Data Analysis of Avocado Dataset .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('avocado.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th>4046</th>\n",
       "      <th>4225</th>\n",
       "      <th>4770</th>\n",
       "      <th>Total Bags</th>\n",
       "      <th>Small Bags</th>\n",
       "      <th>Large Bags</th>\n",
       "      <th>XLarge Bags</th>\n",
       "      <th>type</th>\n",
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       "      <td>0</td>\n",
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       "      <td>2015-12-27</td>\n",
       "      <td>1.33</td>\n",
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       "      <td>1036.74</td>\n",
       "      <td>54454.85</td>\n",
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       "      <td>794.70</td>\n",
       "      <td>109149.67</td>\n",
       "      <td>130.50</td>\n",
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       "      <td>2015-12-06</td>\n",
       "      <td>1.08</td>\n",
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       "      <td>71976.41</td>\n",
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       "      <td>4</td>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>1.28</td>\n",
       "      <td>51039.60</td>\n",
       "      <td>941.48</td>\n",
       "      <td>43838.39</td>\n",
       "      <td>75.78</td>\n",
       "      <td>6183.95</td>\n",
       "      <td>5986.26</td>\n",
       "      <td>197.69</td>\n",
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       "      <td>18244</td>\n",
       "      <td>7</td>\n",
       "      <td>2018-02-04</td>\n",
       "      <td>1.63</td>\n",
       "      <td>17074.83</td>\n",
       "      <td>2046.96</td>\n",
       "      <td>1529.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13498.67</td>\n",
       "      <td>13066.82</td>\n",
       "      <td>431.85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>organic</td>\n",
       "      <td>2018</td>\n",
       "      <td>WestTexNewMexico</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18245</td>\n",
       "      <td>8</td>\n",
       "      <td>2018-01-28</td>\n",
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       "      <td>1191.70</td>\n",
       "      <td>3431.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>9264.84</td>\n",
       "      <td>8940.04</td>\n",
       "      <td>324.80</td>\n",
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       "      <td>727.94</td>\n",
       "      <td>9394.11</td>\n",
       "      <td>9351.80</td>\n",
       "      <td>42.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>organic</td>\n",
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       "      <td>10</td>\n",
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       "      <td>16205.22</td>\n",
       "      <td>1527.63</td>\n",
       "      <td>2981.04</td>\n",
       "      <td>727.01</td>\n",
       "      <td>10969.54</td>\n",
       "      <td>10919.54</td>\n",
       "      <td>50.00</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>2018-01-07</td>\n",
       "      <td>1.62</td>\n",
       "      <td>17489.58</td>\n",
       "      <td>2894.77</td>\n",
       "      <td>2356.13</td>\n",
       "      <td>224.53</td>\n",
       "      <td>12014.15</td>\n",
       "      <td>11988.14</td>\n",
       "      <td>26.01</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>2018</td>\n",
       "      <td>WestTexNewMexico</td>\n",
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       "       Unnamed: 0        Date  AveragePrice  Total Volume     4046       4225  \\\n",
       "0               0  2015-12-27          1.33      64236.62  1036.74   54454.85   \n",
       "1               1  2015-12-20          1.35      54876.98   674.28   44638.81   \n",
       "2               2  2015-12-13          0.93     118220.22   794.70  109149.67   \n",
       "3               3  2015-12-06          1.08      78992.15  1132.00   71976.41   \n",
       "4               4  2015-11-29          1.28      51039.60   941.48   43838.39   \n",
       "...           ...         ...           ...           ...      ...        ...   \n",
       "18244           7  2018-02-04          1.63      17074.83  2046.96    1529.20   \n",
       "18245           8  2018-01-28          1.71      13888.04  1191.70    3431.50   \n",
       "18246           9  2018-01-21          1.87      13766.76  1191.92    2452.79   \n",
       "18247          10  2018-01-14          1.93      16205.22  1527.63    2981.04   \n",
       "18248          11  2018-01-07          1.62      17489.58  2894.77    2356.13   \n",
       "\n",
       "         4770  Total Bags  Small Bags  Large Bags  XLarge Bags          type  \\\n",
       "0       48.16     8696.87     8603.62       93.25          0.0  conventional   \n",
       "1       58.33     9505.56     9408.07       97.49          0.0  conventional   \n",
       "2      130.50     8145.35     8042.21      103.14          0.0  conventional   \n",
       "3       72.58     5811.16     5677.40      133.76          0.0  conventional   \n",
       "4       75.78     6183.95     5986.26      197.69          0.0  conventional   \n",
       "...       ...         ...         ...         ...          ...           ...   \n",
       "18244    0.00    13498.67    13066.82      431.85          0.0       organic   \n",
       "18245    0.00     9264.84     8940.04      324.80          0.0       organic   \n",
       "18246  727.94     9394.11     9351.80       42.31          0.0       organic   \n",
       "18247  727.01    10969.54    10919.54       50.00          0.0       organic   \n",
       "18248  224.53    12014.15    11988.14       26.01          0.0       organic   \n",
       "\n",
       "       year            region  \n",
       "0      2015            Albany  \n",
       "1      2015            Albany  \n",
       "2      2015            Albany  \n",
       "3      2015            Albany  \n",
       "4      2015            Albany  \n",
       "...     ...               ...  \n",
       "18244  2018  WestTexNewMexico  \n",
       "18245  2018  WestTexNewMexico  \n",
       "18246  2018  WestTexNewMexico  \n",
       "18247  2018  WestTexNewMexico  \n",
       "18248  2018  WestTexNewMexico  \n",
       "\n",
       "[18249 rows x 14 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df"
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  {
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       "   Unnamed: 0        Date  AveragePrice  Total Volume     4046       4225  \\\n",
       "0           0  2015-12-27          1.33      64236.62  1036.74   54454.85   \n",
       "1           1  2015-12-20          1.35      54876.98   674.28   44638.81   \n",
       "2           2  2015-12-13          0.93     118220.22   794.70  109149.67   \n",
       "3           3  2015-12-06          1.08      78992.15  1132.00   71976.41   \n",
       "\n",
       "     4770  Total Bags  Small Bags  Large Bags  XLarge Bags          type  \\\n",
       "0   48.16     8696.87     8603.62       93.25          0.0  conventional   \n",
       "1   58.33     9505.56     9408.07       97.49          0.0  conventional   \n",
       "2  130.50     8145.35     8042.21      103.14          0.0  conventional   \n",
       "3   72.58     5811.16     5677.40      133.76          0.0  conventional   \n",
       "\n",
       "   year  region  \n",
       "0  2015  Albany  \n",
       "1  2015  Albany  \n",
       "2  2015  Albany  \n",
       "3  2015  Albany  "
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   "source": [
    "df.head(4)"
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  },
  {
   "cell_type": "code",
   "execution_count": 5,
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       "      <td>6</td>\n",
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       "      <td>727.94</td>\n",
       "      <td>9394.11</td>\n",
       "      <td>9351.80</td>\n",
       "      <td>42.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>organic</td>\n",
       "      <td>2018</td>\n",
       "      <td>WestTexNewMexico</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18247</td>\n",
       "      <td>10</td>\n",
       "      <td>2018-01-14</td>\n",
       "      <td>1.93</td>\n",
       "      <td>16205.22</td>\n",
       "      <td>1527.63</td>\n",
       "      <td>2981.04</td>\n",
       "      <td>727.01</td>\n",
       "      <td>10969.54</td>\n",
       "      <td>10919.54</td>\n",
       "      <td>50.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>organic</td>\n",
       "      <td>2018</td>\n",
       "      <td>WestTexNewMexico</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18248</td>\n",
       "      <td>11</td>\n",
       "      <td>2018-01-07</td>\n",
       "      <td>1.62</td>\n",
       "      <td>17489.58</td>\n",
       "      <td>2894.77</td>\n",
       "      <td>2356.13</td>\n",
       "      <td>224.53</td>\n",
       "      <td>12014.15</td>\n",
       "      <td>11988.14</td>\n",
       "      <td>26.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>organic</td>\n",
       "      <td>2018</td>\n",
       "      <td>WestTexNewMexico</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0        Date  AveragePrice  Total Volume     4046     4225  \\\n",
       "18243           6  2018-02-11          1.57      15986.17  1924.28  1368.32   \n",
       "18244           7  2018-02-04          1.63      17074.83  2046.96  1529.20   \n",
       "18245           8  2018-01-28          1.71      13888.04  1191.70  3431.50   \n",
       "18246           9  2018-01-21          1.87      13766.76  1191.92  2452.79   \n",
       "18247          10  2018-01-14          1.93      16205.22  1527.63  2981.04   \n",
       "18248          11  2018-01-07          1.62      17489.58  2894.77  2356.13   \n",
       "\n",
       "         4770  Total Bags  Small Bags  Large Bags  XLarge Bags     type  year  \\\n",
       "18243    0.00    12693.57    12437.35      256.22          0.0  organic  2018   \n",
       "18244    0.00    13498.67    13066.82      431.85          0.0  organic  2018   \n",
       "18245    0.00     9264.84     8940.04      324.80          0.0  organic  2018   \n",
       "18246  727.94     9394.11     9351.80       42.31          0.0  organic  2018   \n",
       "18247  727.01    10969.54    10919.54       50.00          0.0  organic  2018   \n",
       "18248  224.53    12014.15    11988.14       26.01          0.0  organic  2018   \n",
       "\n",
       "                 region  \n",
       "18243  WestTexNewMexico  \n",
       "18244  WestTexNewMexico  \n",
       "18245  WestTexNewMexico  \n",
       "18246  WestTexNewMexico  \n",
       "18247  WestTexNewMexico  \n",
       "18248  WestTexNewMexico  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.33\n",
       "1    1.35\n",
       "2    0.93\n",
       "3    1.08\n",
       "4    1.28\n",
       "Name: AveragePrice, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['AveragePrice'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_albany=df[df['region']=='Albany']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>51039.60</td>\n",
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       "      <td>43838.39</td>\n",
       "      <td>75.78</td>\n",
       "      <td>6183.95</td>\n",
       "      <td>5986.26</td>\n",
       "      <td>197.69</td>\n",
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       "      <td>2018-02-04</td>\n",
       "      <td>1.52</td>\n",
       "      <td>4124.96</td>\n",
       "      <td>118.38</td>\n",
       "      <td>420.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3586.22</td>\n",
       "      <td>3586.22</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>organic</td>\n",
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       "      <td>Albany</td>\n",
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       "    <tr>\n",
       "      <td>17609</td>\n",
       "      <td>8</td>\n",
       "      <td>2018-01-28</td>\n",
       "      <td>1.32</td>\n",
       "      <td>6987.56</td>\n",
       "      <td>433.66</td>\n",
       "      <td>374.96</td>\n",
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       "      <td>253.01</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>17612</td>\n",
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       "      <td>4816.90</td>\n",
       "      <td>43.51</td>\n",
       "      <td>412.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4361.22</td>\n",
       "      <td>4357.89</td>\n",
       "      <td>3.33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>organic</td>\n",
       "      <td>2018</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
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       "<p>338 rows × 14 columns</p>\n",
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       "       Unnamed: 0        Date  AveragePrice  Total Volume     4046       4225  \\\n",
       "0               0  2015-12-27          1.33      64236.62  1036.74   54454.85   \n",
       "1               1  2015-12-20          1.35      54876.98   674.28   44638.81   \n",
       "2               2  2015-12-13          0.93     118220.22   794.70  109149.67   \n",
       "3               3  2015-12-06          1.08      78992.15  1132.00   71976.41   \n",
       "4               4  2015-11-29          1.28      51039.60   941.48   43838.39   \n",
       "...           ...         ...           ...           ...      ...        ...   \n",
       "17608           7  2018-02-04          1.52       4124.96   118.38     420.36   \n",
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       "17611          10  2018-01-14          1.47       4140.95     7.30     301.87   \n",
       "17612          11  2018-01-07          1.54       4816.90    43.51     412.17   \n",
       "\n",
       "         4770  Total Bags  Small Bags  Large Bags  XLarge Bags          type  \\\n",
       "0       48.16     8696.87     8603.62       93.25          0.0  conventional   \n",
       "1       58.33     9505.56     9408.07       97.49          0.0  conventional   \n",
       "2      130.50     8145.35     8042.21      103.14          0.0  conventional   \n",
       "3       72.58     5811.16     5677.40      133.76          0.0  conventional   \n",
       "4       75.78     6183.95     5986.26      197.69          0.0  conventional   \n",
       "...       ...         ...         ...         ...          ...           ...   \n",
       "17608    0.00     3586.22     3586.22        0.00          0.0       organic   \n",
       "17609    0.00     6178.94     6178.94        0.00          0.0       organic   \n",
       "17610    0.00     3078.86     3078.86        0.00          0.0       organic   \n",
       "17611    0.00     3831.78     3831.78        0.00          0.0       organic   \n",
       "17612    0.00     4361.22     4357.89        3.33          0.0       organic   \n",
       "\n",
       "       year  region  \n",
       "0      2015  Albany  \n",
       "1      2015  Albany  \n",
       "2      2015  Albany  \n",
       "3      2015  Albany  \n",
       "4      2015  Albany  \n",
       "...     ...     ...  \n",
       "17608  2018  Albany  \n",
       "17609  2018  Albany  \n",
       "17610  2018  Albany  \n",
       "17611  2018  Albany  \n",
       "17612  2018  Albany  \n",
       "\n",
       "[338 rows x 14 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_albany"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 9,
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   "source": [
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  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <td>6183.95</td>\n",
       "      <td>5986.26</td>\n",
       "      <td>197.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-02-04</td>\n",
       "      <td>7</td>\n",
       "      <td>1.52</td>\n",
       "      <td>4124.96</td>\n",
       "      <td>118.38</td>\n",
       "      <td>420.36</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3586.22</td>\n",
       "      <td>3586.22</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>organic</td>\n",
       "      <td>2018</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-01-28</td>\n",
       "      <td>8</td>\n",
       "      <td>1.32</td>\n",
       "      <td>6987.56</td>\n",
       "      <td>433.66</td>\n",
       "      <td>374.96</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6178.94</td>\n",
       "      <td>6178.94</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>organic</td>\n",
       "      <td>2018</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-01-21</td>\n",
       "      <td>9</td>\n",
       "      <td>1.54</td>\n",
       "      <td>3346.54</td>\n",
       "      <td>14.67</td>\n",
       "      <td>253.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3078.86</td>\n",
       "      <td>3078.86</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>organic</td>\n",
       "      <td>2018</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-01-14</td>\n",
       "      <td>10</td>\n",
       "      <td>1.47</td>\n",
       "      <td>4140.95</td>\n",
       "      <td>7.30</td>\n",
       "      <td>301.87</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3831.78</td>\n",
       "      <td>3831.78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>organic</td>\n",
       "      <td>2018</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-01-07</td>\n",
       "      <td>11</td>\n",
       "      <td>1.54</td>\n",
       "      <td>4816.90</td>\n",
       "      <td>43.51</td>\n",
       "      <td>412.17</td>\n",
       "      <td>0.00</td>\n",
       "      <td>4361.22</td>\n",
       "      <td>4357.89</td>\n",
       "      <td>3.33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>organic</td>\n",
       "      <td>2018</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>338 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Unnamed: 0  AveragePrice  Total Volume     4046       4225  \\\n",
       "Date                                                                     \n",
       "2015-12-27           0          1.33      64236.62  1036.74   54454.85   \n",
       "2015-12-20           1          1.35      54876.98   674.28   44638.81   \n",
       "2015-12-13           2          0.93     118220.22   794.70  109149.67   \n",
       "2015-12-06           3          1.08      78992.15  1132.00   71976.41   \n",
       "2015-11-29           4          1.28      51039.60   941.48   43838.39   \n",
       "...                ...           ...           ...      ...        ...   \n",
       "2018-02-04           7          1.52       4124.96   118.38     420.36   \n",
       "2018-01-28           8          1.32       6987.56   433.66     374.96   \n",
       "2018-01-21           9          1.54       3346.54    14.67     253.01   \n",
       "2018-01-14          10          1.47       4140.95     7.30     301.87   \n",
       "2018-01-07          11          1.54       4816.90    43.51     412.17   \n",
       "\n",
       "              4770  Total Bags  Small Bags  Large Bags  XLarge Bags  \\\n",
       "Date                                                                  \n",
       "2015-12-27   48.16     8696.87     8603.62       93.25          0.0   \n",
       "2015-12-20   58.33     9505.56     9408.07       97.49          0.0   \n",
       "2015-12-13  130.50     8145.35     8042.21      103.14          0.0   \n",
       "2015-12-06   72.58     5811.16     5677.40      133.76          0.0   \n",
       "2015-11-29   75.78     6183.95     5986.26      197.69          0.0   \n",
       "...            ...         ...         ...         ...          ...   \n",
       "2018-02-04    0.00     3586.22     3586.22        0.00          0.0   \n",
       "2018-01-28    0.00     6178.94     6178.94        0.00          0.0   \n",
       "2018-01-21    0.00     3078.86     3078.86        0.00          0.0   \n",
       "2018-01-14    0.00     3831.78     3831.78        0.00          0.0   \n",
       "2018-01-07    0.00     4361.22     4357.89        3.33          0.0   \n",
       "\n",
       "                    type  year  region  \n",
       "Date                                    \n",
       "2015-12-27  conventional  2015  Albany  \n",
       "2015-12-20  conventional  2015  Albany  \n",
       "2015-12-13  conventional  2015  Albany  \n",
       "2015-12-06  conventional  2015  Albany  \n",
       "2015-11-29  conventional  2015  Albany  \n",
       "...                  ...   ...     ...  \n",
       "2018-02-04       organic  2018  Albany  \n",
       "2018-01-28       organic  2018  Albany  \n",
       "2018-01-21       organic  2018  Albany  \n",
       "2018-01-14       organic  2018  Albany  \n",
       "2018-01-07       organic  2018  Albany  \n",
       "\n",
       "[338 rows x 13 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_albany.set_index('Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_albany.set_index('Date',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>AveragePrice</th>\n",
       "      <th>Total Volume</th>\n",
       "      <th>4046</th>\n",
       "      <th>4225</th>\n",
       "      <th>4770</th>\n",
       "      <th>Total Bags</th>\n",
       "      <th>Small Bags</th>\n",
       "      <th>Large Bags</th>\n",
       "      <th>XLarge Bags</th>\n",
       "      <th>type</th>\n",
       "      <th>year</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2015-12-27</td>\n",
       "      <td>0</td>\n",
       "      <td>1.33</td>\n",
       "      <td>64236.62</td>\n",
       "      <td>1036.74</td>\n",
       "      <td>54454.85</td>\n",
       "      <td>48.16</td>\n",
       "      <td>8696.87</td>\n",
       "      <td>8603.62</td>\n",
       "      <td>93.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-12-20</td>\n",
       "      <td>1</td>\n",
       "      <td>1.35</td>\n",
       "      <td>54876.98</td>\n",
       "      <td>674.28</td>\n",
       "      <td>44638.81</td>\n",
       "      <td>58.33</td>\n",
       "      <td>9505.56</td>\n",
       "      <td>9408.07</td>\n",
       "      <td>97.49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-12-13</td>\n",
       "      <td>2</td>\n",
       "      <td>0.93</td>\n",
       "      <td>118220.22</td>\n",
       "      <td>794.70</td>\n",
       "      <td>109149.67</td>\n",
       "      <td>130.50</td>\n",
       "      <td>8145.35</td>\n",
       "      <td>8042.21</td>\n",
       "      <td>103.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-12-06</td>\n",
       "      <td>3</td>\n",
       "      <td>1.08</td>\n",
       "      <td>78992.15</td>\n",
       "      <td>1132.00</td>\n",
       "      <td>71976.41</td>\n",
       "      <td>72.58</td>\n",
       "      <td>5811.16</td>\n",
       "      <td>5677.40</td>\n",
       "      <td>133.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>4</td>\n",
       "      <td>1.28</td>\n",
       "      <td>51039.60</td>\n",
       "      <td>941.48</td>\n",
       "      <td>43838.39</td>\n",
       "      <td>75.78</td>\n",
       "      <td>6183.95</td>\n",
       "      <td>5986.26</td>\n",
       "      <td>197.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Unnamed: 0  AveragePrice  Total Volume     4046       4225  \\\n",
       "Date                                                                     \n",
       "2015-12-27           0          1.33      64236.62  1036.74   54454.85   \n",
       "2015-12-20           1          1.35      54876.98   674.28   44638.81   \n",
       "2015-12-13           2          0.93     118220.22   794.70  109149.67   \n",
       "2015-12-06           3          1.08      78992.15  1132.00   71976.41   \n",
       "2015-11-29           4          1.28      51039.60   941.48   43838.39   \n",
       "\n",
       "              4770  Total Bags  Small Bags  Large Bags  XLarge Bags  \\\n",
       "Date                                                                  \n",
       "2015-12-27   48.16     8696.87     8603.62       93.25          0.0   \n",
       "2015-12-20   58.33     9505.56     9408.07       97.49          0.0   \n",
       "2015-12-13  130.50     8145.35     8042.21      103.14          0.0   \n",
       "2015-12-06   72.58     5811.16     5677.40      133.76          0.0   \n",
       "2015-11-29   75.78     6183.95     5986.26      197.69          0.0   \n",
       "\n",
       "                    type  year  region  \n",
       "Date                                    \n",
       "2015-12-27  conventional  2015  Albany  \n",
       "2015-12-20  conventional  2015  Albany  \n",
       "2015-12-13  conventional  2015  Albany  \n",
       "2015-12-06  conventional  2015  Albany  \n",
       "2015-11-29  conventional  2015  Albany  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_albany.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x231bb3c7748>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_albany['AveragePrice'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x231bd9c3c18>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1=pd.read_csv('avocado.csv')\n",
    "df1_albany=df1[df['region']=='Albany']\n",
    "df1_albany.set_index('Date',inplace=True)\n",
    "df1_albany['AveragePrice'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x231bd5b6470>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_albany['AveragePrice'].rolling(25).mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\ragha\\appdata\\local\\continuum\\anaconda3\\envs\\tensorflowgpu\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df1_albany.sort_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x231bd458828>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1_albany['AveragePrice'].rolling(25).mean().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\ragha\\appdata\\local\\continuum\\anaconda3\\envs\\tensorflowgpu\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df1_albany['price25ma']=df1_albany['AveragePrice'].rolling(25).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\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>Unnamed: 0</th>\n",
       "      <th>AveragePrice</th>\n",
       "      <th>Total Volume</th>\n",
       "      <th>4046</th>\n",
       "      <th>4225</th>\n",
       "      <th>4770</th>\n",
       "      <th>Total Bags</th>\n",
       "      <th>Small Bags</th>\n",
       "      <th>Large Bags</th>\n",
       "      <th>XLarge Bags</th>\n",
       "      <th>type</th>\n",
       "      <th>year</th>\n",
       "      <th>region</th>\n",
       "      <th>price25ma</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>51</td>\n",
       "      <td>1.22</td>\n",
       "      <td>40873.28</td>\n",
       "      <td>2819.50</td>\n",
       "      <td>28287.42</td>\n",
       "      <td>49.90</td>\n",
       "      <td>9716.46</td>\n",
       "      <td>9186.93</td>\n",
       "      <td>529.53</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>51</td>\n",
       "      <td>1.79</td>\n",
       "      <td>1373.95</td>\n",
       "      <td>57.42</td>\n",
       "      <td>153.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1162.65</td>\n",
       "      <td>1162.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>organic</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-01-11</td>\n",
       "      <td>50</td>\n",
       "      <td>1.24</td>\n",
       "      <td>41195.08</td>\n",
       "      <td>1002.85</td>\n",
       "      <td>31640.34</td>\n",
       "      <td>127.12</td>\n",
       "      <td>8424.77</td>\n",
       "      <td>8036.04</td>\n",
       "      <td>388.73</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-01-11</td>\n",
       "      <td>50</td>\n",
       "      <td>1.77</td>\n",
       "      <td>1182.56</td>\n",
       "      <td>39.00</td>\n",
       "      <td>305.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>838.44</td>\n",
       "      <td>838.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>organic</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2015-01-18</td>\n",
       "      <td>49</td>\n",
       "      <td>1.17</td>\n",
       "      <td>44511.28</td>\n",
       "      <td>914.14</td>\n",
       "      <td>31540.32</td>\n",
       "      <td>135.77</td>\n",
       "      <td>11921.05</td>\n",
       "      <td>11651.09</td>\n",
       "      <td>269.96</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Unnamed: 0  AveragePrice  Total Volume     4046      4225    4770  \\\n",
       "Date                                                                            \n",
       "2015-01-04          51          1.22      40873.28  2819.50  28287.42   49.90   \n",
       "2015-01-04          51          1.79       1373.95    57.42    153.88    0.00   \n",
       "2015-01-11          50          1.24      41195.08  1002.85  31640.34  127.12   \n",
       "2015-01-11          50          1.77       1182.56    39.00    305.12    0.00   \n",
       "2015-01-18          49          1.17      44511.28   914.14  31540.32  135.77   \n",
       "\n",
       "            Total Bags  Small Bags  Large Bags  XLarge Bags          type  \\\n",
       "Date                                                                        \n",
       "2015-01-04     9716.46     9186.93      529.53          0.0  conventional   \n",
       "2015-01-04     1162.65     1162.65        0.00          0.0       organic   \n",
       "2015-01-11     8424.77     8036.04      388.73          0.0  conventional   \n",
       "2015-01-11      838.44      838.44        0.00          0.0       organic   \n",
       "2015-01-18    11921.05    11651.09      269.96          0.0  conventional   \n",
       "\n",
       "            year  region  price25ma  \n",
       "Date                                 \n",
       "2015-01-04  2015  Albany        NaN  \n",
       "2015-01-04  2015  Albany        NaN  \n",
       "2015-01-11  2015  Albany        NaN  \n",
       "2015-01-11  2015  Albany        NaN  \n",
       "2015-01-18  2015  Albany        NaN  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_albany.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>AveragePrice</th>\n",
       "      <th>Total Volume</th>\n",
       "      <th>4046</th>\n",
       "      <th>4225</th>\n",
       "      <th>4770</th>\n",
       "      <th>Total Bags</th>\n",
       "      <th>Small Bags</th>\n",
       "      <th>Large Bags</th>\n",
       "      <th>XLarge Bags</th>\n",
       "      <th>type</th>\n",
       "      <th>year</th>\n",
       "      <th>region</th>\n",
       "      <th>price25ma</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2018-03-11</td>\n",
       "      <td>2</td>\n",
       "      <td>1.68</td>\n",
       "      <td>2570.52</td>\n",
       "      <td>131.67</td>\n",
       "      <td>229.56</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2209.29</td>\n",
       "      <td>2209.29</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>organic</td>\n",
       "      <td>2018</td>\n",
       "      <td>Albany</td>\n",
       "      <td>1.4224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-03-18</td>\n",
       "      <td>1</td>\n",
       "      <td>1.66</td>\n",
       "      <td>3154.45</td>\n",
       "      <td>275.89</td>\n",
       "      <td>297.96</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2580.60</td>\n",
       "      <td>2577.27</td>\n",
       "      <td>3.33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>organic</td>\n",
       "      <td>2018</td>\n",
       "      <td>Albany</td>\n",
       "      <td>1.4316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-03-18</td>\n",
       "      <td>1</td>\n",
       "      <td>1.35</td>\n",
       "      <td>105304.65</td>\n",
       "      <td>13234.86</td>\n",
       "      <td>61037.58</td>\n",
       "      <td>55.00</td>\n",
       "      <td>30977.21</td>\n",
       "      <td>26755.90</td>\n",
       "      <td>3721.31</td>\n",
       "      <td>500.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2018</td>\n",
       "      <td>Albany</td>\n",
       "      <td>1.4276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-03-25</td>\n",
       "      <td>0</td>\n",
       "      <td>1.57</td>\n",
       "      <td>149396.50</td>\n",
       "      <td>16361.69</td>\n",
       "      <td>109045.03</td>\n",
       "      <td>65.45</td>\n",
       "      <td>23924.33</td>\n",
       "      <td>19273.80</td>\n",
       "      <td>4270.53</td>\n",
       "      <td>380.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2018</td>\n",
       "      <td>Albany</td>\n",
       "      <td>1.4272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-03-25</td>\n",
       "      <td>0</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2321.82</td>\n",
       "      <td>42.95</td>\n",
       "      <td>272.41</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2006.46</td>\n",
       "      <td>1996.46</td>\n",
       "      <td>10.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>organic</td>\n",
       "      <td>2018</td>\n",
       "      <td>Albany</td>\n",
       "      <td>1.4368</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Unnamed: 0  AveragePrice  Total Volume      4046       4225  \\\n",
       "Date                                                                      \n",
       "2018-03-11           2          1.68       2570.52    131.67     229.56   \n",
       "2018-03-18           1          1.66       3154.45    275.89     297.96   \n",
       "2018-03-18           1          1.35     105304.65  13234.86   61037.58   \n",
       "2018-03-25           0          1.57     149396.50  16361.69  109045.03   \n",
       "2018-03-25           0          1.71       2321.82     42.95     272.41   \n",
       "\n",
       "             4770  Total Bags  Small Bags  Large Bags  XLarge Bags  \\\n",
       "Date                                                                 \n",
       "2018-03-11   0.00     2209.29     2209.29        0.00          0.0   \n",
       "2018-03-18   0.00     2580.60     2577.27        3.33          0.0   \n",
       "2018-03-18  55.00    30977.21    26755.90     3721.31        500.0   \n",
       "2018-03-25  65.45    23924.33    19273.80     4270.53        380.0   \n",
       "2018-03-25   0.00     2006.46     1996.46       10.00          0.0   \n",
       "\n",
       "                    type  year  region  price25ma  \n",
       "Date                                               \n",
       "2018-03-11       organic  2018  Albany     1.4224  \n",
       "2018-03-18       organic  2018  Albany     1.4316  \n",
       "2018-03-18  conventional  2018  Albany     1.4276  \n",
       "2018-03-25  conventional  2018  Albany     1.4272  \n",
       "2018-03-25       organic  2018  Albany     1.4368  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_albany.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Albany', 'Albany', 'Albany', ..., 'WestTexNewMexico',\n",
       "       'WestTexNewMexico', 'WestTexNewMexico'], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['region'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Albany', 'Atlanta', 'BaltimoreWashington', 'Boise', 'Boston',\n",
       "       'BuffaloRochester', 'California', 'Charlotte', 'Chicago',\n",
       "       'CincinnatiDayton', 'Columbus', 'DallasFtWorth', 'Denver',\n",
       "       'Detroit', 'GrandRapids', 'GreatLakes', 'HarrisburgScranton',\n",
       "       'HartfordSpringfield', 'Houston', 'Indianapolis', 'Jacksonville',\n",
       "       'LasVegas', 'LosAngeles', 'Louisville', 'MiamiFtLauderdale',\n",
       "       'Midsouth', 'Nashville', 'NewOrleansMobile', 'NewYork',\n",
       "       'Northeast', 'NorthernNewEngland', 'Orlando', 'Philadelphia',\n",
       "       'PhoenixTucson', 'Pittsburgh', 'Plains', 'Portland',\n",
       "       'RaleighGreensboro', 'RichmondNorfolk', 'Roanoke', 'Sacramento',\n",
       "       'SanDiego', 'SanFrancisco', 'Seattle', 'SouthCarolina',\n",
       "       'SouthCentral', 'Southeast', 'Spokane', 'StLouis', 'Syracuse',\n",
       "       'Tampa', 'TotalUS', 'West', 'WestTexNewMexico'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['region'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph_df=pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Albany\n",
      "Atlanta\n",
      "BaltimoreWashington\n",
      "Boise\n",
      "Boston\n",
      "BuffaloRochester\n",
      "California\n",
      "Charlotte\n",
      "Chicago\n",
      "CincinnatiDayton\n",
      "Columbus\n",
      "DallasFtWorth\n",
      "Denver\n",
      "Detroit\n",
      "GrandRapids\n",
      "GreatLakes\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Albany_price25ma</th>\n",
       "      <th>Atlanta_price25ma</th>\n",
       "      <th>BaltimoreWashington_price25ma</th>\n",
       "      <th>Boise_price25ma</th>\n",
       "      <th>Boston_price25ma</th>\n",
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       "      <th>Columbus_price25ma</th>\n",
       "      <th>DallasFtWorth_price25ma</th>\n",
       "      <th>Denver_price25ma</th>\n",
       "      <th>Detroit_price25ma</th>\n",
       "      <th>GrandRapids_price25ma</th>\n",
       "      <th>GreatLakes_price25ma</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2018-03-25</td>\n",
       "      <td>1.4368</td>\n",
       "      <td>1.2884</td>\n",
       "      <td>1.3844</td>\n",
       "      <td>1.5016</td>\n",
       "      <td>1.588</td>\n",
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       "      <td>1.4232</td>\n",
       "      <td>1.4916</td>\n",
       "      <td>1.5708</td>\n",
       "      <td>1.2792</td>\n",
       "      <td>1.1704</td>\n",
       "      <td>1.118</td>\n",
       "      <td>1.2888</td>\n",
       "      <td>1.1492</td>\n",
       "      <td>1.3264</td>\n",
       "      <td>1.2788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-03-25</td>\n",
       "      <td>1.4368</td>\n",
       "      <td>1.2884</td>\n",
       "      <td>1.3844</td>\n",
       "      <td>1.5016</td>\n",
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       "      <td>1.1728</td>\n",
       "      <td>1.3164</td>\n",
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       "      <td>2018-03-25</td>\n",
       "      <td>1.4368</td>\n",
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       "      <td>1.5016</td>\n",
       "      <td>1.588</td>\n",
       "      <td>1.2232</td>\n",
       "      <td>1.4232</td>\n",
       "      <td>1.4916</td>\n",
       "      <td>1.5708</td>\n",
       "      <td>1.2792</td>\n",
       "      <td>1.1704</td>\n",
       "      <td>1.118</td>\n",
       "      <td>1.2888</td>\n",
       "      <td>1.1728</td>\n",
       "      <td>1.3164</td>\n",
       "      <td>1.2788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-03-25</td>\n",
       "      <td>1.4368</td>\n",
       "      <td>1.2884</td>\n",
       "      <td>1.3844</td>\n",
       "      <td>1.5016</td>\n",
       "      <td>1.588</td>\n",
       "      <td>1.2232</td>\n",
       "      <td>1.4232</td>\n",
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       "      <td>1.2792</td>\n",
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       "      <td>2018-03-25</td>\n",
       "      <td>1.4368</td>\n",
       "      <td>1.2884</td>\n",
       "      <td>1.3844</td>\n",
       "      <td>1.5016</td>\n",
       "      <td>1.588</td>\n",
       "      <td>1.2232</td>\n",
       "      <td>1.4232</td>\n",
       "      <td>1.4916</td>\n",
       "      <td>1.5708</td>\n",
       "      <td>1.2792</td>\n",
       "      <td>1.1704</td>\n",
       "      <td>1.118</td>\n",
       "      <td>1.2888</td>\n",
       "      <td>1.1728</td>\n",
       "      <td>1.3264</td>\n",
       "      <td>1.2788</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Albany_price25ma  Atlanta_price25ma  \\\n",
       "Date                                              \n",
       "2018-03-25            1.4368             1.2884   \n",
       "2018-03-25            1.4368             1.2884   \n",
       "2018-03-25            1.4368             1.2884   \n",
       "2018-03-25            1.4368             1.2884   \n",
       "2018-03-25            1.4368             1.2884   \n",
       "\n",
       "            BaltimoreWashington_price25ma  Boise_price25ma  Boston_price25ma  \\\n",
       "Date                                                                           \n",
       "2018-03-25                         1.3844           1.5016             1.588   \n",
       "2018-03-25                         1.3844           1.5016             1.588   \n",
       "2018-03-25                         1.3844           1.5016             1.588   \n",
       "2018-03-25                         1.3844           1.5016             1.588   \n",
       "2018-03-25                         1.3844           1.5016             1.588   \n",
       "\n",
       "            BuffaloRochester_price25ma  California_price25ma  \\\n",
       "Date                                                           \n",
       "2018-03-25                      1.2232                1.4232   \n",
       "2018-03-25                      1.2232                1.4232   \n",
       "2018-03-25                      1.2232                1.4232   \n",
       "2018-03-25                      1.2232                1.4232   \n",
       "2018-03-25                      1.2232                1.4232   \n",
       "\n",
       "            Charlotte_price25ma  Chicago_price25ma  \\\n",
       "Date                                                 \n",
       "2018-03-25               1.4916             1.5708   \n",
       "2018-03-25               1.4916             1.5708   \n",
       "2018-03-25               1.4916             1.5708   \n",
       "2018-03-25               1.4916             1.5708   \n",
       "2018-03-25               1.4916             1.5708   \n",
       "\n",
       "            CincinnatiDayton_price25ma  Columbus_price25ma  \\\n",
       "Date                                                         \n",
       "2018-03-25                      1.2792              1.1704   \n",
       "2018-03-25                      1.2792              1.1704   \n",
       "2018-03-25                      1.2792              1.1704   \n",
       "2018-03-25                      1.2792              1.1704   \n",
       "2018-03-25                      1.2792              1.1704   \n",
       "\n",
       "            DallasFtWorth_price25ma  Denver_price25ma  Detroit_price25ma  \\\n",
       "Date                                                                       \n",
       "2018-03-25                    1.118            1.2888             1.1492   \n",
       "2018-03-25                    1.118            1.2888             1.1728   \n",
       "2018-03-25                    1.118            1.2888             1.1728   \n",
       "2018-03-25                    1.118            1.2888             1.1728   \n",
       "2018-03-25                    1.118            1.2888             1.1728   \n",
       "\n",
       "            GrandRapids_price25ma  GreatLakes_price25ma  \n",
       "Date                                                     \n",
       "2018-03-25                 1.3264                1.2788  \n",
       "2018-03-25                 1.3164                1.2568  \n",
       "2018-03-25                 1.3164                1.2788  \n",
       "2018-03-25                 1.3264                1.2568  \n",
       "2018-03-25                 1.3264                1.2788  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for region in df['region'].unique()[:16]:\n",
    "    print(region)\n",
    "    region_df = df.copy()[df['region']==region]\n",
    "    region_df.set_index('Date', inplace=True)\n",
    "    region_df.sort_index(inplace=True)\n",
    "    region_df[\"{}_price25ma\".format(region)] = region_df[\"AveragePrice\"].rolling(25).mean()\n",
    "\n",
    "    if graph_df.empty:\n",
    "        graph_df = region_df[[\"{}_price25ma\".format(region)]]  # note the double square brackets!\n",
    "    else:\n",
    "        graph_df = graph_df.join(region_df[\"{}_price25ma\".format(region)])\n",
    "        \n",
    "\n",
    "        \n",
    "graph_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Date</th>\n",
       "      <th>AveragePrice</th>\n",
       "      <th>Total Volume</th>\n",
       "      <th>4046</th>\n",
       "      <th>4225</th>\n",
       "      <th>4770</th>\n",
       "      <th>Total Bags</th>\n",
       "      <th>Small Bags</th>\n",
       "      <th>Large Bags</th>\n",
       "      <th>XLarge Bags</th>\n",
       "      <th>type</th>\n",
       "      <th>year</th>\n",
       "      <th>region</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>9489</td>\n",
       "      <td>51</td>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>1.24</td>\n",
       "      <td>142349.77</td>\n",
       "      <td>107490.73</td>\n",
       "      <td>25711.96</td>\n",
       "      <td>2.93</td>\n",
       "      <td>9144.15</td>\n",
       "      <td>9144.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>organic</td>\n",
       "      <td>2015</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10269</td>\n",
       "      <td>51</td>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>1.50</td>\n",
       "      <td>6329.83</td>\n",
       "      <td>3730.80</td>\n",
       "      <td>2141.91</td>\n",
       "      <td>0.00</td>\n",
       "      <td>457.12</td>\n",
       "      <td>426.67</td>\n",
       "      <td>30.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>organic</td>\n",
       "      <td>2015</td>\n",
       "      <td>LasVegas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10893</td>\n",
       "      <td>51</td>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>1.12</td>\n",
       "      <td>17296.85</td>\n",
       "      <td>14569.66</td>\n",
       "      <td>1868.59</td>\n",
       "      <td>0.00</td>\n",
       "      <td>858.60</td>\n",
       "      <td>830.00</td>\n",
       "      <td>28.60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>organic</td>\n",
       "      <td>2015</td>\n",
       "      <td>PhoenixTucson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9437</td>\n",
       "      <td>51</td>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>1.73</td>\n",
       "      <td>379.82</td>\n",
       "      <td>0.00</td>\n",
       "      <td>59.82</td>\n",
       "      <td>0.00</td>\n",
       "      <td>320.00</td>\n",
       "      <td>320.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>organic</td>\n",
       "      <td>2015</td>\n",
       "      <td>BuffaloRochester</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11621</td>\n",
       "      <td>51</td>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>1.30</td>\n",
       "      <td>5782.70</td>\n",
       "      <td>723.29</td>\n",
       "      <td>4221.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>838.26</td>\n",
       "      <td>223.33</td>\n",
       "      <td>614.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>organic</td>\n",
       "      <td>2015</td>\n",
       "      <td>Spokane</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0       Date  AveragePrice  Total Volume       4046      4225  \\\n",
       "9489           51 2015-01-04          1.24     142349.77  107490.73  25711.96   \n",
       "10269          51 2015-01-04          1.50       6329.83    3730.80   2141.91   \n",
       "10893          51 2015-01-04          1.12      17296.85   14569.66   1868.59   \n",
       "9437           51 2015-01-04          1.73        379.82       0.00     59.82   \n",
       "11621          51 2015-01-04          1.30       5782.70     723.29   4221.15   \n",
       "\n",
       "       4770  Total Bags  Small Bags  Large Bags  XLarge Bags     type  year  \\\n",
       "9489   2.93     9144.15     9144.15        0.00          0.0  organic  2015   \n",
       "10269  0.00      457.12      426.67       30.45          0.0  organic  2015   \n",
       "10893  0.00      858.60      830.00       28.60          0.0  organic  2015   \n",
       "9437   0.00      320.00      320.00        0.00          0.0  organic  2015   \n",
       "11621  0.00      838.26      223.33      614.93          0.0  organic  2015   \n",
       "\n",
       "                 region  \n",
       "9489         California  \n",
       "10269          LasVegas  \n",
       "10893     PhoenixTucson  \n",
       "9437   BuffaloRochester  \n",
       "11621           Spokane  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"avocado.csv\")\n",
    "df = df.copy()[df['type']=='organic']\n",
    "\n",
    "df[\"Date\"] = pd.to_datetime(df[\"Date\"])\n",
    "\n",
    "df.sort_values(by=\"Date\", ascending=True, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "California\n",
      "LasVegas\n",
      "PhoenixTucson\n",
      "BuffaloRochester\n",
      "Spokane\n",
      "LosAngeles\n",
      "Philadelphia\n",
      "Boston\n",
      "StLouis\n",
      "Louisville\n",
      "Orlando\n",
      "Southeast\n",
      "Boise\n",
      "MiamiFtLauderdale\n",
      "NorthernNewEngland\n",
      "BaltimoreWashington\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\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>California_price25ma</th>\n",
       "      <th>LasVegas_price25ma</th>\n",
       "      <th>PhoenixTucson_price25ma</th>\n",
       "      <th>BuffaloRochester_price25ma</th>\n",
       "      <th>Spokane_price25ma</th>\n",
       "      <th>LosAngeles_price25ma</th>\n",
       "      <th>Philadelphia_price25ma</th>\n",
       "      <th>Boston_price25ma</th>\n",
       "      <th>StLouis_price25ma</th>\n",
       "      <th>Louisville_price25ma</th>\n",
       "      <th>Orlando_price25ma</th>\n",
       "      <th>Southeast_price25ma</th>\n",
       "      <th>Boise_price25ma</th>\n",
       "      <th>MiamiFtLauderdale_price25ma</th>\n",
       "      <th>NorthernNewEngland_price25ma</th>\n",
       "      <th>BaltimoreWashington_price25ma</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2018-02-25</td>\n",
       "      <td>1.9128</td>\n",
       "      <td>1.9120</td>\n",
       "      <td>1.7468</td>\n",
       "      <td>1.2912</td>\n",
       "      <td>2.1544</td>\n",
       "      <td>1.9024</td>\n",
       "      <td>1.6324</td>\n",
       "      <td>1.7636</td>\n",
       "      <td>1.9240</td>\n",
       "      <td>1.7044</td>\n",
       "      <td>1.8728</td>\n",
       "      <td>1.7116</td>\n",
       "      <td>2.0512</td>\n",
       "      <td>1.7140</td>\n",
       "      <td>1.6280</td>\n",
       "      <td>1.5836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-03-04</td>\n",
       "      <td>1.8876</td>\n",
       "      <td>1.8748</td>\n",
       "      <td>1.7404</td>\n",
       "      <td>1.2744</td>\n",
       "      <td>2.1040</td>\n",
       "      <td>1.8656</td>\n",
       "      <td>1.6260</td>\n",
       "      <td>1.7708</td>\n",
       "      <td>1.8868</td>\n",
       "      <td>1.6816</td>\n",
       "      <td>1.8512</td>\n",
       "      <td>1.6868</td>\n",
       "      <td>2.0176</td>\n",
       "      <td>1.7068</td>\n",
       "      <td>1.6224</td>\n",
       "      <td>1.5772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-03-11</td>\n",
       "      <td>1.8636</td>\n",
       "      <td>1.8440</td>\n",
       "      <td>1.7324</td>\n",
       "      <td>1.2652</td>\n",
       "      <td>2.0552</td>\n",
       "      <td>1.8284</td>\n",
       "      <td>1.6300</td>\n",
       "      <td>1.7824</td>\n",
       "      <td>1.8468</td>\n",
       "      <td>1.6508</td>\n",
       "      <td>1.8004</td>\n",
       "      <td>1.6484</td>\n",
       "      <td>1.9836</td>\n",
       "      <td>1.6904</td>\n",
       "      <td>1.6264</td>\n",
       "      <td>1.5732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-03-18</td>\n",
       "      <td>1.8516</td>\n",
       "      <td>1.8204</td>\n",
       "      <td>1.7216</td>\n",
       "      <td>1.2560</td>\n",
       "      <td>2.0012</td>\n",
       "      <td>1.8160</td>\n",
       "      <td>1.6304</td>\n",
       "      <td>1.7932</td>\n",
       "      <td>1.8192</td>\n",
       "      <td>1.6176</td>\n",
       "      <td>1.7712</td>\n",
       "      <td>1.6156</td>\n",
       "      <td>1.9520</td>\n",
       "      <td>1.6692</td>\n",
       "      <td>1.6284</td>\n",
       "      <td>1.5684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2018-03-25</td>\n",
       "      <td>1.8364</td>\n",
       "      <td>1.7968</td>\n",
       "      <td>1.7104</td>\n",
       "      <td>1.2416</td>\n",
       "      <td>1.9496</td>\n",
       "      <td>1.8016</td>\n",
       "      <td>1.6256</td>\n",
       "      <td>1.7984</td>\n",
       "      <td>1.7976</td>\n",
       "      <td>1.5844</td>\n",
       "      <td>1.7352</td>\n",
       "      <td>1.5820</td>\n",
       "      <td>1.9128</td>\n",
       "      <td>1.6392</td>\n",
       "      <td>1.6312</td>\n",
       "      <td>1.5568</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            California_price25ma  LasVegas_price25ma  PhoenixTucson_price25ma  \\\n",
       "Date                                                                            \n",
       "2018-02-25                1.9128              1.9120                   1.7468   \n",
       "2018-03-04                1.8876              1.8748                   1.7404   \n",
       "2018-03-11                1.8636              1.8440                   1.7324   \n",
       "2018-03-18                1.8516              1.8204                   1.7216   \n",
       "2018-03-25                1.8364              1.7968                   1.7104   \n",
       "\n",
       "            BuffaloRochester_price25ma  Spokane_price25ma  \\\n",
       "Date                                                        \n",
       "2018-02-25                      1.2912             2.1544   \n",
       "2018-03-04                      1.2744             2.1040   \n",
       "2018-03-11                      1.2652             2.0552   \n",
       "2018-03-18                      1.2560             2.0012   \n",
       "2018-03-25                      1.2416             1.9496   \n",
       "\n",
       "            LosAngeles_price25ma  Philadelphia_price25ma  Boston_price25ma  \\\n",
       "Date                                                                         \n",
       "2018-02-25                1.9024                  1.6324            1.7636   \n",
       "2018-03-04                1.8656                  1.6260            1.7708   \n",
       "2018-03-11                1.8284                  1.6300            1.7824   \n",
       "2018-03-18                1.8160                  1.6304            1.7932   \n",
       "2018-03-25                1.8016                  1.6256            1.7984   \n",
       "\n",
       "            StLouis_price25ma  Louisville_price25ma  Orlando_price25ma  \\\n",
       "Date                                                                     \n",
       "2018-02-25             1.9240                1.7044             1.8728   \n",
       "2018-03-04             1.8868                1.6816             1.8512   \n",
       "2018-03-11             1.8468                1.6508             1.8004   \n",
       "2018-03-18             1.8192                1.6176             1.7712   \n",
       "2018-03-25             1.7976                1.5844             1.7352   \n",
       "\n",
       "            Southeast_price25ma  Boise_price25ma  MiamiFtLauderdale_price25ma  \\\n",
       "Date                                                                            \n",
       "2018-02-25               1.7116           2.0512                       1.7140   \n",
       "2018-03-04               1.6868           2.0176                       1.7068   \n",
       "2018-03-11               1.6484           1.9836                       1.6904   \n",
       "2018-03-18               1.6156           1.9520                       1.6692   \n",
       "2018-03-25               1.5820           1.9128                       1.6392   \n",
       "\n",
       "            NorthernNewEngland_price25ma  BaltimoreWashington_price25ma  \n",
       "Date                                                                     \n",
       "2018-02-25                        1.6280                         1.5836  \n",
       "2018-03-04                        1.6224                         1.5772  \n",
       "2018-03-11                        1.6264                         1.5732  \n",
       "2018-03-18                        1.6284                         1.5684  \n",
       "2018-03-25                        1.6312                         1.5568  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph_df=pd.DataFrame()\n",
    "for region in df['region'].unique()[:16]:\n",
    "    print(region)\n",
    "    region_df = df.copy()[df['region']==region]\n",
    "    region_df.set_index('Date', inplace=True)\n",
    "    region_df.sort_index(inplace=True)\n",
    "    region_df[\"{}_price25ma\".format(region)] = region_df[\"AveragePrice\"].rolling(25).mean()\n",
    "\n",
    "    if graph_df.empty:\n",
    "        graph_df = region_df[[\"{}_price25ma\".format(region)]]  # note the double square brackets!\n",
    "    else:\n",
    "        graph_df = graph_df.join(region_df[\"{}_price25ma\".format(region)])\n",
    "        \n",
    "\n",
    "        \n",
    "graph_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x231bd635080>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "graph_df.plot(figsize=(8,5),legend=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}