WMS_Co-Relation.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
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    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Correlation Coefficient Formula\n",
    "\n",
    "$ r =  {\\dfrac{n(\\sum_{xy}) - (\\sum_{x})\\thinspace(\\sum_{y})}{\\sqrt{[n\\sum_{x^2}\\thinspace-\\thinspace(\\sum_{x})^2]\\thinspace\\thinspace[n\\sum_{y^2}-\\thinspace(\\sum_{y})^2] }}}$\n",
    "\n",
    "$1 pascal = {\\dfrac{N}{m^2}} = {\\dfrac{kg}{ms^2}}$\n",
    "\n",
    "$N \\thinspace\\thinspace - Newtons \\\\ m \\thinspace\\thinspace - Meter \\\\ kg - Kilogram \\\\ s \\thinspace\\thinspace - Seconds$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import mlab\n",
    "from matplotlib.dates import DateFormatter\n",
    "import matplotlib.dates as mdates "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>client_id</th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>temparature</th>\n",
       "      <th>pressure</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>rain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10101</td>\n",
       "      <td>2016-10-13</td>\n",
       "      <td>12:48:16</td>\n",
       "      <td>2747</td>\n",
       "      <td>91061.0</td>\n",
       "      <td>4265.0</td>\n",
       "      <td>1</td>\n",
       "      <td>150.0</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10101</td>\n",
       "      <td>2016-10-13</td>\n",
       "      <td>12:54:56</td>\n",
       "      <td>2747</td>\n",
       "      <td>91035.0</td>\n",
       "      <td>4275.0</td>\n",
       "      <td>7.14</td>\n",
       "      <td>40.0</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10101</td>\n",
       "      <td>2016-10-13</td>\n",
       "      <td>12:57:16</td>\n",
       "      <td>2747</td>\n",
       "      <td>91044.0</td>\n",
       "      <td>4258.0</td>\n",
       "      <td>8.83</td>\n",
       "      <td>335.0</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10101</td>\n",
       "      <td>2016-10-13</td>\n",
       "      <td>12:59:26</td>\n",
       "      <td>2747</td>\n",
       "      <td>91040.0</td>\n",
       "      <td>7253.0</td>\n",
       "      <td>9.38</td>\n",
       "      <td>330.0</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10101</td>\n",
       "      <td>2016-10-13</td>\n",
       "      <td>13:06:16</td>\n",
       "      <td>2747</td>\n",
       "      <td>91022.0</td>\n",
       "      <td>4252.0</td>\n",
       "      <td>6.24</td>\n",
       "      <td>185.0</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   client_id        date      time temparature  pressure  humidity wind_speed  \\\n",
       "0      10101  2016-10-13  12:48:16        2747   91061.0    4265.0          1   \n",
       "1      10101  2016-10-13  12:54:56        2747   91035.0    4275.0       7.14   \n",
       "2      10101  2016-10-13  12:57:16        2747   91044.0    4258.0       8.83   \n",
       "3      10101  2016-10-13  12:59:26        2747   91040.0    7253.0       9.38   \n",
       "4      10101  2016-10-13  13:06:16        2747   91022.0    4252.0       6.24   \n",
       "\n",
       "   wind_direction      rain  \n",
       "0           150.0  111111.0  \n",
       "1            40.0  111111.0  \n",
       "2           335.0  111111.0  \n",
       "3           330.0  111111.0  \n",
       "4           185.0  111111.0  "
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"wms.csv\", sep=\",\")\n",
    "df.columns = [\n",
    "    \"client_id\",\n",
    "    \"date\",\n",
    "    \"time\",\n",
    "    \"temparature\",\n",
    "    \"pressure\",\n",
    "    \"humidity\",\n",
    "    \"wind_speed\",\n",
    "    \"wind_direction\",\n",
    "    \"rain\",\n",
    "]\n",
    "df.replace(\"wwww\", 0)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1678, 9)"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "client_id           int64\n",
       "date               object\n",
       "time               object\n",
       "temparature        object\n",
       "pressure          float64\n",
       "humidity          float64\n",
       "wind_speed         object\n",
       "wind_direction    float64\n",
       "rain              float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>client_id</th>\n",
       "      <th>pressure</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>rain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1678.0</td>\n",
       "      <td>1668.000000</td>\n",
       "      <td>1678.000000</td>\n",
       "      <td>1678.000000</td>\n",
       "      <td>1677.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10101.0</td>\n",
       "      <td>111061.177050</td>\n",
       "      <td>4721.179118</td>\n",
       "      <td>152.473021</td>\n",
       "      <td>80394.888849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>101565.629728</td>\n",
       "      <td>949.395945</td>\n",
       "      <td>296.495726</td>\n",
       "      <td>46368.228269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10101.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-699.000000</td>\n",
       "      <td>-3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10101.0</td>\n",
       "      <td>91031.750000</td>\n",
       "      <td>4474.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11111.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10101.0</td>\n",
       "      <td>91175.000000</td>\n",
       "      <td>4563.000000</td>\n",
       "      <td>192.000000</td>\n",
       "      <td>111111.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10101.0</td>\n",
       "      <td>91272.000000</td>\n",
       "      <td>5657.000000</td>\n",
       "      <td>282.000000</td>\n",
       "      <td>111111.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10101.0</td>\n",
       "      <td>599109.000000</td>\n",
       "      <td>7253.000000</td>\n",
       "      <td>11111.000000</td>\n",
       "      <td>111111.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       client_id       pressure     humidity  wind_direction           rain\n",
       "count     1678.0    1668.000000  1678.000000     1678.000000    1677.000000\n",
       "mean     10101.0  111061.177050  4721.179118      152.473021   80394.888849\n",
       "std          0.0  101565.629728   949.395945      296.495726   46368.228269\n",
       "min      10101.0       1.000000  -699.000000       -3.000000       0.000000\n",
       "25%      10101.0   91031.750000  4474.000000        1.000000   11111.000000\n",
       "50%      10101.0   91175.000000  4563.000000      192.000000  111111.000000\n",
       "75%      10101.0   91272.000000  5657.000000      282.000000  111111.000000\n",
       "max      10101.0  599109.000000  7253.000000    11111.000000  111111.000000"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "temp = []\n",
    "for i in df.temparature:\n",
    "    a = \"{:4}\".format(i)\n",
    "    b = str(\"{}.{}\".format(a[:2], a[2:]))\n",
    "    temp.append(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "df.temparature = pd.Series(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>client_id</th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>temparature</th>\n",
       "      <th>pressure</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>rain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10101</td>\n",
       "      <td>2016-10-13</td>\n",
       "      <td>12:48:16</td>\n",
       "      <td>27.47</td>\n",
       "      <td>91061.0</td>\n",
       "      <td>4265.0</td>\n",
       "      <td>1</td>\n",
       "      <td>150.0</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10101</td>\n",
       "      <td>2016-10-13</td>\n",
       "      <td>12:54:56</td>\n",
       "      <td>27.47</td>\n",
       "      <td>91035.0</td>\n",
       "      <td>4275.0</td>\n",
       "      <td>7.14</td>\n",
       "      <td>40.0</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10101</td>\n",
       "      <td>2016-10-13</td>\n",
       "      <td>12:57:16</td>\n",
       "      <td>27.47</td>\n",
       "      <td>91044.0</td>\n",
       "      <td>4258.0</td>\n",
       "      <td>8.83</td>\n",
       "      <td>335.0</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10101</td>\n",
       "      <td>2016-10-13</td>\n",
       "      <td>12:59:26</td>\n",
       "      <td>27.47</td>\n",
       "      <td>91040.0</td>\n",
       "      <td>7253.0</td>\n",
       "      <td>9.38</td>\n",
       "      <td>330.0</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10101</td>\n",
       "      <td>2016-10-13</td>\n",
       "      <td>13:06:16</td>\n",
       "      <td>27.47</td>\n",
       "      <td>91022.0</td>\n",
       "      <td>4252.0</td>\n",
       "      <td>6.24</td>\n",
       "      <td>185.0</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   client_id        date      time temparature  pressure  humidity wind_speed  \\\n",
       "0      10101  2016-10-13  12:48:16       27.47   91061.0    4265.0          1   \n",
       "1      10101  2016-10-13  12:54:56       27.47   91035.0    4275.0       7.14   \n",
       "2      10101  2016-10-13  12:57:16       27.47   91044.0    4258.0       8.83   \n",
       "3      10101  2016-10-13  12:59:26       27.47   91040.0    7253.0       9.38   \n",
       "4      10101  2016-10-13  13:06:16       27.47   91022.0    4252.0       6.24   \n",
       "\n",
       "   wind_direction      rain  \n",
       "0           150.0  111111.0  \n",
       "1            40.0  111111.0  \n",
       "2           335.0  111111.0  \n",
       "3           330.0  111111.0  \n",
       "4           185.0  111111.0  "
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Maximum value of Pressure based on date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>client_id</th>\n",
       "      <th>time</th>\n",
       "      <th>temparature</th>\n",
       "      <th>pressure</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>rain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2002-11-16</td>\n",
       "      <td>10101</td>\n",
       "      <td>18:43:34</td>\n",
       "      <td>27.47</td>\n",
       "      <td>91186.0</td>\n",
       "      <td>5657.0</td>\n",
       "      <td>0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-10-13</td>\n",
       "      <td>10101</td>\n",
       "      <td>18:32:04</td>\n",
       "      <td>27.47</td>\n",
       "      <td>91061.0</td>\n",
       "      <td>7253.0</td>\n",
       "      <td>9.89</td>\n",
       "      <td>355.0</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-10-18</td>\n",
       "      <td>10101</td>\n",
       "      <td>21:14:30</td>\n",
       "      <td>27.91</td>\n",
       "      <td>91118.0</td>\n",
       "      <td>4295.0</td>\n",
       "      <td>9.89</td>\n",
       "      <td>355.0</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-10-25</td>\n",
       "      <td>10101</td>\n",
       "      <td>16:22:01</td>\n",
       "      <td>28.06</td>\n",
       "      <td>91288.0</td>\n",
       "      <td>3578.0</td>\n",
       "      <td>9.94</td>\n",
       "      <td>335.0</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-10-26</td>\n",
       "      <td>10101</td>\n",
       "      <td>04:52:35</td>\n",
       "      <td>27.49</td>\n",
       "      <td>91057.0</td>\n",
       "      <td>3450.0</td>\n",
       "      <td>wwww</td>\n",
       "      <td>15.1</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  client_id      time temparature  pressure  humidity wind_speed  \\\n",
       "0  2002-11-16      10101  18:43:34       27.47   91186.0    5657.0          0   \n",
       "1  2016-10-13      10101  18:32:04       27.47   91061.0    7253.0       9.89   \n",
       "2  2016-10-18      10101  21:14:30       27.91   91118.0    4295.0       9.89   \n",
       "3  2016-10-25      10101  16:22:01       28.06   91288.0    3578.0       9.94   \n",
       "4  2016-10-26      10101  04:52:35       27.49   91057.0    3450.0       wwww   \n",
       "\n",
       "   wind_direction      rain  \n",
       "0            80.0  111111.0  \n",
       "1           355.0  111111.0  \n",
       "2           355.0  111111.0  \n",
       "3           335.0  111111.0  \n",
       "4            15.1  111111.0  "
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_max = df.groupby(['date'], axis=0,).max().reset_index()\n",
    "df_max.head(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KpZkplTZumSpZWE9XNEdUhfW6dTM/R82zsJYkSZKkhpQsrGea6j1/viPWbbOwlgobt3VB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\njR7zpNLMlEozUyqty5kaVmE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ltkaLhbUkSZIkdcTUwrqfZwVvj2usXWMtSZIkqQPuvHPD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\nkiRJ6sBgLUmSJElSBwZrSZIkSZI6+AXFhFDgbwBMJQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe30b081790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_pres = [float(\"{:2.2f}\".format(i)) for i in df_max.pressure]\n",
    "max_date = [i for i in df_max.date]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(16, 5))\n",
    "max_date = mdates.datestr2num(max_date)\n",
    "ax.plot(max_date, max_pres)\n",
    "ax.grid(True)\n",
    "ax.legend(loc='right')\n",
    "ax.set_title('Maximum Pressure Value based on Date')\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Pressure(pascal)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Minimum value of Pressure based on date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>client_id</th>\n",
       "      <th>time</th>\n",
       "      <th>temparature</th>\n",
       "      <th>pressure</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>rain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2002-11-16</td>\n",
       "      <td>10101</td>\n",
       "      <td>18:24:49</td>\n",
       "      <td>26.41</td>\n",
       "      <td>91138.00</td>\n",
       "      <td>4625.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-10-13</td>\n",
       "      <td>10101</td>\n",
       "      <td>11:01:00</td>\n",
       "      <td>26.00</td>\n",
       "      <td>90020.00</td>\n",
       "      <td>4037.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-10-18</td>\n",
       "      <td>10101</td>\n",
       "      <td>12:19:35</td>\n",
       "      <td>25.75</td>\n",
       "      <td>90700.00</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-10-25</td>\n",
       "      <td>10101</td>\n",
       "      <td>03:05:48</td>\n",
       "      <td>27.47</td>\n",
       "      <td>78597.00</td>\n",
       "      <td>3450.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-10-26</td>\n",
       "      <td>10101</td>\n",
       "      <td>00:51:41</td>\n",
       "      <td>27.</td>\n",
       "      <td>910.32</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  client_id      time temparature  pressure  humidity wind_speed  \\\n",
       "0  2002-11-16      10101  18:24:49       26.41  91138.00    4625.0          0   \n",
       "1  2016-10-13      10101  11:01:00       26.00  90020.00    4037.0          1   \n",
       "2  2016-10-18      10101  12:19:35       25.75  90700.00      20.0          1   \n",
       "3  2016-10-25      10101  03:05:48       27.47  78597.00    3450.0          1   \n",
       "4  2016-10-26      10101  00:51:41       27.      910.32      34.5          0   \n",
       "\n",
       "   wind_direction      rain  \n",
       "0            -1.0  111111.0  \n",
       "1             5.0  111111.0  \n",
       "2             5.0  111111.0  \n",
       "3             0.0  111111.0  \n",
       "4             0.0  111111.0  "
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_min = df.groupby(['date'], axis=0,).min().reset_index()\n",
    "df_min.head(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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srKXCJm1dkNplnlSamVJpZkqlTVqmvN3W4mBhLUmSJEkNcSr44hC5yOcLREQu9s9AkiRJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe30aed9710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "min_pres = [float(\"{:2.2f}\".format(i)) for i in df_min.pressure]\n",
    "min_date = [i for i in df_min.date]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(16, 5))\n",
    "min_date = mdates.datestr2num(min_date)\n",
    "ax.plot(min_date, min_pres)\n",
    "ax.grid(True)\n",
    "ax.legend(loc='right')\n",
    "ax.set_title('Minimum Pressure Value based on Date')\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Pressure(pascal)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Average(Mean) value of Pressure based on date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>client_id</th>\n",
       "      <th>pressure</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>rain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2002-11-16</td>\n",
       "      <td>10101</td>\n",
       "      <td>91164.666667</td>\n",
       "      <td>5243.291667</td>\n",
       "      <td>9.083333</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-10-13</td>\n",
       "      <td>10101</td>\n",
       "      <td>90937.226415</td>\n",
       "      <td>4496.471698</td>\n",
       "      <td>167.547170</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-10-18</td>\n",
       "      <td>10101</td>\n",
       "      <td>90918.172414</td>\n",
       "      <td>3788.327586</td>\n",
       "      <td>179.224138</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-10-25</td>\n",
       "      <td>10101</td>\n",
       "      <td>90898.241935</td>\n",
       "      <td>3452.677419</td>\n",
       "      <td>187.338710</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-10-26</td>\n",
       "      <td>10101</td>\n",
       "      <td>90217.083214</td>\n",
       "      <td>3419.504464</td>\n",
       "      <td>6.741071</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  client_id      pressure     humidity  wind_direction      rain\n",
       "0  2002-11-16      10101  91164.666667  5243.291667        9.083333  111111.0\n",
       "1  2016-10-13      10101  90937.226415  4496.471698      167.547170  111111.0\n",
       "2  2016-10-18      10101  90918.172414  3788.327586      179.224138  111111.0\n",
       "3  2016-10-25      10101  90898.241935  3452.677419      187.338710  111111.0\n",
       "4  2016-10-26      10101  90217.083214  3419.504464        6.741071  111111.0"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mean = df.groupby(['date'], axis=0).mean().reset_index()\n",
    "df_mean.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dwdUOC2upsHFbF6R2mSeVZqZUmplSaeOWKW+3tTBYWEuSJElSQ5wKvjBELvD5AhGRC/0z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\nwWAtSZIkSVKH324n0fn2QgWkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe30b658150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_pres = [float(\"{:2.2f}\".format(i)) for i in df_mean.pressure]\n",
    "mean_date = [i for i in df_mean.date]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(16, 5))\n",
    "mean_date = mdates.datestr2num(mean_date)\n",
    "ax.plot(mean_date, mean_pres)\n",
    "ax.grid(True)\n",
    "ax.legend(loc='right')\n",
    "ax.set_title('Minimum Pressure Value based on Date')\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Pressure(pascal)')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Temparature vs Pressure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>client_id</th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>temparature</th>\n",
       "      <th>pressure</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>rain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10101</td>\n",
       "      <td>2016-10-13</td>\n",
       "      <td>12:48:16</td>\n",
       "      <td>27.47</td>\n",
       "      <td>91061.0</td>\n",
       "      <td>4265.0</td>\n",
       "      <td>1</td>\n",
       "      <td>150.0</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10101</td>\n",
       "      <td>2016-10-13</td>\n",
       "      <td>12:54:56</td>\n",
       "      <td>27.47</td>\n",
       "      <td>91035.0</td>\n",
       "      <td>4275.0</td>\n",
       "      <td>7.14</td>\n",
       "      <td>40.0</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10101</td>\n",
       "      <td>2016-10-13</td>\n",
       "      <td>12:57:16</td>\n",
       "      <td>27.47</td>\n",
       "      <td>91044.0</td>\n",
       "      <td>4258.0</td>\n",
       "      <td>8.83</td>\n",
       "      <td>335.0</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10101</td>\n",
       "      <td>2016-10-13</td>\n",
       "      <td>12:59:26</td>\n",
       "      <td>27.47</td>\n",
       "      <td>91040.0</td>\n",
       "      <td>7253.0</td>\n",
       "      <td>9.38</td>\n",
       "      <td>330.0</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10101</td>\n",
       "      <td>2016-10-13</td>\n",
       "      <td>13:06:16</td>\n",
       "      <td>27.47</td>\n",
       "      <td>91022.0</td>\n",
       "      <td>4252.0</td>\n",
       "      <td>6.24</td>\n",
       "      <td>185.0</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   client_id        date      time temparature  pressure  humidity wind_speed  \\\n",
       "0      10101  2016-10-13  12:48:16       27.47   91061.0    4265.0          1   \n",
       "1      10101  2016-10-13  12:54:56       27.47   91035.0    4275.0       7.14   \n",
       "2      10101  2016-10-13  12:57:16       27.47   91044.0    4258.0       8.83   \n",
       "3      10101  2016-10-13  12:59:26       27.47   91040.0    7253.0       9.38   \n",
       "4      10101  2016-10-13  13:06:16       27.47   91022.0    4252.0       6.24   \n",
       "\n",
       "   wind_direction      rain  \n",
       "0           150.0  111111.0  \n",
       "1            40.0  111111.0  \n",
       "2           335.0  111111.0  \n",
       "3           330.0  111111.0  \n",
       "4           185.0  111111.0  "
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df > 0].dropna()\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tmp = []\n",
    "for i in df.temparature:\n",
    "    tmp.append(float(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LT+8K4M+rat4LXnt6JUmSJGm0LLieXuCfgS+0xe+tE5NV9Z/9CkqSJEmSpC7M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\nFr2SJEmSpJFl0StJkiRJGln/P+AGkW+SpXUXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe30a87e110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "temparture = [float(\"{:2.2f}\".format(i)) for i in tmp]\n",
    "pressure = [float(\"{:2.2f}\".format(i)) for i in df.pressure]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(16, 5))\n",
    "ax.plot(sorted(pressure), temparture, )\n",
    "ax.grid(True)\n",
    "ax.legend(loc='right')\n",
    "ax.set_title('Temparature vs Pressure')\n",
    "ax.set_xlabel('Pressure(pascal)')\n",
    "ax.set_ylabel('Temparature(Celsius)')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}