm Tiefe weekly mean and median\")\n",
      "bodenfeuchte_25_weekly_mean.plot(color=\"green\", drawstyle=\"steps\")\n",
      "bodenfeuchte_25_weekly_median.plot(color=\"yellow\", drawstyle=\"steps\")\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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DBw8ebPCkTKXjMkdoiut/c22M4fHGoTZ1AkRERERERNQE6e0kUCjMZ/h9bahf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\nYCcBEREREREREf3t/wFw+CBHlzdkBAAAAABJRU5ErkJggg==\n"
      },
      {
       "output_type": "display_data",
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      },
      {
       "output_type": "display_data",
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      },
      {
       "output_type": "display_data",
       "png": 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      },
      {
       "output_type": "display_data",
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IwMWLFzF37lyMGTMGiYmJeP311zF37twnlS+RQZQ+E5VIjlifJFesTZIr1ibJ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\nS4LT3WBzAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Convert to one data frame. There has go to be a better way...\n",
      "df = pd.DataFrame(boden_5_weekly_mean)\n",
      "df[boden_5_weekly_median.name] = boden_5_weekly_median\n",
      "df[boden_10_weekly_mean.name] = boden_10_weekly_mean\n",
      "df[boden_10_weekly_median.name] = boden_10_weekly_median\n",
      "df[luft_2_weekly_mean.name] = luft_2_weekly_mean\n",
      "df[luft_2_weekly_median.name] = luft_2_weekly_median\n",
      "df[bodenfeuchte_10_weekly_mean.name] = bodenfeuchte_10_weekly_mean\n",
      "df[bodenfeuchte_10_weekly_median.name] = bodenfeuchte_10_weekly_median\n",
      "df[bodenfeuchte_25_weekly_mean.name] = bodenfeuchte_25_weekly_mean\n",
      "df[bodenfeuchte_25_weekly_median.name] = bodenfeuchte_25_weekly_median\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Bodentemp 05 cm C weekly mean</th>\n",
        "      <th>Bodentemp 05 cm C weekly median</th>\n",
        "      <th>Bodentemp 10 cm C weekly mean</th>\n",
        "      <th>Bodentemp 10 cm C weekly median</th>\n",
        "      <th>Lufttemperatur 2 m C weekly mean</th>\n",
        "      <th>Lufttemperatur 2 m C weekly median</th>\n",
        "      <th>Bodenfeuchte in 10 cm Tiefe weekly mean</th>\n",
        "      <th>Bodenfeuchte in 10 cm Tiefe weekly median</th>\n",
        "      <th>Bodenfeuchte in 25 cm Tiefe weekly mean</th>\n",
        "      <th>Bodenfeuchte in 25 cm Tiefe weekly median</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td> 4.108383</td>\n",
        "      <td> 4.090</td>\n",
        "      <td> 4.044385</td>\n",
        "      <td> 3.990</td>\n",
        "      <td> 4.822629</td>\n",
        "      <td> 5.475</td>\n",
        "      <td> 53.933284</td>\n",
        "      <td> 53.810</td>\n",
        "      <td> 45.837649</td>\n",
        "      <td> 45.76</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td> 3.871928</td>\n",
        "      <td> 4.390</td>\n",
        "      <td> 3.918851</td>\n",
        "      <td> 4.360</td>\n",
        "      <td> 0.490979</td>\n",
        "      <td> 2.110</td>\n",
        "      <td> 54.130909</td>\n",
        "      <td> 54.260</td>\n",
        "      <td> 46.003217</td>\n",
        "      <td> 46.07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td> 0.695709</td>\n",
        "      <td> 0.630</td>\n",
        "      <td> 0.948664</td>\n",
        "      <td> 0.860</td>\n",
        "      <td>-3.884757</td>\n",
        "      <td>-3.485</td>\n",
        "      <td> 44.903684</td>\n",
        "      <td> 43.645</td>\n",
        "      <td> 45.247166</td>\n",
        "      <td> 45.36</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td> 0.293530</td>\n",
        "      <td> 0.350</td>\n",
        "      <td> 0.449044</td>\n",
        "      <td> 0.510</td>\n",
        "      <td>-3.462747</td>\n",
        "      <td>-3.420</td>\n",
        "      <td> 40.957487</td>\n",
        "      <td> 41.230</td>\n",
        "      <td> 45.035880</td>\n",
        "      <td> 45.04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td> 1.723786</td>\n",
        "      <td> 1.950</td>\n",
        "      <td> 1.681014</td>\n",
        "      <td> 2.040</td>\n",
        "      <td> 3.813797</td>\n",
        "      <td> 3.640</td>\n",
        "      <td> 49.837735</td>\n",
        "      <td> 51.620</td>\n",
        "      <td> 45.066753</td>\n",
        "      <td> 45.14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td> 1.574653</td>\n",
        "      <td> 1.500</td>\n",
        "      <td> 1.725536</td>\n",
        "      <td> 1.700</td>\n",
        "      <td>-1.086687</td>\n",
        "      <td>-1.370</td>\n",
        "      <td> 52.626250</td>\n",
        "      <td> 52.650</td>\n",
        "      <td> 45.091518</td>\n",
        "      <td> 45.08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td> 0.481369</td>\n",
        "      <td> 0.410</td>\n",
        "      <td> 0.655327</td>\n",
        "      <td> 0.580</td>\n",
        "      <td>-1.540704</td>\n",
        "      <td>-1.650</td>\n",
        "      <td> 53.439861</td>\n",
        "      <td> 53.470</td>\n",
        "      <td> 44.958611</td>\n",
        "      <td> 44.96</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td> 0.655605</td>\n",
        "      <td> 0.680</td>\n",
        "      <td> 0.799742</td>\n",
        "      <td> 0.890</td>\n",
        "      <td>-2.751181</td>\n",
        "      <td>-2.835</td>\n",
        "      <td> 53.545079</td>\n",
        "      <td> 53.530</td>\n",
        "      <td> 44.977530</td>\n",
        "      <td> 45.01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td> 0.724086</td>\n",
        "      <td> 0.800</td>\n",
        "      <td> 0.768275</td>\n",
        "      <td> 0.800</td>\n",
        "      <td> 0.617569</td>\n",
        "      <td> 0.470</td>\n",
        "      <td> 53.927037</td>\n",
        "      <td> 53.940</td>\n",
        "      <td> 44.997477</td>\n",
        "      <td> 45.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44</th>\n",
        "      <td> 7.504201</td>\n",
        "      <td> 7.420</td>\n",
        "      <td> 7.536285</td>\n",
        "      <td> 7.475</td>\n",
        "      <td> 7.296476</td>\n",
        "      <td> 7.150</td>\n",
        "      <td> 32.096476</td>\n",
        "      <td> 32.510</td>\n",
        "      <td> 37.744497</td>\n",
        "      <td> 38.46</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>45</th>\n",
        "      <td> 7.519673</td>\n",
        "      <td> 7.460</td>\n",
        "      <td> 7.626915</td>\n",
        "      <td> 7.560</td>\n",
        "      <td> 6.015446</td>\n",
        "      <td> 6.085</td>\n",
        "      <td> 32.921756</td>\n",
        "      <td> 32.800</td>\n",
        "      <td> 41.010357</td>\n",
        "      <td> 40.38</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>46</th>\n",
        "      <td> 6.112153</td>\n",
        "      <td> 5.455</td>\n",
        "      <td> 6.396042</td>\n",
        "      <td> 5.780</td>\n",
        "      <td> 1.583661</td>\n",
        "      <td> 0.545</td>\n",
        "      <td> 33.331290</td>\n",
        "      <td> 33.310</td>\n",
        "      <td> 45.179444</td>\n",
        "      <td> 45.55</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47</th>\n",
        "      <td> 5.671909</td>\n",
        "      <td> 5.835</td>\n",
        "      <td> 5.777286</td>\n",
        "      <td> 5.890</td>\n",
        "      <td> 4.805527</td>\n",
        "      <td> 4.945</td>\n",
        "      <td> 31.979712</td>\n",
        "      <td> 31.940</td>\n",
        "      <td> 40.248757</td>\n",
        "      <td> 39.89</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48</th>\n",
        "      <td> 5.760612</td>\n",
        "      <td> 5.900</td>\n",
        "      <td> 5.936144</td>\n",
        "      <td> 6.070</td>\n",
        "      <td> 3.115826</td>\n",
        "      <td> 3.250</td>\n",
        "      <td> 32.893880</td>\n",
        "      <td> 31.490</td>\n",
        "      <td> 41.153856</td>\n",
        "      <td> 39.46</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49</th>\n",
        "      <td> 2.027851</td>\n",
        "      <td> 1.950</td>\n",
        "      <td> 2.487393</td>\n",
        "      <td> 2.440</td>\n",
        "      <td>-6.161375</td>\n",
        "      <td>-5.440</td>\n",
        "      <td> 43.313696</td>\n",
        "      <td> 41.490</td>\n",
        "      <td> 44.716332</td>\n",
        "      <td> 44.74</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50</th>\n",
        "      <td> 2.233274</td>\n",
        "      <td> 2.380</td>\n",
        "      <td> 2.374623</td>\n",
        "      <td> 2.470</td>\n",
        "      <td>-1.655714</td>\n",
        "      <td>-1.460</td>\n",
        "      <td> 42.873730</td>\n",
        "      <td> 40.630</td>\n",
        "      <td> 44.515129</td>\n",
        "      <td> 44.55</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>51</th>\n",
        "      <td> 2.922083</td>\n",
        "      <td> 2.835</td>\n",
        "      <td> 2.907589</td>\n",
        "      <td> 2.780</td>\n",
        "      <td> 2.328264</td>\n",
        "      <td> 1.915</td>\n",
        "      <td> 50.907411</td>\n",
        "      <td> 51.015</td>\n",
        "      <td> 45.051349</td>\n",
        "      <td> 45.06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>52</th>\n",
        "      <td> 4.355430</td>\n",
        "      <td> 4.410</td>\n",
        "      <td> 4.268320</td>\n",
        "      <td> 4.325</td>\n",
        "      <td> 5.790300</td>\n",
        "      <td> 5.645</td>\n",
        "      <td> 53.080470</td>\n",
        "      <td> 53.210</td>\n",
        "      <td> 45.530780</td>\n",
        "      <td> 45.54</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "    Bodentemp 05 cm C weekly mean  Bodentemp 05 cm C weekly median  \\\n",
        "1                        4.108383                            4.090   \n",
        "2                        3.871928                            4.390   \n",
        "3                        0.695709                            0.630   \n",
        "4                        0.293530                            0.350   \n",
        "5                        1.723786                            1.950   \n",
        "6                        1.574653                            1.500   \n",
        "7                        0.481369                            0.410   \n",
        "8                        0.655605                            0.680   \n",
        "9                        0.724086                            0.800   \n",
        "44                       7.504201                            7.420   \n",
        "45                       7.519673                            7.460   \n",
        "46                       6.112153                            5.455   \n",
        "47                       5.671909                            5.835   \n",
        "48                       5.760612                            5.900   \n",
        "49                       2.027851                            1.950   \n",
        "50                       2.233274                            2.380   \n",
        "51                       2.922083                            2.835   \n",
        "52                       4.355430                            4.410   \n",
        "\n",
        "    Bodentemp 10 cm C weekly mean  Bodentemp 10 cm C weekly median  \\\n",
        "1                        4.044385                            3.990   \n",
        "2                        3.918851                            4.360   \n",
        "3                        0.948664                            0.860   \n",
        "4                        0.449044                            0.510   \n",
        "5                        1.681014                            2.040   \n",
        "6                        1.725536                            1.700   \n",
        "7                        0.655327                            0.580   \n",
        "8                        0.799742                            0.890   \n",
        "9                        0.768275                            0.800   \n",
        "44                       7.536285                            7.475   \n",
        "45                       7.626915                            7.560   \n",
        "46                       6.396042                            5.780   \n",
        "47                       5.777286                            5.890   \n",
        "48                       5.936144                            6.070   \n",
        "49                       2.487393                            2.440   \n",
        "50                       2.374623                            2.470   \n",
        "51                       2.907589                            2.780   \n",
        "52                       4.268320                            4.325   \n",
        "\n",
        "    Lufttemperatur 2 m C weekly mean  Lufttemperatur 2 m C weekly median  \\\n",
        "1                           4.822629                               5.475   \n",
        "2                           0.490979                               2.110   \n",
        "3                          -3.884757                              -3.485   \n",
        "4                          -3.462747                              -3.420   \n",
        "5                           3.813797                               3.640   \n",
        "6                          -1.086687                              -1.370   \n",
        "7                          -1.540704                              -1.650   \n",
        "8                          -2.751181                              -2.835   \n",
        "9                           0.617569                               0.470   \n",
        "44                          7.296476                               7.150   \n",
        "45                          6.015446                               6.085   \n",
        "46                          1.583661                               0.545   \n",
        "47                          4.805527                               4.945   \n",
        "48                          3.115826                               3.250   \n",
        "49                         -6.161375                              -5.440   \n",
        "50                         -1.655714                              -1.460   \n",
        "51                          2.328264                               1.915   \n",
        "52                          5.790300                               5.645   \n",
        "\n",
        "    Bodenfeuchte in 10 cm Tiefe weekly mean  \\\n",
        "1                                 53.933284   \n",
        "2                                 54.130909   \n",
        "3                                 44.903684   \n",
        "4                                 40.957487   \n",
        "5                                 49.837735   \n",
        "6                                 52.626250   \n",
        "7                                 53.439861   \n",
        "8                                 53.545079   \n",
        "9                                 53.927037   \n",
        "44                                32.096476   \n",
        "45                                32.921756   \n",
        "46                                33.331290   \n",
        "47                                31.979712   \n",
        "48                                32.893880   \n",
        "49                                43.313696   \n",
        "50                                42.873730   \n",
        "51                                50.907411   \n",
        "52                                53.080470   \n",
        "\n",
        "    Bodenfeuchte in 10 cm Tiefe weekly median  \\\n",
        "1                                      53.810   \n",
        "2                                      54.260   \n",
        "3                                      43.645   \n",
        "4                                      41.230   \n",
        "5                                      51.620   \n",
        "6                                      52.650   \n",
        "7                                      53.470   \n",
        "8                                      53.530   \n",
        "9                                      53.940   \n",
        "44                                     32.510   \n",
        "45                                     32.800   \n",
        "46                                     33.310   \n",
        "47                                     31.940   \n",
        "48                                     31.490   \n",
        "49                                     41.490   \n",
        "50                                     40.630   \n",
        "51                                     51.015   \n",
        "52                                     53.210   \n",
        "\n",
        "    Bodenfeuchte in 25 cm Tiefe weekly mean  \\\n",
        "1                                 45.837649   \n",
        "2                                 46.003217   \n",
        "3                                 45.247166   \n",
        "4                                 45.035880   \n",
        "5                                 45.066753   \n",
        "6                                 45.091518   \n",
        "7                                 44.958611   \n",
        "8                                 44.977530   \n",
        "9                                 44.997477   \n",
        "44                                37.744497   \n",
        "45                                41.010357   \n",
        "46                                45.179444   \n",
        "47                                40.248757   \n",
        "48                                41.153856   \n",
        "49                                44.716332   \n",
        "50                                44.515129   \n",
        "51                                45.051349   \n",
        "52                                45.530780   \n",
        "\n",
        "    Bodenfeuchte in 25 cm Tiefe weekly median  \n",
        "1                                       45.76  \n",
        "2                                       46.07  \n",
        "3                                       45.36  \n",
        "4                                       45.04  \n",
        "5                                       45.14  \n",
        "6                                       45.08  \n",
        "7                                       44.96  \n",
        "8                                       45.01  \n",
        "9                                       45.00  \n",
        "44                                      38.46  \n",
        "45                                      40.38  \n",
        "46                                      45.55  \n",
        "47                                      39.89  \n",
        "48                                      39.46  \n",
        "49                                      44.74  \n",
        "50                                      44.55  \n",
        "51                                      45.06  \n",
        "52                                      45.54  "
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Convert some more things\n",
      "df2 = pd.DataFrame(boden_5_daily_max)\n",
      "df2[boden_5_daily_min.name] = boden_5_daily_min\n",
      "df2[boden_10_daily_max.name] = boden_10_daily_max\n",
      "df2[boden_10_daily_min.name] = boden_10_daily_min\n",
      "df2[luft_2_daily_max.name] = luft_2_daily_max\n",
      "df2[luft_2_daily_min.name] = luft_2_daily_min\n",
      "df2[bodenfeuchte_10_daily_max.name] = bodenfeuchte_10_daily_max\n",
      "df2[bodenfeuchte_10_daily_min.name] = bodenfeuchte_10_daily_min\n",
      "df2[bodenfeuchte_25_daily_max.name] = bodenfeuchte_25_daily_max\n",
      "df2[bodenfeuchte_25_daily_min.name] = bodenfeuchte_25_daily_min\n",
      "df2[:10]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Bodentemp 05 cm C daily max</th>\n",
        "      <th>Bodentemp 05 cm C daily min</th>\n",
        "      <th>Bodentemp 10 cm C daily max</th>\n",
        "      <th>Bodentemp 10 cm C daily min</th>\n",
        "      <th>Lufttemperatur 2 m C daily max</th>\n",
        "      <th>Lufttemperatur 2 m C daily min</th>\n",
        "      <th>Bodenfeuchte in 10 cm Tiefe daily max</th>\n",
        "      <th>Bodenfeuchte in 10 cm Tiefe daily min</th>\n",
        "      <th>Bodenfeuchte in 25 cm Tiefe daily max</th>\n",
        "      <th>Bodenfeuchte in 25 cm Tiefe daily min</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2012-11-01</th>\n",
        "      <td> 7.54</td>\n",
        "      <td> 6.55</td>\n",
        "      <td> 7.42</td>\n",
        "      <td> 6.88</td>\n",
        "      <td> 10.57</td>\n",
        "      <td> 2.89</td>\n",
        "      <td> 31.17</td>\n",
        "      <td> 30.99</td>\n",
        "      <td> 36.46</td>\n",
        "      <td> 36.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-11-02</th>\n",
        "      <td> 7.68</td>\n",
        "      <td> 7.12</td>\n",
        "      <td> 7.60</td>\n",
        "      <td> 7.32</td>\n",
        "      <td> 10.22</td>\n",
        "      <td> 5.73</td>\n",
        "      <td> 33.91</td>\n",
        "      <td> 30.92</td>\n",
        "      <td> 38.82</td>\n",
        "      <td> 36.39</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-11-03</th>\n",
        "      <td> 8.09</td>\n",
        "      <td> 7.33</td>\n",
        "      <td> 7.95</td>\n",
        "      <td> 7.46</td>\n",
        "      <td> 12.61</td>\n",
        "      <td> 5.87</td>\n",
        "      <td> 33.84</td>\n",
        "      <td> 32.45</td>\n",
        "      <td> 38.84</td>\n",
        "      <td> 38.44</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-11-04</th>\n",
        "      <td> 8.36</td>\n",
        "      <td> 7.78</td>\n",
        "      <td> 8.23</td>\n",
        "      <td> 7.85</td>\n",
        "      <td> 10.19</td>\n",
        "      <td> 5.59</td>\n",
        "      <td> 33.96</td>\n",
        "      <td> 32.41</td>\n",
        "      <td> 39.92</td>\n",
        "      <td> 38.77</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-11-05</th>\n",
        "      <td> 8.45</td>\n",
        "      <td> 8.02</td>\n",
        "      <td> 8.31</td>\n",
        "      <td> 8.11</td>\n",
        "      <td>  9.27</td>\n",
        "      <td> 4.61</td>\n",
        "      <td> 33.47</td>\n",
        "      <td> 32.46</td>\n",
        "      <td> 40.01</td>\n",
        "      <td> 39.33</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-11-06</th>\n",
        "      <td> 8.01</td>\n",
        "      <td> 7.23</td>\n",
        "      <td> 8.14</td>\n",
        "      <td> 7.59</td>\n",
        "      <td>  6.18</td>\n",
        "      <td> 1.33</td>\n",
        "      <td> 34.89</td>\n",
        "      <td> 32.52</td>\n",
        "      <td> 40.89</td>\n",
        "      <td> 39.69</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-11-07</th>\n",
        "      <td> 7.27</td>\n",
        "      <td> 6.96</td>\n",
        "      <td> 7.58</td>\n",
        "      <td> 7.26</td>\n",
        "      <td>  6.36</td>\n",
        "      <td> 3.39</td>\n",
        "      <td> 33.15</td>\n",
        "      <td> 32.44</td>\n",
        "      <td> 40.51</td>\n",
        "      <td> 39.73</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-11-08</th>\n",
        "      <td> 7.53</td>\n",
        "      <td> 7.27</td>\n",
        "      <td> 7.58</td>\n",
        "      <td> 7.38</td>\n",
        "      <td>  6.99</td>\n",
        "      <td> 5.66</td>\n",
        "      <td> 33.11</td>\n",
        "      <td> 32.60</td>\n",
        "      <td> 40.59</td>\n",
        "      <td> 39.90</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-11-09</th>\n",
        "      <td> 8.06</td>\n",
        "      <td> 7.33</td>\n",
        "      <td> 7.89</td>\n",
        "      <td> 7.56</td>\n",
        "      <td>  9.39</td>\n",
        "      <td> 2.56</td>\n",
        "      <td> 32.63</td>\n",
        "      <td> 32.20</td>\n",
        "      <td> 40.96</td>\n",
        "      <td> 40.20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-11-10</th>\n",
        "      <td> 7.43</td>\n",
        "      <td> 6.90</td>\n",
        "      <td> 7.62</td>\n",
        "      <td> 7.19</td>\n",
        "      <td>  8.62</td>\n",
        "      <td> 2.32</td>\n",
        "      <td> 32.41</td>\n",
        "      <td> 31.88</td>\n",
        "      <td> 40.93</td>\n",
        "      <td> 40.63</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "            Bodentemp 05 cm C daily max  Bodentemp 05 cm C daily min  \\\n",
        "2012-11-01                         7.54                         6.55   \n",
        "2012-11-02                         7.68                         7.12   \n",
        "2012-11-03                         8.09                         7.33   \n",
        "2012-11-04                         8.36                         7.78   \n",
        "2012-11-05                         8.45                         8.02   \n",
        "2012-11-06                         8.01                         7.23   \n",
        "2012-11-07                         7.27                         6.96   \n",
        "2012-11-08                         7.53                         7.27   \n",
        "2012-11-09                         8.06                         7.33   \n",
        "2012-11-10                         7.43                         6.90   \n",
        "\n",
        "            Bodentemp 10 cm C daily max  Bodentemp 10 cm C daily min  \\\n",
        "2012-11-01                         7.42                         6.88   \n",
        "2012-11-02                         7.60                         7.32   \n",
        "2012-11-03                         7.95                         7.46   \n",
        "2012-11-04                         8.23                         7.85   \n",
        "2012-11-05                         8.31                         8.11   \n",
        "2012-11-06                         8.14                         7.59   \n",
        "2012-11-07                         7.58                         7.26   \n",
        "2012-11-08                         7.58                         7.38   \n",
        "2012-11-09                         7.89                         7.56   \n",
        "2012-11-10                         7.62                         7.19   \n",
        "\n",
        "            Lufttemperatur 2 m C daily max  Lufttemperatur 2 m C daily min  \\\n",
        "2012-11-01                           10.57                            2.89   \n",
        "2012-11-02                           10.22                            5.73   \n",
        "2012-11-03                           12.61                            5.87   \n",
        "2012-11-04                           10.19                            5.59   \n",
        "2012-11-05                            9.27                            4.61   \n",
        "2012-11-06                            6.18                            1.33   \n",
        "2012-11-07                            6.36                            3.39   \n",
        "2012-11-08                            6.99                            5.66   \n",
        "2012-11-09                            9.39                            2.56   \n",
        "2012-11-10                            8.62                            2.32   \n",
        "\n",
        "            Bodenfeuchte in 10 cm Tiefe daily max  \\\n",
        "2012-11-01                                  31.17   \n",
        "2012-11-02                                  33.91   \n",
        "2012-11-03                                  33.84   \n",
        "2012-11-04                                  33.96   \n",
        "2012-11-05                                  33.47   \n",
        "2012-11-06                                  34.89   \n",
        "2012-11-07                                  33.15   \n",
        "2012-11-08                                  33.11   \n",
        "2012-11-09                                  32.63   \n",
        "2012-11-10                                  32.41   \n",
        "\n",
        "            Bodenfeuchte in 10 cm Tiefe daily min  \\\n",
        "2012-11-01                                  30.99   \n",
        "2012-11-02                                  30.92   \n",
        "2012-11-03                                  32.45   \n",
        "2012-11-04                                  32.41   \n",
        "2012-11-05                                  32.46   \n",
        "2012-11-06                                  32.52   \n",
        "2012-11-07                                  32.44   \n",
        "2012-11-08                                  32.60   \n",
        "2012-11-09                                  32.20   \n",
        "2012-11-10                                  31.88   \n",
        "\n",
        "            Bodenfeuchte in 25 cm Tiefe daily max  \\\n",
        "2012-11-01                                  36.46   \n",
        "2012-11-02                                  38.82   \n",
        "2012-11-03                                  38.84   \n",
        "2012-11-04                                  39.92   \n",
        "2012-11-05                                  40.01   \n",
        "2012-11-06                                  40.89   \n",
        "2012-11-07                                  40.51   \n",
        "2012-11-08                                  40.59   \n",
        "2012-11-09                                  40.96   \n",
        "2012-11-10                                  40.93   \n",
        "\n",
        "            Bodenfeuchte in 25 cm Tiefe daily min  \n",
        "2012-11-01                                  36.00  \n",
        "2012-11-02                                  36.39  \n",
        "2012-11-03                                  38.44  \n",
        "2012-11-04                                  38.77  \n",
        "2012-11-05                                  39.33  \n",
        "2012-11-06                                  39.69  \n",
        "2012-11-07                                  39.73  \n",
        "2012-11-08                                  39.90  \n",
        "2012-11-09                                  40.20  \n",
        "2012-11-10                                  40.63  "
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Write as excel\n",
      "df.to_excel(\"weekly_values.xls\")\n",
      "df2.to_excel(\"daily_values.xls\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    }
   ],
   "metadata": {}
  }
 ]
}