From ed689facdaafb1442f0eb30a7afdc44034fd83f2 Mon Sep 17 00:00:00 2001 From: Data Mad Date: Sun, 10 Apr 2022 03:19:58 +0100 Subject: [PATCH] all business questions answered and new hypothesis as well, now it is documenting the results and take conclusions --- .gitignore | 1 + houses_recommendations.ipynb | 8564 +- images/>10000m2_recommended_houses.png | Bin 0 -> 733664 bytes images/recommended_houses_cloropleth_map.html | 92 +- images/recommended_houses_cloropleth_map.png | Bin 1115335 -> 1133617 bytes images/recommended_houses_density_map.html | 180730 ++------------- images/recommended_houses_density_map.png | Bin 1169483 -> 1346903 bytes 7 files changed, 29565 insertions(+), 159822 deletions(-) create mode 100644 images/>10000m2_recommended_houses.png diff --git a/.gitignore b/.gitignore index f9283d7..aa17e55 100644 --- a/.gitignore +++ b/.gitignore @@ -3,3 +3,4 @@ Projeto_de_Insights_plan.pdf houserocket_env/ .idea/ __pycache__/ +env_houserocket/ diff --git a/houses_recommendations.ipynb b/houses_recommendations.ipynb index 5c89162..16beba2 100644 --- a/houses_recommendations.ipynb +++ b/houses_recommendations.ipynb @@ -14,6 +14,7 @@ { "cell_type": "markdown", "metadata": { + "heading_collapsed": true, "hide_input": true }, "source": [ @@ -24,7 +25,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hidden": true + }, "source": [ "### Library Imports" ] @@ -33,6 +36,7 @@ "cell_type": "code", "execution_count": 1, "metadata": { + "hidden": true, "hide_input": false, "scrolled": true }, @@ -50,15 +54,17 @@ "import geopandas\n", "\n", "from matplotlib import pyplot as plt\n", - "from IPython.core.display import display, HTML\n", - "from IPython.display import Image\n", + "from IPython.display import display, HTML, Image\n", "from helper_functions import *\n", + "from scipy import stats\n", "from folium.plugins import MarkerCluster" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hidden": true + }, "source": [ "### Settings" ] @@ -67,13 +73,13 @@ "cell_type": "code", "execution_count": 2, "metadata": { + "hidden": true, "hide_input": false }, "outputs": [], "source": [ "def jupyter_settings():\n", " %matplotlib inline\n", - " %pylab inline\n", " \n", " plt.style.use( 'bmh' )\n", " plt.rcParams['figure.figsize'] = [20, 12]\n", @@ -95,26 +101,11 @@ "cell_type": "code", "execution_count": 3, "metadata": { + "hidden": true, "hide_input": false, "scrolled": true }, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Populating the interactive namespace from numpy and matplotlib\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/data-madness/Documents/SynologyDrive/Comunidade DS/Python do Zero ao DS/HouseSales-RecommendationSystem/houserocket_env/lib/python3.8/site-packages/IPython/core/magics/pylab.py:159: UserWarning: pylab import has clobbered these variables: ['save']\n", - "`%matplotlib` prevents importing * from pylab and numpy\n", - " warn(\"pylab import has clobbered these variables: %s\" % clobbered +\n" - ] - }, { "data": { "text/html": [ @@ -134,7 +125,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hidden": true + }, "source": [ "### Auxiliar Functions" ] @@ -142,57 +135,67 @@ { "cell_type": "code", "execution_count": 4, - "metadata": {}, + "metadata": { + "hidden": true + }, "outputs": [], "source": [ - "def descriptive_stats(data: pd.DataFrame):\n", + "# Summary Statistical functions\n", + "\n", + "def descriptive_stats(data: pd.DataFrame, bound_dist: int):\n", " \n", " mean = pd.DataFrame(data.mean())\n", " std = pd.DataFrame(data.std())\n", " median = pd.DataFrame(data.median())\n", + " mode = pd.DataFrame(data.mode().T.iloc[:,0])\n", "\n", " q1,q3 = pd.DataFrame(data.quantile(0.25)), pd.DataFrame(data.quantile(0.75))\n", " \n", " iqr = q3[0.75] - q1[0.25]\n", - " q0,q4 = q1[0.25] - (1.5*iqr), q3[0.75] + (1.5*iqr)\n", + " lower_bound, upper_bound = q1[0.25] - (bound_dist*iqr), q3[0.75] + (bound_dist*iqr)\n", "\n", " maximum = pd.DataFrame(data.max())\n", " minimum = pd.DataFrame(data.min())\n", + " \n", + " p1,p5,p95,p99 = pd.DataFrame(data.quantile(0.01)),pd.DataFrame(data.quantile(0.05)),pd.DataFrame(data.quantile(0.95)),pd.DataFrame(data.quantile(0.99)) \n", "\n", " skew = pd.DataFrame(data.skew())\n", " kurtosis = pd.DataFrame(data.kurtosis())\n", "\n", - " descriptive_statistics = pd.concat([mean,std,minimum,q0,q1,median,q3,q4,maximum,skew,kurtosis],axis=1)\n", + " descriptive_statistics = pd.concat([mean,std,minimum,p1,p5,lower_bound,q1,mode,median,q3,upper_bound,p95,p99,maximum,skew,kurtosis],axis=1)\n", " \n", - " descriptive_statistics.columns = ['mean','std','minimum','q0','q1','median','q3','q4','maximum','skew','kurtosis']\n", + " descriptive_statistics.columns = ['mean','std','minimum','p1','p5','lower_bound','q1','mode','median','q3','upper_bound','p95','p99','maximum',\n", + " 'skew','kurtosis']\n", " \n", " return descriptive_statistics\n", "\n", - "def group_descriptive_stats(data: pd.DataFrame, agg_feature: str, info_feature: str):\n", + "def group_descriptive_stats(data: pd.DataFrame, agg_feature: str, info_feature: str, bound_dist: int):\n", " \n", " grouped_data = data[[agg_feature,info_feature]].groupby(agg_feature)\n", " \n", " mean = grouped_data.mean()\n", " std = grouped_data.std()\n", " median = grouped_data.median()\n", + " mode = grouped_data.agg([stats.mode]).apply(lambda x: x[0][0][0],axis=1)\n", "\n", " q1,q3 = grouped_data.quantile(0.25), grouped_data.quantile(0.75)\n", " \n", " iqr = q3 - q1\n", - " q0,q4 = q1 - (1.5*iqr), q3 + (1.5*iqr)\n", + " lower_bound, upper_bound = q1 - (bound_dist*iqr), q3 + (bound_dist*iqr)\n", "\n", " maximum = grouped_data.max()\n", " minimum = grouped_data.min()\n", - "\n", + " \n", + " p1,p5,p95,p99 = grouped_data.quantile(0.01),grouped_data.quantile(0.05),grouped_data.quantile(0.95),grouped_data.quantile(0.99)\n", + " \n", " skew = grouped_data.skew()\n", " kurtosis = grouped_data.apply(pd.DataFrame.kurt).drop(agg_feature,axis=1)\n", " \n", - " grouped_descriptive_statistics = pd.concat([mean,std,minimum,q0,q1,median,q3,q4,maximum,skew,kurtosis],axis=1)\n", + " grouped_descriptive_statistics = pd.concat([mean,std,minimum,p1,p5,lower_bound,q1,mode,median,q3,upper_bound,p95,p99,maximum,skew,kurtosis],axis=1)\n", " \n", - " grouped_descriptive_statistics.columns = ['mean','std','minimum','q0','q1','median','q3','q4','maximum','skew','kurtosis']\n", + " grouped_descriptive_statistics.columns = ['mean','std','minimum','p1','p5','lower_bound','q1','mode','median','q3','upper_bound','p95','p99','maximum','skew','kurtosis']\n", " \n", " return grouped_descriptive_statistics\n", - " \n", "\n", "def select_num_attr(data: pd.DataFrame):\n", " \n", @@ -203,16 +206,68 @@ " return geofile" ] }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "# Outlier identification functions\n", + "\n", + "def z_score(data, k_dev):\n", + " mean = np.round(data.mean(), decimals=2)\n", + " std_dev = np.round(data.std(), decimals=2)\n", + "\n", + " z_scores = [ (x - mean)/std_dev for x in data ]\n", + " return data[(np.abs(z_scores) > k_dev)]\n", + "\n", + "def modified_zscore(data, thresh):\n", + "\n", + " median = np.median(data)\n", + " median_absolute_deviation = np.median([np.abs(x - median) for x in data])\n", + "\n", + " modified_z_scores = [0.6745 * (x - median) / median_absolute_deviation for x in data]\n", + " return data[(np.abs(modified_z_scores) > thresh)]\n", + "\n", + "def iqr(data, dist):\n", + " \n", + " Q1 = data.quantile(0.25)\n", + " Q3 = data.quantile(0.75)\n", + " IQR = Q3 - Q1\n", + "\n", + " return data[(data < (Q1 - (dist * IQR))) | (data > (Q3 + (dist * IQR)))]\n", + "\n", + "def number_iqr_outliers(data: pd.DataFrame,dist: int) -> pd.DataFrame:\n", + " \n", + " outliers = {}\n", + " for feature in data.columns:\n", + " # print(f\"{feature} outliers\", end=\"\\n\")\n", + " # print(iqr(num_attributes[feature], 1.5))\n", + " outliers[feature] = iqr(num_attributes[feature], dist)\n", + " num_outliers = [outliers[feature].shape[0] for feature in outliers.keys()]\n", + " perc_records = [(n_outliers/data.shape[0])*100 for n_outliers in num_outliers]\n", + " outliers_data = {'num_iqr_outliers':num_outliers,'perc_outlier_records':perc_records}\n", + " iqr_outliers = pd.DataFrame(data=outliers_data, index=data.columns.to_list())\n", + " \n", + " return iqr_outliers" + ] + }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hidden": true + }, "source": [ "** **" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true + }, "source": [ "# Data Import and Setup\n", "\n", @@ -221,7 +276,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hidden": true + }, "source": [ "### Import data\n", "** **" @@ -229,8 +286,10 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": 6, + "metadata": { + "hidden": true + }, "outputs": [], "source": [ "orig_df = pd.read_csv('data/kc_house_data.csv')\n", @@ -239,7 +298,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hidden": true + }, "source": [ "### General Info\n", "\n", @@ -248,8 +309,10 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, + "execution_count": 7, + "metadata": { + "hidden": true + }, "outputs": [ { "data": { @@ -429,7 +492,7 @@ "4 1954400510 20150218T000000 510000.00 3 2.00 1680 8080 1.00 0 0 3 8 1680 0 1987 0 98074 47.62 -122.05 1800 7503" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -440,8 +503,10 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, + "execution_count": 8, + "metadata": { + "hidden": true + }, "outputs": [ { "name": "stdout", @@ -484,8 +549,10 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, + "execution_count": 9, + "metadata": { + "hidden": true + }, "outputs": [ { "data": { @@ -767,7 +834,7 @@ "sqft_lot15 21613.00 12768.46 27304.18 651.00 5100.00 7620.00 10083.00 871200.00" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -778,7 +845,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hidden": true + }, "source": [ "** **" ] @@ -793,7 +862,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true + }, "source": [ "### 1.1 Data Dimensionality\n", "** **\n", @@ -803,8 +874,10 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, + "execution_count": 10, + "metadata": { + "hidden": true + }, "outputs": [ { "name": "stdout", @@ -822,7 +895,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true + }, "source": [ "### 1.2 Attributes Name and Description\n", "** **" @@ -830,7 +905,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hidden": true + }, "source": [ "**id** - its a unique identifier number for each unique house\n", "\n", @@ -866,7 +943,7 @@ "\n", "**zipcode** - what zipcode area the house is in\n", "\n", - "**lat** - Lattitude\n", + "**lat** - Latitude\n", "\n", "**long** - Longitude\n", "\n", @@ -879,7 +956,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true + }, "source": [ "### 1.3 Attributes Type and Data type conversions\n", "** **" @@ -887,8 +966,10 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, + "execution_count": 11, + "metadata": { + "hidden": true + }, "outputs": [ { "data": { @@ -917,7 +998,7 @@ "dtype: object" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -928,7 +1009,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hidden": true + }, "source": [ ">#### Date reformat type\n", "\n", @@ -937,8 +1020,10 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, + "execution_count": 12, + "metadata": { + "hidden": true + }, "outputs": [], "source": [ "house_df_eda.date = pd.to_datetime(house_df_eda.date)" @@ -946,8 +1031,10 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, + "execution_count": 13, + "metadata": { + "hidden": true + }, "outputs": [ { "data": { @@ -955,7 +1042,7 @@ "Timestamp('2014-10-13 00:00:00')" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -966,7 +1053,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true + }, "source": [ "### 1.4 Attributes Descriptive Statistics (Summary Statistics)\n", "** **" @@ -974,8 +1063,10 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, + "execution_count": 14, + "metadata": { + "hidden": true + }, "outputs": [], "source": [ "num_attributes = select_num_attr(house_df_eda)" @@ -983,8 +1074,11 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, + "execution_count": 15, + "metadata": { + "hidden": true, + "scrolled": true + }, "outputs": [ { "data": { @@ -996,7 +1090,7 @@ " dtype='object')" ] }, - "execution_count": 14, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1007,8 +1101,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": { + "hidden": true, "scrolled": false }, "outputs": [ @@ -1036,14 +1131,21 @@ " mean\n", " std\n", " minimum\n", - " q0\n", + " p1\n", + " p5\n", + " lower_bound\n", " q1\n", + " mode\n", " median\n", " q3\n", - " q4\n", + " upper_bound\n", + " p95\n", + " p99\n", " maximum\n", " skew\n", " kurtosis\n", + " num_iqr_outliers\n", + " perc_outlier_records\n", " \n", " \n", " \n", @@ -1052,98 +1154,147 @@ " 4580301520.86\n", " 2876565571.31\n", " 1000102.00\n", + " 122033870.92\n", + " 512480335.00\n", " -5655727682.50\n", " 2123049194.00\n", + " 795000620.00\n", " 3904930410.00\n", " 7308900445.00\n", " 15087677321.50\n", + " 9297300429.00\n", + " 9558728047.20\n", " 9900000190.00\n", " 0.24\n", " -1.26\n", + " 0\n", + " 0.00\n", " \n", " \n", " price\n", " 540088.14\n", " 367127.20\n", " 75000.00\n", + " 153500.36\n", + " 210000.00\n", " -162625.00\n", " 321950.00\n", + " 350000.00\n", " 450000.00\n", " 645000.00\n", " 1129575.00\n", + " 1156480.00\n", + " 1964400.00\n", " 7700000.00\n", " 4.02\n", " 34.59\n", + " 1146\n", + " 5.30\n", " \n", " \n", " bedrooms\n", " 3.37\n", " 0.93\n", " 0.00\n", + " 2.00\n", + " 2.00\n", " 1.50\n", " 3.00\n", " 3.00\n", + " 3.00\n", " 4.00\n", " 5.50\n", + " 5.00\n", + " 6.00\n", " 33.00\n", " 1.97\n", " 49.06\n", + " 546\n", + " 2.53\n", " \n", " \n", " bathrooms\n", " 2.11\n", " 0.77\n", " 0.00\n", + " 1.00\n", + " 1.00\n", " 0.62\n", " 1.75\n", + " 2.50\n", " 2.25\n", " 2.50\n", " 3.62\n", + " 3.50\n", + " 4.25\n", " 8.00\n", " 0.51\n", " 1.28\n", + " 571\n", + " 2.64\n", " \n", " \n", " sqft_living\n", " 2079.90\n", " 918.44\n", " 290.00\n", + " 720.00\n", + " 940.00\n", " -257.50\n", " 1427.00\n", + " 1300.00\n", " 1910.00\n", " 2550.00\n", " 4234.50\n", + " 3760.00\n", + " 4978.80\n", " 13540.00\n", " 1.47\n", " 5.24\n", + " 572\n", + " 2.65\n", " \n", " \n", " sqft_lot\n", " 15106.97\n", " 41420.51\n", " 520.00\n", + " 1013.12\n", + " 1800.00\n", " -3432.00\n", " 5040.00\n", + " 5000.00\n", " 7618.00\n", " 10688.00\n", " 19160.00\n", + " 43339.20\n", + " 213008.00\n", " 1651359.00\n", " 13.06\n", " 285.08\n", + " 2425\n", + " 11.22\n", " \n", " \n", " floors\n", " 1.49\n", " 0.54\n", " 1.00\n", + " 1.00\n", + " 1.00\n", " -0.50\n", " 1.00\n", + " 1.00\n", " 1.50\n", " 2.00\n", " 3.50\n", + " 2.00\n", + " 3.00\n", " 3.50\n", " 0.62\n", " -0.48\n", + " 0\n", + " 0.00\n", " \n", " \n", " waterfront\n", @@ -1155,9 +1306,16 @@ " 0.00\n", " 0.00\n", " 0.00\n", + " 0.00\n", + " 0.00\n", + " 0.00\n", + " 0.00\n", + " 0.00\n", " 1.00\n", " 11.39\n", " 127.63\n", + " 163\n", + " 0.75\n", " \n", " \n", " view\n", @@ -1169,79 +1327,121 @@ " 0.00\n", " 0.00\n", " 0.00\n", + " 0.00\n", + " 0.00\n", + " 0.00\n", + " 2.00\n", + " 4.00\n", " 4.00\n", " 3.40\n", " 10.89\n", + " 2124\n", + " 9.83\n", " \n", " \n", " condition\n", " 3.41\n", " 0.65\n", " 1.00\n", + " 3.00\n", + " 3.00\n", " 1.50\n", " 3.00\n", " 3.00\n", + " 3.00\n", " 4.00\n", " 5.50\n", " 5.00\n", + " 5.00\n", + " 5.00\n", " 1.03\n", " 0.53\n", + " 30\n", + " 0.14\n", " \n", " \n", " grade\n", " 7.66\n", " 1.18\n", " 1.00\n", + " 5.00\n", + " 6.00\n", " 5.50\n", " 7.00\n", " 7.00\n", + " 7.00\n", " 8.00\n", " 9.50\n", + " 10.00\n", + " 11.00\n", " 13.00\n", " 0.77\n", " 1.19\n", + " 1911\n", + " 8.84\n", " \n", " \n", " sqft_above\n", " 1788.39\n", " 828.09\n", " 290.00\n", + " 700.00\n", + " 850.00\n", " -340.00\n", " 1190.00\n", + " 1300.00\n", " 1560.00\n", " 2210.00\n", " 3740.00\n", + " 3400.00\n", + " 4370.00\n", " 9410.00\n", " 1.45\n", " 3.40\n", + " 611\n", + " 2.83\n", " \n", " \n", " sqft_basement\n", " 291.51\n", " 442.58\n", " 0.00\n", + " 0.00\n", + " 0.00\n", " -840.00\n", " 0.00\n", " 0.00\n", + " 0.00\n", " 560.00\n", " 1400.00\n", + " 1190.00\n", + " 1660.00\n", " 4820.00\n", " 1.58\n", " 2.72\n", + " 496\n", + " 2.29\n", " \n", " \n", " yr_built\n", " 1971.01\n", " 29.37\n", " 1900.00\n", + " 1904.00\n", + " 1915.00\n", " 1882.00\n", " 1951.00\n", + " 2014.00\n", " 1975.00\n", " 1997.00\n", " 2066.00\n", + " 2011.00\n", + " 2014.00\n", " 2015.00\n", " -0.47\n", " -0.66\n", + " 0\n", + " 0.00\n", " \n", " \n", " yr_renovated\n", @@ -1253,136 +1453,198 @@ " 0.00\n", " 0.00\n", " 0.00\n", + " 0.00\n", + " 0.00\n", + " 0.00\n", + " 0.00\n", + " 2008.00\n", " 2015.00\n", " 4.55\n", " 18.70\n", + " 914\n", + " 4.23\n", " \n", " \n", " zipcode\n", " 98077.94\n", " 53.51\n", " 98001.00\n", + " 98001.00\n", + " 98004.00\n", " 97905.50\n", " 98033.00\n", + " 98103.00\n", " 98065.00\n", " 98118.00\n", " 98245.50\n", + " 98177.00\n", + " 98199.00\n", " 98199.00\n", " 0.41\n", " -0.85\n", + " 0\n", + " 0.00\n", " \n", " \n", " lat\n", " 47.56\n", " 0.14\n", " 47.16\n", + " 47.26\n", + " 47.31\n", " 47.16\n", " 47.47\n", + " 47.53\n", " 47.57\n", " 47.68\n", " 47.99\n", + " 47.75\n", + " 47.77\n", " 47.78\n", " -0.49\n", " -0.68\n", + " 2\n", + " 0.01\n", " \n", " \n", " long\n", " -122.21\n", " 0.14\n", " -122.52\n", + " -122.41\n", + " -122.39\n", " -122.63\n", " -122.33\n", + " -122.29\n", " -122.23\n", " -122.12\n", " -121.82\n", + " -121.98\n", + " -121.78\n", " -121.31\n", " 0.89\n", " 1.05\n", + " 256\n", + " 1.18\n", " \n", " \n", " sqft_living15\n", " 1986.55\n", " 685.39\n", " 399.00\n", + " 950.00\n", + " 1140.00\n", " 185.00\n", " 1490.00\n", + " 1540.00\n", " 1840.00\n", " 2360.00\n", " 3665.00\n", + " 3300.00\n", + " 4080.00\n", " 6210.00\n", " 1.11\n", " 1.60\n", + " 544\n", + " 2.52\n", " \n", " \n", " sqft_lot15\n", " 12768.46\n", " 27304.18\n", " 651.00\n", + " 1191.24\n", + " 1999.20\n", " -2374.50\n", " 5100.00\n", + " 5000.00\n", " 7620.00\n", " 10083.00\n", " 17557.50\n", + " 37062.80\n", + " 157687.00\n", " 871200.00\n", " 9.51\n", " 150.76\n", + " 2194\n", + " 10.15\n", " \n", " \n", "\n", "" ], "text/plain": [ - " mean std minimum q0 q1 median q3 q4 maximum skew kurtosis\n", - "id 4580301520.86 2876565571.31 1000102.00 -5655727682.50 2123049194.00 3904930410.00 7308900445.00 15087677321.50 9900000190.00 0.24 -1.26\n", - "price 540088.14 367127.20 75000.00 -162625.00 321950.00 450000.00 645000.00 1129575.00 7700000.00 4.02 34.59\n", - "bedrooms 3.37 0.93 0.00 1.50 3.00 3.00 4.00 5.50 33.00 1.97 49.06\n", - "bathrooms 2.11 0.77 0.00 0.62 1.75 2.25 2.50 3.62 8.00 0.51 1.28\n", - "sqft_living 2079.90 918.44 290.00 -257.50 1427.00 1910.00 2550.00 4234.50 13540.00 1.47 5.24\n", - "sqft_lot 15106.97 41420.51 520.00 -3432.00 5040.00 7618.00 10688.00 19160.00 1651359.00 13.06 285.08\n", - "floors 1.49 0.54 1.00 -0.50 1.00 1.50 2.00 3.50 3.50 0.62 -0.48\n", - "waterfront 0.01 0.09 0.00 0.00 0.00 0.00 0.00 0.00 1.00 11.39 127.63\n", - "view 0.23 0.77 0.00 0.00 0.00 0.00 0.00 0.00 4.00 3.40 10.89\n", - "condition 3.41 0.65 1.00 1.50 3.00 3.00 4.00 5.50 5.00 1.03 0.53\n", - "grade 7.66 1.18 1.00 5.50 7.00 7.00 8.00 9.50 13.00 0.77 1.19\n", - "sqft_above 1788.39 828.09 290.00 -340.00 1190.00 1560.00 2210.00 3740.00 9410.00 1.45 3.40\n", - "sqft_basement 291.51 442.58 0.00 -840.00 0.00 0.00 560.00 1400.00 4820.00 1.58 2.72\n", - "yr_built 1971.01 29.37 1900.00 1882.00 1951.00 1975.00 1997.00 2066.00 2015.00 -0.47 -0.66\n", - "yr_renovated 84.40 401.68 0.00 0.00 0.00 0.00 0.00 0.00 2015.00 4.55 18.70\n", - "zipcode 98077.94 53.51 98001.00 97905.50 98033.00 98065.00 98118.00 98245.50 98199.00 0.41 -0.85\n", - "lat 47.56 0.14 47.16 47.16 47.47 47.57 47.68 47.99 47.78 -0.49 -0.68\n", - "long -122.21 0.14 -122.52 -122.63 -122.33 -122.23 -122.12 -121.82 -121.31 0.89 1.05\n", - "sqft_living15 1986.55 685.39 399.00 185.00 1490.00 1840.00 2360.00 3665.00 6210.00 1.11 1.60\n", - "sqft_lot15 12768.46 27304.18 651.00 -2374.50 5100.00 7620.00 10083.00 17557.50 871200.00 9.51 150.76" + " mean std minimum p1 p5 lower_bound q1 mode median q3 upper_bound p95 p99 maximum skew kurtosis num_iqr_outliers perc_outlier_records\n", + "id 4580301520.86 2876565571.31 1000102.00 122033870.92 512480335.00 -5655727682.50 2123049194.00 795000620.00 3904930410.00 7308900445.00 15087677321.50 9297300429.00 9558728047.20 9900000190.00 0.24 -1.26 0 0.00\n", + "price 540088.14 367127.20 75000.00 153500.36 210000.00 -162625.00 321950.00 350000.00 450000.00 645000.00 1129575.00 1156480.00 1964400.00 7700000.00 4.02 34.59 1146 5.30\n", + "bedrooms 3.37 0.93 0.00 2.00 2.00 1.50 3.00 3.00 3.00 4.00 5.50 5.00 6.00 33.00 1.97 49.06 546 2.53\n", + "bathrooms 2.11 0.77 0.00 1.00 1.00 0.62 1.75 2.50 2.25 2.50 3.62 3.50 4.25 8.00 0.51 1.28 571 2.64\n", + "sqft_living 2079.90 918.44 290.00 720.00 940.00 -257.50 1427.00 1300.00 1910.00 2550.00 4234.50 3760.00 4978.80 13540.00 1.47 5.24 572 2.65\n", + "sqft_lot 15106.97 41420.51 520.00 1013.12 1800.00 -3432.00 5040.00 5000.00 7618.00 10688.00 19160.00 43339.20 213008.00 1651359.00 13.06 285.08 2425 11.22\n", + "floors 1.49 0.54 1.00 1.00 1.00 -0.50 1.00 1.00 1.50 2.00 3.50 2.00 3.00 3.50 0.62 -0.48 0 0.00\n", + "waterfront 0.01 0.09 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 11.39 127.63 163 0.75\n", + "view 0.23 0.77 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.00 4.00 4.00 3.40 10.89 2124 9.83\n", + "condition 3.41 0.65 1.00 3.00 3.00 1.50 3.00 3.00 3.00 4.00 5.50 5.00 5.00 5.00 1.03 0.53 30 0.14\n", + "grade 7.66 1.18 1.00 5.00 6.00 5.50 7.00 7.00 7.00 8.00 9.50 10.00 11.00 13.00 0.77 1.19 1911 8.84\n", + "sqft_above 1788.39 828.09 290.00 700.00 850.00 -340.00 1190.00 1300.00 1560.00 2210.00 3740.00 3400.00 4370.00 9410.00 1.45 3.40 611 2.83\n", + "sqft_basement 291.51 442.58 0.00 0.00 0.00 -840.00 0.00 0.00 0.00 560.00 1400.00 1190.00 1660.00 4820.00 1.58 2.72 496 2.29\n", + "yr_built 1971.01 29.37 1900.00 1904.00 1915.00 1882.00 1951.00 2014.00 1975.00 1997.00 2066.00 2011.00 2014.00 2015.00 -0.47 -0.66 0 0.00\n", + "yr_renovated 84.40 401.68 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2008.00 2015.00 4.55 18.70 914 4.23\n", + "zipcode 98077.94 53.51 98001.00 98001.00 98004.00 97905.50 98033.00 98103.00 98065.00 98118.00 98245.50 98177.00 98199.00 98199.00 0.41 -0.85 0 0.00\n", + "lat 47.56 0.14 47.16 47.26 47.31 47.16 47.47 47.53 47.57 47.68 47.99 47.75 47.77 47.78 -0.49 -0.68 2 0.01\n", + "long -122.21 0.14 -122.52 -122.41 -122.39 -122.63 -122.33 -122.29 -122.23 -122.12 -121.82 -121.98 -121.78 -121.31 0.89 1.05 256 1.18\n", + "sqft_living15 1986.55 685.39 399.00 950.00 1140.00 185.00 1490.00 1540.00 1840.00 2360.00 3665.00 3300.00 4080.00 6210.00 1.11 1.60 544 2.52\n", + "sqft_lot15 12768.46 27304.18 651.00 1191.24 1999.20 -2374.50 5100.00 5000.00 7620.00 10083.00 17557.50 37062.80 157687.00 871200.00 9.51 150.76 2194 10.15" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "descriptive_stats(num_attributes)" + "bound_dist = 1.5\n", + "pd.concat([descriptive_stats(num_attributes,bound_dist),number_iqr_outliers(num_attributes,bound_dist)],axis=1)" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hidden": true + }, "source": [ - "### 1.5 Identify missing values and Duplicated records\n", - "***" + "> ##### Study each attribute variability, centrality/localization, distribution skewness and kurtosis\n", + "\n", + "**Price :**\n", + "\n", + "Housing price is varying from 75 000.00\\\\$ to 7 700 000.00\\\\$ which reveals a wide range of values. By looking to median price, 50% of the houses on King County costs equal or less than 450 000.00\\\\$, and comparing to the mean value, 540 088.14\\\\$, I can assume that are houses with much higher prices pushing the mean far from the median, so the distribution is skewed right with a skew of 4.02. In terms of more price concentration of houses, by looking at kurtosis there are many houses around the mode of 350 000.00\\\\$. Since it is right skewed the most interesting extreme values are to the left, and 75% of the houses price value are equal or below 645 000.00\\\\$, and 94.70% of the houses are up to 1 129 575.00\\\\$, there being 1146 houses above this value that could be statistically considered outliers." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true + }, "source": [ - ">#### Missing Values" + "### 1.5 Identify missing values and Duplicated records\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hidden": true + }, + "source": [ + ">#### Missing Values" ] }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, + "execution_count": 17, + "metadata": { + "hidden": true + }, "outputs": [ { "data": { @@ -1410,7 +1672,7 @@ "dtype: int64" ] }, - "execution_count": 16, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1421,8 +1683,10 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, + "execution_count": 18, + "metadata": { + "hidden": true + }, "outputs": [ { "data": { @@ -1431,7 +1695,7 @@ "dtype: int64" ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1444,15 +1708,19 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hidden": true + }, "source": [ ">#### Check Duplicates (Houses sold twice or more times)" ] }, { "cell_type": "code", - "execution_count": 18, - "metadata": {}, + "execution_count": 19, + "metadata": { + "hidden": true + }, "outputs": [ { "data": { @@ -1460,7 +1728,7 @@ "21436" ] }, - "execution_count": 18, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1471,8 +1739,10 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": {}, + "execution_count": 20, + "metadata": { + "hidden": true + }, "outputs": [], "source": [ "duplicated_ids = house_df_eda['id'].apply(lambda x: True if (sum(x == house_df_eda['id']) >= 2)\\\n", @@ -1481,15 +1751,19 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hidden": true + }, "source": [ "Drop the duplicates but keep the most recent, because for this business purpose it is relevant the houses that are currently on market or the real estate portfolio." ] }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, + "execution_count": 21, + "metadata": { + "hidden": true + }, "outputs": [ { "data": { @@ -3112,7 +3386,7 @@ "21581 7853420110 2015-05-04 625000.00 0 98065" ] }, - "execution_count": 20, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -3123,7 +3397,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true + }, "source": [ "### 1.6 Data Granularity\n", "***" @@ -3131,16 +3407,20 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hidden": true + }, "source": [ ">#### Geographic Data\n", "\n", - "The geographic data attributes *latitude* and *longitude* are in their finest granularity, but *zipcode* despite being in their purest from in terms of value, it could be transformed into other finer variables to describe specifically the location of the house, for example, the street or a more coarse one as county. " + "The geographic data attributes *latitude* and *longitude* are in their finest granularity, but *zipcode* despite being in their purest form in terms of value, it could be transformed into other finer variables to describe specifically the location of the house, for example, the street or a more coarse one as county." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hidden": true + }, "source": [ ">#### Temporal Data\n", "\n", @@ -3149,8 +3429,10 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": {}, + "execution_count": 22, + "metadata": { + "hidden": true + }, "outputs": [], "source": [ "# house_df_eda['yr_built'].sort_values().unique()" @@ -3158,8 +3440,10 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": {}, + "execution_count": 23, + "metadata": { + "hidden": true + }, "outputs": [], "source": [ "# house_df_eda['yr_renovated'].sort_values().unique()" @@ -3167,8 +3451,9 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": { + "hidden": true, "scrolled": true }, "outputs": [], @@ -3178,7 +3463,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hidden": true + }, "source": [ ">#### Numerical Data \n", "\n", @@ -3191,7 +3478,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true + }, "source": [ "### 1.7 Data Distribution\n", "***" @@ -3199,8 +3488,9 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": { + "hidden": true, "scrolled": true }, "outputs": [], @@ -3211,8 +3501,9 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "metadata": { + "hidden": true, "scrolled": true }, "outputs": [], @@ -3224,8 +3515,10 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": {}, + "execution_count": 27, + "metadata": { + "hidden": true + }, "outputs": [], "source": [ "# identify_best_fit_distribution(num_attributes,'price')" @@ -3233,8 +3526,9 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "metadata": { + "hidden": true, "scrolled": true }, "outputs": [], @@ -3244,7 +3538,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true + }, "source": [ "### 1.8 Data Sparsity\n", "***" @@ -3252,8 +3548,10 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": {}, + "execution_count": 29, + "metadata": { + "hidden": true + }, "outputs": [], "source": [ "# multiple_scatter_plots(house_df_eda.loc[:, house_df_eda.columns != 'id'])" @@ -3261,7 +3559,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hidden": true + }, "source": [ "> #### Correlation Analysis\n", "\n", @@ -3274,8 +3574,10 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": {}, + "execution_count": 30, + "metadata": { + "hidden": true + }, "outputs": [], "source": [ "# fig = plt.figure(figsize=[12, 12])\n", @@ -3287,49 +3589,21 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hidden": true + }, "source": [ "> #### Outlier Analysis\n", "\n", "Outliers still not analyzed" ] }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [], - "source": [ - "# Outlier identification functions\n", - "\n", - "def z_score(data, k_dev):\n", - " mean = np.round(data.mean(), decimals=2)\n", - " std_dev = np.round(data.std(), decimals=2)\n", - "\n", - " z_scores = [ (x - mean)/std_dev for x in data ]\n", - " return data[(np.abs(z_scores) > k_dev)]\n", - "\n", - "def modified_zscore(data, thresh):\n", - "\n", - " median = np.median(data)\n", - " median_absolute_deviation = np.median([np.abs(x - median) for x in data])\n", - "\n", - " modified_z_scores = [0.6745 * (x - median) / median_absolute_deviation for x in data]\n", - " return data[(np.abs(modified_z_scores) > thresh)]\n", - "\n", - "def iqr(data, dist):\n", - " \n", - " Q1 = data.quantile(0.25)\n", - " Q3 = data.quantile(0.75)\n", - " IQR = Q3 - Q1\n", - "\n", - " return data[(data < (Q1 - (dist * IQR))) | (data > (Q3 + (dist * IQR)))]" - ] - }, { "cell_type": "code", "execution_count": 31, - "metadata": {}, + "metadata": { + "hidden": true + }, "outputs": [ { "data": { @@ -3532,7 +3806,9 @@ { "cell_type": "code", "execution_count": 32, - "metadata": {}, + "metadata": { + "hidden": true + }, "outputs": [], "source": [ "outliers = {}\n", @@ -3545,7 +3821,9 @@ { "cell_type": "code", "execution_count": 33, - "metadata": {}, + "metadata": { + "hidden": true + }, "outputs": [ { "data": { @@ -3564,7 +3842,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true + }, "source": [ "### 1.9 Identify Data Inconsistency\n", "***\n", @@ -3575,7 +3855,9 @@ { "cell_type": "code", "execution_count": 34, - "metadata": {}, + "metadata": { + "hidden": true + }, "outputs": [], "source": [ "house_df_eda_inconsistency = house_df_eda.copy(deep=True)" @@ -3584,7 +3866,9 @@ { "cell_type": "code", "execution_count": 35, - "metadata": {}, + "metadata": { + "hidden": true + }, "outputs": [ { "data": { @@ -3976,7 +4260,9 @@ { "cell_type": "code", "execution_count": 36, - "metadata": {}, + "metadata": { + "hidden": true + }, "outputs": [ { "data": { @@ -4068,7 +4354,9 @@ { "cell_type": "code", "execution_count": 37, - "metadata": {}, + "metadata": { + "hidden": true + }, "outputs": [ { "data": { @@ -4094,7 +4382,9 @@ { "cell_type": "code", "execution_count": 38, - "metadata": {}, + "metadata": { + "hidden": true + }, "outputs": [ { "data": { @@ -4114,7 +4404,9 @@ { "cell_type": "code", "execution_count": 39, - "metadata": {}, + "metadata": { + "hidden": true + }, "outputs": [], "source": [ "house_df_eda_inconsistency['living_area_above_and_basement'] = house_df_eda_inconsistency['sqft_living'] - (house_df_eda_inconsistency['sqft_above'] + house_df_eda_inconsistency['sqft_basement'])" @@ -4123,7 +4415,9 @@ { "cell_type": "code", "execution_count": 40, - "metadata": {}, + "metadata": { + "hidden": true + }, "outputs": [ { "data": { @@ -4192,7 +4486,9 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hidden": true + }, "source": [ "#### Inconsistent features values\n", "\n", @@ -4207,7 +4503,7 @@ "\n", "\n", "3. yr_renovated\n", - " 1. 20699 records have the year renovated variable equal to 0\n", + " 1. 20699 records have the year renovated variable equal to 0, saying that they were never renovated.\n", " \n", " \n", "4. sqft_living - (sqft_above - sqft_basement)\n", @@ -4219,11 +4515,20 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true + }, "source": [ "### 1.10 Business Data Assumptions\n", - "***\n", - "\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hidden": true + }, + "source": [ "It is important to do some business data assumption to better analyze the data and take interpretable insights.\n", "\n", "1. **Region granularity**\n", @@ -4252,13 +4557,17 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "hidden": true + }, "outputs": [], "source": [] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true + }, "source": [ "### 1.12 Attributes Usefulness for the tasks\n", "***" @@ -4266,11 +4575,13 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hidden": true + }, "source": [ "First intuition without looking to EDA, and only based on the business understanding. (On the second cycle will be based on the EDA) \n", "\n", - "**Business:** Buy and sell houses in Real Estate market, with some profit.\n", + "**Business:** Buy and sell houses in Real Estate market, by taking profit.\n", "\n", "**Business problems:**\n", "\n", @@ -4280,7 +4591,7 @@ "\n", " 3. To rise the housing selling price, the company should do a renovation. So what would be good renewal changes?\n", "\n", - " This tasks can easily be done as a Classification Task, after doing the labelling task of giving a label to each record, telling if the house should be bought or not. This labelling process comes with some business assumptions of good profit values and empirical rules, and by doing a preliminary analysis we get our first actionable insights that will be presented to business experts. After validating this labelling empirical process, we can train a classification model, to make the decision-making more robust and less biased by the history.\n", + " This tasks can easily be done as a Classification Task, after doing the labelling task of giving a label to each record, telling if the house should be bought or not. This labelling process comes with some business assumptions of what could be good profit values and with empirical rules, and by doing a preliminary analysis we get our first actionable insights that will be presented to business experts. After validating this labelling empirical process, we can train a classification model, to make the decision-making more robust and less biased.\n", " \n", " Useful Attributes for business question:\n", " \n", @@ -4291,14 +4602,19 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true + }, "source": [ - "## 2. Data Preparation" + "# 2. Data Preparation" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true, + "hidden": true + }, "source": [ "### 2.1 Feature Selection\n", "***" @@ -4307,7 +4623,9 @@ { "cell_type": "code", "execution_count": 41, - "metadata": {}, + "metadata": { + "hidden": true + }, "outputs": [], "source": [ "house_df_fs = house_df_eda[['id','date','price','condition','grade','zipcode','bedrooms','bathrooms','sqft_living','sqft_above','sqft_basement','sqft_lot','lat','long']]" @@ -4315,7 +4633,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true, + "hidden": true + }, "source": [ "### 2.2 Feature engineering and derivation (Identify extra useful features based on solution planning)\n", "***\n", @@ -4335,9 +4656,6 @@ " 1. best_season - based on zipcode region of the selected houses, and its selling date\n", " 2. selling_price - based on the price and the season\n", " 3. profit - will result from difference between selling_price and price \n", - " 3. From the selected houses to buy create and use:\n", - " 1.\n", - " 2. \n", " \n", "\n", "The price per m² is being calculated based on the total property size that is living plus lot size, but some records may record the lot size of a condominium and its only a condominium unit, and the price paid is relative to living size only, but for a farmhouse the price should consider the lot size as well.\n", @@ -4367,7 +4685,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true, + "hidden": true + }, "source": [ "#### Square feet to Squared Meters" ] @@ -4375,19 +4696,21 @@ { "cell_type": "code", "execution_count": 42, - "metadata": {}, + "metadata": { + "hidden": true + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/home/data-madness/Documents/SynologyDrive/Comunidade DS/Python do Zero ao DS/HouseSales-RecommendationSystem/houserocket_env/lib/python3.8/site-packages/pandas/core/frame.py:3641: SettingWithCopyWarning: \n", + "/home/data-madness/Documents/SynologyDrive/ComunidadeDS/PythondoZeroaoDS/HouseSales-RecommendationSystem/env_houserocket/lib/python3.8/site-packages/pandas/core/frame.py:3641: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " self[k1] = value[k2]\n", - "/home/data-madness/Documents/SynologyDrive/Comunidade DS/Python do Zero ao DS/HouseSales-RecommendationSystem/houserocket_env/lib/python3.8/site-packages/pandas/core/frame.py:4906: SettingWithCopyWarning: \n", + "/home/data-madness/Documents/SynologyDrive/ComunidadeDS/PythondoZeroaoDS/HouseSales-RecommendationSystem/env_houserocket/lib/python3.8/site-packages/pandas/core/frame.py:4906: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", @@ -4545,7 +4868,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true, + "hidden": true + }, "source": [ "#### House total size = Living size + Lot size" ] @@ -4554,6 +4880,7 @@ "cell_type": "code", "execution_count": 43, "metadata": { + "hidden": true, "scrolled": false }, "outputs": [ @@ -4561,7 +4888,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_28672/3069127675.py:1: SettingWithCopyWarning: \n", + "/tmp/ipykernel_43708/3069127675.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", @@ -4724,7 +5051,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true, + "hidden": true + }, "source": [ "#### Price m²" ] @@ -4732,13 +5062,15 @@ { "cell_type": "code", "execution_count": 44, - "metadata": {}, + "metadata": { + "hidden": true + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_28672/2121288087.py:1: SettingWithCopyWarning: \n", + "/tmp/ipykernel_43708/2121288087.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", @@ -4753,7 +5085,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true, + "hidden": true + }, "source": [ "#### Living_size, lot_size and house_type" ] @@ -4761,7 +5096,21 @@ { "cell_type": "code", "execution_count": 45, - "metadata": {}, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "num_attr_fs = select_num_attr(house_df_fs)\n", + "bound_dist = 1.5" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "hidden": true + }, "outputs": [ { "data": { @@ -4787,11 +5136,16 @@ " mean\n", " std\n", " minimum\n", - " q0\n", + " p1\n", + " p5\n", + " lower_bound\n", " q1\n", + " mode\n", " median\n", " q3\n", - " q4\n", + " upper_bound\n", + " p95\n", + " p99\n", " maximum\n", " skew\n", " kurtosis\n", @@ -4803,11 +5157,16 @@ " 4580301520.86\n", " 2876565571.31\n", " 1000102.00\n", + " 122033870.92\n", + " 512480335.00\n", " -5655727682.50\n", " 2123049194.00\n", + " 795000620.00\n", " 3904930410.00\n", " 7308900445.00\n", " 15087677321.50\n", + " 9297300429.00\n", + " 9558728047.20\n", " 9900000190.00\n", " 0.24\n", " -1.26\n", @@ -4817,11 +5176,16 @@ " 540088.14\n", " 367127.20\n", " 75000.00\n", + " 153500.36\n", + " 210000.00\n", " -162625.00\n", " 321950.00\n", + " 350000.00\n", " 450000.00\n", " 645000.00\n", " 1129575.00\n", + " 1156480.00\n", + " 1964400.00\n", " 7700000.00\n", " 4.02\n", " 34.59\n", @@ -4831,12 +5195,17 @@ " 3.41\n", " 0.65\n", " 1.00\n", + " 3.00\n", + " 3.00\n", " 1.50\n", " 3.00\n", " 3.00\n", + " 3.00\n", " 4.00\n", " 5.50\n", " 5.00\n", + " 5.00\n", + " 5.00\n", " 1.03\n", " 0.53\n", " \n", @@ -4845,11 +5214,16 @@ " 7.66\n", " 1.18\n", " 1.00\n", + " 5.00\n", + " 6.00\n", " 5.50\n", " 7.00\n", " 7.00\n", + " 7.00\n", " 8.00\n", " 9.50\n", + " 10.00\n", + " 11.00\n", " 13.00\n", " 0.77\n", " 1.19\n", @@ -4859,11 +5233,16 @@ " 98077.94\n", " 53.51\n", " 98001.00\n", + " 98001.00\n", + " 98004.00\n", " 97905.50\n", " 98033.00\n", + " 98103.00\n", " 98065.00\n", " 98118.00\n", " 98245.50\n", + " 98177.00\n", + " 98199.00\n", " 98199.00\n", " 0.41\n", " -0.85\n", @@ -4873,11 +5252,16 @@ " 3.37\n", " 0.93\n", " 0.00\n", + " 2.00\n", + " 2.00\n", " 1.50\n", " 3.00\n", " 3.00\n", + " 3.00\n", " 4.00\n", " 5.50\n", + " 5.00\n", + " 6.00\n", " 33.00\n", " 1.97\n", " 49.06\n", @@ -4887,11 +5271,16 @@ " 2.11\n", " 0.77\n", " 0.00\n", + " 1.00\n", + " 1.00\n", " 0.62\n", " 1.75\n", + " 2.50\n", " 2.25\n", " 2.50\n", " 3.62\n", + " 3.50\n", + " 4.25\n", " 8.00\n", " 0.51\n", " 1.28\n", @@ -4901,11 +5290,16 @@ " 47.56\n", " 0.14\n", " 47.16\n", + " 47.26\n", + " 47.31\n", " 47.16\n", " 47.47\n", + " 47.53\n", " 47.57\n", " 47.68\n", " 47.99\n", + " 47.75\n", + " 47.77\n", " 47.78\n", " -0.49\n", " -0.68\n", @@ -4915,11 +5309,16 @@ " -122.21\n", " 0.14\n", " -122.52\n", + " -122.41\n", + " -122.39\n", " -122.63\n", " -122.33\n", + " -122.29\n", " -122.23\n", " -122.12\n", " -121.82\n", + " -121.98\n", + " -121.78\n", " -121.31\n", " 0.89\n", " 1.05\n", @@ -4929,11 +5328,16 @@ " 193.22\n", " 85.32\n", " 26.94\n", + " 66.89\n", + " 87.33\n", " -23.92\n", " 132.57\n", + " 120.77\n", " 177.44\n", " 236.89\n", " 393.39\n", + " 349.30\n", + " 462.53\n", " 1257.87\n", " 1.47\n", " 5.24\n", @@ -4943,11 +5347,16 @@ " 166.14\n", " 76.93\n", " 26.94\n", + " 65.03\n", + " 78.97\n", " -31.59\n", " 110.55\n", + " 120.77\n", " 144.92\n", " 205.31\n", " 347.45\n", + " 315.86\n", + " 405.97\n", " 874.19\n", " 1.45\n", " 3.40\n", @@ -4957,11 +5366,16 @@ " 27.08\n", " 41.12\n", " 0.00\n", + " 0.00\n", + " 0.00\n", " -78.04\n", " 0.00\n", " 0.00\n", + " 0.00\n", " 52.02\n", " 130.06\n", + " 110.55\n", + " 154.21\n", " 447.78\n", " 1.58\n", " 2.72\n", @@ -4971,11 +5385,16 @@ " 1403.44\n", " 3847.97\n", " 48.31\n", + " 94.12\n", + " 167.22\n", " -318.83\n", " 468.22\n", + " 464.50\n", " 707.71\n", " 992.92\n", " 1779.96\n", + " 4026.21\n", + " 19788.44\n", " 153411.25\n", " 13.06\n", " 285.08\n", @@ -4985,11 +5404,16 @@ " 1596.66\n", " 3863.63\n", " 132.20\n", + " 209.60\n", + " 306.77\n", " -177.67\n", " 653.55\n", + " 628.93\n", " 889.52\n", " 1207.70\n", " 2038.92\n", + " 4262.79\n", + " 19966.20\n", " 153532.02\n", " 12.96\n", " 281.22\n", @@ -4999,11 +5423,16 @@ " 642.74\n", " 490.92\n", " 1.75\n", + " 29.63\n", + " 113.44\n", " -503.30\n", " 302.79\n", + " 384.44\n", " 504.22\n", " 840.18\n", " 1646.27\n", + " 1664.28\n", + " 2301.16\n", " 4316.09\n", " 1.63\n", " 3.38\n", @@ -5013,39 +5442,38 @@ "" ], "text/plain": [ - " mean std minimum q0 q1 median q3 q4 maximum skew kurtosis\n", - "id 4580301520.86 2876565571.31 1000102.00 -5655727682.50 2123049194.00 3904930410.00 7308900445.00 15087677321.50 9900000190.00 0.24 -1.26\n", - "price 540088.14 367127.20 75000.00 -162625.00 321950.00 450000.00 645000.00 1129575.00 7700000.00 4.02 34.59\n", - "condition 3.41 0.65 1.00 1.50 3.00 3.00 4.00 5.50 5.00 1.03 0.53\n", - "grade 7.66 1.18 1.00 5.50 7.00 7.00 8.00 9.50 13.00 0.77 1.19\n", - "zipcode 98077.94 53.51 98001.00 97905.50 98033.00 98065.00 98118.00 98245.50 98199.00 0.41 -0.85\n", - "bedrooms 3.37 0.93 0.00 1.50 3.00 3.00 4.00 5.50 33.00 1.97 49.06\n", - "bathrooms 2.11 0.77 0.00 0.62 1.75 2.25 2.50 3.62 8.00 0.51 1.28\n", - "lat 47.56 0.14 47.16 47.16 47.47 47.57 47.68 47.99 47.78 -0.49 -0.68\n", - "long -122.21 0.14 -122.52 -122.63 -122.33 -122.23 -122.12 -121.82 -121.31 0.89 1.05\n", - "m2_living 193.22 85.32 26.94 -23.92 132.57 177.44 236.89 393.39 1257.87 1.47 5.24\n", - "m2_above 166.14 76.93 26.94 -31.59 110.55 144.92 205.31 347.45 874.19 1.45 3.40\n", - "m2_basement 27.08 41.12 0.00 -78.04 0.00 0.00 52.02 130.06 447.78 1.58 2.72\n", - "m2_lot 1403.44 3847.97 48.31 -318.83 468.22 707.71 992.92 1779.96 153411.25 13.06 285.08\n", - "house_total_m2 1596.66 3863.63 132.20 -177.67 653.55 889.52 1207.70 2038.92 153532.02 12.96 281.22\n", - "price_m2 642.74 490.92 1.75 -503.30 302.79 504.22 840.18 1646.27 4316.09 1.63 3.38" + " mean std minimum p1 p5 lower_bound q1 mode median q3 upper_bound p95 p99 maximum skew kurtosis\n", + "id 4580301520.86 2876565571.31 1000102.00 122033870.92 512480335.00 -5655727682.50 2123049194.00 795000620.00 3904930410.00 7308900445.00 15087677321.50 9297300429.00 9558728047.20 9900000190.00 0.24 -1.26\n", + "price 540088.14 367127.20 75000.00 153500.36 210000.00 -162625.00 321950.00 350000.00 450000.00 645000.00 1129575.00 1156480.00 1964400.00 7700000.00 4.02 34.59\n", + "condition 3.41 0.65 1.00 3.00 3.00 1.50 3.00 3.00 3.00 4.00 5.50 5.00 5.00 5.00 1.03 0.53\n", + "grade 7.66 1.18 1.00 5.00 6.00 5.50 7.00 7.00 7.00 8.00 9.50 10.00 11.00 13.00 0.77 1.19\n", + "zipcode 98077.94 53.51 98001.00 98001.00 98004.00 97905.50 98033.00 98103.00 98065.00 98118.00 98245.50 98177.00 98199.00 98199.00 0.41 -0.85\n", + "bedrooms 3.37 0.93 0.00 2.00 2.00 1.50 3.00 3.00 3.00 4.00 5.50 5.00 6.00 33.00 1.97 49.06\n", + "bathrooms 2.11 0.77 0.00 1.00 1.00 0.62 1.75 2.50 2.25 2.50 3.62 3.50 4.25 8.00 0.51 1.28\n", + "lat 47.56 0.14 47.16 47.26 47.31 47.16 47.47 47.53 47.57 47.68 47.99 47.75 47.77 47.78 -0.49 -0.68\n", + "long -122.21 0.14 -122.52 -122.41 -122.39 -122.63 -122.33 -122.29 -122.23 -122.12 -121.82 -121.98 -121.78 -121.31 0.89 1.05\n", + "m2_living 193.22 85.32 26.94 66.89 87.33 -23.92 132.57 120.77 177.44 236.89 393.39 349.30 462.53 1257.87 1.47 5.24\n", + "m2_above 166.14 76.93 26.94 65.03 78.97 -31.59 110.55 120.77 144.92 205.31 347.45 315.86 405.97 874.19 1.45 3.40\n", + "m2_basement 27.08 41.12 0.00 0.00 0.00 -78.04 0.00 0.00 0.00 52.02 130.06 110.55 154.21 447.78 1.58 2.72\n", + "m2_lot 1403.44 3847.97 48.31 94.12 167.22 -318.83 468.22 464.50 707.71 992.92 1779.96 4026.21 19788.44 153411.25 13.06 285.08\n", + "house_total_m2 1596.66 3863.63 132.20 209.60 306.77 -177.67 653.55 628.93 889.52 1207.70 2038.92 4262.79 19966.20 153532.02 12.96 281.22\n", + "price_m2 642.74 490.92 1.75 29.63 113.44 -503.30 302.79 384.44 504.22 840.18 1646.27 1664.28 2301.16 4316.09 1.63 3.38" ] }, - "execution_count": 45, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "num_attr_fs = select_num_attr(house_df_fs)\n", - "\n", - "descriptive_stats(num_attr_fs)" + "descriptive_stats(num_attr_fs,bound_dist)" ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 47, "metadata": { + "hidden": true, "scrolled": true }, "outputs": [ @@ -5073,11 +5501,16 @@ " mean\n", " std\n", " minimum\n", - " q0\n", + " p1\n", + " p5\n", + " lower_bound\n", " q1\n", + " mode\n", " median\n", " q3\n", - " q4\n", + " upper_bound\n", + " p95\n", + " p99\n", " maximum\n", " skew\n", " kurtosis\n", @@ -5095,6 +5528,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -5103,11 +5541,16 @@ " 176.59\n", " 60.87\n", " 71.53\n", + " 79.31\n", + " 93.83\n", " 22.30\n", " 133.78\n", + " 106.83\n", " 169.26\n", " 208.10\n", " 319.58\n", + " 285.14\n", + " 360.26\n", " 505.38\n", " 1.11\n", " 2.61\n", @@ -5117,11 +5560,16 @@ " 151.22\n", " 50.07\n", " 64.10\n", + " 70.53\n", + " 85.47\n", " 5.71\n", " 112.87\n", + " 118.91\n", " 145.85\n", " 184.31\n", " 291.47\n", + " 237.64\n", + " 271.86\n", " 330.72\n", " 0.71\n", " 0.25\n", @@ -5131,11 +5579,16 @@ " 179.19\n", " 68.44\n", " 70.60\n", + " 88.21\n", + " 93.83\n", " 9.77\n", " 130.06\n", + " 120.77\n", " 165.36\n", " 210.26\n", " 330.55\n", + " 295.97\n", + " 422.51\n", " 536.40\n", " 1.83\n", " 5.75\n", @@ -5145,11 +5598,16 @@ " 270.25\n", " 121.55\n", " 79.89\n", + " 88.96\n", + " 114.27\n", " -92.90\n", " 177.44\n", + " 195.09\n", " 247.11\n", " 357.66\n", " 628.00\n", + " 470.63\n", + " 616.08\n", " 932.72\n", " 1.15\n", " 2.76\n", @@ -5159,11 +5617,16 @@ " 246.82\n", " 84.38\n", " 91.04\n", + " 102.84\n", + " 139.68\n", " 49.35\n", " 191.14\n", + " 209.95\n", " 232.71\n", " 285.67\n", " 427.46\n", + " 408.95\n", + " 488.87\n", " 609.70\n", " 1.06\n", " 1.90\n", @@ -5173,11 +5636,16 @@ " 268.32\n", " 108.76\n", " 86.40\n", + " 94.70\n", + " 120.77\n", " 9.64\n", " 196.02\n", + " 212.74\n", " 252.69\n", " 320.27\n", " 506.65\n", + " 477.27\n", + " 644.33\n", " 745.06\n", " 1.16\n", " 2.08\n", @@ -5187,11 +5655,16 @@ " 202.71\n", " 68.17\n", " 90.11\n", + " 100.70\n", + " 111.48\n", " 0.93\n", " 145.85\n", + " 131.92\n", " 194.16\n", " 242.47\n", " 387.39\n", + " 322.36\n", + " 387.95\n", " 456.14\n", " 0.79\n", " 0.68\n", @@ -5201,11 +5674,16 @@ " 198.20\n", " 79.44\n", " 87.33\n", + " 87.33\n", + " 112.50\n", " 15.79\n", " 144.00\n", + " 116.12\n", " 183.94\n", " 229.46\n", " 357.66\n", + " 339.41\n", + " 491.98\n", " 635.44\n", " 1.92\n", " 6.07\n", @@ -5215,11 +5693,16 @@ " 198.58\n", " 88.76\n", " 48.31\n", + " 54.75\n", + " 88.81\n", " -82.68\n", " 130.52\n", + " 137.49\n", " 176.70\n", " 272.66\n", " 485.87\n", + " 335.00\n", + " 399.82\n", " 526.74\n", " 0.76\n", " 0.50\n", @@ -5229,11 +5712,16 @@ " 209.31\n", " 66.20\n", " 73.39\n", + " 91.58\n", + " 114.64\n", " 24.15\n", " 157.93\n", + " 153.28\n", " 204.38\n", " 247.11\n", " 380.89\n", + " 325.89\n", + " 413.66\n", " 454.28\n", " 0.77\n", " 1.04\n", @@ -5243,11 +5731,16 @@ " 196.76\n", " 102.06\n", " 39.95\n", + " 44.09\n", + " 82.82\n", " -52.14\n", " 126.58\n", + " 89.18\n", " 176.97\n", " 245.72\n", " 424.44\n", + " 355.06\n", + " 554.59\n", " 606.64\n", " 1.44\n", " 3.15\n", @@ -5257,11 +5750,16 @@ " 201.74\n", " 70.80\n", " 78.97\n", + " 86.82\n", + " 97.55\n", " 0.35\n", " 147.71\n", + " 234.11\n", " 196.02\n", " 245.95\n", " 393.32\n", + " 316.05\n", + " 389.65\n", " 525.81\n", " 0.89\n", " 1.85\n", @@ -5271,11 +5769,16 @@ " 170.22\n", " 67.38\n", " 43.66\n", + " 64.39\n", + " 83.61\n", " 13.24\n", " 125.41\n", " 155.14\n", + " 155.14\n", " 200.20\n", " 312.38\n", + " 309.08\n", + " 405.97\n", " 441.27\n", " 1.22\n", " 2.10\n", @@ -5285,11 +5788,16 @@ " 184.85\n", " 67.91\n", " 74.32\n", + " 78.04\n", + " 95.50\n", " 8.36\n", " 136.56\n", + " 137.49\n", " 174.65\n", " 222.03\n", " 350.23\n", + " 312.14\n", + " 381.93\n", " 542.91\n", " 1.05\n", " 1.90\n", @@ -5299,11 +5807,16 @@ " 217.15\n", " 120.66\n", " 26.94\n", + " 55.18\n", + " 71.53\n", " -91.04\n", " 129.13\n", + " 71.53\n", " 181.16\n", " 275.91\n", " 496.09\n", + " 431.98\n", + " 562.42\n", " 563.90\n", " 1.07\n", " 0.74\n", @@ -5313,11 +5826,16 @@ " 233.61\n", " 96.42\n", " 55.74\n", + " 89.59\n", + " 109.62\n", " -12.43\n", " 165.59\n", + " 150.50\n", " 219.71\n", " 284.27\n", " 462.29\n", + " 410.62\n", + " 544.77\n", " 729.26\n", " 1.22\n", " 2.71\n", @@ -5327,11 +5845,16 @@ " 197.20\n", " 63.85\n", " 66.89\n", + " 88.25\n", + " 100.26\n", " 25.55\n", " 152.36\n", + " 170.01\n", " 192.30\n", " 236.89\n", " 363.70\n", + " 312.61\n", + " 377.17\n", " 408.76\n", " 0.53\n", " 0.25\n", @@ -5341,11 +5864,16 @@ " 212.20\n", " 74.76\n", " 97.55\n", + " 105.91\n", + " 123.56\n", " 28.80\n", " 159.79\n", + " 186.73\n", " 194.16\n", " 247.11\n", " 378.10\n", + " 351.16\n", + " 455.21\n", " 592.70\n", " 1.59\n", " 4.16\n", @@ -5355,11 +5883,16 @@ " 182.32\n", " 52.59\n", " 74.32\n", + " 93.83\n", + " 106.83\n", " 31.93\n", " 142.79\n", + " 111.48\n", " 173.58\n", " 216.69\n", " 327.54\n", + " 277.40\n", + " 301.21\n", " 416.19\n", " 0.64\n", " 0.68\n", @@ -5369,11 +5902,16 @@ " 179.45\n", " 54.42\n", " 65.03\n", + " 87.86\n", + " 105.26\n", " 56.55\n", " 144.00\n", + " 158.86\n", " 171.40\n", " 202.29\n", " 289.73\n", + " 266.55\n", + " 324.17\n", " 509.09\n", " 1.42\n", " 5.51\n", @@ -5383,11 +5921,16 @@ " 161.46\n", " 63.60\n", " 61.31\n", + " 72.16\n", + " 80.82\n", " -18.12\n", " 111.48\n", + " 77.11\n", " 159.79\n", " 197.88\n", " 327.47\n", + " 253.25\n", + " 339.78\n", " 507.33\n", " 1.58\n", " 6.25\n", @@ -5397,11 +5940,16 @@ " 221.23\n", " 95.29\n", " 74.32\n", + " 82.90\n", + " 95.78\n", " -42.27\n", " 151.43\n", + " 203.45\n", " 202.06\n", " 280.56\n", " 474.25\n", + " 396.17\n", + " 476.28\n", " 744.13\n", " 1.07\n", " 2.06\n", @@ -5411,11 +5959,16 @@ " 183.82\n", " 70.10\n", " 69.67\n", + " 83.61\n", + " 96.99\n", " 9.75\n", " 133.78\n", + " 123.56\n", " 170.94\n", " 216.46\n", " 340.48\n", + " 318.65\n", + " 420.76\n", " 475.65\n", " 1.23\n", " 1.82\n", @@ -5425,11 +5978,16 @@ " 199.52\n", " 64.20\n", " 62.24\n", + " 88.67\n", + " 115.61\n", " 23.11\n", " 152.50\n", + " 260.12\n", " 185.80\n", " 238.75\n", " 368.14\n", + " 325.24\n", + " 375.52\n", " 485.87\n", " 0.82\n", " 0.68\n", @@ -5439,11 +5997,16 @@ " 353.10\n", " 163.92\n", " 113.34\n", + " 113.34\n", + " 131.41\n", " 1.97\n", " 248.97\n", + " 113.34\n", " 330.72\n", " 413.64\n", " 660.64\n", + " 633.49\n", + " 887.37\n", " 918.78\n", " 1.39\n", " 3.09\n", @@ -5453,11 +6016,16 @@ " 288.62\n", " 109.91\n", " 76.18\n", + " 107.28\n", + " 148.64\n", " 16.26\n", " 209.95\n", + " 293.56\n", " 280.56\n", " 339.08\n", " 532.78\n", + " 485.68\n", + " 613.07\n", " 895.56\n", " 1.27\n", " 3.54\n", @@ -5467,11 +6035,16 @@ " 184.15\n", " 69.33\n", " 60.38\n", + " 78.04\n", + " 91.97\n", " -9.60\n", " 130.76\n", + " 149.57\n", " 177.90\n", " 224.33\n", " 364.69\n", + " 299.32\n", + " 377.56\n", " 540.68\n", " 0.99\n", " 2.18\n", @@ -5481,11 +6054,16 @@ " 191.73\n", " 88.38\n", " 55.74\n", + " 82.87\n", + " 93.83\n", " -46.91\n", " 127.27\n", + " 259.19\n", " 170.01\n", " 243.40\n", " 417.59\n", + " 354.88\n", + " 451.12\n", " 689.32\n", " 1.87\n", " 6.56\n", @@ -5495,11 +6073,16 @@ " 219.58\n", " 69.23\n", " 73.39\n", + " 96.97\n", + " 120.44\n", " 9.87\n", " 165.59\n", + " 188.59\n", " 213.58\n", " 269.41\n", " 425.13\n", + " 334.44\n", + " 388.82\n", " 462.64\n", " 0.42\n", " -0.19\n", @@ -5509,11 +6092,16 @@ " 243.48\n", " 110.66\n", " 49.24\n", + " 105.05\n", + " 120.77\n", " -68.75\n", " 157.00\n", + " 157.00\n", " 227.60\n", " 307.50\n", " 533.25\n", + " 415.08\n", + " 504.41\n", " 1257.87\n", " 2.43\n", " 16.88\n", @@ -5523,11 +6111,16 @@ " 165.80\n", " 59.93\n", " 42.73\n", + " 66.90\n", + " 78.04\n", " 20.55\n", " 125.41\n", + " 139.35\n", " 158.39\n", " 195.32\n", " 300.18\n", + " 284.55\n", + " 322.64\n", " 370.67\n", " 0.70\n", " 0.32\n", @@ -5537,11 +6130,16 @@ " 187.39\n", " 74.79\n", " 61.31\n", + " 65.03\n", + " 83.61\n", " -33.56\n", " 129.13\n", + " 149.57\n", " 181.16\n", " 237.59\n", " 400.28\n", + " 314.93\n", + " 386.42\n", " 432.91\n", " 0.53\n", " -0.16\n", @@ -5551,11 +6149,16 @@ " 190.41\n", " 70.13\n", " 63.17\n", + " 72.96\n", + " 94.48\n", " 21.37\n", " 144.00\n", + " 165.36\n", " 183.01\n", " 225.75\n", " 348.37\n", + " 301.09\n", + " 393.95\n", " 680.03\n", " 1.68\n", " 7.85\n", @@ -5565,11 +6168,16 @@ " 223.30\n", " 82.85\n", " 62.24\n", + " 87.03\n", + " 110.55\n", " -7.43\n", " 162.57\n", + " 116.12\n", " 210.42\n", " 275.91\n", " 445.92\n", + " 383.35\n", + " 458.88\n", " 567.62\n", " 0.83\n", " 1.12\n", @@ -5579,11 +6187,16 @@ " 238.88\n", " 85.86\n", " 44.59\n", + " 83.69\n", + " 121.19\n", " 19.86\n", " 181.16\n", + " 181.16\n", " 227.14\n", " 288.69\n", " 449.98\n", + " 382.33\n", + " 482.07\n", " 682.81\n", " 0.99\n", " 2.65\n", @@ -5593,11 +6206,16 @@ " 174.68\n", " 70.17\n", " 35.67\n", + " 42.93\n", + " 77.94\n", " -25.95\n", " 116.71\n", + " 91.97\n", " 174.19\n", " 211.81\n", " 354.47\n", + " 305.78\n", + " 346.02\n", " 351.72\n", " 0.46\n", " -0.26\n", @@ -5607,11 +6225,16 @@ " 220.33\n", " 82.04\n", " 71.53\n", + " 91.19\n", + " 108.88\n", " 25.08\n", " 167.22\n", + " 167.22\n", " 209.95\n", " 261.98\n", " 404.12\n", + " 368.44\n", + " 515.56\n", " 544.39\n", " 1.12\n", " 2.08\n", @@ -5621,11 +6244,16 @@ " 245.80\n", " 82.76\n", " 49.24\n", + " 93.83\n", + " 120.77\n", " 31.59\n", " 190.44\n", + " 93.83\n", " 234.11\n", " 296.35\n", " 455.21\n", + " 392.97\n", + " 462.27\n", " 572.26\n", " 0.61\n", " 0.57\n", @@ -5635,11 +6263,16 @@ " 280.22\n", " 78.50\n", " 107.76\n", + " 129.28\n", + " 154.12\n", " 96.15\n", " 232.71\n", + " 292.63\n", " 277.77\n", " 323.76\n", " 460.32\n", + " 410.99\n", + " 488.84\n", " 650.30\n", " 0.61\n", " 1.47\n", @@ -5649,11 +6282,16 @@ " 265.42\n", " 100.89\n", " 72.46\n", + " 99.38\n", + " 107.76\n", " 22.41\n", " 201.13\n", + " 107.76\n", " 267.09\n", " 320.27\n", " 498.99\n", + " 408.30\n", + " 617.58\n", " 718.12\n", " 0.92\n", " 2.67\n", @@ -5663,11 +6301,16 @@ " 203.98\n", " 69.28\n", " 65.03\n", + " 92.44\n", + " 110.09\n", " 16.40\n", " 151.43\n", + " 159.79\n", " 196.95\n", " 241.45\n", " 376.48\n", + " 325.15\n", + " 412.75\n", " 530.46\n", " 1.02\n", " 2.03\n", @@ -5677,11 +6320,16 @@ " 200.64\n", " 131.26\n", " 65.96\n", + " 65.96\n", + " 88.63\n", " -39.95\n", " 127.27\n", + " 65.96\n", " 167.22\n", " 238.75\n", " 405.97\n", + " 374.02\n", + " 625.59\n", " 1119.44\n", " 3.85\n", " 23.12\n", @@ -5691,11 +6339,16 @@ " 153.36\n", " 58.69\n", " 36.23\n", + " 65.03\n", + " 79.94\n", " 15.44\n", " 115.43\n", + " 131.92\n", " 139.81\n", " 182.08\n", " 282.07\n", + " 274.94\n", + " 346.33\n", " 405.04\n", " 1.25\n", " 1.85\n", @@ -5705,11 +6358,16 @@ " 199.79\n", " 88.66\n", " 78.97\n", + " 81.34\n", + " 93.27\n", " -56.20\n", " 129.13\n", + " 129.13\n", " 177.44\n", " 252.69\n", " 438.02\n", + " 359.52\n", + " 485.61\n", " 517.45\n", " 1.05\n", " 1.02\n", @@ -5719,11 +6377,16 @@ " 138.22\n", " 56.23\n", " 46.45\n", + " 53.29\n", + " 66.89\n", " -2.79\n", " 98.94\n", + " 66.89\n", " 130.06\n", " 166.76\n", " 268.48\n", + " 233.46\n", + " 297.82\n", " 403.19\n", " 1.14\n", " 1.97\n", @@ -5733,11 +6396,16 @@ " 146.74\n", " 56.10\n", " 50.17\n", + " 67.82\n", + " 80.13\n", " 32.40\n", " 114.27\n", + " 122.63\n", " 134.24\n", " 168.85\n", " 250.71\n", + " 253.38\n", + " 366.21\n", " 403.19\n", " 1.75\n", " 4.54\n", @@ -5747,11 +6415,16 @@ " 158.36\n", " 57.60\n", " 39.02\n", + " 62.24\n", + " 71.77\n", " 5.46\n", " 117.98\n", + " 117.98\n", " 152.82\n", " 193.00\n", " 305.52\n", + " 255.47\n", + " 329.10\n", " 354.88\n", " 0.59\n", " 0.39\n", @@ -5761,11 +6434,16 @@ " 190.89\n", " 84.25\n", " 58.53\n", + " 90.34\n", + " 98.47\n", " -10.22\n", " 134.70\n", + " 139.35\n", " 170.94\n", " 231.32\n", " 376.25\n", + " 327.57\n", + " 457.96\n", " 576.91\n", " 1.54\n", " 3.85\n", @@ -5775,11 +6453,16 @@ " 232.13\n", " 103.90\n", " 65.03\n", + " 75.29\n", + " 103.68\n", " -32.51\n", " 157.00\n", + " 218.31\n", " 216.46\n", " 283.34\n", " 472.86\n", + " 429.48\n", + " 539.34\n", " 716.26\n", " 1.14\n", " 1.83\n", @@ -5789,11 +6472,16 @@ " 170.50\n", " 67.16\n", " 57.60\n", + " 68.75\n", + " 83.61\n", " -23.46\n", " 117.98\n", + " 126.34\n", " 158.86\n", " 212.28\n", " 353.72\n", + " 290.68\n", + " 350.21\n", " 536.03\n", " 0.92\n", " 1.36\n", @@ -5803,11 +6491,16 @@ " 172.18\n", " 71.28\n", " 48.31\n", + " 65.57\n", + " 82.17\n", " -5.11\n", " 121.70\n", + " 161.65\n", " 161.18\n", " 206.24\n", " 333.05\n", + " 320.09\n", + " 393.30\n", " 436.63\n", " 1.06\n", " 1.13\n", @@ -5817,11 +6510,16 @@ " 157.19\n", " 63.57\n", " 34.37\n", + " 58.08\n", + " 74.32\n", " -6.50\n", " 113.34\n", + " 135.63\n", " 144.00\n", " 193.23\n", " 313.07\n", + " 269.22\n", + " 348.82\n", " 418.98\n", " 0.96\n", " 1.02\n", @@ -5831,11 +6529,16 @@ " 156.70\n", " 70.81\n", " 36.23\n", + " 62.24\n", + " 72.46\n", " -44.13\n", " 100.10\n", + " 85.47\n", " 146.78\n", " 196.25\n", " 340.48\n", + " 281.12\n", + " 402.60\n", " 508.16\n", " 1.25\n", " 2.46\n", @@ -5845,11 +6548,16 @@ " 186.32\n", " 77.29\n", " 73.39\n", + " 77.88\n", + " 91.04\n", " -4.76\n", " 134.24\n", + " 156.07\n", " 167.22\n", " 226.91\n", " 365.91\n", + " 338.67\n", + " 430.44\n", " 514.67\n", " 1.40\n", " 2.86\n", @@ -5859,11 +6567,16 @@ " 165.83\n", " 64.08\n", " 51.09\n", + " 62.87\n", + " 77.11\n", " -0.58\n", " 121.70\n", + " 143.07\n", " 151.89\n", " 203.22\n", " 325.50\n", + " 287.99\n", + " 352.50\n", " 381.82\n", " 0.88\n", " 0.73\n", @@ -5873,11 +6586,16 @@ " 162.16\n", " 63.90\n", " 57.60\n", + " 69.84\n", + " 83.61\n", " 2.09\n", " 116.78\n", + " 132.85\n", " 148.64\n", " 193.23\n", " 307.92\n", + " 279.21\n", + " 380.82\n", " 418.05\n", " 1.22\n", " 1.83\n", @@ -5887,11 +6605,16 @@ " 143.40\n", " 61.41\n", " 52.02\n", + " 58.09\n", + " 65.96\n", " -18.58\n", " 95.69\n", + " 146.78\n", " 135.17\n", " 171.86\n", " 286.13\n", + " 260.07\n", + " 314.82\n", " 335.37\n", " 0.89\n", " 0.42\n", @@ -5901,11 +6624,16 @@ " 149.98\n", " 51.10\n", " 54.81\n", + " 65.89\n", + " 78.64\n", " 8.83\n", " 112.64\n", + " 153.28\n", " 140.23\n", " 181.85\n", " 285.67\n", + " 246.84\n", + " 291.43\n", " 315.86\n", " 0.71\n", " 0.24\n", @@ -5915,11 +6643,16 @@ " 160.42\n", " 68.71\n", " 39.02\n", + " 56.67\n", + " 73.48\n", " -26.48\n", " 108.69\n", + " 125.41\n", " 147.71\n", " 198.81\n", " 333.98\n", + " 298.21\n", + " 330.54\n", " 484.01\n", " 0.96\n", " 1.31\n", @@ -5929,11 +6662,16 @@ " 177.64\n", " 85.44\n", " 57.60\n", + " 62.63\n", + " 77.39\n", " -35.53\n", " 115.66\n", + " 115.20\n", " 154.21\n", " 216.46\n", " 367.65\n", + " 329.61\n", + " 460.51\n", " 595.49\n", " 1.41\n", " 2.80\n", @@ -5943,11 +6681,16 @@ " 150.63\n", " 61.35\n", " 38.09\n", + " 51.90\n", + " 69.72\n", " -45.99\n", " 100.33\n", + " 79.89\n", " 138.42\n", " 197.88\n", " 344.19\n", + " 255.15\n", + " 304.94\n", " 353.95\n", " 0.60\n", " -0.18\n", @@ -5957,11 +6700,16 @@ " 149.62\n", " 54.43\n", " 67.82\n", + " 76.14\n", + " 86.03\n", " 13.01\n", " 110.55\n", + " 86.40\n", " 145.85\n", " 175.58\n", " 273.13\n", + " 248.04\n", + " 299.96\n", " 314.00\n", " 0.95\n", " 0.67\n", @@ -5971,11 +6719,16 @@ " 165.98\n", " 70.96\n", " 65.03\n", + " 68.98\n", + " 78.97\n", " -31.93\n", " 109.85\n", + " 71.53\n", " 154.21\n", " 204.38\n", " 346.17\n", + " 294.26\n", + " 379.03\n", " 616.86\n", " 1.45\n", " 4.41\n", @@ -5985,11 +6738,16 @@ " 192.11\n", " 86.52\n", " 57.60\n", + " 65.96\n", + " 77.71\n", " -9.29\n", " 135.63\n", + " 130.06\n", " 179.76\n", " 232.25\n", " 377.17\n", + " 336.85\n", + " 465.36\n", " 694.89\n", " 1.52\n", " 4.86\n", @@ -5999,11 +6757,16 @@ " 136.44\n", " 52.17\n", " 35.30\n", + " 55.74\n", + " 70.60\n", " -7.43\n", " 98.47\n", + " 127.27\n", " 128.20\n", " 169.08\n", " 274.98\n", + " 225.93\n", + " 269.71\n", " 412.48\n", " 1.07\n", " 2.49\n", @@ -6013,11 +6776,16 @@ " 215.84\n", " 99.15\n", " 50.17\n", + " 70.68\n", + " 101.73\n", " -9.75\n", " 150.50\n", + " 156.07\n", " 196.95\n", " 257.33\n", " 417.59\n", + " 376.06\n", + " 562.45\n", " 805.44\n", " 2.02\n", " 7.71\n", @@ -6027,11 +6795,16 @@ " 160.66\n", " 65.67\n", " 53.88\n", + " 62.49\n", + " 73.39\n", " -19.97\n", " 109.62\n", + " 79.89\n", " 160.25\n", " 196.02\n", " 325.61\n", + " 282.32\n", + " 358.55\n", " 458.93\n", " 0.90\n", " 1.48\n", @@ -6041,11 +6814,16 @@ " 167.48\n", " 68.45\n", " 62.24\n", + " 63.17\n", + " 73.39\n", " 0.93\n", " 123.56\n", + " 123.56\n", " 157.00\n", " 205.31\n", " 327.94\n", + " 307.27\n", + " 365.47\n", " 415.26\n", " 0.99\n", " 1.20\n", @@ -6055,11 +6833,16 @@ " 162.14\n", " 65.24\n", " 57.60\n", + " 67.82\n", + " 85.38\n", " -4.99\n", " 115.20\n", + " 190.44\n", " 149.57\n", " 195.32\n", " 315.51\n", + " 289.98\n", + " 343.78\n", " 498.87\n", " 1.35\n", " 3.06\n", @@ -6069,11 +6852,16 @@ " 200.83\n", " 87.82\n", " 63.17\n", + " 67.48\n", + " 87.14\n", " -36.70\n", " 137.49\n", + " 127.27\n", " 183.94\n", " 253.62\n", " 427.80\n", + " 363.98\n", + " 414.97\n", " 659.59\n", " 1.09\n", " 2.03\n", @@ -6083,93 +6871,96 @@ "" ], "text/plain": [ - " mean std minimum q0 q1 median q3 q4 maximum skew kurtosis\n", - "zipcode \n", - "98001 176.59 60.87 71.53 22.30 133.78 169.26 208.10 319.58 505.38 1.11 2.61\n", - "98002 151.22 50.07 64.10 5.71 112.87 145.85 184.31 291.47 330.72 0.71 0.25\n", - "98003 179.19 68.44 70.60 9.77 130.06 165.36 210.26 330.55 536.40 1.83 5.75\n", - "98004 270.25 121.55 79.89 -92.90 177.44 247.11 357.66 628.00 932.72 1.15 2.76\n", - "98005 246.82 84.38 91.04 49.35 191.14 232.71 285.67 427.46 609.70 1.06 1.90\n", - "98006 268.32 108.76 86.40 9.64 196.02 252.69 320.27 506.65 745.06 1.16 2.08\n", - "98007 202.71 68.17 90.11 0.93 145.85 194.16 242.47 387.39 456.14 0.79 0.68\n", - "98008 198.20 79.44 87.33 15.79 144.00 183.94 229.46 357.66 635.44 1.92 6.07\n", - "98010 198.58 88.76 48.31 -82.68 130.52 176.70 272.66 485.87 526.74 0.76 0.50\n", - "98011 209.31 66.20 73.39 24.15 157.93 204.38 247.11 380.89 454.28 0.77 1.04\n", - "98014 196.76 102.06 39.95 -52.14 126.58 176.97 245.72 424.44 606.64 1.44 3.15\n", - "98019 201.74 70.80 78.97 0.35 147.71 196.02 245.95 393.32 525.81 0.89 1.85\n", - "98022 170.22 67.38 43.66 13.24 125.41 155.14 200.20 312.38 441.27 1.22 2.10\n", - "98023 184.85 67.91 74.32 8.36 136.56 174.65 222.03 350.23 542.91 1.05 1.90\n", - "98024 217.15 120.66 26.94 -91.04 129.13 181.16 275.91 496.09 563.90 1.07 0.74\n", - "98027 233.61 96.42 55.74 -12.43 165.59 219.71 284.27 462.29 729.26 1.22 2.71\n", - "98028 197.20 63.85 66.89 25.55 152.36 192.30 236.89 363.70 408.76 0.53 0.25\n", - "98029 212.20 74.76 97.55 28.80 159.79 194.16 247.11 378.10 592.70 1.59 4.16\n", - "98030 182.32 52.59 74.32 31.93 142.79 173.58 216.69 327.54 416.19 0.64 0.68\n", - "98031 179.45 54.42 65.03 56.55 144.00 171.40 202.29 289.73 509.09 1.42 5.51\n", - "98032 161.46 63.60 61.31 -18.12 111.48 159.79 197.88 327.47 507.33 1.58 6.25\n", - "98033 221.23 95.29 74.32 -42.27 151.43 202.06 280.56 474.25 744.13 1.07 2.06\n", - "98034 183.82 70.10 69.67 9.75 133.78 170.94 216.46 340.48 475.65 1.23 1.82\n", - "98038 199.52 64.20 62.24 23.11 152.50 185.80 238.75 368.14 485.87 0.82 0.68\n", - "98039 353.10 163.92 113.34 1.97 248.97 330.72 413.64 660.64 918.78 1.39 3.09\n", - "98040 288.62 109.91 76.18 16.26 209.95 280.56 339.08 532.78 895.56 1.27 3.54\n", - "98042 184.15 69.33 60.38 -9.60 130.76 177.90 224.33 364.69 540.68 0.99 2.18\n", - "98045 191.73 88.38 55.74 -46.91 127.27 170.01 243.40 417.59 689.32 1.87 6.56\n", - "98052 219.58 69.23 73.39 9.87 165.59 213.58 269.41 425.13 462.64 0.42 -0.19\n", - "98053 243.48 110.66 49.24 -68.75 157.00 227.60 307.50 533.25 1257.87 2.43 16.88\n", - "98055 165.80 59.93 42.73 20.55 125.41 158.39 195.32 300.18 370.67 0.70 0.32\n", - "98056 187.39 74.79 61.31 -33.56 129.13 181.16 237.59 400.28 432.91 0.53 -0.16\n", - "98058 190.41 70.13 63.17 21.37 144.00 183.01 225.75 348.37 680.03 1.68 7.85\n", - "98059 223.30 82.85 62.24 -7.43 162.57 210.42 275.91 445.92 567.62 0.83 1.12\n", - "98065 238.88 85.86 44.59 19.86 181.16 227.14 288.69 449.98 682.81 0.99 2.65\n", - "98070 174.68 70.17 35.67 -25.95 116.71 174.19 211.81 354.47 351.72 0.46 -0.26\n", - "98072 220.33 82.04 71.53 25.08 167.22 209.95 261.98 404.12 544.39 1.12 2.08\n", - "98074 245.80 82.76 49.24 31.59 190.44 234.11 296.35 455.21 572.26 0.61 0.57\n", - "98075 280.22 78.50 107.76 96.15 232.71 277.77 323.76 460.32 650.30 0.61 1.47\n", - "98077 265.42 100.89 72.46 22.41 201.13 267.09 320.27 498.99 718.12 0.92 2.67\n", - "98092 203.98 69.28 65.03 16.40 151.43 196.95 241.45 376.48 530.46 1.02 2.03\n", - "98102 200.64 131.26 65.96 -39.95 127.27 167.22 238.75 405.97 1119.44 3.85 23.12\n", - "98103 153.36 58.69 36.23 15.44 115.43 139.81 182.08 282.07 405.04 1.25 1.85\n", - "98105 199.79 88.66 78.97 -56.20 129.13 177.44 252.69 438.02 517.45 1.05 1.02\n", - "98106 138.22 56.23 46.45 -2.79 98.94 130.06 166.76 268.48 403.19 1.14 1.97\n", - "98107 146.74 56.10 50.17 32.40 114.27 134.24 168.85 250.71 403.19 1.75 4.54\n", - "98108 158.36 57.60 39.02 5.46 117.98 152.82 193.00 305.52 354.88 0.59 0.39\n", - "98109 190.89 84.25 58.53 -10.22 134.70 170.94 231.32 376.25 576.91 1.54 3.85\n", - "98112 232.13 103.90 65.03 -32.51 157.00 216.46 283.34 472.86 716.26 1.14 1.83\n", - "98115 170.50 67.16 57.60 -23.46 117.98 158.86 212.28 353.72 536.03 0.92 1.36\n", - "98116 172.18 71.28 48.31 -5.11 121.70 161.18 206.24 333.05 436.63 1.06 1.13\n", - "98117 157.19 63.57 34.37 -6.50 113.34 144.00 193.23 313.07 418.98 0.96 1.02\n", - "98118 156.70 70.81 36.23 -44.13 100.10 146.78 196.25 340.48 508.16 1.25 2.46\n", - "98119 186.32 77.29 73.39 -4.76 134.24 167.22 226.91 365.91 514.67 1.40 2.86\n", - "98122 165.83 64.08 51.09 -0.58 121.70 151.89 203.22 325.50 381.82 0.88 0.73\n", - "98125 162.16 63.90 57.60 2.09 116.78 148.64 193.23 307.92 418.05 1.22 1.83\n", - "98126 143.40 61.41 52.02 -18.58 95.69 135.17 171.86 286.13 335.37 0.89 0.42\n", - "98133 149.98 51.10 54.81 8.83 112.64 140.23 181.85 285.67 315.86 0.71 0.24\n", - "98136 160.42 68.71 39.02 -26.48 108.69 147.71 198.81 333.98 484.01 0.96 1.31\n", - "98144 177.64 85.44 57.60 -35.53 115.66 154.21 216.46 367.65 595.49 1.41 2.80\n", - "98146 150.63 61.35 38.09 -45.99 100.33 138.42 197.88 344.19 353.95 0.60 -0.18\n", - "98148 149.62 54.43 67.82 13.01 110.55 145.85 175.58 273.13 314.00 0.95 0.67\n", - "98155 165.98 70.96 65.03 -31.93 109.85 154.21 204.38 346.17 616.86 1.45 4.41\n", - "98166 192.11 86.52 57.60 -9.29 135.63 179.76 232.25 377.17 694.89 1.52 4.86\n", - "98168 136.44 52.17 35.30 -7.43 98.47 128.20 169.08 274.98 412.48 1.07 2.49\n", - "98177 215.84 99.15 50.17 -9.75 150.50 196.95 257.33 417.59 805.44 2.02 7.71\n", - "98178 160.66 65.67 53.88 -19.97 109.62 160.25 196.02 325.61 458.93 0.90 1.48\n", - "98188 167.48 68.45 62.24 0.93 123.56 157.00 205.31 327.94 415.26 0.99 1.20\n", - "98198 162.14 65.24 57.60 -4.99 115.20 149.57 195.32 315.51 498.87 1.35 3.06\n", - "98199 200.83 87.82 63.17 -36.70 137.49 183.94 253.62 427.80 659.59 1.09 2.03" + " mean std minimum p1 p5 lower_bound q1 mode median q3 upper_bound p95 p99 maximum skew kurtosis\n", + "zipcode \n", + "98001 176.59 60.87 71.53 79.31 93.83 22.30 133.78 106.83 169.26 208.10 319.58 285.14 360.26 505.38 1.11 2.61\n", + "98002 151.22 50.07 64.10 70.53 85.47 5.71 112.87 118.91 145.85 184.31 291.47 237.64 271.86 330.72 0.71 0.25\n", + "98003 179.19 68.44 70.60 88.21 93.83 9.77 130.06 120.77 165.36 210.26 330.55 295.97 422.51 536.40 1.83 5.75\n", + "98004 270.25 121.55 79.89 88.96 114.27 -92.90 177.44 195.09 247.11 357.66 628.00 470.63 616.08 932.72 1.15 2.76\n", + "98005 246.82 84.38 91.04 102.84 139.68 49.35 191.14 209.95 232.71 285.67 427.46 408.95 488.87 609.70 1.06 1.90\n", + "98006 268.32 108.76 86.40 94.70 120.77 9.64 196.02 212.74 252.69 320.27 506.65 477.27 644.33 745.06 1.16 2.08\n", + "98007 202.71 68.17 90.11 100.70 111.48 0.93 145.85 131.92 194.16 242.47 387.39 322.36 387.95 456.14 0.79 0.68\n", + "98008 198.20 79.44 87.33 87.33 112.50 15.79 144.00 116.12 183.94 229.46 357.66 339.41 491.98 635.44 1.92 6.07\n", + "98010 198.58 88.76 48.31 54.75 88.81 -82.68 130.52 137.49 176.70 272.66 485.87 335.00 399.82 526.74 0.76 0.50\n", + "98011 209.31 66.20 73.39 91.58 114.64 24.15 157.93 153.28 204.38 247.11 380.89 325.89 413.66 454.28 0.77 1.04\n", + "98014 196.76 102.06 39.95 44.09 82.82 -52.14 126.58 89.18 176.97 245.72 424.44 355.06 554.59 606.64 1.44 3.15\n", + "98019 201.74 70.80 78.97 86.82 97.55 0.35 147.71 234.11 196.02 245.95 393.32 316.05 389.65 525.81 0.89 1.85\n", + "98022 170.22 67.38 43.66 64.39 83.61 13.24 125.41 155.14 155.14 200.20 312.38 309.08 405.97 441.27 1.22 2.10\n", + "98023 184.85 67.91 74.32 78.04 95.50 8.36 136.56 137.49 174.65 222.03 350.23 312.14 381.93 542.91 1.05 1.90\n", + "98024 217.15 120.66 26.94 55.18 71.53 -91.04 129.13 71.53 181.16 275.91 496.09 431.98 562.42 563.90 1.07 0.74\n", + "98027 233.61 96.42 55.74 89.59 109.62 -12.43 165.59 150.50 219.71 284.27 462.29 410.62 544.77 729.26 1.22 2.71\n", + "98028 197.20 63.85 66.89 88.25 100.26 25.55 152.36 170.01 192.30 236.89 363.70 312.61 377.17 408.76 0.53 0.25\n", + "98029 212.20 74.76 97.55 105.91 123.56 28.80 159.79 186.73 194.16 247.11 378.10 351.16 455.21 592.70 1.59 4.16\n", + "98030 182.32 52.59 74.32 93.83 106.83 31.93 142.79 111.48 173.58 216.69 327.54 277.40 301.21 416.19 0.64 0.68\n", + "98031 179.45 54.42 65.03 87.86 105.26 56.55 144.00 158.86 171.40 202.29 289.73 266.55 324.17 509.09 1.42 5.51\n", + "98032 161.46 63.60 61.31 72.16 80.82 -18.12 111.48 77.11 159.79 197.88 327.47 253.25 339.78 507.33 1.58 6.25\n", + "98033 221.23 95.29 74.32 82.90 95.78 -42.27 151.43 203.45 202.06 280.56 474.25 396.17 476.28 744.13 1.07 2.06\n", + "98034 183.82 70.10 69.67 83.61 96.99 9.75 133.78 123.56 170.94 216.46 340.48 318.65 420.76 475.65 1.23 1.82\n", + "98038 199.52 64.20 62.24 88.67 115.61 23.11 152.50 260.12 185.80 238.75 368.14 325.24 375.52 485.87 0.82 0.68\n", + "98039 353.10 163.92 113.34 113.34 131.41 1.97 248.97 113.34 330.72 413.64 660.64 633.49 887.37 918.78 1.39 3.09\n", + "98040 288.62 109.91 76.18 107.28 148.64 16.26 209.95 293.56 280.56 339.08 532.78 485.68 613.07 895.56 1.27 3.54\n", + "98042 184.15 69.33 60.38 78.04 91.97 -9.60 130.76 149.57 177.90 224.33 364.69 299.32 377.56 540.68 0.99 2.18\n", + "98045 191.73 88.38 55.74 82.87 93.83 -46.91 127.27 259.19 170.01 243.40 417.59 354.88 451.12 689.32 1.87 6.56\n", + "98052 219.58 69.23 73.39 96.97 120.44 9.87 165.59 188.59 213.58 269.41 425.13 334.44 388.82 462.64 0.42 -0.19\n", + "98053 243.48 110.66 49.24 105.05 120.77 -68.75 157.00 157.00 227.60 307.50 533.25 415.08 504.41 1257.87 2.43 16.88\n", + "98055 165.80 59.93 42.73 66.90 78.04 20.55 125.41 139.35 158.39 195.32 300.18 284.55 322.64 370.67 0.70 0.32\n", + "98056 187.39 74.79 61.31 65.03 83.61 -33.56 129.13 149.57 181.16 237.59 400.28 314.93 386.42 432.91 0.53 -0.16\n", + "98058 190.41 70.13 63.17 72.96 94.48 21.37 144.00 165.36 183.01 225.75 348.37 301.09 393.95 680.03 1.68 7.85\n", + "98059 223.30 82.85 62.24 87.03 110.55 -7.43 162.57 116.12 210.42 275.91 445.92 383.35 458.88 567.62 0.83 1.12\n", + "98065 238.88 85.86 44.59 83.69 121.19 19.86 181.16 181.16 227.14 288.69 449.98 382.33 482.07 682.81 0.99 2.65\n", + "98070 174.68 70.17 35.67 42.93 77.94 -25.95 116.71 91.97 174.19 211.81 354.47 305.78 346.02 351.72 0.46 -0.26\n", + "98072 220.33 82.04 71.53 91.19 108.88 25.08 167.22 167.22 209.95 261.98 404.12 368.44 515.56 544.39 1.12 2.08\n", + "98074 245.80 82.76 49.24 93.83 120.77 31.59 190.44 93.83 234.11 296.35 455.21 392.97 462.27 572.26 0.61 0.57\n", + "98075 280.22 78.50 107.76 129.28 154.12 96.15 232.71 292.63 277.77 323.76 460.32 410.99 488.84 650.30 0.61 1.47\n", + "98077 265.42 100.89 72.46 99.38 107.76 22.41 201.13 107.76 267.09 320.27 498.99 408.30 617.58 718.12 0.92 2.67\n", + "98092 203.98 69.28 65.03 92.44 110.09 16.40 151.43 159.79 196.95 241.45 376.48 325.15 412.75 530.46 1.02 2.03\n", + "98102 200.64 131.26 65.96 65.96 88.63 -39.95 127.27 65.96 167.22 238.75 405.97 374.02 625.59 1119.44 3.85 23.12\n", + "98103 153.36 58.69 36.23 65.03 79.94 15.44 115.43 131.92 139.81 182.08 282.07 274.94 346.33 405.04 1.25 1.85\n", + "98105 199.79 88.66 78.97 81.34 93.27 -56.20 129.13 129.13 177.44 252.69 438.02 359.52 485.61 517.45 1.05 1.02\n", + "98106 138.22 56.23 46.45 53.29 66.89 -2.79 98.94 66.89 130.06 166.76 268.48 233.46 297.82 403.19 1.14 1.97\n", + "98107 146.74 56.10 50.17 67.82 80.13 32.40 114.27 122.63 134.24 168.85 250.71 253.38 366.21 403.19 1.75 4.54\n", + "98108 158.36 57.60 39.02 62.24 71.77 5.46 117.98 117.98 152.82 193.00 305.52 255.47 329.10 354.88 0.59 0.39\n", + "98109 190.89 84.25 58.53 90.34 98.47 -10.22 134.70 139.35 170.94 231.32 376.25 327.57 457.96 576.91 1.54 3.85\n", + "98112 232.13 103.90 65.03 75.29 103.68 -32.51 157.00 218.31 216.46 283.34 472.86 429.48 539.34 716.26 1.14 1.83\n", + "98115 170.50 67.16 57.60 68.75 83.61 -23.46 117.98 126.34 158.86 212.28 353.72 290.68 350.21 536.03 0.92 1.36\n", + "98116 172.18 71.28 48.31 65.57 82.17 -5.11 121.70 161.65 161.18 206.24 333.05 320.09 393.30 436.63 1.06 1.13\n", + "98117 157.19 63.57 34.37 58.08 74.32 -6.50 113.34 135.63 144.00 193.23 313.07 269.22 348.82 418.98 0.96 1.02\n", + "98118 156.70 70.81 36.23 62.24 72.46 -44.13 100.10 85.47 146.78 196.25 340.48 281.12 402.60 508.16 1.25 2.46\n", + "98119 186.32 77.29 73.39 77.88 91.04 -4.76 134.24 156.07 167.22 226.91 365.91 338.67 430.44 514.67 1.40 2.86\n", + "98122 165.83 64.08 51.09 62.87 77.11 -0.58 121.70 143.07 151.89 203.22 325.50 287.99 352.50 381.82 0.88 0.73\n", + "98125 162.16 63.90 57.60 69.84 83.61 2.09 116.78 132.85 148.64 193.23 307.92 279.21 380.82 418.05 1.22 1.83\n", + "98126 143.40 61.41 52.02 58.09 65.96 -18.58 95.69 146.78 135.17 171.86 286.13 260.07 314.82 335.37 0.89 0.42\n", + "98133 149.98 51.10 54.81 65.89 78.64 8.83 112.64 153.28 140.23 181.85 285.67 246.84 291.43 315.86 0.71 0.24\n", + "98136 160.42 68.71 39.02 56.67 73.48 -26.48 108.69 125.41 147.71 198.81 333.98 298.21 330.54 484.01 0.96 1.31\n", + "98144 177.64 85.44 57.60 62.63 77.39 -35.53 115.66 115.20 154.21 216.46 367.65 329.61 460.51 595.49 1.41 2.80\n", + "98146 150.63 61.35 38.09 51.90 69.72 -45.99 100.33 79.89 138.42 197.88 344.19 255.15 304.94 353.95 0.60 -0.18\n", + "98148 149.62 54.43 67.82 76.14 86.03 13.01 110.55 86.40 145.85 175.58 273.13 248.04 299.96 314.00 0.95 0.67\n", + "98155 165.98 70.96 65.03 68.98 78.97 -31.93 109.85 71.53 154.21 204.38 346.17 294.26 379.03 616.86 1.45 4.41\n", + "98166 192.11 86.52 57.60 65.96 77.71 -9.29 135.63 130.06 179.76 232.25 377.17 336.85 465.36 694.89 1.52 4.86\n", + "98168 136.44 52.17 35.30 55.74 70.60 -7.43 98.47 127.27 128.20 169.08 274.98 225.93 269.71 412.48 1.07 2.49\n", + "98177 215.84 99.15 50.17 70.68 101.73 -9.75 150.50 156.07 196.95 257.33 417.59 376.06 562.45 805.44 2.02 7.71\n", + "98178 160.66 65.67 53.88 62.49 73.39 -19.97 109.62 79.89 160.25 196.02 325.61 282.32 358.55 458.93 0.90 1.48\n", + "98188 167.48 68.45 62.24 63.17 73.39 0.93 123.56 123.56 157.00 205.31 327.94 307.27 365.47 415.26 0.99 1.20\n", + "98198 162.14 65.24 57.60 67.82 85.38 -4.99 115.20 190.44 149.57 195.32 315.51 289.98 343.78 498.87 1.35 3.06\n", + "98199 200.83 87.82 63.17 67.48 87.14 -36.70 137.49 127.27 183.94 253.62 427.80 363.98 414.97 659.59 1.09 2.03" ] }, - "execution_count": 46, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "group_descriptive_stats(num_attr_fs,'zipcode','m2_living')" + "group_descriptive_stats(num_attr_fs,'zipcode','m2_living',bound_dist)" ] }, { "cell_type": "code", - "execution_count": 47, - "metadata": {}, + "execution_count": 48, + "metadata": { + "hidden": true, + "scrolled": true + }, "outputs": [ { "data": { @@ -6195,11 +6986,16 @@ " mean\n", " std\n", " minimum\n", - " q0\n", + " p1\n", + " p5\n", + " lower_bound\n", " q1\n", + " mode\n", " median\n", " q3\n", - " q4\n", + " upper_bound\n", + " p95\n", + " p99\n", " maximum\n", " skew\n", " kurtosis\n", @@ -6217,6 +7013,11 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -6225,11 +7026,16 @@ " 1387.69\n", " 1968.72\n", " 191.75\n", + " 357.74\n", + " 408.76\n", " -328.59\n", " 672.85\n", + " 408.76\n", " 862.76\n", " 1340.48\n", " 2341.92\n", + " 4382.43\n", + " 7684.67\n", " 22256.98\n", " 6.16\n", " 50.57\n", @@ -6239,11 +7045,16 @@ " 698.39\n", " 307.29\n", " 260.12\n", + " 260.12\n", + " 303.08\n", " -16.93\n", " 489.12\n", + " 557.40\n", " 676.50\n", " 826.48\n", " 1332.53\n", + " 1167.02\n", + " 1712.33\n", " 2468.72\n", " 1.74\n", " 6.35\n", @@ -6253,11 +7064,16 @@ " 985.03\n", " 691.78\n", " 120.96\n", + " 190.63\n", + " 570.30\n", " 227.35\n", " 710.62\n", + " 682.81\n", " 819.01\n", " 1032.79\n", " 1516.06\n", + " 1654.35\n", + " 3921.28\n", " 7365.02\n", " 5.30\n", " 37.08\n", @@ -6267,11 +7083,16 @@ " 1217.38\n", " 598.38\n", " 72.37\n", + " 121.44\n", + " 646.81\n", " -195.32\n", " 824.86\n", + " 753.70\n", " 1032.96\n", " 1504.98\n", " 2525.16\n", + " 2113.01\n", + " 3769.53\n", " 4123.27\n", " 1.81\n", " 5.66\n", @@ -6281,11 +7102,16 @@ " 1851.38\n", " 1639.36\n", " 236.99\n", + " 250.08\n", + " 674.85\n", " -1192.00\n", " 922.08\n", + " 1018.18\n", " 1135.24\n", " 2331.46\n", " 4445.54\n", + " 4635.78\n", + " 9229.13\n", " 10453.20\n", " 2.54\n", " 8.63\n", @@ -6295,11 +7121,16 @@ " 1243.32\n", " 814.98\n", " 182.08\n", + " 389.07\n", + " 667.54\n", " 180.14\n", " 845.85\n", + " 780.36\n", " 970.57\n", " 1289.66\n", " 1955.37\n", + " 3103.84\n", + " 4191.00\n", " 8259.65\n", " 3.47\n", " 17.74\n", @@ -6309,11 +7140,16 @@ " 990.57\n", " 715.17\n", " 182.27\n", + " 187.12\n", + " 327.10\n", " 145.25\n", " 698.61\n", + " 715.33\n", " 836.10\n", " 1067.51\n", " 1620.87\n", + " 1543.72\n", + " 4095.27\n", " 5179.73\n", " 3.48\n", " 14.18\n", @@ -6323,11 +7159,16 @@ " 946.51\n", " 600.19\n", " 98.01\n", + " 228.38\n", + " 603.10\n", " 346.35\n", " 712.64\n", + " 743.20\n", " 809.44\n", " 956.82\n", " 1323.11\n", + " 1801.38\n", + " 3925.55\n", " 4815.56\n", " 4.10\n", " 19.52\n", @@ -6337,11 +7178,16 @@ " 5152.73\n", " 7271.09\n", " 447.22\n", + " 485.76\n", + " 668.04\n", " -4645.95\n", " 911.05\n", + " 668.88\n", " 1832.64\n", " 4615.71\n", " 10172.71\n", + " 20743.51\n", + " 30411.48\n", " 32454.71\n", " 2.06\n", " 3.46\n", @@ -6351,11 +7197,16 @@ " 1051.08\n", " 1498.12\n", " 260.21\n", + " 340.33\n", + " 412.06\n", " 205.98\n", " 676.50\n", + " 891.84\n", " 831.18\n", " 990.17\n", " 1460.69\n", + " 1873.80\n", + " 5309.30\n", " 19505.19\n", " 10.15\n", " 120.59\n", @@ -6365,11 +7216,16 @@ " 9002.28\n", " 17079.25\n", " 232.25\n", + " 338.60\n", + " 485.41\n", " -8944.70\n", " 1393.50\n", + " 464.50\n", " 2115.70\n", " 8285.63\n", " 18623.84\n", + " 25901.05\n", + " 88880.22\n", " 108209.36\n", " 3.89\n", " 16.92\n", @@ -6379,11 +7235,16 @@ " 3700.99\n", " 7103.45\n", " 239.40\n", + " 281.28\n", + " 335.41\n", " -998.98\n", " 603.83\n", + " 929.00\n", " 976.89\n", " 1672.36\n", " 3275.17\n", + " 20286.85\n", + " 33810.38\n", " 35732.50\n", " 2.72\n", " 6.81\n", @@ -6393,11 +7254,16 @@ " 6884.08\n", " 14858.53\n", " 319.02\n", + " 404.67\n", + " 473.05\n", " -5027.16\n", " 782.59\n", + " 780.36\n", " 948.83\n", " 4655.75\n", " 10465.50\n", + " 37483.27\n", + " 49300.01\n", " 153411.25\n", " 5.22\n", " 41.70\n", @@ -6407,11 +7273,16 @@ " 972.68\n", " 791.58\n", " 236.89\n", + " 464.07\n", + " 546.09\n", " 305.97\n", " 682.77\n", + " 743.20\n", " 786.86\n", " 933.97\n", " 1310.77\n", + " 1888.53\n", + " 5027.03\n", " 9976.16\n", " 6.32\n", " 52.04\n", @@ -6421,11 +7292,16 @@ " 9450.24\n", " 11951.55\n", " 592.24\n", + " 617.36\n", + " 650.30\n", " -18777.88\n", " 1399.72\n", + " 650.30\n", " 4306.38\n", " 14851.46\n", " 35029.06\n", + " 24482.68\n", + " 45549.87\n", " 81905.66\n", " 3.15\n", " 15.95\n", @@ -6435,11 +7311,16 @@ " 3052.41\n", " 6755.59\n", " 63.45\n", + " 66.05\n", + " 94.79\n", " -4000.91\n", " 440.21\n", + " 557.40\n", " 935.32\n", " 3400.95\n", " 7842.07\n", + " 11419.83\n", + " 23354.01\n", " 99794.85\n", " 8.55\n", " 107.05\n", @@ -6449,11 +7330,16 @@ " 1108.80\n", " 1087.13\n", " 103.68\n", + " 124.49\n", + " 418.05\n", " -115.22\n", " 655.04\n", + " 891.84\n", " 914.88\n", " 1168.54\n", " 1938.80\n", + " 2291.08\n", + " 5803.65\n", " 10926.15\n", " 5.83\n", " 44.62\n", @@ -6463,11 +7349,16 @@ " 806.34\n", " 1818.66\n", " 80.36\n", + " 97.86\n", + " 150.31\n", " -209.44\n", " 334.44\n", + " 464.50\n", " 464.87\n", " 697.03\n", " 1240.91\n", + " 2322.50\n", + " 7847.71\n", " 20395.45\n", " 8.42\n", " 82.41\n", @@ -6477,11 +7368,16 @@ " 1094.24\n", " 2007.99\n", " 237.08\n", + " 355.84\n", + " 529.53\n", " 103.53\n", " 585.01\n", + " 529.53\n", " 708.13\n", " 906.01\n", " 1387.50\n", + " 1703.37\n", + " 13891.01\n", " 20759.62\n", " 7.45\n", " 59.62\n", @@ -6491,11 +7387,16 @@ " 1123.34\n", " 1790.63\n", " 259.01\n", + " 316.41\n", + " 401.02\n", " 302.01\n", " 663.31\n", + " 668.88\n", " 743.20\n", " 904.17\n", " 1265.47\n", + " 2459.06\n", + " 10635.18\n", " 17562.74\n", " 6.40\n", " 45.89\n", @@ -6505,11 +7406,16 @@ " 964.28\n", " 730.87\n", " 374.39\n", + " 452.94\n", + " 532.97\n", " 275.91\n", " 682.81\n", + " 668.88\n", " 789.65\n", " 954.08\n", " 1360.99\n", + " 1851.22\n", + " 2945.97\n", " 7729.19\n", " 6.86\n", " 60.06\n", @@ -6519,11 +7425,16 @@ " 974.88\n", " 735.40\n", " 163.04\n", + " 197.57\n", + " 334.40\n", " 141.27\n", " 672.15\n", + " 668.88\n", " 815.24\n", " 1026.08\n", " 1556.97\n", + " 2314.93\n", + " 4126.78\n", " 7243.60\n", " 4.36\n", " 26.74\n", @@ -6533,11 +7444,16 @@ " 908.02\n", " 1227.02\n", " 126.34\n", + " 201.58\n", + " 372.58\n", " 268.95\n", " 668.88\n", + " 668.88\n", " 743.57\n", " 935.50\n", " 1335.44\n", + " 1642.68\n", + " 3522.86\n", " 27032.04\n", " 18.08\n", " 379.71\n", @@ -6547,11 +7463,16 @@ " 2358.33\n", " 5863.02\n", " 198.06\n", + " 248.75\n", + " 335.03\n", " -197.17\n", " 464.50\n", + " 390.18\n", " 641.43\n", " 905.61\n", " 1567.28\n", + " 13350.08\n", + " 30532.90\n", " 57949.07\n", " 4.97\n", " 30.65\n", @@ -6561,11 +7482,16 @@ " 1616.79\n", " 618.27\n", " 610.54\n", + " 680.91\n", + " 754.26\n", " 410.19\n", " 1281.79\n", + " 754.26\n", " 1596.81\n", " 1862.85\n", " 2734.45\n", + " 2876.27\n", + " 3228.00\n", " 3257.91\n", " 0.70\n", " 0.73\n", @@ -6575,11 +7501,16 @@ " 1273.14\n", " 668.62\n", " 343.73\n", + " 575.46\n", + " 743.20\n", " 98.93\n", " 916.95\n", + " 884.87\n", " 1110.29\n", " 1462.29\n", " 2280.31\n", + " 2171.11\n", + " 3437.18\n", " 8579.04\n", " 5.62\n", " 52.77\n", @@ -6589,11 +7520,16 @@ " 1760.90\n", " 3799.54\n", " 145.02\n", + " 294.24\n", + " 382.26\n", " -612.11\n", " 505.10\n", + " 483.08\n", " 766.42\n", " 1249.90\n", " 2367.10\n", + " 5655.38\n", + " 22484.35\n", " 41195.58\n", " 6.65\n", " 54.04\n", @@ -6603,11 +7539,16 @@ " 4652.25\n", " 9803.31\n", " 278.70\n", + " 464.54\n", + " 557.40\n", " -3385.93\n", " 899.64\n", + " 836.10\n", " 1167.38\n", " 3756.69\n", " 8042.26\n", + " 24604.01\n", + " 40467.24\n", " 95135.92\n", " 5.08\n", " 35.96\n", @@ -6617,11 +7558,16 @@ " 1033.23\n", " 954.66\n", " 72.93\n", + " 191.18\n", + " 322.68\n", " 56.96\n", " 646.79\n", + " 780.36\n", " 806.46\n", " 1040.02\n", " 1629.85\n", + " 3266.64\n", + " 4372.57\n", " 10319.15\n", " 4.25\n", " 26.02\n", @@ -6631,11 +7577,16 @@ " 3302.35\n", " 5406.37\n", " 230.58\n", + " 268.83\n", + " 321.90\n", " -4351.76\n", " 462.64\n", + " 418.05\n", " 796.34\n", " 3672.24\n", " 8486.65\n", + " 19375.71\n", + " 22512.72\n", " 42166.85\n", " 3.05\n", " 11.14\n", @@ -6645,11 +7596,16 @@ " 936.11\n", " 1503.10\n", " 145.48\n", + " 175.23\n", + " 288.41\n", " -89.52\n", " 466.31\n", + " 418.05\n", " 689.04\n", " 836.87\n", " 1392.70\n", + " 1775.93\n", + " 9363.50\n", " 14730.04\n", " 6.51\n", " 47.10\n", @@ -6659,11 +7615,16 @@ " 888.73\n", " 1131.01\n", " 182.83\n", + " 190.46\n", + " 381.35\n", " 13.51\n", " 546.62\n", + " 780.36\n", " 710.68\n", " 902.04\n", " 1435.15\n", + " 1720.51\n", + " 3891.58\n", " 15607.20\n", " 9.91\n", " 115.64\n", @@ -6673,11 +7634,16 @@ " 1813.30\n", " 5245.33\n", " 272.20\n", + " 342.99\n", + " 449.36\n", " 172.91\n", " 685.65\n", + " 702.32\n", " 780.36\n", " 1027.47\n", " 1540.21\n", + " 5101.98\n", + " 20062.41\n", " 91320.51\n", " 12.12\n", " 190.71\n", @@ -6687,11 +7653,16 @@ " 1338.31\n", " 2275.08\n", " 133.40\n", + " 252.95\n", + " 357.83\n", " -346.87\n", " 612.14\n", + " 418.05\n", " 883.85\n", " 1251.48\n", " 2210.49\n", + " 3135.56\n", + " 12517.33\n", " 23956.59\n", " 6.94\n", " 55.22\n", @@ -6701,11 +7672,16 @@ " 1585.36\n", " 6077.56\n", " 233.46\n", + " 254.18\n", + " 345.23\n", " -43.83\n", " 455.81\n", + " 557.40\n", " 575.28\n", " 788.91\n", " 1288.55\n", + " 3682.88\n", + " 24026.19\n", " 85507.30\n", " 10.29\n", " 126.32\n", @@ -6715,11 +7691,16 @@ " 9035.73\n", " 12623.24\n", " 509.93\n", + " 729.95\n", + " 920.82\n", " -10645.42\n", " 1765.75\n", + " 920.82\n", " 4099.68\n", " 10039.87\n", " 22451.04\n", + " 30397.62\n", + " 58563.36\n", " 78343.41\n", " 2.93\n", " 10.47\n", @@ -6729,11 +7710,16 @@ " 2858.32\n", " 3097.75\n", " 232.25\n", + " 273.25\n", + " 343.43\n", " -3692.96\n", " 833.68\n", + " 780.36\n", " 1554.22\n", " 3851.45\n", " 8378.09\n", + " 9172.48\n", + " 14209.67\n", " 22418.81\n", " 2.49\n", " 8.72\n", @@ -6743,11 +7729,16 @@ " 1349.12\n", " 2001.11\n", " 263.74\n", + " 364.93\n", + " 464.50\n", " -2.97\n", " 709.66\n", + " 696.75\n", " 930.11\n", " 1184.75\n", " 1897.39\n", + " 3725.85\n", + " 7617.11\n", " 26384.62\n", " 8.15\n", " 83.69\n", @@ -6757,11 +7748,16 @@ " 1765.31\n", " 2566.96\n", " 164.15\n", + " 349.04\n", + " 426.98\n", " -967.18\n", " 633.11\n", + " 520.24\n", " 1017.72\n", " 1699.98\n", " 3300.27\n", + " 5020.61\n", + " 10093.32\n", " 30026.67\n", " 6.48\n", " 58.67\n", @@ -6771,11 +7767,16 @@ " 5093.61\n", " 5627.59\n", " 518.38\n", + " 847.66\n", + " 903.68\n", " -2429.52\n", " 1919.52\n", + " 4046.72\n", " 3588.45\n", " 4818.89\n", " 9167.93\n", + " 20263.97\n", + " 24195.33\n", " 34235.23\n", " 2.65\n", " 7.19\n", @@ -6785,11 +7786,16 @@ " 3018.75\n", " 5709.88\n", " 176.32\n", + " 274.47\n", + " 349.16\n", " -1910.35\n", " 582.34\n", + " 445.92\n", " 778.04\n", " 2244.14\n", " 4736.83\n", + " 19590.06\n", + " 23491.20\n", " 39617.39\n", " 3.12\n", " 10.38\n", @@ -6799,11 +7805,16 @@ " 335.94\n", " 345.80\n", " 79.62\n", + " 83.62\n", + " 90.95\n", " -301.00\n", " 124.86\n", + " 371.60\n", " 297.28\n", " 408.76\n", " 834.61\n", + " 624.29\n", + " 2263.20\n", " 2564.04\n", " 4.55\n", " 25.76\n", @@ -6813,11 +7824,16 @@ " 323.52\n", " 170.19\n", " 60.48\n", + " 74.14\n", + " 93.44\n", " -305.82\n", " 145.23\n", + " 278.70\n", " 325.15\n", " 445.92\n", " 896.96\n", + " 615.40\n", + " 797.28\n", " 877.90\n", " 0.42\n", " -0.21\n", @@ -6827,11 +7843,16 @@ " 475.76\n", " 307.67\n", " 68.37\n", + " 90.42\n", + " 133.37\n", " 126.58\n", " 360.54\n", + " 371.60\n", " 416.19\n", " 516.52\n", " 750.49\n", + " 930.86\n", + " 1842.42\n", " 2435.00\n", " 3.36\n", " 15.99\n", @@ -6841,11 +7862,16 @@ " 515.99\n", " 363.17\n", " 53.14\n", + " 63.48\n", + " 106.36\n", " -150.64\n", " 346.98\n", + " 464.50\n", " 475.65\n", " 678.73\n", " 1176.35\n", + " 980.53\n", + " 1554.56\n", " 4435.32\n", " 4.38\n", " 41.13\n", @@ -6855,11 +7881,16 @@ " 296.76\n", " 166.72\n", " 69.30\n", + " 79.90\n", + " 102.24\n", " -386.91\n", " 122.91\n", + " 464.50\n", " 304.90\n", " 462.78\n", " 972.59\n", + " 553.22\n", + " 744.94\n", " 870.84\n", " 0.50\n", " -0.29\n", @@ -6869,11 +7900,16 @@ " 483.53\n", " 207.69\n", " 89.00\n", + " 111.07\n", + " 158.58\n", " 59.80\n", " 371.60\n", + " 557.40\n", " 470.54\n", " 579.46\n", " 891.26\n", + " 824.02\n", + " 1097.03\n", " 1648.51\n", " 1.13\n", " 4.97\n", @@ -6883,11 +7919,16 @@ " 336.40\n", " 157.25\n", " 66.24\n", + " 82.60\n", + " 98.55\n", " -128.90\n", " 214.60\n", + " 371.60\n", " 334.44\n", " 443.60\n", " 787.10\n", + " 575.52\n", + " 659.20\n", " 822.72\n", " 0.38\n", " -0.30\n", @@ -6897,11 +7938,16 @@ " 463.61\n", " 290.71\n", " 48.31\n", + " 62.56\n", + " 112.15\n", " 0.00\n", " 334.44\n", + " 371.60\n", " 411.18\n", " 557.40\n", " 891.84\n", + " 860.77\n", + " 1388.33\n", " 2798.15\n", " 3.31\n", " 20.38\n", @@ -6911,11 +7957,16 @@ " 505.76\n", " 248.54\n", " 80.27\n", + " 93.93\n", + " 131.30\n", " 58.53\n", " 379.03\n", + " 592.70\n", " 492.37\n", " 592.70\n", " 913.21\n", + " 888.59\n", + " 1238.40\n", " 2798.33\n", " 2.95\n", " 21.17\n", @@ -6925,11 +7976,16 @@ " 462.59\n", " 286.45\n", " 55.74\n", + " 82.95\n", + " 103.78\n", " 34.35\n", " 346.91\n", + " 534.17\n", " 464.50\n", " 555.29\n", " 867.85\n", + " 755.04\n", + " 1427.59\n", " 3207.56\n", " 4.29\n", " 34.88\n", @@ -6939,11 +7995,16 @@ " 430.72\n", " 215.40\n", " 74.88\n", + " 93.93\n", + " 120.55\n", " 150.96\n", " 347.45\n", + " 464.50\n", " 418.14\n", " 478.44\n", " 674.92\n", + " 755.00\n", + " 1313.38\n", " 2081.89\n", " 2.31\n", " 11.48\n", @@ -6953,11 +8014,16 @@ " 537.00\n", " 425.28\n", " 62.71\n", + " 96.62\n", + " 186.76\n", " 24.27\n", " 377.59\n", + " 668.88\n", " 494.14\n", " 613.14\n", " 966.46\n", + " 907.08\n", + " 1489.46\n", " 8517.16\n", " 13.32\n", " 245.21\n", @@ -6967,11 +8033,16 @@ " 335.85\n", " 178.25\n", " 70.79\n", + " 85.65\n", + " 92.90\n", " -211.00\n", " 183.15\n", " 334.44\n", + " 334.44\n", " 445.92\n", " 840.07\n", + " 637.96\n", + " 855.17\n", " 1003.32\n", " 0.74\n", " 0.75\n", @@ -6981,11 +8052,16 @@ " 325.95\n", " 172.74\n", " 65.40\n", + " 84.25\n", + " 94.80\n", " -240.44\n", " 168.03\n", + " 408.76\n", " 334.44\n", " 440.35\n", " 848.82\n", + " 650.30\n", + " 781.18\n", " 891.84\n", " 0.51\n", " -0.10\n", @@ -6995,11 +8071,16 @@ " 712.82\n", " 358.66\n", " 76.64\n", + " 97.71\n", + " 134.38\n", " 228.78\n", " 579.70\n", + " 668.88\n", " 696.66\n", " 813.64\n", " 1164.56\n", + " 1266.34\n", + " 2074.26\n", " 3455.88\n", " 2.18\n", " 12.35\n", @@ -7009,11 +8090,16 @@ " 475.15\n", " 186.72\n", " 67.35\n", + " 94.85\n", + " 132.08\n", " 57.55\n", " 379.03\n", + " 464.50\n", " 469.52\n", " 593.35\n", " 914.83\n", + " 735.77\n", + " 934.50\n", " 1137.84\n", " -0.03\n", " 0.22\n", @@ -7023,11 +8109,16 @@ " 657.94\n", " 323.77\n", " 77.48\n", + " 83.09\n", + " 105.97\n", " 17.42\n", " 487.72\n", + " 668.88\n", " 678.68\n", " 801.26\n", " 1271.57\n", + " 1116.46\n", + " 1604.07\n", " 2726.34\n", " 0.97\n", " 5.51\n", @@ -7037,11 +8128,16 @@ " 555.76\n", " 458.42\n", " 65.03\n", + " 74.32\n", + " 92.20\n", " 9.29\n", " 371.60\n", + " 557.40\n", " 507.23\n", " 613.14\n", " 975.45\n", + " 1166.13\n", + " 2841.02\n", " 3483.75\n", " 3.58\n", " 17.35\n", @@ -7051,11 +8147,16 @@ " 380.30\n", " 300.38\n", " 62.80\n", + " 76.00\n", + " 91.11\n", " -251.46\n", " 183.11\n", " 371.60\n", + " 371.60\n", " 472.81\n", " 907.38\n", + " 754.12\n", + " 1207.67\n", " 4072.55\n", " 5.92\n", " 66.60\n", @@ -7065,11 +8166,16 @@ " 837.98\n", " 508.80\n", " 232.25\n", + " 272.66\n", + " 379.23\n", " 109.18\n", " 580.39\n", + " 464.50\n", " 752.49\n", " 894.53\n", " 1365.75\n", + " 1698.66\n", + " 2910.28\n", " 5031.46\n", " 4.22\n", " 26.36\n", @@ -7079,11 +8185,16 @@ " 832.45\n", " 254.11\n", " 371.97\n", + " 378.01\n", + " 490.10\n", " 421.81\n", " 720.44\n", + " 718.86\n", " 797.45\n", " 919.52\n", " 1218.15\n", + " 1122.70\n", + " 1708.87\n", " 2184.26\n", " 2.68\n", " 13.98\n", @@ -7093,11 +8204,16 @@ " 955.45\n", " 526.13\n", " 91.23\n", + " 257.37\n", + " 554.68\n", " 196.55\n", " 696.75\n", + " 668.88\n", " 792.67\n", " 1030.21\n", " 1530.41\n", + " 1929.30\n", + " 3393.43\n", " 5082.28\n", " 3.39\n", " 15.86\n", @@ -7107,11 +8223,16 @@ " 1264.31\n", " 1051.00\n", " 167.50\n", + " 172.87\n", + " 553.73\n", " -347.12\n", " 733.26\n", + " 707.90\n", " 973.13\n", " 1453.51\n", " 2533.89\n", + " 2566.27\n", + " 4716.77\n", " 12719.40\n", " 6.02\n", " 56.99\n", @@ -7121,11 +8242,16 @@ " 1046.97\n", " 668.00\n", " 209.03\n", + " 466.48\n", + " 561.45\n", " -5.57\n", " 668.88\n", + " 613.14\n", " 823.74\n", " 1118.52\n", " 1792.97\n", + " 2369.34\n", + " 3897.56\n", " 4613.23\n", " 2.71\n", " 8.58\n", @@ -7135,11 +8261,16 @@ " 1105.92\n", " 962.60\n", " 118.82\n", + " 149.65\n", + " 475.09\n", " 191.51\n", " 698.75\n", + " 752.49\n", " 762.71\n", " 1036.90\n", " 1544.14\n", + " 2695.65\n", + " 6116.18\n", " 7122.18\n", " 3.66\n", " 16.75\n", @@ -7149,11 +8280,16 @@ " 771.92\n", " 470.75\n", " 278.70\n", + " 278.70\n", + " 395.99\n", " 95.32\n", " 557.40\n", + " 557.40\n", " 668.88\n", " 865.46\n", " 1327.54\n", + " 1358.95\n", + " 2010.19\n", " 6312.83\n", " 6.96\n", " 74.64\n", @@ -7163,11 +8299,16 @@ " 940.71\n", " 379.72\n", " 319.11\n", + " 341.73\n", + " 663.47\n", " 408.30\n", " 757.92\n", + " 808.23\n", " 828.02\n", " 991.01\n", " 1340.64\n", + " 1671.41\n", + " 2655.78\n", " 2831.22\n", " 2.71\n", " 9.56\n", @@ -7177,11 +8318,16 @@ " 977.86\n", " 935.95\n", " 181.16\n", + " 221.63\n", + " 335.00\n", " 232.03\n", " 676.87\n", + " 696.75\n", " 797.96\n", " 973.43\n", " 1418.27\n", + " 2014.10\n", + " 4599.40\n", " 12747.18\n", " 8.11\n", " 92.01\n", @@ -7191,11 +8337,16 @@ " 505.03\n", " 273.16\n", " 81.29\n", + " 86.57\n", + " 122.22\n", " 109.53\n", " 390.18\n", + " 557.40\n", " 510.95\n", " 577.28\n", " 857.93\n", + " 795.60\n", + " 1652.52\n", " 2704.41\n", " 3.08\n", " 19.89\n", @@ -7205,105 +8356,107 @@ "" ], "text/plain": [ - " mean std minimum q0 q1 median q3 q4 maximum skew kurtosis\n", - "zipcode \n", - "98001 1387.69 1968.72 191.75 -328.59 672.85 862.76 1340.48 2341.92 22256.98 6.16 50.57\n", - "98002 698.39 307.29 260.12 -16.93 489.12 676.50 826.48 1332.53 2468.72 1.74 6.35\n", - "98003 985.03 691.78 120.96 227.35 710.62 819.01 1032.79 1516.06 7365.02 5.30 37.08\n", - "98004 1217.38 598.38 72.37 -195.32 824.86 1032.96 1504.98 2525.16 4123.27 1.81 5.66\n", - "98005 1851.38 1639.36 236.99 -1192.00 922.08 1135.24 2331.46 4445.54 10453.20 2.54 8.63\n", - "98006 1243.32 814.98 182.08 180.14 845.85 970.57 1289.66 1955.37 8259.65 3.47 17.74\n", - "98007 990.57 715.17 182.27 145.25 698.61 836.10 1067.51 1620.87 5179.73 3.48 14.18\n", - "98008 946.51 600.19 98.01 346.35 712.64 809.44 956.82 1323.11 4815.56 4.10 19.52\n", - "98010 5152.73 7271.09 447.22 -4645.95 911.05 1832.64 4615.71 10172.71 32454.71 2.06 3.46\n", - "98011 1051.08 1498.12 260.21 205.98 676.50 831.18 990.17 1460.69 19505.19 10.15 120.59\n", - "98014 9002.28 17079.25 232.25 -8944.70 1393.50 2115.70 8285.63 18623.84 108209.36 3.89 16.92\n", - "98019 3700.99 7103.45 239.40 -998.98 603.83 976.89 1672.36 3275.17 35732.50 2.72 6.81\n", - "98022 6884.08 14858.53 319.02 -5027.16 782.59 948.83 4655.75 10465.50 153411.25 5.22 41.70\n", - "98023 972.68 791.58 236.89 305.97 682.77 786.86 933.97 1310.77 9976.16 6.32 52.04\n", - "98024 9450.24 11951.55 592.24 -18777.88 1399.72 4306.38 14851.46 35029.06 81905.66 3.15 15.95\n", - "98027 3052.41 6755.59 63.45 -4000.91 440.21 935.32 3400.95 7842.07 99794.85 8.55 107.05\n", - "98028 1108.80 1087.13 103.68 -115.22 655.04 914.88 1168.54 1938.80 10926.15 5.83 44.62\n", - "98029 806.34 1818.66 80.36 -209.44 334.44 464.87 697.03 1240.91 20395.45 8.42 82.41\n", - "98030 1094.24 2007.99 237.08 103.53 585.01 708.13 906.01 1387.50 20759.62 7.45 59.62\n", - "98031 1123.34 1790.63 259.01 302.01 663.31 743.20 904.17 1265.47 17562.74 6.40 45.89\n", - "98032 964.28 730.87 374.39 275.91 682.81 789.65 954.08 1360.99 7729.19 6.86 60.06\n", - "98033 974.88 735.40 163.04 141.27 672.15 815.24 1026.08 1556.97 7243.60 4.36 26.74\n", - "98034 908.02 1227.02 126.34 268.95 668.88 743.57 935.50 1335.44 27032.04 18.08 379.71\n", - "98038 2358.33 5863.02 198.06 -197.17 464.50 641.43 905.61 1567.28 57949.07 4.97 30.65\n", - "98039 1616.79 618.27 610.54 410.19 1281.79 1596.81 1862.85 2734.45 3257.91 0.70 0.73\n", - "98040 1273.14 668.62 343.73 98.93 916.95 1110.29 1462.29 2280.31 8579.04 5.62 52.77\n", - "98042 1760.90 3799.54 145.02 -612.11 505.10 766.42 1249.90 2367.10 41195.58 6.65 54.04\n", - "98045 4652.25 9803.31 278.70 -3385.93 899.64 1167.38 3756.69 8042.26 95135.92 5.08 35.96\n", - "98052 1033.23 954.66 72.93 56.96 646.79 806.46 1040.02 1629.85 10319.15 4.25 26.02\n", - "98053 3302.35 5406.37 230.58 -4351.76 462.64 796.34 3672.24 8486.65 42166.85 3.05 11.14\n", - "98055 936.11 1503.10 145.48 -89.52 466.31 689.04 836.87 1392.70 14730.04 6.51 47.10\n", - "98056 888.73 1131.01 182.83 13.51 546.62 710.68 902.04 1435.15 15607.20 9.91 115.64\n", - "98058 1813.30 5245.33 272.20 172.91 685.65 780.36 1027.47 1540.21 91320.51 12.12 190.71\n", - "98059 1338.31 2275.08 133.40 -346.87 612.14 883.85 1251.48 2210.49 23956.59 6.94 55.22\n", - "98065 1585.36 6077.56 233.46 -43.83 455.81 575.28 788.91 1288.55 85507.30 10.29 126.32\n", - "98070 9035.73 12623.24 509.93 -10645.42 1765.75 4099.68 10039.87 22451.04 78343.41 2.93 10.47\n", - "98072 2858.32 3097.75 232.25 -3692.96 833.68 1554.22 3851.45 8378.09 22418.81 2.49 8.72\n", - "98074 1349.12 2001.11 263.74 -2.97 709.66 930.11 1184.75 1897.39 26384.62 8.15 83.69\n", - "98075 1765.31 2566.96 164.15 -967.18 633.11 1017.72 1699.98 3300.27 30026.67 6.48 58.67\n", - "98077 5093.61 5627.59 518.38 -2429.52 1919.52 3588.45 4818.89 9167.93 34235.23 2.65 7.19\n", - "98092 3018.75 5709.88 176.32 -1910.35 582.34 778.04 2244.14 4736.83 39617.39 3.12 10.38\n", - "98102 335.94 345.80 79.62 -301.00 124.86 297.28 408.76 834.61 2564.04 4.55 25.76\n", - "98103 323.52 170.19 60.48 -305.82 145.23 325.15 445.92 896.96 877.90 0.42 -0.21\n", - "98105 475.76 307.67 68.37 126.58 360.54 416.19 516.52 750.49 2435.00 3.36 15.99\n", - "98106 515.99 363.17 53.14 -150.64 346.98 475.65 678.73 1176.35 4435.32 4.38 41.13\n", - "98107 296.76 166.72 69.30 -386.91 122.91 304.90 462.78 972.59 870.84 0.50 -0.29\n", - "98108 483.53 207.69 89.00 59.80 371.60 470.54 579.46 891.26 1648.51 1.13 4.97\n", - "98109 336.40 157.25 66.24 -128.90 214.60 334.44 443.60 787.10 822.72 0.38 -0.30\n", - "98112 463.61 290.71 48.31 0.00 334.44 411.18 557.40 891.84 2798.15 3.31 20.38\n", - "98115 505.76 248.54 80.27 58.53 379.03 492.37 592.70 913.21 2798.33 2.95 21.17\n", - "98116 462.59 286.45 55.74 34.35 346.91 464.50 555.29 867.85 3207.56 4.29 34.88\n", - "98117 430.72 215.40 74.88 150.96 347.45 418.14 478.44 674.92 2081.89 2.31 11.48\n", - "98118 537.00 425.28 62.71 24.27 377.59 494.14 613.14 966.46 8517.16 13.32 245.21\n", - "98119 335.85 178.25 70.79 -211.00 183.15 334.44 445.92 840.07 1003.32 0.74 0.75\n", - "98122 325.95 172.74 65.40 -240.44 168.03 334.44 440.35 848.82 891.84 0.51 -0.10\n", - "98125 712.82 358.66 76.64 228.78 579.70 696.66 813.64 1164.56 3455.88 2.18 12.35\n", - "98126 475.15 186.72 67.35 57.55 379.03 469.52 593.35 914.83 1137.84 -0.03 0.22\n", - "98133 657.94 323.77 77.48 17.42 487.72 678.68 801.26 1271.57 2726.34 0.97 5.51\n", - "98136 555.76 458.42 65.03 9.29 371.60 507.23 613.14 975.45 3483.75 3.58 17.35\n", - "98144 380.30 300.38 62.80 -251.46 183.11 371.60 472.81 907.38 4072.55 5.92 66.60\n", - "98146 837.98 508.80 232.25 109.18 580.39 752.49 894.53 1365.75 5031.46 4.22 26.36\n", - "98148 832.45 254.11 371.97 421.81 720.44 797.45 919.52 1218.15 2184.26 2.68 13.98\n", - "98155 955.45 526.13 91.23 196.55 696.75 792.67 1030.21 1530.41 5082.28 3.39 15.86\n", - "98166 1264.31 1051.00 167.50 -347.12 733.26 973.13 1453.51 2533.89 12719.40 6.02 56.99\n", - "98168 1046.97 668.00 209.03 -5.57 668.88 823.74 1118.52 1792.97 4613.23 2.71 8.58\n", - "98177 1105.92 962.60 118.82 191.51 698.75 762.71 1036.90 1544.14 7122.18 3.66 16.75\n", - "98178 771.92 470.75 278.70 95.32 557.40 668.88 865.46 1327.54 6312.83 6.96 74.64\n", - "98188 940.71 379.72 319.11 408.30 757.92 828.02 991.01 1340.64 2831.22 2.71 9.56\n", - "98198 977.86 935.95 181.16 232.03 676.87 797.96 973.43 1418.27 12747.18 8.11 92.01\n", - "98199 505.03 273.16 81.29 109.53 390.18 510.95 577.28 857.93 2704.41 3.08 19.89" + " mean std minimum p1 p5 lower_bound q1 mode median q3 upper_bound p95 p99 maximum skew kurtosis\n", + "zipcode \n", + "98001 1387.69 1968.72 191.75 357.74 408.76 -328.59 672.85 408.76 862.76 1340.48 2341.92 4382.43 7684.67 22256.98 6.16 50.57\n", + "98002 698.39 307.29 260.12 260.12 303.08 -16.93 489.12 557.40 676.50 826.48 1332.53 1167.02 1712.33 2468.72 1.74 6.35\n", + "98003 985.03 691.78 120.96 190.63 570.30 227.35 710.62 682.81 819.01 1032.79 1516.06 1654.35 3921.28 7365.02 5.30 37.08\n", + "98004 1217.38 598.38 72.37 121.44 646.81 -195.32 824.86 753.70 1032.96 1504.98 2525.16 2113.01 3769.53 4123.27 1.81 5.66\n", + "98005 1851.38 1639.36 236.99 250.08 674.85 -1192.00 922.08 1018.18 1135.24 2331.46 4445.54 4635.78 9229.13 10453.20 2.54 8.63\n", + "98006 1243.32 814.98 182.08 389.07 667.54 180.14 845.85 780.36 970.57 1289.66 1955.37 3103.84 4191.00 8259.65 3.47 17.74\n", + "98007 990.57 715.17 182.27 187.12 327.10 145.25 698.61 715.33 836.10 1067.51 1620.87 1543.72 4095.27 5179.73 3.48 14.18\n", + "98008 946.51 600.19 98.01 228.38 603.10 346.35 712.64 743.20 809.44 956.82 1323.11 1801.38 3925.55 4815.56 4.10 19.52\n", + "98010 5152.73 7271.09 447.22 485.76 668.04 -4645.95 911.05 668.88 1832.64 4615.71 10172.71 20743.51 30411.48 32454.71 2.06 3.46\n", + "98011 1051.08 1498.12 260.21 340.33 412.06 205.98 676.50 891.84 831.18 990.17 1460.69 1873.80 5309.30 19505.19 10.15 120.59\n", + "98014 9002.28 17079.25 232.25 338.60 485.41 -8944.70 1393.50 464.50 2115.70 8285.63 18623.84 25901.05 88880.22 108209.36 3.89 16.92\n", + "98019 3700.99 7103.45 239.40 281.28 335.41 -998.98 603.83 929.00 976.89 1672.36 3275.17 20286.85 33810.38 35732.50 2.72 6.81\n", + "98022 6884.08 14858.53 319.02 404.67 473.05 -5027.16 782.59 780.36 948.83 4655.75 10465.50 37483.27 49300.01 153411.25 5.22 41.70\n", + "98023 972.68 791.58 236.89 464.07 546.09 305.97 682.77 743.20 786.86 933.97 1310.77 1888.53 5027.03 9976.16 6.32 52.04\n", + "98024 9450.24 11951.55 592.24 617.36 650.30 -18777.88 1399.72 650.30 4306.38 14851.46 35029.06 24482.68 45549.87 81905.66 3.15 15.95\n", + "98027 3052.41 6755.59 63.45 66.05 94.79 -4000.91 440.21 557.40 935.32 3400.95 7842.07 11419.83 23354.01 99794.85 8.55 107.05\n", + "98028 1108.80 1087.13 103.68 124.49 418.05 -115.22 655.04 891.84 914.88 1168.54 1938.80 2291.08 5803.65 10926.15 5.83 44.62\n", + "98029 806.34 1818.66 80.36 97.86 150.31 -209.44 334.44 464.50 464.87 697.03 1240.91 2322.50 7847.71 20395.45 8.42 82.41\n", + "98030 1094.24 2007.99 237.08 355.84 529.53 103.53 585.01 529.53 708.13 906.01 1387.50 1703.37 13891.01 20759.62 7.45 59.62\n", + "98031 1123.34 1790.63 259.01 316.41 401.02 302.01 663.31 668.88 743.20 904.17 1265.47 2459.06 10635.18 17562.74 6.40 45.89\n", + "98032 964.28 730.87 374.39 452.94 532.97 275.91 682.81 668.88 789.65 954.08 1360.99 1851.22 2945.97 7729.19 6.86 60.06\n", + "98033 974.88 735.40 163.04 197.57 334.40 141.27 672.15 668.88 815.24 1026.08 1556.97 2314.93 4126.78 7243.60 4.36 26.74\n", + "98034 908.02 1227.02 126.34 201.58 372.58 268.95 668.88 668.88 743.57 935.50 1335.44 1642.68 3522.86 27032.04 18.08 379.71\n", + "98038 2358.33 5863.02 198.06 248.75 335.03 -197.17 464.50 390.18 641.43 905.61 1567.28 13350.08 30532.90 57949.07 4.97 30.65\n", + "98039 1616.79 618.27 610.54 680.91 754.26 410.19 1281.79 754.26 1596.81 1862.85 2734.45 2876.27 3228.00 3257.91 0.70 0.73\n", + "98040 1273.14 668.62 343.73 575.46 743.20 98.93 916.95 884.87 1110.29 1462.29 2280.31 2171.11 3437.18 8579.04 5.62 52.77\n", + "98042 1760.90 3799.54 145.02 294.24 382.26 -612.11 505.10 483.08 766.42 1249.90 2367.10 5655.38 22484.35 41195.58 6.65 54.04\n", + "98045 4652.25 9803.31 278.70 464.54 557.40 -3385.93 899.64 836.10 1167.38 3756.69 8042.26 24604.01 40467.24 95135.92 5.08 35.96\n", + "98052 1033.23 954.66 72.93 191.18 322.68 56.96 646.79 780.36 806.46 1040.02 1629.85 3266.64 4372.57 10319.15 4.25 26.02\n", + "98053 3302.35 5406.37 230.58 268.83 321.90 -4351.76 462.64 418.05 796.34 3672.24 8486.65 19375.71 22512.72 42166.85 3.05 11.14\n", + "98055 936.11 1503.10 145.48 175.23 288.41 -89.52 466.31 418.05 689.04 836.87 1392.70 1775.93 9363.50 14730.04 6.51 47.10\n", + "98056 888.73 1131.01 182.83 190.46 381.35 13.51 546.62 780.36 710.68 902.04 1435.15 1720.51 3891.58 15607.20 9.91 115.64\n", + "98058 1813.30 5245.33 272.20 342.99 449.36 172.91 685.65 702.32 780.36 1027.47 1540.21 5101.98 20062.41 91320.51 12.12 190.71\n", + "98059 1338.31 2275.08 133.40 252.95 357.83 -346.87 612.14 418.05 883.85 1251.48 2210.49 3135.56 12517.33 23956.59 6.94 55.22\n", + "98065 1585.36 6077.56 233.46 254.18 345.23 -43.83 455.81 557.40 575.28 788.91 1288.55 3682.88 24026.19 85507.30 10.29 126.32\n", + "98070 9035.73 12623.24 509.93 729.95 920.82 -10645.42 1765.75 920.82 4099.68 10039.87 22451.04 30397.62 58563.36 78343.41 2.93 10.47\n", + "98072 2858.32 3097.75 232.25 273.25 343.43 -3692.96 833.68 780.36 1554.22 3851.45 8378.09 9172.48 14209.67 22418.81 2.49 8.72\n", + "98074 1349.12 2001.11 263.74 364.93 464.50 -2.97 709.66 696.75 930.11 1184.75 1897.39 3725.85 7617.11 26384.62 8.15 83.69\n", + "98075 1765.31 2566.96 164.15 349.04 426.98 -967.18 633.11 520.24 1017.72 1699.98 3300.27 5020.61 10093.32 30026.67 6.48 58.67\n", + "98077 5093.61 5627.59 518.38 847.66 903.68 -2429.52 1919.52 4046.72 3588.45 4818.89 9167.93 20263.97 24195.33 34235.23 2.65 7.19\n", + "98092 3018.75 5709.88 176.32 274.47 349.16 -1910.35 582.34 445.92 778.04 2244.14 4736.83 19590.06 23491.20 39617.39 3.12 10.38\n", + "98102 335.94 345.80 79.62 83.62 90.95 -301.00 124.86 371.60 297.28 408.76 834.61 624.29 2263.20 2564.04 4.55 25.76\n", + "98103 323.52 170.19 60.48 74.14 93.44 -305.82 145.23 278.70 325.15 445.92 896.96 615.40 797.28 877.90 0.42 -0.21\n", + "98105 475.76 307.67 68.37 90.42 133.37 126.58 360.54 371.60 416.19 516.52 750.49 930.86 1842.42 2435.00 3.36 15.99\n", + "98106 515.99 363.17 53.14 63.48 106.36 -150.64 346.98 464.50 475.65 678.73 1176.35 980.53 1554.56 4435.32 4.38 41.13\n", + "98107 296.76 166.72 69.30 79.90 102.24 -386.91 122.91 464.50 304.90 462.78 972.59 553.22 744.94 870.84 0.50 -0.29\n", + "98108 483.53 207.69 89.00 111.07 158.58 59.80 371.60 557.40 470.54 579.46 891.26 824.02 1097.03 1648.51 1.13 4.97\n", + "98109 336.40 157.25 66.24 82.60 98.55 -128.90 214.60 371.60 334.44 443.60 787.10 575.52 659.20 822.72 0.38 -0.30\n", + "98112 463.61 290.71 48.31 62.56 112.15 0.00 334.44 371.60 411.18 557.40 891.84 860.77 1388.33 2798.15 3.31 20.38\n", + "98115 505.76 248.54 80.27 93.93 131.30 58.53 379.03 592.70 492.37 592.70 913.21 888.59 1238.40 2798.33 2.95 21.17\n", + "98116 462.59 286.45 55.74 82.95 103.78 34.35 346.91 534.17 464.50 555.29 867.85 755.04 1427.59 3207.56 4.29 34.88\n", + "98117 430.72 215.40 74.88 93.93 120.55 150.96 347.45 464.50 418.14 478.44 674.92 755.00 1313.38 2081.89 2.31 11.48\n", + "98118 537.00 425.28 62.71 96.62 186.76 24.27 377.59 668.88 494.14 613.14 966.46 907.08 1489.46 8517.16 13.32 245.21\n", + "98119 335.85 178.25 70.79 85.65 92.90 -211.00 183.15 334.44 334.44 445.92 840.07 637.96 855.17 1003.32 0.74 0.75\n", + "98122 325.95 172.74 65.40 84.25 94.80 -240.44 168.03 408.76 334.44 440.35 848.82 650.30 781.18 891.84 0.51 -0.10\n", + "98125 712.82 358.66 76.64 97.71 134.38 228.78 579.70 668.88 696.66 813.64 1164.56 1266.34 2074.26 3455.88 2.18 12.35\n", + "98126 475.15 186.72 67.35 94.85 132.08 57.55 379.03 464.50 469.52 593.35 914.83 735.77 934.50 1137.84 -0.03 0.22\n", + "98133 657.94 323.77 77.48 83.09 105.97 17.42 487.72 668.88 678.68 801.26 1271.57 1116.46 1604.07 2726.34 0.97 5.51\n", + "98136 555.76 458.42 65.03 74.32 92.20 9.29 371.60 557.40 507.23 613.14 975.45 1166.13 2841.02 3483.75 3.58 17.35\n", + "98144 380.30 300.38 62.80 76.00 91.11 -251.46 183.11 371.60 371.60 472.81 907.38 754.12 1207.67 4072.55 5.92 66.60\n", + "98146 837.98 508.80 232.25 272.66 379.23 109.18 580.39 464.50 752.49 894.53 1365.75 1698.66 2910.28 5031.46 4.22 26.36\n", + "98148 832.45 254.11 371.97 378.01 490.10 421.81 720.44 718.86 797.45 919.52 1218.15 1122.70 1708.87 2184.26 2.68 13.98\n", + "98155 955.45 526.13 91.23 257.37 554.68 196.55 696.75 668.88 792.67 1030.21 1530.41 1929.30 3393.43 5082.28 3.39 15.86\n", + "98166 1264.31 1051.00 167.50 172.87 553.73 -347.12 733.26 707.90 973.13 1453.51 2533.89 2566.27 4716.77 12719.40 6.02 56.99\n", + "98168 1046.97 668.00 209.03 466.48 561.45 -5.57 668.88 613.14 823.74 1118.52 1792.97 2369.34 3897.56 4613.23 2.71 8.58\n", + "98177 1105.92 962.60 118.82 149.65 475.09 191.51 698.75 752.49 762.71 1036.90 1544.14 2695.65 6116.18 7122.18 3.66 16.75\n", + "98178 771.92 470.75 278.70 278.70 395.99 95.32 557.40 557.40 668.88 865.46 1327.54 1358.95 2010.19 6312.83 6.96 74.64\n", + "98188 940.71 379.72 319.11 341.73 663.47 408.30 757.92 808.23 828.02 991.01 1340.64 1671.41 2655.78 2831.22 2.71 9.56\n", + "98198 977.86 935.95 181.16 221.63 335.00 232.03 676.87 696.75 797.96 973.43 1418.27 2014.10 4599.40 12747.18 8.11 92.01\n", + "98199 505.03 273.16 81.29 86.57 122.22 109.53 390.18 557.40 510.95 577.28 857.93 795.60 1652.52 2704.41 3.08 19.89" ] }, - "execution_count": 47, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "group_descriptive_stats(num_attr_fs,'zipcode','m2_lot')" + "group_descriptive_stats(num_attr_fs,'zipcode','m2_lot',bound_dist)" ] }, { "cell_type": "code", - "execution_count": 48, - "metadata": {}, + "execution_count": 49, + "metadata": { + "hidden": true + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_28672/3041004763.py:1: SettingWithCopyWarning: \n", + "/tmp/ipykernel_43708/3041004763.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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " house_df_fs['living_size'] = house_df_fs['m2_living'].apply(lambda x: 'small_house' if x <= 135\n", - "/tmp/ipykernel_28672/3041004763.py:5: SettingWithCopyWarning: \n", + "/tmp/ipykernel_43708/3041004763.py:5: 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", @@ -7324,8 +8477,10 @@ }, { "cell_type": "code", - "execution_count": 49, - "metadata": {}, + "execution_count": 50, + "metadata": { + "hidden": true + }, "outputs": [ { "data": { @@ -7336,7 +8491,7 @@ "Name: living_size, dtype: int64" ] }, - "execution_count": 49, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" } @@ -7347,8 +8502,10 @@ }, { "cell_type": "code", - "execution_count": 50, - "metadata": {}, + "execution_count": 51, + "metadata": { + "hidden": true + }, "outputs": [ { "data": { @@ -7359,7 +8516,7 @@ "Name: lot_size, dtype: int64" ] }, - "execution_count": 50, + "execution_count": 51, "metadata": {}, "output_type": "execute_result" } @@ -7370,14 +8527,16 @@ }, { "cell_type": "code", - "execution_count": 51, - "metadata": {}, + "execution_count": 52, + "metadata": { + "hidden": true + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/tmp/ipykernel_28672/2379853187.py:1: SettingWithCopyWarning: \n", + "/tmp/ipykernel_43708/2379853187.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", @@ -7396,8 +8555,10 @@ }, { "cell_type": "code", - "execution_count": 52, - "metadata": {}, + "execution_count": 53, + "metadata": { + "hidden": true + }, "outputs": [ { "data": { @@ -7410,7 +8571,7 @@ "Name: house_type, dtype: int64" ] }, - "execution_count": 52, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } @@ -7423,38 +8584,57 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 3. Find Main Insights" + "# 3. Find Main Insights" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true + }, "source": [ "### 3.1 House Recommendation report (Answering Business Team questions)" ] }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "house_df_ins = house_df_fs.copy(deep=True)" + ] + }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true + }, "source": [ "### 3.1.1 Which houses should be bought and for what price?" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true, + "hidden": true + }, "source": [ "#### Business Assumptions\n", "***\n", "\n", - "The recommendation solution will output houses to buy and not to buy only based on the region median price, because residence localization is an important business information, and more specifically based on the median price of that region but taking into account the property size, since its an important house comparison metric, because the price of a house with 60m² that is below the median price of its region does not tell that it is a potential house to buy and sell for a higher price near this region median, since the region property size distribution may be left skewed and the majority contribution for that median price comes from houses with bigger property size. \n", + "The recommendation solution will output houses to buy and not to buy based on the median price of the respective region, because residence localization is an important business information, and more specifically based on the median price of that region but taking into account groups of houses with the same living and lot area size, since its an important house comparison metric, because the price of a house with 60m² that is below the median price of its region does not tell that it is a potential house to buy and sell for a higher price near this region median, since the region property size distribution may be more concentrated on bigger sizes, hence the median price it's also bigger. \n", "\n", - "So, it is better to use a metric like price/m² to do a fair evaluation of the real estate or the property size appreciation for each region, that can be influenced by the mobility access of that region, and what offers in terms of market and public spaces. Denoting that this decision making process will be based on the infraestructure construction area that is the aspect of the house that would remotely be changed, unlike some maintenance or renovation works to preserve the house which could be what could be improved to increase that evaluation. \n", + "So, it is better to use a metric like median price of the aggregation with the finer granularity of the property size, to do a fair evaluation of the real estate or the property size appreciation for each region, that then can be influenced by other variables like the mobility access of that region, and what offers in terms of market and public spaces there are. \n", "\n", + "Denoting that this decision making process will be based on the infraestructure construction area and its locatization that are the aspects of the house that would be less probable to be changed over time, unlike some maintenance or renovation works to preserve the house which could be done to improve and increase that property pricing evaluation. \n", "\n", - "The price value will only be used to calculate the profit, coming from the difference with the selling price obtained through an increase on appreciation price/m2 by using as baseline the current median price/m2, that always suffer some market inflation, because the works expenses always increase each year and consequently other services and the house value, ignoring the possible increase with market appreciation because of higher house demand and less offer.\n", + "The price value will be used to calculate the profit, from the difference with the selling price obtained through an increase on appreciation of the last selling price, from always suffering some market inflation, because the works expenses always increase each year and consequently other services, and the house value itself as a valuable asset, from the possible increase with market appreciation considering a higher house demand and less offering effect, and of course some renovation works.\n", "\n", - "The decision to buy the houses is fundamentally based on the potential profit based on the expenses and the potential margin to increase value to match the median price/m2 of the respective region. The houses not considered to buy, can have different evaluation afterwards.\n", + "The decision to buy the houses is fundamentally based on the potential profit based on the expenses and the potential margin to increase the selling price value. The houses not considered to buy, can have different evaluation afterwards.\n", "\n", "Why using median price and not mean?\n", "\n", @@ -7462,29 +8642,33 @@ "\n", "**1. Identify best houses with comparison within each region**\n", "\n", - "**-** The houses that have a price value lower than the median price/m2, compared with houses that belong to the same region, and are in good conditions (condition $\\epsilon$ [3,5]), can be sold for a higher price, so are good to buy.\n", + "**-** The houses that have a price value lower than the median price, compared with houses that belong to the same region, and are in good conditions (condition $\\epsilon$ [3,5]), can be sold for a higher price, so are good to buy.\n", " \n", - " \n", - "**-** The houses that have a price value lower than the median price/m2, compared with houses that belong to the same region, and are in bad conditions (condition $\\epsilon$ [0,2]), cannot be sold for a higher price, so are not so good to buy.\n", + "**-** The houses that have a price value lower than the median price, compared with houses that belong to the same region, and are in bad conditions (condition $\\epsilon$ [0,2]), cannot be sold for a higher price, so are not so good to buy.\n", + "\n", + "**-** The houses that have a price value higher than the median price, compared with houses that belong to the same region, independently from the condition, are not good to buy in order to make good profit, but are going to be good for future comparison.\n", "\n", - "**-** The houses that have a price value higher than the median price/m2, compared with houses that belong to the same region, independently from the condition, are not good to buy to make good profit, but are going to be good for future comparison.\n", "\n", - "**-** The condition rank 3 takes into consideration that houses from a region that are below the region median price/m2 but has an average condition it's better than low condition houses that would need more renovation and could not have profit by taking into account the expenses, and has more potential to be sold above the median price of the region and get a higher profit since it has more margin for renewal and maintenance to increase the preservation condition unlike the houses with already good conditions. \n", + "**-** The condition rank 3 takes into consideration that houses from a region that are below the region median price but has a medium condition it's better than low condition houses that would need more renovation and could not have profit by taking into account the expenses, and has more potential to be sold above the median price of the region and get a higher profit since it has more margin for renewal and maintenance to increase the preservation condition unlike the houses with already good conditions. \n", + "\n", + "**-** Houses with condition rank 4 or 5, do not need that investment on renewal works but being below the median price, it could be a good buying option since it will suffer from value appreciation throughout time, without the short-term investment on renewing.\n", "\n", "\n", "**2. Define a selling price estimation**\n", "\n", - "**-** Assuming that the houses to buy have preservation condition rank between 3 and 5, there is some expectancy to have more profit gradually from houses with rank 3 to 5, on descending order, since the houses with rank 5 may have been sold below the region median because of bad real estate marketing strategy, or it was sold on a time period of low appreciation, that will be studied on the next business question, and the rank 3 were more probable sold below that appreciation value because of obvious lower preservation conditions and few amenities. \n", + "**-** Assuming that the houses to buy have preservation condition rank between 3 and 5, there is some expectancy to have more profit gradually from houses with rank 3 to 5, on ascending order. Since the houses with rank 5 may have been sold below the region median because of bad real estate marketing strategy, or it was sold on a time period of low appreciation, that will be studied on the next business question. And the rank 3 were more probable sold below that appreciation value because of obvious lower preservation conditions and few amenities. \n", + "\n", + "**-** But since the previous rationale results on a recommendation of over 10 000 houses, half of the portfolio, does not seem to be a very realistic analysis. So it would be better to forget in the beginning the strategy of acquiring as well the houses with condition of 3, that would be more susceptible to short-term investment decisions on renewals.\n", "\n", - "**-** So we set some different levels of increasing expectancy on houses appreciation:\n", + "**-** So I set some different levels of increasing expectancy on houses appreciation:\n", "\n", - " 1. giving a 15% increase estimation on selling price, with region median price/m2 as baseline to calculate the current market price for houses with this property size and then adding the inflation, for houses with condition rank 3,\n", - " 2. 10% for houses with condition rank 4\n", - " 3. and 5% for houses with condition rank 5\n", + " 1. giving a 50% increase estimation on selling price, for houses with their last price being between 50% and 75% below the region median price/m2, for houses with condition rank 4,\n", + " 2. then between 25% and 50% an appreciation of 25%, and from 0 to 25% half of the appreciation. \n", + " 3. and for houses with condition rank 5, the same appreciation model but with less 5% on each interval.\n", " \n", "**3. Ordering houses to buy with defined priorities**\n", "\n", - " 1. Order by percentage_below_median_price/m2, on a descending order\n", + " 1. Order by percentage_below_median_price, on a descending order\n", " 2. then by condition, on a ascending order\n", " 3. then by profit, on a descending order\n", " \n", @@ -7493,21 +8677,26 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true, + "hidden": true + }, "source": [ "#### 3.1.1.1 Houses to buy labelling process\n", "\n", "Labels:\n", "\n", " - 'to buy'\n", - " - 'to compare'\n", + " - 'to consider'\n", + " - 'to compare' (when founding what renovations to do or ammenities to be added on the properties above the median price it can be compared with renewal margin properties that were below the median)\n", " - 'not worth buying'" ] }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 55, "metadata": { + "hidden": true, "scrolled": true }, "outputs": [ @@ -7522,55 +8711,61 @@ "Name: condition, dtype: int64" ] }, - "execution_count": 53, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "house_df_fs['condition'].value_counts()" + "house_df_ins['condition'].value_counts()" ] }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 56, "metadata": { + "hidden": true, "scrolled": true }, "outputs": [], "source": [ "# Calculate its median price per region per living size and per lot size.\n", "\n", - "zipcode_median_price = house_df_fs[['price', 'zipcode','living_size','lot_size']].groupby(['zipcode','living_size','lot_size']).median().reset_index()\n", + "zipcode_median_price = house_df_ins[['price', 'zipcode','living_size','lot_size']].groupby(['zipcode','living_size','lot_size']).median().reset_index()\n", "zipcode_median_price.columns = ['zipcode','living_size','lot_size','median_price']\n", "\n", - "house_df_fs = pd.merge(house_df_fs,zipcode_median_price,on=['zipcode','living_size','lot_size'],how='inner')\n", + "house_df_ins = pd.merge(house_df_fs,zipcode_median_price,on=['zipcode','living_size','lot_size'],how='inner')\n", "\n", "# Calculate its median price per m2 per region per living size and per lot size.\n", "\n", - "zipcode_median_price_m2 = house_df_fs[['price_m2', 'zipcode','living_size','lot_size']].groupby(['zipcode','living_size','lot_size']).median().reset_index()\n", + "zipcode_median_price_m2 = house_df_ins[['price_m2', 'zipcode','living_size','lot_size']].groupby(['zipcode','living_size','lot_size']).median().reset_index()\n", "zipcode_median_price_m2.columns = ['zipcode','living_size','lot_size','median_price_m2']\n", "\n", - "house_df_fs = pd.merge(house_df_fs,zipcode_median_price_m2,on=['zipcode','living_size','lot_size'],how='inner')" + "house_df_ins = pd.merge(house_df_ins,zipcode_median_price_m2,on=['zipcode','living_size','lot_size'],how='inner')" ] }, { "cell_type": "code", - "execution_count": 55, - "metadata": {}, + "execution_count": 57, + "metadata": { + "hidden": true + }, "outputs": [], "source": [ "# Create house recommendation system\n", "\n", - "house_df_fs['status'] = house_df_fs[['price','condition','median_price']].apply(lambda x: 'to buy' if (x[0] < x[2]) & (x[1] >= 3)\\\n", - " else 'to compare' if (x[0] > x[2])\\\n", - " else 'not worth buying', axis = 1)" + "house_df_ins['status'] = house_df_ins[['price','condition','median_price']].apply(lambda x: 'to buy' if (x[0] < x[2]) & (x[1] > 3)\\\n", + " else 'to consider' if (x[0] < x[2]) & (x[1] == 3)\\\n", + " else 'to compare' if (x[0] > x[2]) & (x[1] > 3)\\\n", + " else 'not worth buying', axis = 1)" ] }, { "cell_type": "code", - "execution_count": 56, - "metadata": {}, + "execution_count": 58, + "metadata": { + "hidden": true + }, "outputs": [ { "data": { @@ -7605,137 +8800,166 @@ " \n", " \n", " \n", - " 0\n", - " 7129300520\n", - " 98178\n", - " 221900.00\n", - " 225000.00\n", - " 349.72\n", - " 292.83\n", + " 16159\n", + " 1775910270\n", + " 98072\n", + " 355000.00\n", + " 355500.00\n", + " 222.17\n", + " 330.14\n", " 3\n", - " to buy\n", + " to consider\n", " \n", " \n", - " 1\n", - " 4060000240\n", - " 98178\n", - " 205425.00\n", - " 225000.00\n", - " 288.67\n", - " 292.83\n", + " 18860\n", + " 3323059027\n", + " 98058\n", + " 326000.00\n", + " 408000.00\n", + " 118.07\n", + " 112.75\n", " 4\n", " to buy\n", " \n", " \n", - " 2\n", - " 2976800796\n", - " 98178\n", - " 236000.00\n", - " 225000.00\n", - " 352.93\n", - " 292.83\n", + " 15447\n", + " 4024101440\n", + " 98155\n", + " 375000.00\n", + " 420000.00\n", + " 457.61\n", + " 397.76\n", " 3\n", - " to compare\n", + " to consider\n", " \n", " \n", - " 3\n", - " 3969300030\n", - " 98178\n", - " 165000.00\n", - " 225000.00\n", - " 218.36\n", - " 292.83\n", + " 10622\n", + " 2538400060\n", + " 98075\n", + " 860000.00\n", + " 836500.00\n", + " 843.72\n", + " 697.62\n", " 3\n", - " to buy\n", + " not worth buying\n", " \n", " \n", - " 4\n", - " 3969300030\n", - " 98178\n", - " 239900.00\n", - " 225000.00\n", - " 317.48\n", - " 292.83\n", + " 798\n", + " 7212660960\n", + " 98003\n", + " 297000.00\n", + " 283200.00\n", + " 317.35\n", + " 285.22\n", " 3\n", - " to compare\n", + " not worth buying\n", " \n", " \n", "\n", "" ], "text/plain": [ - " id zipcode price median_price price_m2 median_price_m2 condition status\n", - "0 7129300520 98178 221900.00 225000.00 349.72 292.83 3 to buy\n", - "1 4060000240 98178 205425.00 225000.00 288.67 292.83 4 to buy\n", - "2 2976800796 98178 236000.00 225000.00 352.93 292.83 3 to compare\n", - "3 3969300030 98178 165000.00 225000.00 218.36 292.83 3 to buy\n", - "4 3969300030 98178 239900.00 225000.00 317.48 292.83 3 to compare" + " id zipcode price median_price price_m2 median_price_m2 condition status\n", + "16159 1775910270 98072 355000.00 355500.00 222.17 330.14 3 to consider\n", + "18860 3323059027 98058 326000.00 408000.00 118.07 112.75 4 to buy\n", + "15447 4024101440 98155 375000.00 420000.00 457.61 397.76 3 to consider\n", + "10622 2538400060 98075 860000.00 836500.00 843.72 697.62 3 not worth buying\n", + "798 7212660960 98003 297000.00 283200.00 317.35 285.22 3 not worth buying" ] }, - "execution_count": 56, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "house_df_fs[['id','zipcode','price','median_price','price_m2','median_price_m2','condition','status']].head()" + "house_df_ins[['id','zipcode','price','median_price','price_m2','median_price_m2','condition','status']].sample(5)" ] }, { "cell_type": "code", - "execution_count": 57, - "metadata": {}, + "execution_count": 59, + "metadata": { + "hidden": true + }, "outputs": [ { "data": { "text/plain": [ - "to compare 10592\n", - "to buy 10448\n", - "not worth buying 573\n", + "not worth buying 7495\n", + "to consider 6888\n", + "to compare 3670\n", + "to buy 3560\n", "Name: status, dtype: int64" ] }, - "execution_count": 57, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "house_df_fs.status.value_counts()" + "house_df_ins.status.value_counts()" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "hidden": true + }, "outputs": [], "source": [] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true, + "hidden": true + }, "source": [ "#### 3.1.1.2 Create a selling price business model\n" ] }, { "cell_type": "code", - "execution_count": 58, - "metadata": {}, + "execution_count": 60, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "# create percentage_value_below_median\n", + "\n", + "house_df_ins['%_value_below_median_price'] = (1 - (house_df_ins['price']/house_df_ins['median_price']))*100" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "hidden": true + }, "outputs": [], "source": [ "# set a selling price\n", "\n", - "house_df_fs['selling_price'] = house_df_fs[['median_price','condition','status','price']].apply(lambda x: x[0]*1.15 if (x[1] == 3) & (x[2] == 'to buy')\\\n", - " else x[0]*1.10 if (x[1] == 4) & (x[2] == 'to buy')\\\n", - " else x[0]*1.05 if (x[1] == 5) & (x[2] == 'to buy')\\\n", - " else x[3], axis=1)" + "house_df_ins['selling_price'] = house_df_ins[['price','condition','status','%_value_below_median_price']].apply(lambda x: x[0] if (x[1] == 3) & (x[2] == 'to consider')\\\n", + " else x[0]*1.5 if (x[1] == 4) & (x[2] == 'to buy') & (x[3] > 50 and x[3] < 75)\\\n", + " else x[0]*1.25 if (x[1] == 4) & (x[2] == 'to buy') & (x[3] > 25 and x[3] < 50)\\\n", + " else x[0]*1.125 if (x[1] == 4) & (x[2] == 'to buy') & (x[3] > 0 and x[3] < 25)\\\n", + " else x[0]*1.45 if (x[1] == 5) & (x[2] == 'to buy') & (x[3] > 50 and x[3] < 75)\\\n", + " else x[0]*1.20 if (x[1] == 5) & (x[2] == 'to buy') & (x[3] > 25 and x[3] < 50)\\\n", + " else x[0]*1.075 if (x[1] == 5) & (x[2] == 'to buy') & (x[3] > 0 and x[3] < 25)\\\n", + " else x[0], axis=1)" ] }, { "cell_type": "code", - "execution_count": 59, - "metadata": {}, + "execution_count": 62, + "metadata": { + "hidden": true + }, "outputs": [ { "data": { @@ -7767,6 +8991,7 @@ " condition\n", " status\n", " selling_price\n", + " %_value_below_median_price\n", " \n", " \n", " \n", @@ -7779,8 +9004,9 @@ " 349.72\n", " 292.83\n", " 3\n", - " to buy\n", - " 258750.00\n", + " to consider\n", + " 221900.00\n", + " 1.38\n", " \n", " \n", " 1\n", @@ -7792,7 +9018,8 @@ " 292.83\n", " 4\n", " to buy\n", - " 247500.00\n", + " 231103.12\n", + " 8.70\n", " \n", " \n", " 2\n", @@ -7803,8 +9030,9 @@ " 352.93\n", " 292.83\n", " 3\n", - " to compare\n", + " not worth buying\n", " 236000.00\n", + " -4.89\n", " \n", " \n", " 3\n", @@ -7815,8 +9043,9 @@ " 218.36\n", " 292.83\n", " 3\n", - " to buy\n", - " 258750.00\n", + " to consider\n", + " 165000.00\n", + " 26.67\n", " \n", " \n", " 4\n", @@ -7827,73 +9056,71 @@ " 317.48\n", " 292.83\n", " 3\n", - " to compare\n", + " not worth buying\n", " 239900.00\n", + " -6.62\n", " \n", " \n", "\n", "" ], "text/plain": [ - " id zipcode price median_price price_m2 median_price_m2 condition status selling_price\n", - "0 7129300520 98178 221900.00 225000.00 349.72 292.83 3 to buy 258750.00\n", - "1 4060000240 98178 205425.00 225000.00 288.67 292.83 4 to buy 247500.00\n", - "2 2976800796 98178 236000.00 225000.00 352.93 292.83 3 to compare 236000.00\n", - "3 3969300030 98178 165000.00 225000.00 218.36 292.83 3 to buy 258750.00\n", - "4 3969300030 98178 239900.00 225000.00 317.48 292.83 3 to compare 239900.00" + " id zipcode price median_price price_m2 median_price_m2 condition status selling_price %_value_below_median_price\n", + "0 7129300520 98178 221900.00 225000.00 349.72 292.83 3 to consider 221900.00 1.38\n", + "1 4060000240 98178 205425.00 225000.00 288.67 292.83 4 to buy 231103.12 8.70\n", + "2 2976800796 98178 236000.00 225000.00 352.93 292.83 3 not worth buying 236000.00 -4.89\n", + "3 3969300030 98178 165000.00 225000.00 218.36 292.83 3 to consider 165000.00 26.67\n", + "4 3969300030 98178 239900.00 225000.00 317.48 292.83 3 not worth buying 239900.00 -6.62" ] }, - "execution_count": 59, + "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "house_df_fs[['id','zipcode','price','median_price','price_m2','median_price_m2','condition','status','selling_price']].head()" + "house_df_ins[['id','zipcode','price','median_price','price_m2','median_price_m2','condition','status','selling_price','%_value_below_median_price']].head()" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true, + "hidden": true + }, "source": [ "#### 3.1.1.3 Add a priority order for selected houses to buy" ] }, { "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "# create percentage_value_below_median\n", - "\n", - "house_df_fs['%_value_below_median_price'] = (1 - (house_df_fs['price']/house_df_fs['median_price']))*100" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, + "execution_count": 63, + "metadata": { + "hidden": true + }, "outputs": [], "source": [ "# Create profit variable\n", "\n", - "house_df_fs['profit_est'] = house_df_fs['selling_price'] - house_df_fs['price']" + "house_df_ins['profit_est'] = house_df_ins['selling_price'] - house_df_ins['price']" ] }, { "cell_type": "code", - "execution_count": 62, - "metadata": {}, + "execution_count": 64, + "metadata": { + "hidden": true + }, "outputs": [], "source": [ - "house_df_fs.sort_values(by=['%_value_below_median_price', 'condition', 'profit_est'], ascending=[False,True,False], inplace=True)" + "house_df_ins.sort_values(by=['%_value_below_median_price', 'condition', 'profit_est'], ascending=[False,True,False], inplace=True)" ] }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 65, "metadata": { + "hidden": true, "scrolled": false }, "outputs": [ @@ -7940,41 +9167,13 @@ " median_price\n", " median_price_m2\n", " status\n", - " selling_price\n", " %_value_below_median_price\n", + " selling_price\n", " profit_est\n", " \n", " \n", " \n", " \n", - " 19325\n", - " 3421079032\n", - " 2015-02-17\n", - " 75000.00\n", - " 3\n", - " 3\n", - " 98022\n", - " 1\n", - " 0.00\n", - " 47.26\n", - " -121.91\n", - " 62.24\n", - " 62.24\n", - " 0.00\n", - " 4029.72\n", - " 4091.97\n", - " 18.33\n", - " small_house\n", - " large_terrain\n", - " mansion\n", - " 302000.00\n", - " 21.93\n", - " to buy\n", - " 347300.00\n", - " 75.17\n", - " 272300.00\n", - " \n", - " \n", " 19431\n", " 1238500451\n", " 2015-02-09\n", @@ -7998,573 +9197,605 @@ " 465000.00\n", " 483.99\n", " to buy\n", - " 511500.00\n", " 72.04\n", - " 381500.00\n", - " \n", - " \n", - " 9957\n", - " 2734100734\n", - " 2014-10-15\n", - " 216650.00\n", - " 3\n", - " 7\n", - " 98109\n", - " 3\n", - " 3.50\n", - " 47.54\n", - " -122.32\n", - " 143.07\n", - " 126.34\n", - " 16.72\n", - " 132.57\n", - " 275.63\n", - " 786.01\n", - " medium_house\n", - " small_terrain\n", - " villa\n", - " 740000.00\n", - " 1652.12\n", - " to buy\n", - " 851000.00\n", - " 70.72\n", - " 634350.00\n", + " 195000.00\n", + " 65000.00\n", " \n", " \n", - " 9972\n", - " 2734100732\n", - " 2014-10-15\n", - " 216650.00\n", - " 3\n", - " 7\n", - " 98109\n", + " 11755\n", + " 5111400086\n", + " 2014-05-12\n", + " 110000.00\n", + " 5\n", + " 6\n", + " 98038\n", " 3\n", - " 3.50\n", - " 47.54\n", - " -122.32\n", - " 137.49\n", - " 120.77\n", - " 16.72\n", - " 100.05\n", - " 237.55\n", - " 912.04\n", - " medium_house\n", - " small_terrain\n", - " villa\n", - " 740000.00\n", - " 1652.12\n", + " 1.00\n", + " 47.42\n", + " -122.05\n", + " 116.12\n", + " 116.12\n", + " 0.00\n", + " 4936.98\n", + " 5053.11\n", + " 21.77\n", + " small_house\n", + " large_terrain\n", + " mansion\n", + " 295000.00\n", + " 34.21\n", " to buy\n", - " 851000.00\n", - " 70.72\n", - " 634350.00\n", + " 62.71\n", + " 159500.00\n", + " 49500.00\n", " \n", " \n", - " 20328\n", - " 1049010620\n", - " 2014-05-13\n", - " 90000.00\n", - " 3\n", - " 7\n", - " 98034\n", + " 13663\n", + " 7899800450\n", + " 2014-08-28\n", + " 107000.00\n", + " 4\n", + " 6\n", + " 98106\n", " 2\n", " 1.00\n", - " 47.74\n", - " -122.18\n", - " 73.39\n", - " 73.39\n", + " 47.52\n", + " -122.36\n", + " 62.24\n", + " 62.24\n", " 0.00\n", - " 245.26\n", - " 318.65\n", - " 282.44\n", + " 438.49\n", + " 500.73\n", + " 213.69\n", " small_house\n", " small_terrain\n", - " villa\n", - " 300000.00\n", - " 735.03\n", + " townhouse\n", + " 276000.00\n", + " 631.95\n", " to buy\n", - " 345000.00\n", - " 70.00\n", - " 255000.00\n", + " 61.23\n", + " 160500.00\n", + " 53500.00\n", " \n", " \n", - " 20893\n", - " 6341000020\n", - " 2015-03-04\n", - " 226000.00\n", - " 3\n", - " 7\n", - " 98146\n", + " 21363\n", + " 2354300456\n", + " 2015-03-11\n", + " 130000.00\n", + " 4\n", + " 4\n", + " 98027\n", " 2\n", " 1.00\n", - " 47.49\n", - " -122.34\n", - " 140.28\n", - " 140.28\n", + " 47.53\n", + " -122.03\n", + " 55.74\n", + " 55.74\n", " 0.00\n", - " 1846.29\n", - " 1986.57\n", - " 113.76\n", - " medium_house\n", - " large_terrain\n", - " mansion\n", - " 750000.00\n", - " 227.60\n", + " 139.35\n", + " 195.09\n", + " 666.36\n", + " small_house\n", + " small_terrain\n", + " apartment\n", + " 331000.00\n", + " 1522.92\n", " to buy\n", - " 862500.00\n", - " 69.87\n", - " 636500.00\n", + " 60.73\n", + " 195000.00\n", + " 65000.00\n", " \n", " \n", - " 19467\n", - " 2870000040\n", - " 2014-11-10\n", - " 145000.00\n", - " 3\n", + " 11133\n", + " 3888100226\n", + " 2014-06-30\n", + " 461000.00\n", + " 4\n", " 6\n", " 98033\n", - " 2\n", - " 1.00\n", - " 47.68\n", + " 3\n", + " 1.75\n", + " 47.69\n", " -122.17\n", - " 74.32\n", - " 74.32\n", - " 0.00\n", - " 754.81\n", - " 829.13\n", - " 174.88\n", - " small_house\n", + " 334.44\n", + " 222.96\n", + " 111.48\n", + " 805.07\n", + " 1139.51\n", + " 404.56\n", + " large_house\n", " medium_terrain\n", - " townhouse\n", - " 465000.00\n", - " 483.99\n", - " to buy\n", - " 534750.00\n", - " 68.82\n", - " 389750.00\n", - " \n", - " \n", - " 20080\n", - " 2734100835\n", - " 2015-03-03\n", - " 90000.00\n", - " 3\n", - " 5\n", - " 98108\n", - " 1\n", - " 1.00\n", - " 47.54\n", - " -122.32\n", - " 72.46\n", - " 72.46\n", - " 0.00\n", - " 371.60\n", - " 444.06\n", - " 202.67\n", - " small_house\n", - " small_terrain\n", - " townhouse\n", - " 284500.00\n", - " 717.00\n", + " mansion\n", + " 1135000.00\n", + " 945.68\n", " to buy\n", - " 327175.00\n", - " 68.37\n", - " 237175.00\n", + " 59.38\n", + " 691500.00\n", + " 230500.00\n", " \n", " \n", - " 7895\n", - " 3530210260\n", - " 2014-10-27\n", - " 274975.00\n", - " 3\n", - " 9\n", - " 98077\n", - " 3\n", + " 16258\n", + " 644210020\n", + " 2015-01-05\n", + " 780000.00\n", + " 4\n", + " 8\n", + " 98004\n", + " 4\n", " 2.50\n", - " 47.77\n", - " -122.09\n", - " 281.49\n", - " 281.49\n", - " 0.00\n", - " 4180.87\n", - " 4462.36\n", - " 61.62\n", + " 47.59\n", + " -122.19\n", + " 280.56\n", + " 160.72\n", + " 119.84\n", + " 1408.74\n", + " 1689.29\n", + " 461.73\n", " large_house\n", - " large_terrain\n", + " medium_terrain\n", " mansion\n", - " 856250.00\n", - " 191.28\n", + " 1750000.00\n", + " 1242.36\n", " to buy\n", - " 984687.50\n", - " 67.89\n", - " 709712.50\n", + " 55.43\n", + " 1170000.00\n", + " 390000.00\n", " \n", " \n", - " 20688\n", - " 9169600275\n", - " 2014-07-23\n", - " 280000.00\n", - " 3\n", + " 6551\n", + " 4038000040\n", + " 2015-03-26\n", + " 250000.00\n", + " 4\n", " 7\n", - " 98136\n", - " 2\n", - " 1.00\n", - " 47.52\n", - " -122.39\n", - " 211.81\n", - " 211.81\n", - " 0.00\n", - " 3483.75\n", - " 3695.56\n", - " 75.77\n", - " medium_house\n", - " large_terrain\n", - " mansion\n", - " 865000.00\n", - " 310.60\n", - " to buy\n", - " 994750.00\n", - " 67.63\n", - " 714750.00\n", - " \n", - " \n", - " 13704\n", - " 2114700500\n", - " 2015-04-18\n", - " 90000.00\n", - " 3\n", + " 98008\n", " 4\n", - " 98106\n", - " 1\n", - " 1.00\n", - " 47.53\n", - " -122.35\n", - " 52.02\n", - " 52.02\n", + " 1.75\n", + " 47.61\n", + " -122.12\n", + " 177.44\n", + " 177.44\n", " 0.00\n", - " 382.75\n", - " 434.77\n", - " 207.01\n", - " small_house\n", - " small_terrain\n", + " 766.42\n", + " 943.86\n", + " 264.87\n", + " medium_house\n", + " medium_terrain\n", " townhouse\n", - " 276000.00\n", - " 631.95\n", + " 555000.00\n", + " 542.88\n", " to buy\n", - " 317400.00\n", - " 67.39\n", - " 227400.00\n", + " 54.95\n", + " 375000.00\n", + " 125000.00\n", " \n", " \n", - " 12640\n", - " 424049043\n", - " 2014-08-11\n", - " 450000.00\n", - " 3\n", - " 7\n", - " 98144\n", + " 21410\n", + " 8952900245\n", + " 2014-07-15\n", + " 850000.00\n", + " 4\n", " 9\n", - " 7.50\n", - " 47.59\n", - " -122.30\n", - " 376.25\n", - " 376.25\n", - " 0.00\n", - " 604.22\n", - " 980.47\n", - " 458.97\n", + " 98118\n", + " 6\n", + " 3.25\n", + " 47.55\n", + " -122.27\n", + " 457.07\n", + " 253.62\n", + " 203.45\n", + " 1912.81\n", + " 2369.88\n", + " 358.67\n", " large_house\n", - " medium_terrain\n", - " townhouse\n", - " 1364500.00\n", - " 1292.80\n", + " large_terrain\n", + " mansion\n", + " 1876500.00\n", + " 343.06\n", " to buy\n", - " 1569175.00\n", - " 67.02\n", - " 1119175.00\n", + " 54.70\n", + " 1275000.00\n", + " 425000.00\n", " \n", " \n", - " 2709\n", - " 5016003230\n", - " 2015-02-18\n", - " 169317.00\n", - " 3\n", + " 20086\n", + " 13001215\n", + " 2015-03-05\n", + " 130000.00\n", + " 4\n", " 7\n", - " 98112\n", - " 2\n", + " 98108\n", + " 3\n", " 1.00\n", - " 47.62\n", - " -122.30\n", - " 73.39\n", - " 73.39\n", + " 47.52\n", + " -122.33\n", + " 102.19\n", + " 102.19\n", " 0.00\n", - " 371.60\n", - " 444.99\n", - " 380.50\n", + " 473.79\n", + " 575.98\n", + " 225.70\n", " small_house\n", " small_terrain\n", " townhouse\n", - " 511500.00\n", - " 1492.21\n", + " 284500.00\n", + " 717.00\n", " to buy\n", - " 588225.00\n", - " 66.90\n", - " 418908.00\n", + " 54.31\n", + " 195000.00\n", + " 65000.00\n", " \n", " \n", - " 20434\n", - " 6169901185\n", - " 2014-05-20\n", - " 490000.00\n", - " 3\n", - " 10\n", - " 98119\n", + " 4474\n", + " 7227800040\n", + " 2014-06-04\n", + " 190000.00\n", + " 4\n", " 5\n", - " 3.50\n", - " 47.63\n", - " -122.37\n", - " 414.33\n", - " 304.71\n", - " 109.62\n", - " 276.38\n", - " 690.71\n", - " 709.41\n", - " large_house\n", - " small_terrain\n", - " townhouse\n", - " 1430000.00\n", - " 1834.02\n", + " 98056\n", + " 5\n", + " 2.00\n", + " 47.51\n", + " -122.18\n", + " 162.57\n", + " 162.57\n", + " 0.00\n", + " 955.38\n", + " 1117.96\n", + " 169.95\n", + " medium_house\n", + " medium_terrain\n", + " mansion\n", + " 415000.00\n", + " 422.38\n", " to buy\n", - " 1644500.00\n", - " 65.73\n", - " 1154500.00\n", + " 54.22\n", + " 285000.00\n", + " 95000.00\n", " \n", " \n", - " 19898\n", - " 1721801591\n", - " 2015-02-19\n", - " 89950.00\n", + " 5569\n", + " 1217000340\n", + " 2014-06-06\n", + " 185000.00\n", + " 4\n", + " 7\n", + " 98166\n", " 3\n", - " 5\n", - " 98146\n", - " 1\n", " 1.00\n", - " 47.51\n", - " -122.33\n", - " 52.95\n", - " 52.95\n", - " 0.00\n", - " 379.03\n", - " 431.98\n", - " 208.22\n", - " small_house\n", - " small_terrain\n", + " 47.45\n", + " -122.35\n", + " 170.94\n", + " 85.47\n", + " 85.47\n", + " 752.49\n", + " 923.43\n", + " 200.34\n", + " medium_house\n", + " medium_terrain\n", " townhouse\n", - " 260000.00\n", - " 509.58\n", + " 400475.00\n", + " 349.30\n", " to buy\n", - " 299000.00\n", - " 65.40\n", - " 209050.00\n", + " 53.80\n", + " 277500.00\n", + " 92500.00\n", " \n", " \n", - " 12921\n", - " 3883800011\n", - " 2014-11-05\n", - " 82000.00\n", - " 3\n", + " 6019\n", + " 4311700120\n", + " 2015-03-17\n", + " 105000.00\n", + " 4\n", " 6\n", - " 98146\n", + " 98042\n", " 3\n", " 1.00\n", - " 47.50\n", - " -122.34\n", - " 79.89\n", - " 79.89\n", + " 47.36\n", + " -122.10\n", + " 81.75\n", + " 81.75\n", " 0.00\n", - " 968.58\n", - " 1048.47\n", - " 78.21\n", + " 1682.33\n", + " 1764.08\n", + " 59.52\n", " small_house\n", " medium_terrain\n", " mansion\n", - " 234900.00\n", - " 266.86\n", + " 226000.00\n", + " 233.34\n", " to buy\n", - " 270135.00\n", - " 65.09\n", - " 188135.00\n", + " 53.54\n", + " 157500.00\n", + " 52500.00\n", " \n", " \n", - " 20308\n", - " 7896300592\n", - " 2015-01-14\n", - " 303500.00\n", - " 3\n", - " 8\n", - " 98118\n", + " 20040\n", + " 9232900050\n", + " 2014-08-06\n", + " 300000.00\n", + " 4\n", " 6\n", - " 4.50\n", - " 47.52\n", - " -122.29\n", - " 314.93\n", - " 226.68\n", - " 88.25\n", - " 668.88\n", - " 983.81\n", - " 308.49\n", - " large_house\n", + " 98103\n", + " 2\n", + " 1.75\n", + " 47.70\n", + " -122.36\n", + " 196.95\n", + " 133.78\n", + " 63.17\n", + " 589.91\n", + " 786.86\n", + " 381.26\n", + " medium_house\n", " medium_terrain\n", " townhouse\n", - " 856000.00\n", - " 773.05\n", + " 645000.00\n", + " 846.27\n", " to buy\n", - " 984400.00\n", - " 64.54\n", - " 680900.00\n", + " 53.49\n", + " 450000.00\n", + " 150000.00\n", " \n", " \n", - " 1030\n", - " 723049158\n", - " 2015-03-13\n", - " 135000.00\n", - " 3\n", - " 5\n", - " 98146\n", + " 5340\n", + " 5127001320\n", + " 2014-11-25\n", + " 190000.00\n", " 4\n", - " 1.00\n", - " 47.50\n", - " -122.35\n", - " 135.63\n", - " 135.63\n", + " 7\n", + " 98059\n", + " 3\n", + " 1.75\n", + " 47.47\n", + " -122.15\n", + " 141.21\n", + " 141.21\n", " 0.00\n", - " 1727.85\n", - " 1863.48\n", - " 72.45\n", + " 891.84\n", + " 1033.05\n", + " 183.92\n", " medium_house\n", " medium_terrain\n", " mansion\n", - " 380000.00\n", - " 368.73\n", + " 405000.00\n", + " 369.90\n", " to buy\n", - " 437000.00\n", - " 64.47\n", - " 302000.00\n", + " 53.09\n", + " 285000.00\n", + " 95000.00\n", " \n", " \n", - " 5627\n", - " 7228500094\n", - " 2014-12-12\n", - " 278000.00\n", - " 3\n", + " 10785\n", + " 6840700165\n", + " 2014-07-01\n", + " 202000.00\n", + " 4\n", " 7\n", " 98122\n", - " 4\n", - " 2.00\n", + " 1\n", + " 1.00\n", " 47.61\n", " -122.30\n", - " 137.49\n", - " 137.49\n", + " 54.81\n", + " 54.81\n", " 0.00\n", - " 587.50\n", - " 724.99\n", - " 383.45\n", - " medium_house\n", + " 77.39\n", + " 132.20\n", + " 1528.03\n", + " small_house\n", + " small_terrain\n", + " apartment\n", + " 430000.00\n", + " 1492.01\n", + " to buy\n", + " 53.02\n", + " 303000.00\n", + " 101000.00\n", + " \n", + " \n", + " 2003\n", + " 7335400215\n", + " 2015-05-05\n", + " 95000.00\n", + " 4\n", + " 5\n", + " 98002\n", + " 1\n", + " 0.75\n", + " 47.30\n", + " -122.22\n", + " 70.60\n", + " 70.60\n", + " 0.00\n", + " 533.80\n", + " 604.41\n", + " 157.18\n", + " small_house\n", " medium_terrain\n", " townhouse\n", - " 762000.00\n", - " 904.13\n", + " 200000.00\n", + " 236.30\n", " to buy\n", - " 876300.00\n", - " 63.52\n", - " 598300.00\n", + " 52.50\n", + " 142500.00\n", + " 47500.00\n", " \n", " \n", - " 16776\n", - " 4055700778\n", - " 2014-11-14\n", - " 523000.00\n", - " 3\n", - " 8\n", - " 98034\n", + " 20536\n", + " 3398800055\n", + " 2014-11-19\n", + " 2400000.00\n", " 4\n", - " 2.50\n", - " 47.72\n", - " -122.24\n", - " 295.42\n", - " 196.95\n", - " 98.47\n", - " 1938.82\n", - " 2234.24\n", - " 234.08\n", + " 11\n", + " 98102\n", + " 4\n", + " 3.75\n", + " 47.63\n", + " -122.32\n", + " 379.96\n", + " 315.86\n", + " 64.10\n", + " 2306.24\n", + " 2686.20\n", + " 893.45\n", + " large_house\n", + " large_terrain\n", + " mansion\n", + " 5050000.00\n", + " 1491.93\n", + " to buy\n", + " 52.48\n", + " 3600000.00\n", + " 1200000.00\n", + " \n", + " \n", + " 16820\n", + " 1545807810\n", + " 2014-10-21\n", + " 118000.00\n", + " 4\n", + " 6\n", + " 98038\n", + " 1\n", + " 1.00\n", + " 47.36\n", + " -122.06\n", + " 62.24\n", + " 62.24\n", + " 0.00\n", + " 739.21\n", + " 801.45\n", + " 147.23\n", + " small_house\n", + " medium_terrain\n", + " townhouse\n", + " 247450.00\n", + " 296.60\n", + " to buy\n", + " 52.31\n", + " 177000.00\n", + " 59000.00\n", + " \n", + " \n", + " 10127\n", + " 5495200040\n", + " 2015-01-26\n", + " 610000.00\n", + " 4\n", + " 8\n", + " 98006\n", + " 5\n", + " 3.25\n", + " 47.57\n", + " -122.12\n", + " 324.22\n", + " 175.58\n", + " 148.64\n", + " 2173.86\n", + " 2498.08\n", + " 244.19\n", " large_house\n", " large_terrain\n", " mansion\n", - " 1432500.00\n", - " 453.13\n", + " 1275000.00\n", + " 403.94\n", + " to buy\n", + " 52.16\n", + " 915000.00\n", + " 305000.00\n", + " \n", + " \n", + " 19784\n", + " 5016002275\n", + " 2014-06-02\n", + " 610000.00\n", + " 4\n", + " 8\n", + " 98112\n", + " 5\n", + " 2.50\n", + " 47.62\n", + " -122.30\n", + " 370.67\n", + " 184.87\n", + " 185.80\n", + " 356.64\n", + " 727.31\n", + " 838.70\n", + " large_house\n", + " small_terrain\n", + " townhouse\n", + " 1272000.00\n", + " 1737.98\n", " to buy\n", - " 1647375.00\n", - " 63.49\n", - " 1124375.00\n", + " 52.04\n", + " 915000.00\n", + " 305000.00\n", " \n", " \n", "\n", "" ], "text/plain": [ - " id date price condition grade zipcode bedrooms bathrooms lat long m2_living m2_above m2_basement m2_lot house_total_m2 price_m2 living_size lot_size house_type median_price median_price_m2 status selling_price %_value_below_median_price profit_est\n", - "19325 3421079032 2015-02-17 75000.00 3 3 98022 1 0.00 47.26 -121.91 62.24 62.24 0.00 4029.72 4091.97 18.33 small_house large_terrain mansion 302000.00 21.93 to buy 347300.00 75.17 272300.00\n", - "19431 1238500451 2015-02-09 130000.00 4 7 98033 3 1.00 47.68 -122.18 103.12 103.12 0.00 698.61 801.73 162.15 small_house medium_terrain townhouse 465000.00 483.99 to buy 511500.00 72.04 381500.00\n", - "9957 2734100734 2014-10-15 216650.00 3 7 98109 3 3.50 47.54 -122.32 143.07 126.34 16.72 132.57 275.63 786.01 medium_house small_terrain villa 740000.00 1652.12 to buy 851000.00 70.72 634350.00\n", - "9972 2734100732 2014-10-15 216650.00 3 7 98109 3 3.50 47.54 -122.32 137.49 120.77 16.72 100.05 237.55 912.04 medium_house small_terrain villa 740000.00 1652.12 to buy 851000.00 70.72 634350.00\n", - "20328 1049010620 2014-05-13 90000.00 3 7 98034 2 1.00 47.74 -122.18 73.39 73.39 0.00 245.26 318.65 282.44 small_house small_terrain villa 300000.00 735.03 to buy 345000.00 70.00 255000.00\n", - "20893 6341000020 2015-03-04 226000.00 3 7 98146 2 1.00 47.49 -122.34 140.28 140.28 0.00 1846.29 1986.57 113.76 medium_house large_terrain mansion 750000.00 227.60 to buy 862500.00 69.87 636500.00\n", - "19467 2870000040 2014-11-10 145000.00 3 6 98033 2 1.00 47.68 -122.17 74.32 74.32 0.00 754.81 829.13 174.88 small_house medium_terrain townhouse 465000.00 483.99 to buy 534750.00 68.82 389750.00\n", - "20080 2734100835 2015-03-03 90000.00 3 5 98108 1 1.00 47.54 -122.32 72.46 72.46 0.00 371.60 444.06 202.67 small_house small_terrain townhouse 284500.00 717.00 to buy 327175.00 68.37 237175.00\n", - "7895 3530210260 2014-10-27 274975.00 3 9 98077 3 2.50 47.77 -122.09 281.49 281.49 0.00 4180.87 4462.36 61.62 large_house large_terrain mansion 856250.00 191.28 to buy 984687.50 67.89 709712.50\n", - "20688 9169600275 2014-07-23 280000.00 3 7 98136 2 1.00 47.52 -122.39 211.81 211.81 0.00 3483.75 3695.56 75.77 medium_house large_terrain mansion 865000.00 310.60 to buy 994750.00 67.63 714750.00\n", - "13704 2114700500 2015-04-18 90000.00 3 4 98106 1 1.00 47.53 -122.35 52.02 52.02 0.00 382.75 434.77 207.01 small_house small_terrain townhouse 276000.00 631.95 to buy 317400.00 67.39 227400.00\n", - "12640 424049043 2014-08-11 450000.00 3 7 98144 9 7.50 47.59 -122.30 376.25 376.25 0.00 604.22 980.47 458.97 large_house medium_terrain townhouse 1364500.00 1292.80 to buy 1569175.00 67.02 1119175.00\n", - "2709 5016003230 2015-02-18 169317.00 3 7 98112 2 1.00 47.62 -122.30 73.39 73.39 0.00 371.60 444.99 380.50 small_house small_terrain townhouse 511500.00 1492.21 to buy 588225.00 66.90 418908.00\n", - "20434 6169901185 2014-05-20 490000.00 3 10 98119 5 3.50 47.63 -122.37 414.33 304.71 109.62 276.38 690.71 709.41 large_house small_terrain townhouse 1430000.00 1834.02 to buy 1644500.00 65.73 1154500.00\n", - "19898 1721801591 2015-02-19 89950.00 3 5 98146 1 1.00 47.51 -122.33 52.95 52.95 0.00 379.03 431.98 208.22 small_house small_terrain townhouse 260000.00 509.58 to buy 299000.00 65.40 209050.00\n", - "12921 3883800011 2014-11-05 82000.00 3 6 98146 3 1.00 47.50 -122.34 79.89 79.89 0.00 968.58 1048.47 78.21 small_house medium_terrain mansion 234900.00 266.86 to buy 270135.00 65.09 188135.00\n", - "20308 7896300592 2015-01-14 303500.00 3 8 98118 6 4.50 47.52 -122.29 314.93 226.68 88.25 668.88 983.81 308.49 large_house medium_terrain townhouse 856000.00 773.05 to buy 984400.00 64.54 680900.00\n", - "1030 723049158 2015-03-13 135000.00 3 5 98146 4 1.00 47.50 -122.35 135.63 135.63 0.00 1727.85 1863.48 72.45 medium_house medium_terrain mansion 380000.00 368.73 to buy 437000.00 64.47 302000.00\n", - "5627 7228500094 2014-12-12 278000.00 3 7 98122 4 2.00 47.61 -122.30 137.49 137.49 0.00 587.50 724.99 383.45 medium_house medium_terrain townhouse 762000.00 904.13 to buy 876300.00 63.52 598300.00\n", - "16776 4055700778 2014-11-14 523000.00 3 8 98034 4 2.50 47.72 -122.24 295.42 196.95 98.47 1938.82 2234.24 234.08 large_house large_terrain mansion 1432500.00 453.13 to buy 1647375.00 63.49 1124375.00" + " id date price condition grade zipcode bedrooms bathrooms lat long m2_living m2_above m2_basement m2_lot house_total_m2 price_m2 living_size lot_size house_type median_price median_price_m2 status %_value_below_median_price selling_price profit_est\n", + "19431 1238500451 2015-02-09 130000.00 4 7 98033 3 1.00 47.68 -122.18 103.12 103.12 0.00 698.61 801.73 162.15 small_house medium_terrain townhouse 465000.00 483.99 to buy 72.04 195000.00 65000.00\n", + "11755 5111400086 2014-05-12 110000.00 5 6 98038 3 1.00 47.42 -122.05 116.12 116.12 0.00 4936.98 5053.11 21.77 small_house large_terrain mansion 295000.00 34.21 to buy 62.71 159500.00 49500.00\n", + "13663 7899800450 2014-08-28 107000.00 4 6 98106 2 1.00 47.52 -122.36 62.24 62.24 0.00 438.49 500.73 213.69 small_house small_terrain townhouse 276000.00 631.95 to buy 61.23 160500.00 53500.00\n", + "21363 2354300456 2015-03-11 130000.00 4 4 98027 2 1.00 47.53 -122.03 55.74 55.74 0.00 139.35 195.09 666.36 small_house small_terrain apartment 331000.00 1522.92 to buy 60.73 195000.00 65000.00\n", + "11133 3888100226 2014-06-30 461000.00 4 6 98033 3 1.75 47.69 -122.17 334.44 222.96 111.48 805.07 1139.51 404.56 large_house medium_terrain mansion 1135000.00 945.68 to buy 59.38 691500.00 230500.00\n", + "16258 644210020 2015-01-05 780000.00 4 8 98004 4 2.50 47.59 -122.19 280.56 160.72 119.84 1408.74 1689.29 461.73 large_house medium_terrain mansion 1750000.00 1242.36 to buy 55.43 1170000.00 390000.00\n", + "6551 4038000040 2015-03-26 250000.00 4 7 98008 4 1.75 47.61 -122.12 177.44 177.44 0.00 766.42 943.86 264.87 medium_house medium_terrain townhouse 555000.00 542.88 to buy 54.95 375000.00 125000.00\n", + "21410 8952900245 2014-07-15 850000.00 4 9 98118 6 3.25 47.55 -122.27 457.07 253.62 203.45 1912.81 2369.88 358.67 large_house large_terrain mansion 1876500.00 343.06 to buy 54.70 1275000.00 425000.00\n", + "20086 13001215 2015-03-05 130000.00 4 7 98108 3 1.00 47.52 -122.33 102.19 102.19 0.00 473.79 575.98 225.70 small_house small_terrain townhouse 284500.00 717.00 to buy 54.31 195000.00 65000.00\n", + "4474 7227800040 2014-06-04 190000.00 4 5 98056 5 2.00 47.51 -122.18 162.57 162.57 0.00 955.38 1117.96 169.95 medium_house medium_terrain mansion 415000.00 422.38 to buy 54.22 285000.00 95000.00\n", + "5569 1217000340 2014-06-06 185000.00 4 7 98166 3 1.00 47.45 -122.35 170.94 85.47 85.47 752.49 923.43 200.34 medium_house medium_terrain townhouse 400475.00 349.30 to buy 53.80 277500.00 92500.00\n", + "6019 4311700120 2015-03-17 105000.00 4 6 98042 3 1.00 47.36 -122.10 81.75 81.75 0.00 1682.33 1764.08 59.52 small_house medium_terrain mansion 226000.00 233.34 to buy 53.54 157500.00 52500.00\n", + "20040 9232900050 2014-08-06 300000.00 4 6 98103 2 1.75 47.70 -122.36 196.95 133.78 63.17 589.91 786.86 381.26 medium_house medium_terrain townhouse 645000.00 846.27 to buy 53.49 450000.00 150000.00\n", + "5340 5127001320 2014-11-25 190000.00 4 7 98059 3 1.75 47.47 -122.15 141.21 141.21 0.00 891.84 1033.05 183.92 medium_house medium_terrain mansion 405000.00 369.90 to buy 53.09 285000.00 95000.00\n", + "10785 6840700165 2014-07-01 202000.00 4 7 98122 1 1.00 47.61 -122.30 54.81 54.81 0.00 77.39 132.20 1528.03 small_house small_terrain apartment 430000.00 1492.01 to buy 53.02 303000.00 101000.00\n", + "2003 7335400215 2015-05-05 95000.00 4 5 98002 1 0.75 47.30 -122.22 70.60 70.60 0.00 533.80 604.41 157.18 small_house medium_terrain townhouse 200000.00 236.30 to buy 52.50 142500.00 47500.00\n", + "20536 3398800055 2014-11-19 2400000.00 4 11 98102 4 3.75 47.63 -122.32 379.96 315.86 64.10 2306.24 2686.20 893.45 large_house large_terrain mansion 5050000.00 1491.93 to buy 52.48 3600000.00 1200000.00\n", + "16820 1545807810 2014-10-21 118000.00 4 6 98038 1 1.00 47.36 -122.06 62.24 62.24 0.00 739.21 801.45 147.23 small_house medium_terrain townhouse 247450.00 296.60 to buy 52.31 177000.00 59000.00\n", + "10127 5495200040 2015-01-26 610000.00 4 8 98006 5 3.25 47.57 -122.12 324.22 175.58 148.64 2173.86 2498.08 244.19 large_house large_terrain mansion 1275000.00 403.94 to buy 52.16 915000.00 305000.00\n", + "19784 5016002275 2014-06-02 610000.00 4 8 98112 5 2.50 47.62 -122.30 370.67 184.87 185.80 356.64 727.31 838.70 large_house small_terrain townhouse 1272000.00 1737.98 to buy 52.04 915000.00 305000.00" ] }, - "execution_count": 63, + "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# house_df_fs.loc[(house_df_fs['%_value_below_median_price'] > 0) & (house_df_fs['status'] == 'to buy'),['id','zipcode','price','median_price','price_m2','median_price_m2','%_value_below_median_price_m2','condition','status','selling_price','profit_est']].head(20)\n", - "house_df_fs.loc[(house_df_fs['%_value_below_median_price'] > 0) & (house_df_fs['status'] == 'to buy')].head(20)" + "house_df_ins.loc[(house_df_ins['%_value_below_median_price'] > 0) & (house_df_ins['status'] == 'to buy')].head(20)" ] }, { "cell_type": "code", - "execution_count": 64, - "metadata": {}, + "execution_count": 66, + "metadata": { + "hidden": true + }, "outputs": [], "source": [ - "recommended_houses_ordered = house_df_fs.loc[(house_df_fs['%_value_below_median_price'] > 0) & (house_df_fs['status'] == 'to buy'),:].copy(deep=True)" + "recommended_houses_ordered = house_df_ins.loc[(house_df_ins['%_value_below_median_price'] > 0) & (house_df_ins['status'] == 'to buy'),:].copy(deep=True)" ] }, { "cell_type": "code", - "execution_count": 65, - "metadata": {}, + "execution_count": 67, + "metadata": { + "hidden": true + }, "outputs": [ { "data": { "text/plain": [ - "10448" + "3560" ] }, - "execution_count": 65, + "execution_count": 67, "metadata": {}, "output_type": "execute_result" } @@ -8575,8 +9806,10 @@ }, { "cell_type": "code", - "execution_count": 66, - "metadata": {}, + "execution_count": 68, + "metadata": { + "hidden": true + }, "outputs": [ { "data": { @@ -8621,8 +9854,8 @@ " median_price\n", " median_price_m2\n", " status\n", - " selling_price\n", " %_value_below_median_price\n", + " selling_price\n", " profit_est\n", " \n", " \n", @@ -8651,571 +9884,571 @@ " 465000.00\n", " 483.99\n", " to buy\n", - " 511500.00\n", " 72.04\n", - " 381500.00\n", + " 195000.00\n", + " 65000.00\n", " \n", " \n", - " 19467\n", - " 2870000040\n", - " 2014-11-10\n", - " 145000.00\n", - " 3\n", + " 13663\n", + " 7899800450\n", + " 2014-08-28\n", + " 107000.00\n", + " 4\n", " 6\n", - " 98033\n", + " 98106\n", " 2\n", " 1.00\n", - " 47.68\n", - " -122.17\n", - " 74.32\n", - " 74.32\n", + " 47.52\n", + " -122.36\n", + " 62.24\n", + " 62.24\n", " 0.00\n", - " 754.81\n", - " 829.13\n", - " 174.88\n", + " 438.49\n", + " 500.73\n", + " 213.69\n", " small_house\n", + " small_terrain\n", + " townhouse\n", + " 276000.00\n", + " 631.95\n", + " to buy\n", + " 61.23\n", + " 160500.00\n", + " 53500.00\n", + " \n", + " \n", + " 6551\n", + " 4038000040\n", + " 2015-03-26\n", + " 250000.00\n", + " 4\n", + " 7\n", + " 98008\n", + " 4\n", + " 1.75\n", + " 47.61\n", + " -122.12\n", + " 177.44\n", + " 177.44\n", + " 0.00\n", + " 766.42\n", + " 943.86\n", + " 264.87\n", + " medium_house\n", " medium_terrain\n", " townhouse\n", - " 465000.00\n", - " 483.99\n", + " 555000.00\n", + " 542.88\n", " to buy\n", - " 534750.00\n", - " 68.82\n", - " 389750.00\n", + " 54.95\n", + " 375000.00\n", + " 125000.00\n", " \n", " \n", - " 20080\n", - " 2734100835\n", - " 2015-03-03\n", - " 90000.00\n", - " 3\n", - " 5\n", + " 20086\n", + " 13001215\n", + " 2015-03-05\n", + " 130000.00\n", + " 4\n", + " 7\n", " 98108\n", - " 1\n", + " 3\n", " 1.00\n", - " 47.54\n", - " -122.32\n", - " 72.46\n", - " 72.46\n", + " 47.52\n", + " -122.33\n", + " 102.19\n", + " 102.19\n", " 0.00\n", - " 371.60\n", - " 444.06\n", - " 202.67\n", + " 473.79\n", + " 575.98\n", + " 225.70\n", " small_house\n", " small_terrain\n", " townhouse\n", " 284500.00\n", " 717.00\n", " to buy\n", - " 327175.00\n", - " 68.37\n", - " 237175.00\n", + " 54.31\n", + " 195000.00\n", + " 65000.00\n", " \n", " \n", - " 13704\n", - " 2114700500\n", - " 2015-04-18\n", - " 90000.00\n", - " 3\n", + " 5569\n", + " 1217000340\n", + " 2014-06-06\n", + " 185000.00\n", " 4\n", - " 98106\n", - " 1\n", + " 7\n", + " 98166\n", + " 3\n", " 1.00\n", - " 47.53\n", + " 47.45\n", " -122.35\n", - " 52.02\n", - " 52.02\n", - " 0.00\n", - " 382.75\n", - " 434.77\n", - " 207.01\n", - " small_house\n", - " small_terrain\n", + " 170.94\n", + " 85.47\n", + " 85.47\n", + " 752.49\n", + " 923.43\n", + " 200.34\n", + " medium_house\n", + " medium_terrain\n", " townhouse\n", - " 276000.00\n", - " 631.95\n", + " 400475.00\n", + " 349.30\n", " to buy\n", - " 317400.00\n", - " 67.39\n", - " 227400.00\n", + " 53.80\n", + " 277500.00\n", + " 92500.00\n", " \n", " \n", - " 12640\n", - " 424049043\n", - " 2014-08-11\n", - " 450000.00\n", - " 3\n", - " 7\n", - " 98144\n", - " 9\n", - " 7.50\n", - " 47.59\n", - " -122.30\n", - " 376.25\n", - " 376.25\n", - " 0.00\n", - " 604.22\n", - " 980.47\n", - " 458.97\n", - " large_house\n", + " 20040\n", + " 9232900050\n", + " 2014-08-06\n", + " 300000.00\n", + " 4\n", + " 6\n", + " 98103\n", + " 2\n", + " 1.75\n", + " 47.70\n", + " -122.36\n", + " 196.95\n", + " 133.78\n", + " 63.17\n", + " 589.91\n", + " 786.86\n", + " 381.26\n", + " medium_house\n", " medium_terrain\n", " townhouse\n", - " 1364500.00\n", - " 1292.80\n", + " 645000.00\n", + " 846.27\n", " to buy\n", - " 1569175.00\n", - " 67.02\n", - " 1119175.00\n", + " 53.49\n", + " 450000.00\n", + " 150000.00\n", " \n", " \n", - " 2709\n", - " 5016003230\n", - " 2015-02-18\n", - " 169317.00\n", - " 3\n", - " 7\n", - " 98112\n", - " 2\n", - " 1.00\n", - " 47.62\n", - " -122.30\n", - " 73.39\n", - " 73.39\n", + " 2003\n", + " 7335400215\n", + " 2015-05-05\n", + " 95000.00\n", + " 4\n", + " 5\n", + " 98002\n", + " 1\n", + " 0.75\n", + " 47.30\n", + " -122.22\n", + " 70.60\n", + " 70.60\n", " 0.00\n", - " 371.60\n", - " 444.99\n", - " 380.50\n", + " 533.80\n", + " 604.41\n", + " 157.18\n", " small_house\n", - " small_terrain\n", - " townhouse\n", - " 511500.00\n", - " 1492.21\n", - " to buy\n", - " 588225.00\n", - " 66.90\n", - " 418908.00\n", - " \n", - " \n", - " 20434\n", - " 6169901185\n", - " 2014-05-20\n", - " 490000.00\n", - " 3\n", - " 10\n", - " 98119\n", - " 5\n", - " 3.50\n", - " 47.63\n", - " -122.37\n", - " 414.33\n", - " 304.71\n", - " 109.62\n", - " 276.38\n", - " 690.71\n", - " 709.41\n", - " large_house\n", - " small_terrain\n", + " medium_terrain\n", " townhouse\n", - " 1430000.00\n", - " 1834.02\n", + " 200000.00\n", + " 236.30\n", " to buy\n", - " 1644500.00\n", - " 65.73\n", - " 1154500.00\n", + " 52.50\n", + " 142500.00\n", + " 47500.00\n", " \n", " \n", - " 19898\n", - " 1721801591\n", - " 2015-02-19\n", - " 89950.00\n", - " 3\n", - " 5\n", - " 98146\n", + " 16820\n", + " 1545807810\n", + " 2014-10-21\n", + " 118000.00\n", + " 4\n", + " 6\n", + " 98038\n", " 1\n", " 1.00\n", - " 47.51\n", - " -122.33\n", - " 52.95\n", - " 52.95\n", + " 47.36\n", + " -122.06\n", + " 62.24\n", + " 62.24\n", " 0.00\n", - " 379.03\n", - " 431.98\n", - " 208.22\n", + " 739.21\n", + " 801.45\n", + " 147.23\n", " small_house\n", - " small_terrain\n", + " medium_terrain\n", " townhouse\n", - " 260000.00\n", - " 509.58\n", + " 247450.00\n", + " 296.60\n", " to buy\n", - " 299000.00\n", - " 65.40\n", - " 209050.00\n", + " 52.31\n", + " 177000.00\n", + " 59000.00\n", " \n", " \n", - " 20308\n", - " 7896300592\n", - " 2015-01-14\n", - " 303500.00\n", - " 3\n", + " 19784\n", + " 5016002275\n", + " 2014-06-02\n", + " 610000.00\n", + " 4\n", " 8\n", - " 98118\n", - " 6\n", - " 4.50\n", - " 47.52\n", - " -122.29\n", - " 314.93\n", - " 226.68\n", - " 88.25\n", - " 668.88\n", - " 983.81\n", - " 308.49\n", + " 98112\n", + " 5\n", + " 2.50\n", + " 47.62\n", + " -122.30\n", + " 370.67\n", + " 184.87\n", + " 185.80\n", + " 356.64\n", + " 727.31\n", + " 838.70\n", " large_house\n", - " medium_terrain\n", + " small_terrain\n", " townhouse\n", - " 856000.00\n", - " 773.05\n", + " 1272000.00\n", + " 1737.98\n", " to buy\n", - " 984400.00\n", - " 64.54\n", - " 680900.00\n", + " 52.04\n", + " 915000.00\n", + " 305000.00\n", " \n", " \n", - " 5627\n", - " 7228500094\n", - " 2014-12-12\n", - " 278000.00\n", - " 3\n", - " 7\n", - " 98122\n", + " 4407\n", + " 7227800055\n", + " 2014-09-09\n", + " 199500.00\n", + " 4\n", + " 5\n", + " 98056\n", " 4\n", " 2.00\n", - " 47.61\n", - " -122.30\n", - " 137.49\n", - " 137.49\n", + " 47.51\n", + " -122.18\n", + " 162.57\n", + " 162.57\n", " 0.00\n", - " 587.50\n", - " 724.99\n", - " 383.45\n", + " 753.98\n", + " 916.55\n", + " 217.66\n", " medium_house\n", " medium_terrain\n", " townhouse\n", - " 762000.00\n", - " 904.13\n", + " 415000.00\n", + " 422.38\n", " to buy\n", - " 876300.00\n", - " 63.52\n", - " 598300.00\n", + " 51.93\n", + " 299250.00\n", + " 99750.00\n", " \n", " \n", - " 13692\n", - " 123039604\n", - " 2014-07-01\n", - " 102500.00\n", - " 3\n", - " 5\n", + " 13709\n", + " 3298701025\n", + " 2015-04-27\n", + " 135000.00\n", + " 4\n", + " 6\n", " 98106\n", " 2\n", " 1.00\n", - " 47.51\n", - " -122.36\n", - " 76.18\n", - " 76.18\n", + " 47.52\n", + " -122.35\n", + " 69.67\n", + " 69.67\n", " 0.00\n", - " 401.33\n", - " 477.51\n", - " 214.66\n", + " 484.66\n", + " 554.33\n", + " 243.54\n", " small_house\n", " small_terrain\n", " townhouse\n", " 276000.00\n", " 631.95\n", " to buy\n", - " 317400.00\n", - " 62.86\n", - " 214900.00\n", + " 51.09\n", + " 202500.00\n", + " 67500.00\n", " \n", " \n", - " 18940\n", - " 7129800036\n", - " 2015-01-14\n", - " 109000.00\n", - " 3\n", + " 4112\n", + " 5101402435\n", + " 2014-06-03\n", + " 312000.00\n", " 5\n", - " 98118\n", - " 2\n", - " 0.50\n", - " 47.51\n", - " -122.26\n", - " 53.88\n", - " 53.88\n", - " 0.00\n", - " 641.01\n", - " 694.89\n", - " 156.86\n", - " small_house\n", - " medium_terrain\n", + " 7\n", + " 98115\n", + " 3\n", + " 2.25\n", + " 47.69\n", + " -122.30\n", + " 143.07\n", + " 71.53\n", + " 71.53\n", + " 495.90\n", + " 638.97\n", + " 488.29\n", + " medium_house\n", + " small_terrain\n", " townhouse\n", - " 288500.00\n", - " 392.93\n", + " 630000.00\n", + " 1172.71\n", " to buy\n", - " 331775.00\n", - " 62.22\n", - " 222775.00\n", + " 50.48\n", + " 452400.00\n", + " 140400.00\n", " \n", " \n", - " 13512\n", - " 7454000145\n", - " 2015-03-06\n", - " 122000.00\n", - " 3\n", + " 2827\n", + " 1995200200\n", + " 2014-05-06\n", + " 313950.00\n", + " 4\n", " 6\n", - " 98126\n", - " 2\n", + " 98115\n", + " 3\n", " 1.00\n", - " 47.52\n", - " -122.37\n", - " 68.75\n", - " 68.75\n", - " 0.00\n", - " 635.44\n", - " 704.18\n", - " 173.25\n", - " small_house\n", + " 47.70\n", + " -122.32\n", + " 140.28\n", + " 79.89\n", + " 60.38\n", + " 565.11\n", + " 705.39\n", + " 445.07\n", + " medium_house\n", " medium_terrain\n", " townhouse\n", - " 320975.00\n", - " 453.37\n", + " 626750.00\n", + " 758.28\n", " to buy\n", - " 369121.25\n", - " 61.99\n", - " 247121.25\n", + " 49.91\n", + " 392437.50\n", + " 78487.50\n", " \n", " \n", - " 13682\n", - " 7899800045\n", - " 2014-08-28\n", - " 107000.00\n", - " 3\n", - " 6\n", + " 21556\n", + " 2113700360\n", + " 2014-06-27\n", + " 315000.00\n", + " 4\n", + " 7\n", " 98106\n", - " 3\n", - " 1.50\n", - " 47.52\n", - " -122.36\n", - " 84.54\n", - " 84.54\n", - " 0.00\n", - " 475.65\n", - " 560.19\n", - " 191.01\n", - " small_house\n", + " 6\n", + " 4.00\n", + " 47.53\n", + " -122.35\n", + " 289.85\n", + " 194.16\n", + " 95.69\n", + " 393.90\n", + " 683.74\n", + " 460.70\n", + " large_house\n", " small_terrain\n", " townhouse\n", - " 276000.00\n", - " 631.95\n", + " 616950.00\n", + " 775.09\n", " to buy\n", - " 317400.00\n", - " 61.23\n", - " 210400.00\n", + " 48.94\n", + " 393750.00\n", + " 78750.00\n", " \n", " \n", - " 13663\n", - " 7899800450\n", - " 2014-08-28\n", - " 107000.00\n", + " 1895\n", + " 2917200675\n", + " 2015-01-27\n", + " 340000.00\n", " 4\n", - " 6\n", - " 98106\n", + " 7\n", + " 98103\n", " 2\n", - " 1.00\n", - " 47.52\n", - " -122.36\n", - " 62.24\n", - " 62.24\n", - " 0.00\n", - " 438.49\n", - " 500.73\n", - " 213.69\n", - " small_house\n", + " 1.75\n", + " 47.70\n", + " -122.35\n", + " 139.35\n", + " 113.34\n", + " 26.01\n", + " 386.28\n", + " 525.63\n", + " 646.85\n", + " medium_house\n", " small_terrain\n", " townhouse\n", - " 276000.00\n", - " 631.95\n", + " 640000.00\n", + " 1365.26\n", " to buy\n", - " 303600.00\n", - " 61.23\n", - " 196600.00\n", + " 46.88\n", + " 425000.00\n", + " 85000.00\n", " \n", " \n", - " 9915\n", - " 6450302546\n", - " 2014-10-21\n", - " 130000.00\n", - " 3\n", - " 7\n", - " 98133\n", + " 12959\n", + " 6303401150\n", + " 2014-09-24\n", + " 125000.00\n", + " 4\n", + " 5\n", + " 98146\n", " 2\n", " 1.00\n", - " 47.73\n", - " -122.33\n", - " 78.04\n", - " 78.04\n", + " 47.50\n", + " -122.36\n", + " 75.25\n", + " 75.25\n", " 0.00\n", - " 618.16\n", - " 696.19\n", - " 186.73\n", + " 778.69\n", + " 853.94\n", + " 146.38\n", " small_house\n", " medium_terrain\n", " townhouse\n", - " 334275.00\n", - " 409.12\n", + " 234900.00\n", + " 266.86\n", " to buy\n", - " 384416.25\n", - " 61.11\n", - " 254416.25\n", + " 46.79\n", + " 156250.00\n", + " 31250.00\n", " \n", " \n", - " 973\n", - " 723049156\n", - " 2014-05-23\n", - " 149000.00\n", - " 3\n", - " 6\n", - " 98146\n", - " 3\n", - " 1.00\n", - " 47.49\n", - " -122.34\n", - " 157.93\n", - " 157.93\n", - " 0.00\n", - " 803.12\n", - " 961.05\n", - " 155.04\n", - " medium_house\n", + " 20624\n", + " 7292700005\n", + " 2014-10-14\n", + " 485000.00\n", + " 4\n", + " 8\n", + " 98177\n", + " 4\n", + " 1.75\n", + " 47.77\n", + " -122.36\n", + " 299.14\n", + " 186.73\n", + " 112.41\n", + " 686.72\n", + " 985.85\n", + " 491.96\n", + " large_house\n", " medium_terrain\n", " townhouse\n", - " 380000.00\n", - " 368.73\n", + " 907500.00\n", + " 750.17\n", " to buy\n", - " 437000.00\n", - " 60.79\n", - " 288000.00\n", + " 46.56\n", + " 606250.00\n", + " 121250.00\n", " \n", " \n", - " 12622\n", - " 7999600180\n", - " 2014-05-29\n", - " 83000.00\n", + " 2431\n", + " 7697870600\n", + " 2014-09-09\n", + " 158000.00\n", + " 4\n", + " 7\n", + " 98030\n", " 3\n", - " 5\n", - " 98168\n", - " 2\n", - " 1.00\n", - " 47.47\n", - " -122.27\n", - " 83.61\n", - " 83.61\n", + " 2.50\n", + " 47.37\n", + " -122.18\n", + " 141.21\n", + " 141.21\n", " 0.00\n", - " 797.08\n", - " 880.69\n", - " 94.24\n", - " small_house\n", + " 668.88\n", + " 810.09\n", + " 195.04\n", + " medium_house\n", " medium_terrain\n", " townhouse\n", - " 209950.00\n", - " 234.32\n", + " 295000.00\n", + " 319.92\n", " to buy\n", - " 241442.50\n", - " 60.47\n", - " 158442.50\n", + " 46.44\n", + " 197500.00\n", + " 39500.00\n", " \n", " \n", - " 84\n", - " 1423049019\n", - " 2014-05-23\n", - " 90000.00\n", - " 3\n", - " 5\n", - " 98178\n", - " 2\n", + " 816\n", + " 4222310010\n", + " 2014-12-26\n", + " 152500.00\n", + " 4\n", + " 6\n", + " 98003\n", + " 4\n", " 1.00\n", - " 47.49\n", - " -122.25\n", - " 53.88\n", - " 53.88\n", + " 47.35\n", + " -122.31\n", + " 160.72\n", + " 160.72\n", " 0.00\n", - " 696.75\n", - " 750.63\n", - " 119.90\n", - " small_house\n", + " 682.81\n", + " 843.53\n", + " 180.79\n", + " medium_house\n", " medium_terrain\n", " townhouse\n", - " 225000.00\n", - " 292.83\n", + " 283200.00\n", + " 285.22\n", " to buy\n", - " 258750.00\n", - " 60.00\n", - " 168750.00\n", + " 46.15\n", + " 190625.00\n", + " 38125.00\n", " \n", " \n", - " 17119\n", - " 3613600150\n", - " 2015-01-05\n", - " 300523.00\n", - " 3\n", - " 9\n", - " 98119\n", + " 4365\n", + " 9476200680\n", + " 2015-04-28\n", + " 226000.00\n", + " 4\n", + " 6\n", + " 98056\n", " 3\n", - " 2.50\n", - " 47.65\n", - " -122.37\n", - " 220.17\n", - " 220.17\n", - " 0.00\n", - " 635.44\n", - " 855.61\n", - " 351.24\n", + " 1.75\n", + " 47.49\n", + " -122.19\n", + " 138.42\n", + " 91.97\n", + " 46.45\n", + " 582.39\n", + " 720.81\n", + " 313.54\n", " medium_house\n", " medium_terrain\n", " townhouse\n", - " 750000.00\n", - " 1042.13\n", + " 415000.00\n", + " 422.38\n", " to buy\n", - " 862500.00\n", - " 59.93\n", - " 561977.00\n", + " 45.54\n", + " 282500.00\n", + " 56500.00\n", " \n", " \n", "\n", "" ], "text/plain": [ - " id date price condition grade zipcode bedrooms bathrooms lat long m2_living m2_above m2_basement m2_lot house_total_m2 price_m2 living_size lot_size house_type median_price median_price_m2 status selling_price %_value_below_median_price profit_est\n", - "19431 1238500451 2015-02-09 130000.00 4 7 98033 3 1.00 47.68 -122.18 103.12 103.12 0.00 698.61 801.73 162.15 small_house medium_terrain townhouse 465000.00 483.99 to buy 511500.00 72.04 381500.00\n", - "19467 2870000040 2014-11-10 145000.00 3 6 98033 2 1.00 47.68 -122.17 74.32 74.32 0.00 754.81 829.13 174.88 small_house medium_terrain townhouse 465000.00 483.99 to buy 534750.00 68.82 389750.00\n", - "20080 2734100835 2015-03-03 90000.00 3 5 98108 1 1.00 47.54 -122.32 72.46 72.46 0.00 371.60 444.06 202.67 small_house small_terrain townhouse 284500.00 717.00 to buy 327175.00 68.37 237175.00\n", - "13704 2114700500 2015-04-18 90000.00 3 4 98106 1 1.00 47.53 -122.35 52.02 52.02 0.00 382.75 434.77 207.01 small_house small_terrain townhouse 276000.00 631.95 to buy 317400.00 67.39 227400.00\n", - "12640 424049043 2014-08-11 450000.00 3 7 98144 9 7.50 47.59 -122.30 376.25 376.25 0.00 604.22 980.47 458.97 large_house medium_terrain townhouse 1364500.00 1292.80 to buy 1569175.00 67.02 1119175.00\n", - "2709 5016003230 2015-02-18 169317.00 3 7 98112 2 1.00 47.62 -122.30 73.39 73.39 0.00 371.60 444.99 380.50 small_house small_terrain townhouse 511500.00 1492.21 to buy 588225.00 66.90 418908.00\n", - "20434 6169901185 2014-05-20 490000.00 3 10 98119 5 3.50 47.63 -122.37 414.33 304.71 109.62 276.38 690.71 709.41 large_house small_terrain townhouse 1430000.00 1834.02 to buy 1644500.00 65.73 1154500.00\n", - "19898 1721801591 2015-02-19 89950.00 3 5 98146 1 1.00 47.51 -122.33 52.95 52.95 0.00 379.03 431.98 208.22 small_house small_terrain townhouse 260000.00 509.58 to buy 299000.00 65.40 209050.00\n", - "20308 7896300592 2015-01-14 303500.00 3 8 98118 6 4.50 47.52 -122.29 314.93 226.68 88.25 668.88 983.81 308.49 large_house medium_terrain townhouse 856000.00 773.05 to buy 984400.00 64.54 680900.00\n", - "5627 7228500094 2014-12-12 278000.00 3 7 98122 4 2.00 47.61 -122.30 137.49 137.49 0.00 587.50 724.99 383.45 medium_house medium_terrain townhouse 762000.00 904.13 to buy 876300.00 63.52 598300.00\n", - "13692 123039604 2014-07-01 102500.00 3 5 98106 2 1.00 47.51 -122.36 76.18 76.18 0.00 401.33 477.51 214.66 small_house small_terrain townhouse 276000.00 631.95 to buy 317400.00 62.86 214900.00\n", - "18940 7129800036 2015-01-14 109000.00 3 5 98118 2 0.50 47.51 -122.26 53.88 53.88 0.00 641.01 694.89 156.86 small_house medium_terrain townhouse 288500.00 392.93 to buy 331775.00 62.22 222775.00\n", - "13512 7454000145 2015-03-06 122000.00 3 6 98126 2 1.00 47.52 -122.37 68.75 68.75 0.00 635.44 704.18 173.25 small_house medium_terrain townhouse 320975.00 453.37 to buy 369121.25 61.99 247121.25\n", - "13682 7899800045 2014-08-28 107000.00 3 6 98106 3 1.50 47.52 -122.36 84.54 84.54 0.00 475.65 560.19 191.01 small_house small_terrain townhouse 276000.00 631.95 to buy 317400.00 61.23 210400.00\n", - "13663 7899800450 2014-08-28 107000.00 4 6 98106 2 1.00 47.52 -122.36 62.24 62.24 0.00 438.49 500.73 213.69 small_house small_terrain townhouse 276000.00 631.95 to buy 303600.00 61.23 196600.00\n", - "9915 6450302546 2014-10-21 130000.00 3 7 98133 2 1.00 47.73 -122.33 78.04 78.04 0.00 618.16 696.19 186.73 small_house medium_terrain townhouse 334275.00 409.12 to buy 384416.25 61.11 254416.25\n", - "973 723049156 2014-05-23 149000.00 3 6 98146 3 1.00 47.49 -122.34 157.93 157.93 0.00 803.12 961.05 155.04 medium_house medium_terrain townhouse 380000.00 368.73 to buy 437000.00 60.79 288000.00\n", - "12622 7999600180 2014-05-29 83000.00 3 5 98168 2 1.00 47.47 -122.27 83.61 83.61 0.00 797.08 880.69 94.24 small_house medium_terrain townhouse 209950.00 234.32 to buy 241442.50 60.47 158442.50\n", - "84 1423049019 2014-05-23 90000.00 3 5 98178 2 1.00 47.49 -122.25 53.88 53.88 0.00 696.75 750.63 119.90 small_house medium_terrain townhouse 225000.00 292.83 to buy 258750.00 60.00 168750.00\n", - "17119 3613600150 2015-01-05 300523.00 3 9 98119 3 2.50 47.65 -122.37 220.17 220.17 0.00 635.44 855.61 351.24 medium_house medium_terrain townhouse 750000.00 1042.13 to buy 862500.00 59.93 561977.00" + " id date price condition grade zipcode bedrooms bathrooms lat long m2_living m2_above m2_basement m2_lot house_total_m2 price_m2 living_size lot_size house_type median_price median_price_m2 status %_value_below_median_price selling_price profit_est\n", + "19431 1238500451 2015-02-09 130000.00 4 7 98033 3 1.00 47.68 -122.18 103.12 103.12 0.00 698.61 801.73 162.15 small_house medium_terrain townhouse 465000.00 483.99 to buy 72.04 195000.00 65000.00\n", + "13663 7899800450 2014-08-28 107000.00 4 6 98106 2 1.00 47.52 -122.36 62.24 62.24 0.00 438.49 500.73 213.69 small_house small_terrain townhouse 276000.00 631.95 to buy 61.23 160500.00 53500.00\n", + "6551 4038000040 2015-03-26 250000.00 4 7 98008 4 1.75 47.61 -122.12 177.44 177.44 0.00 766.42 943.86 264.87 medium_house medium_terrain townhouse 555000.00 542.88 to buy 54.95 375000.00 125000.00\n", + "20086 13001215 2015-03-05 130000.00 4 7 98108 3 1.00 47.52 -122.33 102.19 102.19 0.00 473.79 575.98 225.70 small_house small_terrain townhouse 284500.00 717.00 to buy 54.31 195000.00 65000.00\n", + "5569 1217000340 2014-06-06 185000.00 4 7 98166 3 1.00 47.45 -122.35 170.94 85.47 85.47 752.49 923.43 200.34 medium_house medium_terrain townhouse 400475.00 349.30 to buy 53.80 277500.00 92500.00\n", + "20040 9232900050 2014-08-06 300000.00 4 6 98103 2 1.75 47.70 -122.36 196.95 133.78 63.17 589.91 786.86 381.26 medium_house medium_terrain townhouse 645000.00 846.27 to buy 53.49 450000.00 150000.00\n", + "2003 7335400215 2015-05-05 95000.00 4 5 98002 1 0.75 47.30 -122.22 70.60 70.60 0.00 533.80 604.41 157.18 small_house medium_terrain townhouse 200000.00 236.30 to buy 52.50 142500.00 47500.00\n", + "16820 1545807810 2014-10-21 118000.00 4 6 98038 1 1.00 47.36 -122.06 62.24 62.24 0.00 739.21 801.45 147.23 small_house medium_terrain townhouse 247450.00 296.60 to buy 52.31 177000.00 59000.00\n", + "19784 5016002275 2014-06-02 610000.00 4 8 98112 5 2.50 47.62 -122.30 370.67 184.87 185.80 356.64 727.31 838.70 large_house small_terrain townhouse 1272000.00 1737.98 to buy 52.04 915000.00 305000.00\n", + "4407 7227800055 2014-09-09 199500.00 4 5 98056 4 2.00 47.51 -122.18 162.57 162.57 0.00 753.98 916.55 217.66 medium_house medium_terrain townhouse 415000.00 422.38 to buy 51.93 299250.00 99750.00\n", + "13709 3298701025 2015-04-27 135000.00 4 6 98106 2 1.00 47.52 -122.35 69.67 69.67 0.00 484.66 554.33 243.54 small_house small_terrain townhouse 276000.00 631.95 to buy 51.09 202500.00 67500.00\n", + "4112 5101402435 2014-06-03 312000.00 5 7 98115 3 2.25 47.69 -122.30 143.07 71.53 71.53 495.90 638.97 488.29 medium_house small_terrain townhouse 630000.00 1172.71 to buy 50.48 452400.00 140400.00\n", + "2827 1995200200 2014-05-06 313950.00 4 6 98115 3 1.00 47.70 -122.32 140.28 79.89 60.38 565.11 705.39 445.07 medium_house medium_terrain townhouse 626750.00 758.28 to buy 49.91 392437.50 78487.50\n", + "21556 2113700360 2014-06-27 315000.00 4 7 98106 6 4.00 47.53 -122.35 289.85 194.16 95.69 393.90 683.74 460.70 large_house small_terrain townhouse 616950.00 775.09 to buy 48.94 393750.00 78750.00\n", + "1895 2917200675 2015-01-27 340000.00 4 7 98103 2 1.75 47.70 -122.35 139.35 113.34 26.01 386.28 525.63 646.85 medium_house small_terrain townhouse 640000.00 1365.26 to buy 46.88 425000.00 85000.00\n", + "12959 6303401150 2014-09-24 125000.00 4 5 98146 2 1.00 47.50 -122.36 75.25 75.25 0.00 778.69 853.94 146.38 small_house medium_terrain townhouse 234900.00 266.86 to buy 46.79 156250.00 31250.00\n", + "20624 7292700005 2014-10-14 485000.00 4 8 98177 4 1.75 47.77 -122.36 299.14 186.73 112.41 686.72 985.85 491.96 large_house medium_terrain townhouse 907500.00 750.17 to buy 46.56 606250.00 121250.00\n", + "2431 7697870600 2014-09-09 158000.00 4 7 98030 3 2.50 47.37 -122.18 141.21 141.21 0.00 668.88 810.09 195.04 medium_house medium_terrain townhouse 295000.00 319.92 to buy 46.44 197500.00 39500.00\n", + "816 4222310010 2014-12-26 152500.00 4 6 98003 4 1.00 47.35 -122.31 160.72 160.72 0.00 682.81 843.53 180.79 medium_house medium_terrain townhouse 283200.00 285.22 to buy 46.15 190625.00 38125.00\n", + "4365 9476200680 2015-04-28 226000.00 4 6 98056 3 1.75 47.49 -122.19 138.42 91.97 46.45 582.39 720.81 313.54 medium_house medium_terrain townhouse 415000.00 422.38 to buy 45.54 282500.00 56500.00" ] }, - "execution_count": 66, + "execution_count": 68, "metadata": {}, "output_type": "execute_result" } @@ -9226,67 +10459,27 @@ }, { "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Next Step**\n", - "\n", - "- [ ] A seguir a obter as casas finais deveria fazer um mapa com a distribuição das mesmas com a cor pela condition e o tamanho pelo valor de mercado (última venda) para assinalar as mais caras e baratas de adquirir. \n", - "- [ ] Depois um mapa de densidade pela variável profit da região.\n", - "- [ ] Ver a localização das casas com mais de 10000 m² de house_total_m2, i.e. 1 hectare, to take insights." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### 3.1.1.4 Visualize recommended houses" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Scatter Map" - ] - }, - { - "cell_type": "code", - "execution_count": 67, "metadata": { - "scrolled": true + "heading_collapsed": true, + "hidden": true }, - "outputs": [], "source": [ - "# recommended_houses_map = recommended_houses_ordered[['id','zipcode','condition', 'lat', 'long', 'price','bedrooms']]\n", - "\n", - "# map = px.scatter_mapbox(recommended_houses_map, lat='lat', lon='long',\n", - "# hover_name='id',\n", - "# hover_data=['price'],\n", - "# size= 'price',\n", - "# color='condition',\n", - "# color_discrete_sequence=['red','green'],\n", - "# size_max=30,\n", - "# zoom=10,\n", - "# height=300)\n", - "\n", - "# map.update_layout(mapbox_style='open-street-map')\n", - "# map.update_layout(height=600, margin={'r':0, 't':0, 'l':0, 'b':0})\n", - "\n", - "# map.show()" + "#### 3.1.1.4 Visualize recommended houses" ] }, { "cell_type": "markdown", "metadata": { - "heading_collapsed": true + "heading_collapsed": true, + "hidden": true }, "source": [ - "#### Recommended Houses Density map" + "##### Recommended Houses Density map" ] }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 69, "metadata": { "hidden": true }, @@ -9314,16 +10507,19 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hidden": true + }, "source": [ - "#### Density Map Image" + "###### Density Map Image" ] }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 70, "metadata": { - "scrolled": true + "hidden": true, + "scrolled": false }, "outputs": [ { @@ -9335,7 +10531,7 @@ "" ] }, - "execution_count": 69, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } @@ -9347,15 +10543,16 @@ { "cell_type": "markdown", "metadata": { - "heading_collapsed": true + "heading_collapsed": true, + "hidden": true }, "source": [ - "#### Recommended Houses Profit per zipcode" + "##### Recommended Houses Profit per zipcode" ] }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 71, "metadata": { "hidden": true }, @@ -9365,7 +10562,7 @@ "# url = 'https://opendata.arcgis.com/datasets/83fc2e72903343aabff6de8cb445b81c_2.geojson'\n", "# geofile = get_geofile(url)\n", "\n", - "# zipcode_profit_df = recommended_houses_ordered[['profit_est', 'zipcode']].groupby('zipcode').mean().reset_index()\n", + "# zipcode_profit_df = recommended_houses_ordered[['profit_est', 'zipcode']].groupby('zipcode').median().reset_index()\n", "# zipcode_profit_df.columns = ['zipcode', 'profit_est']\n", "\n", "# geofile = geofile[geofile['ZIP'].isin(recommended_houses_ordered['zipcode'].tolist())]\n", @@ -9392,89 +10589,1647 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hidden": true + }, "source": [ - "#### Cloropleth Map Image" + "###### Cloropleth Map Image" ] }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 72, "metadata": { - "scrolled": true + "hidden": true, + "scrolled": false }, "outputs": [ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" ] }, - "execution_count": 71, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "Image(url= \"images/recommended_houses_cloropleth_map.png\", width=800, height=800)" + "Image(url= \"images/recommended_houses_cloropleth_map.png\", width=600, height=600)" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true, + "hidden": true + }, + "source": [ + "##### Properties Scatter Map" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hidden": true + }, "source": [ - "### 3.1.2 Once its bought when it's the best time period to sell it and for what price?" + ">#### Location of certain specific houses\n", + "\n", + "In this specific selection it can be seen that the properties considered country houses with more than 10000m² of total property size are actually outside town, closer to the mountains." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 73, + "metadata": { + "hidden": true, + "scrolled": false + }, "outputs": [], - "source": [] + "source": [ + "# # For houses with more than 9999 m²\n", + "\n", + "# specific_houses = recommended_houses_ordered.loc[recommended_houses_ordered['house_total_m2'] > 9999]\n", + "\n", + "# map = px.scatter_mapbox(specific_houses, lat='lat', lon='long',\n", + "# hover_name='id',\n", + "# hover_data=['price'],\n", + "# size_max=30,\n", + "# zoom=10,\n", + "# height=300)\n", + "\n", + "# map.update_layout(mapbox_style='open-street-map')\n", + "# map.update_layout(height=800, margin={'r':0, 't':0, 'l':0, 'b':0})\n", + "\n", + "# map.show()" + ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] + "execution_count": 74, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "10000m2_recommended_houses.png\" width=\"600\" height=\"600\"/>" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Image(url= 'images/>10000m2_recommended_houses.png', width=600, height=600) " + ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "heading_collapsed": true + }, "source": [ - "### 3.1.3 To rise the housing selling price, the company should do a renovation. So what would be good renewal changes?\n", + "### 3.1.2 Once its bought when it's the best time period to sell it and for what price?\n", "\n", - "Now to understand what would be good renewal changes, we must have a discretization of the house total size in m2, to group the houses respectively, and compare the houses that were sold with a price/m2 below the median of its region and now below the median of this total size group median price, what are the amenities that must be added or renewed to increase its market appreciation. \n", + "After having a value appreciation on the previous conditions based on real estate natural appreciation and some renewal works, the selling price can also suffer from other appreciation by taking into account the best season in which the houses on the same category were sold for a higher value.\n", + "\n", + "So for all houses where its appreciated selling price are still below the best season median price of its zipcode, living and lot size category, the selling price will get more 5% of appreciation rate. If it is already above, there is no extra season appreciation." + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "# Extract a season column through date\n", "\n", - "This way a new categorical variable must represent the discretization of the properties sizes within each region and get a more normal distribution of property size. \n", + "house_df_ins['season'] = house_df_ins[['price','date']].apply(lambda x: 'spring' if pd.to_datetime(x[1]).month in (4,5,6)\n", + " else 'summer' if pd.to_datetime(x[1]).month in (7,8,9)\n", + " else 'autumn' if pd.to_datetime(x[1]).month in (10,11,12)\n", + " else 'winter', axis=1)\n", "\n", - "Then calculate the respective median price/m2 for each sub group on each region, and do a visualization to compare the amenities of the houses from each region within its respective property size interval that are below the median price/m2 against the houses that are above, and finally find what must be renewed or built. " + "house_df_ins.loc[(house_df_ins['date'].dt.month == 3) & (house_df_ins['date'].dt.day > 19),'season'] = 'spring'\n", + "house_df_ins.loc[(house_df_ins['date'].dt.month == 6) & (house_df_ins['date'].dt.day > 20),'season'] = 'summer'\n", + "house_df_ins.loc[(house_df_ins['date'].dt.month == 9) & (house_df_ins['date'].dt.day > 21),'season'] = 'autumn'\n", + "house_df_ins.loc[(house_df_ins['date'].dt.month == 12) & (house_df_ins['date'].dt.day > 20),'season'] = 'winter'" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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zipcodeliving_sizelot_sizeseasonmedian_price
098001large_houselarge_terrainautumn675000.00
198001large_houselarge_terrainspring639900.00
298001large_houselarge_terrainsummer515000.00
398001large_housemedium_terrainautumn510900.00
498001large_housemedium_terrainspring470000.00
..................
180398199small_housemedium_terrainwinter529000.00
180498199small_housesmall_terrainautumn457500.00
180598199small_housesmall_terrainspring425000.00
180698199small_housesmall_terrainsummer441000.00
180798199small_housesmall_terrainwinter473000.00
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1808 rows × 5 columns

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" + ], + "text/plain": [ + " zipcode living_size lot_size season median_price\n", + "0 98001 large_house large_terrain autumn 675000.00\n", + "1 98001 large_house large_terrain spring 639900.00\n", + "2 98001 large_house large_terrain summer 515000.00\n", + "3 98001 large_house medium_terrain autumn 510900.00\n", + "4 98001 large_house medium_terrain spring 470000.00\n", + "... ... ... ... ... ...\n", + "1803 98199 small_house medium_terrain winter 529000.00\n", + "1804 98199 small_house small_terrain autumn 457500.00\n", + "1805 98199 small_house small_terrain spring 425000.00\n", + "1806 98199 small_house small_terrain summer 441000.00\n", + "1807 98199 small_house small_terrain winter 473000.00\n", + "\n", + "[1808 rows x 5 columns]" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "groups_zip_sizes_season = house_df_ins[['price', 'zipcode','living_size','lot_size','season']].groupby(['zipcode','living_size','lot_size','season'])\n", + "\n", + "median_price_per_season = groups_zip_sizes_season.median().reset_index()\n", + "\n", + "median_price_per_season.columns = ['zipcode','living_size','lot_size','season', 'median_price']\n", + "\n", + "median_price_per_season" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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zipcodeliving_sizelot_size
098001large_houselarge_terrain
198001large_housemedium_terrain
298001large_housesmall_terrain
398001medium_houselarge_terrain
498001medium_housemedium_terrain
............
53498199medium_houselarge_terrain
53598199medium_housemedium_terrain
53698199medium_housesmall_terrain
53798199small_housemedium_terrain
53898199small_housesmall_terrain
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539 rows × 3 columns

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" + ], + "text/plain": [ + " zipcode living_size lot_size\n", + "0 98001 large_house large_terrain\n", + "1 98001 large_house medium_terrain\n", + "2 98001 large_house small_terrain\n", + "3 98001 medium_house large_terrain\n", + "4 98001 medium_house medium_terrain\n", + ".. ... ... ...\n", + "534 98199 medium_house large_terrain\n", + "535 98199 medium_house medium_terrain\n", + "536 98199 medium_house small_terrain\n", + "537 98199 small_house medium_terrain\n", + "538 98199 small_house small_terrain\n", + "\n", + "[539 rows x 3 columns]" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "unique_indices = median_price_per_season[['zipcode','living_size','lot_size']].drop_duplicates()\n", + "\n", + "unique_indices.reset_index(drop=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "best_season_df = pd.DataFrame(columns=['zipcode','living_size','lot_size','best_season','best_season_median_price'])\n", + "for row in unique_indices.iterrows():\n", + " \n", + " rows_indeces = (median_price_per_season['zipcode'] == row[1]['zipcode']) & \\\n", + " (median_price_per_season['living_size'] == row[1]['living_size']) & \\\n", + " (median_price_per_season['lot_size'] == row[1]['lot_size'])\n", + " \n", + " unique_df = median_price_per_season.loc[rows_indeces]\n", + " max_median_price = unique_df['median_price'].max()\n", + " \n", + " row_append = unique_df.loc[unique_df['median_price'] == max_median_price]\n", + " row_append.columns = ['zipcode','living_size','lot_size','best_season','best_season_median_price']\n", + " \n", + " best_season_df = best_season_df.append(row_append,ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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zipcodeliving_sizelot_sizebest_seasonbest_season_median_price
098001large_houselarge_terrainautumn675000.00
198001large_housemedium_terrainautumn510900.00
298001large_housesmall_terrainsummer330000.00
398001medium_houselarge_terrainautumn340000.00
498001medium_housemedium_terrainwinter285500.00
..................
53698199medium_houselarge_terrainautumn957250.00
53798199medium_housemedium_terrainspring782500.00
53898199medium_housesmall_terrainwinter655000.00
53998199small_housemedium_terrainsummer545000.00
54098199small_housesmall_terrainwinter473000.00
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541 rows × 5 columns

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" + ], + "text/plain": [ + " zipcode living_size lot_size best_season best_season_median_price\n", + "0 98001 large_house large_terrain autumn 675000.00\n", + "1 98001 large_house medium_terrain autumn 510900.00\n", + "2 98001 large_house small_terrain summer 330000.00\n", + "3 98001 medium_house large_terrain autumn 340000.00\n", + "4 98001 medium_house medium_terrain winter 285500.00\n", + ".. ... ... ... ... ...\n", + "536 98199 medium_house large_terrain autumn 957250.00\n", + "537 98199 medium_house medium_terrain spring 782500.00\n", + "538 98199 medium_house small_terrain winter 655000.00\n", + "539 98199 small_house medium_terrain summer 545000.00\n", + "540 98199 small_house small_terrain winter 473000.00\n", + "\n", + "[541 rows x 5 columns]" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# best_season_df = pd.concat([unique_indices.reset_index(drop=True),pd.DataFrame(best_season_rows),pd.DataFrame(best_season_price_rows)],axis=1)\n", + "# best_season_df.columns = ['zipcode','living_size','lot_size','best_season','best_season_median_price']\n", + "\n", + "best_season_df" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "houses_best_selling_price = pd.merge(recommended_houses_ordered,best_season_df,how='left',on=['zipcode','living_size','lot_size'])" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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iddatepriceconditiongradezipcodebedroomsbathroomslatlongm2_livingm2_abovem2_basementm2_lothouse_total_m2price_m2living_sizelot_sizehouse_typemedian_pricemedian_price_m2status%_value_below_median_priceselling_priceprofit_estbest_seasonbest_season_median_price
210886059000602014-06-03545000.00479810731.7547.66-122.36168.15168.150.00278.70446.851219.65medium_housesmall_terraintownhouse605000.001247.09to buy9.92613125.0068125.00summer612000.00
215232918007802015-02-03375000.00479805642.5047.49-122.18194.16136.5657.60773.39967.55387.58medium_housemedium_terraintownhouse415000.00422.38to buy9.64421875.0046875.00summer419500.00
216213778002772014-12-15696000.00489819963.2547.65-122.40269.41213.6755.74594.56863.97805.58medium_housemedium_terraintownhouse769500.00965.72to buy9.55783000.0087000.00spring782500.00
216473998001502014-06-06535500.00489807231.5047.75-122.11160.72160.720.003739.223899.94137.31medium_houselarge_terrainmansion592000.00141.85to buy9.54602437.5066937.50spring600000.00
218376637005512015-04-07336000.00469812532.0047.73-122.3098.4798.470.001092.971191.44282.01small_housemedium_terrainmansion371000.00453.71to buy9.43378000.0042000.00autumn377500.00
....................................................................................
355915622003802014-09-16560000.00489800741.7547.62-122.14161.65161.650.00817.52979.17571.92medium_housemedium_terraintownhouse560500.00545.12to buy0.09630000.0070000.00winter601500.00
356081212007002014-07-13579000.00489805242.2547.72-122.11188.59188.590.00814.181002.76577.40medium_housemedium_terrainmansion579500.00550.27to buy0.09651375.0072375.00spring597389.50
356279416002202014-06-10219900.00469800331.0047.32-122.3390.1190.110.00719.23809.34271.70small_housemedium_terraintownhouse219950.00224.58to buy0.02247387.5027487.50winter230500.00
356332937004802014-10-20414950.00479813341.7547.75-122.35204.38102.19102.19793.83998.21415.69medium_housemedium_terraintownhouse415000.00427.25to buy0.01466818.7551868.75winter449000.00
356540241012542014-12-04419995.00479815532.2547.76-122.31170.01123.5646.45696.75866.76484.56medium_housemedium_terraintownhouse420000.00397.76to buy0.00472494.3852499.38summer435000.00
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763 rows × 27 columns

\n", + "
" + ], + "text/plain": [ + " id date price condition grade zipcode bedrooms bathrooms lat long m2_living m2_above m2_basement m2_lot house_total_m2 price_m2 living_size lot_size house_type median_price median_price_m2 status %_value_below_median_price selling_price profit_est best_season best_season_median_price\n", + "2108 8605900060 2014-06-03 545000.00 4 7 98107 3 1.75 47.66 -122.36 168.15 168.15 0.00 278.70 446.85 1219.65 medium_house small_terrain townhouse 605000.00 1247.09 to buy 9.92 613125.00 68125.00 summer 612000.00\n", + "2152 3291800780 2015-02-03 375000.00 4 7 98056 4 2.50 47.49 -122.18 194.16 136.56 57.60 773.39 967.55 387.58 medium_house medium_terrain townhouse 415000.00 422.38 to buy 9.64 421875.00 46875.00 summer 419500.00\n", + "2162 1377800277 2014-12-15 696000.00 4 8 98199 6 3.25 47.65 -122.40 269.41 213.67 55.74 594.56 863.97 805.58 medium_house medium_terrain townhouse 769500.00 965.72 to buy 9.55 783000.00 87000.00 spring 782500.00\n", + "2164 7399800150 2014-06-06 535500.00 4 8 98072 3 1.50 47.75 -122.11 160.72 160.72 0.00 3739.22 3899.94 137.31 medium_house large_terrain mansion 592000.00 141.85 to buy 9.54 602437.50 66937.50 spring 600000.00\n", + "2183 7663700551 2015-04-07 336000.00 4 6 98125 3 2.00 47.73 -122.30 98.47 98.47 0.00 1092.97 1191.44 282.01 small_house medium_terrain mansion 371000.00 453.71 to buy 9.43 378000.00 42000.00 autumn 377500.00\n", + "... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n", + "3559 1562200380 2014-09-16 560000.00 4 8 98007 4 1.75 47.62 -122.14 161.65 161.65 0.00 817.52 979.17 571.92 medium_house medium_terrain townhouse 560500.00 545.12 to buy 0.09 630000.00 70000.00 winter 601500.00\n", + "3560 8121200700 2014-07-13 579000.00 4 8 98052 4 2.25 47.72 -122.11 188.59 188.59 0.00 814.18 1002.76 577.40 medium_house medium_terrain mansion 579500.00 550.27 to buy 0.09 651375.00 72375.00 spring 597389.50\n", + "3562 7941600220 2014-06-10 219900.00 4 6 98003 3 1.00 47.32 -122.33 90.11 90.11 0.00 719.23 809.34 271.70 small_house medium_terrain townhouse 219950.00 224.58 to buy 0.02 247387.50 27487.50 winter 230500.00\n", + "3563 3293700480 2014-10-20 414950.00 4 7 98133 4 1.75 47.75 -122.35 204.38 102.19 102.19 793.83 998.21 415.69 medium_house medium_terrain townhouse 415000.00 427.25 to buy 0.01 466818.75 51868.75 winter 449000.00\n", + "3565 4024101254 2014-12-04 419995.00 4 7 98155 3 2.25 47.76 -122.31 170.01 123.56 46.45 696.75 866.76 484.56 medium_house medium_terrain townhouse 420000.00 397.76 to buy 0.00 472494.38 52499.38 summer 435000.00\n", + "\n", + "[763 rows x 27 columns]" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "houses_best_selling_price.loc[houses_best_selling_price['selling_price'] > houses_best_selling_price['best_season_median_price']]" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "hidden": true + }, + "outputs": [], + "source": [ + "# set a best season selling price\n", + "\n", + "houses_best_selling_price['best_season_selling_price'] = houses_best_selling_price[['selling_price','best_season_median_price']].apply(lambda x: x[0]*1.05 if (x[0] < x[1])\\\n", + " else x[0], axis=1)\n", + "\n", + "houses_best_selling_price['profit_est'] = houses_best_selling_price['best_season_selling_price'] - houses_best_selling_price['price']" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": { + "hidden": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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idzipcodeliving_sizelot_sizehouse_total_m2conditionprice_m2median_price_m2pricemedian_pricebest_seasonbest_season_median_price%_value_below_median_priceselling_pricebest_season_selling_priceprofit_est
0123850045198033small_housemedium_terrain801.734162.15483.99130000.00465000.00spring530000.0072.04195000.00204750.0074750.00
1511140008698038small_houselarge_terrain5053.11521.7734.21110000.00295000.00summer375000.0062.71159500.00167475.0057475.00
2789980045098106small_housesmall_terrain500.734213.69631.95107000.00276000.00autumn299000.0061.23160500.00168525.0061525.00
3235430045698027small_housesmall_terrain195.094666.361522.92130000.00331000.00summer355000.0060.73195000.00204750.0074750.00
4388810022698033large_housemedium_terrain1139.514404.56945.68461000.001135000.00winter1575000.0059.38691500.00726075.00265075.00
564421002098004large_housemedium_terrain1689.294461.731242.36780000.001750000.00winter1942500.0055.431170000.001228500.00448500.00
6403800004098008medium_housemedium_terrain943.864264.87542.88250000.00555000.00summer575000.0054.95375000.00393750.00143750.00
7895290024598118large_houselarge_terrain2369.884358.67343.06850000.001876500.00winter2903000.0054.701275000.001338750.00488750.00
81300121598108small_housesmall_terrain575.984225.70717.00130000.00284500.00spring331000.0054.31195000.00204750.0074750.00
9722780004098056medium_housemedium_terrain1117.964169.95422.38190000.00415000.00summer419500.0054.22285000.00299250.00109250.00
10121700034098166medium_housemedium_terrain923.434200.34349.30185000.00400475.00spring444500.0053.80277500.00291375.00106375.00
11431170012098042small_housemedium_terrain1764.08459.52233.34105000.00226000.00winter240975.0053.54157500.00165375.0060375.00
12923290005098103medium_housemedium_terrain786.864381.26846.27300000.00645000.00winter662600.0053.49450000.00472500.00172500.00
13512700132098059medium_housemedium_terrain1033.054183.92369.90190000.00405000.00autumn416500.0053.09285000.00299250.00109250.00
14684070016598122small_housesmall_terrain132.2041528.031492.01202000.00430000.00spring449250.0053.02303000.00318150.00116150.00
15733540021598002small_housemedium_terrain604.414157.18236.3095000.00200000.00winter208000.0052.50142500.00149625.0054625.00
16339880005598102large_houselarge_terrain2686.204893.451491.932400000.005050000.00autumn5050000.0052.483600000.003780000.001380000.00
17154580781098038small_housemedium_terrain801.454147.23296.60118000.00247450.00winter256000.0052.31177000.00185850.0067850.00
18549520004098006large_houselarge_terrain2498.084244.19403.94610000.001275000.00spring1407500.0052.16915000.00960750.00350750.00
19501600227598112large_housesmall_terrain727.314838.701737.98610000.001272000.00summer1287500.0052.04915000.00960750.00350750.00
\n", + "
" + ], + "text/plain": [ + " id zipcode living_size lot_size house_total_m2 condition price_m2 median_price_m2 price median_price best_season best_season_median_price %_value_below_median_price selling_price best_season_selling_price profit_est\n", + "0 1238500451 98033 small_house medium_terrain 801.73 4 162.15 483.99 130000.00 465000.00 spring 530000.00 72.04 195000.00 204750.00 74750.00\n", + "1 5111400086 98038 small_house large_terrain 5053.11 5 21.77 34.21 110000.00 295000.00 summer 375000.00 62.71 159500.00 167475.00 57475.00\n", + "2 7899800450 98106 small_house small_terrain 500.73 4 213.69 631.95 107000.00 276000.00 autumn 299000.00 61.23 160500.00 168525.00 61525.00\n", + "3 2354300456 98027 small_house small_terrain 195.09 4 666.36 1522.92 130000.00 331000.00 summer 355000.00 60.73 195000.00 204750.00 74750.00\n", + "4 3888100226 98033 large_house medium_terrain 1139.51 4 404.56 945.68 461000.00 1135000.00 winter 1575000.00 59.38 691500.00 726075.00 265075.00\n", + "5 644210020 98004 large_house medium_terrain 1689.29 4 461.73 1242.36 780000.00 1750000.00 winter 1942500.00 55.43 1170000.00 1228500.00 448500.00\n", + "6 4038000040 98008 medium_house medium_terrain 943.86 4 264.87 542.88 250000.00 555000.00 summer 575000.00 54.95 375000.00 393750.00 143750.00\n", + "7 8952900245 98118 large_house large_terrain 2369.88 4 358.67 343.06 850000.00 1876500.00 winter 2903000.00 54.70 1275000.00 1338750.00 488750.00\n", + "8 13001215 98108 small_house small_terrain 575.98 4 225.70 717.00 130000.00 284500.00 spring 331000.00 54.31 195000.00 204750.00 74750.00\n", + "9 7227800040 98056 medium_house medium_terrain 1117.96 4 169.95 422.38 190000.00 415000.00 summer 419500.00 54.22 285000.00 299250.00 109250.00\n", + "10 1217000340 98166 medium_house medium_terrain 923.43 4 200.34 349.30 185000.00 400475.00 spring 444500.00 53.80 277500.00 291375.00 106375.00\n", + "11 4311700120 98042 small_house medium_terrain 1764.08 4 59.52 233.34 105000.00 226000.00 winter 240975.00 53.54 157500.00 165375.00 60375.00\n", + "12 9232900050 98103 medium_house medium_terrain 786.86 4 381.26 846.27 300000.00 645000.00 winter 662600.00 53.49 450000.00 472500.00 172500.00\n", + "13 5127001320 98059 medium_house medium_terrain 1033.05 4 183.92 369.90 190000.00 405000.00 autumn 416500.00 53.09 285000.00 299250.00 109250.00\n", + "14 6840700165 98122 small_house small_terrain 132.20 4 1528.03 1492.01 202000.00 430000.00 spring 449250.00 53.02 303000.00 318150.00 116150.00\n", + "15 7335400215 98002 small_house medium_terrain 604.41 4 157.18 236.30 95000.00 200000.00 winter 208000.00 52.50 142500.00 149625.00 54625.00\n", + "16 3398800055 98102 large_house large_terrain 2686.20 4 893.45 1491.93 2400000.00 5050000.00 autumn 5050000.00 52.48 3600000.00 3780000.00 1380000.00\n", + "17 1545807810 98038 small_house medium_terrain 801.45 4 147.23 296.60 118000.00 247450.00 winter 256000.00 52.31 177000.00 185850.00 67850.00\n", + "18 5495200040 98006 large_house large_terrain 2498.08 4 244.19 403.94 610000.00 1275000.00 spring 1407500.00 52.16 915000.00 960750.00 350750.00\n", + "19 5016002275 98112 large_house small_terrain 727.31 4 838.70 1737.98 610000.00 1272000.00 summer 1287500.00 52.04 915000.00 960750.00 350750.00" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "houses_best_selling_price[['id','zipcode','living_size','lot_size','house_total_m2','condition','price_m2','median_price_m2','price','median_price','best_season','best_season_median_price','%_value_below_median_price','selling_price','best_season_selling_price','profit_est']].head(20)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1.3 To rise the housing selling price, the company should do a renovation. So what would be good renewal changes?\n", + "\n", + "Now to understand what would be good renewal changes, we must compare the recommended houses that were sold with a price/m2 below the median of its region, living and lot size, in terms of amenities with the ones already above the median and then analyze what must be added or renewed to increase its market appreciation. \n", + "\n", + "- By seeing a visualization of price vs sqft_lot, colored by different amenities that will illustrate the boundaries of which amenity should be improved for the house with a price and a fixed size.\n", + "- Compare by only see the price vs amenity\n", + "- This would be more suitable if the ameties were like the bedroom having a closet, the kitchen having oven etc." + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [], + "source": [ + "def renovation_histograms(house_df: pd.DataFrame,zipcode: int):\n", + "\n", + " ''' \n", + " For a specific zipcode region, returns a set of histograms, one \n", + " on each column for each living_size and on each row for \n", + " lot_size.\n", + " On each graph the x-axis will be the amenity and the y-axis \n", + " the number of amenities.\n", + "\n", + " Parameters\n", + " ----------\n", + " zipcode : int\n", + " amenity : string or list\n", + "\n", + " Returns\n", + " ----------\n", + " plots a set of histograms\n", + " ''' \n", + "\n", + " unique_zipcodes = house_df['zipcode'].unique()\n", + " if zipcode in unique_zipcodes:\n", + " \n", + " zipcode_houses_df = house_df.loc[house_df.zipcode == zipcode,:]\n", + " columns = zipcode_houses_df['living_size'].unique()\n", + " rows = zipcode_houses_df['lot_size'].unique()\n", + " \n", + "# houses_to_buy_df = zipcode_houses_df.loc[zipcode_houses_df.status == 'to buy',:]\n", + "# houses_to_compare_df = zipcode_houses_df.loc[zipcode_houses_df.status == 'to compare',:]\n", + " \n", + "# houses_to_buy_grouped_by_amenity = houses_to_buy_df.groupby(by=['living_size','lot_size',amenity]).median().reset_index()\n", + "# houses_to_compare_grouped_by_amenity = houses_to_compare_df.groupby(by=['living_size','lot_size',amenity]).median().reset_index()\n", + " \n", + "# houses_to_buy_grouped_by_amenity['status'] = 'to_buy'\n", + "# houses_to_compare_grouped_by_amenity['status'] = 'to_compare'\n", + "# houses_grouped_by_amenity = pd.concat([houses_to_buy_grouped_by_amenity,houses_to_compare_grouped_by_amenity],ignore_index=True)\n", + " \n", + " \n", + "# houses_grouped_by_amenity.hist(,by=[amenity,'status'])\n", + " houses_status_df = zipcode_houses_df.loc[(zipcode_houses_df.status == 'to buy') |\n", + " (zipcode_houses_df.status == 'to compare'),:]\n", + " \n", + " \n", + " houses_to_buy_df = zipcode_houses_df.loc[zipcode_houses_df.status == 'to buy',:]\n", + " houses_to_compare_df = zipcode_houses_df.loc[zipcode_houses_df.status == 'to compare',:]\n", + " \n", + " houses_to_buy_grouped = houses_to_buy_df.groupby(by=['living_size','lot_size']).median().reset_index()\n", + " houses_to_compare_grouped = houses_to_compare_df.groupby(by=['living_size','lot_size']).median().reset_index()\n", + " \n", + " houses_to_buy_grouped.rename(columns = {'bedrooms':'houses_to_buy_bedrooms'}, inplace = True)\n", + " houses_to_buy_grouped.rename(columns = {'bathrooms':'houses_to_buy_bathrooms'}, inplace = True)\n", + " houses_to_compare_grouped.rename(columns = {'bedrooms':'houses_to_compare_bedrooms'}, inplace = True)\n", + " houses_to_compare_grouped.rename(columns = {'bathrooms':'houses_to_compare_bathrooms'}, inplace = True)\n", + " \n", + " fig, ax = plt.subplots()\n", + " houses_to_compare_grouped['property_size'] = houses_to_compare_grouped['living_size']+'_'+houses_to_compare_grouped['lot_size']\n", + " houses_to_compare_grouped.plot.bar(x='property_size',y=['houses_to_compare_bedrooms','houses_to_compare_bathrooms'],ax=ax)\n", + " houses_to_buy_grouped['property_size'] = houses_to_buy_grouped['living_size']+'_'+houses_to_buy_grouped['lot_size']\n", + " houses_to_buy_grouped.plot.bar(x='property_size',y=['houses_to_buy_bedrooms','houses_to_buy_bathrooms'],ax=ax,color=['lightblue','orange'])\n", + " " ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] + "execution_count": 87, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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mzpyZ3r3XzkEHDUp1dXWuuOLS/OlPD6S2tm0qleS88y5Jx44d58g5evSrufPO29LY2Jinnnoim276gwwcuEfuu+/u3HjjtalUKvnmN5fNEUccnc6dF//M1/vGG6/n3HPPyvvvT0hTU1MGDBiYH/1o64wd+1bOPPOUfPDBxFRXV2fffQ/IhhtumOTjI5/22eeXeeSREfnwww/zm98ck6eeeiKPP/731NfX58QTT88KK3TPyJFP5dxzz07Pnr3y0ksvZpFF2uXoo4eme/cemTBhfIYOPSZTp07NzJkzs/76G2T//Q9J8vFRNa+//lqmTp2Sd999JxdffGWef/7ZXHPNFZkxY2batGmTgw76dVZbbfXP/Vo+9dQTueqqYZk0aVL69ds8v/jFAUmSG2+8Ln/+84NpaKhPbW3bHHbYkenVa+WcffbpSZJf/vLnqVSqcv75lyRJXnttdA4+eL+89967WXXV1TN48PGpVCo5+eShqa6uzpgxb2batGm56qobct11V+WBB+5NknzrW6vmV786PO3bt8+0adPy29+emRdeeD5JssUWW+WnP909ycdHH6288rfywgvP55133s5OO/0kXbp0ya233pzx48dl//0PSb9+m+Wjjz7KSScdlzfeeC3V1TVZfvluOfHE0+biu/rzzXXRc9hhh6WpqSlrrbVWfv3rX2fRRRed6yf5rHO7s/Dr0mXOH0DAgmH8sTBqaGxKddWC+QSuCA2NTcYeFMTYK4f33qtKTc2COefP3D5Px44dctVV1+WZZ/5fBg/+TTbbbPO8//77OemkIbnoosvSvXuP3HXXHTnhhGNzxRXXpKrq46NNPtn/f16/4YZrsttuu+cHP9giTU1NmTJlSmpqqnLBBb/NWmutlcGDj0tjY2OOO+6Y3Hff8Hz/+5vm5ptvyN13P5h27dpl6tSpadu27admX3nllbL99jtm+vTpOfjgQUk+Ln8uueSCXHXV9VlyyS655JIL89vfnpmTTz79U19rfX19jjrq0Oy33wHZdNPNkyQffvhBamqqcsIJx2a77XbINttsl9dffy377bd3brrp1nTu3DlJsthii+aqq67Ln//8xxx11KE58cRTc+CBB+faa6/KtddekeOPPznV1VUZPfqVHHroEenTZ63cc8/wnHzycbnqquvTqdNiOfvsc9O+ffvU18/KIYcckCeffDTrrbdBqqoqeeGF53P11denU6fOGTv2rVx99eU599zfpa6uQ157bXQGDTood95572d+HSuVSt588/UMG3ZVZs6cmX322SNrrLFGNtxwo2y9df8MHPizJMkTTzyes846NZdffk1+85ujcvvtf8iwYVc1T9+rVCp5/fXXcv75F6Wqqio/+9mAjBz5ZL773b6pVCp59dWXc9FFl2WRRRbJ3//+tzz44H257LKr0r59XU44YUiuuebyHHjgIbnmmsuTNOWGG/6QadOmZu+990ivXitl/fU3SKVSyfjx7+Xiiy/LhAkTstNO2+YnP9k1l112VZ5//rkceeRh+cEPfpCnnnos06dPzU033ZokmTRp0qd+b1RVVc3Tz8i5Knquv/76fOMb38jMmTNz8skn54QTTshZZ501108yYcKUNDY2zfX9WTh06dIx48ZNLjoGtErGHwurLl065raX3i46RovZYeVvGHtQAL/3yqOxsTH19Y0L5Lnm9nm+//0fpL6+MaussmrGjRuXqVOn59lnn82KK66U5ZZbIfX1jdlii61z5pmnZtKkyWlsbEpTU1Pz/v/zeu/ea+WKKy7LmDFjss46fbPqqqulvr4xjzwyIv/853O5/vrrkiQfffRRllxyqbRr1z5duy6XoUOPzbrr9s36638vbdsu8pnZGxub/udooY9vf/LJJ9K37wbp1GmJ1Nc3pn//7bPHHrt+5uNfe+311Nc3ZOONN22+T13dopk0aXJeeeWlbLHF1qmvb8xyy62Qnj1XynPPPZv11vt4zZtNNtk89fWN6dlz5SSV9O27YerrG9Or1yr5y18eSn19YxoaGrPsssvlO9/pnfr6xmy++Y9y2mkn5cMPJ6Wqqjq/+91vM2rUs0maMmHChLz44ktZZ5310tjYlL5910+HDoulvr4xf//73zN27Nj84hd7/8fXsz7vvTcuiy++xKe+tqampmyxxVZJqlJb2y79+m3+P+/Phnn++edz7bVXZtKkD1NVVZW33hoz23tUX/+/35dNTU3ZcMONU1398VSulVZaOW+9NSZrrbVumpqasvHGm6ZNm7apr2/M448/ln79Nk/btu3T0NCUrbfePueee1bq6xvzxBOP55BDDktDQ1Patm2fTTf9QR5//LGsu+56aWpqyiabbJrGxqRz5yWy2GKL5Xvf26T5/R037r1MnTo93bv3zOuvv57TTz81vXuvlfXX3/BTv7aNjY1z/Iysqqp85kE1c1X0fOMb30iS1NbWZtddd80vf/nLuXkYAAAAFKq2tjZJUl1dnSRpaGj43PtXV1fPdqDCzJkzmi/vvPOu2WCDjfLkk4/nt789I+us0zf77rt/kqaccspZ6dp12Tn2d8klV2bUqGcycuRT2Wuv3XL22eenZ89e8+GVzV+fvE9VVVWprf3f9Wyqqqq+8D1Lkt///vpMnjwpl156Vdq2bZvTTz95tvdukUX+d0HspqamfPe76+XYY0/4yrlnzZqVY4/9TS64YFhWXnmVjB8/Lttt96PPfUzbtrXNl6uqqmd7fe3bL/KVMyVJ7X+cbe7j93TO78OuXZfNddfdnKeeejKPPfa3XHrp73L11Tc1nwjry/rCY92mTZuWyZM/bo6amppy77335lvf+tZXelIAAAAoyqqrrp7Ro1/Om2++kSS5776706vXymnfvi5duy7XvNbOrFmz8pe/PNT8uDFj3kzXrstmu+12zI9/PKB5fZYNNtgo1113dXNh8MEHH+Tf//5Xpk2bmg8++CC9e6+Vvfb6RXr0WDGvvTb6M3PV1dVl6tQpzdf79Fk7jz76t0yYMD5JMnz4HVlnnXU/8/HLL98t1dXVeeihPzVv+/DDD9K+fV169lwp9913d5KP1/EZPfrlrLbad+bxnUv+9a+xeeaZp5Mkf/zj/enRo2fq6jpk8uTJWWKJJdO2bduMG/de/vrXEZ+5j3XX7ZvHH390tvfik/fy8zzwwH2pr6/P9OnT89BDf0qfPutk5swZaWhoyFJLLZ0kue22P8z2mPbtZ39P58Xaa6+bhx76Y6ZNm5qmpqbcffcdWWed7zbfds89d6apqSnTpk3Nn//8YPNtc+u9995NVVV1Ntpokxx88KH54IOJmTx50pfK+p++8IieCRMm5KCDDkpDQ0MaGxuz4oor5rjjjvvKTwwAAEC51c+c8T+nQp//+/0qOnfunMGDT8jxxx+ThoaGdOrUOUOGnJgkWW211bP22utm4MCds+SSXdKzZ6/mouWWW27KyJH/SJs2NWnTpjaDBh2eJDnkkENz4YXnZY89BqRSqaRNm9ocfPChqampyTHHHJGZM2eksbExK620Sjbe+PufmWujjb6fo48+PHvssWvzYsz77XdgBg064H8WY+6aww8/+jMfX1NTk9NOOzvnnHNGrrpqWCqVqgwYsFu22GKrHHfcSTnzzFNy8803pLq6OoMHn5DOnTvP81S7Hj16ZvjwO3LWWaemXbt2GTz4+CTJj3/8kxx77G8ycODO6dJl6ay11jqfuY/llls+Q4acmNNOOzEzZsxIff2srL76GvnWt1b93Ofu1q1bfvnLnzcvxvzJqdb32usX2Wefn2XRRRfL97+/6WyP+clPfpqDD94vbdu2a16MeW6tt94GGT36lfziF3smSVZZ5dvZffe9kiR77LF3zjnnjPzsZx+fpv2HP9wyffuuP0/7Hz361Vx88QVJksbGhuy22x5Zcsku87SPT1Npampq8cVzrNHz9WSuNBTH+GNhZY0eoCX4vVce77zzZpZZplvRMZhLNTVV81T0jBz5VH73u3Nz+eXXtmAq/q9PG1eft0bPglkOHQAAAIAWN9enVwcAAAC+mokT38+gQQfOsX3jjb+fPffcZ672MXz4Hbn11pvn2H7MMcelV6+Vv3LGz9Knz9otejTPo4/+NZdccuEc23/xi/2z3nobttjzlo2iBwAAABaQzp0Xz1VX3fCV9tG//3bp33+7+RNoIbLeehsqdOYDU7cAAAAASkLRAwAAAFASih4AAACAkrBGDwAAAC2iU+f2aVNTPd/3O6u+IR9MnDbf9wtloOgBAACgRbSpqc5tL7093/e7w8rfmO/7hLIwdQsAAIBS2nDDtTNt2tfnyJ+RI5/KE0889pX2sdNO/fPaa6/Op0SfbX68t2+//e9stdWm8ykRn1D0AAAAwELg6af/8ZWLnrKor68vOsLXlqlbAAAAlNYtt9yUhx/+73z44Yc54ICDs8kmHx9B8thjf88ll1yQxsbGdOrUOYcffnSWXXa53Hvv8Pz974/kpJPOSJLZro8a9UzOOeeMNDY2pb6+Prvv/vNsvvkWmTp1Ss4//5yMHv1KZs6cmd69185BBw1KdXV1rrji0vzpTw+ktrZtKpXkvPMuSceOHefIOXr0q7nzztvS2NiYp556Iptu+oMMHLhH7rvv7tx447WpVCr55jeXzRFHHJ3OnRf/3Nf8wAP35cknH8/UqVOy884DsuOOuyT5+CicBx98OO3bt0+S9O3bJw8++HDuuOOWvP322zn00N8kSd5/f0J2331A/vCHu9KuXbvPfJ4bb7w2jzwyIjNmfJRf/OKA5vf2+eefy8UXn5+pU6cmSfbee7+sv/6GSZJbb705N998Q+rq6rLeehs27+vtt/+dvfcemB/9qH9Gjnwy22yzfdZe+7s588xT8sEHE1NdXZ199z0gffuu/7lfv5Ejn8q5556db3971Tz//KjU1NRk8OATcuWVw/L666Oz1FJL5+STz8wiiyySRx757wwbdlGqqqrT0FCfQYOOSJ8+a3/ue/t1oOgBAACgtOrq6nLZZdfk2Wf/X4YMOSqbbLJpJk58PyedNCTnn39punfvkbvvviPHHz84w4Zd/bn7uv76qzNgwMBsvvkWaWpqypQpU5Ik559/TtZcs0+OPPLYNDY25vjjB+eee+7KJpv0y80335A777w/bdu2y7RpU1Nb2/ZT973iij2z7bY7ZPr06TnwwF8lSV577dVcfPEFufzy67Lkkktm2LCLcs45Z+aEE0793JwTJ76fK664Lu+/PyF77vnTrLFGn/Ts2esz77/11ttmt912zi9/eVDat2+fu+66PZtv/sPPLXmSpKqqKldddUPGjHkj++23V9ZYo3dqatrkrLNOyZlnnpcll1wy48ePzz77/CzXXPP7vPvuO7nmmity5ZXXZ/HFl8hZZ5022/4+/PDDfOtb325+/fvss3u23Xb7bL31dnn99ddy4IH75LrrbknS9LlfvzfeeC2DBw/Nb34zOGeffXoOPfSgXHLJlVlqqaVz2GEH509/eiD9+2+Xyy67JEcccUxWW+07aWhoyEcfTf/c1/t1oegBAACgtDbd9IdJklVXXT3jx4/LjBkz8vzzz2XFFVdK9+49kiRbbrlNzj779EybNvVz99Wnz9q5+uor8q9/jc066/TNqquuliT5618fzgsvPJ+bbro+SfLRRx9lqaWWTl1dh3TtulxOPPG4rLtu36y//vfSvn3dXGcfOfKprLfeBllyySWTJNtuu0P22GPXL3zc1ltvmyRZfPElsv76G+bpp//xuUXPoosulg022CgPPHBv+vffLnfddXvOPfeiuX6e5ZdfISuttHKef35Uqqur8/bb/85hhx3cfL9KpZJ//eutjBr1bNZff8MsvvgS//N6ts9f/vLH5vvV1rZNv36bJ0mmTZuaV199OVtuuU2SpHv3HunZ8+PnSPK5X7/ll++WXr1WTpKsvPLKeffdt7PUUkv/z/VvZezYt5Ika621ds4777+yySb90rfv+unRo+cXvuavA0UPAAAApVVbW5skqa7++DTvDQ0Nn3v/6urqNDY2NV+fOXNG8+Wdd941G2ywUZ588vH89rdnZJ11+mbfffdP0pRTTjkrXbsuO8f+Lrnkyowa9UxGjnwqe+21W84++/zPLV1aUnV1dZqaGpMkM2bMmO22nXbaJSecMDidO3fOCit0z3LLLf+lnqOpKVlxxV753e+GzXHbqFHPfu5jF1mkXSqVypd63v/0n0dNVVVVN38PfHy9qvl74OCDD83o0a/mH/94Mscee2R22eWn2Wab7b/y8xdN0QMAAECLmFXf0CKnQp9V//llzRdZddXVc9ppJ+TNN99It24r5L777k6vXiunffu6dO26XPNaO5VKJX/5y0Pp2LFDkmTMmDez/PLd0rXrsmnfvn3uu+/uJMkGG2yU6667OocddmSqq6vzwQcfZNq0qenUqVOmTZue3r3XSu/ea+W5557Na6+N/syip66uLuPHj2u+3qfP2rn22qsyYcL4LLHEkhk+/I6ss866X/j67rvv7nznO2tm4sSJefTRv2WnnX6SJOnaddm88MI/s/ba6+aPf7x/tsesuGLPLLroYjnvvP/Kr399xFy9j/fcc1f22GPvvPXWmLzyyktZddXVU11dk7Fjx2TkyKea17t54YXns8oq307v3mvl+uuvzsSJ76dz58Vz9913fua+27evS8+eK+W+++7OVlttkzfeeD2jR7+cVVddPUk+8+s3L8aMeSMrrtgzK67YM9OnT8sLL/xT0QMAAACf5YOJC+epzTt37pzBg0/I8ccfk4aGhnTq1DlDhpyYJFlttdWz9trrZuDAnbPkkl3Ss2evTJgwPsnHCzuPHPmPtGlTkzZtajNo0OFJkkMOOTQXXnhe9thjQCqVStq0qc3BBx+ampqaHHPMEZk5c0YaGxuz0kqrZOONv/+ZuTba6Ps5+ujDs8ceuzYvxrzffgdm0KAD/mcx5q45/PCjv/D1LbZYp/z857tl6tQpGThwj6y44sdTkg46aFDOPPOU1NV1SL9+m83xuP79t8sll/wu66//vbl6HxsaGrLnnrvmo48+yuGH/+8i0aed9l/53e/Ozbnnnp36+ln55je75vTTz0nPnr0ycOCe+eUv90r79nVZb70NPnf/xx13Us4885TcfPMNqa6uzuDBJ6Rz585J8plfv3lx0UUXZOzYMamurkmHDh1y1FFD5nkfC6NKU1NT0xff7auZMGHKbIe+8fXQpUvHjBs3uegY0CoZfyysunTpmNteervoGC1mh5W/YexBAfzeK4933nkzyyzTregYzKWamqrU1zc2Xz/ttBOz/PLdsuuuPyswFf/Xp42rqqpKlliiw6fev2pBhAIAAAAWTuPHj8uAATtk7Ni3ssMOOxcdh6/I1C0AAABYQCZOfD+DBh04x/aNN/5+9txzn7nax/Dhd+TWW2+eY/sxxxzXfLapebHkkl1y4423zbH9yiuHZcSIv8yx/ZxzLmiepsXCx9QtPpNDaKE4xh8LK1O3gJbg9155mLr19fJ/p26xcDJ1CwAAAKCVUvQAAAAAlISiBwAAAKAkLMYMAABAi1iiU3Wq2rSf7/ttnDUtEz5omO/7hTJQ9AAAANAiqtq0T26ozP/97tqU5IsX8N5ww7Xz4IMPp337+V82tYSRI59KfX191l2375fex0479c8ZZ5yTHj16zsdkc/oy7+3bb/87TzzxWLbddofmbQsqb2ti6hYAAAAsBJ5++h954onHio7RYt5++9+5667bv9Rj6+vr53Oa8nJEDwAAAKV1yy035eGH/zsffvhhDjjg4GyyyaZJksce+3suueSCNDY2plOnzjn88KOz7LLL5d57h+fvf38kJ510RpLMdn3UqGdyzjlnpLGxKfX19dl9959n8823yNSpU3L++edk9OhXMnPmzPTuvXYOOmhQqqurc8UVl+ZPf3ogtbVtU6kk5513STp27DhHztGjX82dd96WxsbGPPXUE9l00x9k4MA9ct99d+fGG69NpVLJN7+5bI444uh07rz4577mBx64L08++XimTp2SnXcekB133CXJnEfh9O3bJw8++HDuuOOWvP322zn00N8kSd5/f0J2331A/vCHu9KuXbvPfJ4bb7w2jzwyIjNmfJRf/OKA5vf2+OMHZ8yYNzNr1sx07bpcjjpqSBZddNH813+dkbff/lf22GPXLLvsss3v8UMP/Smnn35yJkwYnwEDdmvOu9NO/bPppj/IyJFPpkePnjnkkMPy29+emRdeeD5JssUWW+WnP909STJ27Fs588xT8sEHE1NdXZ199z0gffuu3/y699nnl3nkkRH58MMP85vfHJOnnnoijz/+99TX1+fEE0/PCit0z5gxb+Tkk4/PRx99lMbGhvzoR/2z664Dv+hbbKGj6AEAAKC06urqctll1+TZZ/9fhgw5KptssmkmTnw/J500JOeff2m6d++Ru+++I8cfPzjDhl39ufu6/vqrM2DAwGy++RZpamrKlClTkiTnn39O1lyzT4488tg0Njbm+OMH55577somm/TLzTffkDvvvD9t27bLtGlTU1vb9lP3veKKPbPttjtk+vTpOfDAXyVJXnvt1Vx88QW5/PLrsuSSS2bYsItyzjln5oQTTv3cnBMnvp8rrrgu778/IXvu+dOssUaf9OzZ6zPvv/XW22a33XbOL395UNq3b5+77ro9m2/+w88teZKkqqoqV111Q8aMeSP77bdX1lijdzp3XjyHHHJYOnXqlCS59NILc/31V+eXvzwov/71Efnd787N5ZdfO9t+Pvroo1xyyZV5++1/52c/2yU/+lH/5jJq6tSpGTbsmiTJhReel8bGxlxzze8zbdrU/OIXP0+PHj2z3nob5PjjB2fbbbfP1ltvl9dffy0HHrhPrrvulnTu3DlJ0qFDx1x22TV56KE/5aijDs3Qoadkv/0OzPXXX51rrrkiQ4acmNtuuyUbbrhRBg7cM0kyadKkz339CytTtwAAACitTTf9YZJk1VVXz/jx4zJjxow8//xzWXHFldK9e48kyZZbbpNXX30506ZN/dx99emzdq6++opcddVl+ec/n28+Muevf304N954bfbYY9f8/Oe75aWXXsxbb41JXV2HdO26XE488bjcddftmTZtempq5v54i5Ejn8p6622QJZdcMkmy7bY75KmnnvjCx2299bZJksUXXyLrr79hnn76H597/0UXXSwbbLBRHnjg3tTX1+euu27P9tv/eK6fZ/nlV8hKK62c558flSS5//678/Of75af/WyX/PGPD+SVV17+3P1sttkPkiTf+MY307Hjohk37r3m27bYYqvmy0899UT6998+lUoldXUdstlmP8hTTz2RadOm5tVXX86WW26TJOnevUd69vzfPEmy6aYfP8fKK6+SpJINNvje/1z/VsaOfStJsuaavTN8+B0ZNuyi/OMfT37qkVdfB47oAQAAoLRqa2uTJNXV1UmShobPP1tXdXV1Ghubmq/PnDmj+fLOO++aDTbYKE8++Xh++9szss46fbPvvvsnacopp5yVrl2XnWN/l1xyZUaNeiYjRz6VvfbaLWefff7nHl3Tkqqrq9PU1JgkmTFjxmy37bTTLjnhhMHp3LlzVlihe5Zbbvkv9RzPPPN07rjj1lx00RXp3LlzHnzw/tx1122f+5hPvkbJx0cJNTT873o87dsv8qVyfNZzVFVVpba2zf95vo+/JzbZZNOsttp38sQTj+W6667KPffclSFDTpwvz78gOaIHAACAFtE4a1qya9N8/9c4a9pXyrXqqqtn9OiX8+abbyRJ7rvv7vTqtXLat69L167LNa+1M2vWrPzlLw81P27MmDfTteuy2W67HfPjHw9oXitmgw02ynXXXd1cGHzwwQf597//lWnTpuaDDz5I795rZa+9fpEePVbMa6+N/sxcdXV1mTp1SvP1Pn3WzqOP/i0TJoxPkgwffkfWWWfdL3x99913d5Jk4sSJefTRv6VPn7WTJF27LpsXXvhnkuSPf7x/tsesuGLPLLroYjnvvP/KDjt88dE8SXLPPXclSd56a0xeeeWlrLrq6pk8eXLq6jpkscUWy8yZM5vv8/Hr6zDb65tXa6+9bu655840NTVl2rSp+fOfH8w663w37dvXpWfPlZpf9xtvvJ7Ro1/OqquuPk/7Hzv2rSy++BLZcsv+2XPPffLPfz7/pbMWyRE9AAAAtIgJHzRkbk6DvqB17tw5gwefkOOPPyYNDQ3p1Klz85Ebq622etZee90MHLhzllyyS3r27NVctNxyy00ZOfIfadOmJm3a1GbQoMOTJIcccmguvPC87LHHgFQqlbRpU5uDDz40NTU1OeaYIzJz5ow0NjZmpZVWycYbf/8zc2200fdz9NGHZ489dm1ejHm//Q7MoEEH/M9izF1z+OFHf+HrW2yxTvn5z3fL1KlTMnDgHllxxY9PXX7QQYNy5pmnpK6uQ/r122yOx/Xvv10uueR3WX/9783V+9jQ0JA999w1H330UQ4//ONFovv2XT8PPnhfBgzYIYst1ilrrtm7uTBZccWeWX75bhk4cOd067ZC82LMc2uPPfbOOeeckZ/97OPFmn/4wy2bF1w+7riTcuaZp+Tmm29IdXV1Bg8+oXl9nrn10EN/zIMP3p82bWpSqVRyyCGHztPjFxaVpqampi++21czYcKU2Q594+uhS5eOGTdu4fuhDK2B8cfCqkuXjrntpbeLjtFidlj5G8YeFMDvvfJ45503s8wy3YqOwVyqqalKfX1j8/XTTjsxyy/fLbvu+rMCU/F/fdq4qqqqZIklOnzq/U3dAgAAgFZs/PhxGTBgh4wd+1Z22GHnouPwFZm6BQAAAAvIxInvZ9CgA+fYvvHG38+ee+4zV/sYPvyO3HrrzXNsP+aY49Kr18rznGnJJbvkxhvnXDD5yiuHZcSIv8yx/ZxzLkjnzovP8/OwYJi6xWdyCC0Ux/hjYWXqFtAS/N4rD1O3vl7+79QtFk6mbgEAAFCYBXAsAbQaX2Y8KXoAAACYL2pqajN16iRlD8wHTU1NmTp1UmpqaufpcdboAQAAYL7o3LlLJk4clylTPig6CnOhqqoqjY2mbi3Mampq07lzl3l7TAtlAQAAoJWprq7Jkkt+o+gYzCXrY5WTqVsAAAAAJaHoAQAAACgJRQ8AAABASSh6AAAAAEpC0QMAAABQEooeAAAAgJJQ9AAAAACUhKIHAAAAoCQUPQAAAAAloegBAAAAKAlFDwAAAEBJKHoAAAAASkLRAwAAAFASih4AAACAklD0AAAAAJSEogcAAACgJBQ9AAAAACVRU3SAr7uOiy6Sdm3L+TbOnNVQdAQAAABgHpSzoViA2rWtSf9D7yw6RosYfva2RUcAAAAA5oGpWwAAAAAloegBAAAAKAlFDwAAAEBJKHoAAAAASkLRAwAAAFASih4AAACAklD0AAAAAJSEogcAAACgJBQ9AAAAACWh6AEAAAAoCUUPAAAAQEkoegAAAABKQtEDAAAAUBKKHgAAAICSUPQAAAAAlISiBwAAAKAkFD0AAAAAJaHoAQAAACgJRQ8AAABASSh6AAAAAEpC0QMAAABQEooeAAAAgJJQ9AAAAACUhKIHAAAAoCQUPQAAAAAloegBAAAAKAlFDwAAAEBJKHoAAAAASkLRAwAAAFASih4AAACAklD0AAAAAJSEogcAAACgJBQ9AAAAACWh6AEAAAAoCUUPAAAAQEkoegAAAABKQtEDAAAAUBKKHgAAAICSUPQAAAAAlISiBwAAAKAkFD0AAAAAJaHoAQAAACgJRQ8AAABASSh6AAAAAEpC0QMAAABQEooeAAAAgJJQ9AAAAACUhKIHAAAAoCQUPQAAAAAloegBAAAAKAlFDwAAAEBJKHoAAAAASkLRAwAAAFASih4AAACAklD0AAAAAJSEogcAAACgJBQ9AAAAACUxT0XPBRdckJVXXjkvv/xyS+UBAAAA4Eua66Ln+eefz//7f/8vXbt2bck8AAAAAHxJc1X0zJw5MyeccEKGDh3awnEAAAAA+LJq5uZO5557brbZZpssu+yyX+pJlliiw5d63NdBQ2NThp+9bdExWkRDY1O6dOlYdAz4VPWNjampKu8yY/WNjcYfFMTYY2E0c1ZDattUFx2jxcyc1WDsQUGMvfL5wqLn6aefznPPPZfDDjvsSz/JhAlT0tjY9KUfvzDr0qVjbnvp7aJjtIgdVv5Gxo2bXHQM+FRlHnuJ8cfCqzX8Z9DYY2HUpUvH9D/0zqJjtJjhZ29r7EEBunTpaOx9TVVVVT7zoJov/Dj8ySefzOjRo7PpppumX79+eeedd7LXXnvlr3/963wPCgAAAMCX94VH9Oy7777Zd999m6/369cvF198cVZaaaUWDQYAAADAvCnvAhcAAAAArcxcLcb8nx566KGWyAEAAADAV+SIHgAAAICSUPQAAAAAlISiBwAAAKAkFD0AAAAAJaHoAQAAACgJRQ8AAABASSh6AAAAAEpC0QMAAABQEooeAAAAgJJQ9AAAAACUhKIHAAAAoCQUPQAAAAAloegBAAAAKAlFDwAAAEBJKHoAAAAASkLRAwAAAFASih4AAACAklD0AAAAAJSEogcAAACgJBQ9AAAAACWh6AEAAAAoCUUPAAAAQEkoegAAAABKQtEDAAAAUBKKHgAAAICSUPQAAAAAlISiBwAAAKAkFD0AAAAAJaHoAQAAACgJRQ8AAABASSh6AAAAAEpC0QMAAABQEooeAAAAgJJQ9AAAAACUhKIHAAAAoCQUPQAAAAAloegBAAAAKAlFDwAAAEBJKHoAAAAASkLRAwAAAFASih4AAACAklD0AAAAAJSEogcAAACgJBQ9AAAAACWh6AEAAAAoCUUPAAAAQEkoegAAAABKQtEDAAAAUBKKHgAAAICSUPQAAAAAlISiBwAAAKAkFD0AAAAAJaHoAQAAACgJRQ8AAABASSh6AAAAAEpC0QMAAABQEooeAAAAgJJQ9AAAAACUhKIHAAAAoCQUPQAAAAAloegBAAAAKAlFDwAAAEBJKHoAAAAASkLRAwAAAFASih4AAACAklD0AAAAAJSEogcAAACgJBQ9AAAAACWh6AEAAAAoCUUPAAAAQEkoegAAAABKQtEDAAAAUBKKHgAAAICSUPQAAAAAlISiBwAAAKAkFD0AAAAAJaHoAQAAACgJRQ8AAABASSh6AAAAAEpC0QMAAABQEooeAAAAgJJQ9AAAAACUhKIHAAAAoCQUPQAAAAAloegBAAAAKAlFDwAAAEBJKHoAAAAASqKm6AAs3Lp06Vh0hBZTP3NGJn44s+gYAAAAMN8oevhcr528Y9ERWkyPY25NougBAACgPEzdAgAAACgJRQ8AAABASSh6AAAAAEpC0QMAAABQEooeAAAAgJJQ9AAAAACUhKIHAAAAoCQUPQAAAAAloegBAAAAKAlFDwAAAEBJKHoAAAAASkLRAwAAAFASih4AAACAklD0AAAAAJSEogcAAACgJBQ9AAAAACWh6AEAAAAoCUUPAAAAQEkoegAAAABKQtEDAAAAUBKKHgAAAICSUPQAAAAAlISiBwAAAKAkFD0AAAAAJaHoAQAAACgJRQ8AAABASdTMzZ3233//jB07NlVVVWnfvn2OPfbYfOtb32rpbAAAAADMg7kqek4//fR07NgxSfKnP/0pRx99dG6//fYWDQYAAADAvJmrqVuflDxJMmXKlFQqlRYLBAAAAMCXM1dH9CTJMccck7/97W9pamrKZZddNk9PssQSHeY5GCwIXbp0/OI7QUF8f0IxjD0WRg2NTRl+9rZFx2gxDY1Nxh4LpfrGxtRUlXdp2/rGRmOvhOa66Dn55JOTJHfccUfOOOOMDBs2bK6fZMKEKWlsbJr3dF8DBsXX27hxk4uOwJfUGsae708WRsYeFKNLl4657aW3i47RYnZY+RvGHgslY4+FVVVV5TMPqpnnanK77bbL448/nokTJ37lYAAAAADMP19Y9EydOjVvv/2/DeZDDz2UxRZbLJ06dWrJXAAAAADMoy+cujV9+vQccsghmT59eqqqqrLYYovl4osvtiAzAAAAwELmC4ueJZdcMjfffPOCyAIAAADAV1De5cMBAAAAWhlFDwAAAEBJKHoAAAAASkLRAwAAAFASih4AAACAklD0AAAAAJSEogcAAACgJBQ9AAAAACWh6AEAAAAoCUUPAAAAQEkoegAAAABKQtEDAAAAUBKKHgAAAICSUPQAAAAAlISiBwAAAKAkFD0AAAAAJaHoAQAAACgJRQ8AAABASSh6AAAAAEpC0QMAAABQEooeAAAAgJJQ9AAAAACUhKIHAAAAoCQUPQAAAAAloegBAAAAKAlFDwAAAEBJKHoAAAAASkLRAwAAAFASih4AAACAklD0AAAAAJSEogcAAACgJBQ9AAAAACWh6AEAAAAoCUUPAAAAQEkoegAAAABKQtEDAAAAUBKKHgAAAICSUPQAAAAAlISiBwAAAKAkFD0AAAAAJaHoAQAAACiJmqIDsBBr+Cg9jrm16BQtp+GjohMAAADAfKXo4bNVt0tuqBSdouXs2pRkVtEpAAAAYL4xdQsAAACgJBQ9AAAAACWh6AEAAAAoCUUPAAAAQEkoegAAAABKQtEDAAAAUBKKHgAAAICSUPQAAAAAlISiBwAAAKAkFD0AAAAAJaHoAQAAACgJRQ8AAABASSh6AAAAAEqipugAAHy6Ll06Fh2hRdTPnJGJH84sOgYAAJSSogdgIfXayTsWHaFF9Djm1iSKHgAAaAmmbgEAAACUhKIHAAAAoCQUPQAAAAAloegBAAAAKAlFDwAAAEBJKHoAAAAASkLRAwAAAFASih4AAACAklD0AAAAAJSEogcAAACgJBQ9AAAAACWh6AEAAAAoCUUPAAAAQEkoegAAAABKoqboAAAAC5MuXToWHaHF1M+ckYkfziw6BnwqYw9g/lD0AAD8h9dO3rHoCC2mxzG3JvHHJgsnYw9g/jB1CwAAAKAkFD0AAAAAJaHoAQAAACgJRQ8AAABASSh6AAAAAEpC0QMAAABQEooeAAAAgJJQ9AAAAACUhKIHAAAAoCQUPQAAAAAloegBAAAAKAlFDwAAAEBJKHoAAAAASqKm6AAAfIqGj9LjmFuLTtEyGj4qOgF8tjKPvcT4A4BWQNEDsDCqbpfcUCk6RcvYtSnJrKJTwKcr89hLjD8AaAVM3QIAAAAoCUUPAAAAQEkoegAAAABKQtEDAAAAUBKKHgAAAICSUPQAAAAAlISiBwAAAKAkFD0AAAAAJaHoAQAAACgJRQ8AAABASSh6AAAAAEpC0QMAAABQEooeAAAAgJJQ9AAAAACUhKIHAAAAoCQUPQAAAAAloegBAAAAKAlFDwAAAEBJKHoAAAAASkLRAwAAAFASih4AAACAklD0AAAAAJSEogcAAACgJBQ9AAAAACWh6AEAAAAoCUUPAAAAQEnUfNEdJk6cmCOOOCJjxoxJbW1tunXrlhNOOCGLL774gsgHAAAAwFz6wiN6KpVK9t577zzwwAMZPnx4lltuuZx11lkLIhsAAAAA8+ALi55OnTrlu9/9bvP1NddcM//+979bNBQAAAAA8+4Lp279p8bGxtx4443p16/fPD3JEkt0mKf7w4LSpUvHoiNAq2TsQXGMPxZKDR+lxzG3Fp2i5TR8ZOyx0PK9WT7zVPSceOKJad++fXbbbbd5epIJE6aksbFpnh7zdWFQfL2NGze56Ah8Scbe15ux9/Vl7H39GX9fT6Ufe9XtkhsqRadoObs2GXtfU6Ufe/F74euqqqrymQfVzHXRc/rpp+fNN9/MxRdfnKoqJ+sCAAAAWNjMVdHzX//1X3nuuedy6aWXpra2tqUzAQAAAPAlfGHR88orr+SSSy7JCiuskJ/85CdJkmWXXTa/+93vWjwcAAAAAHPvC4ueXr165aWXXloQWQAAAAD4Ciy2AwAAAFASih4AAACAklD0AAAAAJSEogcAAACgJBQ9AAAAACWh6AEAAAAoCUUPAAAAQEkoegAAAABKQtEDAAAAUBKKHgAAAICSUPQAAAAAlISiBwAAAKAkFD0AAAAAJaHoAQAAACgJRQ8AAABASSh6AAAAAEpC0QMAAABQEooeAAAAgJJQ9AAAAACUhKIHAAAAoCQUPQAAAAAloegBAAAAKAlFDwAAAEBJKHoAAAAASkLRAwAAAFASih4AAACAklD0AAAAAJSEogcAAACgJBQ9AAAAACWh6AEAAAAoCUUPAAAAQEkoegAAAABKQtEDAAAAUBKKHgAAAICSUPQAAAAAlISiBwAAAKAkFD0AAAAAJaHoAQAAACgJRQ8AAABASSh6AAAAAEpC0QMAAABQEooeAAAAgJJQ9AAAAACUhKIHAAAAoCQUPQAAAAAloegBAAAAKAlFDwAAAEBJKHoAAAAASkLRAwAAAFASih4AAACAklD0AAAAAJSEogcAAACgJBQ9AAAAACWh6AEAAAAoCUUPAAAAQEkoegAAAABKQtEDAAAAUBKKHgAAAICSUPQAAAAAlISiBwAAAKAkFD0AAAAAJaHoAQAAACgJRQ8AAABASSh6AAAAAEpC0QMAAABQEooeAAAAgJJQ9AAAAACUhKIHAAAAoCQUPQAAAAAloegBAAAAKAlFDwAAAEBJKHoAAAAASqKm6AAAAABAMbp06Vh0hBZTP3NGJn44s+gYC5yiBwAAAFqp107esegILabHMbcmaX1Fj6lbAAAAACWh6AEAAAAoCUUPAAAAQEkoegAAAABKQtEDAAAAUBKKHgAAAICSUPQAAAAAlISiBwAAAKAkFD0AAAAAJaHoAQAAACgJRQ8AAABASSh6AAAAAEpC0QMAAABQEjVFBwAAAAAK0PBRehxza9EpWk7DR0UnKISiBwAAAFqj6nbJDZWiU7ScXZuSzCo6xQJn6hYAAABASSh6AAAAAEpC0QMAAABQEooeAAAAgJJQ9AAAAACUhKIHAAAAoCQUPQAAAAAloegBAAAAKAlFDwAAAEBJKHoAAAAASkLRAwAAAFASih4AAACAklD0AAAAAJSEogcAAACgJBQ9AAAAACWh6AEAAAAoCUUPAAAAQEkoegAAAABKQtEDAAAAUBKKHgAAAICSUPQAAAAAlISiBwAAAKAkFD0AAAAAJaHoAQAAACgJRQ8AAABASSh6AAAAAEriC4ue008/Pf369cvKK6+cl19+eUFkAgAAAOBL+MKiZ9NNN83111+frl27Log8AAAAAHxJNV90h7XXXntB5AAAAADgK/rComd+WGKJDgviaWCedenSsegI0CoZe1Ac4w+KYexBMVrj2FsgRc+ECVPS2Ni0IJ5qgWuN3zRlMm7c5KIj8CUZe19vxt7Xl7H39Wf8fT0Ze19/xt7Xk7H39VfWsVdVVfnMg2qcdQsAAACgJBQ9AAAAACXxhUXPSSedlI022ijvvPNO9txzz2y11VYLIhcAAAAA8+gL1+gZPHhwBg8evCCyAAAAAPAVmLoFAAAAUBKKHgAAAICSUPQAAAAAlISiBwAAAKAkFD0AAAAAJaHoAQAAACgJRQ8AAABASSh6AAAAAEpC0QMAAABQEooeAAAAgJJQ9AAAAACUhKIHAAAAoCQUPQAAAAAloegBAAAAKAlFDwAAAEBJKHoAAAAASkLRAwAAAFASih4AAACAklD0AAAAAJSEogcAAACgJBQ9AAAAACWh6AEAAAAoCUUPAAAAQEkoegAAAABKQtEDAAAAUBKKHgAAAICSUPQAAAAAlISiBwAAAKAkFD0AAAAAJaHoAQAAACgJRQ8AAABASSh6AAAAAEpC0QMAAABQEooeAAAAgJJQ9AAAAACUhKIHAAAAoCQUPQAAAAAloegBAAAAKAlFDwAAAEBJKHoAAAAASkLRAwAAAFASih4AAACAklD0AAAAAJSEogcAAACgJBQ9AAAAACWh6AEAAAAoCUUPAAAAQEkoegAAAABKQtEDAAAAUBKKHgAAAICSUPQAAAAAlISiBwAAAKAkFD0AAAAAJaHoAQAAACgJRQ8AAABASSh6AAAAAEpC0QMAAABQEooeAAAAgJJQ9AAAAACUhKIHAAAAoCQUPQAAAAAloegBAAAAKAlFDwAAAEBJKHoAAAAASkLRAwAAAFASih4AAACAklD0AAAAAJSEogcAAACgJBQ9AAAAACWh6AEAAAAoCUUPAAAAQEkoegAAAABKQtEDAAAAUBKKHgAAAICSUPQAAAAAlISiBwAAAKAkFD0AAAAAJaHoAQAAACgJRQ8AAABASSh6AAAAAEpC0QMAAABQEooeAAAAgJJQ9AAAAACUhKIHAAAAoCQUPQAAAAAloegBAAAAKAlFDwAAAEBJKHoAAAAASkLRAwAAAFASih4AAACAklD0AAAAAJSEogcAAACgJBQ9AAAAACWh6AEAAAAoCUUPAAAAQEkoegAAAABKQtEDAAAAUBKKHgAAAICSUPQAAAAAlISiBwAAAKAkFD0AAAAAJaHoAQAAACgJRQ8AAABASSh6AAAAAEpC0QMAAABQEooeAAAAgJJQ9AAAAACUhKIHAAAAoCQUPQAAAAAloegBAAAAKAlFDwAAAEBJKHoAAAAASkLRAwAAAFASih4AAACAklD0AAAAAJSEogcAAACgJOaq6Hn99dezyy675Ic//GF22WWXvPHGGy0cCwAAAIB5NVdFz3HHHZddd901DzzwQHbdddcMGTKkpXMBAAAAMI9qvugOEyZMyD//+c9ceeWVSZKtt946J554Yt5///0svvjic/UkVVWVr5ZyIde+prroCC2nrlvRCVpU2b83y67UYy8p9fgz9r7ejL2vN+Pv68vY+3oz9r6+jL2vt7KOvc97XZWmpqamz3vwc889l9/85je55557mrdtueWWOfPMM7PqqqvOv5QAAAAAfCUWYwYAAAAoiS8ser7xjW/k3XffTUNDQ5KkoaEh7733Xr7xjW+0eDgAAAAA5t4XFj1LLLFEvvWtb+Xuu+9Oktx999351re+Ndfr8wAAAACwYHzhGj1JMnr06Bx55JGZNGlSFl100Zx++unp0aPHgsgHAAAAwFyaq6IHAAAAgIWfxZgBAAAASkLRAwAAAFASih4AAACAklD0AAAAAJSEogcAAACgJBQ9AAAAACVRU3QAFk7Tp0/PO++8k4aGhuZtPXv2LDARtA6PPvpoxowZk/r6+uZtP/3pTwtMBK2DsQcAlIWihzlcf/31Oeuss9KpU6dUKpUkSaVSyZ///OeCk0G5HXnkkXnuuefy7W9/O9XV1UXHgVbD2INiTJgwIddee23eeuut2UrWc889t8BUUH6vvfZaLrroojnG3i233FJgKuYnRQ9zuOKKK3L33Xena9euRUeBVuXpp5/O3XffnTZt2hQdBVoVYw+KcdBBB2XFFVfMeuutp2SFBejXv/51tthii+ywww7GXkkpephDly5dlDxQgGWWWaboCNAqGXtQjEmTJuXEE08sOga0Oo2Njdlvv/2KjkELUvQwh/XXXz9nnHFGttpqq7Rt27Z5uzV6oGWtsMIK2WOPPbLZZpultra2ebt1QqBlGXtQjF69euXdd9/N0ksvXXQUaFXWXHPNvPjii1lllVWKjkILUfQwhzvuuCNJcv/99zdvs0YPtLyZM2dm+eWXz8svv1x0FGhVjD0oxqRJk7LNNtukd+/es324aI0eaFnPPvtsbrvttnTv3n22sWeNnvKoNDU1NRUdAgAAaF1uv/32T92+/fbbL+Ak0Lo88cQTn7p93XXXXcBJaCmKHprNnDkztbW1mT59+qfevsgiiyzgRNA6/OMf/8haa62VESNGfOrtG2+88QJOBK2DsQcAlJGpWzTbZZddcvvtt6d3796pVCr5zw6wUqnkhRdeKDAdlNftt9+etdZaK5dddtkct1UqFX9sQgsx9qAYV199dXbfffecccYZn3r7EUccsYATQetw5pln5vDDD8/BBx+cSqUyx+2mTZaHI3oAAIAF5qabbspPfvKTXHDBBZ96+4EHHriAE0Hr8NBDD6Vfv36mTbYCih6AhcjkyZPz+uuvZ8aMGc3b1llnnQITQetg7AEAZWHqFnN48cUXc9xxx+XFF1/MzJkzm7ebugUt6957783pp5+eSZMmZamllsqYMWOyyiqrfOanLsD8YexBcf7617/mhRdemK1kdUQPtKz6+vrceuutc4y9U089tcBUzE9VRQdg4TN06ND86le/Srdu3TJixIjsu+++GTRoUNGxoPQuvvji3HbbbenWrVseeOCBXHbZZVl99dWLjgWlZ+xBMc4666wMGzYsV111Vd57773ceOONeeONN4qOBaU3ZMiQjBw5Mv/93/+dFVZYIc8991zatWtXdCzmI0UPc5g5c2bWW2+9NDU1ZamllsqgQYPywAMPFB0LSq+mpiZLLLFEGhoakiQbbLBBRo0aVXAqKD9jD4oxYsSIXH755VliiSVywgkn5LbbbsuHH35YdCwovVGjRuX0009Px44d84tf/CI33HBDXn311aJjMR+ZusUcqqo+7v8WW2yxvPjii1l66aUzceLEglNB+dXW1qapqSndunXLtddem65du2batGlFx4LSM/agGLW1tampqUmlUsmsWbOy9NJL55133ik6FpRe27ZtkyTV1dWZPn16OnbsmAkTJhScivlJ0cMcttpqq0ycODH77rtvBgwYkMbGxhx88MFFx4LSO+SQQzJlypQcdthhGTp0aCZPnpzjjjuu6FhQesYeFKOuri7Tp09P7969c+SRR6ZLly6mj8ACsNhii+XDDz/M9773veyzzz7p3Llzll566aJjMR856xazaWxszMsvv5xVVlklSTJr1qzMmDEjHTp0KDgZlFtDQ0NuueWW7LLLLkVHgVbF2IPijB8/PosuumgaGhpy5ZVXZvLkyRk4cGC++c1vFh0NSq2hoSHV1dVpbGzM8OHDM3ny5Gy33Xb+5isRRQ9z6N+/f4YPH150DGh1dthhh9x2221Fx4BWx9iDBa+hoSHHHXdcTjrppKKjQKvS0NCQ/fffP5dccknRUWhBFmNmDt26dcvYsWOLjgGtzne/+93cf//9RceAVsfYgwWvuro6L730UtExoNWprq7OBx98kMbGxqKj0IIc0cMc9txzzzzzzDNZa6210r59++bt5557boGpoPz69u2bDz74IO3atcsiiyySpqamVCqVPProo0VHg1Iz9qAYZ599dqZOnZrttttutv9z9uzZs8BUUH6nnHJKxo4dm6233jp1dXXN2zfeeOMCUzE/KXqYw+233/6p27fffvsFnARalxdeeCGLLrroHNu7du1aQBpoPYw9KEa/fv3m2FapVPLnP/+5gDTQegwcOHCObZVKJddcc00BaWgJih5mY84mFKOpqSlbbbVV7r333qKjQKti7EExGhsb8/DDD2eTTTYpOgq0Kk5C0DpYo4fZfDJnU/8HC1alUsk3vvGNfPjhh0VHgVbF2INiVFVVWRYAClBdXZ3f//73RceghdUUHYCFzxprrJEDDjjAnE1YwDp06JDtt98+G2200WxrFRxxxBEFpoLyM/agGKusskqeffbZfOc73yk6CrQqn5yEYIsttig6Ci1E0cMcXnjhhSTJjTfe2LytUqkoeqCF9erVK7169So6BrQ6xh4U4/nnn8+AAQPSrVu32UrWW265pcBUUH633357rrzySichKDFr9AAAAAvcE0888anb11133QWcBFqXf/3rX5+63UkIykPRwxyamppyyy235M0338xhhx2WsWPH5r333kufPn2KjgalNmHChJx66ql5++23c/311+fFF1/M008/nQEDBhQdDUrN2INivf/++1l88cWLjgGtypQpU/Lmm29m1VVXLToKLcBizMzh1FNPzWOPPZY//elPSZK6urqccsopBaeC8hs8eHDWWmutTJo0KUnSo0eP3HDDDQWngvIz9qAYzzzzTL7//e9n++23T5KMGjUqxx57bMGpoPxGjBiRrbbaKgcddFCSj8fefvvtV3Aq5idFD3N4/PHHc9ZZZ6Vdu3ZJks6dO2fGjBkFp4Lye/fddzNgwIBUV1cnSWpra1NV5cc0tDRjD4px6qmnZtiwYencuXOSZPXVV8/IkSMLTgXld9555+WWW27JoosumuTjsTdmzJiCUzE/+V8Mc2jbtm0qlUrz9cbGxgLTQOtRUzP7+viTJk2K2bXQ8ow9KMasWbPSs2fP2ba1adOmoDTQunTp0mW267W1tQUloSU46xZzWGmllXLXXXelqakpY8eOzaWXXpq11lqr6FhQeptvvnmGDBmSqVOn5rbbbssNN9yQHXfcsehYUHrGHhSjtrY2U6dObf6A8dVXX03btm0LTgXlV1dXl/HjxzePvccffzwdO3YsOBXzk8WYmcOUKVNy2mmn5aGHHkqS9OvXL0cddVTq6uoKTgbld9ddd+Whhx5KU1NT+vXrl2233bboSNAqGHuw4I0YMSIXXXRR3nrrrXzve9/LI488kjPPPDPrr79+0dGg1J555pkMHTo0Y8eOzSqrrJI33ngjF110UVZbbbWiozGfKHqYw5QpU9KhQ4cv3AbMX48++mjWW2+9L9wGzF/GHhTnrbfeyiOPPJKmpqZsuOGG6datW9GRoFWYPHly85pYvXv3bl6vh3KwRg9zGDhw4FxtA+avM844Y662AfOXsQfFGDZsWJZbbrnsuuuu+elPf5pu3bpl2LBhRceC0jv55JPTsWPHbLzxxtl4442z6KKL5uSTTy46FvORNXpoVl9fn1mzZqWxsTEfffRR80KUkydPzvTp0wtOB+X15ptv5o033siUKVMyYsSI5u3GHrQsYw+Kde+992afffb5wm3A/PXUU0/Nse3JJ58sIAktRdFDs4svvjgXXHBBKpVK1lxzzebtHTp0yJ577llcMCi5kSNH5rbbbsv48eNz2WWXNW/v0KFDjjzyyAKTQbkZe1CMv/3tb/nrX/+a9957b7aj56ZMmeKMd9CC7rvvvtx3333517/+lUMOOaR5+5QpU9KuXbsCkzG/WaOHOZxwwgkZMmTIZ97+/vvvZ/HFF1+AiaB1uO2227LDDjt85u0PP/xwNtpoowWYCFoHYw8WrCeeeCJPPPFEbrrppvzkJz9p3t6hQ4dsttlmWXbZZQtMB+X14osv5p///GfOP//8HHzwwc3bO3TokPXWW8+arCWi6GGebb/99rn99tuLjgGtjrEHxTD2oGW8/PLLWWmllT7z9gsvvDD777//AkwErcMHH3yQTp06febtQ4cOzdChQxdYHuY/izEzz3SDUAxjD4ph7EHL+LySJ0n++Mc/LqAk0Lp8XsmTfHz6db7eFD3Ms0qlUnQEaJWMPSiGsQfFULICfDmKHgAAYKGjZAX4chQ9zDOfrkAxjD0ohrEHAHydKHqYZ5tvvnnREaBVGjRoUNERoNTef//9T91u7EExlKxQjKoqNcHXnbNuMYcJEybk1FNPzdtvv53rr78+L774Yp5++ukMGDCg6GhQaq+99louuuiivPXWW6mvr2/efssttxSYCsrvmWeeya9+9as0NjZmxIgRGTVqVG6++eaceOKJRUeDVmHmzJlpaGhovr7IIosk+bh8XXzxxYuKBaU2ffr0vPPOO7ONvZ49exaYiPmppugALHwGDx6cjTbaKDfccEOSpEePHjn88MMVPdDCDjnkkGy77bbZYYcdUl1dXXQcaDVOPfXUDBs2LIcddliSZPXVV8+RRx5ZcCoovwceeCCnnHJK3nvvvSQfH8FTqVTywgsvJImSB1rINddck3POOSedOnVqXgurUqnkz3/+c8HJmF8UPczh3XffzYABA/L73/8+SVJbW+vwPVgAampqsvfeexcdA1qdWbNmzfEpZps2bQpKA63HGWeckfPPPz+rrbaa/2vCAnT11Vfn/vvvz9JLL110FFqIn6jMoaZm9v5v0qRJ5kjDAvC9730vI0aMKDoGtDq1tbWZOnVq86ear776atq2bVtwKii/Ll265Dvf+Y6SBxawZZZZRslTctboYQ7Dhg3LW2+9lUcffTS//OUvc8MNN6R///7Zfffdi44Gpfboo49m//33T1VVVWpra5sPYX/00UeLjgalNmLEiOb1sb73ve/lkUceyZlnnpn111+/6GhQavfcc09eeeWVbL755rOVq9YJgZb12GOPZfjw4dl4441nG3sbb7xxgamYnxQ9fKq77rorDz30UJqamtKvX79su+22RUeC0tt8881z6KGHZtVVV53t082uXbsWmApah7feeiuPPPJImpqasuGGG6Zbt25FR4LSu+KKK3LeeeelU6dOzb/3rBMCLe/UU0/N8OHD071799nG3jXXXFNwMuYXRQ/AQmKnnXZyhi0AWo3vf//7+f3vf5+lllqq6CjQqvTr1y/33ntv2rVrV3QUWojFmJnDwQcf3LxOwSc6duyYNddcMzvssIN51NBCNttss9x444350Y9+NNthtJ+cZhZoGX379v3M33uHH354unTpUlAyKLdvfvObSh4owHLLLTfHuqyUiyN6mMOJJ56Y5557LltttVWS5N57703Pnj3z9ttvp3v37hk8eHDBCaGcVllllebLlUpljtPMAi3j/PPPz6RJk7LjjjsmSe64445UV1dnkUUWyXPPPZeLL7644IRQTqeffnrefffdbLHFFtYJgQVoyJAhGT16dDbbbLPU1tY2b//pT39aYCrmJzUec3jxxRdz7bXXNg/6XXbZJXvssUeuvvrqbLfddsWGgxJ78cUXi44ArdLDDz+cP/zhD83XjzzyyOy444659dZbmz/0AOa/5557Lkly7bXXNm+rVCqKHmhhs2bNyvLLL5+XX3656Ci0EEUPcxg/fnzatGnTfL2mpiYTJ05MbW3tbI0vMH9Nnz79U7ebugUta9KkSfnggw/SqVOnJMnEiRMzZcqUJJnt9yEwf/1nwQMsOKeeemrREWhhih7msO6662bfffdtPtPW8OHDs/baa2fq1KmKHmhBvXv3nm3K1idM3YKWNXDgwGy77bbNRxE8/PDD2XvvvTN16tT06dOn4HRQXiNGjPjU7Y7ogZZ1/fXXf+p2U7fKwxo9zGHWrFn5/e9/n8cffzzJx8XPT37yE59qwgI0Y8aMDB8+PBMnTsw+++xTdBwotcmTJ+ff//53nnjiiSTJOuusM9uaWUDLGDhwYPPlmTNn5oUXXsi3v/3t3HTTTQWmgvI76qijmi/PmDEjjz/+eNZYY41ceOGFBaZiflL0MJuGhobsv//+ueSSS4qOAiTN64QALaOpqSlbbbVV7r333qKjQKv36quv5vLLLzetBBaw9957LyeccEIuuOCCoqMwn5i6xWyqq6vzwQcfzDF1BGh5/7lGT2NjY0aNGpXJkycXmAjKr1Kp5Bvf+EY+/PDDLLbYYkXHgVatZ8+eef7554uOAa3OUkstlTfeeKPoGMxHih7msMYaa+SAAw7I1ltvnbq6uubt5ktDy/rPNXqqq6vTrVu3HHPMMUXHgtLr0KFDtt9++2y00UZp37598/YjjjiiwFRQfv+5Rs8nH3DU1PjzBFraf67R09TUlFGjRmXxxRcvMBHzm5+kzOGThV9vvPHG5m1OdQktz+nVoRi9evVKr169io4Brc5ll13WfLmmpibLL798zj333AITQevw3HPPNV+urq7OiiuuONu6PXz9WaMHoGAzZ85MbW2t06sDAABfmaKHTzV58uS8/vrrmTFjRvO2ddZZp8BEUF7bb799br/99qyyyirNU7c+UalUnF4dFoC//vWveeGFF2b7vXfggQcWmAjK6x//+EfWWmstp1eHBey+++7Lj370I6dXbwVM3WIO9957b04//fRMmjQpSy21VMaMGZNVVlklt99+e9HRoJQ+GVumbkExzjrrrIwaNSqvvvpqNt100/z5z3/OeuutV3QsKK3bb789a6211mxTtz5huQBoOa+88kp+9KMfzTZ1i3JyRA9z2GabbXLllVdmr732yh133JG//e1veeCBB3LCCScUHQ1K6bOmbH3C1C1oWf3798/tt9+eHXbYIXfddVfefffdDB48OMOGDSs6GgDAPHNED3OoqanJEksskYaGhiTJBhtskLPOOqvgVFBen5xt67OYugUtq7a2NjU1NalUKpk1a1aWXnrpvPPOO0XHgtL6rClbn3BED7SMz5qy9QlTt8pD0cMcamtr09TUlG7duuXaa69N165dM23atKJjQWl9MmXrwgsvTG1tbXbZZZc0NTXlD3/4Q2bNmlVwOii/urq6TJ8+Pb17986RRx6ZLl26pF27dkXHgtL6ZMrWzJkzM2rUqKy00kpJkpdffjnf+c53FD3QQj6ZsjVx4sQ88cQTzdOUH3300Xz3u99V9JSIqVvM4dFHH81qq62WCRMmZOjQoZk8eXIOPfTQrL/++kVHg1L7ZFHm/7TDDjvktttuKygRtA7jx4/PoosumoaGhlx55ZWZPHlyBg4cmG9+85tFR4NS+/Wvf53dd989a6yxRpLk2WefzdVXX52zzz674GRQbvvuu2+OPfbYLLfcckmSt956KyeffHIuvvjigpMxvziihzl80ux27NgxV111VbFhoBX56KOP8uabb6Zbt25JkjFjxnzh+j3AV7fkkks2X95///0LTAKtyyuvvNJc8iTJd77znbz88ssFJoLW4d///ndzyZMkyy23XMaOHVtgIuY3RQ/NzNmEYg0aNCg777xzVltttSTJP//5z5x44okFp4LyOvjggz93faxzzz13AaaB1meRRRbJnXfemW233TZJctdddzkBASwASy65ZH73u9/lxz/+cZLk1ltvne1DD77+TN2i2VFHHfW5t5966qkLKAm0XhMmTMgzzzyTJFlzzTWz+OKLF5wIyuv/TpX8v7bffvsFlARap9GjR+fwww/PK6+8kkqlkpVWWimnn356VlxxxaKjQam9++67Ofnkk/P4448nSfr27Zujjz46Sy+9dMHJmF8UPcyzW265JTvttFPRMaCUXn/99YwePTqbbbZZpk6dmlmzZqVTp05Fx4JWbejQoRk6dGjRMaC0pkyZkiTp0KFDwUkAyqGq6AB8/XzRFC/gy7n99tvzy1/+svnouXfffTe/+tWvig0FNB9lB8xfn5xh8uKLL06HDh0yduzYjBw5suhYUHrTp0/Pb3/72xx66KFJPj667k9/+lPBqZifFD3MMweBQcu4+uqrc+utt6Zjx45Jkh49emT8+PEFpwKAlnHqqafmsccea/4Ds66uLqecckrBqaD8hg4dmvr6+rz44otJkmWWWSYXXHBBwamYnxQ9zLPPW7gS+PLatGmTurq62bZVV1cXlAYAWtbjjz+es846K+3atUuSdO7cOTNmzCg4FZTfSy+9lMMOOyxt2rRJ8nHJ2tjYWHAq5idFD8BColOnTnn99deby9Q777wzyyyzTMGpAKBltG3bdrYPEP2hCQtGbW3tbNdnzJhh1kbJOL0688wPAWgZRx99dA499NC8/vrr6devX9q1a5eLL7646FjQ6lVV+VwMWsJKK62Uu+66K01NTRk7dmwuvfTSrLXWWkXHgtJbe+21c/HFF2fmzJl5/PHHc+WVV6Zfv35Fx2I+ctYtPtWUKVPy5ptvZtVVV53jthdffDGrrLJKAamg/BoaGvLGG2+kqakp3bt3N3ULFpBHH300o0ePzm677Zbx48dn8uTJ6d69e9GxoNSmTJmS0047LQ899FCSpF+/fjnqqKPmmMYMzF+zZs3KZZddloceeihNTU3p169f9t1339TUOA6kLBQ9zGHEiBEZMmRIqqur89BDD2XUqFH53e9+58gCWACmT5+ed955Jw0NDc3bevbsWWAiKL9LL700I0aMyLhx4/Lggw/mnXfeyaBBg3LjjTcWHQ0AYJ6p7JjDeeedl1tuuSX77LNPkmT11VfPmDFjCk4F5XfNNdfknHPOSadOnZrXLKhUKvnzn/9ccDIot7vvvju33nprfvzjHyf5+OwjU6ZMKTgVtA6PPvpoxowZk/r6+uZtP/3pTwtMBOX30Ucf5e67755j7B1xxBEFpmJ+UvTwqbp06TLb9f+7YBcw/1199dW5//77s/TSSxcdBVqVdu3aNZ955BPOMAkt7/DDD89LL72UVVZZxVRlWIAOPPDAVFVVZdVVV/V3XkkpephDXV1dxo8f3/yf3McffzwdO3YsOBWU3zLLLKPkgQIss8wyeeqpp1KpVNLY2JiLL744vXr1KjoWlN6oUaNyzz33KHlgAXv77bdzzz33FB2DFqToYQ6HHXZY9tlnn4wdOzYDBw7MG2+8kYsuuqjoWFB6Bx10UI455phsvPHGadu2bfP2jTfeuMBUUH7HHntsfvOb3+SVV17JGmuskbXXXjtnnXVW0bGg9JZffvlMnz49HTp0KDoKtCq9evXKe++9l6WWWqroKLQQizHzqSZPnpyRI0cmSXr37p1FF1204ERQfqeeemqGDx+e7t27N5/OuVKp5Jprrik4GbQO06dPT2NjozP+wAIyevToHHbYYVlrrbVmmz5inRBoWa+++mr23nvvrLLKKrN9uHjuuecWmIr5yRE9zGHKlCmpq6vLxhtvnJdffjmPPPJINt98c/M3oYX98Y9/zEMPPZR27doVHQValSeffDLf/va3U1dXlz/84Q8ZNWpU9tlnnyy33HJFR4NSO+mkk7L00kunY8eOpm/BAnTEEUekX79++fa3v23slZSihzn87Gc/y3XXXZepU6dmr732ykorrZRHHnkkp512WtHRoNSWW2651NT4sQwL2gknnJC77rorr7zySq688spss802OeaYYxxNBy3snXfeyX333Vd0DGh1Zs2alSFDhhQdgxbkLwrm0NTUlPbt2+eee+7JzjvvnIMOOij9+/cvOhaUXrdu3bL77rtns802m+0IOqeZhZZVU1OTSqWShx9+OAMGDMjAgQNz//33Fx0LSm/llVe2TggUYM0118xLL72UlVdeuegotBBFD3OYMWNGZs6cmb/97W/ZbbfdkqR5vRCg5cyaNSvLL798Xn755aKjQKtSX1+fZ555Jn/84x9z4oknJkkaGhoKTgXlN3ny5PTv3z+9e/e2TggsQM8++2x23HHHdO/efbaxd8sttxSYivlJ0cMcttxyy2ywwQbp1q1b+vTpk3Hjxs32AwBoGaeeeurn3n7LLbdkp512WkBpoPU45JBDMmTIkPTt2ze9evXK66+/nm7duhUdC0pv6623ztZbb110DGh1jjnmmKIj0MKcdYtP9eGHH6Zjx46pqqrK1KlTM2XKlCy99NJFx4JWbfvtt8/tt99edAwAWCCGDh2aoUOHFh0DWp39998/F154YdEx+Aoc0cMcRowY8anbFT1QLL08tIwzzjjjU7c7xTMU65lnnik6ArRK//73v4uOwFek6GEOl112WfPlmTNn5oUXXsi3v/3tbLzxxgWmAiqVStERoJTat2/ffHnGjBn57//+76y22moFJgKA4vg/59efooc5XHvttbNdf/XVV3P55ZcXlAYAWtaBBx442/Vf/OIXOeSQQwpKAwDw1TiVEl+oZ8+eef7554uOAa2eqVuwYNTV1TlsHQD42nJED3P4zzV6GhsbM2rUqNTU+FaBBWHKlCl58803s+qqq85x22mnnVZAIii//1yjp6mpKc8991xWXHHFAhMBSVJV5TNpKMIyyyxTdAS+ImfdYg4DBw5svlxTU5Pll18+e++9d5ZbbrkCU0H5jRgxIkOGDEl1dXUeeuihjBo1Kr/73e9y8cUXFx0NSu2CCy5ovlxdXZ1u3bpl8803T5s2bQpMBa3Do48+mtGjR2e33XbL+PHjM3ny5HTv3r3oWFBqTU1NueWWW/LGG2/k8MMPz9ixY/Pee++lT58+RUdjPlH0ACwkdtxxx1x88cXZZ599cscddyRJttxyy9x7773FBgOAFnDppZdmxIgRGTduXB588MG88847GTRoUG688caio0GpnXLKKZkwYUKef/753H///Zk4cWL22Wef3HLLLUVHYz5xPCRzaGpqyk033ZSDDz44Bx98cG6++WZrg8AC0qVLl9mu19bWFpQEWo/3338/gwYNSt++fdO3b98ceuihef/994uOBaV3991356qrrmo+890yyyyTKVOmFJwKyu/xxx/PWWedlXbt2iVJOnfunBkzZhScivlJ0cMczjjjjNx///3ZbLPNstlmm+X+++/PmWeeWXQsKL26urqMHz+++ZSWjz/+eDp27FhwKii/4447LiussELuvPPO3HHHHenWrVuGDBlSdCwovXbt2s0xRdJpnaHltW3bdrax1tjYWGAaWoIVdpnDX//619x+++3NCzD/6Ec/yg477JAjjjii4GRQbocddlj22WefjB07NgMHDswbb7yRiy66qOhYUHpjxozJ+eef33z94IMPzrbbbltgImgdlllmmTz11FOpVCppbGzMxRdfnF69ehUdC0pvpZVWyl133ZWmpqaMHTs2l156adZaa62iYzEfKXr4VP/Z8PpkBRaM73znO7nmmmsycuTIJEnv3r2z6KKLFpwKyq+xsTETJkzIEksskSSZMGGCTzdhATj22GPzm9/8Jq+88krWWGONrL322jnrrLOKjgWld+SRR+a0007LuHHj8uMf/zj9+vXLb37zm6JjMR9ZjJk5nH766XnppZey/fbbJ0nuuOOOrLTSSgY/tLApU6akffv2qaqqyssvv5xXXnklm2++uXV6oIXdcccdOfvss7PJJpsk+fgMeIceeqijemABmT59ehobG1NXV1d0FIBSUPQwh8bGxtx000157LHHkiTrrbdedtlll1RVWdIJWtIOO+yQ6667LlOnTs0OO+yQlVZaKV26dMlpp51WdDQovZdffjlPPPFEkuS73/2u6SOwADz55JP59re/nbq6uvzhD3/IqFGjss8++2S55ZYrOhqU2r333puNNtooHTp0yLnnnptnn302gwYNymqrrVZ0NOYTRQ/AQmL77bfP7bffnj/84Q955513ctBBB6V///4ZPnx40dEAYL7r379/7rrrrrz66qs55JBDss022+Tvf/97rrnmmqKjQal98v/LZ599NieeeGJ+9rOf5frrr89NN91UdDTmE2v0MIcJEybk2muvzVtvvZX6+vrm7eeee26BqaD8ZsyYkZkzZ+Zvf/tbdttttyRxJB0sACNHjsyZZ56Zt956Kw0NDWlqakqlUsmjjz5adDQotZqamlQqlTz88MMZMGBABg4cmPvvv7/oWFB6n5x0529/+1t+/OMfp3///rniiisKTsX8pOhhDgcddFBWXHHFrLfeeqmuri46DrQaW265ZTbYYIN069Ytffr0ybhx49K2bduiY0HpHXPMMdl///2z5pprKldhAaqvr88zzzyTP/7xjznxxBOTJA0NDQWngvKrVCq59957c++99+bCCy9MksyaNavgVMxPih7mMGnSpOZftsCCc+CBB2bgwIHp2LFjqqqq0r59+9lO+Qy0jHbt2qV///5Fx4BW55BDDsmQIUPSt2/f9OrVK6+//nq6detWdCwovcGDB+eyyy7LTjvtlOWWWy5vvPFGvvvd7xYdi/nIGj3MYdCgQTnyyCOz9NJLFx0FWpURI0Z86vaNN954ASeB1uWcc85Jnz59jDUAoBQUPTQ7+OCDU6lUMmXKlDz33HPp3bv3bNNGrNEDLWvgwIHNl2fOnJkXXngh3/72ty2MBy2kb9++qVQqaWpqygcffJC6urrU1tZaowcWkDPOOONTtx9xxBELOAm0Lp/83fd/+XuvPEzdotn3v//95stbb711gUmgdbr22mtnu/7qq6/m8ssvLygNlN+tt95adARo1dq3b998ecaMGfnv//5vp3eGBeA//+6bMWNGHnjggay44ooFJmJ+c0QP82zo0KEZOnRo0TGgVdhmm21y1113FR0DWrWddtopt9xyS9ExoPSmTJmSQw45xIccsIDNnDkze+211xwfOvL15Yge5tkzzzxTdAQopf9co6exsTGjRo1qPv0lUJz6+vqiI0CrUFdXl3//+99Fx4BWp1Kp5N133y06BvORvyAAFhKXXXZZ8+Wamposv/zy5krDQuDT1jEAvrr/XKOnqakpzz33nOkjsAD85xo9TU1Neemll7L++usXnIr5SdEDsJBwuCwArcl/rtFTXV2dAQMGZPPNNy8wEbQO/7lGT3V1dfbaa6+sscYaBSZiflP0ACwkmpqa8vvf/z5///vfkyQbbrhhfvzjHzuaAApmOUNoGQceeGDREaBV2n777ZMk06ZNSzJ76Uo5VBUdgK+fqirfNtASzjjjjNx///3ZbLPNstlmm+X+++/PmWeeWXQsaPV8ygkt4/3338+gQYPSt2/f9O3bN4ceemjef//9omNB6b311lvZeeedm8feT37yk7z11ltFx2I+ctYtPtWjjz6a0aNHZ7fddsv48eMzefLkdO/evehYUGr9+/fP7bff3rwA86xZs7LDDjtk+PDhBSeD8nv00UczZsyY2RZe/ulPf1pgIii/gw46KD179sxPfvKTNDU15eabb87LL7+cCy64oOhoUGp77rlnttpqq+y4445Jkttuuy133313rrzyyoKTMb+YusUcLr300owYMSLjxo3Lbrvtlvr6+hx99NH5/+3de1DVdf7H8dc5yE0hMxW8jDgtKbg6EguKd13SGbygZaJ5a1R0SzJNXXTD0bVCXcV2clyN3IuNTqG73lBYL5WG7Q6TMcuEyyKWMOmIHkxgBUW8cH5/NJyN0Gp+wfno9zwfM4zn+/l+j98XzBwu7/N5fz4ZGRmmowGW9+02LVq2APdITk5WcXGxwsPD5eXlZToO4DHOnz+vzZs3u44XLlyoCRMmGEwEeIaKigpNmjTJdfzss89qx44dBhOhuVHoQRNZWVnau3evEhISJEmdOnVSTU2N4VSA9Q0ZMkTz5s1z9U0fOHBAQ4YMMZwKsL7Tp08rOzubIg/gZvX19bp69arat28vSbp69arq6+sNpwKsz263q6SkRD/72c8kSaWlpfwMtBgKPWjCz89P3t7ejcaYWQC0vOTkZO3atUsffPCBJGnkyJGaMmWK4VSA9YWEhKi2tlYBAQGmowAeJTExUU8//bRGjBghScrJydHSpUvNhgI8wOLFizV9+nT16tXLtb36hg0bTMdCM2KNHjSxcOFCPf/881qzZo327t2r9PR0nTt3Tm+++abpaAAANLtz587p17/+taKiouTj4+MaX7ZsmcFUgGc4e/asTp06JUmKiYlRjx49DCcCPMPVq1dVUFAg6ZtNBx577DHDidCcKPSgiStXrmj58uU6deqUbDaboqOjtXHjRte0WgAt4+rVq9q5c6cuXLjQaEHYTZs2GUwFWN/s2bPl6+urXr16NZq6ztbPAAAru3Xrlu7eves69vf3N5gGzYlCD+6rtrZW9fX1atOmjekogEeYNm2aQkNDFRER0eiPzYY1ewC0jNGjR+vw4cOmYwAe51//+pfS0tJ04cIF3b17V06nUzabTbm5uaajAZZ27Ngxpaam6sqVK5Lkeu0VFRUZTobmwho9aCInJ6fJWEBAgHr27KnAwEADiQDPcO3aNb3xxhumYwAeJywsTOXl5QoKCjIdBfAoK1asUFJSkp588knZ7XbTcQCPkZaWprfeeovXnoVR6EETW7du1enTpxUWFibpm97psLAwORwOpaam6pe//KXhhIA19ejRQw6HQ8HBwaajAB6lurpa8fHxioyMlK+vr2uctkmgZfn5+Sk+Pt50DMDjtG3bVr/4xS9Mx0ALotCDJkJCQrRy5Ur16dNHklRYWKjt27crLS1NS5YsodADNLOFCxfKZrOppqZG48eP549NwM3GjRuncePGmY4BeJxhw4YpJydHw4cPNx0F8Ai1tbWSpFGjRun999/XmDFjGv3OyRo91sEaPWgiPj5ehw4dajQ2fvx4HTx40PUvgOazf//+7z3PGj0AACsZMGCAbDabnE6nqqqq1KZNG/n4+LBGD9DCwsPDXa+9Bg3HrNFjLczoQRP+/v7KyspyvbuZlZUlPz8/Sd98IwDQvH5sIWf16tVavXp1y4YBPFDDrLrvYjYd0DL27t1rOgLgkc6cOfOjrquoqGC79YcchR40sW7dOiUnJyslJUWS9MQTT2jDhg26ceOGli1bZjgd4Lk+//xz0xEAS/p2S3JdXZ2OHj2q0NBQg4kAa+vateuPum7SpEnas2dPC6cB8F2JiYk/OOMcDzYKPWjk7t27ysvL0759+1RTUyPpmx23GgwePNhUNAAAWsR3Z9VNnDhRiYmJhtIAaHDnzh3TEQCPxOouDz/2UkMjXl5e2r17t6RvCjzfLvIAAOAJbDabHA6H6RiAx2PJAMAMXnsPP2b0oImYmBgdOXJEcXFxpqMAANDivr1Gj9PpVHFxsQYNGmQ4FQAAwP8PhR40sX//fm3fvl1+fn7y9/dnBwTgAWG3MwkTaAnfXqPHy8tLiYmJioiIMJgIgET7CGAKr72HH4UeNMFOCIA5ubm5OnfunGbMmKGvv/5a1dXVevzxxyXx2gRayo/d+Q6Ae1FwBcwYNWqU6Qj4iWxOynUA8EDYtm2bcnJydOXKFR07dkyXL1/W4sWLlZGRYToaYElpaWlKTk5me3XAoNzcXJ0/f77RwsvTp083mAiwrvfee+97z/Pasw5m9KCJS5cuKS0tTWfOnFFdXZ1r/KOPPjKYCrC+rKws7d27VwkJCZKkTp06uXa/A9D8oqKiJDVu3QLgPsnJySouLlZ4eLi8vLxMxwEs79///rfpCHATCj1oIiUlRWPGjFFRUZE2btyojIwMhYSEmI4FWJ6fn5+8vb0bjbHrAdByYmNjJdG6BZhy+vRpZWdnU+QB3GTdunWmI8BNKPSgicrKSiUkJGjHjh2KjIxURESEpkyZogULFpiOBlhap06dlJeXJ5vNpvr6eqWnp6tHjx6mYwGWdb+WrQa0bgEtKyQkRLW1tQoICDAdBfAIOTk533t++PDhbkqClkahB000zCho3bq1ysrK1KFDB1VUVBhOBVjfypUrtXz5cn3xxReKiIhQdHS0Nm7caDoWYFkNLVsFBQUqKCjQ+PHjJX3TRtm3b1+T0QCPsHz5cs2cOVNRUVHy8fFxjS9btsxgKsC6/vSnP933nM1mo9BjIRR60ER0dLSqqqo0depUTZw4UT4+PoqLizMdC7C8jh076i9/+Ytqa2tVX1+vNm3amI4EWFpDy9bu3bv13nvvyc/PT5I0ZcoUzZo1y2AywDOkpqYqODhYgYGBtG8BbrBz507TEeAmFHrQxPLlyyVJTz/9tPr376+amhr17NnTcCrA+u41nTYgIEA9e/ZUYGCggUSAZ6isrGw0m8Db21uVlZUGEwGe4fLlyzp8+LDpGIBHqq6uVmlpaaPNd/r162cwEZoThR641NbWNhlr166d2rVrp9raWvn7+xtIBXiOrVu36vTp0woLC5MknT17VmFhYXI4HEpNTWVnIKCFxMTEaN68ea4ZPpmZmYqJiTGcCrC+sLAwlZeXKygoyHQUwKP8/e9/1/r163Xt2jUFBQXp/PnzCg8P1/79+01HQzOh0AOXyMhI2Ww2OZ1O11jDsc1mU1FRkcF0gPWFhIRo5cqV6tOnjySpsLBQ27dvV1pampYsWUKhB2ghK1eu1K5du3T06FFJ0ogRIzR58mTDqQDrq66uVnx8vCIjI+Xr6+saZyF0oGWlp6dr3759SkxM1IEDB/TPf/7T9TMQ1kChBy5nzpwxHQHwaGfOnHEVeSSpd+/eOnv2rEJDQxsVYAE0L29vb82cOVMzZ840HQXwKOPGjdO4ceNMxwA8TqtWrdS+fXvdvXtXkjR48GA2ALEYCj0A8IDw9/dXVlaW65ferKws1+Kw37cFNICfprS0VCkpKXI4HDp+/LgKCwt1/Phxvfzyy6ajAZbW0C4JwL18fHzkdDrVvXt37dy5U127dtWNGzdMx0Izsjl5mxgAHgjnzp1TcnKyvvzyS0nSE088oQ0bNqhLly7Kz8/X4MGDDScErGnWrFmaM2eO3nzzTWVmZqq+vl7x8fHKzs42HQ2wtIULF97zjQxat4CWlZubqz59+ujq1atavXq1qqurtXTpUg0aNMh0NDQTZvQAwAPg7t27ysvL0759+1RTUyPpmx23GlDkAVpOdXW1hg0bpt///veSJLvdLm9vb8OpAOv79tpzdXV1Onr0qEJDQw0mAjzDwIEDJUmBgYF69913zYZBi6DQAwAPAC8vL+3evVtTpkxpVOAB0PK8vLx0+/Zt18wCh8Mhu91uOBVgfd9t3Zo4caISExMNpQE8x82bN5WVlaXz58/rzp07rvFly5YZTIXmxG8xAPCAiImJ0ZEjR0zHADzOtGnTtGDBAlVWVmrz5s2aNm2a5syZYzoW4HFsNpscDofpGIDlLViwQMeOHZOXl5dat27t+oB1sEYPADwgBgwYoKqqKvn5+cnf319Op1M2m025ubmmowGWl5eXpxMnTsjpdCo2NlbR0dGmIwGW9+01epxOp4qLizVw4ECtXr3abDDA4saOHcs6dBZHoQcAHhAXL16853jXrl3dnAQAgJa3f/9+12MvLy91795dERERBhMBnuGVV15RSkqKgoKCTEdBC6HQAwAAPFpJSYnS09ObrFWwZ88eg6kAAGgZX375pebOnavw8HD5+vq6xtnxzjpYjBkAHhCXLl1SWlqazpw5o7q6Otf4Rx99ZDAVYH2LFi3ShAkT9Mwzz8jLy8t0HMDy0tLSlJyczPbqgCHLli1TbGysfv7zn/Nzz6Io9ADAAyIlJUVjxoxRUVGRNm7cqIyMDIWEhJiOBVheq1atNHfuXNMxAI8RFRUlqfH26gDc5/bt21q1apXpGGhBFHoA4AFRWVmphIQE7dixQ5GRkYqIiNCUKVO0YMEC09EASxs6dKhycnI0fPhw01EAjxAbGyup6fbqANzjySefVHFxscLCwkxHQQuh0AMADwhvb29JUuvWrVVWVqYOHTqooqLCcCrA+gYOHKikpCTZ7Xb5+Piw4x3Qwu7XstWA1i2gZRUUFOjZZ5/V448/3miNHtamsw4KPQDwgIiOjlZVVZWmTp2qiRMnysfHR3FxcaZjAZa3atUqrVu3Tr1795bdbjcdB7C8hpatgoICFRQUaPz48ZKkrKws9e3b12Q0wCOsWLHC9biurk7Xrl1Tx44dDSZCc2PXLQB4AJWVlammpkY9e/Y0HQWwvEmTJvEuJmDAc889p3fffVd+fn6SpJs3b2rWrFnatWuX4WSAtS1evFivv/66vL29NWHCBFVWVuqFF15QYmKi6WhoJrxtBQCG1dbWNvlo166dunXrptraWtPxAMsbOXKkMjIyVFVV1eh1CKBlVVZWysfHx3Xs7e2tyspKg4kAz1BaWqrAwEB9/PHHiomJ0cmTJ3XgwAHTsdCMaN0CAMMiIyNls9n07QmWDcc2m01FRUUG0wHW99Zbb0mSXnvtNV57gBvFxMRo3rx5rkWZMzMzFRMTYzgVYH137tyRJH322WcaPny4/Pz8aF22GFq3AAAAALjd7du3tWvXLp06dUqSNGDAAE2ePNm1OQGAlrFo0SJdv35dJSUlysrKkt1u15QpU5SZmWk6GpoJhR4AAAAAADzEzZs39Y9//ENhYWHq1q2bHA6HiouLNWzYMNPR0Ewo9AAAAABwu9LSUqWkpMjhcOj48eMqLCzU8ePH9fLLL5uOBgAPNRrxAAAAALjda6+9pvnz5yswMFCS1KtXLx05csRwKgB4+FHoAQAAAOB21dXVGjZsmGw2myTJbrezPg8ANAMKPQAAAADczsvLS7dv33YVehwOBzv/AEAz4DspAAAAALebNm2aFixYoMrKSm3evFnTpk3TnDlzTMcCgIceizEDAAAAMCIvL08nTpyQ0+lUbGysoqOjTUcCgIcehR4AAAAAAACLaGU6AAAAAADPU1JSovT0dJ0/f1537txxje/Zs8dgKgB4+DGjBwAAAIDbxcfHa8KECerdu7e8vLxc4/379zeYCgAefszoAQAAAOB2rVq10ty5c03HAADLYdctAAAAAG43dOhQ5eTkmI4BAJbDjB4AAAAAbjdw4EAlJSXJbrfLx8dHTqdTNptNubm5pqMBwEONNXoAAAAAuN2oUaO0dOlS9e7dW3b7/xoNunbtajAVADz8mNEDAAAAwO3atm2ruLg40zEAwHJYowcAAACA240cOVIZGRmqqqpSbW2t6wMA8NPQugUAAADA7cLDw12PbTaba42eoqIig6kA4OFHoQcAAAAAAMAiaN0CAAAAAACwCAo9AAAAAAAAFkGhBwAAwA0iIyN14cIF0zEAAIDFUegBAAAe79NPP9WwYcNa9B75+fnq1q1bi94DAACAQg8AAHjg3Llzx5L3AgAAaGkUegAAgNvExsbqnXfe0ZgxY9SvXz+9+uqrqqurc82o2bZtmwYPHqxXX31Vt27d0po1azRkyBANGTJEa9as0a1btyT9bwZOenq6YmJiFBsbq4MHD7ruc+vWLa1fv14jRozQoEGDtGrVKt28ebPRcxvutWTJEs2bN0/l5eWKjIxUZGSkHA6HIiIiVFlZ6fo/CwsLNWDAAN2+ffu+n99XX32lGTNmKCoqSjExMXrllVdc58LCwvTVV1/J4XC47hMZGamIiAiFhYW5rtuzZ49Gjx6tfv36KTExURcvXmyuLz8AAPAAFHoAAIBbHTp0SH/+85/1wQcfqLS0VFu3bpUkff311/rvf/+rEydO6I033tDbb7+tzz//XJmZmTp48KBOnz7turbh+srKSn3yySf63e9+p1WrVqmkpESStHHjRpWWlurAgQM6duyYysvLtWXLlkbPbbjXhg0b9Mc//lFBQUHKz89Xfn6+goOD1b9/fx0+fNj1nMzMTI0dO1be3t73/dw2bdqkwYMH67PPPtPJkyc1Y8aMJtcEBwe77pOfn6+RI0dq7NixkqQPP/xQ77zzjv7whz8oNzdXUVFRWrp06U/7ggMAAI9CoQcAALjV9OnT1blzZz366KOaP3++srOzJUl2u10LFy6Uj4+P/Pz8dOjQIb300ktq3769HnvsMb300kuNZu1I0qJFi+Tj46P+/ftr+PDhOnz4sJxOp/76178qJSVFjz76qAICAvTCCy+47nOve93LM88847rf3bt3lZ2drQkTJnzv59aqVSuVlZWpvLxcvr6+io6O/t7rt23bptLSUq1du1aStGvXLv3qV79SaGioWrVqpRdffFFFRUXM6gEAAD9aK9MBAACAZ+ncubPrcZcuXVReXi5JateunXx9fV3nysvL1aVLl3teK0mPPPKIWrdu3eR8RUWFamtrNXHiRNc5p9Op+vp61/F373UvTz31lH7729/qwoULKi0tVUBAgPr27fu9z0lOTtamTZs0adIktW3bVrNnz9akSZPueW1OTo527Nihv/3tb65iU1lZmdauXav169c3yu5wONS1a9fvvTcAAIBEoQcAALjZpUuXXI/LysoUFBQkSbLZbI2uCwoKUllZmXr06OF6XsO1knTt2jXduHHDVey5dOmSevTooXbt2snPz0/Z2dkKDg6+Z4bv3uu7x5Lk6+ur0aNH6+DBgyopKfnB2TyS1LFjR6WmpkqS8vLyNHv2bPXr10/du3dvdF1JSYl+85vfaPPmzY0KX507d9aLL76o8ePH/+C9AAAA7oXWLQAA4Fbvv/++Ll++rKqqKqWnp2vMmDH3vG7s2LF6++23VVFRoYqKCm3ZskXx8fGNrtm8ebNu3bqlvLw8ffzxx4qLi5PdbldCQoLWrl2rq1evSpIcDoc++eST+2Zq3769qqqqVF1d3Wh8woQJ2r9/v44fP/6jCj2HDx/W5cuXJUlt27aVzWaT3d74162amholJSVp8eLFTVq7nnvuOW3btk1ffPGFJKm6urrROkEAAAA/hBk9AADArcaNG6c5c+aovLxcTz31lObPn6+CgoIm1yUlJen69euu2S1xcXFKSkpyne/QoYMeeeQRDR06VP7+/lq9erVCQ0MlfdNCtWXLFk2ePFmVlZUKDg7W1KlTNXTo0HtmCg0N1dixYzVy5EjXejzBwcGKioqS3W5X7969f1Tr1OnTp7V27VrV1NSoffv2WrFihbp169bomsLCQpWWlmrdunVat26dazw/P1+jRo3S9evXtWTJEl28eFGBgYEaNGiQRo8e/cNfWAAAAEk2p9PpNB0CAAB4htjYWKWmpmrQoEE/6f/59NNPlZycrJMnTzZTsvt7/vnnFR8fr4SEhBa/FwAAwE9F6xYAAMB9FBQU6D//+Q8zagAAwEOD1i0AAIB7WL58uT788EOtWLFCAQEBrvFVq1bp0KFDTa6Pj4/X66+/7s6IAAAATdC6BQAAAAAAYBG0bgEAAAAAAFgEhR4AAAAAAACLoNADAAAAAABgERR6AAAAAAAALIJCDwAAAAAAgEX8H9CH+9fpm3MFAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "renovation_histograms(house_df_ins, 98005)" + ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "##### Conclusions\n", + "\n", + "For example, on the zipcode 98005, the houses to compare, that were the ones above the median price and their condition were higher than 3, have more bathroom amenities on general, and also for properties with living and lot large and medium size." + ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## 4. Set new business hypothesis\n", + "# 4. Set new business hypothesis\n", "***\n", "\n", "1. Houses that have a water view, are 20% more expensive on average.\n", @@ -9492,2523 +12247,378 @@ "5. Houses with 3 bathrooms have a MoM (Month over Month) growth of 15%.\n", "\n", "\n", - "6. Houses in the mountains are valued 10% more in the Summer than in the Winter.\n", - "\n", - "\n", - "7. Houses with sqft_living" + "6. Overall the houses North of the county are generally more expensive than South properties." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.1 Test New Business Hypothesis\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.1.1. Houses that have a water view, are 20% more expensive on average." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 127, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average price of houses with water view: 1661876.0245398772\n", + "\n", + "Average price of houses without water view: 531563.5998135199\n", + "\n", + "Average price percentage of houses with a water view above houses without a water view: 212.64%\n" + ] + } + ], + "source": [ + "price_per_water_front = house_df_eda[['price','waterfront']].groupby('waterfront').mean().reset_index()\n", + "\n", + "print('Average price of houses with water view: ', price_per_water_front.loc[price_per_water_front['waterfront']==1,'price'].values[0],end='\\n\\n')\n", + "print('Average price of houses without water view: ', price_per_water_front.loc[price_per_water_front['waterfront']==0,'price'].values[0],end='\\n\\n')\n", + "print(\"Average price percentage of houses with a water view above houses without a water view: \",\n", + " str(round(((price_per_water_front.loc[price_per_water_front['waterfront']==1,'price'].values[0])/(price_per_water_front.loc[price_per_water_front['waterfront']==0,'price'].values[0]))*100,2)-100)+'%')" + ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### 4.1 Test New Business Hypothesis\n", - "***" + "#### 4.1.2. Houses with year built older than 1955, are 50% cheaper on average. " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 128, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average price of houses older than 1955: 537050.9076898118\n", + "\n", + "Average price of houses after 1955: 542917.1649756227\n", + "\n", + "Average price percentage of houses older than 1955 above houses built after 1955: -1.0799999999999983%\n" + ] + } + ], + "source": [ + "print('Average price of houses older than 1955: ', house_df_eda.loc[house_df_eda.yr_built < 1955,'price'].mean(),end='\\n\\n')\n", + "print('Average price of houses after 1955: ', house_df_eda.loc[house_df_eda.yr_built > 1955,'price'].mean(),end='\\n\\n')\n", + "print(\"Average price percentage of houses older than 1955 above houses built after 1955: \",\n", + " str(round((house_df_eda.loc[house_df_eda.yr_built < 1955,'price'].mean()/house_df_eda.loc[house_df_eda.yr_built > 1955,'price'].mean())*100,2)-100)+'%')\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.1.3. Houses without basement are 40% bigger than with basement on average. " + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 129, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total size of houses without basement: 1691.9929444689928\n", + "\n", + "Total size of houses with basement: 1449.2179325556733\n", + "\n", + "Average total size percentage of houses without basement above houses with basement: 16.75%\n" + ] + } + ], + "source": [ + "print('Total size of houses without basement: ', house_df_ins.loc[house_df_ins.m2_basement == 0,'house_total_m2'].mean(),end='\\n\\n')\n", + "print('Total size of houses with basement: ', house_df_ins.loc[house_df_ins.m2_basement > 0,'house_total_m2'].mean(),end='\\n\\n')\n", + "print(\"Average total size percentage of houses without basement above houses with basement: \",\n", + " str(round(((house_df_ins.loc[house_df_ins.m2_basement == 0,'house_total_m2'].mean())/(house_df_ins.loc[house_df_ins.m2_basement > 0,'house_total_m2'].mean())*100)-100,2))+'%')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.1.4. The growth rate of the houses price YoY (Year over Year) is 10%. " + ] }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 134, "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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" + ], "text/plain": [ - "0 20699\n", - "1 914\n", - "Name: renovated, dtype: int64" + " year price price_perc_YoY\n", + "0 2014 539181.43 0.00\n", + "1 2015 541988.99 0.52" ] }, - "execution_count": 72, + "execution_count": 134, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "house_df_eda['renovated'] = house_df_eda['yr_renovated'].apply(lambda x: 1 if x!=0 else 0)\n", - "house_df_eda['renovated'].value_counts()" + "house_df_eda['year'] = house_df_eda['date'].apply(lambda x: x.year)\n", + "house_price_per_year = house_df_eda[['year','price']].groupby('year').mean().reset_index()\n", + "\n", + "perc_series_array = np.zeros(len(house_price_per_year))\n", + "perc_series_array[:-1] = house_price_per_year.loc[1:,'price']\n", + "perc_serie = pd.Series(perc_series_array)\n", + "perc_serie\n", + "\n", + "price_perc_YoY = np.zeros(len(house_price_per_year))\n", + "price_perc_YoY[1:] = (((perc_serie/house_price_per_year['price'])*100)-100)[:-1]\n", + "\n", + "house_price_per_year['price_perc_YoY'] = price_perc_YoY\n", + "house_price_per_year" ] }, { - "cell_type": "code", - "execution_count": 73, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "house_df_eda['region'] = house_df_eda['lat'].apply(lambda x: 'North' if x > 47.46 else 'South')\n", - "\n", - "sampleNorth = house_df_eda[house_df_eda['region'] == 'North'].sample(100)\n", - "sampleSouth = house_df_eda[house_df_eda['region'] == 'South'].sample(100)\n", - "\n", - "house_df_sampled = pd.concat([sampleNorth,sampleSouth])\n", - "\n", - "house_df_sampled['price_cube'] = house_df_sampled['price']**1.5" + "#### 4.1.5. Houses with 3 bathrooms have a MoM (Month over Month) growth of 15%. " ] }, { "cell_type": "code", - "execution_count": 74, - "metadata": { - "scrolled": false - }, + "execution_count": 135, + "metadata": {}, "outputs": [ { "data": { "text/html": [ - " \n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "customdata": [ - [ - 475000 - ], - [ - 2180000 - ], - [ - 680000 - ], - [ - 857500 - ], - [ - 510000 - ], - [ - 229000 - ], - [ - 382880 - ], - [ - 600000 - ], - [ - 535800 - ], - [ - 324000 - ], - [ - 885000 - ], - [ - 481000 - ], - [ - 760000 - ], - [ - 325000 - ], - [ - 472500 - ], - [ - 273500 - ], - [ - 1400000 - ], - [ - 560000 - ], - [ - 402500 - ], - [ - 269000 - ], - [ - 352500 - ], - [ - 328000 - ], - [ - 552500 - ], - [ - 635000 - ], - [ - 1698890 - ], - [ - 306888 - ], - [ - 675000 - ], - [ - 256950 - ], - [ - 425000 - ], - [ - 585000 - ], - [ - 1775000 - ], - [ - 575000 - ], - [ - 430000 - ], - [ - 203700 - ], - [ - 1080000 - ], - [ - 750000 - ], - [ - 725000 - ], - [ - 317500 - ], - [ - 356000 - ], - [ - 375000 - ], - [ - 640000 - ], - [ - 350000 - ], - [ - 662000 - ], - [ - 552100 - ], - [ - 1355000 - ], - [ - 1050000 - ], - [ - 722500 - ], - [ - 589000 - ], - [ - 730000 - ], - [ - 581000 - ], - [ - 900000 - ], - [ - 406500 - ], - [ - 269900 - ], - [ - 670000 - ], - [ - 420000 - ], - [ - 299900 - ], - [ - 207000 - ], - [ - 450000 - ], - [ - 309500 - ], - [ - 344000 - ], - [ - 600000 - ], - [ - 241000 - ], - [ - 580135 - ], - [ - 1035480 - ], - [ - 439000 - ], - [ - 530000 - ], - [ - 199000 - ], - [ - 325000 - ], - [ - 313950 - ], - [ - 875000 - ], - [ - 654000 - ], - [ - 300000 - ], - [ - 825000 - ], - [ - 588000 - ], - [ - 899100 - ], - [ - 525000 - ], - [ - 528000 - ], - [ - 372000 - ], - [ - 1100000 - ], - [ - 523000 - ], - [ - 402000 - ], - [ - 515000 - ], - [ - 640000 - ], - [ - 580000 - ], - [ - 1245000 - ], - [ - 760000 - ], - [ - 497000 - ], - [ - 530000 - ], - [ - 424950 - ], - [ - 430000 - ], - [ - 567500 - ], - [ - 555000 - ], - [ - 650000 - ], - [ - 350000 - ], - [ - 923990 - ], - [ - 667000 - ], - [ - 952000 - ], - [ - 1200000 - ], - [ - 561000 - ], - [ - 516000 - ] - ], - "hovertemplate": "%{hovertext}

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\n", + "" + ], + "text/plain": [ + " year month price price_perc_MoM\n", + "0 2014 5 548080.28 0.00\n", + "1 2014 6 558002.20 1.81\n", + "2 2014 7 544788.76 -2.37\n", + "3 2014 8 536445.28 -1.53\n", + "4 2014 9 529253.82 -1.34\n", + "5 2014 10 539026.97 1.85\n", + "6 2014 11 521961.01 -3.17\n", + "7 2014 12 524461.87 0.48\n", + "8 2015 1 525870.89 0.27\n", + "9 2015 2 507851.37 -3.43\n", + "10 2015 3 543977.19 7.11\n", + "11 2015 4 561837.77 3.28\n", + "12 2015 5 558126.81 -0.66" ] }, + "execution_count": 135, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "data_map = house_df_sampled[['id','zipcode', 'lat', 'long', 'price','bedrooms','renovated','region','price_cube']]\n", + "house_df_eda['year'] = house_df_eda['date'].apply(lambda x: x.year)\n", + "house_df_eda['month'] = house_df_eda['date'].apply(lambda x: x.month)\n", + "house_price_per_month = house_df_eda[['year','month','price']].groupby(by=['year','month']).mean().reset_index()\n", "\n", - "map = px.scatter_mapbox(data_map, lat='lat', lon='long',\n", - " hover_name='id',\n", - " hover_data=['price'],\n", - " size= 'price_cube',\n", - " color='region',\n", - " color_discrete_sequence=['red','green'],\n", - " size_max=30,\n", - " zoom=10,\n", - " height=300)\n", + "perc_series_array = np.zeros(len(house_price_per_month))\n", + "perc_series_array[:-1] = house_price_per_month.loc[1:,'price']\n", + "perc_serie = pd.Series(perc_series_array)\n", + "perc_serie\n", "\n", - "map.update_layout(mapbox_style='open-street-map')\n", - "map.update_layout(height=600, margin={'r':0, 't':0, 'l':0, 'b':0})\n", + "price_perc_MoM = np.zeros(len(house_price_per_month))\n", + "price_perc_MoM[1:] = (((perc_serie/house_price_per_month['price'])*100)-100)[:-1]\n", "\n", - "map.show()" + "house_price_per_month['price_perc_MoM'] = price_perc_MoM\n", + "house_price_per_month" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 4.1.6. Houses at North of the county are generally more expensive than South houses." ] }, { @@ -12016,7 +12626,13 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "house_df_eda['region'] = house_df_eda['lat'].apply(lambda x: 'North' if x > 47.46 else 'South')\n", + "\n", + "house_grouped_by_region = house_df_eda.groupby(by='region').median().reset_index()\n", + "\n", + "house_grouped_by_region.plot.bar(x='region',y='price')" + ] }, { "cell_type": "markdown", @@ -12037,9 +12653,9 @@ ], "metadata": { "kernelspec": { - "display_name": "houserocket_env", + "display_name": "env_houserocket", "language": "python", - "name": "houserocket_env" + "name": "env_houserocket" }, "language_info": { "codemirror_mode": { diff --git a/images/>10000m2_recommended_houses.png b/images/>10000m2_recommended_houses.png new file mode 100644 index 0000000000000000000000000000000000000000..2dc90f7f9e6bcc57e6b481160538f31f8b9371d9 GIT binary patch literal 733664 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