Perform preliminary data wrangling for #9
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@ -29,7 +29,24 @@
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"cell_type": "code",
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"execution_count": 50,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import seaborn as sns"
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]
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},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Data Wrangling"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 76,
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [
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{
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@ -53,205 +70,421 @@
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" <thead>\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th>GEOID10</th>\n",
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" <th>name</th>\n",
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" <th>Tax_Mjoint</th>\n",
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" <th>lat</th>\n",
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" <th>Mjoint_MF</th>\n",
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" <th>long</th>\n",
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" <th>Mjoint_SS</th>\n",
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" <th>Mjoint_FF</th>\n",
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" <th>Mjoint_MM</th>\n",
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" <th>TaxRate_SS</th>\n",
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" <th>TaxRate_FF</th>\n",
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" <th>TaxRate_MM</th>\n",
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" <th>Cns_TotHH</th>\n",
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" <th>...</th>\n",
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" <th>FF_Cns</th>\n",
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" <th>FF_Index</th>\n",
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" <th>MM_Tax</th>\n",
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" <th>MM_Cns</th>\n",
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" <th>MM_Index</th>\n",
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" <th>SS_Index</th>\n",
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" <th>SS_Index_Weight</th>\n",
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" <th>Parade_Weight</th>\n",
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" <th>Bars_Weight</th>\n",
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" <th>TOTINDEX</th>\n",
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" </tr>\n",
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" </tr>\n",
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" </thead>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tbody>\n",
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" <tr>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <th>0</th>\n",
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" <td>90069</td>\n",
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" <td>Hancock OH</td>\n",
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" <td>2120</td>\n",
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" <td>41.000471</td>\n",
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" <td>1689</td>\n",
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" <td>-83.666033</td>\n",
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" <td>431</td>\n",
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" <td>61</td>\n",
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" <td>370</td>\n",
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" <td>203.301887</td>\n",
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" <td>28.773585</td>\n",
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" <td>174.528302</td>\n",
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" <td>12551</td>\n",
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" <td>...</td>\n",
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" <td>1.847099</td>\n",
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" <td>6.724415</td>\n",
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" <td>29.583721</td>\n",
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" <td>18.704533</td>\n",
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" <td>48.288254</td>\n",
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" <td>55.012669</td>\n",
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" <td>39.429995</td>\n",
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" <td>10</td>\n",
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" <td>17.647059</td>\n",
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" <td>67.077054</td>\n",
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" </tr>\n",
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" </tr>\n",
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" <tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <th>1</th>\n",
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" <td>94114</td>\n",
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" <td>Stafford VA</td>\n",
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" <td>5080</td>\n",
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" <td>38.413261</td>\n",
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" <td>4036</td>\n",
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" <td>-77.451334</td>\n",
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" <td>1044</td>\n",
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" <td>170</td>\n",
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" <td>874</td>\n",
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" <td>205.511811</td>\n",
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" <td>33.464567</td>\n",
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" <td>172.047244</td>\n",
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" <td>16456</td>\n",
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" <td>...</td>\n",
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" <td>4.161579</td>\n",
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" <td>9.834048</td>\n",
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" <td>29.163165</td>\n",
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" <td>19.415304</td>\n",
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" <td>48.578469</td>\n",
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" <td>58.412517</td>\n",
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" <td>41.866815</td>\n",
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" <td>0</td>\n",
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" <td>20.000000</td>\n",
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" <td>61.866815</td>\n",
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" </tr>\n",
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" </tr>\n",
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" <tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <th>2</th>\n",
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" <td>10011</td>\n",
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" <td>Webster NE</td>\n",
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" <td>5790</td>\n",
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" <td>40.180646</td>\n",
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" <td>5166</td>\n",
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" <td>-98.498590</td>\n",
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" <td>624</td>\n",
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" <td>97</td>\n",
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" <td>527</td>\n",
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" <td>107.772021</td>\n",
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" <td>16.753022</td>\n",
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" <td>91.018998</td>\n",
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" <td>29762</td>\n",
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" <td>...</td>\n",
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" <td>1.531029</td>\n",
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" <td>4.370779</td>\n",
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" <td>15.428332</td>\n",
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" <td>10.932081</td>\n",
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" <td>26.360413</td>\n",
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" <td>30.731192</td>\n",
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" <td>22.026394</td>\n",
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" <td>10</td>\n",
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" <td>5.882353</td>\n",
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" <td>37.908747</td>\n",
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" </tr>\n",
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" </tr>\n",
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" <tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <th>3</th>\n",
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" <td>10014</td>\n",
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" <td>Dimmit TX</td>\n",
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" <td>3510</td>\n",
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" <td>28.423587</td>\n",
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" <td>3229</td>\n",
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" <td>-99.765871</td>\n",
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" <td>281</td>\n",
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" <td>74</td>\n",
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" <td>207</td>\n",
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" <td>80.056980</td>\n",
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" <td>21.082621</td>\n",
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" <td>58.974359</td>\n",
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" <td>18786</td>\n",
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" <td>...</td>\n",
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" <td>2.482293</td>\n",
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" <td>6.055939</td>\n",
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" <td>9.996551</td>\n",
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" <td>5.943318</td>\n",
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" <td>15.939869</td>\n",
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" <td>21.995808</td>\n",
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" <td>15.765361</td>\n",
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" <td>10</td>\n",
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" <td>11.764706</td>\n",
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" <td>37.530067</td>\n",
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" </tr>\n",
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" </tr>\n",
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" <tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <th>4</th>\n",
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" <td>94103</td>\n",
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" <td>Cedar IA</td>\n",
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" <td>2660</td>\n",
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" <td>41.772360</td>\n",
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" <td>2417</td>\n",
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" <td>-91.132610</td>\n",
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" <td>243</td>\n",
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" <td>34</td>\n",
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" <td>209</td>\n",
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" <td>91.353383</td>\n",
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" <td>12.781955</td>\n",
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" <td>78.571429</td>\n",
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" <td>12728</td>\n",
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" <td>...</td>\n",
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" <td>0.837431</td>\n",
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" <td>3.004058</td>\n",
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" <td>13.318386</td>\n",
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" <td>4.961779</td>\n",
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" <td>18.280165</td>\n",
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" <td>21.284224</td>\n",
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" <td>15.255337</td>\n",
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" <td>10</td>\n",
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" <td>10.588235</td>\n",
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" <td>35.843573</td>\n",
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" </tr>\n",
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" </tr>\n",
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" </tbody>\n",
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" </tbody>\n",
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"</table>\n",
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"</table>\n",
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"<p>5 rows × 29 columns</p>\n",
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"</div>"
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"</div>"
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],
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],
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"text/plain": [
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"text/plain": [
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" GEOID10 Tax_Mjoint Mjoint_MF Mjoint_SS Mjoint_FF Mjoint_MM \\\n",
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" name lat long\n",
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"0 90069 2120 1689 431 61 370 \n",
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"0 Hancock OH 41.000471 -83.666033\n",
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"1 94114 5080 4036 1044 170 874 \n",
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"1 Stafford VA 38.413261 -77.451334\n",
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"2 10011 5790 5166 624 97 527 \n",
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"2 Webster NE 40.180646 -98.498590\n",
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"3 10014 3510 3229 281 74 207 \n",
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"3 Dimmit TX 28.423587 -99.765871\n",
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"4 94103 2660 2417 243 34 209 \n",
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"4 Cedar IA 41.772360 -91.132610"
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"\n",
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" TaxRate_SS TaxRate_FF TaxRate_MM Cns_TotHH ... FF_Cns FF_Index \\\n",
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"0 203.301887 28.773585 174.528302 12551 ... 1.847099 6.724415 \n",
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"1 205.511811 33.464567 172.047244 16456 ... 4.161579 9.834048 \n",
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"2 107.772021 16.753022 91.018998 29762 ... 1.531029 4.370779 \n",
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"3 80.056980 21.082621 58.974359 18786 ... 2.482293 6.055939 \n",
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"4 91.353383 12.781955 78.571429 12728 ... 0.837431 3.004058 \n",
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"\n",
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" MM_Tax MM_Cns MM_Index SS_Index SS_Index_Weight Parade_Weight \\\n",
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"0 29.583721 18.704533 48.288254 55.012669 39.429995 10 \n",
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"1 29.163165 19.415304 48.578469 58.412517 41.866815 0 \n",
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"2 15.428332 10.932081 26.360413 30.731192 22.026394 10 \n",
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"3 9.996551 5.943318 15.939869 21.995808 15.765361 10 \n",
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"4 13.318386 4.961779 18.280165 21.284224 15.255337 10 \n",
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"\n",
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" Bars_Weight TOTINDEX \n",
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"0 17.647059 67.077054 \n",
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"1 20.000000 61.866815 \n",
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"2 5.882353 37.908747 \n",
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"3 11.764706 37.530067 \n",
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"4 10.588235 35.843573 \n",
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"\n",
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"[5 rows x 29 columns]"
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]
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]
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"execution_count": 2,
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"execution_count": 76,
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"metadata": {},
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"metadata": {},
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"output_type": "execute_result"
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"output_type": "execute_result"
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}
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}
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],
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],
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"source": [
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"source": [
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"import pandas as pd\n",
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"## counties - Relating US counties to their long/lat position on the Earth\n",
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"counties = pd.read_csv(\"../data/raw/us-county-boundaries.csv\", sep=\";\")\n",
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"\n",
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"\n",
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"gaybourhoods = pd.read_csv(\"../data/raw/gaybourhoods.csv\")\n",
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"counties = counties.rename({\n",
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"gaybourhoods.head(5)"
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" \"NAME\": \"name\",\n",
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" \"INTPTLAT\": \"lat\",\n",
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" \"INTPTLON\": \"long\",\n",
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"}, axis=\"columns\")\n",
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"\n",
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"# Combine the county name with the state code\n",
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"def combine_name_state(row):\n",
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" row[\"name\"] = f\"{row['name']} {row['STUSAB']}\"\n",
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" return row\n",
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"\n",
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"counties = counties.apply(combine_name_state, axis=\"columns\")\n",
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"\n",
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"# We don't need this column anymore\n",
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"counties = counties.drop([\"STUSAB\"], axis=\"columns\")\n",
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"\n",
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"counties.to_csv(\"../data/processed/us-county-boundaries.csv\")\n",
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"counties.head()"
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]
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]
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 81,
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [
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"source": []
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>county</th>\n",
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" <th>party</th>\n",
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" <th>votes</th>\n",
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" <th>total</th>\n",
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" <th>percent</th>\n",
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" <th>lat</th>\n",
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" <th>long</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>Autauga AL</td>\n",
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" <td>Democrat</td>\n",
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" <td>6363</td>\n",
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" <td>23932</td>\n",
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" <td>0.265878</td>\n",
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" <td>32.532237</td>\n",
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" <td>-86.646439</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>Autauga AL</td>\n",
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" <td>Republican</td>\n",
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" <td>17379</td>\n",
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" <td>23932</td>\n",
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||||||
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" <td>0.726183</td>\n",
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" <td>32.532237</td>\n",
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" <td>-86.646439</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>Autauga AL</td>\n",
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" <td>Other</td>\n",
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||||||
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" <td>190</td>\n",
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||||||
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" <td>23932</td>\n",
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||||||
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" <td>0.007939</td>\n",
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||||||
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" <td>32.532237</td>\n",
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||||||
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" <td>-86.646439</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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||||||
|
" <td>Baldwin AL</td>\n",
|
||||||
|
" <td>Democrat</td>\n",
|
||||||
|
" <td>18424</td>\n",
|
||||||
|
" <td>85338</td>\n",
|
||||||
|
" <td>0.215894</td>\n",
|
||||||
|
" <td>30.659218</td>\n",
|
||||||
|
" <td>-87.746067</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>4</th>\n",
|
||||||
|
" <td>Baldwin AL</td>\n",
|
||||||
|
" <td>Republican</td>\n",
|
||||||
|
" <td>66016</td>\n",
|
||||||
|
" <td>85338</td>\n",
|
||||||
|
" <td>0.773583</td>\n",
|
||||||
|
" <td>30.659218</td>\n",
|
||||||
|
" <td>-87.746067</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </tbody>\n",
|
||||||
|
"</table>\n",
|
||||||
|
"</div>"
|
||||||
|
],
|
||||||
|
"text/plain": [
|
||||||
|
" county party votes total percent lat long\n",
|
||||||
|
"0 Autauga AL Democrat 6363 23932 0.265878 32.532237 -86.646439\n",
|
||||||
|
"1 Autauga AL Republican 17379 23932 0.726183 32.532237 -86.646439\n",
|
||||||
|
"2 Autauga AL Other 190 23932 0.007939 32.532237 -86.646439\n",
|
||||||
|
"3 Baldwin AL Democrat 18424 85338 0.215894 30.659218 -87.746067\n",
|
||||||
|
"4 Baldwin AL Republican 66016 85338 0.773583 30.659218 -87.746067"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 81,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"## pol - Election results from the 2012 American presidential election\n",
|
||||||
|
"pol = pd.read_csv(\"../data/raw/countypres_2000-2020.csv\")\n",
|
||||||
|
"\n",
|
||||||
|
"# We only want 2012--the latest election before the gb data was collected\n",
|
||||||
|
"\n",
|
||||||
|
"pol = pol[pol[\"year\"] == 2012].reset_index()\n",
|
||||||
|
"\n",
|
||||||
|
"# Get rid of undesireable columns\n",
|
||||||
|
"pol = pol.drop([\n",
|
||||||
|
" \"year\", \"state\", \"county_fips\", \"office\",\n",
|
||||||
|
" \"candidate\", \"version\", \"mode\", \"index\",\n",
|
||||||
|
"], axis=\"columns\")\n",
|
||||||
|
"\n",
|
||||||
|
"# Change the column names to make them a little more friendly\n",
|
||||||
|
"pol.rename({\n",
|
||||||
|
" \"county_name\": \"county\",\n",
|
||||||
|
" \"state_po\": \"state\",\n",
|
||||||
|
" \"candidatevotes\": \"votes\",\n",
|
||||||
|
" \"totalvotes\": \"total\"\n",
|
||||||
|
"}, axis=\"columns\", inplace=True)\n",
|
||||||
|
"\n",
|
||||||
|
"# Make cells lowercase\n",
|
||||||
|
"pol[\"county\"] = pol[\"county\"].apply(lambda x: x.capitalize())\n",
|
||||||
|
"pol[\"party\"] = pol[\"party\"].apply(lambda x: x.capitalize())\n",
|
||||||
|
"\n",
|
||||||
|
"# Combine the county name with the state code\n",
|
||||||
|
"def combine_name_state(row):\n",
|
||||||
|
" row[\"county\"] = f\"{row['county']} {row['state']}\"\n",
|
||||||
|
" return row\n",
|
||||||
|
"\n",
|
||||||
|
"pol = pol.apply(combine_name_state, axis=\"columns\")\n",
|
||||||
|
"\n",
|
||||||
|
"# Add a percent column which will be useful when graphing\n",
|
||||||
|
"pol[\"percent\"] = pol[\"votes\"] / pol[\"total\"]\n",
|
||||||
|
"\n",
|
||||||
|
"# Attach long/lat data to each row\n",
|
||||||
|
"pol = pol.merge(counties, left_on=\"county\", right_on=\"name\")\n",
|
||||||
|
"\n",
|
||||||
|
"# Now we can get rid of the state columns\n",
|
||||||
|
"pol = pol.drop([\"state\", \"name\"], axis=\"columns\")\n",
|
||||||
|
"\n",
|
||||||
|
"pol.to_csv(\"../data/processed/election-2012.csv\", index=False)\n",
|
||||||
|
"pol.head()"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 87,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/html": [
|
||||||
|
"<div>\n",
|
||||||
|
"<style scoped>\n",
|
||||||
|
" .dataframe tbody tr th:only-of-type {\n",
|
||||||
|
" vertical-align: middle;\n",
|
||||||
|
" }\n",
|
||||||
|
"\n",
|
||||||
|
" .dataframe tbody tr th {\n",
|
||||||
|
" vertical-align: top;\n",
|
||||||
|
" }\n",
|
||||||
|
"\n",
|
||||||
|
" .dataframe thead th {\n",
|
||||||
|
" text-align: right;\n",
|
||||||
|
" }\n",
|
||||||
|
"</style>\n",
|
||||||
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||||||
|
" <thead>\n",
|
||||||
|
" <tr style=\"text-align: right;\">\n",
|
||||||
|
" <th></th>\n",
|
||||||
|
" <th>Tax_Mjoint</th>\n",
|
||||||
|
" <th>TaxRate_SS</th>\n",
|
||||||
|
" <th>TaxRate_FF</th>\n",
|
||||||
|
" <th>TaxRate_MM</th>\n",
|
||||||
|
" <th>Cns_RateSS</th>\n",
|
||||||
|
" <th>Cns_RateFF</th>\n",
|
||||||
|
" <th>Cns_RateMM</th>\n",
|
||||||
|
" <th>CountBars</th>\n",
|
||||||
|
" <th>FF_Index</th>\n",
|
||||||
|
" <th>MM_Index</th>\n",
|
||||||
|
" <th>SS_Index</th>\n",
|
||||||
|
" <th>TOTINDEX</th>\n",
|
||||||
|
" <th>lat</th>\n",
|
||||||
|
" <th>long</th>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </thead>\n",
|
||||||
|
" <tbody>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>0</th>\n",
|
||||||
|
" <td>2120</td>\n",
|
||||||
|
" <td>203.301887</td>\n",
|
||||||
|
" <td>28.773585</td>\n",
|
||||||
|
" <td>174.528302</td>\n",
|
||||||
|
" <td>77.125329</td>\n",
|
||||||
|
" <td>6.931719</td>\n",
|
||||||
|
" <td>70.193610</td>\n",
|
||||||
|
" <td>15</td>\n",
|
||||||
|
" <td>6.724415</td>\n",
|
||||||
|
" <td>48.288254</td>\n",
|
||||||
|
" <td>55.012669</td>\n",
|
||||||
|
" <td>67.077054</td>\n",
|
||||||
|
" <td>34.093828</td>\n",
|
||||||
|
" <td>-118.381697</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>1</th>\n",
|
||||||
|
" <td>5080</td>\n",
|
||||||
|
" <td>205.511811</td>\n",
|
||||||
|
" <td>33.464567</td>\n",
|
||||||
|
" <td>172.047244</td>\n",
|
||||||
|
" <td>88.478367</td>\n",
|
||||||
|
" <td>15.617404</td>\n",
|
||||||
|
" <td>72.860963</td>\n",
|
||||||
|
" <td>17</td>\n",
|
||||||
|
" <td>9.834048</td>\n",
|
||||||
|
" <td>48.578469</td>\n",
|
||||||
|
" <td>58.412517</td>\n",
|
||||||
|
" <td>61.866815</td>\n",
|
||||||
|
" <td>37.758057</td>\n",
|
||||||
|
" <td>-122.435410</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>2</th>\n",
|
||||||
|
" <td>5790</td>\n",
|
||||||
|
" <td>107.772021</td>\n",
|
||||||
|
" <td>16.753022</td>\n",
|
||||||
|
" <td>91.018998</td>\n",
|
||||||
|
" <td>46.771050</td>\n",
|
||||||
|
" <td>5.745582</td>\n",
|
||||||
|
" <td>41.025469</td>\n",
|
||||||
|
" <td>5</td>\n",
|
||||||
|
" <td>4.370779</td>\n",
|
||||||
|
" <td>26.360413</td>\n",
|
||||||
|
" <td>30.731192</td>\n",
|
||||||
|
" <td>37.908747</td>\n",
|
||||||
|
" <td>40.742039</td>\n",
|
||||||
|
" <td>-74.000620</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>3</th>\n",
|
||||||
|
" <td>3510</td>\n",
|
||||||
|
" <td>80.056980</td>\n",
|
||||||
|
" <td>21.082621</td>\n",
|
||||||
|
" <td>58.974359</td>\n",
|
||||||
|
" <td>31.619291</td>\n",
|
||||||
|
" <td>9.315448</td>\n",
|
||||||
|
" <td>22.303843</td>\n",
|
||||||
|
" <td>10</td>\n",
|
||||||
|
" <td>6.055939</td>\n",
|
||||||
|
" <td>15.939869</td>\n",
|
||||||
|
" <td>21.995808</td>\n",
|
||||||
|
" <td>37.530067</td>\n",
|
||||||
|
" <td>40.734012</td>\n",
|
||||||
|
" <td>-74.006746</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" <tr>\n",
|
||||||
|
" <th>4</th>\n",
|
||||||
|
" <td>2660</td>\n",
|
||||||
|
" <td>91.353383</td>\n",
|
||||||
|
" <td>12.781955</td>\n",
|
||||||
|
" <td>78.571429</td>\n",
|
||||||
|
" <td>21.763042</td>\n",
|
||||||
|
" <td>3.142678</td>\n",
|
||||||
|
" <td>18.620365</td>\n",
|
||||||
|
" <td>9</td>\n",
|
||||||
|
" <td>3.004058</td>\n",
|
||||||
|
" <td>18.280165</td>\n",
|
||||||
|
" <td>21.284224</td>\n",
|
||||||
|
" <td>35.843573</td>\n",
|
||||||
|
" <td>37.773134</td>\n",
|
||||||
|
" <td>-122.411167</td>\n",
|
||||||
|
" </tr>\n",
|
||||||
|
" </tbody>\n",
|
||||||
|
"</table>\n",
|
||||||
|
"</div>"
|
||||||
|
],
|
||||||
|
"text/plain": [
|
||||||
|
" Tax_Mjoint TaxRate_SS TaxRate_FF TaxRate_MM Cns_RateSS Cns_RateFF \\\n",
|
||||||
|
"0 2120 203.301887 28.773585 174.528302 77.125329 6.931719 \n",
|
||||||
|
"1 5080 205.511811 33.464567 172.047244 88.478367 15.617404 \n",
|
||||||
|
"2 5790 107.772021 16.753022 91.018998 46.771050 5.745582 \n",
|
||||||
|
"3 3510 80.056980 21.082621 58.974359 31.619291 9.315448 \n",
|
||||||
|
"4 2660 91.353383 12.781955 78.571429 21.763042 3.142678 \n",
|
||||||
|
"\n",
|
||||||
|
" Cns_RateMM CountBars FF_Index MM_Index SS_Index TOTINDEX \\\n",
|
||||||
|
"0 70.193610 15 6.724415 48.288254 55.012669 67.077054 \n",
|
||||||
|
"1 72.860963 17 9.834048 48.578469 58.412517 61.866815 \n",
|
||||||
|
"2 41.025469 5 4.370779 26.360413 30.731192 37.908747 \n",
|
||||||
|
"3 22.303843 10 6.055939 15.939869 21.995808 37.530067 \n",
|
||||||
|
"4 18.620365 9 3.004058 18.280165 21.284224 35.843573 \n",
|
||||||
|
"\n",
|
||||||
|
" lat long \n",
|
||||||
|
"0 34.093828 -118.381697 \n",
|
||||||
|
"1 37.758057 -122.435410 \n",
|
||||||
|
"2 40.742039 -74.000620 \n",
|
||||||
|
"3 40.734012 -74.006746 \n",
|
||||||
|
"4 37.773134 -122.411167 "
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 87,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"## gb - the gaybourhoods dataset\n",
|
||||||
|
"gb = pd.read_csv(\"../data/raw/gaybourhoods.csv\")\n",
|
||||||
|
"cords = pd.read_csv(\"../data/raw/zip_lat_long.csv\")\n",
|
||||||
|
"\n",
|
||||||
|
"# Let's add long/lat columns to gb\n",
|
||||||
|
"gb = gb.merge(cords, left_on=\"GEOID10\", right_on=\"ZIP\")\n",
|
||||||
|
"\n",
|
||||||
|
"# Get rid of unneeded columns\n",
|
||||||
|
"gb = gb.drop([\n",
|
||||||
|
" \"Mjoint_MF\", \"Mjoint_SS\", \"Mjoint_FF\", \"Mjoint_MM\",\n",
|
||||||
|
" \"Cns_TotHH\", \"Cns_UPSS\", \"Cns_UPFF\", \"Cns_UPMM\",\n",
|
||||||
|
" \"ParadeFlag\", \"FF_Tax\", \"FF_Cns\", \"MM_Tax\", \"MM_Cns\",\n",
|
||||||
|
" \"SS_Index_Weight\", \"Parade_Weight\", \"Bars_Weight\",\n",
|
||||||
|
" \"GEOID10\", \"ZIP\",\n",
|
||||||
|
"], axis=\"columns\")\n",
|
||||||
|
"\n",
|
||||||
|
"# There's a lot of info baked into some of these columns. Especially the composite indexes.\n",
|
||||||
|
"# We'll leave their names as is for easy reference even if they're a little ugly.\n",
|
||||||
|
"gb = gb.rename({\n",
|
||||||
|
" \"LAT\": \"lat\",\n",
|
||||||
|
" \"LNG\": \"long\",\n",
|
||||||
|
"}, axis=\"columns\")\n",
|
||||||
|
"\n",
|
||||||
|
"gb.to_csv(\"../data/processed/gaybourhoods-nat.csv\")\n",
|
||||||
|
"gb.head()"
|
||||||
|
]
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
"metadata": {
|
"metadata": {
|
||||||
|
|
|
||||||
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Reference in New Issue