10_python_pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas\n",
    "\n",
    "Pandas is python module that makes data science easy and effective\n",
    "\n",
    "Main Data strcuture is Data Frames\n",
    "\n",
    "Data frame is a main object in pandas. It is used to represent data with rows and columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EST</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>DewPoint</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Sea Level PressureIn</th>\n",
       "      <th>VisibilityMiles</th>\n",
       "      <th>WindSpeedMPH</th>\n",
       "      <th>PrecipitationIn</th>\n",
       "      <th>CloudCover</th>\n",
       "      <th>Events</th>\n",
       "      <th>WindDirDegrees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>38</td>\n",
       "      <td>23</td>\n",
       "      <td>52</td>\n",
       "      <td>30.03</td>\n",
       "      <td>10</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2016</td>\n",
       "      <td>36</td>\n",
       "      <td>18</td>\n",
       "      <td>46</td>\n",
       "      <td>30.02</td>\n",
       "      <td>10</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2016</td>\n",
       "      <td>40</td>\n",
       "      <td>21</td>\n",
       "      <td>47</td>\n",
       "      <td>29.86</td>\n",
       "      <td>10</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2016</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>44</td>\n",
       "      <td>30.05</td>\n",
       "      <td>10</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2016</td>\n",
       "      <td>20</td>\n",
       "      <td>-3</td>\n",
       "      <td>41</td>\n",
       "      <td>30.57</td>\n",
       "      <td>10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/6/2016</td>\n",
       "      <td>33</td>\n",
       "      <td>4</td>\n",
       "      <td>35</td>\n",
       "      <td>30.50</td>\n",
       "      <td>10</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/7/2016</td>\n",
       "      <td>39</td>\n",
       "      <td>11</td>\n",
       "      <td>33</td>\n",
       "      <td>30.28</td>\n",
       "      <td>10</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1/8/2016</td>\n",
       "      <td>39</td>\n",
       "      <td>29</td>\n",
       "      <td>64</td>\n",
       "      <td>30.20</td>\n",
       "      <td>10</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1/9/2016</td>\n",
       "      <td>44</td>\n",
       "      <td>38</td>\n",
       "      <td>77</td>\n",
       "      <td>30.16</td>\n",
       "      <td>9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>T</td>\n",
       "      <td>8</td>\n",
       "      <td>Rain</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1/10/2016</td>\n",
       "      <td>50</td>\n",
       "      <td>46</td>\n",
       "      <td>71</td>\n",
       "      <td>29.59</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.8</td>\n",
       "      <td>7</td>\n",
       "      <td>Rain</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1/11/2016</td>\n",
       "      <td>33</td>\n",
       "      <td>8</td>\n",
       "      <td>37</td>\n",
       "      <td>29.92</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1/12/2016</td>\n",
       "      <td>35</td>\n",
       "      <td>15</td>\n",
       "      <td>53</td>\n",
       "      <td>29.85</td>\n",
       "      <td>10</td>\n",
       "      <td>6.0</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1/13/2016</td>\n",
       "      <td>26</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "      <td>29.94</td>\n",
       "      <td>10</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1/14/2016</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>47</td>\n",
       "      <td>29.95</td>\n",
       "      <td>10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>T</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1/15/2016</td>\n",
       "      <td>43</td>\n",
       "      <td>31</td>\n",
       "      <td>62</td>\n",
       "      <td>29.82</td>\n",
       "      <td>9</td>\n",
       "      <td>5.0</td>\n",
       "      <td>T</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1/16/2016</td>\n",
       "      <td>47</td>\n",
       "      <td>37</td>\n",
       "      <td>70</td>\n",
       "      <td>29.52</td>\n",
       "      <td>8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>7</td>\n",
       "      <td>Rain</td>\n",
       "      <td>340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1/17/2016</td>\n",
       "      <td>36</td>\n",
       "      <td>23</td>\n",
       "      <td>66</td>\n",
       "      <td>29.78</td>\n",
       "      <td>8</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>6</td>\n",
       "      <td>Fog-Snow</td>\n",
       "      <td>345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1/18/2016</td>\n",
       "      <td>25</td>\n",
       "      <td>6</td>\n",
       "      <td>53</td>\n",
       "      <td>29.83</td>\n",
       "      <td>9</td>\n",
       "      <td>12.0</td>\n",
       "      <td>T</td>\n",
       "      <td>2</td>\n",
       "      <td>Snow</td>\n",
       "      <td>293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1/19/2016</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>42</td>\n",
       "      <td>30.03</td>\n",
       "      <td>10</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1/20/2016</td>\n",
       "      <td>32</td>\n",
       "      <td>15</td>\n",
       "      <td>49</td>\n",
       "      <td>30.13</td>\n",
       "      <td>10</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1/21/2016</td>\n",
       "      <td>31</td>\n",
       "      <td>11</td>\n",
       "      <td>45</td>\n",
       "      <td>30.15</td>\n",
       "      <td>10</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1/22/2016</td>\n",
       "      <td>26</td>\n",
       "      <td>6</td>\n",
       "      <td>41</td>\n",
       "      <td>30.21</td>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.01</td>\n",
       "      <td>3</td>\n",
       "      <td>Snow</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1/23/2016</td>\n",
       "      <td>26</td>\n",
       "      <td>21</td>\n",
       "      <td>78</td>\n",
       "      <td>29.77</td>\n",
       "      <td>1</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>8</td>\n",
       "      <td>Fog-Snow</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1/24/2016</td>\n",
       "      <td>28</td>\n",
       "      <td>11</td>\n",
       "      <td>53</td>\n",
       "      <td>29.92</td>\n",
       "      <td>8</td>\n",
       "      <td>6.0</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>Snow</td>\n",
       "      <td>327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1/25/2016</td>\n",
       "      <td>34</td>\n",
       "      <td>18</td>\n",
       "      <td>54</td>\n",
       "      <td>30.25</td>\n",
       "      <td>10</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1/26/2016</td>\n",
       "      <td>43</td>\n",
       "      <td>29</td>\n",
       "      <td>56</td>\n",
       "      <td>30.03</td>\n",
       "      <td>10</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1/27/2016</td>\n",
       "      <td>41</td>\n",
       "      <td>22</td>\n",
       "      <td>45</td>\n",
       "      <td>30.03</td>\n",
       "      <td>10</td>\n",
       "      <td>7.0</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>Rain</td>\n",
       "      <td>311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1/28/2016</td>\n",
       "      <td>37</td>\n",
       "      <td>20</td>\n",
       "      <td>51</td>\n",
       "      <td>29.90</td>\n",
       "      <td>10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1/29/2016</td>\n",
       "      <td>36</td>\n",
       "      <td>21</td>\n",
       "      <td>50</td>\n",
       "      <td>29.58</td>\n",
       "      <td>10</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1/30/2016</td>\n",
       "      <td>34</td>\n",
       "      <td>16</td>\n",
       "      <td>46</td>\n",
       "      <td>30.01</td>\n",
       "      <td>10</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1/31/2016</td>\n",
       "      <td>46</td>\n",
       "      <td>28</td>\n",
       "      <td>52</td>\n",
       "      <td>29.90</td>\n",
       "      <td>10</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          EST  Temperature  DewPoint  Humidity  Sea Level PressureIn  \\\n",
       "0    1/1/2016           38        23        52                 30.03   \n",
       "1    1/2/2016           36        18        46                 30.02   \n",
       "2    1/3/2016           40        21        47                 29.86   \n",
       "3    1/4/2016           25         9        44                 30.05   \n",
       "4    1/5/2016           20        -3        41                 30.57   \n",
       "5    1/6/2016           33         4        35                 30.50   \n",
       "6    1/7/2016           39        11        33                 30.28   \n",
       "7    1/8/2016           39        29        64                 30.20   \n",
       "8    1/9/2016           44        38        77                 30.16   \n",
       "9   1/10/2016           50        46        71                 29.59   \n",
       "10  1/11/2016           33         8        37                 29.92   \n",
       "11  1/12/2016           35        15        53                 29.85   \n",
       "12  1/13/2016           26         4        42                 29.94   \n",
       "13  1/14/2016           30        12        47                 29.95   \n",
       "14  1/15/2016           43        31        62                 29.82   \n",
       "15  1/16/2016           47        37        70                 29.52   \n",
       "16  1/17/2016           36        23        66                 29.78   \n",
       "17  1/18/2016           25         6        53                 29.83   \n",
       "18  1/19/2016           22         3        42                 30.03   \n",
       "19  1/20/2016           32        15        49                 30.13   \n",
       "20  1/21/2016           31        11        45                 30.15   \n",
       "21  1/22/2016           26         6        41                 30.21   \n",
       "22  1/23/2016           26        21        78                 29.77   \n",
       "23  1/24/2016           28        11        53                 29.92   \n",
       "24  1/25/2016           34        18        54                 30.25   \n",
       "25  1/26/2016           43        29        56                 30.03   \n",
       "26  1/27/2016           41        22        45                 30.03   \n",
       "27  1/28/2016           37        20        51                 29.90   \n",
       "28  1/29/2016           36        21        50                 29.58   \n",
       "29  1/30/2016           34        16        46                 30.01   \n",
       "30  1/31/2016           46        28        52                 29.90   \n",
       "\n",
       "    VisibilityMiles  WindSpeedMPH PrecipitationIn  CloudCover    Events  \\\n",
       "0                10           8.0               0           5       NaN   \n",
       "1                10           7.0               0           3       NaN   \n",
       "2                10           8.0               0           1       NaN   \n",
       "3                10           9.0               0           3       NaN   \n",
       "4                10           5.0               0           0       NaN   \n",
       "5                10           4.0               0           0       NaN   \n",
       "6                10           2.0               0           3       NaN   \n",
       "7                10           4.0               0           8       NaN   \n",
       "8                 9           8.0               T           8      Rain   \n",
       "9                 4           NaN             1.8           7      Rain   \n",
       "10               10           NaN               0           1       NaN   \n",
       "11               10           6.0               T           4       NaN   \n",
       "12               10          10.0               0           0       NaN   \n",
       "13               10           5.0               T           7       NaN   \n",
       "14                9           5.0               T           2       NaN   \n",
       "15                8           7.0            0.24           7      Rain   \n",
       "16                8           6.0            0.05           6  Fog-Snow   \n",
       "17                9          12.0               T           2      Snow   \n",
       "18               10          11.0               0           1       NaN   \n",
       "19               10           6.0               0           2       NaN   \n",
       "20               10           6.0               0           1       NaN   \n",
       "21                9           NaN            0.01           3      Snow   \n",
       "22                1          16.0            2.31           8  Fog-Snow   \n",
       "23                8           6.0               T           3      Snow   \n",
       "24               10           3.0               0           2       NaN   \n",
       "25               10           7.0               0           2       NaN   \n",
       "26               10           7.0               T           3      Rain   \n",
       "27               10           5.0               0           1       NaN   \n",
       "28               10           8.0               0           4       NaN   \n",
       "29               10           7.0               0           0       NaN   \n",
       "30               10           5.0               0           0       NaN   \n",
       "\n",
       "    WindDirDegrees  \n",
       "0              281  \n",
       "1              275  \n",
       "2              277  \n",
       "3              345  \n",
       "4              333  \n",
       "5              259  \n",
       "6              293  \n",
       "7               79  \n",
       "8               76  \n",
       "9              109  \n",
       "10             289  \n",
       "11             235  \n",
       "12             284  \n",
       "13             266  \n",
       "14             101  \n",
       "15             340  \n",
       "16             345  \n",
       "17             293  \n",
       "18             293  \n",
       "19             302  \n",
       "20             312  \n",
       "21              34  \n",
       "22              42  \n",
       "23             327  \n",
       "24             286  \n",
       "25             244  \n",
       "26             311  \n",
       "27             234  \n",
       "28             298  \n",
       "29             257  \n",
       "30             241  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"Data/nyc_weather.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Temperature'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8      1/9/2016\n",
       "9     1/10/2016\n",
       "15    1/16/2016\n",
       "26    1/27/2016\n",
       "Name: EST, dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to know which dates it rains\n",
    "df['EST'][df['Events'] == 'Rain']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.892857142857143"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average wind speed\n",
    "df['WindSpeedMPH'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017           32          6   Rain\n",
       "1  1/2/2017           35          7  Sunny\n",
       "2  1/3/2017           28          2   Snow\n",
       "3  1/4/2017           24          7   Snow\n",
       "4  1/5/2017           32          4   Rain\n",
       "5  1/6/2017           31          2  Sunny"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"Data/weather_data.csv\")   #read weather.csv data\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0   1  2     3\n",
       "0  1/1/2017  32  6  Rain\n",
       "1  1/1/2017  32  6  Rain\n",
       "2  1/1/2017  32  6  Rain\n",
       "3  1/1/2017  32  6  Rain"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list of tuples\n",
    "weather_data = [('1/1/2017', 32, 6, 'Rain'),\n",
    "                ('1/1/2017', 32, 6, 'Rain'),\n",
    "                ('1/1/2017', 32, 6, 'Rain'),\n",
    "               ('1/1/2017', 32, 6, 'Rain')]\n",
    "df = pd.DataFrame(weather_data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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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>temperature</th>\n",
       "      <th>Windspeed</th>\n",
       "      <th>weather</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "        day  temperature  Windspeed weather\n",
       "0  1/1/2017           32          6    Rain\n",
       "1  1/1/2017           32          6    Rain\n",
       "2  1/1/2017           32          6    Rain\n",
       "3  1/1/2017           32          6    Rain"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(weather_data, columns= ['day', 'temperature', 'Windspeed', 'weather'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 4)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Data/weather_data.csv\")   #read weather.csv data\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
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       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017           32          6   Rain\n",
       "1  1/2/2017           35          7  Sunny\n",
       "2  1/3/2017           28          2   Snow\n",
       "3  1/4/2017           24          7   Snow\n",
       "4  1/5/2017           32          4   Rain"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()   # First five"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "1  1/2/2017           35          7  Sunny\n",
       "2  1/3/2017           28          2   Snow\n",
       "3  1/4/2017           24          7   Snow\n",
       "4  1/5/2017           32          4   Rain\n",
       "5  1/6/2017           31          2  Sunny"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail() # last five"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "1  1/2/2017           35          7  Sunny\n",
       "2  1/3/2017           28          2   Snow"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# slicing\n",
    "df[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['day', 'temperature', 'windspeed', 'event'], dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1/1/2017\n",
       "1    1/2/2017\n",
       "2    1/3/2017\n",
       "3    1/4/2017\n",
       "4    1/5/2017\n",
       "5    1/6/2017\n",
       "Name: day, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.day            # print particular column data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1/1/2017\n",
       "1    1/2/2017\n",
       "2    1/3/2017\n",
       "3    1/4/2017\n",
       "4    1/5/2017\n",
       "5    1/6/2017\n",
       "Name: day, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df['day']         # Another way of accessing column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  event\n",
       "0  1/1/2017   Rain\n",
       "1  1/2/2017  Sunny\n",
       "2  1/3/2017   Snow\n",
       "3  1/4/2017   Snow\n",
       "4  1/5/2017   Rain\n",
       "5  1/6/2017  Sunny"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get two or more columns\n",
    "df[['day', 'event']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# max temperature\n",
    "df['temperature'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['temperature'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     6.000000\n",
       "mean     30.333333\n",
       "std       3.829708\n",
       "min      24.000000\n",
       "25%      28.750000\n",
       "50%      31.500000\n",
       "75%      32.000000\n",
       "max      35.000000\n",
       "Name: temperature, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['temperature'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "1  1/2/2017           35          7  Sunny"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select rows which has he maximum temperature\n",
    "df[df.temperature == df.temperature.max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1/2/2017\n",
       "Name: day, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select only day column which has the maximum temperature \n",
    "df.day[df.temperature == df.temperature.max()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read and Write CSV and XLS files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017           32          6   Rain\n",
       "1  1/2/2017           35          7  Sunny\n",
       "2  1/3/2017           28          2   Snow\n",
       "3  1/4/2017           24          7   Snow\n",
       "4  1/5/2017           32          4   Rain\n",
       "5  1/6/2017           31          2  Sunny"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"Data/weather_data.csv\")   #read weather.csv data\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017           32          6   Rain\n",
       "1  1/2/2017           35          7  Sunny\n",
       "2  1/3/2017           28          2   Snow\n",
       "3  1/4/2017           24          7   Snow\n",
       "4  1/5/2017           32          4   Rain\n",
       "5  1/6/2017           31          2  Sunny"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pip3 install xlrd, if not working\n",
    "\n",
    "# read excel files\n",
    "df = pd.read_excel('Data/weather_data.xlsx')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write df to csv\n",
    "df.to_csv('Data/new.csv')\n",
    "\n",
    "# This also stores the index here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('Data/new_noIndex.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# INSTALL: pip3 install openpyxl if not already installed or working\n",
    "\n",
    "#write DF to Excel\n",
    "df.to_excel('Data/new.xlsx', sheet_name='weather_data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Group By"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>90</td>\n",
       "      <td>5</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>85</td>\n",
       "      <td>12</td>\n",
       "      <td>Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>87</td>\n",
       "      <td>15</td>\n",
       "      <td>Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>92</td>\n",
       "      <td>5</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>paris</td>\n",
       "      <td>45</td>\n",
       "      <td>20</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>paris</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>paris</td>\n",
       "      <td>54</td>\n",
       "      <td>8</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>paris</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         day      city  temperature  windspeed   event\n",
       "0   1/1/2017  new york           32          6    Rain\n",
       "1   1/2/2017  new york           36          7   Sunny\n",
       "2   1/3/2017  new york           28         12    Snow\n",
       "3   1/4/2017  new york           33          7   Sunny\n",
       "4   1/1/2017    mumbai           90          5   Sunny\n",
       "5   1/2/2017    mumbai           85         12     Fog\n",
       "6   1/3/2017    mumbai           87         15     Fog\n",
       "7   1/4/2017    mumbai           92          5    Rain\n",
       "8   1/1/2017     paris           45         20   Sunny\n",
       "9   1/2/2017     paris           50         13  Cloudy\n",
       "10  1/3/2017     paris           54          8  Cloudy\n",
       "11  1/4/2017     paris           42         10  Cloudy"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('Data/weather_data_cities.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.DataFrameGroupBy object at 0x000001B7B8956470>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = df.groupby('city')\n",
    "g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mumbai\n",
      "        day    city  temperature  windspeed  event\n",
      "4  1/1/2017  mumbai           90          5  Sunny\n",
      "5  1/2/2017  mumbai           85         12    Fog\n",
      "6  1/3/2017  mumbai           87         15    Fog\n",
      "7  1/4/2017  mumbai           92          5   Rain\n",
      "new york\n",
      "        day      city  temperature  windspeed  event\n",
      "0  1/1/2017  new york           32          6   Rain\n",
      "1  1/2/2017  new york           36          7  Sunny\n",
      "2  1/3/2017  new york           28         12   Snow\n",
      "3  1/4/2017  new york           33          7  Sunny\n",
      "paris\n",
      "         day   city  temperature  windspeed   event\n",
      "8   1/1/2017  paris           45         20   Sunny\n",
      "9   1/2/2017  paris           50         13  Cloudy\n",
      "10  1/3/2017  paris           54          8  Cloudy\n",
      "11  1/4/2017  paris           42         10  Cloudy\n"
     ]
    }
   ],
   "source": [
    "for city, city_df in g:\n",
    "    print(city)\n",
    "    print(city_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day      city  temperature  windspeed  event\n",
       "0  1/1/2017  new york           32          6   Rain\n",
       "1  1/2/2017  new york           36          7  Sunny\n",
       "2  1/3/2017  new york           28         12   Snow\n",
       "3  1/4/2017  new york           33          7  Sunny"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# or to get a specific group\n",
    "g.get_group('new york')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               day  temperature  windspeed  event\n",
      "city                                             \n",
      "mumbai    1/4/2017           92         15  Sunny\n",
      "new york  1/4/2017           36         12  Sunny\n",
      "paris     1/4/2017           54         20  Sunny\n"
     ]
    }
   ],
   "source": [
    "# Find max temp in each of the cities\n",
    "print(g.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          temperature  windspeed\n",
      "city                            \n",
      "mumbai          88.50       9.25\n",
      "new york        32.25       8.00\n",
      "paris           47.75      12.75\n"
     ]
    }
   ],
   "source": [
    "print(g.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         temperature                                                   \\\n",
      "               count   mean       std   min    25%   50%    75%   max   \n",
      "city                                                                    \n",
      "mumbai           4.0  88.50  3.109126  85.0  86.50  88.5  90.50  92.0   \n",
      "new york         4.0  32.25  3.304038  28.0  31.00  32.5  33.75  36.0   \n",
      "paris            4.0  47.75  5.315073  42.0  44.25  47.5  51.00  54.0   \n",
      "\n",
      "         windspeed                                                 \n",
      "             count   mean       std  min   25%   50%    75%   max  \n",
      "city                                                               \n",
      "mumbai         4.0   9.25  5.057997  5.0  5.00   8.5  12.75  15.0  \n",
      "new york       4.0   8.00  2.708013  6.0  6.75   7.0   8.25  12.0  \n",
      "paris          4.0  12.75  5.251984  8.0  9.50  11.5  14.75  20.0  \n"
     ]
    }
   ],
   "source": [
    "print(g.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Concatenate Data Frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>humidity</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mumbai</td>\n",
       "      <td>80</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Delhi</td>\n",
       "      <td>60</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bangalore</td>\n",
       "      <td>78</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        city  humidity  temperature\n",
       "0     Mumbai        80           32\n",
       "1      Delhi        60           45\n",
       "2  Bangalore        78           30"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "india_weather = pd.DataFrame({\n",
    "    \"city\": [\"Mumbai\", \"Delhi\", \"Bangalore\"],\n",
    "    \"temperature\": [32,45,30],\n",
    "    \"humidity\": [80,60,78]\n",
    "})\n",
    "india_weather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>humidity</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>new york</td>\n",
       "      <td>68</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chicago</td>\n",
       "      <td>65</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>orlando</td>\n",
       "      <td>75</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  humidity  temperature\n",
       "0  new york        68           21\n",
       "1   chicago        65           14\n",
       "2   orlando        75           35"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us_weather = pd.DataFrame({\n",
    "     \"city\": [\"new york\",\"chicago\",\"orlando\"],\n",
    "     \"temperature\": [21,14,35],\n",
    "     \"humidity\": [68, 65, 75]\n",
    "})\n",
    "us_weather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>humidity</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mumbai</td>\n",
       "      <td>80</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Delhi</td>\n",
       "      <td>60</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bangalore</td>\n",
       "      <td>78</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>new york</td>\n",
       "      <td>68</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chicago</td>\n",
       "      <td>65</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>orlando</td>\n",
       "      <td>75</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        city  humidity  temperature\n",
       "0     Mumbai        80           32\n",
       "1      Delhi        60           45\n",
       "2  Bangalore        78           30\n",
       "0   new york        68           21\n",
       "1    chicago        65           14\n",
       "2    orlando        75           35"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##############Important: Indices remians same##################\n",
    "df = pd.concat([india_weather, us_weather])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>humidity</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mumbai</td>\n",
       "      <td>80</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Delhi</td>\n",
       "      <td>60</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bangalore</td>\n",
       "      <td>78</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>new york</td>\n",
       "      <td>68</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chicago</td>\n",
       "      <td>65</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>orlando</td>\n",
       "      <td>75</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        city  humidity  temperature\n",
       "0     Mumbai        80           32\n",
       "1      Delhi        60           45\n",
       "2  Bangalore        78           30\n",
       "3   new york        68           21\n",
       "4    chicago        65           14\n",
       "5    orlando        75           35"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# If we want continuous index\n",
    "df = pd.concat([india_weather, us_weather], ignore_index=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>humidity</th>\n",
       "      <th>temperature</th>\n",
       "      <th>city</th>\n",
       "      <th>humidity</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mumbai</td>\n",
       "      <td>80</td>\n",
       "      <td>32</td>\n",
       "      <td>new york</td>\n",
       "      <td>68</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Delhi</td>\n",
       "      <td>60</td>\n",
       "      <td>45</td>\n",
       "      <td>chicago</td>\n",
       "      <td>65</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bangalore</td>\n",
       "      <td>78</td>\n",
       "      <td>30</td>\n",
       "      <td>orlando</td>\n",
       "      <td>75</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        city  humidity  temperature      city  humidity  temperature\n",
       "0     Mumbai        80           32  new york        68           21\n",
       "1      Delhi        60           45   chicago        65           14\n",
       "2  Bangalore        78           30   orlando        75           35"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Concatenate along the row\n",
    "df = pd.concat([india_weather, us_weather], axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge Data Frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hyderabad</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        city  temperature\n",
       "0     mumbai           32\n",
       "1      delhi           45\n",
       "2   banglore           30\n",
       "3  hyderabad           40"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temperature_df = pd.DataFrame({\n",
    "    \"city\": [\"mumbai\",\"delhi\",\"banglore\", 'hyderabad'],\n",
    "    \"temperature\": [32,45,30,40]\n",
    "})\n",
    "temperature_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>delhi</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  humidity\n",
       "0     delhi        68\n",
       "1    mumbai        65\n",
       "2  banglore        75"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "humidity_df = pd.DataFrame({\n",
    "    \"city\": [\"delhi\",\"mumbai\",\"banglore\"],\n",
    "    \"humidity\": [68, 65, 75]})\n",
    "humidity_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>32</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>45</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>30</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature  humidity\n",
       "0    mumbai           32        65\n",
       "1     delhi           45        68\n",
       "2  banglore           30        75"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#################### WE don't have Hydearbad in the humidity_df, so it is skipped###################\n",
    "df = pd.merge(temperature_df, humidity_df, on='city')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>32</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>45</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>30</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hyderabad</td>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        city  temperature  humidity\n",
       "0     mumbai           32      65.0\n",
       "1      delhi           45      68.0\n",
       "2   banglore           30      75.0\n",
       "3  hyderabad           40       NaN"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Outer Join\n",
    "df = pd.merge(temperature_df, humidity_df, on='city', how='outer')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numerical Indexing (.loc vs iloc)\n",
    "\n",
    "location and index locations\n",
    "\n",
    "loc uses index to access the Dataframes and iloc uses row numbers\n",
    "\n",
    "By default indices are same as the row numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0\n",
       "0   1\n",
       "1   2\n",
       "2   3\n",
       "3   4\n",
       "4   5\n",
       "5   6\n",
       "6   7\n",
       "7   8\n",
       "8   9\n",
       "9  19"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([1,2,3,4,5,6,7,8,9,19]) # index=[49,48,47,46,45,1,2,3,4,5])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>49</th>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0\n",
       "49   1\n",
       "48   2\n",
       "47   3\n",
       "46   4\n",
       "45   5\n",
       "1    6\n",
       "2    7\n",
       "3    8\n",
       "4    9\n",
       "5   19"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([1,2,3,4,5,6,7,8,9,19], index=[49,48,47,46,45,1,2,3,4,5])\n",
    "df\n",
    "\n",
    "# There are two ways to access the data, by using the row number and another using the index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4\n",
       "Name: 46, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[46]          # Use index to access data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4\n",
       "Name: 46, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3]          # Use row number to access the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    0\n",
       "49  1\n",
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       "47  3\n",
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       "2   7"
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     "execution_count": 80,
     "metadata": {},
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   ],
   "source": [
    "df.loc[:2]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
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   "source": [
    "df.iloc[:2]"
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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