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April 7, 2017 05:33
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{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": [ | |
"0it [00:00, ?it/s]\n", | |
"TqdmDeprecationWarning: Please use `tqdm.pandas(...)` instead of `tqdm_pandas(tqdm(...))`.\n", | |
"/Users/dan/miniconda3/lib/python3.5/site-packages/IPython/core/interactiveshell.py:2705: DtypeWarning: Columns (60,63) have mixed types. Specify dtype option on import or set low_memory=False.\n", | |
" interactivity=interactivity, compiler=compiler, result=result)\n" | |
] | |
} | |
], | |
"source": [ | |
"import pandas as pd\n", | |
"import numpy as np\n", | |
"import seaborn as sns\n", | |
"import matplotlib.pyplot as plt\n", | |
"from tqdm import tqdm, tqdm_pandas\n", | |
"%matplotlib inline\n", | |
"tqdm_pandas(tqdm())\n", | |
"\n", | |
"df = pd.read_csv('./property-assessment-fy2017.csv', index_col=0)\n", | |
"jp = df[df.ZIPCODE == '02130_']" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"jp = jp[jp.LU.isin(['CD', 'R1', 'R2', 'R3', 'RL'])]" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 8, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"chestnut = jp[(jp.ST_NAME == 'CHESTNUT') & (jp.ST_NAME_SUF == 'AV')]\n", | |
"house = chestnut[chestnut.ST_NUM == '150'].iloc[0]\n", | |
"same_style = chestnut[chestnut.R_BLDG_STYL == house.R_BLDG_STYL]\n", | |
"prices_for_same_style = chestnut[chestnut.R_BLDG_STYL == house.R_BLDG_STYL].GROSS_TAX" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## Getting an idea of JP" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"First, we can look at just the types of different land units:" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 9, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.axes._subplots.AxesSubplot at 0x11479cba8>" | |
] | |
}, | |
"execution_count": 9, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
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| |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x11461c3c8>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"jp.LU.value_counts().plot(kind='bar')" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 10, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.axes._subplots.AxesSubplot at 0x1147b5f60>" | |
] | |
}, | |
"execution_count": 10, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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| |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x1147bd8d0>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"jp.groupby('LU').GROSS_TAX.mean().plot(kind='bar')" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## Single-Families and 150 Chestnut Avenue" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Okay, well, we know that 150 chestnut is a single-family unit, so let's just compare with those for now.\n", | |
"\n", | |
"It might be good to take a peek at where 150 chestnut is on the distribution of square feet / gross tax:" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 11, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"[<matplotlib.lines.Line2D at 0x112f7c7f0>]" | |
] | |
}, | |
"execution_count": 11, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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bgc8CWGtvAp4xZhF4vbX2S/uv/wzw1hNouzBh7CIickE02n1c9/j/8lzXpdkZTCkiERGR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yKjmqYpg91ycnJG3Q3cXj9FiIR2GMnKOX3XokQk+AEbjnNKJQJBIF8sSaRFBMfZ+SnegR160rwk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\nkiRJkqTG/ATaOxhH/Kb6CQAAAABJRU5ErkJggg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x115a50668>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"from sklearn.preprocessing import minmax_scale\n", | |
"\n", | |
"f, ax = plt.subplots(figsize=(15,7))\n", | |
"single_family = jp[jp.LU == 'R1']\n", | |
"house_land_ratio = pd.Series(minmax_scale(single_family['AV_LAND'] / single_family['GROSS_TAX']), index=single_family.index)\n", | |
"s = [100+(3**(n*8)) for n in house_land_ratio]\n", | |
"\n", | |
"#s = [100+(3**(n*8)) for n in minmax_scale(single_family['AV_LAND'])]\n", | |
"\n", | |
"single_family.plot(kind='scatter', x='LAND_SF', y='GROSS_TAX', alpha=0.2, s=s, ax=ax)\n", | |
"\n", | |
"plt.xlabel('Square feet')\n", | |
"plt.ylabel(\"gross appraisal (inc. tax)\")\n", | |
"\n", | |
"plt.plot(house.LAND_SF, house.GROSS_TAX, marker='o', markersize=10, color=\"red\", alpha=0.8)\n" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Okay, so from this plot it seems like 150 chestnut is on the upper end of the total gross appraisal value for its square footage. Let's look at that in more detail -- it's not clear to me why that might be.\n", | |
"\n", | |
"The size of the points corresponds to the \"house land ratio\" -- the value of the land vs the gross value of the property. \n", | |
"\n", | |
"A hunch is that the seemingly high valuation might have something to do with the ratio of the house size to the land it's on. First, I want to check out the \"house land ratio\" more. \n", | |
"\n", | |
"150 Chestnut's house land ratio:" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"visually, they're all pretty similar in house/land ratio. it doesn't seem like the house is that much different -- where is it on the distribution?" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 44, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": [ | |
"/Users/dan/miniconda3/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", | |
" y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n" | |
] | |
}, | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.text.Text at 0x11efa80b8>" | |
] | |
}, | |
"execution_count": 44, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
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LHJ1ItzsUIZpCRgIIUqkk39m1H38gSDJrurQtForcv/sggWCEQKhzJ0SPRb0c\nGs/y3JEZzt8YaXc4QjSc1KQFAP5AkEAwTAHTDj0UCeDzB9sc1epig+bi5nNHZ9sciRDNIUlanCZt\n16Q76Q7hK/F7XawbHGDf8VkKRWmXFr1HkrQ4TaqcpP3dkaQBLt4cYSFf5MCJuXaHIkTDSZIWp0nn\nui9Jv2BrFICnDk61ORIhGk+StDhNKpvH43Li83bOPNKruWhTGLfLyZ4DkqRF75EkLU6xLItUdpFw\n0IPD0dl9pCt5PU4u2TbI8XiG6eT86i8Qoous2gVPKeUEvgAooAT8mtZ6b7MDE62XWyhQLFmEA509\n0rCaF14wwtMHp9lzcIrrr9zU7nCEaJh6atKvAyyt9SuADwAfaW5Iol3KFw3DXdQeXXb5jhEAnpIm\nD9FjVk3SWuu7gF+1/9wOzDQzINE+5YEs4WD3Jen1QwHWDwfYe2SGxYJ0xRO9o642aa11SSn1D8Bf\nAV9uakSibVLZPEBXNneAafJYyBd5/rgMbBG9o+5h4VrrW5VS64CHlVIv0FpXncR3aCiA293+ngGx\nWOcOZW61WCyM07n0eDmvt0QoOE0unwFgYyxMyO8hl/HidHoIh3yn1m30snN9vZM8o6NhotEwL3/R\nJr6z+xiHJtJc/5JtNctCGFIWSzq5LOq5cPgmYLPW+mPAPFC0/1U1M5NtXHRnKRYLE4/L9JWwVBal\nkhniHY9nzlgnmUyRziwwk5zH5XRQKhRIpYtkMnmcziID/qUeE41edq6vz2YWSCRS5PNO1ocHcLsc\n7N47wWuv3lqzLISURaVOKYtaPxT11KS/Bvy9UuoBe/3f1lrnGxib6ACnut8Fuqv7nWVZZrY+2/b1\nQQ6cTJHMLBAJdvZNC4Sox6pJWmudBd7YglhEG+UXSywWSl3XHp3LZnjgsWkGh03vjgGPAwt4XJ/k\n+hef397ghGgAGcwiAEjPmxascJdMrFTJ5w8QCIYJBMNs3TgEwL7j7T99FaIRJEkLANK5AtC9PTvK\nRqI+PC4H+44nV19ZiC4gSVoAkJ4vJ+nuq0lXcjocxAYHmErmmeyAi9hCnCtJ0gKATK57mzuWW2/f\nCGDvYRl3JbqfJGkBmJq002Fu7trt1p26W4skadH9JEkLwLRJB/0enM7u6X5XS8jvIuhzcfCktEuL\n7idJWpDOFcgXLKLB7r5oWOZwONi2PkRibp659EK7wxHinEiSFkzOmhF8kR5J0pZlsXHIDAHYs3+M\nZHKOZHKjZtmOAAARs0lEQVSOubk5LMtqc3RCrE3dc3eI3lVO0r1Sk85lM8zMmKllHnp6gmTGfD6n\n4xjXXraFSCTazvCEWBNJ0oLJmd6qSQOsGw7iODLLbKZEIGjmRHAisxmI7iPNHWKpJh3qnSTtdjkY\nDA8wNTdPqSRNHKJ7SZIWTM7O43E7GPC0f4rZRooN+iiWLKZTcvFQdC9J0n2uUCyRSC4Q9ru7ava7\nesQG/QDEZ6tOfS5EV5Ak3efiszlKJQgHeu/yhCRp0QskSfe58Skzv0XY33tJOhzwMOBxkZidX31l\nITqUJOk+Nz7du0na4XAwEh0gnVtkIV/zZkJCdDRJ0n1urJyke7C5A2AkYu6HOJWU2rToTpKk+9z4\nVBanA0K+Hk3SUUnSortJku5z49NZRiIDPTGxUjWnatJzkqRFd5Ik3cdS2Tzp3CLrhnztDqVpAj43\nPq9LkrToWiue4yql3MAXge2AF/iw1vrfWxCXaIExu2fH+sHeTdIOh4ORiI8TiQzzcvFQdKHVatJv\nAhJa6+uAnwE+0/yQRKscm0wDcN6Iv82RNNepduk5GXkous9qSfpfgA/Yjx3AYnPDEa10bNLcUfu8\n0UCbI2mucpJOJGWCJdF9Vmzu0FpnAZRSYeCrwB+2IijRGscmM7icDtYNDnDwZLujaZ6RiLmd1lRS\natKi+6za70optQX4GvAZrfVXVlt/aCiA293+iXpisXC7Q+gYsVgYp3PpMUCxZHEikWHrhjAb1kcJ\nHZklGDq9bTqX8eJ0eghXLG/0sla8Tyg4QMDnZmpugdHRMNGo7Bsgx0ilTi6L1S4crgfuBX5Da31/\nPRucmck2Iq5zEouFicdT7Q6jI5TLolQKAhCPZwDT9S6/WGTDUIBEIkU6s0CJ03tAZDJ5nM4iA/75\npi1r1fsMhwc4Hs9w6Og0mzZIpyY5RpZ0SlnU+qFYbW99PzAIfEApdb9S6ntKqYFGBydar3zRcMu6\nUJsjaY1hu7/0sXj7KxFCrMVqbdLvBt7dolhEC/Vbkh6NLiXpl7U5FiHWQs77+tTxPkvSp2rSk5k2\nRyLE2kiS7lPHJtNEgt6euq/hSgI+N4EBlzR3iK4jSboPZecXmUrO900tumwkOsBcZpHZtHTFE91D\nknQfOm738NgS668kPRoxZw2Hx9t/JV+IekmS7kNHJ0yS6seaNMDhsWSbIxGifpKk+9D+E3MAnH9e\npM2RtFZ55KHUpEU36c2Z3kVNlmXx/LFZQn43fleeZHKRVCoJVrsjaz7/gIvBkIcj4yksy+q5u6OL\n3iRJus8cG0swk85z3oiPHzw9DsB0YoJAMEIg1LlDYxtlayzIU4dmmU3nGQrLuCzR+aS5o88cHDP9\nozeMhgkEzT+fP9jmqFpnc8zM+HfwpLRLi+4gSbrPHB43PTvW9fBE/yu5YKO5WLrv2GybIxGiPpKk\n+8yh8TROx9K9//rNtvVB3C4nzx2daXcoQtRFknQfsSyLE4ksQ2EPLld/fvUet5MLN0U4PpkmnZN7\nWIjO159Hap8qFC1KFoxE+mMoeC2XbB3CAvRRafIQnU+SdB9ZLJQAGAn3eZLeNgSAliYP0QUkSfeR\nxaKdpPu8Jn3+xghet7RLi+4gSbpPWJbFYqHEllgAn7f9tzdrJ4/byY5NUY7HMySzcnNa0dkkSfeJ\nvN3Ucem2aJsj6QzlJo990i4tOpwk6T6RXzRJeuf2/k3SlmWRSiVJJufYOuIB4PF941hWH4yJF11L\nknQfKFkWi4UiTsfSiLt+lM2meeCxozy0Z4zj8SQ+r5NHdILpGalNi84lSboPHBpLUrLA63bh7PNJ\nhXz+AIFgmGAowo5NgxSK8MzhuXaHJURNdSVppdTVSqn7mx2MaI4n908B4PHIb3KlHZvMVK0P66k2\nRyJEbasetUqp9wJfAGTKsC715P4EAF63JOlKg6EBhkIenjs6x1xGenmIzlTPUbsfeH2zAxHNcWwi\nxbHJNF63U+ZPrmLbOj8lC3Y9M97uUISoatUkrbX+OlBoQSyiwSzL4p4f7AdMLbpUKvXNBP/12rLO\nj8vp4HuPnyC/WGx3OEKcoeGT/g8NBXC72z9YIhbr/QnsVzMzM8t3HzmOx+3AwvSV3rX3BMFQlHBo\naRa8XMaL0+k5bVmt5Y1e1tr3gWDQd9pyJwFufMkmvr3rON/efZy33nJZ7QLtMXKMLOnkslhLkq7r\nXHlmJnuWoTROLBYmHpf72O3ee5JcvsSFm6M8ZDd1lCw3mcw8A/75U+tlMnmczuJpy2otb/SyVr6P\nee70z57NLHDDC9fz+PPT3PXAAdSmCGrrULXi7ClyjCzplLKo9UOxlitJcpLcZR6xey2UezGI6rwe\nJ2977QvAAX/3rb0cHjMDXpb/k0Evoh3qqklrrY8A1zY5FtFA8/kCTx4wN5xdN+hvdzgdb8emKK//\nyQv42oMH+dCXdnP+ugEuu2AIj90jJpfNcOPVFxKJ9O+ITdEe0ierR/3o6XHyhRIXbgpJr4463Xzt\ndn7nF65kKOzlwMQCdz88wTNHc+Dy4w/0z30gRWeRJN2DLMviu48ex+V0cPGWzr0g0gkq5/NIJufY\nMuzinT+zhcu2h/G6XTx7ZIZv/fAwk7ML7Q5V9KmG9+4Q7ffM4WnGprJcdfEwgQE3pXYH1MFy2QwP\nPDbN4PDIqWXTiQm2j0a48uILeO7oDI/qOA/umWI0GuDmV0hzh2gtqUn3oO/uPg7AdZeva3Mk3aE8\nn0f5n89vmjacTgeXbh/mppduxed18rWHjvH4vniboxX9RpJ0j5mYzvLUgSl2bIqwdb20ozbCuiE/\nr9g5gsft4G+/tZcTiUy7QxJ9RJJ0j7Asi2Ryjru+b0YYvvzSEVKpJJb0nGyIwZCH//mq7Szki3zm\n354ityCDcEVrSJLuEalUkrse3Meu5xKE/W4yuXnu332Q+Zxc8GoEy7K4aIObV125nomZHHfe84z0\nnRYtIUm6hxyKF7EsuOKiGMFQ5FTbqjh35gLjUYaCTgZDHh5+boovfftZMxeKEE0kSbpHJOYWODKR\nJRr0sn2jdLtrBp8/QCgc4forN+FyOthzNMdMSqY4Fc0lSbpH3Lv7JBbwwh0jfX/3lWaLhga46pJ1\nLBYsvvAf+6V9WjSVJOkesP/EHI/oaaJBN9ukFt0SF2+JcsGGACencvzNN5+hWJLe6KI5JEl3uVLJ\n4sv37QPgRTuiUotuEYfDwZUXRrlkS4SnDkzx+W88Q3Z+sd1hiR4kSbrLPfjkSY5MpLjq4mFGo3KH\ns1ZyOhzcetMFXLw5yqP74tz2xUd49vC09PgQDSXDwrtQeb6J2XSef/2v/Qx4nNxweZSDE9LdrpUs\ny2JxIcM7bt7BfbvHuO/RMW7/5yfYHAtw/QtjvOjCYdyupXpQOByRya7EmkmS7kKpVJL7fvw8jxyY\nJ7tQ5MUXRtm99yiBYIRASNqkW6Vy3o+w38GrrhhFH0tzPJ7ly/95hH978BgXnBfkovOCFPI5mepU\nnBVJ0l3q6LRFfC7P1vUhdu5Yz1RcTrHboTzvB0AgCJs3jHD02AmOJhY5NrXI3iMpDo5lecHWEMWS\nfEdi7SRJd6F9x5M8cySFf8D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| |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x11e91d208>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"house_land_ratio = single_family['AV_LAND'] / single_family['GROSS_TAX']\n", | |
"sns.distplot(house_land_ratio)\n", | |
"plt.axvline(house_land_ratio.ix[house.name])\n", | |
"plt.title(\"Distribution of the ratio of land value to gross property value\")" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 12, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"0.23262416063947242" | |
] | |
}, | |
"execution_count": 12, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"house_land_ratio.ix[house.name]" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Wow, that's really low on that distribution. This means that only ~20% of the total value comes from the land itself -- the mean for single family homes is closer to 1/3 of the property value.\n", | |
"\n", | |
"Maybe its because the house has a low square footage in comparison to other properties with the same house size?" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 30, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": [ | |
"/Users/dan/miniconda3/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", | |
" y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n" | |
] | |
}, | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.lines.Line2D at 0x11cb5e8d0>" | |
] | |
}, | |
"execution_count": 30, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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Nd9ywgIOnkjz36hnWrmzk2stiMxClMaUjl5L73UClqt4MPAA8mJmhqq+q6m2q+ibgK8B/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\nQmCC\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x11cb5e898>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"sns.distplot(np.log(single_family.LAND_SF.dropna()))\n", | |
"plt.title('Distribution of land square footage (log scale)')\n", | |
"plt.axvline(np.log(house.LAND_SF))" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Huh, so the house is also really low on the total distributionof square footage for single family homes in JP -- This is a log scale, mind you.\n", | |
"\n", | |
"Maybe we should just look at homes that have a square footage close to 150 chestnut's. Let's narrow our exploration to homes within 1 standard deviation of the log-distribution of land square footage of 150 chestnut.\n", | |
"\n", | |
"I'm kind of interested in making sure the value difference isn't caused by floor size (we have 3, but it's listed as 2). let's make sure:" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 46, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": [ | |
"/Users/dan/miniconda3/lib/python3.5/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n", | |
"A value is trying to be set on a copy of a slice from a DataFrame.\n", | |
"Try using .loc[row_indexer,col_indexer] = value instead\n", | |
"\n", | |
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", | |
" from ipykernel import kernelapp as app\n" | |
] | |
}, | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.text.Text at 0x11c0f3f28>" | |
] | |
}, | |
"execution_count": 46, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
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RM7NsOdDNzDLhQDczy4QD3cwsEw50M7NM/DcQs/YidEIerAAAAABJRU5ErkJg\ngg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x11f450978>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"sf_range = np.log(house.LAND_SF) - np.log(single_family.LAND_SF).std(), np.log(house.LAND_SF) + np.log(single_family.LAND_SF).std()\n", | |
"single_family['log_sf'] = np.log(single_family.LAND_SF)\n", | |
"sf_in_range = single_family[(single_family.log_sf >= sf_range[0]) & (single_family.log_sf <= sf_range[1])]\n", | |
"single_family.ix[sf_in_range.index].NUM_FLOORS.value_counts().plot(kind='bar')\n", | |
"plt.title('distribution of # floors for single family homes in range')" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Okay, so most are 2 floors. Nothing is skewing the distribution.\n", | |
"\n", | |
"We can then look at the value of just the buildings themselves for properties with similar square footage:" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 29, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": [ | |
"/Users/dan/miniconda3/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", | |
" y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n" | |
] | |
}, | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.text.Text at 0x11cae3438>" | |
] | |
}, | |
"execution_count": 29, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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8Hg9XLZtGdzTGy2/UpLo4xgzIEpPJSPvd60vzbETesFx+/jRyvB7+9JoNgjDp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\nPTM4S0xmXMViTmvJ44HlC6y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| |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x11caa8c50>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"sns.distplot(sf_in_range.AV_BLDG)\n", | |
"plt.axvline(house.AV_BLDG)\n", | |
"plt.title('Distribution of building value')" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Okay, so we're above the mean on the building value for houses with similar square footage to ours...\n", | |
"\n", | |
"What about land value?" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 28, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": [ | |
"/Users/dan/miniconda3/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", | |
" y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n" | |
] | |
}, | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.text.Text at 0x11c7bc6a0>" | |
] | |
}, | |
"execution_count": 28, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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gNmAQWKOqj4lIHfB9oBw4BNyiqn0ZqBsGvg4sAkLAnar6YiYOhCl8LR3JFk8R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\n3qwGkbIS3r1iPrsOdrFpeys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| |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x11b84bd68>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"sns.distplot(sf_in_range.AV_LAND)\n", | |
"plt.axvline(house.AV_LAND)\n", | |
"plt.title('Distribution of Land value')" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Hm. The building is higher-value than most other properties in this gross tax range -- the land is right on the mean, but the building is worth more than usual. Maybe it has more living space than other similar-SF homes?" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 53, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": [ | |
"/Users/dan/miniconda3/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", | |
" y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n" | |
] | |
}, | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.text.Text at 0x1258ad320>" | |
] | |
}, | |
"execution_count": 53, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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ba5ZN4sOXTj3ndS2aWUau38eug410dkVTF6RSI9iAo6eMMR7gQWAR0AncJiIHE8pvB74A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\nRK5CYII=\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x125758fd0>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"sns.distplot(sf_in_range.LIVING_AREA)\n", | |
"plt.axvline(house.LIVING_AREA)\n", | |
"plt.title('Distribution of raw living area')" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Yeah! But this really only makes sense when we normalize it by the amount of square feet on the land:" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 48, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": [ | |
"/Users/dan/miniconda3/lib/python3.5/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: \n", | |
"A value is trying to be set on a copy of a slice from a DataFrame.\n", | |
"Try using .loc[row_indexer,col_indexer] = value instead\n", | |
"\n", | |
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", | |
" if __name__ == '__main__':\n", | |
"/Users/dan/miniconda3/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", | |
" y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n" | |
] | |
}, | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.text.Text at 0x11d3c4d30>" | |
] | |
}, | |
"execution_count": 48, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
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u+yB+f3FG7efadrr1ZrNdP6PU14epqpr5uL/p5Ru449Ej3P/Mcd56neDz+aZs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\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x11c0f8c88>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"sf_in_range['living_area_sf_ratio'] = (sf_in_range.LIVING_AREA / sf_in_range.LAND_SF)\n", | |
"sns.distplot(sf_in_range.living_area_sf_ratio)\n", | |
"plt.axvline(house.LIVING_AREA / house.LAND_SF)\n", | |
"plt.title('Ratio of living area to home land Square Feet')" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Wow, yeah, I was right -- it is way way higher in the ratio of land square feet and livable space. I wonder if there's a strong relationship there generally, between the ratio of sqft and livable space to the building value. I'm specifically looking at building value here, since that was the thing that was unusually high.\n", | |
"\n", | |
"Let's look at it. We can fit a linear model, too, to see how different 150 chestnut is from the mean:" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 50, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.text.Text at 0x12548cf28>" | |
] | |
}, | |
"execution_count": 50, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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f8BrwaSHEZ4A1gE1KOSKEOCaEuFtK+ew82+akUkrWVFL5nErpS6H9aPS6iMb0tM9z6f+p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\nCoWijigRVigUijry/wGtBO5OptwOdAAAAABJRU5ErkJggg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x11f4bacf8>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"sns.lmplot(x='living_area_sf_ratio',y='AV_BLDG',data=sf_in_range,fit_reg=True)\n", | |
"plt.plot((house.LIVING_AREA / house.LAND_SF), house.GROSS_TAX, marker='o', markersize=10, color=\"red\", alpha=0.8)\n", | |
"plt.title('land SF / living area SF ratio against building value')" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Wow, 150 chestnut as a building is high-priced for its living-area-sf-ratio, but it looks like the amount of square footage it has over its land size is really driving its price." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 43, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.text.Text at 0x11e920400>" | |
] | |
}, | |
"execution_count": 43, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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KRRVRIqxQKBRVRPmEFYoUqoOyohooEVYoUqgOyopqoNwRCkUKVSNCUQ2UCCsUKVSyh6Ia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\nrkJggg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x11e7c6668>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"sns.lmplot(x='living_area_sf_ratio',y='GROSS_TAX',data=sf_in_range,fit_reg=True)\n", | |
"plt.plot((house.LIVING_AREA / house.LAND_SF), house.GROSS_TAX, marker='o', markersize=10, color=\"red\", alpha=0.8)\n", | |
"plt.title('land SF / living area SF ratio against total gross value')" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"Wow, that's kind of amazing. It's right on the regression line! So although we have a high building value with a really high ratio of living space : square footage, the land price itself is very cheap it seems." | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"But -- is it a good deal for the price? I think a good way of telling this for this particular house is to see where it lies on a similar plot comparing the living area amount to similarly priced homes:" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 57, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stderr", | |
"output_type": "stream", | |
"text": [ | |
"/Users/dan/miniconda3/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", | |
" y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n" | |
] | |
}, | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.text.Text at 0x126346fd0>" | |
] | |
}, | |
"execution_count": 57, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
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saVSISDyJv7aKmmpfsUOZttOXhfB6PGzaZUnDmFJnSaMCDCdSDI2U35VTWYHa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\n/z/+N7IpZ2C+cAAAAABJRU5ErkJggg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x126267b38>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"price_range = house.GROSS_TAX - single_family.GROSS_TAX.std(), house.GROSS_TAX + single_family.GROSS_TAX.std()\n", | |
"price_in_range = single_family[(single_family.GROSS_TAX >= price_range[0]) & (single_family.GROSS_TAX <= price_range[1])]\n", | |
"sns.distplot(price_in_range.LIVING_AREA)\n", | |
"plt.axvline(house.LIVING_AREA)\n", | |
"plt.title(\"Distribution of living area size for similarly-priced homes\")" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 62, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"450.72478854912174" | |
] | |
}, | |
"execution_count": 62, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"house.LIVING_AREA - price_in_range.LIVING_AREA.mean()" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## Conclusions\n", | |
"\n", | |
"### Developers will not be interested in the house\n", | |
"\n", | |
"Developers will probably be looking for a property that has a relatively high land value:gross value ratio -- so that they're not primarily purchasing the house when they buy a property. This property's value is driven by the house itself, not by the land, so developers are likely not to be interested for purely economical reasons. \n", | |
"\n", | |
"### This house has an unusally high amount of living area for its price and land size\n", | |
"\n", | |
"This unusual ratio of living area to land size is what is driving the appraisal price for the house. \n", | |
"\n", | |
"The amount of living area will be a major factor in determining how many tenants the house can hold. If you were to spend the same amount of gross money on another single-family home in the JP area, you would likely buy a place with ~450-500 sqft less of living space. That's the equivalent of two rentable bedrooms.\n", | |
"\n", | |
"However, other buyers are likely not looking to rent the house. They are likely looking at it for single-family living, and not for development, since the land value is low. I would think that few people buy single-family homes to rent as a landlord and not as an owner-occupied unit." | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"---" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## My notes\n", | |
"\n", | |
"For surveying the neighborhood:" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 63, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"same_style = chestnut[chestnut.LU == house.LU]\n", | |
"prices_for_same_style" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 67, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/html": [ | |
"<div>\n", | |
"<table border=\"1\" class=\"dataframe\">\n", | |
" <thead>\n", | |
" <tr style=\"text-align: right;\">\n", | |
" <th></th>\n", | |
" <th>ST_NUM</th>\n", | |
" <th>GROSS_TAX</th>\n", | |
" <th>diff</th>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>PID</th>\n", | |
" <th></th>\n", | |
" <th></th>\n", | |
" <th></th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>1002515000_</th>\n", | |
" <td>107</td>\n", | |
" <td>550785</td>\n", | |
" <td>-131635</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1002605000_</th>\n", | |
" <td>35</td>\n", | |
" <td>392360</td>\n", | |
" <td>-290060</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1002606000_</th>\n", | |
" <td>37</td>\n", | |
" <td>526428</td>\n", | |
" <td>-155992</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1002607000_</th>\n", | |
" <td>39</td>\n", | |
" <td>426777</td>\n", | |
" <td>-255643</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1002608000_</th>\n", | |
" <td>41</td>\n", | |
" <td>519757</td>\n", | |
" <td>-162663</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1002630000_</th>\n", | |
" <td>46</td>\n", | |
" <td>680301</td>\n", | |
" <td>-2119</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1900066000_</th>\n", | |
" <td>110</td>\n", | |
" <td>429742</td>\n", | |
" <td>-252678</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1900643000_</th>\n", | |
" <td>179</td>\n", | |
" <td>596746</td>\n", | |
" <td>-85674</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"</div>" | |
], | |
"text/plain": [ | |
" ST_NUM GROSS_TAX diff\n", | |
"PID \n", | |
"1002515000_ 107 550785 -131635\n", | |
"1002605000_ 35 392360 -290060\n", | |
"1002606000_ 37 526428 -155992\n", | |
"1002607000_ 39 426777 -255643\n", | |
"1002608000_ 41 519757 -162663\n", | |
"1002630000_ 46 680301 -2119\n", | |
"1900066000_ 110 429742 -252678\n", | |
"1900643000_ 179 596746 -85674" | |
] | |
}, | |
"execution_count": 67, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"less = same_style[same_style.GROSS_TAX < house.GROSS_TAX][['ST_NUM', 'GROSS_TAX']]\n", | |
"less['diff'] = less.GROSS_TAX - house.GROSS_TAX\n", | |
"less" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"These are the houses that are appraised as more:" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 68, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/html": [ | |
"<div>\n", | |
"<table border=\"1\" class=\"dataframe\">\n", | |
" <thead>\n", | |
" <tr style=\"text-align: right;\">\n", | |
" <th></th>\n", | |
" <th>ST_NUM</th>\n", | |
" <th>GROSS_TAX</th>\n", | |
" <th>diff</th>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>PID</th>\n", | |
" <th></th>\n", | |
" <th></th>\n", | |
" <th></th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>1900063000_</th>\n", | |
" <td>148</td>\n", | |
" <td>877593</td>\n", | |
" <td>195173</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1900064000_</th>\n", | |
" <td>142</td>\n", | |
" <td>1187880</td>\n", | |
" <td>505460</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1900353000_</th>\n", | |
" <td>143</td>\n", | |
" <td>840422</td>\n", | |
" <td>158002</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1900546000_</th>\n", | |
" <td>280</td>\n", | |
" <td>845929</td>\n", | |
" <td>163509</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1900547000_</th>\n", | |
" <td>274</td>\n", | |
" <td>957336</td>\n", | |
" <td>274916</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1900616000_</th>\n", | |
" <td>206</td>\n", | |
" <td>1016428</td>\n", | |
" <td>334008</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1901008000_</th>\n", | |
" <td>195</td>\n", | |
" <td>864779</td>\n", | |
" <td>182359</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1901012000_</th>\n", | |
" <td>205 R</td>\n", | |
" <td>786201</td>\n", | |
" <td>103781</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1901021000_</th>\n", | |
" <td>233</td>\n", | |
" <td>1875171</td>\n", | |
" <td>1192751</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"</div>" | |
], | |
"text/plain": [ | |
" ST_NUM GROSS_TAX diff\n", | |
"PID \n", | |
"1900063000_ 148 877593 195173\n", | |
"1900064000_ 142 1187880 505460\n", | |
"1900353000_ 143 840422 158002\n", | |
"1900546000_ 280 845929 163509\n", | |
"1900547000_ 274 957336 274916\n", | |
"1900616000_ 206 1016428 334008\n", | |
"1901008000_ 195 864779 182359\n", | |
"1901012000_ 205 R 786201 103781\n", | |
"1901021000_ 233 1875171 1192751" | |
] | |
}, | |
"execution_count": 68, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"more = same_style[same_style.GROSS_TAX > house.GROSS_TAX][['ST_NUM', 'GROSS_TAX']]\n", | |
"more['diff'] = more.GROSS_TAX - house.GROSS_TAX\n", | |
"more" | |
] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "Python 3", | |
"language": "python", | |
"name": "python3" | |
}, | |
"nav_menu": {}, | |
"toc": { | |
"navigate_menu": true, | |
"number_sections": true, | |
"sideBar": true, | |
"threshold": 6, | |
"toc_cell": false, | |
"toc_section_display": "block", | |
"toc_window_display": false | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 0 | |
} |
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