darksky_data_winds.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dark Sky API: 4ff1d8e73db78778122dfacfabf261f2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from darksky import forecast\n",
    "from datetime import datetime as dt\n",
    "from datetime import date, timedelta\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Time Machine Call, we can get the observed weather conditions for a date in the past or future. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "key = \"4ff1d8e73db78778122dfacfabf261f2\"\n",
    "Livemore = key, 37.681, -121.775"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = dt(2018, 1, 17, 12).isoformat()\n",
    "live = forecast(*Livemore, t)\n",
    "mydata = pd.DataFrame(live['hourly']['data'])\n",
    "mydata['LAT'] = 37.681\n",
    "mydata['LON'] = -121.775\n",
    "mydata['YEAR'] = 2018\n",
    "mydata['MONTH'] = 1\n",
    "mydata['DAY'] = 17\n",
    "mydata['HOUR'] = [i for i in range(24)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(18, 32):  \n",
    "    t = dt(2018, 1, i, 12).isoformat()\n",
    "    live = forecast(*Livemore, t)\n",
    "    tempdata = pd.DataFrame(live['hourly']['data'])\n",
    "    tempdata['LAT'] = 37.681\n",
    "    tempdata['LON'] = -121.775\n",
    "    tempdata['YEAR'] = 2018\n",
    "    tempdata['MONTH'] = 1\n",
    "    tempdata['DAY'] = i\n",
    "    tempdata['HOUR'] = [i for i in range(24)]\n",
    "    mydata = mydata.append(tempdata)\n",
    "    \n",
    "Jan_winds = mydata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save it as csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "Jan_winds.to_csv(\"Jan_winds.csv\", sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DAY</th>\n",
       "      <th>HOUR</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LON</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>apparentTemperature</th>\n",
       "      <th>cloudCover</th>\n",
       "      <th>dewPoint</th>\n",
       "      <th>humidity</th>\n",
       "      <th>...</th>\n",
       "      <th>precipIntensity</th>\n",
       "      <th>precipProbability</th>\n",
       "      <th>precipType</th>\n",
       "      <th>pressure</th>\n",
       "      <th>summary</th>\n",
       "      <th>temperature</th>\n",
       "      <th>time</th>\n",
       "      <th>visibility</th>\n",
       "      <th>windBearing</th>\n",
       "      <th>windSpeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>37.681</td>\n",
       "      <td>-121.775</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>52.57</td>\n",
       "      <td>0.02</td>\n",
       "      <td>51.01</td>\n",
       "      <td>0.94</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1023.75</td>\n",
       "      <td>Clear</td>\n",
       "      <td>52.57</td>\n",
       "      <td>1516176000</td>\n",
       "      <td>6.61</td>\n",
       "      <td>79</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>37.681</td>\n",
       "      <td>-121.775</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>51.65</td>\n",
       "      <td>0.02</td>\n",
       "      <td>50.40</td>\n",
       "      <td>0.95</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1023.27</td>\n",
       "      <td>Clear</td>\n",
       "      <td>51.65</td>\n",
       "      <td>1516179600</td>\n",
       "      <td>5.52</td>\n",
       "      <td>334</td>\n",
       "      <td>2.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>37.681</td>\n",
       "      <td>-121.775</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>51.51</td>\n",
       "      <td>0.02</td>\n",
       "      <td>50.21</td>\n",
       "      <td>0.95</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1023.43</td>\n",
       "      <td>Clear</td>\n",
       "      <td>51.51</td>\n",
       "      <td>1516183200</td>\n",
       "      <td>5.40</td>\n",
       "      <td>20</td>\n",
       "      <td>2.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>37.681</td>\n",
       "      <td>-121.775</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>49.80</td>\n",
       "      <td>0.05</td>\n",
       "      <td>49.18</td>\n",
       "      <td>0.98</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1023.60</td>\n",
       "      <td>Clear</td>\n",
       "      <td>49.80</td>\n",
       "      <td>1516186800</td>\n",
       "      <td>2.82</td>\n",
       "      <td>68</td>\n",
       "      <td>2.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>37.681</td>\n",
       "      <td>-121.775</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>50.08</td>\n",
       "      <td>0.31</td>\n",
       "      <td>49.47</td>\n",
       "      <td>0.98</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1023.65</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>50.08</td>\n",
       "      <td>1516190400</td>\n",
       "      <td>3.04</td>\n",
       "      <td>117</td>\n",
       "      <td>2.21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   DAY  HOUR     LAT      LON  MONTH  YEAR  apparentTemperature  cloudCover  \\\n",
       "0   17     0  37.681 -121.775      1  2018                52.57        0.02   \n",
       "1   17     1  37.681 -121.775      1  2018                51.65        0.02   \n",
       "2   17     2  37.681 -121.775      1  2018                51.51        0.02   \n",
       "3   17     3  37.681 -121.775      1  2018                49.80        0.05   \n",
       "4   17     4  37.681 -121.775      1  2018                50.08        0.31   \n",
       "\n",
       "   dewPoint  humidity    ...     precipIntensity  precipProbability  \\\n",
       "0     51.01      0.94    ...                 0.0                0.0   \n",
       "1     50.40      0.95    ...                 0.0                0.0   \n",
       "2     50.21      0.95    ...                 0.0                0.0   \n",
       "3     49.18      0.98    ...                 0.0                0.0   \n",
       "4     49.47      0.98    ...                 0.0                0.0   \n",
       "\n",
       "   precipType pressure        summary temperature        time  visibility  \\\n",
       "0         NaN  1023.75          Clear       52.57  1516176000        6.61   \n",
       "1         NaN  1023.27          Clear       51.65  1516179600        5.52   \n",
       "2         NaN  1023.43          Clear       51.51  1516183200        5.40   \n",
       "3         NaN  1023.60          Clear       49.80  1516186800        2.82   \n",
       "4         NaN  1023.65  Partly Cloudy       50.08  1516190400        3.04   \n",
       "\n",
       "   windBearing  windSpeed  \n",
       "0           79       0.42  \n",
       "1          334       2.55  \n",
       "2           20       2.37  \n",
       "3           68       2.24  \n",
       "4          117       2.21  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Jan_winds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "t = dt(2018, 2, 9, 12).isoformat()\n",
    "live = forecast(*Livemore, t)\n",
    "mydata = pd.DataFrame(live['hourly']['data'])\n",
    "mydata['LAT'] = 37.681\n",
    "mydata['LON'] = -121.775\n",
    "mydata['YEAR'] = 2018\n",
    "mydata['MONTH'] = 2\n",
    "mydata['DAY'] = 9\n",
    "mydata['HOUR'] = [i for i in range(24)]    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(10, 24):  \n",
    "    t = dt(2018, 2, i, 12).isoformat()\n",
    "    live = forecast(*Livemore, t)\n",
    "    tempdata = pd.DataFrame(live['hourly']['data'])\n",
    "    tempdata['LAT'] = 37.681\n",
    "    tempdata['LON'] = -121.775\n",
    "    tempdata['YEAR'] = 2018\n",
    "    tempdata['MONTH'] = 2\n",
    "    tempdata['DAY'] = i\n",
    "    tempdata['HOUR'] = [i for i in range(24)]\n",
    "    mydata = mydata.append(tempdata)\n",
    "    \n",
    "Feb_winds = mydata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DAY</th>\n",
       "      <th>HOUR</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LON</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>apparentTemperature</th>\n",
       "      <th>cloudCover</th>\n",
       "      <th>dewPoint</th>\n",
       "      <th>humidity</th>\n",
       "      <th>...</th>\n",
       "      <th>precipIntensity</th>\n",
       "      <th>precipProbability</th>\n",
       "      <th>precipType</th>\n",
       "      <th>pressure</th>\n",
       "      <th>summary</th>\n",
       "      <th>temperature</th>\n",
       "      <th>time</th>\n",
       "      <th>visibility</th>\n",
       "      <th>windBearing</th>\n",
       "      <th>windSpeed</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>37.681</td>\n",
       "      <td>-121.775</td>\n",
       "      <td>2</td>\n",
       "      <td>2018</td>\n",
       "      <td>52.20</td>\n",
       "      <td>0.02</td>\n",
       "      <td>43.04</td>\n",
       "      <td>0.71</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1019.82</td>\n",
       "      <td>Clear</td>\n",
       "      <td>52.20</td>\n",
       "      <td>1518163200</td>\n",
       "      <td>8.65</td>\n",
       "      <td>90</td>\n",
       "      <td>2.07</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>37.681</td>\n",
       "      <td>-121.775</td>\n",
       "      <td>2</td>\n",
       "      <td>2018</td>\n",
       "      <td>51.41</td>\n",
       "      <td>0.00</td>\n",
       "      <td>42.95</td>\n",
       "      <td>0.73</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1019.42</td>\n",
       "      <td>Clear</td>\n",
       "      <td>51.41</td>\n",
       "      <td>1518166800</td>\n",
       "      <td>9.32</td>\n",
       "      <td>97</td>\n",
       "      <td>0.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>37.681</td>\n",
       "      <td>-121.775</td>\n",
       "      <td>2</td>\n",
       "      <td>2018</td>\n",
       "      <td>49.52</td>\n",
       "      <td>0.00</td>\n",
       "      <td>42.52</td>\n",
       "      <td>0.77</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1019.18</td>\n",
       "      <td>Clear</td>\n",
       "      <td>49.52</td>\n",
       "      <td>1518170400</td>\n",
       "      <td>9.32</td>\n",
       "      <td>79</td>\n",
       "      <td>1.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>37.681</td>\n",
       "      <td>-121.775</td>\n",
       "      <td>2</td>\n",
       "      <td>2018</td>\n",
       "      <td>49.34</td>\n",
       "      <td>0.00</td>\n",
       "      <td>42.66</td>\n",
       "      <td>0.78</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1019.01</td>\n",
       "      <td>Clear</td>\n",
       "      <td>49.34</td>\n",
       "      <td>1518174000</td>\n",
       "      <td>9.32</td>\n",
       "      <td>114</td>\n",
       "      <td>0.71</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>37.681</td>\n",
       "      <td>-121.775</td>\n",
       "      <td>2</td>\n",
       "      <td>2018</td>\n",
       "      <td>48.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>41.40</td>\n",
       "      <td>0.78</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1018.72</td>\n",
       "      <td>Clear</td>\n",
       "      <td>48.01</td>\n",
       "      <td>1518177600</td>\n",
       "      <td>9.32</td>\n",
       "      <td>72</td>\n",
       "      <td>1.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   DAY  HOUR     LAT      LON  MONTH  YEAR  apparentTemperature  cloudCover  \\\n",
       "0    9     0  37.681 -121.775      2  2018                52.20        0.02   \n",
       "1    9     1  37.681 -121.775      2  2018                51.41        0.00   \n",
       "2    9     2  37.681 -121.775      2  2018                49.52        0.00   \n",
       "3    9     3  37.681 -121.775      2  2018                49.34        0.00   \n",
       "4    9     4  37.681 -121.775      2  2018                48.01        0.00   \n",
       "\n",
       "   dewPoint  humidity    ...     precipIntensity  precipProbability  \\\n",
       "0     43.04      0.71    ...                 0.0                0.0   \n",
       "1     42.95      0.73    ...                 0.0                0.0   \n",
       "2     42.52      0.77    ...                 0.0                0.0   \n",
       "3     42.66      0.78    ...                 0.0                0.0   \n",
       "4     41.40      0.78    ...                 0.0                0.0   \n",
       "\n",
       "   precipType pressure  summary temperature        time  visibility  \\\n",
       "0         NaN  1019.82    Clear       52.20  1518163200        8.65   \n",
       "1         NaN  1019.42    Clear       51.41  1518166800        9.32   \n",
       "2         NaN  1019.18    Clear       49.52  1518170400        9.32   \n",
       "3         NaN  1019.01    Clear       49.34  1518174000        9.32   \n",
       "4         NaN  1018.72    Clear       48.01  1518177600        9.32   \n",
       "\n",
       "   windBearing  windSpeed  \n",
       "0           90       2.07  \n",
       "1           97       0.53  \n",
       "2           79       1.88  \n",
       "3          114       0.71  \n",
       "4           72       1.63  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "Feb_winds.to_csv(\"Feb_winds.csv\", sep=',')\n",
    "Feb_winds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DAY</th>\n",
       "      <th>HOUR</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LON</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>apparentTemperature</th>\n",
       "      <th>cloudCover</th>\n",
       "      <th>dewPoint</th>\n",
       "      <th>humidity</th>\n",
       "      <th>...</th>\n",
       "      <th>precipIntensity</th>\n",
       "      <th>precipProbability</th>\n",
       "      <th>precipType</th>\n",
       "      <th>pressure</th>\n",
       "      <th>summary</th>\n",
       "      <th>temperature</th>\n",
       "      <th>time</th>\n",
       "      <th>visibility</th>\n",
       "      <th>windBearing</th>\n",
       "      <th>windSpeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>37.681</td>\n",
       "      <td>-121.775</td>\n",
       "      <td>3</td>\n",
       "      <td>2018</td>\n",
       "      <td>54.55</td>\n",
       "      <td>0.29</td>\n",
       "      <td>48.85</td>\n",
       "      <td>0.81</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1018.97</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>54.55</td>\n",
       "      <td>1520838000</td>\n",
       "      <td>9.86</td>\n",
       "      <td>35</td>\n",
       "      <td>1.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>37.681</td>\n",
       "      <td>-121.775</td>\n",
       "      <td>3</td>\n",
       "      <td>2018</td>\n",
       "      <td>52.59</td>\n",
       "      <td>0.04</td>\n",
       "      <td>49.10</td>\n",
       "      <td>0.88</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1018.77</td>\n",
       "      <td>Clear</td>\n",
       "      <td>52.59</td>\n",
       "      <td>1520841600</td>\n",
       "      <td>9.81</td>\n",
       "      <td>73</td>\n",
       "      <td>2.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>37.681</td>\n",
       "      <td>-121.775</td>\n",
       "      <td>3</td>\n",
       "      <td>2018</td>\n",
       "      <td>52.09</td>\n",
       "      <td>0.02</td>\n",
       "      <td>48.97</td>\n",
       "      <td>0.89</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1018.97</td>\n",
       "      <td>Clear</td>\n",
       "      <td>52.09</td>\n",
       "      <td>1520845200</td>\n",
       "      <td>9.86</td>\n",
       "      <td>299</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>37.681</td>\n",
       "      <td>-121.775</td>\n",
       "      <td>3</td>\n",
       "      <td>2018</td>\n",
       "      <td>51.77</td>\n",
       "      <td>0.00</td>\n",
       "      <td>48.22</td>\n",
       "      <td>0.88</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1018.78</td>\n",
       "      <td>Clear</td>\n",
       "      <td>51.77</td>\n",
       "      <td>1520848800</td>\n",
       "      <td>9.86</td>\n",
       "      <td>53</td>\n",
       "      <td>1.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>37.681</td>\n",
       "      <td>-121.775</td>\n",
       "      <td>3</td>\n",
       "      <td>2018</td>\n",
       "      <td>50.53</td>\n",
       "      <td>0.15</td>\n",
       "      <td>48.49</td>\n",
       "      <td>0.93</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1017.97</td>\n",
       "      <td>Clear</td>\n",
       "      <td>50.53</td>\n",
       "      <td>1520852400</td>\n",
       "      <td>9.32</td>\n",
       "      <td>86</td>\n",
       "      <td>1.97</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   DAY  HOUR     LAT      LON  MONTH  YEAR  apparentTemperature  cloudCover  \\\n",
       "0   12     0  37.681 -121.775      3  2018                54.55        0.29   \n",
       "1   12     1  37.681 -121.775      3  2018                52.59        0.04   \n",
       "2   12     2  37.681 -121.775      3  2018                52.09        0.02   \n",
       "3   12     3  37.681 -121.775      3  2018                51.77        0.00   \n",
       "4   12     4  37.681 -121.775      3  2018                50.53        0.15   \n",
       "\n",
       "   dewPoint  humidity    ...     precipIntensity  precipProbability  \\\n",
       "0     48.85      0.81    ...                 0.0                0.0   \n",
       "1     49.10      0.88    ...                 0.0                0.0   \n",
       "2     48.97      0.89    ...                 0.0                0.0   \n",
       "3     48.22      0.88    ...                 0.0                0.0   \n",
       "4     48.49      0.93    ...                 0.0                0.0   \n",
       "\n",
       "   precipType pressure        summary temperature        time  visibility  \\\n",
       "0         NaN  1018.97  Partly Cloudy       54.55  1520838000        9.86   \n",
       "1         NaN  1018.77          Clear       52.59  1520841600        9.81   \n",
       "2         NaN  1018.97          Clear       52.09  1520845200        9.86   \n",
       "3         NaN  1018.78          Clear       51.77  1520848800        9.86   \n",
       "4         NaN  1017.97          Clear       50.53  1520852400        9.32   \n",
       "\n",
       "   windBearing  windSpeed  \n",
       "0           35       1.49  \n",
       "1           73       2.26  \n",
       "2          299       0.97  \n",
       "3           53       1.41  \n",
       "4           86       1.97  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "t = dt(2018, 3, 12, 12).isoformat()\n",
    "live = forecast(*Livemore, t)\n",
    "mydata = pd.DataFrame(live['hourly']['data'])\n",
    "mydata['LAT'] = 37.681\n",
    "mydata['LON'] = -121.775\n",
    "mydata['YEAR'] = 2018\n",
    "mydata['MONTH'] = 3\n",
    "mydata['DAY'] = 12\n",
    "mydata['HOUR'] = [i for i in range(24)]\n",
    "    \n",
    "for i in range(13, 27):  \n",
    "    t = dt(2018, 3, i, 12).isoformat()\n",
    "    live = forecast(*Livemore, t)\n",
    "    tempdata = pd.DataFrame(live['hourly']['data'])\n",
    "    tempdata['LAT'] = 37.681\n",
    "    tempdata['LON'] = -121.775\n",
    "    tempdata['YEAR'] = 2018\n",
    "    tempdata['MONTH'] = 3\n",
    "    tempdata['DAY'] = i\n",
    "    tempdata['HOUR'] = [i for i in range(24)]\n",
    "    mydata = mydata.append(tempdata)\n",
    "\n",
    "Mar_winds = mydata    \n",
    "Mar_winds.to_csv(\"Mar_winds.csv\", sep=',')\n",
    "Mar_winds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <td>-121.775</td>\n",
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       "      <td>37.681</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1020.52</td>\n",
       "      <td>Clear</td>\n",
       "      <td>49.90</td>\n",
       "      <td>1523264400</td>\n",
       "      <td>9.86</td>\n",
       "      <td>62</td>\n",
       "      <td>1.41</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
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       "      <td>37.681</td>\n",
       "      <td>-121.775</td>\n",
       "      <td>4</td>\n",
       "      <td>2018</td>\n",
       "      <td>49.23</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.91</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1020.01</td>\n",
       "      <td>Clear</td>\n",
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       "      <td>37.681</td>\n",
       "      <td>-121.775</td>\n",
       "      <td>4</td>\n",
       "      <td>2018</td>\n",
       "      <td>48.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.46</td>\n",
       "      <td>0.91</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1019.58</td>\n",
       "      <td>Clear</td>\n",
       "      <td>48.86</td>\n",
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       "      <td>9.86</td>\n",
       "      <td>144</td>\n",
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       "<p>5 rows × 21 columns</p>\n",
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      ],
      "text/plain": [
       "   DAY  HOUR     LAT      LON  MONTH  YEAR  apparentTemperature  cloudCover  \\\n",
       "0    9     0  37.681 -121.775      4  2018                51.19         0.0   \n",
       "1    9     1  37.681 -121.775      4  2018                51.12         0.0   \n",
       "2    9     2  37.681 -121.775      4  2018                49.90         0.0   \n",
       "3    9     3  37.681 -121.775      4  2018                49.23         0.0   \n",
       "4    9     4  37.681 -121.775      4  2018                48.86         0.0   \n",
       "\n",
       "   dewPoint  humidity    ...     precipIntensity  precipProbability  \\\n",
       "0     47.59      0.87    ...                 0.0                0.0   \n",
       "1     47.09      0.86    ...                 0.0                0.0   \n",
       "2     47.28      0.91    ...                 0.0                0.0   \n",
       "3     46.83      0.91    ...                 0.0                0.0   \n",
       "4     46.46      0.91    ...                 0.0                0.0   \n",
       "\n",
       "   precipType pressure  summary temperature        time  visibility  \\\n",
       "0         NaN  1020.67    Clear       51.19  1523257200        9.86   \n",
       "1         NaN  1020.40    Clear       51.12  1523260800        9.86   \n",
       "2         NaN  1020.52    Clear       49.90  1523264400        9.86   \n",
       "3         NaN  1020.01    Clear       49.23  1523268000        9.86   \n",
       "4         NaN  1019.58    Clear       48.86  1523271600        9.86   \n",
       "\n",
       "   windBearing  windSpeed  \n",
       "0          100       3.06  \n",
       "1          164       1.61  \n",
       "2           62       1.41  \n",
       "3           86       1.84  \n",
       "4          144       2.22  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = dt(2018, 4, 9, 12).isoformat()\n",
    "live = forecast(*Livemore, t)\n",
    "mydata = pd.DataFrame(live['hourly']['data'])\n",
    "mydata['LAT'] = 37.681\n",
    "mydata['LON'] = -121.775\n",
    "mydata['YEAR'] = 2018\n",
    "mydata['MONTH'] = 4\n",
    "mydata['DAY'] = 9\n",
    "mydata['HOUR'] = [i for i in range(24)]\n",
    "    \n",
    "for i in range(10, 24):  \n",
    "    t = dt(2018, 4, i, 12).isoformat()\n",
    "    live = forecast(*Livemore, t)\n",
    "    tempdata = pd.DataFrame(live['hourly']['data'])\n",
    "    tempdata['LAT'] = 37.681\n",
    "    tempdata['LON'] = -121.775\n",
    "    tempdata['YEAR'] = 2018\n",
    "    tempdata['MONTH'] = 4\n",
    "    tempdata['DAY'] = i\n",
    "    tempdata['HOUR'] = [i for i in range(24)]\n",
    "    mydata = mydata.append(tempdata)\n",
    "    \n",
    "Apr_winds = mydata\n",
    "Apr_winds.to_csv(\"Apr_winds.csv\", sep=',')\n",
    "Apr_winds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "      <td>50.40</td>\n",
       "      <td>0.95</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1023.27</td>\n",
       "      <td>Clear</td>\n",
       "      <td>51.65</td>\n",
       "      <td>1516179600</td>\n",
       "      <td>5.52</td>\n",
       "      <td>334</td>\n",
       "      <td>2.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>37.681</td>\n",
       "      <td>-121.775</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>51.51</td>\n",
       "      <td>0.02</td>\n",
       "      <td>50.21</td>\n",
       "      <td>0.95</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1023.43</td>\n",
       "      <td>Clear</td>\n",
       "      <td>51.51</td>\n",
       "      <td>1516183200</td>\n",
       "      <td>5.40</td>\n",
       "      <td>20</td>\n",
       "      <td>2.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>37.681</td>\n",
       "      <td>-121.775</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>49.80</td>\n",
       "      <td>0.05</td>\n",
       "      <td>49.18</td>\n",
       "      <td>0.98</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1023.60</td>\n",
       "      <td>Clear</td>\n",
       "      <td>49.80</td>\n",
       "      <td>1516186800</td>\n",
       "      <td>2.82</td>\n",
       "      <td>68</td>\n",
       "      <td>2.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>37.681</td>\n",
       "      <td>-121.775</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "      <td>50.08</td>\n",
       "      <td>0.31</td>\n",
       "      <td>49.47</td>\n",
       "      <td>0.98</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1023.65</td>\n",
       "      <td>Partly Cloudy</td>\n",
       "      <td>50.08</td>\n",
       "      <td>1516190400</td>\n",
       "      <td>3.04</td>\n",
       "      <td>117</td>\n",
       "      <td>2.21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   DAY  HOUR     LAT      LON  MONTH  YEAR  apparentTemperature  cloudCover  \\\n",
       "0   17     0  37.681 -121.775      1  2018                52.57        0.02   \n",
       "1   17     1  37.681 -121.775      1  2018                51.65        0.02   \n",
       "2   17     2  37.681 -121.775      1  2018                51.51        0.02   \n",
       "3   17     3  37.681 -121.775      1  2018                49.80        0.05   \n",
       "4   17     4  37.681 -121.775      1  2018                50.08        0.31   \n",
       "\n",
       "   dewPoint  humidity    ...     precipIntensity  precipProbability  \\\n",
       "0     51.01      0.94    ...                 0.0                0.0   \n",
       "1     50.40      0.95    ...                 0.0                0.0   \n",
       "2     50.21      0.95    ...                 0.0                0.0   \n",
       "3     49.18      0.98    ...                 0.0                0.0   \n",
       "4     49.47      0.98    ...                 0.0                0.0   \n",
       "\n",
       "   precipType pressure        summary temperature        time  visibility  \\\n",
       "0         NaN  1023.75          Clear       52.57  1516176000        6.61   \n",
       "1         NaN  1023.27          Clear       51.65  1516179600        5.52   \n",
       "2         NaN  1023.43          Clear       51.51  1516183200        5.40   \n",
       "3         NaN  1023.60          Clear       49.80  1516186800        2.82   \n",
       "4         NaN  1023.65  Partly Cloudy       50.08  1516190400        3.04   \n",
       "\n",
       "   windBearing  windSpeed  \n",
       "0           79       0.42  \n",
       "1          334       2.55  \n",
       "2           20       2.37  \n",
       "3           68       2.24  \n",
       "4          117       2.21  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Winds_week = pd.concat([Jan_winds, Feb_winds, Mar_winds, Apr_winds])\n",
    "Winds_week.to_csv(\"Winds_week.csv\", sep=',')\n",
    "Winds_week.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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