akaihola/Teewasser.ipynb

## Teewasser.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import io\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rc('figure', figsize=(18, 9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_temperatures(*tsvs):\n",
    "    columns = {}\n",
    "    for index, tsv in enumerate(tsvs):\n",
    "        columns[str(index)] = pd.read_csv(io.StringIO(tsv), sep=' ', index_col=0, header=None).iloc[:, 0]\n",
    "    df = pd.DataFrame(columns).interpolate(method='index', limit_area='inside')\n",
    "    display(df.plot())\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_temperatures(*tsvs):\n",
    "    columns = [pd.read_csv(io.StringIO(tsv), sep=' ', index_col=0, header=None)\n",
    "               for tsv in tsvs]\n",
    "    df = pd.concat(columns, axis=1).interpolate(method='index', limit_area='inside')\n",
    "    df.columns = [str(i) for i in range(len(columns))]\n",
    "    display(df.plot())\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "m1 = \"\"\"\\\n",
    "0 82\n",
    "1 78\n",
    "2 74\n",
    "3 74\n",
    "22 68\n",
    "34 65\n",
    "59 62\n",
    "86 62\n",
    "95 61\n",
    "150 58\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "m2 = \"\"\"\\\n",
    "1 81\n",
    "6 77\n",
    "7 76\n",
    "10 75\n",
    "12 73\n",
    "16 71\n",
    "25 70\n",
    "33 69\n",
    "69 64\n",
    "97 62\n",
    "108 62\n",
    "122 61\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "m3 = '''\\\n",
    "1 82\n",
    "4 80\n",
    "9 77\n",
    "20 72\n",
    "25 71\n",
    "38 69\n",
    "44 68\n",
    "52 67\n",
    "66 65\n",
    "104 63\n",
    "148 62\n",
    "201 59\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "m4 = '''\\\n",
    "0 82\n",
    "7 76\n",
    "16 74\n",
    "52 66\n",
    "175 62\n",
    "214 59\n",
    "290 53\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f34beec6d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = plot_temperatures(m1, m2, m3, m4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>82.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>78.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>81.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>74.000000</td>\n",
       "      <td>80.200000</td>\n",
       "      <td>81.333333</td>\n",
       "      <td>80.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>74.000000</td>\n",
       "      <td>79.400000</td>\n",
       "      <td>80.666667</td>\n",
       "      <td>79.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>73.684211</td>\n",
       "      <td>78.600000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>78.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>73.052632</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>78.800000</td>\n",
       "      <td>76.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>72.736842</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>78.200000</td>\n",
       "      <td>76.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>72.105263</td>\n",
       "      <td>75.333333</td>\n",
       "      <td>77.000000</td>\n",
       "      <td>75.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>71.789474</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>76.545455</td>\n",
       "      <td>75.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>71.157895</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>75.636364</td>\n",
       "      <td>74.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>69.894737</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>73.818182</td>\n",
       "      <td>74.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>68.631579</td>\n",
       "      <td>70.555556</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>68.000000</td>\n",
       "      <td>70.333333</td>\n",
       "      <td>71.600000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>67.250000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>65.250000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>69.769231</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>65.000000</td>\n",
       "      <td>68.861111</td>\n",
       "      <td>69.615385</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>64.520000</td>\n",
       "      <td>68.305556</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>63.800000</td>\n",
       "      <td>67.472222</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>62.840000</td>\n",
       "      <td>66.361111</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>62.000000</td>\n",
       "      <td>65.388889</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>62.000000</td>\n",
       "      <td>64.416667</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>62.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>64.842105</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>62.000000</td>\n",
       "      <td>62.785714</td>\n",
       "      <td>63.947368</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>62.142857</td>\n",
       "      <td>63.473684</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>60.890909</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>63.368421</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>60.509091</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>60.290909</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>62.909091</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>59.527273</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>62.590909</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>58.109091</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>58.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61.886792</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             0          1          2          3\n",
       "0                                              \n",
       "0    82.000000        NaN        NaN  82.000000\n",
       "1    78.000000  81.000000  82.000000  81.142857\n",
       "2    74.000000  80.200000  81.333333  80.285714\n",
       "3    74.000000  79.400000  80.666667  79.428571\n",
       "4    73.684211  78.600000  80.000000  78.571429\n",
       "6    73.052632  77.000000  78.800000  76.857143\n",
       "7    72.736842  76.000000  78.200000  76.000000\n",
       "9    72.105263  75.333333  77.000000  75.555556\n",
       "10   71.789474  75.000000  76.545455  75.333333\n",
       "12   71.157895  73.000000  75.636364  74.888889\n",
       "16   69.894737  71.000000  73.818182  74.000000\n",
       "20   68.631579  70.555556  72.000000        NaN\n",
       "22   68.000000  70.333333  71.600000        NaN\n",
       "25   67.250000  70.000000  71.000000        NaN\n",
       "33   65.250000  69.000000  69.769231        NaN\n",
       "34   65.000000  68.861111  69.615385        NaN\n",
       "38   64.520000  68.305556  69.000000        NaN\n",
       "44   63.800000  67.472222  68.000000        NaN\n",
       "52   62.840000  66.361111  67.000000        NaN\n",
       "59   62.000000  65.388889  66.000000        NaN\n",
       "66   62.000000  64.416667  65.000000        NaN\n",
       "69   62.000000  64.000000  64.842105        NaN\n",
       "86   62.000000  62.785714  63.947368        NaN\n",
       "95   61.000000  62.142857  63.473684        NaN\n",
       "97   60.890909  62.000000  63.368421        NaN\n",
       "104  60.509091  62.000000  63.000000        NaN\n",
       "108  60.290909  62.000000  62.909091        NaN\n",
       "122  59.527273  61.000000  62.590909        NaN\n",
       "148  58.109091        NaN  62.000000        NaN\n",
       "150  58.000000        NaN  61.886792        NaN\n",
       "201        NaN        NaN  59.000000        NaN"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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