WMS_Time-Series.ipynb

{
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   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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    "deletable": true,
    "editable": 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": 4,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "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",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "df.wind_speed = df.wind_speed.replace(\"wwww\", \"0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "df.wind_speed = df.wind_speed.astype(float).head(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>2747.0</td>\n",
       "      <td>91061.0</td>\n",
       "      <td>4265.0</td>\n",
       "      <td>1.00</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>2747.0</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>2747.0</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>2747.0</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>2747.0</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  \\\n",
       "0      10101  2016-10-13  12:48:16       2747.0   91061.0    4265.0   \n",
       "1      10101  2016-10-13  12:54:56       2747.0   91035.0    4275.0   \n",
       "2      10101  2016-10-13  12:57:16       2747.0   91044.0    4258.0   \n",
       "3      10101  2016-10-13  12:59:26       2747.0   91040.0    7253.0   \n",
       "4      10101  2016-10-13  13:06:16       2747.0   91022.0    4252.0   \n",
       "\n",
       "   wind_speed  wind_direction      rain  \n",
       "0        1.00           150.0  111111.0  \n",
       "1        7.14            40.0  111111.0  \n",
       "2        8.83           335.0  111111.0  \n",
       "3        9.38           330.0  111111.0  \n",
       "4        6.24           185.0  111111.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "df[['temparature', 'pressure', 'humidity', 'wind_speed', 'wind_direction', 'rain']] = df[['temparature', 'pressure', 'humidity', 'wind_speed', 'wind_direction', 'rain']].apply(pd.to_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>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>count</th>\n",
       "      <td>1678.0</td>\n",
       "      <td>1677.000000</td>\n",
       "      <td>1668.000000</td>\n",
       "      <td>1678.000000</td>\n",
       "      <td>5.0000</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>2568.403101</td>\n",
       "      <td>111061.177050</td>\n",
       "      <td>4721.179118</td>\n",
       "      <td>6.5180</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>541.269877</td>\n",
       "      <td>101565.629728</td>\n",
       "      <td>949.395945</td>\n",
       "      <td>3.3335</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>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-699.000000</td>\n",
       "      <td>1.0000</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>2537.000000</td>\n",
       "      <td>91031.750000</td>\n",
       "      <td>4474.000000</td>\n",
       "      <td>6.2400</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>2747.000000</td>\n",
       "      <td>91175.000000</td>\n",
       "      <td>4563.000000</td>\n",
       "      <td>7.1400</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>2747.000000</td>\n",
       "      <td>91272.000000</td>\n",
       "      <td>5657.000000</td>\n",
       "      <td>8.8300</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>6040.000000</td>\n",
       "      <td>599109.000000</td>\n",
       "      <td>7253.000000</td>\n",
       "      <td>9.3800</td>\n",
       "      <td>11111.000000</td>\n",
       "      <td>111111.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       client_id  temparature       pressure     humidity  wind_speed  \\\n",
       "count     1678.0  1677.000000    1668.000000  1678.000000      5.0000   \n",
       "mean     10101.0  2568.403101  111061.177050  4721.179118      6.5180   \n",
       "std          0.0   541.269877  101565.629728   949.395945      3.3335   \n",
       "min      10101.0     2.000000       1.000000  -699.000000      1.0000   \n",
       "25%      10101.0  2537.000000   91031.750000  4474.000000      6.2400   \n",
       "50%      10101.0  2747.000000   91175.000000  4563.000000      7.1400   \n",
       "75%      10101.0  2747.000000   91272.000000  5657.000000      8.8300   \n",
       "max      10101.0  6040.000000  599109.000000  7253.000000      9.3800   \n",
       "\n",
       "       wind_direction           rain  \n",
       "count     1678.000000    1677.000000  \n",
       "mean       152.473021   80394.888849  \n",
       "std        296.495726   46368.228269  \n",
       "min         -3.000000       0.000000  \n",
       "25%          1.000000   11111.000000  \n",
       "50%        192.000000  111111.000000  \n",
       "75%        282.000000  111111.000000  \n",
       "max      11111.000000  111111.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    111111.0\n",
       "1    111111.0\n",
       "2    111111.0\n",
       "3    111111.0\n",
       "4    111111.0\n",
       "Name: rain, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rain = df['rain']\n",
    "rain.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from dateutil.parser import parse\n",
    "df['date'] = df['date'].apply(parse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# df_date_year = df['date'].apply(lambda x: x.strftime(\"%Y\"))\n",
    "# df_date_year.head(5)\n",
    "df_temp_date = df['date']\n",
    "df_date = df['date'].apply(lambda x: x.strftime(\"%Y\"))\n",
    "df_month = df['date'].apply(lambda x: x.strftime(\"%m\"))\n",
    "df['date'] = df_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "df_date_mean = df.groupby(['date'], axis=0).mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>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</td>\n",
       "      <td>10101</td>\n",
       "      <td>2742.583333</td>\n",
       "      <td>91164.666667</td>\n",
       "      <td>5243.291667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.083333</td>\n",
       "      <td>111111.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>10101</td>\n",
       "      <td>2587.366225</td>\n",
       "      <td>91111.679232</td>\n",
       "      <td>4750.294176</td>\n",
       "      <td>6.518</td>\n",
       "      <td>161.230311</td>\n",
       "      <td>86673.913907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>10101</td>\n",
       "      <td>2338.930070</td>\n",
       "      <td>341295.819549</td>\n",
       "      <td>4325.909510</td>\n",
       "      <td>NaN</td>\n",
       "      <td>84.005105</td>\n",
       "      <td>8936.745455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   date  client_id  temparature       pressure     humidity  wind_speed  \\\n",
       "0  2002      10101  2742.583333   91164.666667  5243.291667         NaN   \n",
       "1  2016      10101  2587.366225   91111.679232  4750.294176       6.518   \n",
       "2  2017      10101  2338.930070  341295.819549  4325.909510         NaN   \n",
       "\n",
       "   wind_direction           rain  \n",
       "0        9.083333  111111.000000  \n",
       "1      161.230311   86673.913907  \n",
       "2       84.005105    8936.745455  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_date_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "df_mean_year = [i for i in df_date_mean['date']]\n",
    "df_mean_rain = [float(\"{:2.2f}\".format(i)) for i in df_date_mean['rain']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ERNq7P/75jzz38XMMGDUg7lJazZJ3l7C37c3lX71cB3uaoBlo2WlmxilnnknO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\ntt1TjgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4d1c7bc0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "area = np.pi * (20 * np.array([3, 2, 1]))**2\n",
    "colors = (\"red\", \"green\", \"blue\")\n",
    "fig, ax = plt.subplots(figsize=(16, 5))\n",
    "ax.scatter(df_mean_year, df_mean_rain, c = colors, s=area, alpha=0.5)\n",
    "for i, j in zip(df_mean_year, df_mean_rain):\n",
    "    ax.annotate(str(j), xy=(i, j))\n",
    "ax.set_title('Average Raining based on Year')\n",
    "ax.set_xlabel('Year')\n",
    "ax.set_ylabel('Rain(mm)')\n",
    "ax.grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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</td>\n",
       "      <td>12:48:16</td>\n",
       "      <td>2747.0</td>\n",
       "      <td>91061.0</td>\n",
       "      <td>4265.0</td>\n",
       "      <td>1.00</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</td>\n",
       "      <td>12:54:56</td>\n",
       "      <td>2747.0</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</td>\n",
       "      <td>12:57:16</td>\n",
       "      <td>2747.0</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</td>\n",
       "      <td>12:59:26</td>\n",
       "      <td>2747.0</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</td>\n",
       "      <td>13:06:16</td>\n",
       "      <td>2747.0</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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10101</td>\n",
       "      <td>2016</td>\n",
       "      <td>13:13:06</td>\n",
       "      <td>2747.0</td>\n",
       "      <td>91009.0</td>\n",
       "      <td>4191.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.0</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10101</td>\n",
       "      <td>2016</td>\n",
       "      <td>13:15:17</td>\n",
       "      <td>2747.0</td>\n",
       "      <td>91012.0</td>\n",
       "      <td>4091.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120.0</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10101</td>\n",
       "      <td>2016</td>\n",
       "      <td>13:22:17</td>\n",
       "      <td>2747.0</td>\n",
       "      <td>91004.0</td>\n",
       "      <td>4077.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>130.0</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10101</td>\n",
       "      <td>2016</td>\n",
       "      <td>13:24:17</td>\n",
       "      <td>2747.0</td>\n",
       "      <td>91010.0</td>\n",
       "      <td>4084.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>305.0</td>\n",
       "      <td>111111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10101</td>\n",
       "      <td>2016</td>\n",
       "      <td>13:26:37</td>\n",
       "      <td>2747.0</td>\n",
       "      <td>91013.0</td>\n",
       "      <td>4040.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>285.0</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  12:48:16       2747.0   91061.0    4265.0        1.00   \n",
       "1      10101  2016  12:54:56       2747.0   91035.0    4275.0        7.14   \n",
       "2      10101  2016  12:57:16       2747.0   91044.0    4258.0        8.83   \n",
       "3      10101  2016  12:59:26       2747.0   91040.0    7253.0        9.38   \n",
       "4      10101  2016  13:06:16       2747.0   91022.0    4252.0        6.24   \n",
       "5      10101  2016  13:13:06       2747.0   91009.0    4191.0         NaN   \n",
       "6      10101  2016  13:15:17       2747.0   91012.0    4091.0         NaN   \n",
       "7      10101  2016  13:22:17       2747.0   91004.0    4077.0         NaN   \n",
       "8      10101  2016  13:24:17       2747.0   91010.0    4084.0         NaN   \n",
       "9      10101  2016  13:26:37       2747.0   91013.0    4040.0         NaN   \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  \n",
       "5           100.0  111111.0  \n",
       "6           120.0  111111.0  \n",
       "7           130.0  111111.0  \n",
       "8           305.0  111111.0  \n",
       "9           285.0  111111.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4265\n",
       "1    4275\n",
       "2    4258\n",
       "3    7253\n",
       "4    4252\n",
       "Name: humidity, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_humidity = df['humidity'].apply(int)\n",
    "df_humidity.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "df_humidity = df_humidity.apply(lambda x: x * 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "df2 = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
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      "text/plain": [
       "   client_id  date      time  temparature  pressure  humidity  wind_speed  \\\n",
       "0      10101  2016  12:48:16       2747.0   91061.0    4265.0        1.00   \n",
       "1      10101  2016  12:54:56       2747.0   91035.0    4275.0        7.14   \n",
       "2      10101  2016  12:57:16       2747.0   91044.0    4258.0        8.83   \n",
       "3      10101  2016  12:59:26       2747.0   91040.0    7253.0        9.38   \n",
       "4      10101  2016  13:06:16       2747.0   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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "df2.date = df_temp_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2016-10-13\n",
       "1   2016-10-13\n",
       "2   2016-10-13\n",
       "3   2016-10-13\n",
       "4   2016-10-13\n",
       "Name: date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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       "   client_id       date      time  temparature  pressure  humidity  \\\n",
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       "1      10101 2016-10-13  12:54:56       2747.0   91035.0    4275.0   \n",
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       "3      10101 2016-10-13  12:59:26       2747.0   91040.0    7253.0   \n",
       "4      10101 2016-10-13  13:06:16       2747.0   91022.0    4252.0   \n",
       "\n",
       "   wind_speed  wind_direction      rain  \n",
       "0        1.00           150.0  111111.0  \n",
       "1        7.14            40.0  111111.0  \n",
       "2        8.83           335.0  111111.0  \n",
       "3        9.38           330.0  111111.0  \n",
       "4        6.24           185.0  111111.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      "text/plain": [
       "   client_id       date      time  temparature  pressure  humidity  \\\n",
       "0      10101 2016-10-13  12:48:16       2747.0   91061.0    4265.0   \n",
       "1      10101 2016-10-13  12:54:56       2747.0   91035.0    4275.0   \n",
       "2      10101 2016-10-13  12:57:16       2747.0   91044.0    4258.0   \n",
       "3      10101 2016-10-13  12:59:26       2747.0   91040.0    7253.0   \n",
       "4      10101 2016-10-13  13:06:16       2747.0   91022.0    4252.0   \n",
       "\n",
       "   wind_speed  wind_direction      rain  \n",
       "0        1.00           150.0  111111.0  \n",
       "1        7.14            40.0  111111.0  \n",
       "2        8.83           335.0  111111.0  \n",
       "3        9.38           330.0  111111.0  \n",
       "4        6.24           185.0  111111.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dropna(how=\"any\", axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['humidity'].isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>1535</th>\n",
       "      <td>10101</td>\n",
       "      <td>2017-04-10</td>\n",
       "      <td>17:18:37</td>\n",
       "      <td>2537.0</td>\n",
       "      <td>91192.0</td>\n",
       "      <td>4618.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1536</th>\n",
       "      <td>10101</td>\n",
       "      <td>2017-04-10</td>\n",
       "      <td>15:18:37</td>\n",
       "      <td>2837.0</td>\n",
       "      <td>91192.0</td>\n",
       "      <td>4718.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1537</th>\n",
       "      <td>10101</td>\n",
       "      <td>2017-04-11</td>\n",
       "      <td>15:18:37</td>\n",
       "      <td>2837.0</td>\n",
       "      <td>93192.0</td>\n",
       "      <td>4718.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1538</th>\n",
       "      <td>10101</td>\n",
       "      <td>2017-04-11</td>\n",
       "      <td>14:18:37</td>\n",
       "      <td>2837.0</td>\n",
       "      <td>93192.0</td>\n",
       "      <td>4718.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1539</th>\n",
       "      <td>10101</td>\n",
       "      <td>2017-04-12</td>\n",
       "      <td>15:18:37</td>\n",
       "      <td>2237.0</td>\n",
       "      <td>93192.0</td>\n",
       "      <td>4418.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11111.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      client_id       date      time  temparature  pressure  humidity  \\\n",
       "1535      10101 2017-04-10  17:18:37       2537.0   91192.0    4618.0   \n",
       "1536      10101 2017-04-10  15:18:37       2837.0   91192.0    4718.0   \n",
       "1537      10101 2017-04-11  15:18:37       2837.0   93192.0    4718.0   \n",
       "1538      10101 2017-04-11  14:18:37       2837.0   93192.0    4718.0   \n",
       "1539      10101 2017-04-12  15:18:37       2237.0   93192.0    4418.0   \n",
       "\n",
       "      wind_speed  wind_direction     rain  \n",
       "1535         NaN             1.0  11111.0  \n",
       "1536         NaN             1.0  11111.0  \n",
       "1537         NaN             1.0  11111.0  \n",
       "1538         NaN             1.0  11111.0  \n",
       "1539         NaN             1.0  11111.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df2[df2.humidity > 0]\n",
    "df2 = df2[(df2['date'] > '2017-01-01') & (df2['date'] < '2017-12-30')]\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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l5syeMU+1URa1Wo0lS85g06ZVPPzwTWzatIolS86gVqv5Pq4Qs24ui1lJkqSSWLRoEXAv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\nWMxKkiRJkjrO/wdJxvzQYipURgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4d480f24d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2_humi = pd.Series(df2.humidity.apply(lambda x: x * 0.01))\n",
    "df2_date = pd.Series(df2.date)\n",
    "\n",
    "df2_humidity = pd.Series(df2_humi, index=df2_date)\n",
    "fig, ax = plt.subplots(figsize=(16, 5))\n",
    "ax.scatter(df2_humidity.index, df2_humi)\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y %B'))\n",
    "ax.set_title('Humidity in the year of 2016')\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Humidity(%)')\n",
    "ax.grid(True)\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
}