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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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"# Sami Almuallim"
]
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},
{
"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Research question/interests\n",
"\n",
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"**How are the different metrics of pride represented in this data set correlated?** Answering this question will provide a foundation upon which we can work to answer the more complicated questions that follow.\n",
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"\n",
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"- This will probably be the simplest research question, requiring only the data contained in our original data set. To explore this topic, we will use different visualization methods discussed in class to develop a better understanding of the data.\n",
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"\n",
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"**Is there a positive or a negative correlation between taxes paid and the pride of a given queer neighbourhood?** Taxes are influenced by a variety of socio-economic factors and we hope that in analyzing both tax data and our quantification of queerness on a geographic level, we'll be able to gleam insight into the question of how queerness and class are interrelated.\n",
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"\n",
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"- Similar again to the first research question posed, we'll need to find another data set containing geographically located tax data, which should be easy to acquire from the US government (for example, [in our cursory research, we found this data set from the IRS](https://www.irs.gov/statistics/soi-tax-stats-individual-income-tax-statistics-2018-zip-code-data-soi)).\n",
"- This would bring the number of data sets used in this project up to three, which might present some challenges in terms of the amount of data wrangling necessary to bring it all together.\n",
"- To measure this, we would rank the neighbourhoods presented in the gaybourhoods data set by pride (an open question which we will explore in a separate research question)"
]
},
{
"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
"outputs": [
{
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>GEOID10</th>\n",
" <th>Tax_Mjoint</th>\n",
" <th>Mjoint_MF</th>\n",
" <th>Mjoint_SS</th>\n",
" <th>Mjoint_FF</th>\n",
" <th>Mjoint_MM</th>\n",
" <th>TaxRate_SS</th>\n",
" <th>TaxRate_FF</th>\n",
" <th>TaxRate_MM</th>\n",
" <th>Cns_TotHH</th>\n",
" <th>...</th>\n",
" <th>FF_Cns</th>\n",
" <th>FF_Index</th>\n",
" <th>MM_Tax</th>\n",
" <th>MM_Cns</th>\n",
" <th>MM_Index</th>\n",
" <th>SS_Index</th>\n",
" <th>SS_Index_Weight</th>\n",
" <th>Parade_Weight</th>\n",
" <th>Bars_Weight</th>\n",
" <th>TOTINDEX</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>90069</td>\n",
" <td>2120</td>\n",
" <td>1689</td>\n",
" <td>431</td>\n",
" <td>61</td>\n",
" <td>370</td>\n",
" <td>203.301887</td>\n",
" <td>28.773585</td>\n",
" <td>174.528302</td>\n",
" <td>12551</td>\n",
" <td>...</td>\n",
" <td>1.847099</td>\n",
" <td>6.724415</td>\n",
" <td>29.583721</td>\n",
" <td>18.704533</td>\n",
" <td>48.288254</td>\n",
" <td>55.012669</td>\n",
" <td>39.429995</td>\n",
" <td>10</td>\n",
" <td>17.647059</td>\n",
" <td>67.077054</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>94114</td>\n",
" <td>5080</td>\n",
" <td>4036</td>\n",
" <td>1044</td>\n",
" <td>170</td>\n",
" <td>874</td>\n",
" <td>205.511811</td>\n",
" <td>33.464567</td>\n",
" <td>172.047244</td>\n",
" <td>16456</td>\n",
" <td>...</td>\n",
" <td>4.161579</td>\n",
" <td>9.834048</td>\n",
" <td>29.163165</td>\n",
" <td>19.415304</td>\n",
" <td>48.578469</td>\n",
" <td>58.412517</td>\n",
" <td>41.866815</td>\n",
" <td>0</td>\n",
" <td>20.000000</td>\n",
" <td>61.866815</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10011</td>\n",
" <td>5790</td>\n",
" <td>5166</td>\n",
" <td>624</td>\n",
" <td>97</td>\n",
" <td>527</td>\n",
" <td>107.772021</td>\n",
" <td>16.753022</td>\n",
" <td>91.018998</td>\n",
" <td>29762</td>\n",
" <td>...</td>\n",
" <td>1.531029</td>\n",
" <td>4.370779</td>\n",
" <td>15.428332</td>\n",
" <td>10.932081</td>\n",
" <td>26.360413</td>\n",
" <td>30.731192</td>\n",
" <td>22.026394</td>\n",
" <td>10</td>\n",
" <td>5.882353</td>\n",
" <td>37.908747</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>10014</td>\n",
" <td>3510</td>\n",
" <td>3229</td>\n",
" <td>281</td>\n",
" <td>74</td>\n",
" <td>207</td>\n",
" <td>80.056980</td>\n",
" <td>21.082621</td>\n",
" <td>58.974359</td>\n",
" <td>18786</td>\n",
" <td>...</td>\n",
" <td>2.482293</td>\n",
" <td>6.055939</td>\n",
" <td>9.996551</td>\n",
" <td>5.943318</td>\n",
" <td>15.939869</td>\n",
" <td>21.995808</td>\n",
" <td>15.765361</td>\n",
" <td>10</td>\n",
" <td>11.764706</td>\n",
" <td>37.530067</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>94103</td>\n",
" <td>2660</td>\n",
" <td>2417</td>\n",
" <td>243</td>\n",
" <td>34</td>\n",
" <td>209</td>\n",
" <td>91.353383</td>\n",
" <td>12.781955</td>\n",
" <td>78.571429</td>\n",
" <td>12728</td>\n",
" <td>...</td>\n",
" <td>0.837431</td>\n",
" <td>3.004058</td>\n",
" <td>13.318386</td>\n",
" <td>4.961779</td>\n",
" <td>18.280165</td>\n",
" <td>21.284224</td>\n",
" <td>15.255337</td>\n",
" <td>10</td>\n",
" <td>10.588235</td>\n",
" <td>35.843573</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows ×
"</div>"
],
"text/plain": [
" GEOID10 Tax_Mjoint Mjoint_MF Mjoint_SS Mjoint_FF Mjoint_MM \\\n",
"0 90069 2120 1689 431 61 370 \n",
"1 94114 5080 4036 1044 170 874 \n",
"2 10011 5790 5166 624 97 527 \n",
"3 10014 3510 3229 281 74 207 \n",
"4 94103 2660 2417 243 34 209 \n",
"\n",
" TaxRate_SS TaxRate_FF TaxRate_MM Cns_TotHH ... FF_Cns FF_Index \\\n",
"0 203.301887 28.773585 174.528302 12551 ... 1.847099 6.724415 \n",
"1 205.511811 33.464567 172.047244 16456 ... 4.161579 9.834048 \n",
"2 107.772021 16.753022 91.018998 29762 ... 1.531029 4.370779 \n",
"3 80.056980 21.082621 58.974359 18786 ... 2.482293 6.055939 \n",
"4 91.353383 12.781955 78.571429 12728 ... 0.837431 3.004058 \n",
"\n",
" MM_Tax MM_Cns MM_Index SS_Index SS_Index_Weight Parade_Weight \\\n",
"0 29.583721 18.704533 48.288254 55.012669 39.429995 10 \n",
"1 29.163165 19.415304 48.578469 58.412517 41.866815 0 \n",
"2 15.428332 10.932081 26.360413 30.731192 22.026394 10 \n",
"3 9.996551 5.943318 15.939869 21.995808 15.765361 10 \n",
"4 13.318386 4.961779 18.280165 21.284224 15.255337 10 \n",
"\n",
" Bars_Weight TOTINDEX \n",
"0 17.647059 67.077054 \n",
"1 20.000000 61.866815 \n",
"2 5.882353 37.908747 \n",
"3 11.764706 37.530067 \n",
"4 10.588235 35.843573 \n",
"\n",
"[5 rows x 29 columns]"
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},
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"execution_count": 1,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
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"import seaborn as sns\n",
"import numpy as np\n",
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"\n",
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"gaybourhoods = pd.read_csv(\"../data/raw/gaybourhoods.csv\")\n",
"gaybourhoods.head(5)"
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]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data wrangling"
]
},
{
"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
"# NOTE: This cell will not work unless this file is in the repository. The source\n",
"# can be found linked in the references section of the readme, however, it is too\n",
"# big for GitHub to handle.\n",
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"\n",
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"#irs = pd.read_csv(\"../data/raw/irs_2015.csv\")\n",
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"\n",
"# Naively splitting the IRS data set in two. More formal data wrangling will\n",
"# come later\n",
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"#irs1 = irs.head(int(irs.shape[0] / 2))\n",
"#irs2 = irs.tail(int(irs.shape[0] / 2))\n",
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"\n",
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"#irs1.to_csv(\"../data/processed/irs_2015_1\", index=False)\n",
"#irs2.to_csv(\"../data/processed/irs_2015_2\", index=False)"
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]
},
{
"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
"# Now these two datasets can be joined and worked with\n",
"irs = pd.concat([\n",
" pd.read_csv(\"../data/processed/irs_2015_1\"),\n",
" pd.read_csv(\"../data/processed/irs_2015_2\")\n",
"])\n",
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"# irs.head()\n",
"\n",
"\n",
"#selected data: ZIPCODE - this will be used in conjunction with the rest of the set\n",
" # N2 - population of zip code\n",
" \n",
" #data of intrest\n",
" # A11900\tTotal overpayments amount\n",
" # AGI_STUB - metric for income\n",
"\n",
"# print(irs.loc[irs['zipcode']==90069])\n",
"# df = {irs['zipcode'], irs['N2']}\n",
"\n"
]
},
{
"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
"outputs": [
{
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"data": {
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" <th></th>\n",
" <th>zip</th>\n",
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" <td>166698.000000</td>\n",
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" <td>166698.00000</td>\n",
" <td>1.666980e+05</td>\n",
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" <tr>\n",
" <th>mean</th>\n",
" <td>48877.636432</td>\n",
" <td>3.432536e+03</td>\n",
" <td>3.50000</td>\n",
" <td>1.844871e+03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>27146.337114</td>\n",
" <td>6.676873e+04</td>\n",
" <td>1.70783</td>\n",
" <td>5.785610e+04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000e+00</td>\n",
" <td>1.00000</td>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>27040.000000</td>\n",
" <td>1.400000e+02</td>\n",
" <td>2.00000</td>\n",
" <td>1.600000e+01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>48879.000000</td>\n",
" <td>5.100000e+02</td>\n",
" <td>3.50000</td>\n",
" <td>1.440000e+02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>70607.000000</td>\n",
" <td>2.000000e+03</td>\n",
" <td>5.00000</td>\n",
" <td>6.310000e+02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>99999.000000</td>\n",
" <td>9.566490e+06</td>\n",
" <td>6.00000</td>\n",
" <td>1.557123e+07</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" zip population income overall tax paid\n",
"count 166698.000000 1.666980e+05 166698.00000 1.666980e+05\n",
"mean 48877.636432 3.432536e+03 3.50000 1.844871e+03\n",
"std 27146.337114 6.676873e+04 1.70783 5.785610e+04\n",
"min 0.000000 0.000000e+00 1.00000 0.000000e+00\n",
"25% 27040.000000 1.400000e+02 2.00000 1.600000e+01\n",
"50% 48879.000000 5.100000e+02 3.50000 1.440000e+02\n",
"75% 70607.000000 2.000000e+03 5.00000 6.310000e+02\n",
"max 99999.000000 9.566490e+06 6.00000 1.557123e+07"
]
},
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"execution_count": 4,
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"metadata": {},
"output_type": "execute_result"
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}
],
"source": [
"#wrangle tax\n",
"taxdf = pd.DataFrame(zip(irs['zipcode'], irs['N2'], irs['agi_stub'], irs['A11901']))\n",
"taxdf.columns=('zip', 'population', 'income', 'overall tax paid')\n",
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"taxdf.describe()"
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]
},
{
"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
"outputs": [
{
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"data": {
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" <th></th>\n",
" <th>zip</th>\n",
" <th>gay tax rate</th>\n",
" </tr>\n",
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" <tr>\n",
" <th>count</th>\n",
" <td>2328.000000</td>\n",
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" <tr>\n",
" <th>mean</th>\n",
" <td>48616.478522</td>\n",
" <td>4103.440722</td>\n",
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" <tr>\n",
" <th>std</th>\n",
" <td>35481.240641</td>\n",
" <td>3140.699446</td>\n",
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" <tr>\n",
" <th>min</th>\n",
" <td>1730.000000</td>\n",
" <td>0.000000</td>\n",
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" <th>25%</th>\n",
" <td>11362.750000</td>\n",
" <td>1767.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>46351.000000</td>\n",
" <td>3635.000000</td>\n",
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" <th>75%</th>\n",
" <td>80234.250000</td>\n",
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],
"text/plain": [
" zip gay tax rate\n",
"count 2328.000000 2328.000000\n",
"mean 48616.478522 4103.440722\n",
"std 35481.240641 3140.699446\n",
"min 1730.000000 0.000000\n",
"25% 11362.750000 1767.500000\n",
"50% 46351.000000 3635.000000\n",
"75% 80234.250000 5745.000000\n",
"max 98686.000000 24560.000000"
]
},
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"execution_count": 5,
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"metadata": {},
"output_type": "execute_result"
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}
],
"source": [
"#wrangle gay\n",
"gaydf = pd.DataFrame(zip(gaybourhoods['GEOID10'], gaybourhoods['Tax_Mjoint']))\n",
"gaydf.columns=(('zip', 'gay tax rate'))\n",
"\n",
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"gaydf.describe()"
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]
},
{
"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
"outputs": [
{
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"data": {
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" <th></th>\n",
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" <th>overall tax paid</th>\n",
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" <td>2184.000000</td>\n",
" <td>2184.000000</td>\n",
" <td>2184.000000</td>\n",
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" <td>2184.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
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" <td>48935.203297</td>\n",
" <td>26691.730769</td>\n",
" <td>4373.997253</td>\n",
" <td>596.719322</td>\n",
" <td>1.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
2023-04-13 19:45:53 +00:00
" <td>35451.335807</td>\n",
" <td>17960.713867</td>\n",
" <td>3054.620840</td>\n",
" <td>615.174358</td>\n",
" <td>0.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
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" <td>1730.000000</td>\n",
" <td>160.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
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" <td>11360.750000</td>\n",
" <td>13337.500000</td>\n",
" <td>2110.000000</td>\n",
" <td>217.000000</td>\n",
" <td>1.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
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" <td>60023.500000</td>\n",
" <td>24070.000000</td>\n",
" <td>3900.000000</td>\n",
" <td>434.000000</td>\n",
" <td>1.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
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" <td>80227.250000</td>\n",
" <td>35640.000000</td>\n",
" <td>5902.500000</td>\n",
" <td>777.250000</td>\n",
" <td>1.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
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" <td>98686.000000</td>\n",
" <td>114420.000000</td>\n",
" <td>24560.000000</td>\n",
" <td>9166.000000</td>\n",
" <td>1.0</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
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" zip population gay tax rate overall tax paid income\n",
"count 2184.000000 2184.000000 2184.000000 2184.000000 2184.0\n",
"mean 48935.203297 26691.730769 4373.997253 596.719322 1.0\n",
"std 35451.335807 17960.713867 3054.620840 615.174358 0.0\n",
"min 1730.000000 160.000000 0.000000 0.000000 1.0\n",
"25% 11360.750000 13337.500000 2110.000000 217.000000 1.0\n",
"50% 60023.500000 24070.000000 3900.000000 434.000000 1.0\n",
"75% 80227.250000 35640.000000 5902.500000 777.250000 1.0\n",
"max 98686.000000 114420.000000 24560.000000 9166.000000 1.0"
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]
},
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"execution_count": 6,
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"metadata": {},
"output_type": "execute_result"
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}
],
"source": [
"#merge\n",
"df = pd.merge(taxdf, gaydf)\n",
"\n",
"# print(df)\n",
"\n",
"df2 = df.groupby(df['zip']).aggregate({ 'zip':'first',\n",
" 'population': 'sum',\n",
" 'gay tax rate':'first',\n",
" 'overall tax paid':'first',\n",
" 'income':'first'\n",
" })\n",
"\n",
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"df2.to_csv(\"../data/processed/tax_and_gay.csv\")\n",
"df2.describe()"
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]
},
{
"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
{
"data": {
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" <td>3260</td>\n",
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" <th>1760</th>\n",
" <td>1760</td>\n",
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" <td>7880</td>\n",
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" <td>1770</td>\n",
" <td>4310.0</td>\n",
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" <td>98682</td>\n",
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" <td>703.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98683</th>\n",
" <td>98683</td>\n",
" <td>30700.0</td>\n",
" <td>6470</td>\n",
" <td>358.0</td>\n",
" <td>1</td>\n",
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" <tr>\n",
" <th>98684</th>\n",
" <td>98684</td>\n",
" <td>27630.0</td>\n",
" <td>5390</td>\n",
" <td>371.0</td>\n",
" <td>1</td>\n",
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" <tr>\n",
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" <td>4120</td>\n",
" <td>215.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2184 rows ×
"</div>"
],
"text/plain": [
" zip population gay tax rate overall tax paid income\n",
"zip \n",
"1730 1730 13570.0 3260 150.0 1\n",
"1731 1731 2450.0 550 0.0 1\n",
"1742 1742 17170.0 4220 297.0 1\n",
"1760 1760 34350.0 7880 468.0 1\n",
"1770 1770 4310.0 1060 46.0 1\n",
"... ... ... ... ... ...\n",
"98682 98682 57010.0 11080 703.0 1\n",
"98683 98683 30700.0 6470 358.0 1\n",
"98684 98684 27630.0 5390 371.0 1\n",
"98685 98685 27540.0 6490 298.0 1\n",
"98686 98686 17800.0 4120 215.0 1\n",
"\n",
"[2184 rows x 5 columns]"
]
},
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"execution_count": 7,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2"
]
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},
{
"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
"outputs": [
{
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"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>zip</th>\n",
" <th>population</th>\n",
" <th>gay tax rate</th>\n",
" <th>overall tax paid</th>\n",
" <th>income</th>\n",
" <th>lat</th>\n",
" <th>long</th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>2184.000000</td>\n",
" <td>2184.000000</td>\n",
" <td>2184.000000</td>\n",
" <td>2184.000000</td>\n",
" <td>2184.0</td>\n",
" <td>2184.000000</td>\n",
" <td>2184.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>48935.203297</td>\n",
" <td>26691.730769</td>\n",
" <td>4373.997253</td>\n",
" <td>596.719322</td>\n",
" <td>1.0</td>\n",
" <td>38.016518</td>\n",
" <td>-91.296804</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>35451.335807</td>\n",
" <td>17960.713867</td>\n",
" <td>3054.620840</td>\n",
" <td>615.174358</td>\n",
" <td>0.0</td>\n",
" <td>5.210272</td>\n",
" <td>18.476699</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1730.000000</td>\n",
" <td>160.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.0</td>\n",
" <td>25.572213</td>\n",
" <td>-123.118977</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>11360.750000</td>\n",
" <td>13337.500000</td>\n",
" <td>2110.000000</td>\n",
" <td>217.000000</td>\n",
" <td>1.0</td>\n",
" <td>33.997027</td>\n",
" <td>-105.037767</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>60023.500000</td>\n",
" <td>24070.000000</td>\n",
" <td>3900.000000</td>\n",
" <td>434.000000</td>\n",
" <td>1.0</td>\n",
" <td>39.930150</td>\n",
" <td>-87.603617</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>80227.250000</td>\n",
" <td>35640.000000</td>\n",
" <td>5902.500000</td>\n",
" <td>777.250000</td>\n",
" <td>1.0</td>\n",
" <td>40.960828</td>\n",
" <td>-74.310179</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>98686.000000</td>\n",
" <td>114420.000000</td>\n",
" <td>24560.000000</td>\n",
" <td>9166.000000</td>\n",
" <td>1.0</td>\n",
" <td>47.916786</td>\n",
" <td>-70.758184</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" zip population gay tax rate overall tax paid income \\\n",
"count 2184.000000 2184.000000 2184.000000 2184.000000 2184.0 \n",
"mean 48935.203297 26691.730769 4373.997253 596.719322 1.0 \n",
"std 35451.335807 17960.713867 3054.620840 615.174358 0.0 \n",
"min 1730.000000 160.000000 0.000000 0.000000 1.0 \n",
"25% 11360.750000 13337.500000 2110.000000 217.000000 1.0 \n",
"50% 60023.500000 24070.000000 3900.000000 434.000000 1.0 \n",
"75% 80227.250000 35640.000000 5902.500000 777.250000 1.0 \n",
"max 98686.000000 114420.000000 24560.000000 9166.000000 1.0 \n",
"\n",
" lat long \n",
"count 2184.000000 2184.000000 \n",
"mean 38.016518 -91.296804 \n",
"std 5.210272 18.476699 \n",
"min 25.572213 -123.118977 \n",
"25% 33.997027 -105.037767 \n",
"50% 39.930150 -87.603617 \n",
"75% 40.960828 -74.310179 \n",
"max 47.916786 -70.758184 "
]
},
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"execution_count": 8,
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"metadata": {},
"output_type": "execute_result"
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}
],
"source": [
"## take the dataset & add long/lat\n",
"## props to Nat for creating the backbone I used to zipcode -> long/lat\n",
"\n",
"cords = pd.read_csv(\"../data/raw/zip_lat_long.csv\")\n",
"gaydf = pd.read_csv(\"../data/processed/tax_and_gay.csv\")\n",
"\n",
"# Let's add long/lat columns to gb\n",
"gaydf = gaydf.merge(cords, left_on=\"zip\", right_on=\"ZIP\")\n",
"\n",
"# // unneded was already filtered out\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",
"gaydf = gaydf.rename({\n",
" \"LAT\": \"lat\",\n",
" \"LNG\": \"long\",\n",
"}, axis=\"columns\")\n",
"\n",
"# gb.to_csv(\"../data/processed/gaybourhoods-nat.csv\")\n",
"# gb.head()\n",
"\n",
"# unperson unneccesary zip codes\n",
"del gaydf['zip.1']\n",
"del gaydf['ZIP']\n",
"\n",
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"gaydf.describe()"
]
},
{
"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
"outputs": [
{
"data": {
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" <th>zip</th>\n",
" <th>population</th>\n",
" <th>gay tax rate</th>\n",
" <th>overall tax paid</th>\n",
" <th>income</th>\n",
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" <th>lat</th>\n",
" <th>long</th>\n",
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" <th>0</th>\n",
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" <td>1730</td>\n",
" <td>13570.0</td>\n",
" <td>3260</td>\n",
" <td>150.0</td>\n",
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" <td>1</td>\n",
2023-04-13 19:45:53 +00:00
" <td>42.499295</td>\n",
" <td>-71.281889</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
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" <td>1731</td>\n",
" <td>2450.0</td>\n",
" <td>550</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>42.456748</td>\n",
" <td>-71.279484</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
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" <td>1742</td>\n",
" <td>17170.0</td>\n",
" <td>4220</td>\n",
" <td>297.0</td>\n",
2023-04-13 18:37:53 +00:00
" <td>1</td>\n",
2023-04-13 19:45:53 +00:00
" <td>42.462911</td>\n",
" <td>-71.364496</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
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" <td>1760</td>\n",
" <td>34350.0</td>\n",
" <td>7880</td>\n",
" <td>468.0</td>\n",
2023-04-13 18:37:53 +00:00
" <td>1</td>\n",
2023-04-13 19:45:53 +00:00
" <td>42.284822</td>\n",
" <td>-71.348811</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
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" <td>1770</td>\n",
" <td>4310.0</td>\n",
" <td>1060</td>\n",
" <td>46.0</td>\n",
2023-04-13 18:37:53 +00:00
" <td>1</td>\n",
2023-04-13 19:45:53 +00:00
" <td>42.231947</td>\n",
" <td>-71.372963</td>\n",
2023-04-13 18:37:53 +00:00
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" </tr>\n",
" <tr>\n",
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" <th>2179</th>\n",
" <td>98682</td>\n",
" <td>57010.0</td>\n",
" <td>11080</td>\n",
" <td>703.0</td>\n",
" <td>1</td>\n",
" <td>45.673209</td>\n",
" <td>-122.481745</td>\n",
2023-04-13 18:37:53 +00:00
" </tr>\n",
" <tr>\n",
2023-04-13 19:45:53 +00:00
" <th>2180</th>\n",
" <td>98683</td>\n",
" <td>30700.0</td>\n",
" <td>6470</td>\n",
" <td>358.0</td>\n",
" <td>1</td>\n",
" <td>45.603287</td>\n",
" <td>-122.510170</td>\n",
2023-04-13 18:37:53 +00:00
" </tr>\n",
" <tr>\n",
2023-04-13 19:45:53 +00:00
" <th>2181</th>\n",
" <td>98684</td>\n",
" <td>27630.0</td>\n",
" <td>5390</td>\n",
" <td>371.0</td>\n",
" <td>1</td>\n",
" <td>45.630556</td>\n",
" <td>-122.514839</td>\n",
2023-04-13 18:37:53 +00:00
" </tr>\n",
" <tr>\n",
2023-04-13 19:45:53 +00:00
" <th>2182</th>\n",
" <td>98685</td>\n",
" <td>27540.0</td>\n",
" <td>6490</td>\n",
" <td>298.0</td>\n",
" <td>1</td>\n",
" <td>45.715211</td>\n",
" <td>-122.693165</td>\n",
2023-04-13 18:37:53 +00:00
" </tr>\n",
" <tr>\n",
2023-04-13 19:45:53 +00:00
" <th>2183</th>\n",
" <td>98686</td>\n",
" <td>17800.0</td>\n",
" <td>4120</td>\n",
" <td>215.0</td>\n",
" <td>1</td>\n",
" <td>45.723392</td>\n",
" <td>-122.624397</td>\n",
2023-04-13 18:37:53 +00:00
" </tr>\n",
" </tbody>\n",
"</table>\n",
2023-04-13 19:45:53 +00:00
"<p>2184 rows ×
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"</div>"
],
"text/plain": [
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" zip population gay tax rate overall tax paid income lat \\\n",
"0 1730 13570.0 3260 150.0 1 42.499295 \n",
"1 1731 2450.0 550 0.0 1 42.456748 \n",
"2 1742 17170.0 4220 297.0 1 42.462911 \n",
"3 1760 34350.0 7880 468.0 1 42.284822 \n",
"4 1770 4310.0 1060 46.0 1 42.231947 \n",
"... ... ... ... ... ... ... \n",
"2179 98682 57010.0 11080 703.0 1 45.673209 \n",
"2180 98683 30700.0 6470 358.0 1 45.603287 \n",
"2181 98684 27630.0 5390 371.0 1 45.630556 \n",
"2182 98685 27540.0 6490 298.0 1 45.715211 \n",
"2183 98686 17800.0 4120 215.0 1 45.723392 \n",
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"\n",
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" long \n",
"0 -71.281889 \n",
"1 -71.279484 \n",
"2 -71.364496 \n",
"3 -71.348811 \n",
"4 -71.372963 \n",
"... ... \n",
"2179 -122.481745 \n",
"2180 -122.510170 \n",
"2181 -122.514839 \n",
"2182 -122.693165 \n",
"2183 -122.624397 \n",
"\n",
"[2184 rows x 7 columns]"
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]
},
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"execution_count": 9,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gaydf"
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]
},
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{
"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"#compare taxes paid by queers to taxes paid by general\n",
"\n",
"plot1 = sns.scatterplot(data=gaydf, x=\"long\", y=\"lat\")\n",
"_ = plot1.set(xlabel=\"Long\", ylabel=\"Lat\", title=\"t plot\")"
]
},
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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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"The next step is to compare the gay data to the typical data, so the following models are used to compare the taxes each group paid"
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]
},
{
"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"gaydfgaytaxplot = gaydf.copy()\n",
"\n",
"# gaydf.head()\n",
"\n",
"del gaydfgaytaxplot['zip']\n",
"del gaydfgaytaxplot['population']\n",
"del gaydfgaytaxplot['overall tax paid']\n",
"del gaydfgaytaxplot['income']\n",
"# del gaydfgaytaxplot['gay tax rate']\n",
"\n",
"plot2 = sns.scatterplot(data=gaydfgaytaxplot, x=\"long\", y=\"lat\", hue='gay tax rate')\n",
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"_ = plot2.set(xlabel=\"Longitude\", ylabel=\"Latitude\", title=\"Taxes paid by Queer communities in the US\")\n"
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]
},
{
"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gaydfgentaxplot = gaydf.copy()\n",
"\n",
"# gaydf.head()\n",
"\n",
"del gaydfgentaxplot['zip']\n",
"del gaydfgentaxplot['population']\n",
"# del gaydfgentaxplot['overall tax paid']\n",
"del gaydfgentaxplot['income']\n",
"# del gaydfgaytaxplot['gay tax rate']\n",
"\n",
"plot2 = sns.scatterplot(data=gaydfgentaxplot, x=\"long\", y=\"lat\", hue='overall tax paid')\n",
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"_ = plot2.set(xlabel=\"Longitude\", ylabel=\"Latitude\", title=\"Taxes paid by typical communities in the US\")"
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]
},
{
"cell_type": "code",
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"execution_count": 13,
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"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>gay tax rate</th>\n",
" <th>overall tax paid</th>\n",
" <th>lat</th>\n",
" <th>long</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>2184.000000</td>\n",
" <td>2184.000000</td>\n",
" <td>2184.000000</td>\n",
" <td>2184.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>4373.997253</td>\n",
" <td>596.719322</td>\n",
" <td>38.016518</td>\n",
" <td>-91.296804</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>3054.620840</td>\n",
" <td>615.174358</td>\n",
" <td>5.210272</td>\n",
" <td>18.476699</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>25.572213</td>\n",
" <td>-123.118977</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2110.000000</td>\n",
" <td>217.000000</td>\n",
" <td>33.997027</td>\n",
" <td>-105.037767</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>3900.000000</td>\n",
" <td>434.000000</td>\n",
" <td>39.930150</td>\n",
" <td>-87.603617</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>5902.500000</td>\n",
" <td>777.250000</td>\n",
" <td>40.960828</td>\n",
" <td>-74.310179</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>24560.000000</td>\n",
" <td>9166.000000</td>\n",
" <td>47.916786</td>\n",
" <td>-70.758184</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" gay tax rate overall tax paid lat long\n",
"count 2184.000000 2184.000000 2184.000000 2184.000000\n",
"mean 4373.997253 596.719322 38.016518 -91.296804\n",
"std 3054.620840 615.174358 5.210272 18.476699\n",
"min 0.000000 0.000000 25.572213 -123.118977\n",
"25% 2110.000000 217.000000 33.997027 -105.037767\n",
"50% 3900.000000 434.000000 39.930150 -87.603617\n",
"75% 5902.500000 777.250000 40.960828 -74.310179\n",
"max 24560.000000 9166.000000 47.916786 -70.758184"
]
},
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"execution_count": 13,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gaydfhybridplot = pd.merge(gaydfgentaxplot, gaydfgaytaxplot)\n",
"\n",
"gaydfhybridplot.describe()\n"
]
},
{
"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 1000x1000 with 20 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
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"plt = sns.pairplot(gaydfhybridplot)\n",
"_ = plt.figure.suptitle(\"Complete Decomposition of Taxation Statistics in Queer Communities\")\n",
"plt.figure.subplots_adjust(top=0.925)"
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]
},
{
"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 500x500 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gaydflight = gaydfhybridplot.copy()\n",
"\n",
"del gaydflight['lat']; del gaydflight['long']\n",
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"\n",
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"plt = sns.pairplot(gaydflight)\n",
"_ = plt.figure.suptitle(\"Decomposition of Taxation Statistics in Queer Communities\")\n",
"plt.figure.subplots_adjust(top=0.925)\n"
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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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"As we can infer by taking the first derivative of the corelation line of this graph, queer communities pay significantly more taxes then typical neighborhoods. An explanation for this is queer people through one mechanism or another (queer folk being more likely to be politically progressive, live in cities, or get educations for example) end up corelating strongly with demographics who pay more tax\n",
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"\n",
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"Do note that the analysis is severely limited by severe sampling bias as only hyper urban geographical stratum have been surveyed by gayborhoods"
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**How are the different metrics of pride represented in this data set correlated?**\n"
]
},
{
"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>GEOID10</th>\n",
" <th>Tax_Mjoint</th>\n",
" <th>Mjoint_MF</th>\n",
" <th>Mjoint_SS</th>\n",
" <th>Mjoint_FF</th>\n",
" <th>Mjoint_MM</th>\n",
" <th>TaxRate_SS</th>\n",
" <th>TaxRate_FF</th>\n",
" <th>TaxRate_MM</th>\n",
" <th>Cns_TotHH</th>\n",
" <th>...</th>\n",
" <th>FF_Cns</th>\n",
" <th>FF_Index</th>\n",
" <th>MM_Tax</th>\n",
" <th>MM_Cns</th>\n",
" <th>MM_Index</th>\n",
" <th>SS_Index</th>\n",
" <th>SS_Index_Weight</th>\n",
" <th>Parade_Weight</th>\n",
" <th>Bars_Weight</th>\n",
" <th>TOTINDEX</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>90069</td>\n",
" <td>2120</td>\n",
" <td>1689</td>\n",
" <td>431</td>\n",
" <td>61</td>\n",
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" <td>174.528302</td>\n",
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" <td>1.847099</td>\n",
" <td>6.724415</td>\n",
" <td>29.583721</td>\n",
" <td>18.704533</td>\n",
" <td>48.288254</td>\n",
" <td>55.012669</td>\n",
" <td>39.429995</td>\n",
" <td>10</td>\n",
" <td>17.647059</td>\n",
" <td>67.077054</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>94114</td>\n",
" <td>5080</td>\n",
" <td>4036</td>\n",
" <td>1044</td>\n",
" <td>170</td>\n",
" <td>874</td>\n",
" <td>205.511811</td>\n",
" <td>33.464567</td>\n",
" <td>172.047244</td>\n",
" <td>16456</td>\n",
" <td>...</td>\n",
" <td>4.161579</td>\n",
" <td>9.834048</td>\n",
" <td>29.163165</td>\n",
" <td>19.415304</td>\n",
" <td>48.578469</td>\n",
" <td>58.412517</td>\n",
" <td>41.866815</td>\n",
" <td>0</td>\n",
" <td>20.000000</td>\n",
" <td>61.866815</td>\n",
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" <tr>\n",
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" <td>10011</td>\n",
" <td>5790</td>\n",
" <td>5166</td>\n",
" <td>624</td>\n",
" <td>97</td>\n",
" <td>527</td>\n",
" <td>107.772021</td>\n",
" <td>16.753022</td>\n",
" <td>91.018998</td>\n",
" <td>29762</td>\n",
" <td>...</td>\n",
" <td>1.531029</td>\n",
" <td>4.370779</td>\n",
" <td>15.428332</td>\n",
" <td>10.932081</td>\n",
" <td>26.360413</td>\n",
" <td>30.731192</td>\n",
" <td>22.026394</td>\n",
" <td>10</td>\n",
" <td>5.882353</td>\n",
" <td>37.908747</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>10014</td>\n",
" <td>3510</td>\n",
" <td>3229</td>\n",
" <td>281</td>\n",
" <td>74</td>\n",
" <td>207</td>\n",
" <td>80.056980</td>\n",
" <td>21.082621</td>\n",
" <td>58.974359</td>\n",
" <td>18786</td>\n",
" <td>...</td>\n",
" <td>2.482293</td>\n",
" <td>6.055939</td>\n",
" <td>9.996551</td>\n",
" <td>5.943318</td>\n",
" <td>15.939869</td>\n",
" <td>21.995808</td>\n",
" <td>15.765361</td>\n",
" <td>10</td>\n",
" <td>11.764706</td>\n",
" <td>37.530067</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>94103</td>\n",
" <td>2660</td>\n",
" <td>2417</td>\n",
" <td>243</td>\n",
" <td>34</td>\n",
" <td>209</td>\n",
" <td>91.353383</td>\n",
" <td>12.781955</td>\n",
" <td>78.571429</td>\n",
" <td>12728</td>\n",
" <td>...</td>\n",
" <td>0.837431</td>\n",
" <td>3.004058</td>\n",
" <td>13.318386</td>\n",
" <td>4.961779</td>\n",
" <td>18.280165</td>\n",
" <td>21.284224</td>\n",
" <td>15.255337</td>\n",
" <td>10</td>\n",
" <td>10.588235</td>\n",
" <td>35.843573</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows ×
"</div>"
],
"text/plain": [
" GEOID10 Tax_Mjoint Mjoint_MF Mjoint_SS Mjoint_FF Mjoint_MM \\\n",
"0 90069 2120 1689 431 61 370 \n",
"1 94114 5080 4036 1044 170 874 \n",
"2 10011 5790 5166 624 97 527 \n",
"3 10014 3510 3229 281 74 207 \n",
"4 94103 2660 2417 243 34 209 \n",
"\n",
" TaxRate_SS TaxRate_FF TaxRate_MM Cns_TotHH ... FF_Cns FF_Index \\\n",
"0 203.301887 28.773585 174.528302 12551 ... 1.847099 6.724415 \n",
"1 205.511811 33.464567 172.047244 16456 ... 4.161579 9.834048 \n",
"2 107.772021 16.753022 91.018998 29762 ... 1.531029 4.370779 \n",
"3 80.056980 21.082621 58.974359 18786 ... 2.482293 6.055939 \n",
"4 91.353383 12.781955 78.571429 12728 ... 0.837431 3.004058 \n",
"\n",
" MM_Tax MM_Cns MM_Index SS_Index SS_Index_Weight Parade_Weight \\\n",
"0 29.583721 18.704533 48.288254 55.012669 39.429995 10 \n",
"1 29.163165 19.415304 48.578469 58.412517 41.866815 0 \n",
"2 15.428332 10.932081 26.360413 30.731192 22.026394 10 \n",
"3 9.996551 5.943318 15.939869 21.995808 15.765361 10 \n",
"4 13.318386 4.961779 18.280165 21.284224 15.255337 10 \n",
"\n",
" Bars_Weight TOTINDEX \n",
"0 17.647059 67.077054 \n",
"1 20.000000 61.866815 \n",
"2 5.882353 37.908747 \n",
"3 11.764706 37.530067 \n",
"4 10.588235 35.843573 \n",
"\n",
"[5 rows x 29 columns]"
]
},
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"execution_count": 16,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import seaborn as sns\n",
"import numpy as np\n",
"\n",
"gaybourhoods = pd.read_csv(\"../data/raw/gaybourhoods.csv\")\n",
"gaybourhoods.head(5)"
]
},
{
"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
"outputs": [
{
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"data": {
"text/html": [
"<div>\n",
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" }\n",
"\n",
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" 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>pride parade index</th>\n",
" <th>gay bars index</th>\n",
" <th>lat</th>\n",
" <th>long</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>2328.000000</td>\n",
" <td>2328.000000</td>\n",
" <td>2328.000000</td>\n",
" <td>2328.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.017612</td>\n",
" <td>0.118127</td>\n",
" <td>38.044304</td>\n",
" <td>-91.221236</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.131563</td>\n",
" <td>0.861863</td>\n",
" <td>5.148365</td>\n",
" <td>18.533499</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>25.572213</td>\n",
" <td>-123.118977</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>34.021932</td>\n",
" <td>-105.049099</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>39.899977</td>\n",
" <td>-87.494097</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>40.912413</td>\n",
" <td>-74.288743</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.000000</td>\n",
" <td>17.000000</td>\n",
" <td>47.916786</td>\n",
" <td>-70.758184</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" pride parade index gay bars index lat long\n",
"count 2328.000000 2328.000000 2328.000000 2328.000000\n",
"mean 0.017612 0.118127 38.044304 -91.221236\n",
"std 0.131563 0.861863 5.148365 18.533499\n",
"min 0.000000 0.000000 25.572213 -123.118977\n",
"25% 0.000000 0.000000 34.021932 -105.049099\n",
"50% 0.000000 0.000000 39.899977 -87.494097\n",
"75% 0.000000 0.000000 40.912413 -74.288743\n",
"max 1.000000 17.000000 47.916786 -70.758184"
]
},
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"execution_count": 17,
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"metadata": {},
"output_type": "execute_result"
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}
],
"source": [
"#wrangle gay\n",
"# gaydf = pd.DataFrame(zip(gaybourhoods['GEOID10'], gaybourhoods['Parade_Weight'], gaybourhoods['Bars_Weight']))\n",
"\n",
"\n",
"# print(gaydf.describe())\n",
"# print(gaydf)\n",
"\n",
"cords = pd.read_csv(\"../data/raw/zip_lat_long.csv\")\n",
"\n",
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"# gaydf = pd.DataFrame(zip(gaybourhoods['GEOID10'], gaybourhoods['ParadeFlag'], gaybourhoods['CountBars']))\n",
"# gaydf.columns=(('zip', 'pride parade index', 'gay bars index'))\n",
"# gaydf = gaydf.merge(cords, left_on=\"zip\", right_on=\"ZIP\")\n",
"\n",
"\n",
"def loadprideindexes(secdf):\n",
" gaydf = pd.DataFrame(zip(gaybourhoods['GEOID10'], gaybourhoods['ParadeFlag'], gaybourhoods['CountBars']))\n",
" gaydf.columns=(('zip', 'pride parade index', 'gay bars index'))\n",
" gaydf = gaydf.merge(secdf, left_on=\"zip\", right_on=\"ZIP\")\n",
" \n",
" return gaydf\n",
" \n",
" # gaydf = (\n",
" # pd.DataFrame(zip(gaybourhoods['GEOID10'], gaybourhoods['ParadeFlag'], gaybourhoods['CountBars']))\n",
" # .columns=(('zip', 'pride parade index', 'gay bars index'))\n",
" # # .merge(cords, left_on=\"zip\", right_on=\"ZIP\")\n",
" # )\n",
"\n",
"gaydf = loadprideindexes(cords)\n",
"\n",
"def notzip(dat):\n",
" del dat['zip']\n",
" del dat['ZIP']\n",
"\n",
"notzip(gaydf)\n",
"\n",
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"# // unneded was already filtered out\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",
"gaydf = gaydf.rename({\n",
" \"LAT\": \"lat\",\n",
" \"LNG\": \"long\",\n",
"}, axis=\"columns\")\n",
"\n",
"# unperson unneccesary zip codes\n",
"# del gaydf['zip.1']\n",
"# del gaydf['ZIP']\n",
"\n",
"\n",
"gaydf.to_csv(\"../data/processed/gay_pride\", index=False)\n",
"\n",
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"gaydf.describe()"
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]
},
{
"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
"outputs": [],
"source": [
"gaydf = pd.read_csv(\"../data/processed/gay_pride\")"
]
},
{
"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gaydfprideplot = gaydf.copy()\n",
"\n",
"plot3 = sns.scatterplot(data=gaydfprideplot, x=\"long\", y=\"lat\", hue=\"pride parade index\")\n",
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"_ = plot3.set(xlabel=\"long\", ylabel=\"lat\", title = \"heatmap of pride index\")"
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
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"It seemed that most communities did not have a pride parade, but a number of them still did"
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]
},
{
"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gaydfgaybarsplot = gaydf.copy()\n",
"\n",
"plot4 = sns.scatterplot(data=gaydfprideplot, x=\"long\", y=\"lat\", hue=\"gay bars index\")\n",
"_ = plot4.set(xlabel=\"long\", ylabel=\"lat\", title = \"heatmap of gay bars index\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"it looks like certain communities had large numbers of bars but most didn't\n",
"\n",
"the next step is to find the distribution of gay bars"
]
},
{
"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot5 = sns.histplot(gaydfgaybarsplot[\"gay bars index\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"looks like the hypothesis was right - most communities with gay bars had multiple"
]
},
{
"cell_type": "code",
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"execution_count": 22,
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"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>pride parade index</th>\n",
" <th>gay bars index</th>\n",
" <th>lat</th>\n",
" <th>long</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>2328.000000</td>\n",
" <td>2328.000000</td>\n",
" <td>2328.000000</td>\n",
" <td>2328.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.017612</td>\n",
" <td>0.118127</td>\n",
" <td>38.044304</td>\n",
" <td>-91.221236</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.131563</td>\n",
" <td>0.861863</td>\n",
" <td>5.148365</td>\n",
" <td>18.533499</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>25.572213</td>\n",
" <td>-123.118977</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>34.021932</td>\n",
" <td>-105.049099</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>39.899977</td>\n",
" <td>-87.494097</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>40.912413</td>\n",
" <td>-74.288743</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.000000</td>\n",
" <td>17.000000</td>\n",
" <td>47.916786</td>\n",
" <td>-70.758184</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
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" pride parade index gay bars index lat long\n",
"count 2328.000000 2328.000000 2328.000000 2328.000000\n",
"mean 0.017612 0.118127 38.044304 -91.221236\n",
"std 0.131563 0.861863 5.148365 18.533499\n",
"min 0.000000 0.000000 25.572213 -123.118977\n",
"25% 0.000000 0.000000 34.021932 -105.049099\n",
"50% 0.000000 0.000000 39.899977 -87.494097\n",
"75% 0.000000 0.000000 40.912413 -74.288743\n",
"max 1.000000 17.000000 47.916786 -70.758184"
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]
},
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"execution_count": 22,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gaydfhybridplot2 = pd.merge(gaydfprideplot, gaydfgaybarsplot)\n",
"\n",
"gaydfhybridplot2.describe()"
]
},
{
"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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"<seaborn.axisgrid.PairGrid at 0x7f6445ecacb0>"
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]
},
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"execution_count": 23,
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"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 1000x1000 with 20 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
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"# del gaydfhybridplot2['zip']; del gaydfhybridplot2['ZIP']\n",
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"\n",
"sns.pairplot(gaydfhybridplot2)"
]
},
{
"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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"<seaborn.axisgrid.PairGrid at 0x7f644683a2c0>"
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]
},
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"execution_count": 24,
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"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 500x500 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gaydflight2 = gaydfhybridplot2.copy()\n",
"\n",
"del gaydflight2['lat']; del gaydflight2['long']\n",
"\n",
"sns.pairplot(gaydflight2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"to find a correlation we remove the zero value & find correlation"
]
},
{
"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 500x500 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gaydflight3 = gaydflight2[gaydflight2['gay bars index'] != 0]\n",
"# gaydflight3 = gaydflight3[gaydflight3['pride parade index'] != 0]\n",
"\n",
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"plt = sns.pairplot(gaydflight3)\n",
"_ = plt.figure.suptitle(\"Decomposition of Gay Bars/Parade Statistics\")\n",
"plt.figure.subplots_adjust(top=0.925)"
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]
},
{
"cell_type": "code",
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"execution_count": 26,
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"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
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"<seaborn.axisgrid.FacetGrid at 0x7f64466a7ca0>"
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]
},
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"execution_count": 26,
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"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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"text/plain": [
"<Figure size 613.986x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gaydflightparade = gaydflight3[gaydflight3['pride parade index'] != 0]\n",
"\n",
"gaydflightnoparade = gaydflight3[gaydflight3['pride parade index'] != 1]\n",
"\n",
"g = sns.catplot(\n",
" data=gaydflight3, kind=\"bar\",\n",
" x='pride parade index', y='gay bars index', hue='gay bars index'\n",
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")\n",
"\n",
"g.set(title=\"Gay Bars vs. Passing Pride Parades\", ylabel=\"Gay Bars\", xlabel=\"Passing Pride Parades\")"
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]
},
{
"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
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"<seaborn.axisgrid.FacetGrid at 0x7f6445a04ac0>"
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]
},
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"execution_count": 27,
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"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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"text/plain": [
"<Figure size 642.986x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gaydflightparade = gaydflight3[gaydflight3['pride parade index'] != 0]\n",
"\n",
"gaydflightnoparade = gaydflight3[gaydflight3['pride parade index'] != 1]\n",
"\n",
"g = sns.catplot(\n",
" data=gaydflight3, kind=\"bar\",\n",
" x='gay bars index', y='gay bars index', hue='pride parade index'\n",
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")\n",
"\n",
"g.set(xlabel=\"Gay Bars\", ylabel=\"Gay Bars\", title=\"Gay Bars vs. Passing Pride Parades\")"
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"the above shows that my hypothesis 'more gay bars <==> more pride parades' should be rejected\n",
"\n",
"against my expectation, more gay bars are located in regions pride parades don't pass through\n",
"\n",
"the obvious explanation I can see is each community has a constant amount of queer activity such that more queer people exist such that the communities with more of A have less B\n",
"\n",
"alternatively this correlation should be discarded due to the fact that it exists mostly in zip areas with more then 10 bars\n",
"\n",
"a final explanation would be to cite the sampling bias, which while isn't as extreme as the tax DF from the other research question, is still big enough of a sampling bias to disregard the results"
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]
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}
],
"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
"name": "python3"
},
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"language_info": {
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"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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"version": "3.10.10"
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},
"vscode": {
"interpreter": {
"hash": "b2baa059f790e7ad780c83135aaea020c73a7a7a6921010b599b8b664933698d"
}
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}
},
"nbformat": 4,
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"nbformat_minor": 4
}
