inventory.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inventory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inv = pd.read_csv('datasets\\inventory.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RegionID</th>\n",
       "      <th>SizeRank</th>\n",
       "      <th>RegionName</th>\n",
       "      <th>RegionType</th>\n",
       "      <th>StateName</th>\n",
       "      <th>1/31/2018</th>\n",
       "      <th>2/28/2018</th>\n",
       "      <th>3/31/2018</th>\n",
       "      <th>4/30/2018</th>\n",
       "      <th>5/31/2018</th>\n",
       "      <th>...</th>\n",
       "      <th>4/30/2022</th>\n",
       "      <th>5/31/2022</th>\n",
       "      <th>6/30/2022</th>\n",
       "      <th>7/31/2022</th>\n",
       "      <th>8/31/2022</th>\n",
       "      <th>9/30/2022</th>\n",
       "      <th>10/31/2022</th>\n",
       "      <th>11/30/2022</th>\n",
       "      <th>12/31/2022</th>\n",
       "      <th>1/31/2023</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>102001</td>\n",
       "      <td>0</td>\n",
       "      <td>United States</td>\n",
       "      <td>country</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1360756.0</td>\n",
       "      <td>1388126.0</td>\n",
       "      <td>1515704.0</td>\n",
       "      <td>1596749.0</td>\n",
       "      <td>1664787.0</td>\n",
       "      <td>...</td>\n",
       "      <td>828870.0</td>\n",
       "      <td>925373.0</td>\n",
       "      <td>1028861.0</td>\n",
       "      <td>1084624.0</td>\n",
       "      <td>1099632.0</td>\n",
       "      <td>1057986.0</td>\n",
       "      <td>1038846.0</td>\n",
       "      <td>947250.0</td>\n",
       "      <td>833473.0</td>\n",
       "      <td>825077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>394913</td>\n",
       "      <td>1</td>\n",
       "      <td>New York, NY</td>\n",
       "      <td>msa</td>\n",
       "      <td>NY</td>\n",
       "      <td>66530.0</td>\n",
       "      <td>74575.0</td>\n",
       "      <td>80016.0</td>\n",
       "      <td>86445.0</td>\n",
       "      <td>91131.0</td>\n",
       "      <td>...</td>\n",
       "      <td>50277.0</td>\n",
       "      <td>54827.0</td>\n",
       "      <td>57773.0</td>\n",
       "      <td>57781.0</td>\n",
       "      <td>55530.0</td>\n",
       "      <td>55297.0</td>\n",
       "      <td>52892.0</td>\n",
       "      <td>48441.0</td>\n",
       "      <td>41459.0</td>\n",
       "      <td>40242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>753899</td>\n",
       "      <td>2</td>\n",
       "      <td>Los Angeles, CA</td>\n",
       "      <td>msa</td>\n",
       "      <td>CA</td>\n",
       "      <td>19893.0</td>\n",
       "      <td>21994.0</td>\n",
       "      <td>24106.0</td>\n",
       "      <td>25253.0</td>\n",
       "      <td>27455.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16511.0</td>\n",
       "      <td>18495.0</td>\n",
       "      <td>20270.0</td>\n",
       "      <td>21782.0</td>\n",
       "      <td>22018.0</td>\n",
       "      <td>20697.0</td>\n",
       "      <td>20120.0</td>\n",
       "      <td>18321.0</td>\n",
       "      <td>15293.0</td>\n",
       "      <td>14749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>394463</td>\n",
       "      <td>3</td>\n",
       "      <td>Chicago, IL</td>\n",
       "      <td>msa</td>\n",
       "      <td>IL</td>\n",
       "      <td>34633.0</td>\n",
       "      <td>37046.0</td>\n",
       "      <td>44063.0</td>\n",
       "      <td>45649.0</td>\n",
       "      <td>47560.0</td>\n",
       "      <td>...</td>\n",
       "      <td>26083.0</td>\n",
       "      <td>28480.0</td>\n",
       "      <td>30780.0</td>\n",
       "      <td>31504.0</td>\n",
       "      <td>31121.0</td>\n",
       "      <td>29720.0</td>\n",
       "      <td>28587.0</td>\n",
       "      <td>24979.0</td>\n",
       "      <td>20643.0</td>\n",
       "      <td>19972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>394514</td>\n",
       "      <td>4</td>\n",
       "      <td>Dallas, TX</td>\n",
       "      <td>msa</td>\n",
       "      <td>TX</td>\n",
       "      <td>23048.0</td>\n",
       "      <td>23331.0</td>\n",
       "      <td>25749.0</td>\n",
       "      <td>28549.0</td>\n",
       "      <td>30376.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15108.0</td>\n",
       "      <td>17305.0</td>\n",
       "      <td>20837.0</td>\n",
       "      <td>23004.0</td>\n",
       "      <td>22932.0</td>\n",
       "      <td>22345.0</td>\n",
       "      <td>21766.0</td>\n",
       "      <td>20113.0</td>\n",
       "      <td>17805.0</td>\n",
       "      <td>17416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   RegionID  SizeRank       RegionName RegionType StateName  1/31/2018  \\\n",
       "0    102001         0    United States    country       NaN  1360756.0   \n",
       "1    394913         1     New York, NY        msa        NY    66530.0   \n",
       "2    753899         2  Los Angeles, CA        msa        CA    19893.0   \n",
       "3    394463         3      Chicago, IL        msa        IL    34633.0   \n",
       "4    394514         4       Dallas, TX        msa        TX    23048.0   \n",
       "\n",
       "   2/28/2018  3/31/2018  4/30/2018  5/31/2018  ...  4/30/2022  5/31/2022  \\\n",
       "0  1388126.0  1515704.0  1596749.0  1664787.0  ...   828870.0   925373.0   \n",
       "1    74575.0    80016.0    86445.0    91131.0  ...    50277.0    54827.0   \n",
       "2    21994.0    24106.0    25253.0    27455.0  ...    16511.0    18495.0   \n",
       "3    37046.0    44063.0    45649.0    47560.0  ...    26083.0    28480.0   \n",
       "4    23331.0    25749.0    28549.0    30376.0  ...    15108.0    17305.0   \n",
       "\n",
       "   6/30/2022  7/31/2022  8/31/2022  9/30/2022  10/31/2022  11/30/2022  \\\n",
       "0  1028861.0  1084624.0  1099632.0  1057986.0   1038846.0    947250.0   \n",
       "1    57773.0    57781.0    55530.0    55297.0     52892.0     48441.0   \n",
       "2    20270.0    21782.0    22018.0    20697.0     20120.0     18321.0   \n",
       "3    30780.0    31504.0    31121.0    29720.0     28587.0     24979.0   \n",
       "4    20837.0    23004.0    22932.0    22345.0     21766.0     20113.0   \n",
       "\n",
       "   12/31/2022  1/31/2023  \n",
       "0    833473.0     825077  \n",
       "1     41459.0      40242  \n",
       "2     15293.0      14749  \n",
       "3     20643.0      19972  \n",
       "4     17805.0      17416  \n",
       "\n",
       "[5 rows x 66 columns]"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "inv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inv.drop(columns=['RegionID', 'SizeRank', 'RegionType'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['RegionName', 'StateName', '1/31/2018', '2/28/2018', '3/31/2018',\n",
       "       '4/30/2018', '5/31/2018', '6/30/2018', '7/31/2018', '8/31/2018',\n",
       "       '9/30/2018', '10/31/2018', '11/30/2018', '12/31/2018', '1/31/2019',\n",
       "       '2/28/2019', '3/31/2019', '4/30/2019', '5/31/2019', '6/30/2019',\n",
       "       '7/31/2019', '8/31/2019', '9/30/2019', '10/31/2019', '11/30/2019',\n",
       "       '12/31/2019', '1/31/2020', '2/29/2020', '3/31/2020', '4/30/2020',\n",
       "       '5/31/2020', '6/30/2020', '7/31/2020', '8/31/2020', '9/30/2020',\n",
       "       '10/31/2020', '11/30/2020', '12/31/2020', '1/31/2021', '2/28/2021',\n",
       "       '3/31/2021', '4/30/2021', '5/31/2021', '6/30/2021', '7/31/2021',\n",
       "       '8/31/2021', '9/30/2021', '10/31/2021', '11/30/2021', '12/31/2021',\n",
       "       '1/31/2022', '2/28/2022', '3/31/2022', '4/30/2022', '5/31/2022',\n",
       "       '6/30/2022', '7/31/2022', '8/31/2022', '9/30/2022', '10/31/2022',\n",
       "       '11/30/2022', '12/31/2022', '1/31/2023'],\n",
       "      dtype='object')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "inv.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inv.rename(columns={'RegionName':'region_name', 'StateName':'state_name'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>region_name</th>\n",
       "      <th>state_name</th>\n",
       "      <th>1/31/2018</th>\n",
       "      <th>2/28/2018</th>\n",
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       "      <th>5/31/2022</th>\n",
       "      <th>6/30/2022</th>\n",
       "      <th>7/31/2022</th>\n",
       "      <th>8/31/2022</th>\n",
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       "      <th>10/31/2022</th>\n",
       "      <th>11/30/2022</th>\n",
       "      <th>12/31/2022</th>\n",
       "      <th>1/31/2023</th>\n",
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       "      <th>0</th>\n",
       "      <td>United States</td>\n",
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       "      <td>1360756.0</td>\n",
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       "      <td>1515704.0</td>\n",
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       "      <td>1720301.0</td>\n",
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       "      <td>833473.0</td>\n",
       "      <td>825077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>New York, NY</td>\n",
       "      <td>NY</td>\n",
       "      <td>66530.0</td>\n",
       "      <td>74575.0</td>\n",
       "      <td>80016.0</td>\n",
       "      <td>86445.0</td>\n",
       "      <td>91131.0</td>\n",
       "      <td>92625.0</td>\n",
       "      <td>91887.0</td>\n",
       "      <td>89244.0</td>\n",
       "      <td>...</td>\n",
       "      <td>50277.0</td>\n",
       "      <td>54827.0</td>\n",
       "      <td>57773.0</td>\n",
       "      <td>57781.0</td>\n",
       "      <td>55530.0</td>\n",
       "      <td>55297.0</td>\n",
       "      <td>52892.0</td>\n",
       "      <td>48441.0</td>\n",
       "      <td>41459.0</td>\n",
       "      <td>40242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Los Angeles, CA</td>\n",
       "      <td>CA</td>\n",
       "      <td>19893.0</td>\n",
       "      <td>21994.0</td>\n",
       "      <td>24106.0</td>\n",
       "      <td>25253.0</td>\n",
       "      <td>27455.0</td>\n",
       "      <td>28620.0</td>\n",
       "      <td>30357.0</td>\n",
       "      <td>30646.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16511.0</td>\n",
       "      <td>18495.0</td>\n",
       "      <td>20270.0</td>\n",
       "      <td>21782.0</td>\n",
       "      <td>22018.0</td>\n",
       "      <td>20697.0</td>\n",
       "      <td>20120.0</td>\n",
       "      <td>18321.0</td>\n",
       "      <td>15293.0</td>\n",
       "      <td>14749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chicago, IL</td>\n",
       "      <td>IL</td>\n",
       "      <td>34633.0</td>\n",
       "      <td>37046.0</td>\n",
       "      <td>44063.0</td>\n",
       "      <td>45649.0</td>\n",
       "      <td>47560.0</td>\n",
       "      <td>49268.0</td>\n",
       "      <td>50125.0</td>\n",
       "      <td>49953.0</td>\n",
       "      <td>...</td>\n",
       "      <td>26083.0</td>\n",
       "      <td>28480.0</td>\n",
       "      <td>30780.0</td>\n",
       "      <td>31504.0</td>\n",
       "      <td>31121.0</td>\n",
       "      <td>29720.0</td>\n",
       "      <td>28587.0</td>\n",
       "      <td>24979.0</td>\n",
       "      <td>20643.0</td>\n",
       "      <td>19972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dallas, TX</td>\n",
       "      <td>TX</td>\n",
       "      <td>23048.0</td>\n",
       "      <td>23331.0</td>\n",
       "      <td>25749.0</td>\n",
       "      <td>28549.0</td>\n",
       "      <td>30376.0</td>\n",
       "      <td>32545.0</td>\n",
       "      <td>34304.0</td>\n",
       "      <td>33852.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15108.0</td>\n",
       "      <td>17305.0</td>\n",
       "      <td>20837.0</td>\n",
       "      <td>23004.0</td>\n",
       "      <td>22932.0</td>\n",
       "      <td>22345.0</td>\n",
       "      <td>21766.0</td>\n",
       "      <td>20113.0</td>\n",
       "      <td>17805.0</td>\n",
       "      <td>17416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       region_name state_name  1/31/2018  2/28/2018  3/31/2018  4/30/2018  \\\n",
       "0    United States        NaN  1360756.0  1388126.0  1515704.0  1596749.0   \n",
       "1     New York, NY         NY    66530.0    74575.0    80016.0    86445.0   \n",
       "2  Los Angeles, CA         CA    19893.0    21994.0    24106.0    25253.0   \n",
       "3      Chicago, IL         IL    34633.0    37046.0    44063.0    45649.0   \n",
       "4       Dallas, TX         TX    23048.0    23331.0    25749.0    28549.0   \n",
       "\n",
       "   5/31/2018  6/30/2018  7/31/2018  8/31/2018  ...  4/30/2022  5/31/2022  \\\n",
       "0  1664787.0  1720301.0  1742336.0  1737523.0  ...   828870.0   925373.0   \n",
       "1    91131.0    92625.0    91887.0    89244.0  ...    50277.0    54827.0   \n",
       "2    27455.0    28620.0    30357.0    30646.0  ...    16511.0    18495.0   \n",
       "3    47560.0    49268.0    50125.0    49953.0  ...    26083.0    28480.0   \n",
       "4    30376.0    32545.0    34304.0    33852.0  ...    15108.0    17305.0   \n",
       "\n",
       "   6/30/2022  7/31/2022  8/31/2022  9/30/2022  10/31/2022  11/30/2022  \\\n",
       "0  1028861.0  1084624.0  1099632.0  1057986.0   1038846.0    947250.0   \n",
       "1    57773.0    57781.0    55530.0    55297.0     52892.0     48441.0   \n",
       "2    20270.0    21782.0    22018.0    20697.0     20120.0     18321.0   \n",
       "3    30780.0    31504.0    31121.0    29720.0     28587.0     24979.0   \n",
       "4    20837.0    23004.0    22932.0    22345.0     21766.0     20113.0   \n",
       "\n",
       "   12/31/2022  1/31/2023  \n",
       "0    833473.0     825077  \n",
       "1     41459.0      40242  \n",
       "2     15293.0      14749  \n",
       "3     20643.0      19972  \n",
       "4     17805.0      17416  \n",
       "\n",
       "[5 rows x 63 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "inv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inv.state_name = inv.state_name.fillna('USA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region_name</th>\n",
       "      <th>state_name</th>\n",
       "      <th>1/31/2018</th>\n",
       "      <th>2/28/2018</th>\n",
       "      <th>3/31/2018</th>\n",
       "      <th>4/30/2018</th>\n",
       "      <th>5/31/2018</th>\n",
       "      <th>6/30/2018</th>\n",
       "      <th>7/31/2018</th>\n",
       "      <th>8/31/2018</th>\n",
       "      <th>...</th>\n",
       "      <th>4/30/2022</th>\n",
       "      <th>5/31/2022</th>\n",
       "      <th>6/30/2022</th>\n",
       "      <th>7/31/2022</th>\n",
       "      <th>8/31/2022</th>\n",
       "      <th>9/30/2022</th>\n",
       "      <th>10/31/2022</th>\n",
       "      <th>11/30/2022</th>\n",
       "      <th>12/31/2022</th>\n",
       "      <th>1/31/2023</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>1360756.0</td>\n",
       "      <td>1388126.0</td>\n",
       "      <td>1515704.0</td>\n",
       "      <td>1596749.0</td>\n",
       "      <td>1664787.0</td>\n",
       "      <td>1720301.0</td>\n",
       "      <td>1742336.0</td>\n",
       "      <td>1737523.0</td>\n",
       "      <td>...</td>\n",
       "      <td>828870.0</td>\n",
       "      <td>925373.0</td>\n",
       "      <td>1028861.0</td>\n",
       "      <td>1084624.0</td>\n",
       "      <td>1099632.0</td>\n",
       "      <td>1057986.0</td>\n",
       "      <td>1038846.0</td>\n",
       "      <td>947250.0</td>\n",
       "      <td>833473.0</td>\n",
       "      <td>825077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>New York, NY</td>\n",
       "      <td>NY</td>\n",
       "      <td>66530.0</td>\n",
       "      <td>74575.0</td>\n",
       "      <td>80016.0</td>\n",
       "      <td>86445.0</td>\n",
       "      <td>91131.0</td>\n",
       "      <td>92625.0</td>\n",
       "      <td>91887.0</td>\n",
       "      <td>89244.0</td>\n",
       "      <td>...</td>\n",
       "      <td>50277.0</td>\n",
       "      <td>54827.0</td>\n",
       "      <td>57773.0</td>\n",
       "      <td>57781.0</td>\n",
       "      <td>55530.0</td>\n",
       "      <td>55297.0</td>\n",
       "      <td>52892.0</td>\n",
       "      <td>48441.0</td>\n",
       "      <td>41459.0</td>\n",
       "      <td>40242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Los Angeles, CA</td>\n",
       "      <td>CA</td>\n",
       "      <td>19893.0</td>\n",
       "      <td>21994.0</td>\n",
       "      <td>24106.0</td>\n",
       "      <td>25253.0</td>\n",
       "      <td>27455.0</td>\n",
       "      <td>28620.0</td>\n",
       "      <td>30357.0</td>\n",
       "      <td>30646.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16511.0</td>\n",
       "      <td>18495.0</td>\n",
       "      <td>20270.0</td>\n",
       "      <td>21782.0</td>\n",
       "      <td>22018.0</td>\n",
       "      <td>20697.0</td>\n",
       "      <td>20120.0</td>\n",
       "      <td>18321.0</td>\n",
       "      <td>15293.0</td>\n",
       "      <td>14749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chicago, IL</td>\n",
       "      <td>IL</td>\n",
       "      <td>34633.0</td>\n",
       "      <td>37046.0</td>\n",
       "      <td>44063.0</td>\n",
       "      <td>45649.0</td>\n",
       "      <td>47560.0</td>\n",
       "      <td>49268.0</td>\n",
       "      <td>50125.0</td>\n",
       "      <td>49953.0</td>\n",
       "      <td>...</td>\n",
       "      <td>26083.0</td>\n",
       "      <td>28480.0</td>\n",
       "      <td>30780.0</td>\n",
       "      <td>31504.0</td>\n",
       "      <td>31121.0</td>\n",
       "      <td>29720.0</td>\n",
       "      <td>28587.0</td>\n",
       "      <td>24979.0</td>\n",
       "      <td>20643.0</td>\n",
       "      <td>19972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dallas, TX</td>\n",
       "      <td>TX</td>\n",
       "      <td>23048.0</td>\n",
       "      <td>23331.0</td>\n",
       "      <td>25749.0</td>\n",
       "      <td>28549.0</td>\n",
       "      <td>30376.0</td>\n",
       "      <td>32545.0</td>\n",
       "      <td>34304.0</td>\n",
       "      <td>33852.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15108.0</td>\n",
       "      <td>17305.0</td>\n",
       "      <td>20837.0</td>\n",
       "      <td>23004.0</td>\n",
       "      <td>22932.0</td>\n",
       "      <td>22345.0</td>\n",
       "      <td>21766.0</td>\n",
       "      <td>20113.0</td>\n",
       "      <td>17805.0</td>\n",
       "      <td>17416</td>\n",
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       "       region_name state_name  1/31/2018  2/28/2018  3/31/2018  4/30/2018  \\\n",
       "0    United States        USA  1360756.0  1388126.0  1515704.0  1596749.0   \n",
       "1     New York, NY         NY    66530.0    74575.0    80016.0    86445.0   \n",
       "2  Los Angeles, CA         CA    19893.0    21994.0    24106.0    25253.0   \n",
       "3      Chicago, IL         IL    34633.0    37046.0    44063.0    45649.0   \n",
       "4       Dallas, TX         TX    23048.0    23331.0    25749.0    28549.0   \n",
       "\n",
       "   5/31/2018  6/30/2018  7/31/2018  8/31/2018  ...  4/30/2022  5/31/2022  \\\n",
       "0  1664787.0  1720301.0  1742336.0  1737523.0  ...   828870.0   925373.0   \n",
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       "2    27455.0    28620.0    30357.0    30646.0  ...    16511.0    18495.0   \n",
       "3    47560.0    49268.0    50125.0    49953.0  ...    26083.0    28480.0   \n",
       "4    30376.0    32545.0    34304.0    33852.0  ...    15108.0    17305.0   \n",
       "\n",
       "   6/30/2022  7/31/2022  8/31/2022  9/30/2022  10/31/2022  11/30/2022  \\\n",
       "0  1028861.0  1084624.0  1099632.0  1057986.0   1038846.0    947250.0   \n",
       "1    57773.0    57781.0    55530.0    55297.0     52892.0     48441.0   \n",
       "2    20270.0    21782.0    22018.0    20697.0     20120.0     18321.0   \n",
       "3    30780.0    31504.0    31121.0    29720.0     28587.0     24979.0   \n",
       "4    20837.0    23004.0    22932.0    22345.0     21766.0     20113.0   \n",
       "\n",
       "   12/31/2022  1/31/2023  \n",
       "0    833473.0     825077  \n",
       "1     41459.0      40242  \n",
       "2     15293.0      14749  \n",
       "3     20643.0      19972  \n",
       "4     17805.0      17416  \n",
       "\n",
       "[5 rows x 63 columns]"
      ]
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   "execution_count": null,
   "metadata": {},
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 928 entries, 0 to 927\n",
      "Data columns (total 63 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   region_name  928 non-null    object \n",
      " 1   state_name   928 non-null    object \n",
      " 2   1/31/2018    899 non-null    float64\n",
      " 3   2/28/2018    899 non-null    float64\n",
      " 4   3/31/2018    899 non-null    float64\n",
      " 5   4/30/2018    899 non-null    float64\n",
      " 6   5/31/2018    899 non-null    float64\n",
      " 7   6/30/2018    900 non-null    float64\n",
      " 8   7/31/2018    900 non-null    float64\n",
      " 9   8/31/2018    900 non-null    float64\n",
      " 10  9/30/2018    900 non-null    float64\n",
      " 11  10/31/2018   900 non-null    float64\n",
      " 12  11/30/2018   900 non-null    float64\n",
      " 13  12/31/2018   900 non-null    float64\n",
      " 14  1/31/2019    900 non-null    float64\n",
      " 15  2/28/2019    900 non-null    float64\n",
      " 16  3/31/2019    900 non-null    float64\n",
      " 17  4/30/2019    900 non-null    float64\n",
      " 18  5/31/2019    900 non-null    float64\n",
      " 19  6/30/2019    900 non-null    float64\n",
      " 20  7/31/2019    900 non-null    float64\n",
      " 21  8/31/2019    900 non-null    float64\n",
      " 22  9/30/2019    900 non-null    float64\n",
      " 23  10/31/2019   900 non-null    float64\n",
      " 24  11/30/2019   900 non-null    float64\n",
      " 25  12/31/2019   900 non-null    float64\n",
      " 26  1/31/2020    900 non-null    float64\n",
      " 27  2/29/2020    900 non-null    float64\n",
      " 28  3/31/2020    900 non-null    float64\n",
      " 29  4/30/2020    898 non-null    float64\n",
      " 30  5/31/2020    899 non-null    float64\n",
      " 31  6/30/2020    899 non-null    float64\n",
      " 32  7/31/2020    900 non-null    float64\n",
      " 33  8/31/2020    900 non-null    float64\n",
      " 34  9/30/2020    900 non-null    float64\n",
      " 35  10/31/2020   894 non-null    float64\n",
      " 36  11/30/2020   897 non-null    float64\n",
      " 37  12/31/2020   899 non-null    float64\n",
      " 38  1/31/2021    897 non-null    float64\n",
      " 39  2/28/2021    885 non-null    float64\n",
      " 40  3/31/2021    908 non-null    float64\n",
      " 41  4/30/2021    922 non-null    float64\n",
      " 42  5/31/2021    920 non-null    float64\n",
      " 43  6/30/2021    916 non-null    float64\n",
      " 44  7/31/2021    915 non-null    float64\n",
      " 45  8/31/2021    922 non-null    float64\n",
      " 46  9/30/2021    919 non-null    float64\n",
      " 47  10/31/2021   921 non-null    float64\n",
      " 48  11/30/2021   922 non-null    float64\n",
      " 49  12/31/2021   922 non-null    float64\n",
      " 50  1/31/2022    921 non-null    float64\n",
      " 51  2/28/2022    923 non-null    float64\n",
      " 52  3/31/2022    923 non-null    float64\n",
      " 53  4/30/2022    923 non-null    float64\n",
      " 54  5/31/2022    923 non-null    float64\n",
      " 55  6/30/2022    923 non-null    float64\n",
      " 56  7/31/2022    923 non-null    float64\n",
      " 57  8/31/2022    923 non-null    float64\n",
      " 58  9/30/2022    923 non-null    float64\n",
      " 59  10/31/2022   924 non-null    float64\n",
      " 60  11/30/2022   925 non-null    float64\n",
      " 61  12/31/2022   925 non-null    float64\n",
      " 62  1/31/2023    928 non-null    int64  \n",
      "dtypes: float64(60), int64(1), object(2)\n",
      "memory usage: 456.9+ KB\n"
     ]
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   "source": [
    "inv.info()"
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "region_name     0\n",
       "state_name      0\n",
       "1/31/2018      29\n",
       "2/28/2018      29\n",
       "3/31/2018      29\n",
       "               ..\n",
       "9/30/2022       5\n",
       "10/31/2022      4\n",
       "11/30/2022      3\n",
       "12/31/2022      3\n",
       "1/31/2023       0\n",
       "Length: 63, dtype: int64"
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   "source": [
    "inv.isna().sum()"
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_inv = inv.groupby('state_name').mean()"
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  {
   "cell_type": "code",
   "execution_count": null,
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       "state_name\n",
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       "AZ     2.883060e+03\n",
       "CA     2.508355e+03\n",
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       "FL     4.761755e+03\n",
       "GA     1.149202e+03\n",
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       "ID     5.382663e+02\n",
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       "IN     4.562994e+02\n",
       "KS     3.028931e+02\n",
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       "NY     3.447903e+03\n",
       "OH     8.489802e+02\n",
       "OK     7.281727e+02\n",
       "OR     8.597278e+02\n",
       "PA     1.382999e+03\n",
       "RI     4.874197e+03\n",
       "SC     1.607092e+03\n",
       "SD     2.487686e+02\n",
       "TN     1.015477e+03\n",
       "TX     1.464277e+03\n",
       "USA    1.241510e+06\n",
       "UT     1.181835e+03\n",
       "VA     2.610444e+03\n",
       "VT     4.691352e+02\n",
       "WA     9.314410e+02\n",
       "WI     7.661413e+02\n",
       "WV     4.310149e+02\n",
       "WY     2.636758e+02\n",
       "dtype: float64"
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   "source": [
    "state_inv.mean(axis=1)"
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           "ticks": "",
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        "title": {
         "text": "Overall Mean Inventory 2018-2022"
        },
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   ],
   "source": [
    "px.scatter(state_inv.mean(axis=1), log_y=True, title='Overall Mean Inventory 2018-2022')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ga_inv = inv.query(\"state_name=='GA'\").drop(columns='state_name')\n",
    "pa_inv = inv.query(\"state_name=='PA'\").drop(columns='state_name')\n",
    "md_inv = inv.query(\"state_name=='MD'\").drop(columns='state_name')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Georgia Inventory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region_name</th>\n",
       "      <th>1/31/2018</th>\n",
       "      <th>2/28/2018</th>\n",
       "      <th>3/31/2018</th>\n",
       "      <th>4/30/2018</th>\n",
       "      <th>5/31/2018</th>\n",
       "      <th>6/30/2018</th>\n",
       "      <th>7/31/2018</th>\n",
       "      <th>8/31/2018</th>\n",
       "      <th>9/30/2018</th>\n",
       "      <th>...</th>\n",
       "      <th>4/30/2022</th>\n",
       "      <th>5/31/2022</th>\n",
       "      <th>6/30/2022</th>\n",
       "      <th>7/31/2022</th>\n",
       "      <th>8/31/2022</th>\n",
       "      <th>9/30/2022</th>\n",
       "      <th>10/31/2022</th>\n",
       "      <th>11/30/2022</th>\n",
       "      <th>12/31/2022</th>\n",
       "      <th>1/31/2023</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Atlanta, GA</td>\n",
       "      <td>24273.0</td>\n",
       "      <td>24201.0</td>\n",
       "      <td>26816.0</td>\n",
       "      <td>28053.0</td>\n",
       "      <td>28795.0</td>\n",
       "      <td>30746.0</td>\n",
       "      <td>32008.0</td>\n",
       "      <td>31709.0</td>\n",
       "      <td>30306.0</td>\n",
       "      <td>...</td>\n",
       "      <td>18222.0</td>\n",
       "      <td>20029.0</td>\n",
       "      <td>22451.0</td>\n",
       "      <td>23897.0</td>\n",
       "      <td>24825.0</td>\n",
       "      <td>24081.0</td>\n",
       "      <td>23658.0</td>\n",
       "      <td>21739.0</td>\n",
       "      <td>19522.0</td>\n",
       "      <td>18834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Augusta, GA</td>\n",
       "      <td>3329.0</td>\n",
       "      <td>3292.0</td>\n",
       "      <td>3450.0</td>\n",
       "      <td>3538.0</td>\n",
       "      <td>3589.0</td>\n",
       "      <td>3576.0</td>\n",
       "      <td>3485.0</td>\n",
       "      <td>3463.0</td>\n",
       "      <td>3357.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1585.0</td>\n",
       "      <td>1790.0</td>\n",
       "      <td>1872.0</td>\n",
       "      <td>1908.0</td>\n",
       "      <td>1940.0</td>\n",
       "      <td>1841.0</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>1794.0</td>\n",
       "      <td>1636.0</td>\n",
       "      <td>1668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>Savannah, GA</td>\n",
       "      <td>2545.0</td>\n",
       "      <td>2787.0</td>\n",
       "      <td>2976.0</td>\n",
       "      <td>3023.0</td>\n",
       "      <td>2978.0</td>\n",
       "      <td>2963.0</td>\n",
       "      <td>2872.0</td>\n",
       "      <td>2808.0</td>\n",
       "      <td>2635.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1123.0</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>1360.0</td>\n",
       "      <td>1379.0</td>\n",
       "      <td>1283.0</td>\n",
       "      <td>1278.0</td>\n",
       "      <td>1167.0</td>\n",
       "      <td>1088.0</td>\n",
       "      <td>1089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>Columbus, GA</td>\n",
       "      <td>1846.0</td>\n",
       "      <td>1786.0</td>\n",
       "      <td>1866.0</td>\n",
       "      <td>1940.0</td>\n",
       "      <td>1931.0</td>\n",
       "      <td>1936.0</td>\n",
       "      <td>1925.0</td>\n",
       "      <td>1914.0</td>\n",
       "      <td>1878.0</td>\n",
       "      <td>...</td>\n",
       "      <td>959.0</td>\n",
       "      <td>1069.0</td>\n",
       "      <td>1051.0</td>\n",
       "      <td>1088.0</td>\n",
       "      <td>1136.0</td>\n",
       "      <td>1105.0</td>\n",
       "      <td>1163.0</td>\n",
       "      <td>1068.0</td>\n",
       "      <td>912.0</td>\n",
       "      <td>882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>Macon, GA</td>\n",
       "      <td>951.0</td>\n",
       "      <td>906.0</td>\n",
       "      <td>991.0</td>\n",
       "      <td>1034.0</td>\n",
       "      <td>1035.0</td>\n",
       "      <td>1136.0</td>\n",
       "      <td>1102.0</td>\n",
       "      <td>1137.0</td>\n",
       "      <td>1086.0</td>\n",
       "      <td>...</td>\n",
       "      <td>907.0</td>\n",
       "      <td>930.0</td>\n",
       "      <td>993.0</td>\n",
       "      <td>969.0</td>\n",
       "      <td>1026.0</td>\n",
       "      <td>1004.0</td>\n",
       "      <td>1002.0</td>\n",
       "      <td>954.0</td>\n",
       "      <td>888.0</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 62 columns</p>\n",
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      ],
      "text/plain": [
       "      region_name  1/31/2018  2/28/2018  3/31/2018  4/30/2018  5/31/2018  \\\n",
       "9     Atlanta, GA    24273.0    24201.0    26816.0    28053.0    28795.0   \n",
       "95    Augusta, GA     3329.0     3292.0     3450.0     3538.0     3589.0   \n",
       "138  Savannah, GA     2545.0     2787.0     2976.0     3023.0     2978.0   \n",
       "161  Columbus, GA     1846.0     1786.0     1866.0     1940.0     1931.0   \n",
       "201     Macon, GA      951.0      906.0      991.0     1034.0     1035.0   \n",
       "\n",
       "     6/30/2018  7/31/2018  8/31/2018  9/30/2018  ...  4/30/2022  5/31/2022  \\\n",
       "9      30746.0    32008.0    31709.0    30306.0  ...    18222.0    20029.0   \n",
       "95      3576.0     3485.0     3463.0     3357.0  ...     1585.0     1790.0   \n",
       "138     2963.0     2872.0     2808.0     2635.0  ...     1123.0     1203.0   \n",
       "161     1936.0     1925.0     1914.0     1878.0  ...      959.0     1069.0   \n",
       "201     1136.0     1102.0     1137.0     1086.0  ...      907.0      930.0   \n",
       "\n",
       "     6/30/2022  7/31/2022  8/31/2022  9/30/2022  10/31/2022  11/30/2022  \\\n",
       "9      22451.0    23897.0    24825.0    24081.0     23658.0     21739.0   \n",
       "95      1872.0     1908.0     1940.0     1841.0      1875.0      1794.0   \n",
       "138     1310.0     1360.0     1379.0     1283.0      1278.0      1167.0   \n",
       "161     1051.0     1088.0     1136.0     1105.0      1163.0      1068.0   \n",
       "201      993.0      969.0     1026.0     1004.0      1002.0       954.0   \n",
       "\n",
       "     12/31/2022  1/31/2023  \n",
       "9       19522.0      18834  \n",
       "95       1636.0       1668  \n",
       "138      1088.0       1089  \n",
       "161       912.0        882  \n",
       "201       888.0        889  \n",
       "\n",
       "[5 rows x 62 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ga_inv.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['region_name', '1/31/2018', '2/28/2018', '3/31/2018', '4/30/2018',\n",
       "       '5/31/2018', '6/30/2018', '7/31/2018', '8/31/2018', '9/30/2018',\n",
       "       '10/31/2018', '11/30/2018', '12/31/2018', '1/31/2019', '2/28/2019',\n",
       "       '3/31/2019', '4/30/2019', '5/31/2019', '6/30/2019', '7/31/2019',\n",
       "       '8/31/2019', '9/30/2019', '10/31/2019', '11/30/2019', '12/31/2019',\n",
       "       '1/31/2020', '2/29/2020', '3/31/2020', '4/30/2020', '5/31/2020',\n",
       "       '6/30/2020', '7/31/2020', '8/31/2020', '9/30/2020', '10/31/2020',\n",
       "       '11/30/2020', '12/31/2020', '1/31/2021', '2/28/2021', '3/31/2021',\n",
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  {
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   "metadata": {},
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   "source": [
    "ga_inv_2018 = ga_inv.loc[:,'1/31/2018': '12/31/2018'].mean(axis=1)\n",
    "ga_inv_2019 = ga_inv.loc[:,'1/31/2019': '12/31/2019'].mean(axis=1)\n",
    "ga_inv_2020 = ga_inv.loc[:,'1/31/2020': '12/31/2020'].mean(axis=1)\n",
    "ga_inv_2021 = ga_inv.loc[:,'1/31/2021': '12/31/2021'].mean(axis=1)\n",
    "ga_inv_2022 = ga_inv.loc[:,'1/31/2022': '12/31/2022'].mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
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   "source": [
    "ga_inv_2018.head()"
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ga_inv_2018_full = pd.concat([ga_inv.region_name, ga_inv_2018], axis=1)\n",
    "ga_inv_2019_full = pd.concat([ga_inv.region_name, ga_inv_2019], axis=1)\n",
    "ga_inv_2020_full = pd.concat([ga_inv.region_name, ga_inv_2020], axis=1)\n",
    "ga_inv_2021_full = pd.concat([ga_inv.region_name, ga_inv_2021], axis=1)\n",
    "ga_inv_2022_full = pd.concat([ga_inv.region_name, ga_inv_2022], axis=1)"
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ga_inv_2018_full.rename(columns={0:'Mean'}, inplace=True)\n",
    "ga_inv_2019_full.rename(columns={0:'Mean'}, inplace=True)\n",
    "ga_inv_2020_full.rename(columns={0:'Mean'}, inplace=True)\n",
    "ga_inv_2021_full.rename(columns={0:'Mean'}, inplace=True)\n",
    "ga_inv_2022_full.rename(columns={0:'Mean'}, inplace=True)"
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "merge1 = pd.concat([ga_inv_2018_full, ga_inv_2019], axis=1)\n",
    "merge2 =pd.concat([merge1, ga_inv_2020], axis=1)\n",
    "merge3 = pd.concat([merge2, ga_inv_2021], axis=1)\n",
    "final_merge = pd.concat([merge3, ga_inv_2022], axis=1)"
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_merge.columns=['region_name','2018', '2019', '2020', '2021', '2022']"
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   "metadata": {},
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   ],
   "source": [
    "px.scatter(final_merge, x='region_name', y=['2018', '2019', '2020', '2021', '2022'], log_y=True, title='Georgia Mean Home Inventory', template='plotly')"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
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       "      <th>region_name</th>\n",
       "      <th>Mean</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Atlanta, GA</td>\n",
       "      <td>28436.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Augusta, GA</td>\n",
       "      <td>3380.750000</td>\n",
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       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>Savannah, GA</td>\n",
       "      <td>2774.916667</td>\n",
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       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>Columbus, GA</td>\n",
       "      <td>1862.666667</td>\n",
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       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>Macon, GA</td>\n",
       "      <td>1034.833333</td>\n",
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       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>Athens, GA</td>\n",
       "      <td>1031.083333</td>\n",
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       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>Gainesville, GA</td>\n",
       "      <td>1066.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>Warner Robins, GA</td>\n",
       "      <td>848.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>Valdosta, GA</td>\n",
       "      <td>859.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>Albany, GA</td>\n",
       "      <td>766.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>Dalton, GA</td>\n",
       "      <td>518.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>Brunswick, GA</td>\n",
       "      <td>1395.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>LaGrange, GA</td>\n",
       "      <td>514.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>Rome, GA</td>\n",
       "      <td>500.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>Hinesville, GA</td>\n",
       "      <td>595.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>Statesboro, GA</td>\n",
       "      <td>435.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>Jefferson, GA</td>\n",
       "      <td>394.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>Dublin, GA</td>\n",
       "      <td>200.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>Calhoun, GA</td>\n",
       "      <td>256.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>St. Marys, GA</td>\n",
       "      <td>391.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>Waycross, GA</td>\n",
       "      <td>248.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>Milledgeville, GA</td>\n",
       "      <td>345.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>Douglas, GA</td>\n",
       "      <td>165.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>632</th>\n",
       "      <td>Cornelia, GA</td>\n",
       "      <td>310.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>Moultrie, GA</td>\n",
       "      <td>162.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>Thomasville, GA</td>\n",
       "      <td>334.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>Cedartown, GA</td>\n",
       "      <td>206.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>Tifton, GA</td>\n",
       "      <td>173.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>Vidalia, GA</td>\n",
       "      <td>164.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>Americus, GA</td>\n",
       "      <td>169.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>807</th>\n",
       "      <td>Jesup, GA</td>\n",
       "      <td>147.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>839</th>\n",
       "      <td>Thomaston, GA</td>\n",
       "      <td>125.833333</td>\n",
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       "    <tr>\n",
       "      <th>840</th>\n",
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       "      <td>147.666667</td>\n",
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       "    <tr>\n",
       "      <th>844</th>\n",
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       "      <td>191.083333</td>\n",
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       "    <tr>\n",
       "      <th>855</th>\n",
       "      <td>Summerville, GA</td>\n",
       "      <td>112.083333</td>\n",
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       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>Cordele, GA</td>\n",
       "      <td>126.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>Fitzgerald, GA</td>\n",
       "      <td>70.250000</td>\n",
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       "           region_name          Mean\n",
       "9          Atlanta, GA  28436.583333\n",
       "95         Augusta, GA   3380.750000\n",
       "138       Savannah, GA   2774.916667\n",
       "161       Columbus, GA   1862.666667\n",
       "201          Macon, GA   1034.833333\n",
       "213         Athens, GA   1031.083333\n",
       "219    Gainesville, GA   1066.083333\n",
       "232  Warner Robins, GA    848.250000\n",
       "291       Valdosta, GA    859.583333\n",
       "296         Albany, GA    766.666667\n",
       "301         Dalton, GA    518.583333\n",
       "346      Brunswick, GA   1395.666667\n",
       "374       LaGrange, GA    514.333333\n",
       "393           Rome, GA    500.250000\n",
       "431     Hinesville, GA    595.916667\n",
       "441     Statesboro, GA    435.500000\n",
       "456      Jefferson, GA    394.666667\n",
       "506         Dublin, GA    200.916667\n",
       "529        Calhoun, GA    256.916667\n",
       "558      St. Marys, GA    391.083333\n",
       "559       Waycross, GA    248.916667\n",
       "574  Milledgeville, GA    345.666667\n",
       "583        Douglas, GA    165.250000\n",
       "632       Cornelia, GA    310.500000\n",
       "639       Moultrie, GA    162.333333\n",
       "654    Thomasville, GA    334.916667\n",
       "667      Cedartown, GA    206.333333\n",
       "684         Tifton, GA    173.000000\n",
       "745        Vidalia, GA    164.166667\n",
       "767       Americus, GA    169.750000\n",
       "807          Jesup, GA    147.000000\n",
       "839      Thomaston, GA    125.833333\n",
       "840     Bainbridge, GA    147.666667\n",
       "844         Toccoa, GA    191.083333\n",
       "855    Summerville, GA    112.083333\n",
       "878        Cordele, GA    126.833333\n",
       "917     Fitzgerald, GA     70.250000"
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   ],
   "source": [
    "px.scatter(ga_inv_2018_full, x='region_name', y='Mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ga_list = ga_inv_2018_full.region_name.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[           region_name          Mean\n",
       " 9          Atlanta, GA  28436.583333\n",
       " 95         Augusta, GA   3380.750000\n",
       " 138       Savannah, GA   2774.916667\n",
       " 161       Columbus, GA   1862.666667\n",
       " 201          Macon, GA   1034.833333\n",
       " 213         Athens, GA   1031.083333\n",
       " 219    Gainesville, GA   1066.083333\n",
       " 232  Warner Robins, GA    848.250000\n",
       " 291       Valdosta, GA    859.583333\n",
       " 296         Albany, GA    766.666667\n",
       " 301         Dalton, GA    518.583333\n",
       " 346      Brunswick, GA   1395.666667\n",
       " 374       LaGrange, GA    514.333333\n",
       " 393           Rome, GA    500.250000\n",
       " 431     Hinesville, GA    595.916667\n",
       " 441     Statesboro, GA    435.500000\n",
       " 456      Jefferson, GA    394.666667\n",
       " 506         Dublin, GA    200.916667\n",
       " 529        Calhoun, GA    256.916667\n",
       " 558      St. Marys, GA    391.083333\n",
       " 559       Waycross, GA    248.916667\n",
       " 574  Milledgeville, GA    345.666667\n",
       " 583        Douglas, GA    165.250000\n",
       " 632       Cornelia, GA    310.500000\n",
       " 639       Moultrie, GA    162.333333\n",
       " 654    Thomasville, GA    334.916667\n",
       " 667      Cedartown, GA    206.333333\n",
       " 684         Tifton, GA    173.000000\n",
       " 745        Vidalia, GA    164.166667\n",
       " 767       Americus, GA    169.750000\n",
       " 807          Jesup, GA    147.000000\n",
       " 839      Thomaston, GA    125.833333\n",
       " 840     Bainbridge, GA    147.666667\n",
       " 844         Toccoa, GA    191.083333\n",
       " 855    Summerville, GA    112.083333\n",
       " 878        Cordele, GA    126.833333\n",
       " 917     Fitzgerald, GA     70.250000,\n",
       "            region_name          Mean\n",
       " 9          Atlanta, GA  31717.000000\n",
       " 95         Augusta, GA   3302.666667\n",
       " 138       Savannah, GA   2722.750000\n",
       " 161       Columbus, GA   1596.666667\n",
       " 201          Macon, GA   1012.833333\n",
       " 213         Athens, GA    974.666667\n",
       " 219    Gainesville, GA   1124.583333\n",
       " 232  Warner Robins, GA    794.416667\n",
       " 291       Valdosta, GA    780.500000\n",
       " 296         Albany, GA    733.500000\n",
       " 301         Dalton, GA    533.916667\n",
       " 346      Brunswick, GA   1260.666667\n",
       " 374       LaGrange, GA    490.666667\n",
       " 393           Rome, GA    476.833333\n",
       " 431     Hinesville, GA    573.916667\n",
       " 441     Statesboro, GA    383.833333\n",
       " 456      Jefferson, GA    469.750000\n",
       " 506         Dublin, GA    195.583333\n",
       " 529        Calhoun, GA    278.750000\n",
       " 558      St. Marys, GA    364.916667\n",
       " 559       Waycross, GA    234.666667\n",
       " 574  Milledgeville, GA    363.750000\n",
       " 583        Douglas, GA    141.833333\n",
       " 632       Cornelia, GA    286.750000\n",
       " 639       Moultrie, GA    134.916667\n",
       " 654    Thomasville, GA    289.416667\n",
       " 667      Cedartown, GA    177.750000\n",
       " 684         Tifton, GA    148.833333\n",
       " 745        Vidalia, GA    150.666667\n",
       " 767       Americus, GA    147.833333\n",
       " 807          Jesup, GA    144.916667\n",
       " 839      Thomaston, GA    122.500000\n",
       " 840     Bainbridge, GA     88.916667\n",
       " 844         Toccoa, GA    149.583333\n",
       " 855    Summerville, GA    111.416667\n",
       " 878        Cordele, GA    114.416667\n",
       " 917     Fitzgerald, GA     79.083333,\n",
       "            region_name          Mean\n",
       " 9          Atlanta, GA  27003.250000\n",
       " 95         Augusta, GA   2458.833333\n",
       " 138       Savannah, GA   2344.000000\n",
       " 161       Columbus, GA   1122.750000\n",
       " 201          Macon, GA    817.333333\n",
       " 213         Athens, GA    855.000000\n",
       " 219    Gainesville, GA    952.666667\n",
       " 232  Warner Robins, GA    633.416667\n",
       " 291       Valdosta, GA    598.083333\n",
       " 296         Albany, GA    552.083333\n",
       " 301         Dalton, GA    399.166667\n",
       " 346      Brunswick, GA   1046.666667\n",
       " 374       LaGrange, GA    390.833333\n",
       " 393           Rome, GA    401.500000\n",
       " 431     Hinesville, GA    442.500000\n",
       " 441     Statesboro, GA    293.166667\n",
       " 456      Jefferson, GA    420.500000\n",
       " 506         Dublin, GA    150.000000\n",
       " 529        Calhoun, GA    213.833333\n",
       " 558      St. Marys, GA    279.000000\n",
       " 559       Waycross, GA    197.083333\n",
       " 574  Milledgeville, GA    268.666667\n",
       " 583        Douglas, GA    117.000000\n",
       " 632       Cornelia, GA    226.833333\n",
       " 639       Moultrie, GA     89.666667\n",
       " 654    Thomasville, GA    227.416667\n",
       " 667      Cedartown, GA    142.416667\n",
       " 684         Tifton, GA    107.583333\n",
       " 745        Vidalia, GA    134.833333\n",
       " 767       Americus, GA    104.833333\n",
       " 807          Jesup, GA    101.916667\n",
       " 839      Thomaston, GA     86.916667\n",
       " 840     Bainbridge, GA     80.000000\n",
       " 844         Toccoa, GA    106.333333\n",
       " 855    Summerville, GA     84.833333\n",
       " 878        Cordele, GA     90.666667\n",
       " 917     Fitzgerald, GA     59.083333,\n",
       "            region_name          Mean\n",
       " 9          Atlanta, GA  20580.750000\n",
       " 95         Augusta, GA   1814.416667\n",
       " 138       Savannah, GA   1483.166667\n",
       " 161       Columbus, GA   1001.833333\n",
       " 201          Macon, GA    746.000000\n",
       " 213         Athens, GA    559.750000\n",
       " 219    Gainesville, GA    779.000000\n",
       " 232  Warner Robins, GA    492.166667\n",
       " 291       Valdosta, GA    494.250000\n",
       " 296         Albany, GA    410.916667\n",
       " 301         Dalton, GA    307.833333\n",
       " 346      Brunswick, GA    573.416667\n",
       " 374       LaGrange, GA    326.000000\n",
       " 393           Rome, GA    305.583333\n",
       " 431     Hinesville, GA    325.250000\n",
       " 441     Statesboro, GA    202.083333\n",
       " 456      Jefferson, GA    366.750000\n",
       " 506         Dublin, GA    121.666667\n",
       " 529        Calhoun, GA    169.250000\n",
       " 558      St. Marys, GA    210.583333\n",
       " 559       Waycross, GA    172.000000\n",
       " 574  Milledgeville, GA    360.083333\n",
       " 583        Douglas, GA     83.333333\n",
       " 632       Cornelia, GA    145.916667\n",
       " 639       Moultrie, GA    114.000000\n",
       " 654    Thomasville, GA    177.000000\n",
       " 667      Cedartown, GA    121.583333\n",
       " 684         Tifton, GA     93.250000\n",
       " 745        Vidalia, GA     81.750000\n",
       " 767       Americus, GA     73.666667\n",
       " 807          Jesup, GA     68.333333\n",
       " 839      Thomaston, GA     71.416667\n",
       " 840     Bainbridge, GA     66.833333\n",
       " 844         Toccoa, GA    172.083333\n",
       " 855    Summerville, GA     59.750000\n",
       " 878        Cordele, GA     63.083333\n",
       " 917     Fitzgerald, GA     46.500000,\n",
       "            region_name          Mean\n",
       " 9          Atlanta, GA  20699.500000\n",
       " 95         Augusta, GA   1715.250000\n",
       " 138       Savannah, GA   1185.666667\n",
       " 161       Columbus, GA   1014.916667\n",
       " 201          Macon, GA    927.833333\n",
       " 213         Athens, GA    495.583333\n",
       " 219    Gainesville, GA    795.916667\n",
       " 232  Warner Robins, GA    542.666667\n",
       " 291       Valdosta, GA    494.916667\n",
       " 296         Albany, GA    364.916667\n",
       " 301         Dalton, GA    393.666667\n",
       " 346      Brunswick, GA    512.500000\n",
       " 374       LaGrange, GA    357.500000\n",
       " 393           Rome, GA    306.000000\n",
       " 431     Hinesville, GA    328.500000\n",
       " 441     Statesboro, GA    176.750000\n",
       " 456      Jefferson, GA    401.666667\n",
       " 506         Dublin, GA    130.083333\n",
       " 529        Calhoun, GA    206.083333\n",
       " 558      St. Marys, GA    267.083333\n",
       " 559       Waycross, GA    195.666667\n",
       " 574  Milledgeville, GA    437.333333\n",
       " 583        Douglas, GA     86.666667\n",
       " 632       Cornelia, GA    181.583333\n",
       " 639       Moultrie, GA    144.166667\n",
       " 654    Thomasville, GA    204.166667\n",
       " 667      Cedartown, GA    122.250000\n",
       " 684         Tifton, GA     96.916667\n",
       " 745        Vidalia, GA     68.250000\n",
       " 767       Americus, GA     72.750000\n",
       " 807          Jesup, GA     88.083333\n",
       " 839      Thomaston, GA    100.250000\n",
       " 840     Bainbridge, GA     74.166667\n",
       " 844         Toccoa, GA    210.416667\n",
       " 855    Summerville, GA     72.416667\n",
       " 878        Cordele, GA     51.666667\n",
       " 917     Fitzgerald, GA     71.000000]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "[ga_inv_2018_full, \n",
    "ga_inv_2019_full, \n",
    "ga_inv_2020_full, \n",
    "ga_inv_2021_full, \n",
    "ga_inv_2022_full]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\XIX\\AppData\\Local\\Temp\\ipykernel_12128\\1923333078.py:1: FutureWarning:\n",
      "\n",
      "Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "9      25575.065574\n",
       "95      2520.180328\n",
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       "219      939.639344\n",
       "232      659.557377\n",
       "291      642.229508\n",
       "296      561.409836\n",
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       "346      950.278689\n",
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       "393      396.065574\n",
       "431      451.442623\n",
       "441      295.770492\n",
       "456      410.245902\n",
       "506      159.131148\n",
       "529      224.377049\n",
       "558      301.360656\n",
       "559      209.655738\n",
       "574      356.000000\n",
       "583      118.459016\n",
       "632      229.540984\n",
       "639      129.032787\n",
       "654      245.786885\n",
       "667      153.573770\n",
       "684      123.540984\n",
       "745      118.934426\n",
       "767      112.983607\n",
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       "dtype: float64"
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     "metadata": {},
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   ],
   "source": [
    "ga_inv.mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\XIX\\AppData\\Local\\Temp\\ipykernel_12128\\369696664.py:1: FutureWarning:\n",
      "\n",
      "Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
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    "### Pennsylvania Inventory"
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   "source": [
    "px.line(pa_inv, x='region_name', y= time_frame, log_y=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pa_inv_2018 = pa_inv.loc[:,'1/31/2018': '12/31/2018'].mean(axis=1)\n",
    "pa_inv_2019 = pa_inv.loc[:,'1/31/2019': '12/31/2019'].mean(axis=1)\n",
    "pa_inv_2020 = pa_inv.loc[:,'1/31/2020': '12/31/2020'].mean(axis=1)\n",
    "pa_inv_2021 = pa_inv.loc[:,'1/31/2021': '12/31/2021'].mean(axis=1)\n",
    "pa_inv_2022 = pa_inv.loc[:,'1/31/2022': '12/31/2022'].mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pa_inv_2018_full = pd.concat([pa_inv.region_name, pa_inv_2018], axis=1)\n",
    "pa_inv_2019_full = pd.concat([pa_inv.region_name, pa_inv_2019], axis=1)\n",
    "pa_inv_2020_full = pd.concat([pa_inv.region_name, pa_inv_2020], axis=1)\n",
    "pa_inv_2021_full = pd.concat([pa_inv.region_name, pa_inv_2021], axis=1)\n",
    "pa_inv_2022_full = pd.concat([pa_inv.region_name, pa_inv_2022], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pa_inv_2018_full.rename(columns={0:'Mean'}, inplace=True)\n",
    "pa_inv_2019_full.rename(columns={0:'Mean'}, inplace=True)\n",
    "pa_inv_2020_full.rename(columns={0:'Mean'}, inplace=True)\n",
    "pa_inv_2021_full.rename(columns={0:'Mean'}, inplace=True)\n",
    "pa_inv_2022_full.rename(columns={0:'Mean'}, inplace=True)"
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  {
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    "merge4 = pd.concat([pa_inv_2018_full, pa_inv_2019], axis=1)\n",
    "merge5 =pd.concat([merge4, pa_inv_2020], axis=1)\n",
    "merge6 = pd.concat([merge5, pa_inv_2021], axis=1)\n",
    "final_merge2 = pd.concat([merge6, pa_inv_2022], axis=1)"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Maryland Inventory"
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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  },
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "md_inv_2018 = md_inv.loc[:,'1/31/2018': '12/31/2018'].mean(axis=1)\n",
    "md_inv_2019 = md_inv.loc[:,'1/31/2019': '12/31/2019'].mean(axis=1)\n",
    "md_inv_2020 = md_inv.loc[:,'1/31/2020': '12/31/2020'].mean(axis=1)\n",
    "md_inv_2021 = md_inv.loc[:,'1/31/2021': '12/31/2021'].mean(axis=1)\n",
    "md_inv_2022 = md_inv.loc[:,'1/31/2022': '12/31/2022'].mean(axis=1)"
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  {
   "cell_type": "code",
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   "source": [
    "md_inv_2018_full = pd.concat([md_inv.region_name, md_inv_2018], axis=1)\n",
    "md_inv_2019_full = pd.concat([md_inv.region_name, md_inv_2019], axis=1)\n",
    "md_inv_2020_full = pd.concat([md_inv.region_name, md_inv_2020], axis=1)\n",
    "md_inv_2021_full = pd.concat([md_inv.region_name, md_inv_2021], axis=1)\n",
    "md_inv_2022_full = pd.concat([md_inv.region_name, md_inv_2022], axis=1)"
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   "cell_type": "code",
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   "source": [
    "merge7 = pd.concat([md_inv_2018_full, md_inv_2019], axis=1)\n",
    "merge8 =pd.concat([merge7, md_inv_2020], axis=1)\n",
    "merge9 = pd.concat([merge8, md_inv_2021], axis=1)\n",
    "final_merge3 = pd.concat([merge9, md_inv_2022], axis=1)"
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   "execution_count": null,
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    "final_merge3.columns=['region_name','2018', '2019', '2020', '2021', '2022']"
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           "bgcolor": "#E5ECF6",
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          "scene": {
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           "bgcolor": "#E5ECF6",
           "caxis": {
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          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
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           "ticks": "",
           "title": {
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           "zerolinewidth": 2
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          "yaxis": {
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        },
        "title": {
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        },
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "px.box(final_merge3, x='region_name', y=['2018', '2019', '2020', '2021', '2022'], log_y=True, title='Maryland Mean Home Inventory', template='plotly')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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