├── 001_Seaborn_Loading_Dataset.ipynb ├── 002_Seaborn_Controlling_Aesthetics.ipynb ├── 003_Matplotlib_vs_Seaborn.ipynb ├── 004_Seaborn_Color_Palettes.ipynb ├── 005_Seaborn_LM_Plot_and_Reg_Plot.ipynb ├── 006_Seaborn_Scatter_Plot_and_Joint_Plot.ipynb ├── 007_Seaborn_Additional_Regression_Plots.ipynb ├── 008_Seaborn_Distribution_Plots.ipynb ├── 009_Seaborn_Categorical_Swarm_Plot.ipynb ├── 010_Seaborn_Categorical_Strip_Plot.ipynb ├── 011_Seaborn_Categorical_Box_Plot.ipynb ├── 012_Seaborn_Categorical_Violin_Plot.ipynb ├── 013_Seaborn_Categorical_Bar_Plot_Point_Plot_and_Count_Plot.ipynb ├── 014_Seaborn_Categorical_Factor_Plot.ipynb ├── 015_Seaborn_TimeSeries_and_LetterValue_Plot.ipynb ├── 016_Seaborn_PairGrid_Plot.ipynb ├── 017_Seaborn_FacetGrid_Plot.ipynb ├── 018_Seaborn_Heat_Map.ipynb ├── 019_Seaborn_Cluster_Map.ipynb ├── LICENSE ├── Python Seaborn Statistical Data Visualization.pdf ├── README.md ├── datasets ├── Labour Data.csv ├── PoliceKillingsUS.csv ├── Score Book.csv ├── University.csv ├── anscombe.csv ├── brain_networks.csv ├── empty ├── exercise.csv ├── flights.csv ├── iris.csv ├── nyc_taxi.csv ├── planets.csv ├── tips.csv └── titanic.csv └── img ├── BP.png ├── SCC.png └── dnld_rep.png /018_Seaborn_Heat_Map.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "\n", 8 | "All the IPython Notebooks in **[Python Seaborn Module](https://github.com/milaan9/12_Python_Seaborn_Module)** lecture series by **[Dr. Milaan Parmar](https://www.linkedin.com/in/milaanparmar/)** are available @ **[GitHub](https://github.com/milaan9)**\n", 9 | "" 10 | ] 11 | }, 12 | { 13 | "cell_type": "markdown", 14 | "metadata": {}, 15 | "source": [ 16 | "\"Open" 17 | ] 18 | }, 19 | { 20 | "cell_type": "markdown", 21 | "metadata": {}, 22 | "source": [ 23 | "# What is heat map?\n", 24 | "\n", 25 | "A heat map (or heatmap) is a graphical representation of data where the individual values contained in a matrix are represented as colors. It is a bit like looking a data table from above. It is really useful to display a general view of numerical data, not to extract specific data point. It is quite straight forward to make a heat map, as shown on the examples below. However be careful to understand the underlying mechanisms. You will probably need to normalise your matrix, choose a relevant colour palette, use cluster analysis and thus permute the rows and the columns of the matrix to place similar values near each other according to the clustering.\n", 26 | "\n", 27 | "A **[heatmap](http://seaborn.pydata.org/generated/seaborn.heatmap.html?highlight=heatmap#seaborn.heatmap)** is a plot of rectangular data as a color-encoded matrix. As parameter it takes a 2D dataset. That dataset can be coerced into an ndarray.\n", 28 | "\n", 29 | "This is a great way to visualize data, because it can show the relation between variabels including time. For instance, the number of fligths through the years.\n", 30 | "\n", 31 | "Various types of heatmap can be found **[here](https://python-graph-gallery.com/90-heatmaps-with-various-input-format/)**" 32 | ] 33 | }, 34 | { 35 | "cell_type": "code", 36 | "execution_count": 1, 37 | "metadata": { 38 | "ExecuteTime": { 39 | "end_time": "2021-07-18T17:50:40.741915Z", 40 | "start_time": "2021-07-18T17:50:14.092290Z" 41 | } 42 | }, 43 | "outputs": [], 44 | "source": [ 45 | "import numpy as np\n", 46 | "import pandas as pd\n", 47 | "import seaborn as sns\n", 48 | "import matplotlib.pyplot as plt\n", 49 | "\n", 50 | "%matplotlib inline" 51 | ] 52 | }, 53 | { 54 | "cell_type": "markdown", 55 | "metadata": {}, 56 | "source": [ 57 | "### heatmap\n", 58 | "\n", 59 | "The heatmap plot below is based on random values generated by numpy. Many parameters are possible, this just shows the most basic plot." 60 | ] 61 | }, 62 | { 63 | "cell_type": "code", 64 | "execution_count": 2, 65 | "metadata": { 66 | "ExecuteTime": { 67 | "end_time": "2021-07-18T17:50:42.787791Z", 68 | "start_time": "2021-07-18T17:50:40.744845Z" 69 | } 70 | }, 71 | "outputs": [ 72 | { 73 | "data": { 74 | "text/plain": [ 75 | "" 76 | ] 77 | }, 78 | "execution_count": 2, 79 | "metadata": {}, 80 | "output_type": "execute_result" 81 | }, 82 | { 83 | "data": { 84 | "image/png": "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\n", 85 | "text/plain": [ 86 | "
" 87 | ] 88 | }, 89 | "metadata": { 90 | "needs_background": "light" 91 | }, 92 | "output_type": "display_data" 93 | } 94 | ], 95 | "source": [ 96 | "# Plot a heatmap for a numpy array:\n", 97 | "\n", 98 | "uniform_data = np.random.rand(10,12)\n", 99 | "#uniform_data = np.arange(1,17).reshape(4,4)\n", 100 | "sns.heatmap(uniform_data)" 101 | ] 102 | }, 103 | { 104 | "cell_type": "code", 105 | "execution_count": 3, 106 | "metadata": { 107 | "ExecuteTime": { 108 | "end_time": "2021-07-18T17:50:43.224313Z", 109 | "start_time": "2021-07-18T17:50:42.797561Z" 110 | } 111 | }, 112 | "outputs": [ 113 | { 114 | "data": { 115 | "text/plain": [ 116 | "array([[1, 2, 3, 4],\n", 117 | " [2, 3, 4, 1],\n", 118 | " [5, 4, 2, 1],\n", 119 | " [6, 7, 8, 5]])" 120 | ] 121 | }, 122 | "execution_count": 3, 123 | "metadata": {}, 124 | "output_type": "execute_result" 125 | }, 126 | { 127 | "data": { 128 | "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVQAAAD8CAYAAAAoqlyCAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAOkElEQVR4nO3da4ym5V3H8e9vF1bOoIJkWVBaURrFCJRsjZtiBWoBEUjTKDVtPZBOX9gWjImlL5TURNMXhtRXphNaDylQK4fYEEU2ykrQQlkoEmBBW1phObiQSspKW5iZvy/2WRk2+5x2rod79p7vZ3OHmec+XXmy/PZ/Xdd9SFUhSVq5dV03QJL6wkCVpEYMVElqxECVpEYMVElqxECVpEYMVEkaIsnvJnk0ySNJbkpy2KjtDVRJ2o8km4CPA+dU1RnAeuCKUfsYqJI03CHA4UkOAY4Anh238Wxbs2GTt2INnH38aV03YdXYsmFj101YNd75Peuavd77/I1Z6TFee/HJiTNnwwk//hFgbtlH81U1D1BVzyT5U+Ap4LvAnVV156jjzTxQJWm1GoTn/P7WJflB4DLgLcBLwN8m+UBVfWHY8fynUVK/LC1Ovox2AfDNqnqhql4DbgV+ftQOVqiS+mVxodWRngJ+LskR7Onynw9sH7WDgSqpV6qWGh2n7ktyM/AgsAB8jSHDA3sZqJL6ZalNoAJU1bXAtZNub6BK6pdGFeqBMFAl9cv4yaaZMVAl9YsVqiS1Ue1m+admoErql4aTUtMyUCX1i11+SWrESSlJasQKVZIacVJKkhpxUkqS2qhyDFWS2nAMVZIascsvSY1YoUpSI4uvdXZqA1VSv9jll6RG7PJLUiNWqJLUiIEqSW3Uap6USvI24DJgE1DAs8CXq2rHjNsmSdPrcAx13aiVST4BfBEI8FXg/sHPNyW5ZvbNk6QpLS1NvjQ2rkK9EvjpqnpDDZ3kOuBR4NP72ynJHDAHkPXHsm7dkQ2aKkkTaFShJjkd+JtlH70V+MOq+sywfcYF6hJwEvBf+3y+cbBuv6pqHpgHOGTDphpzDklqp1HlWVVPAGcCJFkPPAPcNmqfcYF6NfBPSf4TeHrw2Y8CpwEfXUFbJWk2ZjOGej7wjarat7h8g5GBWlV3JPlJYDN7JqUC7ATury6fkSVJwyxM/oDp5cOTA/ODHva+rgBuGne8sbP8VbUE3DtxCyWpS1NUqMuHJ4dJsgG4FPjkuON5Haqkfmk/e38R8GBV/fe4DQ1USf3Sfgz1/UzQ3QcDVVLfNKxQkxwBvBv4yCTbG6iS+qVhhVpVrwA/POn2Bqqkfplilr81A1VSv1R39xIZqJL6xcf3SVIjBqokNeIrUCSpkcXu7oo3UCX1i11+SWrEQJWkRhxDlaQ2asnrUCWpDbv8ktSIs/yS1IgVqiQ1YqBKUiM+HEWSGrFClaRG+nzZ1NnHnzbrUxw0tmzY2HUTVo13fm9d101YNX712//SdRNWjSaPhnaWX5LaKLv8ktRIn7v8kvSm8l5+SWrEClWSGlloNymV5DjgeuAMoIDfrqqvDNveQJXUL227/H8G3FFV70uyAThi1MYGqqR+adTlT3IMcC7wmwBV9Srw6qh9vBhQUq/U0tLES5K5JNuXLXPLDvVW4AXgL5J8Lcn1SY4cdW4DVVK/LNXES1XNV9U5y5b5ZUc6BDgb+POqOgv4X+CaUac2UCX1yxSBOsZOYGdV3Tf4/Wb2BOxQjqFK6pdGt55W1fNJnk5yelU9AZwPPDZqHwNVUq80fqfUx4AbBjP8TwK/NWpjA1VSvzQM1Kp6CDhn0u0NVEn94sNRJKkRbz2VpEYMVElqoxbt8ktSG1aoktRG48umpmKgSuoXA1WSGuluCNVAldQvteCklCS1YYUqSW04KSVJrVihSlIbXVaoB/yA6SQjH2MlSZ1YmmJpbCVP7P/UsBXL39Oy65XnVnAKSZpOLUy+tDayy5/k4WGrgBOH7Td4L8s8wOaTfqG7+lvSmtP2LdLTGTeGeiLwHuB/9vk8wL/NpEWStBKrOFBvB44aPLX6DZJsm0WDJGklVm2FWlVXjlj36+2bI0krs2oDVZIONrWYzs5toErqFStUSWqklqxQJamJlhVqkm8BLwOLwEJVjXyltIEqqVeqmleov1hVL06yoYEqqVccQ5WkRpbazvIXcGeSAj47uAt0KANVUq9MMymVZA6YW/bR/D6huaWqnk3yI8DWJI9X1d3DjmegSuqVaQJ1+XNHhqx/dvDfXUluAzYDQwN1JU+bkqRVp2ryZZQkRyY5eu/PwC8Bj4zaxwpVUq80vA71ROC2JLAnK2+sqjtG7WCgSuqVVpdNVdWTwM9Os4+BKqlXFr2XX5LamMGF/RMzUCX1ivfyS1Ij42bvZ8lAldQrVqiS1MjiUneX1xuoknrFLr8kNbLkLL8kteFlU5LUSK+7/DefZGbvtX2nz6LZ69Prnum6Ceopu/yS1Iiz/JLUSIc9fgNVUr/Y5ZekRpzll6RGOnzpqYEqqV8KK1RJamLBLr8ktWGFKkmNOIYqSY1YoUpSI1aoktTIYuMKNcl6YDvwTFVdMmpbA1VSr8zgDShXATuAY8Zt6OOPJPXKEpl4GSfJycAvA9dPcm4DVVKv1BTLBD4D/D4TDs0aqJJ6ZWmKJclcku3Llrm9x0lyCbCrqh6Y9NyOoUrqlaVMPohaVfPA/JDVW4BLk1wMHAYck+QLVfWBYcezQpXUK4tTLKNU1Ser6uSqOhW4AvjnUWEKVqiSemYGs/wTM1Al9coks/fTqqptwLZx2xmoknrFV6BIUiN2+SWpEe/ll6RGFjusUMdeNpXkbUnOT3LUPp9fOLtmSdKBmebC/tZGBmqSjwN/B3wMeCTJZctW/8kM2iNJK7JqAxX4MPD2qroceBfwB0muGqwbWlgvv53rxheeadJQSZpEZfKltXFjqOurajdAVX0rybuAm5P8GCMCdfntXE+dc36XVzFIWmO6nJQaV6E+n+TMvb8MwvUS4HjgZ2bYLkk6IK1uPT0Q4yrUDwELyz+oqgXgQ0k+O4P2SNKKrNrrUKtq54h1/9q+OZK0Ml6HKkmNGKiS1Ij38ktSI6t2DFWSDjazmL2flIEqqVeWOuz0G6iSesVJKUlqxEkpSWrEClWSGlmIY6iS1IRdfklqxC6/JDXiZVOS1EirOE1yGHA38APsycqbq+raUfsYqJJ6pWGX//vAeVW1O8mhwD1J/qGq7h22g4EqqVcWG9WoVVXA7sGvhw6WkQcf+9ZTSTqYTPOSvuXvvxssc8uPlWR9koeAXcDWqrpv1LmtUCX1Sk1RoS5//92Q9YvAmUmOA25LckZVPTJseytUSb0yi9dIV9VLwDbgwlHbGaiSemWJmngZJckJg8qUJIcDFwCPj9rHLr+kXml4FepG4K+SrGdP8fmlqrp91A4GqqReWWg3y/8wcNY0+xioknplmkmp1mYeqD90/rGzPsVB46JzN3fdhFXjV95xaddNWDWeu/DDXTehV7yXX5Ia6XWFKklvJitUSWpksaxQJakJH98nSY04hipJjTiGKkmN2OWXpEbs8ktSI87yS1IjdvklqREnpSSpEcdQJakRu/yS1Eg5KSVJbbR6jfSBMFAl9YpdfklqxC6/JDVihSpJjXjZlCQ10uWtp+s6O7MkzcASNfEySpJTktyVZEeSR5NcNe7cVqiSeqXhGOoC8HtV9WCSo4EHkmytqseG7WCgSuqVVrP8VfUc8Nzg55eT7AA2AUMD1S6/pF6ZpsufZC7J9mXL3P6OmeRU4CzgvlHntkKV1CvTzPJX1TwwP2qbJEcBtwBXV9V3Rm07NlCTbN5z3ro/yU8BFwKPV9XfT9xqSXqTLFa7B/glOZQ9YXpDVd06bvuRgZrkWuAi4JAkW4F3ANuAa5KcVVV/vPImS1I7rcZQkwT4HLCjqq6bZJ9xY6jvA7YA5wK/A1xeVX8EvAf4tREN+f9xic8/9M2JGi9JLbS6bIo92fdB4LwkDw2Wi0ftMK7Lv1BVi8ArSb6xd/ygqr6bZGhdvXxcYvcn3tvdVbaS1pxWd0pV1T1AptlnXKC+muSIqnoFePveD5McS7dvGpCk/VpaxQ9HObeqvg9Q9YaR3kOB35hZqyTpAK3ae/n3hul+Pn8ReHEmLZKkFWg5yz8tr0OV1CurucsvSQeVVdvll6SDjRWqJDVihSpJjSzWYmfnNlAl9Yov6ZOkRnxJnyQ1YoUqSY04yy9JjTjLL0mNeOupJDXiGKokNeIYqiQ1YoUqSY14HaokNWKFKkmNOMsvSY04KSVJjXTZ5V/X2ZklaQZqij/jJPl8kl1JHpnk3AaqpF6pqomXCfwlcOGk57bLL6lXWo6hVtXdSU6ddPt0Od7wZkoyV1XzXbdjNfC7eJ3fxevW4neRZA6YW/bR/L7fwSBQb6+qM8Yebw0F6vaqOqfrdqwGfhev87t4nd/F/k0TqI6hSlIjBqokNbKWAnVNjQ2N4XfxOr+L1/ld7CPJTcBXgNOT7Exy5cjt18oYqiTN2lqqUCVppgxUSWqk94Ga5MIkTyT5epJrum5Pl6a9ja6vkpyS5K4kO5I8muSqrtvUlSSHJflqkn8ffBef6rpNB7Nej6EmWQ/8B/BuYCdwP/D+qnqs04Z1JMm5wG7grye5pq6vkmwENlbVg0mOBh4ALl+Lfy+SBDiyqnYnORS4B7iqqu7tuGkHpb5XqJuBr1fVk1X1KvBF4LKO29SZqrob+HbX7ehaVT1XVQ8Ofn4Z2AFs6rZV3ag9dg9+PXSw9LfKmrG+B+om4Ollv+9kjf6Po/0b3AVzFnBfx03pTJL1SR4CdgFbq2rNfhcr1fdAzX4+819fAZDkKOAW4Oqq+k7X7elKVS1W1ZnAycDmJGt2OGil+h6oO4FTlv1+MvBsR23RKjIYL7wFuKGqbu26PatBVb0EbGOKx9XpjfoeqPcDP5HkLUk2AFcAX+64TerYYCLmc8COqrqu6/Z0KckJSY4b/Hw4cAHweKeNOoj1OlCragH4KPCP7Jl4+FJVPdptq7oz7W10PbYF+CBwXpKHBsvFXTeqIxuBu5I8zJ4CZGtV3d5xmw5avb5sSpLeTL2uUCXpzWSgSlIjBqokNWKgSlIjBqokNWKgSlIjBqokNfJ/IRNWuyzvdDcAAAAASUVORK5CYII=\n", 129 | "text/plain": [ 130 | "
" 131 | ] 132 | }, 133 | "metadata": { 134 | "needs_background": "light" 135 | }, 136 | "output_type": "display_data" 137 | } 138 | ], 139 | "source": [ 140 | "x = np.array([[1,2,3,4],[2,3,4,1],[5,4,2,1],[6,7,8,5]])\n", 141 | "sns.heatmap(x)\n", 142 | "x" 143 | ] 144 | }, 145 | { 146 | "cell_type": "markdown", 147 | "metadata": {}, 148 | "source": [ 149 | "**Three** main types of **input** exist to plot heatmap, let’s study them one by one." 150 | ] 151 | }, 152 | { 153 | "cell_type": "markdown", 154 | "metadata": {}, 155 | "source": [ 156 | "#### Wide format (untidy)\n", 157 | "\n", 158 | "We call **‘wide format‘** or **‘untidy format‘** a matrix where each row is an individual, and each column represents an observation. In this case, a **heatmap** consists to make a visual representation of the matrix: each square of the heatmap represents a cell. The color of the cell changes following its value." 159 | ] 160 | }, 161 | { 162 | "cell_type": "code", 163 | "execution_count": 4, 164 | "metadata": { 165 | "ExecuteTime": { 166 | "end_time": "2021-07-18T17:50:43.691106Z", 167 | "start_time": "2021-07-18T17:50:43.238962Z" 168 | }, 169 | "scrolled": true 170 | }, 171 | "outputs": [ 172 | { 173 | "data": { 174 | "image/png": "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\n", 175 | "text/plain": [ 176 | "
" 177 | ] 178 | }, 179 | "metadata": { 180 | "needs_background": "light" 181 | }, 182 | "output_type": "display_data" 183 | } 184 | ], 185 | "source": [ 186 | "df = pd.DataFrame(np.random.random((5,5)), columns=[\"a\",\"b\",\"c\",\"d\",\"e\"])\n", 187 | "\n", 188 | "# Default heatmap: just a visualization of this square matrix\n", 189 | "p1 = sns.heatmap(df)" 190 | ] 191 | }, 192 | { 193 | "cell_type": "markdown", 194 | "metadata": {}, 195 | "source": [ 196 | "### heatmap colors\n", 197 | "\n", 198 | "The heatmap colors plot below uses random data again. This time it’s using a different color map (cmap), with the ‘Blues’ palette which as nothing but colors of bue. It also uses square blocks." 199 | ] 200 | }, 201 | { 202 | "cell_type": "code", 203 | "execution_count": 5, 204 | "metadata": { 205 | "ExecuteTime": { 206 | "end_time": "2021-07-18T17:50:44.107116Z", 207 | "start_time": "2021-07-18T17:50:43.708194Z" 208 | }, 209 | "scrolled": true 210 | }, 211 | "outputs": [ 212 | { 213 | "data": { 214 | "image/png": "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\n", 215 | "text/plain": [ 216 | "
" 217 | ] 218 | }, 219 | "metadata": { 220 | "needs_background": "light" 221 | }, 222 | "output_type": "display_data" 223 | } 224 | ], 225 | "source": [ 226 | "corr = df.corr()\n", 227 | "\n", 228 | "ax1 = sns.heatmap(corr, cbar=0, linewidths=2,vmax=1, vmin=0, square=True, cmap='Blues')\n", 229 | "plt.show()" 230 | ] 231 | }, 232 | { 233 | "cell_type": "markdown", 234 | "metadata": {}, 235 | "source": [ 236 | "### heatmap data\n", 237 | "\n", 238 | "The heatmap data plot is similar, but uses a different color palette. It uses the airline or flights dataset that’s included in seaborn." 239 | ] 240 | }, 241 | { 242 | "cell_type": "code", 243 | "execution_count": 6, 244 | "metadata": { 245 | "ExecuteTime": { 246 | "end_time": "2021-07-18T17:50:45.328780Z", 247 | "start_time": "2021-07-18T17:50:44.113950Z" 248 | } 249 | }, 250 | "outputs": [ 251 | { 252 | "data": { 253 | "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAElCAYAAADjk4nIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAA5GElEQVR4nO3deVyUVfs/8M/IsIhEbuACpGYJaOb6hKSCghvLiANlLqAt5k5pZZkafU0rNX2wNLXlqVwzExRFBJfUXFPpeSQJs0VRzFjcAJVt5vz+4MckoTLMcIYZ5vPuNa+X3HPPda6Z4OJw7nOfoxBCCBARkdVoUNcJEBGRabHwExFZGRZ+IiIrw8JPRGRlWPiJiKwMCz8RkZVh4adKPD09cfXq1UrH4uPjMWHCBKPiPv/881XimoKnpydUKhXCwsJ0j9mzZ+ueu3r1Kvbu3Yv58+ffN05WVha6det21+cuXryI6Ojouz43c+ZM9O3bV9d2cHAwYmJikJubW23u94tLZAxlXSdA1uHw4cN11vbq1avRtGnTez4fGBiIwMBAg+P/+eefOHfu3D2ff/bZZ/HCCy8AAIQQ+OSTTzBu3DjEx8fDxsbG4LhEhmLhpxopKSnB4sWLceLECWg0GnTs2BFz5syBk5MT9u3bh08++QQlJSW4evUqhg0bhmnTpuHNN98EAIwdOxaffvopRo8ejdDQUBw7dgw3btzAuHHj8OOPPyI9PR1KpRIrV65EixYt7hnvhx9+wOLFi9G6dWv88ccfcHBwwIIFC9C+fXuD3lN8fDxSUlLwySefIDMzE7NmzcKNGzfg4uICIQSGDh2KJ554AhqNBjExMfjpp59QUFCAGTNmYMCAAZgzZw6ys7Pxwgsv4D//+c9921IoFJg4cSK2bNmCw4cPw8/PD6tWrcLevXtRVFSE27dv44033kBAQECVuHc7b+DAgQa9Z7JygugOHTp0EKGhoWLo0KG6h7+/vxg/frwQQohly5aJBQsWCK1WK4QQYsmSJeLtt98WWq1WREZGinPnzgkhhPjrr7+Et7e3uHLlii5uxb/79+8v3nvvPSGEEDt27BBeXl4iIyNDCCHE5MmTxcqVK+8b79ixY8LLy0ucOHFCCCHEhg0bhFqt1vv95OXlVcopLi5O9/6GDx8u1q9fL4QQ4rfffhNdunQRcXFx4uLFi6JDhw4iOTlZCCHErl27RGBgoBBCiGPHjomQkJC7tv/GG2+Izz//vMrx6Oho8dlnn4msrCwRFRUlbt++LYQQIjExUYSGhlaJe7/ziGqKPX6q4p9DIxU9YgDYv38/CgoKcOTIEQBAaWkpmjVrBoVCgVWrVmH//v1ITEzE77//DiEEbt++fdc2Bg0aBADw8PBA8+bN4eXlBQB46KGHcOPGjWrjeXl5oWfPngCAiIgIvPPOO7h27RqaNGlS7fu5lxs3biAtLQ3r1q0DALRv3x69evXSPW9ra4vBgwfr2r9y5Uq1Me9FoVCgYcOGcHNzw6JFi7B9+3ZkZmbi1KlTuHnzZpXz9T2PSB+8uEs1otVqMWvWLCQkJCAhIQHffvstPvzwQ9y6dQtqtRrp6eno2LEjXn/9dSiVSoh7LAVlZ2en+7etrW2V56uLd7ex8fuNl+uj4vV35nxnzDvzVCgUBrcjhEB6ejo6dOiA9PR0PPPMMygsLETv3r0xbty4u75G3/OI9MHCTzXSp08frF+/HiUlJdBqtXjrrbfw73//G5mZmSgsLMS0adMQEBCAH374QXcOUF5Ay8rK9G6nunhnzpzBmTNnAADffPMNunXrBmdnZ6Pem5OTE7p37474+HgA5bNqjh49Wm2Rt7GxQWlpqV5taDQafPzxx2jSpAn+9a9/4cSJE3jsscfw3HPP4YknnsDevXuh0WiqxL3feUQ1xaEeqpHJkydj4cKFUKvV0Gg08Pb2xsyZM+Ho6Ih+/fohKCgIdnZ26NChAx555BFkZmbioYcewpAhQxAVFYVly5bp1Y6np+c949nZ2aF58+ZYunQpLl26hKZNm2LRokW18v4WLlyI2bNnY8OGDWjRogXc3d3h4OBw39c88sgjsLe3x1NPPYVvv/22yi+Kr776Ctu2bYNCoYBGo0Hnzp3x6aefAgBCQ0Oxa9cuBAUFQavVon///rhx4wYKCwsrxV21atU9z3NycqqV907WQyHu9bc4kZn64YcfMG/ePCQmJtZ67JUrV2LQoEFo3749CgoKMHToUHz22Wd45JFHar0torrCHj/RHdq2bYvp06ejQYMG0Gg0ePHFF1n0qd5hj5+IyMrw4i4RkZVh4ScisjL1bozf3sGj1mM2UMj7/ejq+KCUuM3sjJvaeC9NlY2kxAUAO4Vx8/DvpU0DObNe2gm76k8ykJt+s0NrzEWj/5TamnjQpkRKXABo62n4jXL302LfAaNjlOb9ofe5ts0fNrq92sIePxGRlal3PX4iIpPRWuZNdCz8RESGkjR0JhsLPxGRgYTQ1nUKBmHhJyIylNYyC7/JL+7Gx8dj5syZpm6WiKj2Ca3+jxr47rvvEB4ejqCgIN22oEeOHIFKpcKgQYMQGxurOzcjIwPh4eEYPHgwZs+erddiiJzVQ0RkKK1G/4eeLl68iLfffhsrVqzAtm3b8PPPP+PAgQOYNWsWVqxYgaSkJJw+fRoHDpRPR50xYwZiYmKQkpICIQQ2bdpUbRt1VviPHz+OkSNHQq1WIzAwEHv27AFQvjn1/PnzMXLkSAQEBCAuLq6uUiQiuj8JPf7du3cjODgYLVu2hK2tLWJjY9GwYUO0adMGHh4eUCqVUKlUSE5OxqVLl1BUVISuXbsCAMLDw5GcnFxtG3U2xr9u3TrMnz8f7du3x9GjR/Hee+9hwIABAIC//voLGzZswNmzZzFmzBhERETUVZpERPckajCrJz8/H/n5+VWOOzs7V9pLIjMzE7a2tpg4cSIuX76Mfv364dFHH4WLi4vuHFdXV2RnZyMnJ6fScRcXF2RnZ1ebS50V/g8++AD79u1DcnJylW3kevfuDYVCgQ4dOuD69et1lSIR0f3V4OLu6tWrsXz58irHp06diujoaN3XGo0GJ0+exNq1a+Ho6IhJkybBwcGh0j4PQggoFApotdq7Hq+OSQr/yZMn4eHhgRYtWkAIARsbG4waNQo+Pj7w8fGBr68vXnvtNd359vb2AIzb3o6ISLoaDOGMHTsWarW6yvF/7hzXvHlz+Pr66vaJHjBgAJKTkyttA5qbmwtXV1e0bNkSubm5uuN5eXlwdXWtNheTjPHHxcXpxvB/+eUXeHh44Pz583j55Zfh5+fHbeSIyDLV4OKus7Mz3N3dqzz+Wfj79++PQ4cOIT8/HxqNBgcPHsSQIUNw7tw5ZGZmQqPRIDExEX5+fnBzc4O9vT1SU1MBAAkJCfDz86s2bZP0+MePH4/XX38d69atQ8uWLbF06VJcu3YNISEhUCqV6NWrF4qKinDr1i1TpENEVDsk3MDVpUsXjBs3DqNGjUJpaSl69+6NkSNH4uGHH0Z0dDSKi4vh7++PIUOGAAAWL16MOXPmoLCwEJ06dcKYMWOqbaPebcTC1TnLcXXOv3F1zr9xdc6/1cbqnMWnd+t9rv1jA41ur7bwzl0iIkNZ6J27LPxERAYSwjKvTbLwExEZiou0mQdlg9ofJ7ZrIO9jspE0rm2nsLz/tYWaYilxixo0lBK3FPIujxU2kDOVuQHkfL8pNLZS4gJAYa6DlLgtaiMIh3qIiKwMe/xERFZGI2n6lWQs/EREhuJQDxGRleFQDxGRlWGP/+6ysrIwZMgQtG/fvtLxVatWoVWrVlXOX7ZsGQBUWq2OiMgssfDfm6urKxISEkzRFBGRyQgLvbhbZztw5eXlYfLkyQgPD0dERASOHDmiey4tLQ1PP/00QkJCsHr16rpKkYjo/iTtuSubSXr8OTk5CAsL032tUqmQnp6OiIgIBAYGIicnB6NGjcLWrVsBlK81vWHDBmi1WoSHh+OJJ56At7e3KVIlItIfh3ru7W5DPT4+Pvjjjz/w0UcfAQDKyspw8eJFAEBwcDAcHR0BlK9Nffz4cRZ+IjI/ZtaT11edzerRarVYvXo1GjduDKD8r4JmzZphz549UCqVlc6782siIrNhoT3+Ohvj79WrFzZs2AAA+O2336BSqXD79m0AQEpKCkpKSnDjxg3s378fvXr1qqs0iYjujWP8NTNnzhzExMRApVIBABYtWgQnp/INM1q3bo0RI0aguLgYEyZMqDIVlIjILJTJ2dhGtnq3A1cjx7a1HlPm6pxNHB6QEtfVTs7OXk429lLiAkCJVs4PUXvbxlLiPiLkrBoJAM01clbndNbK+XFvLmlnLwB42OW6lLjtT6cYHeN24r/1Prdh6CtGt1dbOHhORGQoCx3jZ+EnIjKUmY3d64uFn4jIUOzxmwdH29ofg7a3kbe7UHM7ZylxGzaQk3NbGznXJABAK2dzKLSGnZS4D0u8W7+NuC0lrm0DOYWq9cM3pMQFAFtHM97Xlj1+IiIrY6Gzelj4iYgMZaGTIln4iYgMxTF+IiIrw8JPRGRleHGXiMjKaMx4xtF9SF+kLSsrC56enoiJial0PCMjA56enoiPj5edAhGRHFqt/g8zYpIef+PGjXHw4EFoNBrY2JRP1k5KSkLTpk1N0TwRkRxmVtD1ZZJlmRs1agRvb2+cOHFCd+zw4cN48sknAQDr1q3D008/jdDQUKjVavzxxx8AgICAAEybNg2DBw/GlStXTJEqEZH+LHRZZpOtxx8UFISUlPLV8NLS0uDp6QlbW1sUFhZiz549WLt2LRITE9GvXz+sX79e9zo/Pz+kpKSgWbNmpkqViEgvQiv0fpgTkxX+gIAAfP/999Bqtdi5cyeCgoIAAE5OTliyZAl27NiBJUuWYN++fbh165budV26dDFVikRENWOhY/wmK/yNGjWCl5cXUlNTcezYMd0wz+XLl/HMM8+goKAAfn5+UKvVuHOLAHt7eeu/ExEZRaPR/1EDUVFRCAkJQVhYGMLCwnDq1CkcOXIEKpUKgwYNQmxsrO7cjIwMhIeHY/DgwZg9ezbK9FhGwqRbLwYFBWHJkiV47LHHdPvoOjo6ok2bNnj22WfRuXNn7NmzBxoLnSJFRFZGQo9fCIHz588jISFB9/D09MSsWbOwYsUKJCUl4fTp0zhw4AAAYMaMGYiJiUFKSgqEENi0aVO1bZi08Pfv3x8ZGRkIDg7WHbO1tYVWq0VwcDDUajXatWuHrKwsU6ZFRGQYCYW/YnLL888/j6FDh2LdunVIS0tDmzZt4OHhAaVSCZVKheTkZFy6dAlFRUXo2rUrACA8PBzJycnVtiF9Oqe7uzu+++47AOXDPadOndI9t2DBAgBAZGTkXV9b8ToiIrNUg0Xa8vPzkZ+fX+W4s7MznJ2dK53n6+uLt956C6WlpRgzZgzGjRsHFxcX3Tmurq7Izs5GTk5OpeMuLi7Izs6uNhfeuUtEZKga9ORXr16N5cuXVzk+depUREdH677u1q0bunXrpvv6qaeewkcffYQePXrojgkhoFAooNVqoVAoqhyvDgs/EZGhajBNc+zYsVCr1VWO39nbB4CTJ0+itLQUvr6+AMqLuZubG3Jzc3Xn5ObmwtXVFS1btqx0PC8vD66urtXmUu8Kv5CwPnaZVt7F5luaYilxbRRyLt/InJRWIukmlwZ69IAMoYGcuABQopXz/09W3NLbkrZPA+Dcw0FabKPVYCLKP4d07qWgoAAfffQRNm7ciNLSUmzZsgVz587FtGnTkJmZCXd3dyQmJiIiIgJubm6wt7dHamoqevTogYSEBPj5+VXbRr0r/EREpiIkzM/v378/Tp06hWHDhkGr1WLUqFHo1q0bFixYgOjoaBQXF8Pf3x9DhgwBACxevBhz5sxBYWEhOnXqhDFjxlTbhkLI6CLXoebOHWo9prKBvN5Mc/sHpcR9QNlQSlxvW3l3UMvq8T+skPNZdCiR1+N3l/SXoCxtW12XFrtZHzn7Rz+wPMnoGDffrb7IVmg0e43R7dUW9viJiAxlZmvw6IuFn4jIUGa2Bo++WPiJiAxVZpmrDLDwExEZykKHeky6ZAMAnD17Fp6enrolmomILJZW6P8wIyYv/HFxcRgyZAi++eYbUzdNRFSrhFar98OcmLTwl5aWYvv27Zg2bRrS09Nx4cIFAOVr9S9atAjDhg3DsGHD8PPPPwMoX5p06tSpGDx4MDIyMkyZKhFR9djjr96BAwfQunVrtGvXDgMGDKjU63d0dMTWrVvx0ksv4Y033tAdrxgW8vb2NmWqRETVY+GvXlxcHEJDQwEAwcHBiI+PR0lJCQBg+PDhAMp7/9nZ2bh69SoA4PHHHzdlikRE+pO0EYtsJpvVc+XKFRw8eBDp6elYs2YNhBDIz8/H7t27yxNR/p2KVquFjU353bIODma8TgcRWTVz20tXXyYr/AkJCejVqxc+//xz3bFly5Zh48aNAIAdO3YgKioKu3fvRvv27fHgg3KWMiAiqjUs/Pe3ZcsWTJ8+vdKx0aNH4/PPP4eTkxN+/PFHbN68GQ0bNtRt0EJEZNbMbLaOvkxW+Ldv317lWNOmTXHq1CkEBATg1Vdfhbu7e6Xn165da6r0iIhqjj1+IiIrw8JvOO6tS0SWSGg41GMWSjRltR5TI3Ecz66hnP8FDSTtDlUqcW0SO0m7hjWQ1Cmzgbzenl0DOZ+znY2caYWNXEqkxAUARUMnabGNxh4/EZF14XROIiJrw8JPRGRlLHOIn4WfiMhQoswyKz8LPxGRoSyz7rPwExEZylIv7kpbnTMrKwuenp6IiYmpdDwjIwOenp6Ij4+X1TQRkWloa/AwI1J7/I0bN8bBgweh0Wh0q20mJSWhadOmMpslIjIJ9vjvolGjRvD29saJEyd0xw4fPownn3wSALBu3To8/fTTCA0NhVqtxh9//IGjR49ixIgRuvPj4+Px9ttvy0yTiMgwFtrjl74RS1BQkG5j9bS0NHh6esLW1haFhYXYs2cP1q5di8TERPTr1w/r169Hr169kJubq9uWcevWrQgPD5edJhFRjYky/R/mRHrhDwgIwPfffw+tVoudO3ciKCgIAODk5IQlS5Zgx44dWLJkCfbt24dbt25BoVBArVZj27Zt+PPPP3HlyhV06dJFdppERDUmtPo/zIn0wt+oUSN4eXkhNTUVx44d0w3zXL58Gc888wwKCgrg5+cHtVoNIcrHy9RqNXbs2IHExESEhYXJTpGIyDAc6rm3oKAgLFmyBI899phui0VHR0e0adMGzz77LDp37ow9e/ZA8//3pXRzc0PLli2xceNGFn4iMlvs8d9H//79kZGRgeDgYN0xW1tbaLVaBAcHQ61Wo127dsjKytI9HxwcjPbt26NFixamSJGIqMYstfBLm87p7u6uW2e/UaNGOHXqlO65iq0VIyMj7/rasrIyHD16FE8//bSs9IiIjCY0cpY/l80kPf6aEEKgb9++UCgUGDBgQF2nQ0R0T+zx1xKFQoGjR4/WdRpERNUSWsvs8Ztd4TeWo6197cdUOtR6zApKhY2UuK2VD0iJ66SQ9y3TVNK346Olcn44H7O/ISUuALTqkC8lro2TnM/C9mF5d+Pb+PSQFttYsnvyCxcuxLVr17BgwQIcOXIE77//PoqLixEUFITp06cDKF8GZ/bs2bh58yZ69uyJuXPn6ibR3IvZDfUQEVkKIRR6P2rq6NGj2LJlCwCgqKgIs2bNwooVK5CUlITTp0/jwIEDAIAZM2YgJiYGKSkpEEJg06ZN1cZm4SciMpCsMf7r168jNjYWEydOBFC+6kGbNm3g4eEBpVIJlUqF5ORkXLp0CUVFRejatSsAIDw8HMnJydXGr3dDPUREpqKtwaye/Px85OdXHcJzdnaGs7NzpWMxMTGYPn06Ll++DADIycmBi4uL7nlXV1dkZ2dXOe7i4oLs7Oxqc2HhJyIyUE0u7q5evRrLly+vcnzq1KmIjo7Wff3tt9+iVatW8PX11S1fr9VqoVD83ZYQAgqF4p7Hq8PCT0RkoJoU/rFjx0KtVlc5/s/eflJSEnJzcxEWFoYbN27g1q1buHTpkm5pewDIzc2Fq6srWrZsidzcXN3xvLw8uLq6VpuLXoW/qKgIu3btwtWrV3Xr6QDAc889p8/LiYjqJVGD5fjvNqRzN19++aXu3/Hx8Th+/Djmzp2LQYMGITMzE+7u7khMTERERATc3Nxgb2+P1NRU9OjRAwkJCfDz86u2Db0K/6uvvorLly+jQ4cOev0ZcT8Vb6Ti7l0iIktlqnn89vb2WLBgAaKjo1FcXAx/f38MGTIEALB48WLMmTMHhYWF6NSpE8aMGVNtPL0K/9mzZ5GSkoIGDTgJiIiogiHTNGsiPDxctx+Jr68vtm3bVuUcLy8vbN68uUZx9arkzZo1Q1lZ7e4kEBUVhR9++AFA+f68AQEBAICZM2di/vz5GDlyJAICAhAXF1er7RIR1RaNRqH3w5zct8dfMdbk4uKCqKgoBAYGwtbWVve8rDH+v/76Cxs2bMDZs2cxZswYRERESGmHiMgYsnv8sty38J89exZA+W5ZTk5OOHfunEmS6t27NxQKBTp06IDr16+bpE0iopqql2v1vP/++wCAPXv2VFkpc+vWrXo3cvLkSXh4eKBFixYQQuimJVXMEPrnMJK9ffl6O8ZeSCYikqkms3rMyX0L/3fffYeysjIsWrQIQohKhXrZsmUYNmyYXo3ExcXhsccew+jRo/HLL7/Aw8MDBQUF+O2339CrVy/s2bPH6DdCRGRq9bLHn5GRgWPHjuHKlStYs2bN3y9SKvHss8/q3cj48ePx+uuvY926dWjZsiWWLl2KzMxMzJw5E3FxcQgMDDT4DRAR1RWN1jJnOiqEqP6PlfXr12P06NGmyMdoLRt713pMmcsyN7er/oYOQ7SxbSwlbnNF7S97XUHWsswdS+T8cD7OZZl1bB9uIiUuIG9Z5oZPzTE6Rlpbld7nPn5+u9Ht1Ra9ftLUajW2bNmCGzdu8M5dIqL/T1sfZ/VUmDlzJrKysmrlzl0iovqiXk7nrHDmzBkkJSVVu6sLEZE1qZezeiq0bNlSdh61pqGNXa3HfFDpWOsxKzS2aSglrqOkLRJljcMDwENlcsbiPRsUSonr4lEgJS4A2HvLufbToIWcLRIbPN5NSlwAUPoMlRbbWPV6qKdDhw4YM2YM+vbtCweHvy90coyfiKyZpc7q0avw37x5E23atMGFCxdk50NEZDEsdKRHv8JfcQfvpUuXUFZWhjZt2khNiojIEtTroZ7MzExMnjwZOTk50Gq1aNKkCT755BO0b99edn5ERGbLUmf16DVA9c4772DcuHE4ceIEUlNTMWnSJMydO1d2bkREZk1bg4c50avwX7lypdJekREREbh27ZrRjcfHx2PmzJn3fH7mzJm6zYaJiMyNgELvhznRa6hHo9Hg+vXraNy4MQDg6tWrMnMiIrIIZfV5qCcyMhLPPPMMli5dig8//BAjR47EyJEjay2Je+3GRURkzup1j/+ZZ55Bo0aNsGvXLgDAmDFj8MQTT0hNjIjI3Jnb2L2+9Cr8CxYswLp16+Dk5AQAOH78OJYvX46jR49KTY6IyJyZW09eX3oV/l27duHgwYNo0sT4pVdruhsXEZG5stQev15j/G3btoWzc+2sHRIXF6fbcatiN64mTZrgt99+AwDuxkVEFkMDhd4Pc6JXjz8qKgqRkZHw8fGptELn1KlTa9wgd+MiovrCQnde1K/wf/rpp3ByckJBgfGrEbZr1w7ffvttpWOPP/44kpKSdF9X/EJZsGCB0e0REcmiNbOevL70Kvy3b9/G119/LTsXIiKLYqmLtOk1xt+uXTucOXNGdi5ERBbFUpds0KvHf/nyZTz11FNwc3ODnd3fG51s324+mwcTEZma1kK3otWr8L/yyiuy86Ba5ggbKXHtJd6iXiIpdGGJrZS4ZcXyNuFQNJDzYSicGkmJCwd5u9SJm9flBG5ufAiN8SHqhF6Fn3fpEhFVVa9n9RARUVX1elYPERFVZamzelj4iYgMZKlDPZa5RTwRkRmQNZ3zww8/RHBwMEJCQvDll18CAI4cOQKVSoVBgwYhNjZWd25GRgbCw8MxePBgzJ49W6/1zkxS+M+ePQtPT0+kpKSYojkiIpPQKPR/6Ov48eM4duwYtm3bhri4OKxduxZnzpzBrFmzsGLFCiQlJeH06dM4cOAAAGDGjBmIiYlBSkoKhBDYtGlTtW2YpPDHxcVhyJAh+Oabb0zRHBGRScjo8T/xxBNYs2YNlEolrly5Ao1Gg/z8fLRp0wYeHh5QKpVQqVRITk7GpUuXUFRUhK5duwIAwsPDkZycXG0b0gt/aWkptm/fjmnTpiE9PR0XLlwAAAQEBCArKwsA8MMPPyAqKgpA+V8H4eHhCAsLw7x58zBw4EDZKRIRGaQmhT8/Px9ZWVlVHvn5+VXi2tra4qOPPkJISAh8fX2Rk5MDFxcX3fOurq7Izs6uctzFxQXZ2dnV5i298B84cACtW7dGu3btMGDAgGp7/TNnzsTLL7+MhIQEeHh4QKOx1FskiKi+Ewr9H6tXr0ZgYGCVx+rVq+8a+6WXXsLRo0dx+fJlnD9/Hoo77hIWQkChUECr1d71eHWkz+qJi4tDaGgoACA4OBivvfYaXn755buee/36dVy6dAn+/v4AgIiICKxZs0Z2ikREBqnJEM7YsWOhVqurHP/nXie///47SkpK4O3tjYYNG2LQoEFITk7WbVoFALm5uXB1dUXLli2Rm5urO56XlwdXV9dqc5Fa+K9cuYKDBw8iPT0da9asgRAC+fn52L17N4Cqu27Z2NjojhERmbuajEc4OzvrtaFVVlYWPvroI92KyHv37sWIESOwaNEiZGZmwt3dHYmJiYiIiICbmxvs7e2RmpqKHj16ICEhAX5+ftW2IbXwJyQkoFevXvj88891x5YtW4aNGzfqdt3y8PDA3r17AQAPPPAAPDw8cODAAfj7+3MROCIyazLm8fv7+yMtLQ3Dhg2DjY0NBg0ahJCQEDRt2hTR0dEoLi6Gv78/hgwZAgBYvHgx5syZg8LCQnTq1Aljxoyptg2FkNjFVqlUmD59OgICAnTHrl69iv79++P111/Hl19+iQcffBB9+vTBjz/+iLVr1+L333/HrFmzUFJSAk9PT6SlpVXapKU67Zp1qfX30djWqdZjVnCxfUBK3PY2tbNV5j+1EnIWPAOAJpLuhulcUiwl7iNtr0iJCwCNe8v5nmvQzl1KXEXHrlLiAoDNIz2lxLVr093oGLEPRep97vQL64xur7ZI7fHfrcfetGlTnDp1CgAwevToKs/v3LkTy5Ytg6urK3bt2oWbN2/KTJGIyGDmts6+vsxuyYbWrVvj+eefh1KphLOzM9599926TomI6K4s9Yqk2RX+8PBwhIeH13UaRETVstS1esyu8BMRWQpLvcuo3hX+25qSWo9pb1P7MSvc1MqJnaW4JSVuaQMHKXEBoNBGzoXjB5X2UuLaXWgiJW65a3LCHpazd/aDPhekxAUA4ZkmJa7dFOMv7motdLCn3hV+IiJT4cVdIiIrY5n9fRZ+IiKDscdPRGRlyhSW2edn4SciMpBlln0TFP7k5GR8+umnKCsrgxACYWFhGDdunOxmiYik41DPXWRnZ2PhwoWIj49HkyZNcPPmTURFRaFdu3YIDAyU2TQRkXScznkX165dQ2lpKYqKigAAjRo1woIFC2Bvb4+0tDS8//77KCoqQpMmTTB37lx4eHggKioKXl5eOHnyJIqLizFr1iz06dNHZppERAaxzLIvufB7eXkhMDAQAwYMgLe3N3x8fKBSqdCqVStER0dj1apVaN26NQ4ePIi33noLX331FQCgsLAQW7ZsQUZGBl588UV89913sLOzk5kqEVGNcajnHubOnYvJkyfj0KFDOHToEIYPH47x48fj4sWLmDRpku68wsJC3b+HDx8OAPD29oaLiwt++eUXdO7cWXaqREQ1orHQPr/Uwr9//37cunULwcHBiIiIQEREBDZt2oTt27fD3d0dCQkJAACNRoO8vDzd6+7cYkyr1UKp5OQjIjI/ltrjl7rZuoODA5YsWYKsrCwA5VstZmRkoGvXrrhx4wZOnjwJoHxf3tdee033uoqNV3766Sfk5+ejQ4cOMtMkIjKIqMF/5kRqV7pXr16YOnUqJk6ciNLSUgBA3759ER0djYCAALz77rsoLi6Gk5MTFi5cqHvdxYsXdZsSx8bGVvoLgIjIXFhqj1/6GIparb7rzvLdunXD5s2b7/qaMWPGwMfHR3ZqRERG4XROIiIrY5ll3wwL/9q1a+s6BSIivZRZaOk3u8JPRGQpzO2irb7qXeEvLCmq9ZhCyPufa6OQM7HKVlJcmdPAihVyLpU9oGwoJW6pRk5cACg5L+eTbmgjZ7NARYPrUuICgLP2D2mxjcWLu0REVoY9fiIiK8MePxGRldFIHAaWiYWfiMhAnMdPRGRlLHWMX+paPXe6efMm5s6di4EDB2Lo0KEYNWoUjh49es/zCwoKMGXKFFOlR0RUY9oaPMyJSXr8QghMnDgR3t7e2LFjB+zs7PDzzz9j/PjxWLJkyV2XZ7hx4wYyMjJMkR4RkUEsdajHJD3+48eP488//8Sbb76p21ClY8eOmDRpElasWIGMjAw8/fTTUKlUiIyMxF9//YX58+cjJyeHvX4iMluWujqnSQr/Tz/9hMceewwKhaLS8X/961/46aef8Nprr2Hy5MnYvn07goODsXr1asyZMweurq74+OOPTZEiEVGNaYTQ+2FOTDLUo1AooNFUvWOwtLQUWq0Wubm56N+/PwBg1KhRAKBbw5+IyFxxqOc+unTpgtOnT+vW5K/wv//9D507d670l0BxcTEuXrxoirSIiIwi6+Lu8uXLERISgpCQECxatAgAcOTIEahUKgwaNAixsbG6czMyMhAeHo7Bgwdj9uzZKCsrqza+SQp/z5498cgjj+C9997TFf/Tp09j5cqVmDp1Klq0aIFDhw4BABISEvDhhx9CqVTq9QaIiOqKjDH+I0eO4NChQ9iyZQu2bt2K9PR0JCYmYtasWVixYgWSkpJw+vRpHDhwAAAwY8YMxMTEICUlBUIIbNq0qdo2TDadc/ny5bCzs0NoaCiCg4Px7rvv4oMPPoCPjw8++OADfPzxxwgLC0NSUhJef/11NGvWDK1bt0ZUVJSpUiQiqhEthN4Pfbm4uGDmzJmws7ODra0t2rdvj/Pnz6NNmzbw8PCAUqmESqVCcnIyLl26hKKiInTt2hUAEB4ejuTk5GrbMNkNXA4ODnjzzTfx5ptvVnnO09MTX3/9dZXjGzduNEVqREQGqcnKvfn5+cjPz69y3NnZGc7OzrqvH330Ud2/z58/j507dyIyMhIuLi66466ursjOzkZOTk6l4y4uLsjOzq42F965S0RkIE0NevKrV6/G8uXLqxyfOnUqoqOjqxz/9ddfMWHCBLz++uuwsbHB+fPndc8JIaBQKKDVaitdI604Xh0WfiIiA9VkCGfs2LF33X/8zt5+hdTUVLz00kuYNWsWQkJCcPz4ceTm5uqez83NhaurK1q2bFnpeF5eHlxdXavNhYWfiMhANRnq+eeQzr1cvnwZU6ZMQWxsLHx9fQGUz4w8d+4cMjMz4e7ujsTERERERMDNzQ329vZITU1Fjx49kJCQAD8/v2rbqHeFv6ispNZjlmnl7FoEAA5KOylx7RvYSolbpJU306rERs6OVvY2cuYwaGzspcQtJye2jaRp5w0uyJvPXlRwS0rcB2ohhox5/P/5z39QXFyMBQsW6I6NGDECCxYsQHR0NIqLi+Hv748hQ4YAABYvXow5c+agsLAQnTp1wpgxY6ptQyFk7itYB5R2brUfs4FNrces0KJRYylxm9lV37MwhJ1CXl+hiaTC72bTSEpcdyGv8Htoqh+nNYSswt8JN+UEBtC0iZzC3/50itEx+rkP0Pvc/Vl7jG6vttS7Hj8RkamY21IM+mLhJyIykKUu2cDCT0RkIBZ+IiIrY6mXSFn4iYgMZKk9fqlr9WRlZcHT0xOHDx+udDwgIIDLLhORxeNGLPdga2uLt956C4WFhbKbIiIyKY3Q6v0wJ9ILv6urK5588kksXLiwynOrVq1CcHAwVCoVFixYAI1Gg/fffx9ffPGF7pzo6Gjs3r1bdppERDUmhND7YU5MsizzzJkzcejQoUpDPt9//z2+++47xMXFYcuWLcjMzMTGjRsRFhaGxMREAEBhYSH++9//wt/f3xRpEhHViIxlmU3BJIXfyckJ8+bNqzTkc+zYMYSEhKBhw4ZQKpWIiIjA0aNH0bFjR5SUlCAzMxN79uxBQECAboN2IiJzYqlj/Cab1dOnT59KQz5abdUxr4odt4YOHYqkpCT897//xfjx402VIhFRjWjNbAhHXybbgQv4e8gnJycHvXr1wo4dO1BUVISysjLExcWhV69eAACVSoWkpCRkZmaiR48epkyRiEhv7PHroWLI54UXXkC/fv2Qn5+PiIgIlJWVoU+fPoiMjAQAtGrVCk2aNEG3bt302lSAiKgumNtsHX1xdU59YnJ1Th2uzvk3rs75N2tdnbODS0+9zz2be9Lo9moL79wlIjKQuQ3h6IuFn4jIQJZ6cbfeFX57Ze3vPNXYXs5QAQA8aCsndhMbRylxlQp5w17NG8gZ6mknaUimpaThGABoVSpnpzNZszlatb0hKTLg2FreDnjGYo+fiMjKaIT5/lK6HxZ+IiIDWercGBZ+IiIDmdtSDPpi4SciMhB7/EREVoazeoiIrAxn9dxFVlYWhgwZgvbt2wMAioqK0L17d7z66qto3ry5zKaJiKSz1CUbTLIRS0JCAhISEpCcnIzmzZvjpZdekt0sEZF0lroRi0mHehQKBaKjo9G7d2+cOXMG33//PXbu3AmNRoM+ffpgxowZUCgU+Oqrr/D111/DxsYG/fv3x4wZM0yZJhGRXix1jN+kyzIDgJ2dHdq0aYMzZ87g9OnT2Lx5M7Zu3Yrs7Gxs27YNaWlp2LBhAzZv3oxt27YhPT0dp0+fNnWaRETVYo+/BhQKBdasWYOrV68iPDwcQPn4f+vWrZGXl4f+/fvjgQceAAB89dVXdZEiEVG1OI9fTyUlJTh37hx8fHygUqnw3HPPAQDy8/NhY2ODzZs3V1qDPzs7Gw0bNoSzs5xlhomIDGVuPXl9mXSoR6vVYtmyZejSpQsiIiKQkJCAmzdvoqysDFOmTEFKSgp69uyJAwcO6I6/+uqrHOohIrOkEVq9H+ZEeo8/JycHYWFhAMoLv7e3N/7973/jwQcfxJkzZzB8+HBoNBr07dsXarUaCoUCkZGRGDFiBLRaLQYOHIgnn3xSdppERDVmqRd3690OXI0c29Z6TJnLMjezlzOE5aJ8QEpcmcsyu0paltkLcuJyWea/PdY2R1JkecsyN91ywOgYDg4P6X1uUdEFo9urLbxzl4jIQLxzl4jIyljqgAkLPxGRgTjGT0REFsHkd+4SEVHdYuEnIrIyLPxERFaGhZ+IyMqw8BMRWRkWfiIiK8PCT0RkZVj4iYisDAs/EZGVYeEnIrIyLPxERFaGhZ+IyMpYXeEvLS1Fbm4url+/XtepkJFu3bpV1ynUe9euXau1WPzZMx9WU/ivXLmCSZMmoVu3bvDz80NQUBB8fHwQExNjVAG5ffs2Fi9ejAEDBqBz587o0qULBg4ciHnz5qGgoKAW30HtuXz5MiZPnozw8HCsWLECGs3fOxxNmDDB4LgFBQWIjY3FF198gezsbIwYMQLdu3fHiy++iOzs7NpIvZLRo0cbHWPp0qUAgPz8fLz22mvw8fFB79698fbbb6OwsNDguJcuXcIrr7yCCxcu4PLly4iKikK3bt0QGRmJCxcM34mpe/fuSEpKMvj193P58mXMmDEDMTExuHjxIlQqFYKDgzFw4ECcOXPG4Lj82TM/VrMs88SJExEWFob+/fsjMTERhYWFGDp0KL744gtcunQJsbGxBsWdMmUKOnXqhPDwcLi4uAAAcnNzsXXrVqSmpuKzzz4zOOfly5ff9/mpU6caFPe5555DaGgoPD09sXz5cmg0GqxcuRJKpRLDhg3D1q1bDYo7efJktG/fHtnZ2Th+/DgmTZqEoUOHIikpCbt378aqVasMigsAnTt3RllZ+XaEQggoFArdJhgKhQIZGRkGxVWr1diyZQtmzJiBVq1aYdy4cdBqtVi3bh0yMjLw8ccfGxR31KhRCAsLg1qtxssvv4yAgACoVCp89913WLt2Lb7++muD4gYGBsLNzQ2Ojo547bXX8MgjjxgU527GjBmDQYMG4datW/jqq6/wf//3fxg0aBBSU1OxZMkSbNiwwaC4lvizV+8JKzF06NBKX6vVat2/g4KCDI57v9eGhIQYHFcIIWJjY0XXrl3Fhx9+KJYtW1blYahhw4bp/q3VasX06dPFtGnThBBChIWFGRxXpVIJIYQoKSkRTz755D3bNER6eroYOXKkSElJ0R0zJtcKFXmFhoZWeS44ONjouEIIERERUem5u7VVk7harVZs2rRJBAYGihdeeEHEx8eLCxcuiOLiYoPjClH58+zbt2+l5/7581MTlvizV99ZzVCPra0tTpw4AQA4cuQIGjUq30D9p59+goODg8FxmzZtip07d0Kr1eqOCSGwY8cONGnSxKicp02bhuDgYDRs2BBTp06t8jCUUqnEr7/+CqC8t7xw4UJcvXoVMTExlYZ9DIn7xx9/wNbWFl9++aXu+M8//wyFwriNyTt27IgvvvgCR44cwZtvvombN28aHRMo7yEmJSWhZcuW+N///qc7npaWBnt7e4Pjurq6YtOmTQCAnj174sCB8o29Dx48iMaNGxuTMhQKBZ5++mns2rULUVFR+PHHHzF58mT4+PgYFdfJyQkbN27E559/Do1Gg3379gEAUlNTjfosLPFnr96r6988pnLq1CnRr18/4evrKwICAkR6ero4c+aMUKvVIi0tzeC4f/75p5gwYYLo3r278Pf3F/7+/qJ79+5iwoQJ4tKlS0bnXVBQILZs2WJ0nDudPHlS9O/fX2zbtk137ObNm2LSpEnCy8vL4LgnTpwQgwYNEmVlZbpju3fvFn379hWpqalG5XynPXv2iKeeekoMHDjQ6FhbtmwR8+bNE8OHDxdTp04VQgjx5Zdfit69e4uTJ08aHDcnJ0c8++yzwtfXV6jVauHl5SV69uwpQkNDxblz5wyOWxt/5dxLVlaWmDFjhnj11VfFhQsXxMiRI4WPj4/w9/c36mfknz97p0+ftoifvfrMasb4K1y9ehVNmzat9bhlZWW4du0atFotmjVrBqXS/LczLikpgZ2dXaVjGRkZ8Pb2rtU2lEolGjSo3T8u8/LysH//fjz11FO1GhcACgsL4ejoWCs5X7t2DRcvXkRZWRlcXFzg4eFhVDxZ37+maI8/e+bDaoZ6Ktz5jRcZGWl0vIoLXlqtFuvXr8ekSZMwatQofPbZZ7qLkcbGLikpwdKlSxEeHo7hw4fj008/NSr2nRfp7oz72Wef4dFHH63VfCMjI/Gf//yn1j+L8ePHY9OmTbX2WdyZ8/PPP290zhVxGzVqhL1792L+/Pl49dVXjf6+SE5OrpJvbXxP3JnznbEnTpxodM4VcZ2cnKp8vxkTt6CgAEuWLEFeXh7s7e2xdOlSDBs2DG+88QauXr1qcFxrYDU9/sDAwCrHsrOz0aJFCwDA3r17DYpbMStk3rx5yM/Px/PPPw8hBNavXw8AePfddw3OWVZsS4triTlbWlxLzHncuHHo2LEjxo8fj7lz58Ld3R2hoaHYu3cvjh8/js8//9yguFahLsaX6sK+fftEUFCQ2LFjh8jKyhIXL14UwcHBIisrS2RlZRkct2L2hkqlEhqNRndcq9WKwYMHG5WzrNiWFldmbMaVH1tW3DtnC905U0gI42ZOWQOrGerp168f1q5di23btmHLli1o3bo17Ozs4ObmBjc3N4Pj3rhxA6dOnYKbm1ulG3P+/PNP2NraGpWzrNiWFtcSc7a0uJaYs7OzMw4fPgwA8Pb2xs8//wwA+OWXX4yaLWQV6vo3T11Ys2aNiIyMNLqHJIQQy5YtExMmTBC9e/cW48ePF0IIsXnzZuHj4yN27dpllrEtLa4l5mxpcS0x599//10MHjxYqNVq8eKLL4ouXbqIsLAw0bdvX3Hq1CmD41oDqxnj/6dff/0VKSkpRs2H/6fbt2+jYcOG+Ouvv2BnZ1erMxhkxba0uDJjM6782DLipqen48KFC7qZU926dTPqvgNrYDXznrRaLdasWYO9e/ciNzcXtra2eOihh7Bjxw6EhITUetyQkBAEBwdLydnY2JYW1xJztrS4lpjzveJevXrV6M+ivrOaHv97772H0tJS9OvXDykpKfDy8oKrqyvWrVsHX19fTJkyxaziWmLO/CwsN64l5izzs6j36nakyXQq1pERQgiNRiNGjBghhBCiuLjYqLF+WXFlxra0uDJjM6782JYW1xpYzawejUaDK1euAChfn6WoqAhA+RrhxtzpJyuuJebMz8Jy41pizjI/i3qvrn/zmEpcXJzw8/MTL7/8sujXr5/Yvn27OH/+vOjXr5/YvHmz2cW1xJz5WVhuXEvMWeZnUd9ZzRg/AJw7dw6//PILvLy80LZtW5SUlODWrVtGr5YoK64l5szPwnLjWmLOMj+L+sxqCv+ff/553+dbt25tVnFlxra0uDJjM6782JYW1xpYTeFXqVQ4f/48XF1d8c+3rFAoDF6rR1ZcS8yZn4XlxrXEnGV+FvWeqceW6kpBQYFQqVRGrbFuyrgyY1taXJmxGVd+bEuLaw2spvALUb4hxJw5cywmrszYlhZXZmzGlR/b0uLWd1Yz1ENEROWsZh4/ERGVY+EnIrIyLPxERFaGhZ+IyMpwQQuySHPmzEGzZs0wffp0AEBCQgJ27dqFiIgIrFy5EqWlpXBwcMAbb7yBbt26IS8vDzExMbhy5Qpyc3Ph5uaGpUuXolmzZggICMDjjz+OX375Ba+88goGDhxYx++OSC72+MkijR49GnFxcSgrKwMAbNq0CX379kVsbCw+/fRTbN26FfPmzUN0dDRu3bqFHTt2oGvXrvjmm2+wd+9eODg4ICEhQRfv0Ucfxc6dO1n0ySqwx08WydvbG+7u7ti/fz/atWuHnJwcaDQa5OTk4Nlnn9Wdp1AocOHCBYwdOxYnT57El19+ifPnz+PXX39Fly5ddOf17NmzDt4FUd1g4SeLVdHrb9u2LYYPHw6tVgtfX18sXbpUd87ly5fh6uqKDz74AGlpaYiIiICPjw/Kysoq3ebv6OhYB++AqG5wqIcs1uDBg5GRkYGUlBRERETA19cXhw8fxu+//w4AOHDgAIYOHYqioiIcOnQIY8eOxbBhw9CsWTMcOXIEGo2mjt8BUd1gj58slp2dHQYPHoy8vDw0bdoUTZs2xTvvvINXXnkFQggolUqsXLkSjRo1wpQpU7Bo0SJ8+OGHsLW1Rffu3XHhwoW6fgtEdYJLNpDFunXrFiIjIxETE4OuXbvWdTpEFoNDPWSRDh48iH79+qFv374s+kQ1xB4/EZGVYY+fiMjKsPATEVkZFn4iIivDwk9EZGVY+ImIrMz/A6uEWCqXXE3tAAAAAElFTkSuQmCC\n", 254 | "text/plain": [ 255 | "
" 256 | ] 257 | }, 258 | "metadata": {}, 259 | "output_type": "display_data" 260 | } 261 | ], 262 | "source": [ 263 | "sns.set()\n", 264 | "flights = sns.load_dataset(\"flights\")\n", 265 | "flights = flights.pivot(\"month\", \"year\", \"passengers\")\n", 266 | "ax = sns.heatmap(flights)\n", 267 | "plt.title(\"Heatmap Flight Data\")\n", 268 | "plt.show()" 269 | ] 270 | }, 271 | { 272 | "cell_type": "markdown", 273 | "metadata": {}, 274 | "source": [ 275 | "In next class we'll learn **[Cluster Map](https://github.com/milaan9/12_Python_Seaborn_Module/blob/main/019_Seaborn_Cluster_Map.ipynb)**" 276 | ] 277 | }, 278 | { 279 | "cell_type": "code", 280 | "execution_count": null, 281 | "metadata": {}, 282 | "outputs": [], 283 | "source": [] 284 | } 285 | ], 286 | "metadata": { 287 | "hide_input": false, 288 | "kernelspec": { 289 | "display_name": "Python 3", 290 | "language": "python", 291 | "name": "python3" 292 | }, 293 | "language_info": { 294 | "codemirror_mode": { 295 | "name": "ipython", 296 | "version": 3 297 | }, 298 | "file_extension": ".py", 299 | "mimetype": "text/x-python", 300 | "name": "python", 301 | "nbconvert_exporter": "python", 302 | "pygments_lexer": "ipython3", 303 | "version": "3.8.8" 304 | }, 305 | "toc": { 306 | "base_numbering": 1, 307 | "nav_menu": {}, 308 | "number_sections": true, 309 | "sideBar": true, 310 | "skip_h1_title": false, 311 | "title_cell": "Table of Contents", 312 | "title_sidebar": "Contents", 313 | "toc_cell": false, 314 | "toc_position": {}, 315 | "toc_section_display": true, 316 | "toc_window_display": false 317 | }, 318 | "varInspector": { 319 | "cols": { 320 | "lenName": 16, 321 | "lenType": 16, 322 | "lenVar": 40 323 | }, 324 | "kernels_config": { 325 | "python": { 326 | "delete_cmd_postfix": "", 327 | "delete_cmd_prefix": "del ", 328 | "library": "var_list.py", 329 | "varRefreshCmd": "print(var_dic_list())" 330 | }, 331 | "r": { 332 | "delete_cmd_postfix": ") ", 333 | "delete_cmd_prefix": "rm(", 334 | "library": "var_list.r", 335 | "varRefreshCmd": "cat(var_dic_list()) " 336 | } 337 | }, 338 | "types_to_exclude": [ 339 | "module", 340 | "function", 341 | "builtin_function_or_method", 342 | "instance", 343 | "_Feature" 344 | ], 345 | "window_display": false 346 | } 347 | }, 348 | "nbformat": 4, 349 | "nbformat_minor": 2 350 | } 351 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2021 milaan9 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /Python Seaborn Statistical Data Visualization.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/milaan9/12_Python_Seaborn_Module/92d2863a362f089e14b009fe28114f014542cfc8/Python Seaborn Statistical Data Visualization.pdf -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 |

2 | Last Commit 3 | 4 | 5 | 6 | 7 | Stars Badge 8 | Forks Badge 9 | Size 10 | Pull Requests Badge 11 | Issues Badge 12 | Language 13 | MIT License 14 |

15 | 16 | 17 |

18 | binder 19 | colab 20 |

21 | 22 | # 12_Python_Seaborn_Module 23 | 24 | 25 | ## Introduction 👋 26 | 27 | From the website, “Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informational statistical graphs.” 28 | 29 | Seaborn excels at doing Exploratory Data Analysis (EDA) which is an important early step in any data analysis project. Seaborn uses a “dataset-oriented” API that offers a consistent way to create multiple visualizations that show the relationships between many variables. In practice, Seaborn works best when using Pandas dataframes and when the data is in tidy format. 30 | 31 | ## What’s New? 32 | In my opinion the most interesting new plot is the [relationship](https://seaborn.pydata.org/generated/seaborn.relplot.html#seaborn.relplot) plot or `relplot()` function which allows you to plot with the new `scatterplot()` and `lineplot()` on data-aware grids. Prior to this release, scatter plots were shoe-horned into seaborn by using the base matplotlib function `plt.scatter` and were not particularly powerful. The `lineplot()` is replacing the `tsplot()` function which was not as useful as it could be. These two changes open up a lot of new possibilities for the types of EDA that are very common in Data Science/Analysis projects. 33 | 34 | The other useful update is a brand new [introduction](https://seaborn.pydata.org/introduction.html) document which very clearly lays out what Seaborn is and how to use it. In the past, one of the biggest challenges with Seaborn was figuring out how to have the “Seaborn mindset.” This introduction goes a long way towards smoothing the transition. 35 | 36 | --- 37 | 38 | ## Table of contents 📋 39 | 40 | | **No.** | **Name** | 41 | | ------- | -------- | 42 | | 01 | **[Seaborn_Loading_Dataset](https://github.com/milaan9/12_Python_Seaborn_Module/blob/main/001_Seaborn_Loading_Dataset.ipynb)** | 43 | | 02 | **[Seaborn_Controlling_Aesthetics](https://github.com/milaan9/12_Python_Seaborn_Module/blob/main/002_Seaborn_Controlling_Aesthetics.ipynb)** | 44 | | 03 | **[Seaborn_Matplotlib_vs_Seaborn](https://github.com/milaan9/12_Python_Seaborn_Module/blob/main/003_Seaborn_Matplotlib_vs_Seaborn.ipynb)** | 45 | | 04 | **[Seaborn_Color_Palettes](https://github.com/milaan9/12_Python_Seaborn_Module/blob/main/004_Seaborn_Color_Palettes.ipynb)** | 46 | | 05 | **[Seaborn_LM Plot_&_Reg_Plot](https://github.com/milaan9/12_Python_Seaborn_Module/blob/main/005_Seaborn_LM%20Plot_%26_Reg_Plot.ipynb)** | 47 | | 06 | **[Seaborn_Scatter_Plot_&_Joint_Plot](https://github.com/milaan9/12_Python_Seaborn_Module/blob/main/006_Seaborn_Scatter_Plot_%26_Joint_Plot.ipynb)** | 48 | | 07 | **[Seaborn_Additional_Regression_Plots](https://github.com/milaan9/12_Python_Seaborn_Module/blob/main/007_Seaborn_Additional_Regression_Plots.ipynb)** | 49 | | 08 | **[Seaborn_Categorical_Data_Plot](https://github.com/milaan9/12_Python_Seaborn_Module/blob/main/008_Seaborn_Categorical_Data_Plot.ipynb)** | 50 | | 09 | **[Seaborn_Dist_Plot](https://github.com/milaan9/12_Python_Seaborn_Module/blob/main/009_Seaborn_Dist_Plot.ipynb)** | 51 | | 10 | **[Seaborn_Strip_Plot](https://github.com/milaan9/12_Python_Seaborn_Module/blob/main/010_Seaborn_Strip_Plot.ipynb)** | 52 | | 11 | **[Seaborn_Box_Plot](https://github.com/milaan9/12_Python_Seaborn_Module/blob/main/011_Seaborn_Box_Plot.ipynb)** | 53 | | 12 | **[Seaborn_Violin_Plot](https://github.com/milaan9/12_Python_Seaborn_Module/blob/main/012_Seaborn_Violin_Plot.ipynb)** | 54 | | 13 | **[Seaborn_Bar_Plot_and_Count_Plot](https://github.com/milaan9/12_Python_Seaborn_Module/blob/main/013_Seaborn_Bar_Plot_and_Count_Plot.ipynb)** | 55 | | 14 | **[Seaborn_TimeSeries_and_LetterValue_Plot](XXX)** | 56 | | 15 | **[Seaborn_Factor_Plot](https://github.com/milaan9/12_Python_Seaborn_Module/blob/main/015_Seaborn_Factor_Plot.ipynb)** | 57 | | 16 | **[Seaborn_PairGrid_Plot](https://github.com/milaan9/12_Python_Seaborn_Module/blob/main/016_Seaborn_PairGrid_Plot.ipynb)** | 58 | | 17 | **[Seaborn_FacetGrid_Plot](https://github.com/milaan9/12_Python_Seaborn_Module/blob/main/017_Seaborn_FacetGrid_Plot.ipynb)** | 59 | | 18 | **[Seaborn_Heat_Map](https://github.com/milaan9/12_Python_Seaborn_Module/blob/main/018_Seaborn_Heat_Map.ipynb)** | 60 | | 19 | **[Seaborn_Cluster_Map](https://github.com/milaan9/12_Python_Seaborn_Module/blob/main/019_Seaborn_Cluster_Map.ipynb)** | 61 | | | **[datasets](https://github.com/milaan9/12_Python_Seaborn_Module/tree/main/datasets)** | 62 | | 11 | **[Python Seaborn Statistical Data Visualization.pdf](https://github.com/milaan9/12_Python_Seaborn_Module/blob/main/Python%20Seaborn%20Statistical%20Data%20Visualization.pdf)** | 63 | 64 | These are online **read-only** versions. However you can **`Run ▶`** all the codes **online** by clicking here ➞ binder 65 | 66 | 67 | --- 68 | 69 | ## Install Seaborn Module: 70 | 71 | Open your [![Anaconda](https://img.shields.io/badge/Anaconda-342B029.svg?&style=flate&logo=anaconda&logoColor=white)](https://www.anaconda.com/products/individual) Prompt propmt and type and run the following command (individually): 72 | 73 | - pip install seaborn 74 | 75 | 76 | Once Installed now we can import it inside our python code. 77 | 78 | --- 79 | 80 | ## Frequently asked questions ❔ 81 | 82 | ### How can I thank you for writing and sharing this tutorial? 🌷 83 | 84 | You can Star Badge and Fork Badge Starring and Forking is free for you, but it tells me and other people that it was helpful and you like this tutorial. 85 | 86 | Go [**`here`**](https://github.com/milaan9/12_Python_Seaborn_Module) if you aren't here already and click ➞ **`✰ Star`** and **`ⵖ Fork`** button in the top right corner. You will be asked to create a GitHub account if you don't already have one. 87 | 88 | --- 89 | 90 | ### How can I read this tutorial without an Internet connection? GIF 91 | 92 | 1. Go [**`here`**](https://github.com/milaan9/12_Python_Seaborn_Module) and click the big green ➞ **`Code`** button in the top right of the page, then click ➞ [**`Download ZIP`**](https://github.com/milaan9/12_Python_Seaborn_Module/archive/refs/heads/main.zip). 93 | 94 | ![Download ZIP](img/dnld_rep.png) 95 | 96 | 2. Extract the ZIP and open it. Unfortunately I don't have any more specific instructions because how exactly this is done depends on which operating system you run. 97 | 98 | 3. Launch ipython notebook from the folder which contains the notebooks. Open each one of them 99 | 100 | **`Kernel > Restart & Clear Output`** 101 | 102 | This will clear all the outputs and now you can understand each statement and learn interactively. 103 | 104 | If you have git and you know how to use it, you can also clone the repository instead of downloading a zip and extracting it. An advantage with doing it this way is that you don't need to download the whole tutorial again to get the latest version of it, all you need to do is to pull with git and run ipython notebook again. 105 | 106 | --- 107 | 108 | ## Authors ✍️ 109 | 110 | I'm Dr. Milaan Parmar and I have written this tutorial. If you think you can add/correct/edit and enhance this tutorial you are most welcome🙏 111 | 112 | See [github's contributors page](https://github.com/milaan9/12_Python_Seaborn_Module/graphs/contributors) for details. 113 | 114 | If you have trouble with this tutorial please tell me about it by [Create an issue on GitHub](https://github.com/milaan9/12_Python_Seaborn_Module/issues/new). and I'll make this tutorial better. This is probably the best choice if you had trouble following the tutorial, and something in it should be explained better. You will be asked to create a GitHub account if you don't already have one. 115 | 116 | If you like this tutorial, please [give it a ⭐ star](https://github.com/milaan9/12_Python_Seaborn_Module). 117 | 118 | --- 119 | 120 | ## Licence 📜 121 | 122 | You may use this tutorial freely at your own risk. See [LICENSE](./LICENSE). 123 | -------------------------------------------------------------------------------- /datasets/Labour Data.csv: -------------------------------------------------------------------------------- 1 | Country,Annual Income,Average Family members,Birth Rate 2 | Lithuania,22949,2,10.1 3 | Latvia,22389,4,9.7 4 | Hungary,22911,4,9.8 5 | Luxembourg,62636,3,3.85 6 | United States,60154,3,3.9 7 | Switzerland,60124,4,4.3 8 | Denmark,52580,3,7.7 9 | Australia,52063,3,5.2 10 | Ireland,51681,4,8.5 11 | Belgium,49587,2,8.7 12 | Canada,48403,3,8.8 13 | Austria,48295,3,6.8 14 | Germany,46389,3,8.6 15 | France,42992,4,5.9 16 | United Kingdom,42835,4,4.9 17 | Sweden,42816,3,6.8 18 | New Zealand,39397,2,5.4 19 | Japan,39113,2,7.7 20 | Spain,37333,3,7.2 21 | Italy,35397,4,8.6 22 | Slovenia,34965,3,8.2 23 | Israel,34023,3,7.6 24 | South Korea,32399,3,8.3 25 | Estonia,28621,4,7.6 26 | Chile,28434,3,9.1 27 | Poland,25921,2,9.5 28 | Greece,25124,2,8.4 29 | Portugal,24529,4,9 30 | Czech Republic,23722,3,9.3 31 | Slovak Republic,23508,3,9.7 32 | Mexico,19311,4,13.5 33 | India,16580,5,19 34 | Pakistan,15800,5,16.3 35 | Bangladesh,12940,5,13.8 36 | Iceland,55984,3,5.4 37 | Norway,53643,3,4.5 38 | Netherlands,52833,4,6.4 39 | Finland,42127,3,5.9 40 | -------------------------------------------------------------------------------- /datasets/PoliceKillingsUS.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/milaan9/12_Python_Seaborn_Module/92d2863a362f089e14b009fe28114f014542cfc8/datasets/PoliceKillingsUS.csv -------------------------------------------------------------------------------- /datasets/Score Book.csv: -------------------------------------------------------------------------------- 1 | Student ID,Analysis,Machine Learning,Artificial Intelligence 2 | 1,2.393412671,3.324129347,0.039631096 3 | 2,3.228434178,3.109298649,5.621414891 4 | 3,6.611171616,3.603703815,4.830770027 5 | 4,4.55351888,5.030113742,4.84697594 6 | 5,4.151165323,6.555816091,4.126257144 7 | 6,9.077035502,7.478125262,3.321003992 8 | 7,7.039631096,8.201300407,2.667565198 9 | 8,3.021068686,8.306264399,1.976072963 10 | 9,5.011522874,6.622167472,1.220086585 11 | 10,2.006190799,4.272699487,0.656404386 12 | 11,3.324129347,2.393412671,0.32202138 13 | 12,3.109298649,1.228434178,2.15599814 14 | 13,3.603703815,0.611171616,0.076945636 15 | 14,5.030113742,0.30351888,0.039381467 16 | 15,6.555816091,0.151165323,0.021178522 17 | 16,7.478125262,3.077035502,8.733030097 18 | 17,8.201300407,0.039631096,0.006440428 19 | 18,8.306264399,2.021068686,0.003664553 20 | 19,6.622167472,0.011522874,3.002106869 21 | 20,4.272699487,0.006190799,0.001308056 22 | 21,2.006190799,4.272699487,0.656404386 23 | 22,3.324129347,2.393412671,0.32202138 24 | 23,4.109298649,5.228434178,2.15599814 25 | 24,3.603703815,2.611171616,0.076945636 26 | 25,6.892500004,5.85721035,6.100588246 27 | -------------------------------------------------------------------------------- /datasets/University.csv: -------------------------------------------------------------------------------- 1 | university_name,total_students,students_enrolled,gender_dominance,education_level,time 2 | Harvard,1582568,12587,Male,High School,Day and Night 3 | Oxford,1568291,54682,Female,Graduate,Night 4 | Luke,1822565,54808,Female,Graduate,Day and Night 5 | Cambridge,785269,24865,Male,Post-Graduate,Day 6 | MIT,64154651,258745,Female,High School,Day and Night 7 | Xavier's,6611852,5698,Male,High School,Day 8 | Cornell,5455131,98547,Male,Graduate,Night 9 | Harvard,785280,42530,Female,Post-Graduate,Day 10 | Oxford,2534156,22897,Male,Post-Graduate,Day and Night 11 | Luke,5425,89,Female,Ph.D,Night 12 | Cambridge,41154894,134981,Male,Graduate,Day and Night 13 | MIT,18547802,254856,Female,Graduate,Day 14 | Xavier's,8527025,21530,Female,High School,Day and Night 15 | Cornell,89652710,72080,Female,Post-Graduate,Day 16 | Harvard,23746520,43250,Male,High School,Day 17 | Oxford,24167204,21560,Female,Graduate,Day and Night 18 | Luke,1822565,54269,Male,High School,Night 19 | Cambridge,585555,56810,Male,Post-Graduate,Day and Night 20 | MIT,9856921,64730,Female,Graduate,Day 21 | Xavier's,12345678,548925,Female,High School,Night 22 | Cornell,9802,152,Male,Ph.D,Day 23 | Harvard,2525560,23541,Male,Post-Graduate,Day 24 | Oxford,1568291,85788,Female,Graduate,Night 25 | Luke,7999952,989012,Female,Diploma,Day and Night 26 | Cambridge,24573045,54256,Male,Graduate,Night 27 | MIT,1822565,78046,Female,Post-Graduate,Day 28 | Xavier's,9078202,11135,Female,Post-Graduate,Day and Night 29 | Cornell,4562082,136542,Male,Graduate,Night 30 | Harvard,1582568,18256,Male,High School,Day and Night 31 | Oxford,4560825,14305,Female,High School,Day and Night 32 | Luke,9805412,58460,Male,Diploma,Day 33 | Cambridge,8552449,49610,Male,Post-Graduate,Night 34 | MIT,4608792,34950,Female,Diploma,Day 35 | Xavier's,24587259,35142,Female,High School,Day and Night 36 | Cornell,1822565,19872,Female,Diploma,Night 37 | Harvard,1582568,14235,Male,Graduate,Day and Night 38 | Oxford,5234,320,Male,Ph.D,Day 39 | Luke,4560872,14811,Male,Diploma,Day 40 | Cambridge,9167890,87587,Female,High School,Night 41 | MIT,8552449,89046,Male,Graduate,Night 42 | Xavier's,4658904,75802,Male,High School,Day 43 | Cornell,1582560,65810,Male,Post-Graduate,Day and Night 44 | Harvard,8629,87,Male,Ph.D,Day and Night 45 | Oxford,1568291,64735,Female,Graduate,Night 46 | Luke,1582568,24585,Male,Post-Graduate,Day 47 | Cambridge,99999990,7777852,Female,Graduate,Day 48 | MIT,8552449,254013,Male,High School,Day and Night 49 | Xavier's,24592580,45267,Female,Post-Graduate,Day and Night 50 | Cornell,1822565,98052,Male,Graduate,Day 51 | -------------------------------------------------------------------------------- /datasets/anscombe.csv: -------------------------------------------------------------------------------- 1 | dataset,x,y 2 | I,10.0,8.04 3 | I,8.0,6.95 4 | I,13.0,7.58 5 | I,9.0,8.81 6 | I,11.0,8.33 7 | I,14.0,9.96 8 | I,6.0,7.24 9 | I,4.0,4.26 10 | I,12.0,10.84 11 | I,7.0,4.82 12 | I,5.0,5.68 13 | II,10.0,9.14 14 | II,8.0,8.14 15 | II,13.0,8.74 16 | II,9.0,8.77 17 | II,11.0,9.26 18 | II,14.0,8.1 19 | II,6.0,6.13 20 | II,4.0,3.1 21 | II,12.0,9.13 22 | II,7.0,7.26 23 | II,5.0,4.74 24 | III,10.0,7.46 25 | III,8.0,6.77 26 | III,13.0,12.74 27 | III,9.0,7.11 28 | III,11.0,7.81 29 | III,14.0,8.84 30 | III,6.0,6.08 31 | III,4.0,5.39 32 | III,12.0,8.15 33 | III,7.0,6.42 34 | III,5.0,5.73 35 | IV,8.0,6.58 36 | IV,8.0,5.76 37 | IV,8.0,7.71 38 | IV,8.0,8.84 39 | IV,8.0,8.47 40 | IV,8.0,7.04 41 | IV,8.0,5.25 42 | IV,19.0,12.5 43 | IV,8.0,5.56 44 | IV,8.0,7.91 45 | IV,8.0,6.89 46 | -------------------------------------------------------------------------------- /datasets/empty: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /datasets/exercise.csv: -------------------------------------------------------------------------------- 1 | ,id,diet,pulse,time,kind 2 | 0,1,low fat,85,1 min,rest 3 | 1,1,low fat,85,15 min,rest 4 | 2,1,low fat,88,30 min,rest 5 | 3,2,low fat,90,1 min,rest 6 | 4,2,low fat,92,15 min,rest 7 | 5,2,low fat,93,30 min,rest 8 | 6,3,low fat,97,1 min,rest 9 | 7,3,low fat,97,15 min,rest 10 | 8,3,low fat,94,30 min,rest 11 | 9,4,low fat,80,1 min,rest 12 | 10,4,low fat,82,15 min,rest 13 | 11,4,low fat,83,30 min,rest 14 | 12,5,low fat,91,1 min,rest 15 | 13,5,low fat,92,15 min,rest 16 | 14,5,low fat,91,30 min,rest 17 | 15,6,no fat,83,1 min,rest 18 | 16,6,no fat,83,15 min,rest 19 | 17,6,no fat,84,30 min,rest 20 | 18,7,no fat,87,1 min,rest 21 | 19,7,no fat,88,15 min,rest 22 | 20,7,no fat,90,30 min,rest 23 | 21,8,no fat,92,1 min,rest 24 | 22,8,no fat,94,15 min,rest 25 | 23,8,no fat,95,30 min,rest 26 | 24,9,no fat,97,1 min,rest 27 | 25,9,no fat,99,15 min,rest 28 | 26,9,no fat,96,30 min,rest 29 | 27,10,no fat,100,1 min,rest 30 | 28,10,no fat,97,15 min,rest 31 | 29,10,no fat,100,30 min,rest 32 | 30,11,low fat,86,1 min,walking 33 | 31,11,low fat,86,15 min,walking 34 | 32,11,low fat,84,30 min,walking 35 | 33,12,low fat,93,1 min,walking 36 | 34,12,low fat,103,15 min,walking 37 | 35,12,low fat,104,30 min,walking 38 | 36,13,low fat,90,1 min,walking 39 | 37,13,low fat,92,15 min,walking 40 | 38,13,low fat,93,30 min,walking 41 | 39,14,low fat,95,1 min,walking 42 | 40,14,low fat,96,15 min,walking 43 | 41,14,low fat,100,30 min,walking 44 | 42,15,low fat,89,1 min,walking 45 | 43,15,low fat,96,15 min,walking 46 | 44,15,low fat,95,30 min,walking 47 | 45,16,no fat,84,1 min,walking 48 | 46,16,no fat,86,15 min,walking 49 | 47,16,no fat,89,30 min,walking 50 | 48,17,no fat,103,1 min,walking 51 | 49,17,no fat,109,15 min,walking 52 | 50,17,no fat,90,30 min,walking 53 | 51,18,no fat,92,1 min,walking 54 | 52,18,no fat,96,15 min,walking 55 | 53,18,no fat,101,30 min,walking 56 | 54,19,no fat,97,1 min,walking 57 | 55,19,no fat,98,15 min,walking 58 | 56,19,no fat,100,30 min,walking 59 | 57,20,no fat,102,1 min,walking 60 | 58,20,no fat,104,15 min,walking 61 | 59,20,no fat,103,30 min,walking 62 | 60,21,low fat,93,1 min,running 63 | 61,21,low fat,98,15 min,running 64 | 62,21,low fat,110,30 min,running 65 | 63,22,low fat,98,1 min,running 66 | 64,22,low fat,104,15 min,running 67 | 65,22,low fat,112,30 min,running 68 | 66,23,low fat,98,1 min,running 69 | 67,23,low fat,105,15 min,running 70 | 68,23,low fat,99,30 min,running 71 | 69,24,low fat,87,1 min,running 72 | 70,24,low fat,132,15 min,running 73 | 71,24,low fat,120,30 min,running 74 | 72,25,low fat,94,1 min,running 75 | 73,25,low fat,110,15 min,running 76 | 74,25,low fat,116,30 min,running 77 | 75,26,no fat,95,1 min,running 78 | 76,26,no fat,126,15 min,running 79 | 77,26,no fat,143,30 min,running 80 | 78,27,no fat,100,1 min,running 81 | 79,27,no fat,126,15 min,running 82 | 80,27,no fat,140,30 min,running 83 | 81,28,no fat,103,1 min,running 84 | 82,28,no fat,124,15 min,running 85 | 83,28,no fat,140,30 min,running 86 | 84,29,no fat,94,1 min,running 87 | 85,29,no fat,135,15 min,running 88 | 86,29,no fat,130,30 min,running 89 | 87,30,no fat,99,1 min,running 90 | 88,30,no fat,111,15 min,running 91 | 89,30,no fat,150,30 min,running 92 | -------------------------------------------------------------------------------- /datasets/flights.csv: -------------------------------------------------------------------------------- 1 | year,month,passengers 2 | 1949,January,112 3 | 1949,February,118 4 | 1949,March,132 5 | 1949,April,129 6 | 1949,May,121 7 | 1949,June,135 8 | 1949,July,148 9 | 1949,August,148 10 | 1949,September,136 11 | 1949,October,119 12 | 1949,November,104 13 | 1949,December,118 14 | 1950,January,115 15 | 1950,February,126 16 | 1950,March,141 17 | 1950,April,135 18 | 1950,May,125 19 | 1950,June,149 20 | 1950,July,170 21 | 1950,August,170 22 | 1950,September,158 23 | 1950,October,133 24 | 1950,November,114 25 | 1950,December,140 26 | 1951,January,145 27 | 1951,February,150 28 | 1951,March,178 29 | 1951,April,163 30 | 1951,May,172 31 | 1951,June,178 32 | 1951,July,199 33 | 1951,August,199 34 | 1951,September,184 35 | 1951,October,162 36 | 1951,November,146 37 | 1951,December,166 38 | 1952,January,171 39 | 1952,February,180 40 | 1952,March,193 41 | 1952,April,181 42 | 1952,May,183 43 | 1952,June,218 44 | 1952,July,230 45 | 1952,August,242 46 | 1952,September,209 47 | 1952,October,191 48 | 1952,November,172 49 | 1952,December,194 50 | 1953,January,196 51 | 1953,February,196 52 | 1953,March,236 53 | 1953,April,235 54 | 1953,May,229 55 | 1953,June,243 56 | 1953,July,264 57 | 1953,August,272 58 | 1953,September,237 59 | 1953,October,211 60 | 1953,November,180 61 | 1953,December,201 62 | 1954,January,204 63 | 1954,February,188 64 | 1954,March,235 65 | 1954,April,227 66 | 1954,May,234 67 | 1954,June,264 68 | 1954,July,302 69 | 1954,August,293 70 | 1954,September,259 71 | 1954,October,229 72 | 1954,November,203 73 | 1954,December,229 74 | 1955,January,242 75 | 1955,February,233 76 | 1955,March,267 77 | 1955,April,269 78 | 1955,May,270 79 | 1955,June,315 80 | 1955,July,364 81 | 1955,August,347 82 | 1955,September,312 83 | 1955,October,274 84 | 1955,November,237 85 | 1955,December,278 86 | 1956,January,284 87 | 1956,February,277 88 | 1956,March,317 89 | 1956,April,313 90 | 1956,May,318 91 | 1956,June,374 92 | 1956,July,413 93 | 1956,August,405 94 | 1956,September,355 95 | 1956,October,306 96 | 1956,November,271 97 | 1956,December,306 98 | 1957,January,315 99 | 1957,February,301 100 | 1957,March,356 101 | 1957,April,348 102 | 1957,May,355 103 | 1957,June,422 104 | 1957,July,465 105 | 1957,August,467 106 | 1957,September,404 107 | 1957,October,347 108 | 1957,November,305 109 | 1957,December,336 110 | 1958,January,340 111 | 1958,February,318 112 | 1958,March,362 113 | 1958,April,348 114 | 1958,May,363 115 | 1958,June,435 116 | 1958,July,491 117 | 1958,August,505 118 | 1958,September,404 119 | 1958,October,359 120 | 1958,November,310 121 | 1958,December,337 122 | 1959,January,360 123 | 1959,February,342 124 | 1959,March,406 125 | 1959,April,396 126 | 1959,May,420 127 | 1959,June,472 128 | 1959,July,548 129 | 1959,August,559 130 | 1959,September,463 131 | 1959,October,407 132 | 1959,November,362 133 | 1959,December,405 134 | 1960,January,417 135 | 1960,February,391 136 | 1960,March,419 137 | 1960,April,461 138 | 1960,May,472 139 | 1960,June,535 140 | 1960,July,622 141 | 1960,August,606 142 | 1960,September,508 143 | 1960,October,461 144 | 1960,November,390 145 | 1960,December,432 146 | -------------------------------------------------------------------------------- /datasets/iris.csv: -------------------------------------------------------------------------------- 1 | sepal_length,sepal_width,petal_length,petal_width,species 2 | 5.1,3.5,1.4,0.2,setosa 3 | 4.9,3.0,1.4,0.2,setosa 4 | 4.7,3.2,1.3,0.2,setosa 5 | 4.6,3.1,1.5,0.2,setosa 6 | 5.0,3.6,1.4,0.2,setosa 7 | 5.4,3.9,1.7,0.4,setosa 8 | 4.6,3.4,1.4,0.3,setosa 9 | 5.0,3.4,1.5,0.2,setosa 10 | 4.4,2.9,1.4,0.2,setosa 11 | 4.9,3.1,1.5,0.1,setosa 12 | 5.4,3.7,1.5,0.2,setosa 13 | 4.8,3.4,1.6,0.2,setosa 14 | 4.8,3.0,1.4,0.1,setosa 15 | 4.3,3.0,1.1,0.1,setosa 16 | 5.8,4.0,1.2,0.2,setosa 17 | 5.7,4.4,1.5,0.4,setosa 18 | 5.4,3.9,1.3,0.4,setosa 19 | 5.1,3.5,1.4,0.3,setosa 20 | 5.7,3.8,1.7,0.3,setosa 21 | 5.1,3.8,1.5,0.3,setosa 22 | 5.4,3.4,1.7,0.2,setosa 23 | 5.1,3.7,1.5,0.4,setosa 24 | 4.6,3.6,1.0,0.2,setosa 25 | 5.1,3.3,1.7,0.5,setosa 26 | 4.8,3.4,1.9,0.2,setosa 27 | 5.0,3.0,1.6,0.2,setosa 28 | 5.0,3.4,1.6,0.4,setosa 29 | 5.2,3.5,1.5,0.2,setosa 30 | 5.2,3.4,1.4,0.2,setosa 31 | 4.7,3.2,1.6,0.2,setosa 32 | 4.8,3.1,1.6,0.2,setosa 33 | 5.4,3.4,1.5,0.4,setosa 34 | 5.2,4.1,1.5,0.1,setosa 35 | 5.5,4.2,1.4,0.2,setosa 36 | 4.9,3.1,1.5,0.2,setosa 37 | 5.0,3.2,1.2,0.2,setosa 38 | 5.5,3.5,1.3,0.2,setosa 39 | 4.9,3.6,1.4,0.1,setosa 40 | 4.4,3.0,1.3,0.2,setosa 41 | 5.1,3.4,1.5,0.2,setosa 42 | 5.0,3.5,1.3,0.3,setosa 43 | 4.5,2.3,1.3,0.3,setosa 44 | 4.4,3.2,1.3,0.2,setosa 45 | 5.0,3.5,1.6,0.6,setosa 46 | 5.1,3.8,1.9,0.4,setosa 47 | 4.8,3.0,1.4,0.3,setosa 48 | 5.1,3.8,1.6,0.2,setosa 49 | 4.6,3.2,1.4,0.2,setosa 50 | 5.3,3.7,1.5,0.2,setosa 51 | 5.0,3.3,1.4,0.2,setosa 52 | 7.0,3.2,4.7,1.4,versicolor 53 | 6.4,3.2,4.5,1.5,versicolor 54 | 6.9,3.1,4.9,1.5,versicolor 55 | 5.5,2.3,4.0,1.3,versicolor 56 | 6.5,2.8,4.6,1.5,versicolor 57 | 5.7,2.8,4.5,1.3,versicolor 58 | 6.3,3.3,4.7,1.6,versicolor 59 | 4.9,2.4,3.3,1.0,versicolor 60 | 6.6,2.9,4.6,1.3,versicolor 61 | 5.2,2.7,3.9,1.4,versicolor 62 | 5.0,2.0,3.5,1.0,versicolor 63 | 5.9,3.0,4.2,1.5,versicolor 64 | 6.0,2.2,4.0,1.0,versicolor 65 | 6.1,2.9,4.7,1.4,versicolor 66 | 5.6,2.9,3.6,1.3,versicolor 67 | 6.7,3.1,4.4,1.4,versicolor 68 | 5.6,3.0,4.5,1.5,versicolor 69 | 5.8,2.7,4.1,1.0,versicolor 70 | 6.2,2.2,4.5,1.5,versicolor 71 | 5.6,2.5,3.9,1.1,versicolor 72 | 5.9,3.2,4.8,1.8,versicolor 73 | 6.1,2.8,4.0,1.3,versicolor 74 | 6.3,2.5,4.9,1.5,versicolor 75 | 6.1,2.8,4.7,1.2,versicolor 76 | 6.4,2.9,4.3,1.3,versicolor 77 | 6.6,3.0,4.4,1.4,versicolor 78 | 6.8,2.8,4.8,1.4,versicolor 79 | 6.7,3.0,5.0,1.7,versicolor 80 | 6.0,2.9,4.5,1.5,versicolor 81 | 5.7,2.6,3.5,1.0,versicolor 82 | 5.5,2.4,3.8,1.1,versicolor 83 | 5.5,2.4,3.7,1.0,versicolor 84 | 5.8,2.7,3.9,1.2,versicolor 85 | 6.0,2.7,5.1,1.6,versicolor 86 | 5.4,3.0,4.5,1.5,versicolor 87 | 6.0,3.4,4.5,1.6,versicolor 88 | 6.7,3.1,4.7,1.5,versicolor 89 | 6.3,2.3,4.4,1.3,versicolor 90 | 5.6,3.0,4.1,1.3,versicolor 91 | 5.5,2.5,4.0,1.3,versicolor 92 | 5.5,2.6,4.4,1.2,versicolor 93 | 6.1,3.0,4.6,1.4,versicolor 94 | 5.8,2.6,4.0,1.2,versicolor 95 | 5.0,2.3,3.3,1.0,versicolor 96 | 5.6,2.7,4.2,1.3,versicolor 97 | 5.7,3.0,4.2,1.2,versicolor 98 | 5.7,2.9,4.2,1.3,versicolor 99 | 6.2,2.9,4.3,1.3,versicolor 100 | 5.1,2.5,3.0,1.1,versicolor 101 | 5.7,2.8,4.1,1.3,versicolor 102 | 6.3,3.3,6.0,2.5,virginica 103 | 5.8,2.7,5.1,1.9,virginica 104 | 7.1,3.0,5.9,2.1,virginica 105 | 6.3,2.9,5.6,1.8,virginica 106 | 6.5,3.0,5.8,2.2,virginica 107 | 7.6,3.0,6.6,2.1,virginica 108 | 4.9,2.5,4.5,1.7,virginica 109 | 7.3,2.9,6.3,1.8,virginica 110 | 6.7,2.5,5.8,1.8,virginica 111 | 7.2,3.6,6.1,2.5,virginica 112 | 6.5,3.2,5.1,2.0,virginica 113 | 6.4,2.7,5.3,1.9,virginica 114 | 6.8,3.0,5.5,2.1,virginica 115 | 5.7,2.5,5.0,2.0,virginica 116 | 5.8,2.8,5.1,2.4,virginica 117 | 6.4,3.2,5.3,2.3,virginica 118 | 6.5,3.0,5.5,1.8,virginica 119 | 7.7,3.8,6.7,2.2,virginica 120 | 7.7,2.6,6.9,2.3,virginica 121 | 6.0,2.2,5.0,1.5,virginica 122 | 6.9,3.2,5.7,2.3,virginica 123 | 5.6,2.8,4.9,2.0,virginica 124 | 7.7,2.8,6.7,2.0,virginica 125 | 6.3,2.7,4.9,1.8,virginica 126 | 6.7,3.3,5.7,2.1,virginica 127 | 7.2,3.2,6.0,1.8,virginica 128 | 6.2,2.8,4.8,1.8,virginica 129 | 6.1,3.0,4.9,1.8,virginica 130 | 6.4,2.8,5.6,2.1,virginica 131 | 7.2,3.0,5.8,1.6,virginica 132 | 7.4,2.8,6.1,1.9,virginica 133 | 7.9,3.8,6.4,2.0,virginica 134 | 6.4,2.8,5.6,2.2,virginica 135 | 6.3,2.8,5.1,1.5,virginica 136 | 6.1,2.6,5.6,1.4,virginica 137 | 7.7,3.0,6.1,2.3,virginica 138 | 6.3,3.4,5.6,2.4,virginica 139 | 6.4,3.1,5.5,1.8,virginica 140 | 6.0,3.0,4.8,1.8,virginica 141 | 6.9,3.1,5.4,2.1,virginica 142 | 6.7,3.1,5.6,2.4,virginica 143 | 6.9,3.1,5.1,2.3,virginica 144 | 5.8,2.7,5.1,1.9,virginica 145 | 6.8,3.2,5.9,2.3,virginica 146 | 6.7,3.3,5.7,2.5,virginica 147 | 6.7,3.0,5.2,2.3,virginica 148 | 6.3,2.5,5.0,1.9,virginica 149 | 6.5,3.0,5.2,2.0,virginica 150 | 6.2,3.4,5.4,2.3,virginica 151 | 5.9,3.0,5.1,1.8,virginica 152 | -------------------------------------------------------------------------------- /datasets/planets.csv: -------------------------------------------------------------------------------- 1 | method,number,orbital_period,mass,distance,year 2 | Radial Velocity,1,269.3,7.1,77.4,2006 3 | Radial Velocity,1,874.774,2.21,56.95,2008 4 | Radial Velocity,1,763.0,2.6,19.84,2011 5 | Radial Velocity,1,326.03,19.4,110.62,2007 6 | Radial Velocity,1,516.22,10.5,119.47,2009 7 | Radial Velocity,1,185.84,4.8,76.39,2008 8 | Radial Velocity,1,1773.4,4.64,18.15,2002 9 | Radial Velocity,1,798.5,,21.41,1996 10 | Radial Velocity,1,993.3,10.3,73.1,2008 11 | Radial Velocity,2,452.8,1.99,74.79,2010 12 | Radial Velocity,2,883.0,0.86,74.79,2010 13 | Radial Velocity,1,335.1,9.88,39.43,2009 14 | Radial Velocity,1,479.1,3.88,97.28,2008 15 | Radial Velocity,3,1078.0,2.53,14.08,1996 16 | Radial Velocity,3,2391.0,0.54,14.08,2001 17 | Radial Velocity,3,14002.0,1.64,14.08,2009 18 | Radial Velocity,1,4.230785,0.472,15.36,1995 19 | Radial Velocity,5,14.651,0.8,12.53,1996 20 | Radial Velocity,5,44.38,0.165,12.53,2004 21 | Radial Velocity,5,4909.0,3.53,12.53,2002 22 | Radial Velocity,5,0.73654,,12.53,2011 23 | Radial Velocity,5,261.2,0.172,12.53,2007 24 | Radial Velocity,3,4.215,0.016,8.52,2009 25 | Radial Velocity,3,38.021,0.057,8.52,2009 26 | Radial Velocity,3,123.01,0.072,8.52,2009 27 | Radial Velocity,1,116.6884,,18.11,1996 28 | Radial Velocity,1,691.9,,81.5,2012 29 | Radial Velocity,1,952.7,5.3,97.18,2008 30 | Radial Velocity,1,181.4,3.2,45.52,2013 31 | Imaging,1,,,45.52,2005 32 | Imaging,1,,,165.0,2007 33 | Imaging,1,,,140.0,2004 34 | Eclipse Timing Variations,1,10220.0,6.05,,2009 35 | Imaging,1,,,,2008 36 | Imaging,1,,,145.0,2013 37 | Imaging,1,,,139.0,2004 38 | Imaging,1,,,18.39,2006 39 | Eclipse Timing Variations,2,5767.0,,130.72,2008 40 | Eclipse Timing Variations,2,3321.0,,130.72,2008 41 | Eclipse Timing Variations,2,5573.55,,500.0,2010 42 | Eclipse Timing Variations,2,2883.5,,500.0,2010 43 | Eclipse Timing Variations,1,2900.0,,,2011 44 | Eclipse Timing Variations,1,4343.5,4.2,,2012 45 | Eclipse Timing Variations,2,5840.0,,,2011 46 | Eclipse Timing Variations,2,1916.25,,,2011 47 | Radial Velocity,1,380.8,1.8,20.21,2010 48 | Radial Velocity,1,3.2357,0.0036,1.35,2012 49 | Imaging,1,6000.0,,19.28,2008 50 | Radial Velocity,1,2502.0,1.55,3.22,2000 51 | Radial Velocity,1,417.9,,70.42,2012 52 | Radial Velocity,1,594.9,7.6,47.53,2006 53 | Radial Velocity,1,428.5,8.78,38.52,2009 54 | Radial Velocity,1,903.3,1.85,13.79,2003 55 | Radial Velocity,1,1251.0,,31.12,2007 56 | Imaging,1,,,52.03,2012 57 | Radial Velocity,1,136.75,2.8,62.66,2007 58 | Radial Velocity,2,530.32,,46.84,2012 59 | Radial Velocity,2,3186.0,,46.84,2012 60 | Radial Velocity,1,277.02,1.7,80.64,2013 61 | Radial Velocity,1,187.83,,84.03,2012 62 | Radial Velocity,1,1630.0,,56.31,2012 63 | Radial Velocity,1,39.845,1.04,17.43,1997 64 | Radial Velocity,1,3.3135,3.9,15.6,1996 65 | Radial Velocity,1,305.5,20.6,92.51,2013 66 | Radial Velocity,4,4.617033,0.6876,13.47,1996 67 | Radial Velocity,4,241.258,1.981,13.47,1999 68 | Radial Velocity,4,1276.46,4.132,13.47,1999 69 | Radial Velocity,4,3848.86,1.059,13.47,2010 70 | Imaging,1,318280.0,,7.69,2008 71 | Imaging,1,,,145.0,2008 72 | Imaging,1,,,36.0,2013 73 | Imaging,1,,,140.0,2010 74 | Imaging,1,4639.15,,12.21,2009 75 | Imaging,1,,,52.4,2004 76 | Imaging,1,7336.5,,25.0,2009 77 | Imaging,1,8679.7,,26.67,2009 78 | Radial Velocity,1,655.6,5.1,37.54,2008 79 | Radial Velocity,1,714.3,10.6,,2007 80 | Radial Velocity,1,3.48777,,80.0,2000 81 | Radial Velocity,2,5.6,0.045,42.09,2009 82 | Radial Velocity,2,237.6,0.33,42.09,2009 83 | Radial Velocity,2,3.8728,0.027,20.1,2013 84 | Radial Velocity,2,125.94,0.17,20.1,2013 85 | Radial Velocity,1,268.94,1.47,50.03,2009 86 | Radial Velocity,1,137.48,1.11,175.44,2013 87 | Radial Velocity,2,379.63,21.42,,2009 88 | Radial Velocity,2,621.99,12.47,,2009 89 | Radial Velocity,1,578.2,,,2012 90 | Radial Velocity,1,392.6,0.91,,2011 91 | Imaging,1,10037.5,,23.1,2011 92 | Imaging,1,,,,2006 93 | Transit,1,1.5089557,,,2008 94 | Transit,1,1.7429935,,200.0,2008 95 | Transit,1,4.2568,,680.0,2008 96 | Transit,1,9.20205,,,2008 97 | Transit,1,4.0378962,,,2009 98 | Transit,1,8.886593,,,2009 99 | Transit,2,0.853585,,150.0,2009 100 | Radial Velocity,2,3.698,,150.0,2009 101 | Transit,1,6.21229,,380.0,2010 102 | Transit,1,95.2738,,460.0,2009 103 | Transit,1,13.2406,,345.0,2010 104 | Transit,1,2.99433,,560.0,2010 105 | Transit,1,2.828042,,1150.0,2010 106 | Transit,1,4.03519,,1060.0,2010 107 | Transit,1,1.51214,,1340.0,2010 108 | Transit,1,5.35227,,840.0,2011 109 | Transit,1,3.7681,,920.0,2011 110 | Transit,1,1.9000693,,870.0,2011 111 | Transit,1,3.89713,,770.0,2011 112 | Transit,1,9.24285,,1230.0,2011 113 | Transit,1,3.6313,,600.0,2011 114 | Transit,1,3.57532,,,2014 115 | Astrometry,1,246.36,,20.77,2013 116 | Radial Velocity,1,15.76491,3.91,10.91,1999 117 | Radial Velocity,3,8.631,0.035,14.97,2013 118 | Radial Velocity,3,25.6,0.024,14.97,2013 119 | Radial Velocity,3,603.0,0.079,14.97,2013 120 | Radial Velocity,1,2288.0,0.82,12.12,2009 121 | Radial Velocity,2,692.0,1.894,15.1,2007 122 | Radial Velocity,2,7100.0,1.6,15.1,2011 123 | Radial Velocity,1,4100.0,2.3,20.03,2013 124 | Radial Velocity,1,7.3709,0.018,9.04,2011 125 | Radial Velocity,1,2.64385,,10.23,2004 126 | Imaging,1,,,17.95,2013 127 | Radial Velocity,4,5.36874,0.049,6.27,2005 128 | Radial Velocity,4,12.9292,0.017,6.27,2005 129 | Radial Velocity,4,66.8,0.022,6.27,2005 130 | Radial Velocity,4,3.14942,0.006,6.27,2005 131 | Radial Velocity,1,598.3,0.328,10.32,2009 132 | Radial Velocity,6,7.2004,0.018,6.8,2011 133 | Radial Velocity,6,28.14,0.012,6.8,2011 134 | Radial Velocity,6,91.61,0.016,6.8,2013 135 | Radial Velocity,6,62.24,0.008,6.8,2013 136 | Radial Velocity,6,39.026,0.008,6.8,2013 137 | Radial Velocity,6,256.2,0.014,6.8,2013 138 | Radial Velocity,1,4.6938,0.035,4.54,2007 139 | Radial Velocity,3,1050.3,4.95,16.13,2010 140 | Radial Velocity,3,3.6,0.014,16.13,2012 141 | Radial Velocity,3,35.37,0.036,16.13,2012 142 | Radial Velocity,1,3416.0,0.64,4.94,2008 143 | Radial Velocity,1,1845.0,0.91,8.77,2006 144 | Radial Velocity,4,61.1166,2.2756,4.7,1998 145 | Radial Velocity,4,30.0881,0.7142,4.7,2000 146 | Radial Velocity,4,1.93778,0.021,4.7,2005 147 | Radial Velocity,4,124.26,0.046,4.7,2010 148 | Transit,1,1.58040482,,,2009 149 | Radial Velocity,1,133.71,3.37,17.62,2000 150 | Radial Velocity,1,3.33714,,25.2,2012 151 | Radial Velocity,1,2.64561,0.022,19.8,2010 152 | Imaging,1,,,145.0,2010 153 | Transit,1,4.4652934,,139.0,2006 154 | Transit,1,5.6334729,,135.32,2007 155 | Transit,1,2.899736,,138.0,2007 156 | Transit,1,3.056536,,314.0,2007 157 | Transit,1,2.788491,,342.0,2007 158 | Transit,1,3.852985,,261.0,2007 159 | Transit,1,3.0763776,,230.0,2008 160 | Transit,1,3.92289,,480.0,2008 161 | Transit,1,3.2130598,,142.5,2009 162 | Transit,2,2.91626,,214.0,2009 163 | Radial Velocity,2,428.5,15.2,214.0,2009 164 | Transit,1,4.627669,,205.0,2010 165 | Transit,1,10.863502,,190.0,2010 166 | Transit,1,2.77596,,235.0,2010 167 | Transit,2,10.338523,,90.0,2010 168 | Radial Velocity,2,5584.0,3.4,90.0,2010 169 | Transit,1,5.508023,,166.0,2010 170 | Transit,1,4.008778,,215.0,2010 171 | Transit,1,2.875317,,70.0,2010 172 | Transit,1,4.124481,,254.0,2010 173 | Transit,1,3.21222,,82.0,2010 174 | Transit,1,1.212884,,393.0,2010 175 | Transit,1,3.35524,,396.0,2010 176 | Transit,1,3.652836,,297.0,2010 177 | Transit,1,4.234516,,134.0,2010 178 | Transit,1,3.039577,,204.0,2011 179 | Transit,1,3.257215,,395.0,2011 180 | Transit,1,5.723186,,322.0,2011 181 | Transit,1,2.810595,,193.0,2011 182 | Transit,2,5.005425,,354.0,2011 183 | Radial Velocity,2,1022.0,3.4,354.0,2011 184 | Transit,1,2.150008,,283.0,2011 185 | Transit,1,3.474474,,387.0,2011 186 | Transit,1,5.452654,,257.0,2011 187 | Transit,1,3.646706,,535.0,2011 188 | Transit,1,1.327347,,317.0,2011 189 | Transit,1,2.797436,,411.0,2011 190 | Transit,1,4.640382,,249.0,2012 191 | Transit,1,3.54387,,642.0,2012 192 | Transit,1,4.457243,,501.0,2012 193 | Transit,1,2.694047,,344.0,2012 194 | Transit,1,4.641878,,447.0,2012 195 | Transit,1,3.332687,,542.0,2012 196 | Transit,1,2.691548,,322.0,2014 197 | Transit,1,3.446459,,303.0,2012 198 | Transit,1,1.354133,,360.0,2013 199 | Transit,1,3.547851,,453.0,2013 200 | Radial Velocity,2,349.7,1.25,25.64,2001 201 | Radial Velocity,2,6005.0,5.3,25.64,2012 202 | Radial Velocity,2,5.7727,0.024,23.44,2009 203 | Radial Velocity,2,13.505,0.0186,23.44,2011 204 | Radial Velocity,1,431.8,3.1,167.5,2011 205 | Radial Velocity,1,533.0,6.1,7.01,2010 206 | Radial Velocity,1,1183.0,4.9,89.85,2002 207 | Radial Velocity,1,3.4442,0.48,53.71,2005 208 | Radial Velocity,1,311.6,1.6,115.21,2013 209 | Radial Velocity,1,62.218,0.229,11.11,2003 210 | Radial Velocity,1,526.62,1.56,44.05,2007 211 | Radial Velocity,1,829.0,0.8,32.7,2001 212 | Radial Velocity,1,15.609,0.0405,21.85,2005 213 | Radial Velocity,1,431.88,2.07,77.82,2001 214 | Radial Velocity,1,356.0,2.3,131.41,2009 215 | Radial Velocity,2,360.2,2.37,56.5,2012 216 | Radial Velocity,2,2732.0,2.37,56.5,2012 217 | Radial Velocity,1,675.0,1.94,100.0,2007 218 | Radial Velocity,1,777.0,1.96,53.28,2009 219 | Radial Velocity,1,792.6,,58.17,2012 220 | Radial Velocity,1,177.11,7.6,150.6,2011 221 | Radial Velocity,1,22.09,0.48,40.32,2003 222 | Radial Velocity,1,2496.0,1.65,55.01,2009 223 | Radial Velocity,1,615.0,0.29,35.88,2011 224 | Radial Velocity,2,1275.0,1.11,38.52,2011 225 | Radial Velocity,2,4046.0,2.0,38.52,2011 226 | Radial Velocity,1,5.3978,0.029,16.82,2008 227 | Radial Velocity,1,1313.0,0.63,56.34,2010 228 | Radial Velocity,1,227.0,1.8,44.15,2002 229 | Radial Velocity,1,1634.0,14.2,38.26,2009 230 | Radial Velocity,2,30.052,0.7,52.85,2009 231 | Radial Velocity,2,192.9,1.82,52.85,2009 232 | Radial Velocity,6,5.75962,0.041,39.39,2010 233 | Radial Velocity,6,16.3567,0.038,39.39,2010 234 | Radial Velocity,6,49.747,0.08,39.39,2010 235 | Radial Velocity,6,122.72,0.074,39.39,2010 236 | Radial Velocity,6,602.0,0.067,39.39,2010 237 | Radial Velocity,6,2248.0,0.205,39.39,2010 238 | Radial Velocity,1,989.2,0.94,17.35,2006 239 | Radial Velocity,1,1075.2,6.21,32.56,1999 240 | Radial Velocity,2,1270.2,3.44,53.82,2007 241 | Radial Velocity,2,170.455,0.82,53.82,2008 242 | Radial Velocity,1,711.0,6.54,66.49,2005 243 | Radial Velocity,2,1945.0,0.622,33.98,2005 244 | Radial Velocity,2,37.91,0.0788,33.98,2008 245 | Radial Velocity,2,262.709,2.3,37.16,2000 246 | Radial Velocity,2,1708.0,1.92,37.16,2002 247 | Radial Velocity,1,471.6,14.0,300.3,2005 248 | Radial Velocity,2,14.182,0.0325,28.6,2011 249 | Radial Velocity,2,53.832,0.036,28.6,2011 250 | Radial Velocity,2,19.382,0.865,66.89,2013 251 | Radial Velocity,2,931.0,5.13,66.89,2013 252 | Radial Velocity,1,4218.0,1.88,45.52,2009 253 | Radial Velocity,1,75.523,0.26,35.91,2000 254 | Radial Velocity,1,17.24,0.0696,25.54,2008 255 | Radial Velocity,1,990.0,4.4,59.84,2009 256 | Radial Velocity,1,465.1,14.3,50.18,2009 257 | Radial Velocity,1,359.9,4.6,,2007 258 | Radial Velocity,1,21.21663,,78.25,2007 259 | Radial Velocity,1,772.0,2.7,127.88,2011 260 | Radial Velocity,1,466.2,1.37,22.38,2003 261 | Radial Velocity,2,11.849,0.0378,43.08,2011 262 | Radial Velocity,2,33.823,0.0422,43.08,2011 263 | Radial Velocity,1,500.0,1.07,27.13,2002 264 | Radial Velocity,3,18.315,0.0085,6.06,2011 265 | Radial Velocity,3,40.114,0.00755,6.06,2011 266 | Radial Velocity,3,90.309,0.0151,6.06,2011 267 | Radial Velocity,2,29.15,0.0379,35.89,2011 268 | Radial Velocity,2,85.131,0.0496,35.89,2011 269 | Radial Velocity,1,591.9,1.9,36.02,2006 270 | Radial Velocity,1,380.85,1.99,44.5,2008 271 | Radial Velocity,2,22.656,0.0322,32.31,2011 272 | Radial Velocity,2,53.881,0.06472,32.31,2011 273 | Radial Velocity,1,1214.0,1.5,89.13,2006 274 | Radial Velocity,1,738.459,2.61,34.6,2001 275 | Radial Velocity,1,528.07,13.65,31.79,2011 276 | Radial Velocity,1,1561.0,7.71,51.97,2003 277 | Radial Velocity,1,3668.0,4.01,46.51,2006 278 | Radial Velocity,1,1845.0,0.95,56.05,2010 279 | Radial Velocity,1,423.841,1.28,18.24,2000 280 | Radial Velocity,1,2208.0,1.45,44.54,2012 281 | Radial Velocity,1,17.991,0.62,42.37,2005 282 | Radial Velocity,1,1117.0,1.16,56.18,2010 283 | Radial Velocity,1,385.9,5.59,39.56,2001 284 | Radial Velocity,1,387.1,1.7,168.92,2011 285 | Radial Velocity,1,2819.654,9.17,54.71,2002 286 | Radial Velocity,1,1159.2,1.373,26.5,2009 287 | Radial Velocity,1,912.0,1.8,121.07,2011 288 | Radial Velocity,1,466.0,0.5,53.82,2010 289 | Radial Velocity,3,16.546,0.0363,38.01,2011 290 | Radial Velocity,3,51.284,0.0498,38.01,2011 291 | Radial Velocity,3,274.49,0.0519,38.01,2011 292 | Radial Velocity,1,326.6,1.3,136.8,2011 293 | Radial Velocity,1,18.179,0.33,86.88,2006 294 | Radial Velocity,1,157.54,3.04,117.37,2009 295 | Radial Velocity,1,1049.0,0.79,45.01,2009 296 | Radial Velocity,1,388.0,9.1,20.98,2005 297 | Radial Velocity,1,363.2,,37.78,2010 298 | Radial Velocity,3,154.46,0.61,33.24,2002 299 | Radial Velocity,3,2295.0,0.683,33.24,2002 300 | Radial Velocity,3,843.6,0.624,33.24,2005 301 | Radial Velocity,1,2063.818,10.35,18.21,2001 302 | Radial Velocity,2,55.0,2.3,42.88,2004 303 | Radial Velocity,2,2720.0,3.366,42.88,2012 304 | Radial Velocity,3,5.6363,0.0117,25.59,2011 305 | Radial Velocity,3,14.025,0.0187,25.59,2011 306 | Radial Velocity,3,33.941,0.0162,25.59,2011 307 | Radial Velocity,2,14.3098,0.839,42.43,2000 308 | Radial Velocity,2,2140.2,13.38,42.43,2000 309 | Radial Velocity,1,696.3,10.7,99.4,2009 310 | Radial Velocity,1,407.15,0.0961,15.56,2011 311 | Radial Velocity,6,4.3123,0.0126,12.83,2008 312 | Radial Velocity,6,9.6184,0.0208,12.83,2008 313 | Radial Velocity,6,20.432,0.0299,12.83,2008 314 | Radial Velocity,6,34.62,0.011,12.83,2012 315 | Radial Velocity,6,51.76,0.0164,12.83,2012 316 | Radial Velocity,6,197.8,0.0223,12.83,2012 317 | Radial Velocity,1,264.15,4.01,33.33,2002 318 | Radial Velocity,1,963.0,2.54,43.03,2004 319 | Radial Velocity,1,1.3283,18.37,43.03,2003 320 | Radial Velocity,2,18.357,0.039,52.03,2013 321 | Radial Velocity,2,25.648,0.027,52.03,2013 322 | Radial Velocity,1,327.8,0.6,54.94,2010 323 | Radial Velocity,1,2371.0,25.0,37.05,2008 324 | Radial Velocity,1,36.96,2.49,93.2,2007 325 | Radial Velocity,1,472.0,0.58,50.43,2010 326 | Radial Velocity,1,5.8872,0.04,22.04,2011 327 | Radial Velocity,2,226.93,0.1872,32.58,2008 328 | Radial Velocity,2,342.85,0.6579,32.58,2008 329 | Radial Velocity,1,890.76,1.79,48.95,2004 330 | Radial Velocity,1,43.6,0.47,36.14,2008 331 | Radial Velocity,1,3.024,0.249,33.41,2000 332 | Radial Velocity,2,4.0845,0.07167000000000001,37.84,2008 333 | Radial Velocity,2,1353.6,0.35061,37.84,2008 334 | Radial Velocity,2,430.0,5.0,121.36,2002 335 | Radial Velocity,2,2500.0,7.0,121.36,2008 336 | Radial Velocity,1,700.0,1.16,87.41,2008 337 | Radial Velocity,1,4.9437,0.115,40.73,2002 338 | Radial Velocity,1,2582.7,1.71,47.26,2005 339 | Radial Velocity,1,1279.0,4.9,31.03,2002 340 | Radial Velocity,2,14.07,0.0413,34.07,2011 341 | Radial Velocity,2,95.415,0.0565,34.07,2011 342 | Radial Velocity,1,118.96,1.13,28.07,2000 343 | Radial Velocity,1,303.0,5.25,92.51,2003 344 | Radial Velocity,2,201.83,3.1548,25.7,2008 345 | Radial Velocity,2,607.06,7.4634,25.7,2008 346 | Radial Velocity,1,2.817822,0.38,35.8,2005 347 | Radial Velocity,1,589.64,2.9,10.34,2006 348 | Radial Velocity,1,358.0,0.64,32.62,2009 349 | Radial Velocity,2,572.4,1.26,35.59,2003 350 | Radial Velocity,2,152.6,0.17,35.59,2011 351 | Radial Velocity,1,480.5,6.0,80.06,2012 352 | Radial Velocity,1,1973.0,2.82,55.04,2005 353 | Radial Velocity,1,6.276,1.9,58.82,2001 354 | Radial Velocity,3,8.667,0.033,12.58,2006 355 | Radial Velocity,3,31.56,0.038,12.58,2006 356 | Radial Velocity,3,197.0,0.058,12.58,2006 357 | Radial Velocity,1,851.8,6.1,,2007 358 | Radial Velocity,1,2231.0,2.0,28.76,2003 359 | Radial Velocity,1,3383.0,3.15,51.36,2002 360 | Radial Velocity,1,1260.0,3.06,53.05,2008 361 | Radial Velocity,1,2.54858,1.87,36.52,2003 362 | Radial Velocity,2,188.9,2.25,94.61,2002 363 | Radial Velocity,2,379.1,2.25,94.61,2005 364 | Radial Velocity,1,1800.0,1.15,96.99,2008 365 | Radial Velocity,3,51.645,1.8,64.56,2003 366 | Radial Velocity,3,2473.0,8.06,64.56,2003 367 | Radial Velocity,3,346.6,0.396,64.56,2007 368 | Radial Velocity,1,3.51,0.42,28.94,1999 369 | Radial Velocity,1,418.2,2.51,80.58,2007 370 | Radial Velocity,1,3.971,0.197,59.7,2002 371 | Radial Velocity,1,5.7361,,41.27,2012 372 | Radial Velocity,1,1966.1,1.34,48.64,2011 373 | Radial Velocity,1,111.4357,,29.04,2001 374 | Radial Velocity,1,1001.7,6.86,32.56,2005 375 | Radial Velocity,1,184.02,2.7,88.26,2007 376 | Radial Velocity,2,441.47,,27.46,2003 377 | Radial Velocity,2,220.078,,27.46,2003 378 | Radial Velocity,1,705.0,1.3,112.23,2011 379 | Radial Velocity,1,2.985625,0.4,43.53,2002 380 | Radial Velocity,1,788.0,0.132,33.96,2009 381 | Radial Velocity,1,58.43,0.01133,11.15,2011 382 | Radial Velocity,1,2.1375,1.5,91.16,2006 383 | Radial Velocity,1,1695.0,0.92,42.48,2009 384 | Radial Velocity,1,1475.0,7.0,72.57,2009 385 | Radial Velocity,1,2754.0,1.78,18.06,2009 386 | Radial Velocity,1,3.416,0.22,74.46,2004 387 | Radial Velocity,1,2157.0,1.78,30.88,2009 388 | Radial Velocity,1,256.78,8.44,38.99,1999 389 | Radial Velocity,1,49.77,0.057,22.09,2009 390 | Radial Velocity,1,325.81,3.86,32.32,2000 391 | Radial Velocity,1,143.58,0.37,28.9,2005 392 | Radial Velocity,2,13.186,0.0263,42.52,2011 393 | Radial Velocity,2,46.025,0.0318,42.52,2011 394 | Imaging,1,,,91.57,2013 395 | Radial Velocity,1,507.0,1.2,149.03,2009 396 | Radial Velocity,1,361.1,0.9,132.8,2011 397 | Radial Velocity,1,498.9,0.68,84.03,2009 398 | Radial Velocity,1,647.3,4.0,221.24,2011 399 | Radial Velocity,2,8.1256,0.0284,26.21,2011 400 | Radial Velocity,2,103.49,0.04,26.21,2011 401 | Radial Velocity,1,9.494,0.026,21.3,2010 402 | Radial Velocity,1,436.9,1.8,150.38,2011 403 | Radial Velocity,1,4951.0,6.8,42.77,2012 404 | Radial Velocity,1,439.3,0.502,60.46,2005 405 | Radial Velocity,2,17.054,0.087,17.99,2004 406 | Radial Velocity,2,4970.0,0.36,17.99,2010 407 | Radial Velocity,1,868.0,1.4,130.89,2011 408 | Radial Velocity,1,157.57,1.7,140.85,2011 409 | Radial Velocity,1,383.7,1.16,52.8,2007 410 | Radial Velocity,1,70.46,0.3,30.5,2005 411 | Radial Velocity,1,20.8133,0.172,42.0,2005 412 | Radial Velocity,1,4.113775,0.45,28.98,2006 413 | Radial Velocity,2,127.58,5.9,164.2,2008 414 | Radial Velocity,2,520.0,2.6,164.2,2008 415 | Radial Velocity,1,122.1,0.05,9.24,2010 416 | Radial Velocity,1,778.1,5.9,138.5,2011 417 | Radial Velocity,1,6.495,0.96,121.07,2010 418 | Radial Velocity,1,47.84,0.098,49.33,2009 419 | Radial Velocity,1,5.8881,0.367,53.08,2013 420 | Radial Velocity,1,55.806,0.186,20.82,2009 421 | Radial Velocity,1,199.505,8.3,102.04,2003 422 | Radial Velocity,1,1531.0,6.92,37.44,2002 423 | Radial Velocity,1,2890.0,11.0,87.87,2011 424 | Radial Velocity,1,3630.0,9.61,36.36,2012 425 | Imaging,1,,,91.83,2013 426 | Radial Velocity,1,48.056,0.21,51.26,2005 427 | Radial Velocity,1,10.8985,0.261,38.56,2002 428 | Radial Velocity,1,443.4,2.6,138.5,2011 429 | Radial Velocity,2,395.8,1.29,68.54,2002 430 | Radial Velocity,2,1624.0,0.99,68.54,2005 431 | Radial Velocity,1,68.27,0.77,65.62,2010 432 | Radial Velocity,2,7.8543,0.054,56.92,2013 433 | Radial Velocity,2,30.93,0.076,56.92,2013 434 | Radial Velocity,1,5.24,0.28,59.03,2005 435 | Radial Velocity,1,835.477,11.09,97.66,2009 436 | Radial Velocity,1,1143.0,6.8,28.88,2003 437 | Radial Velocity,1,324.0,2.83,37.42,2013 438 | Radial Velocity,2,263.3,0.27,15.71,2010 439 | Radial Velocity,2,1657.0,0.71,15.71,2010 440 | Radial Velocity,2,937.7,1.24,28.04,2003 441 | Radial Velocity,2,1046.0,,28.04,2011 442 | Radial Velocity,1,3827.0,0.48,20.48,2014 443 | Radial Velocity,1,83.888,11.68,40.57,1989 444 | Radial Velocity,1,493.7,1.1,20.43,2001 445 | Radial Velocity,1,1114.0,0.95,35.0,2002 446 | Radial Velocity,1,670.0,2.1,110.62,2011 447 | Radial Velocity,1,2597.0,1.88,33.01,2004 448 | Radial Velocity,1,25.827,0.178,38.02,2004 449 | Radial Velocity,1,6.1335,2.13,88.57,2005 450 | Radial Velocity,1,2082.0,4.5,97.66,2013 451 | Radial Velocity,1,63.33,1.22,44.37,2003 452 | Radial Velocity,1,344.95,3.71,133.16,2003 453 | Radial Velocity,3,559.4,3.0,52.83,2007 454 | Radial Velocity,3,4.1547,0.058,52.83,2009 455 | Radial Velocity,3,3008.0,7.2,52.83,2009 456 | Radial Velocity,1,9.6737,0.041,27.45,2009 457 | Radial Velocity,1,1244.0,0.38,68.35,2009 458 | Radial Velocity,1,948.12,0.224,38.05,2011 459 | Radial Velocity,2,454.2,1.45,16.57,2002 460 | Radial Velocity,2,923.8,3.24,16.57,2005 461 | Radial Velocity,1,1840.0,1.6,67.61,2009 462 | Radial Velocity,1,10.7085,1.04,29.76,1999 463 | Radial Velocity,1,883.0,2.2,121.36,2011 464 | Radial Velocity,1,1951.0,18.15,57.21,2008 465 | Radial Velocity,1,974.0,5.61,70.97,2007 466 | Radial Velocity,1,1544.0,1.49,98.52,2011 467 | Radial Velocity,2,3.27,0.0351,24.15,2011 468 | Radial Velocity,2,1160.9,0.1507,24.15,2011 469 | Radial Velocity,2,258.19,1.59,25.65,1999 470 | Radial Velocity,2,5000.0,0.82,25.65,2009 471 | Radial Velocity,3,12.083,0.0292,26.91,2011 472 | Radial Velocity,3,59.519,0.0382,26.91,2011 473 | Radial Velocity,3,459.26,0.121,26.91,2011 474 | Radial Velocity,1,464.3,2.0,106.38,2009 475 | Radial Velocity,3,11.577,0.0166,14.56,2011 476 | Radial Velocity,3,27.582,0.0358,14.56,2011 477 | Radial Velocity,3,106.72,0.03,14.56,2011 478 | Radial Velocity,1,330.0,0.223,38.45,2011 479 | Radial Velocity,1,1125.7,9.76,121.36,2008 480 | Radial Velocity,1,653.22,9.7,33.46,2002 481 | Radial Velocity,1,1928.0,5.1,35.87,2005 482 | Radial Velocity,1,1299.0,1.9,106.38,2011 483 | Radial Velocity,1,386.3,1.62,34.57,2003 484 | Radial Velocity,1,1057.0,3.12,59.35,2008 485 | Radial Velocity,1,176.3,2.9,126.1,2010 486 | Radial Velocity,1,103.95,5.76,55.19,2008 487 | Radial Velocity,2,44.236,2.12,42.68,2009 488 | Radial Velocity,2,1008.0,6.56,42.68,2009 489 | Radial Velocity,1,528.4,1.21,12.87,2003 490 | Radial Velocity,1,1027.0,0.85,53.05,2009 491 | Radial Velocity,1,331.5,0.96,59.28,2009 492 | Radial Velocity,1,2.8758911,,78.86,2005 493 | Radial Velocity,1,4.072,1.33,63.49,2005 494 | Radial Velocity,1,2.391,15.5,76.51,2009 495 | Radial Velocity,1,1096.2,0.168,29.55,2011 496 | Radial Velocity,1,2097.0,3.0,85.18,2009 497 | Radial Velocity,1,689.0,1.5,182.82,2011 498 | Radial Velocity,1,499.4,2.73,51.87,2008 499 | Radial Velocity,1,18.596,0.0193,18.08,2011 500 | Radial Velocity,1,3342.0,0.947,18.06,2006 501 | Radial Velocity,1,163.9,5.02,65.79,2008 502 | Radial Velocity,2,408.6,2.24,68.54,2004 503 | Radial Velocity,2,3452.0,2.58,68.54,2014 504 | Radial Velocity,2,194.3,0.85,43.4,2007 505 | Radial Velocity,2,391.9,0.82,43.4,2007 506 | Radial Velocity,1,131.05,9.71,35.37,2011 507 | Radial Velocity,1,842.0,0.75,55.1,2009 508 | Radial Velocity,1,4.6455,0.013,24.05,2010 509 | Radial Velocity,1,359.5546,10.57,49.0,2007 510 | Radial Velocity,1,104.84,0.12,33.48,2011 511 | Radial Velocity,1,521.0,1.8,114.15,2011 512 | Radial Velocity,2,12.62,1.13,68.59,2013 513 | Radial Velocity,2,248.4,1.9,68.59,2013 514 | Radial Velocity,2,1178.4,2.1,52.72,2006 515 | Radial Velocity,2,352.3,0.73,52.72,2012 516 | Radial Velocity,4,643.25,,15.28,2000 517 | Radial Velocity,4,4205.8,1.814,15.28,2004 518 | Radial Velocity,4,9.6386,,15.28,2004 519 | Radial Velocity,4,310.55,,15.28,2006 520 | Radial Velocity,1,8.428198,14.4,31.26,2002 521 | Radial Velocity,2,75.29,0.77,69.44,2010 522 | Radial Velocity,2,1314.0,2.29,69.44,2010 523 | Radial Velocity,1,282.4,0.48,51.81,2010 524 | Radial Velocity,1,606.4,2.7,37.98,2009 525 | Radial Velocity,1,1155.0,0.36,21.92,2005 526 | Radial Velocity,1,5144.0,3.53,42.99,2012 527 | Radial Velocity,1,420.77,1.7,50.0,2007 528 | Radial Velocity,2,58.11289,8.02,37.88,1998 529 | Radial Velocity,2,1749.5,18.1,37.88,2000 530 | Radial Velocity,1,6.403,0.23,43.12,2002 531 | Radial Velocity,2,225.62,2.88,36.32,2000 532 | Radial Velocity,2,2102.0,4.04,36.32,2003 533 | Radial Velocity,1,1145.0,0.67,64.98,2007 534 | Radial Velocity,1,538.0,1.83,,2007 535 | Radial Velocity,1,1523.0,2.6,44.05,2009 536 | Radial Velocity,1,323.6,2.7,134.95,2008 537 | Radial Velocity,1,1290.0,7.8,67.02,2009 538 | Radial Velocity,1,297.3,0.61,127.55,2007 539 | Astrometry,1,1016.0,,14.98,2010 540 | Radial Velocity,2,406.6,1.49,59.03,1999 541 | Radial Velocity,2,110.9,0.15,59.03,2010 542 | Radial Velocity,1,71.484,7.03,46.73,2001 543 | Radial Velocity,1,14.476,0.08,63.69,2008 544 | Radial Velocity,1,3.0925,0.95,27.05,2000 545 | Radial Velocity,1,396.03,,131.75,2010 546 | Radial Velocity,1,479.0,1.6,114.55,2009 547 | Radial Velocity,1,663.0,3.3,115.47,2009 548 | Radial Velocity,3,9.3743,0.0238,26.15,2008 549 | Radial Velocity,3,962.0,0.64,26.15,2008 550 | Radial Velocity,3,2172.0,0.54,26.15,2008 551 | Radial Velocity,1,956.0,0.37,55.93,2009 552 | Radial Velocity,2,634.23,3.69,52.83,2004 553 | Radial Velocity,2,2950.0,3.82,52.83,2008 554 | Radial Velocity,1,6.838,0.94,47.37,2006 555 | Radial Velocity,1,986.0,0.75,44.98,2006 556 | Radial Velocity,2,3.097,0.52,47.92,1998 557 | Radial Velocity,2,3810.0,1.99,47.92,1999 558 | Radial Velocity,1,456.46,1.26,52.63,2004 559 | Radial Velocity,1,14.275,0.0316,17.72,2011 560 | Radial Velocity,1,2.21857578,,19.25,2005 561 | Radial Velocity,1,1136.1,5.93,62.11,2002 562 | Radial Velocity,2,2891.0,1.502,15.89,2003 563 | Radial Velocity,2,17.1,0.057,15.89,2005 564 | Radial Velocity,1,1038.1,1.9,54.23,2007 565 | Radial Velocity,1,4885.0,3.1,189.39,2009 566 | Radial Velocity,1,24.348,0.72,19.89,1999 567 | Radial Velocity,2,74.72,0.05318,8.82,2011 568 | Radial Velocity,2,525.8,0.07552,8.82,2011 569 | Radial Velocity,1,351.5,2.5,67.39,2007 570 | Radial Velocity,1,18.20163,3.7,37.35,1999 571 | Radial Velocity,1,1289.0,3.0,46.93,2002 572 | Radial Velocity,1,3638.0,6.9,43.57,2012 573 | Radial Velocity,1,1333.0,2.58,32.99,2007 574 | Radial Velocity,1,1035.7,0.79,32.83,2011 575 | Radial Velocity,2,613.8,1.85,68.35,2010 576 | Radial Velocity,2,825.0,0.895,68.35,2010 577 | Radial Velocity,2,255.87,17.4,46.34,2002 578 | Radial Velocity,2,1383.4,2.44,46.34,2004 579 | Imaging,1,,,40.85,2006 580 | Radial Velocity,3,1931.0,4.05,47.3,2009 581 | Radial Velocity,3,34.873,0.054,47.3,2011 582 | Radial Velocity,3,2831.6,1.68,47.3,2012 583 | Radial Velocity,1,1733.0,0.266,26.95,2011 584 | Radial Velocity,1,279.8,1.37,90.33,2008 585 | Radial Velocity,1,610.0,2.2,170.07,2009 586 | Radial Velocity,2,161.97,,55.31,2012 587 | Radial Velocity,2,1155.7,,55.31,2012 588 | Radial Velocity,1,123.0,0.45,43.99,2004 589 | Radial Velocity,1,875.5,9.9,352.11,2012 590 | Radial Velocity,1,3.52474859,,47.08,1999 591 | Radial Velocity,1,442.1,1.23,21.29,1998 592 | Radial Velocity,1,354.8,,55.93,2007 593 | Radial Velocity,1,2.245715,0.45,52.72,2005 594 | Radial Velocity,1,373.3,2.3,121.8,2009 595 | Radial Velocity,1,951.0,4.5,40.75,2001 596 | Radial Velocity,2,7.2825,0.0087,21.52,2011 597 | Radial Velocity,2,10.866,0.0097,21.52,2011 598 | Radial Velocity,2,191.99,0.101,38.64,2011 599 | Radial Velocity,2,2277.0,0.246,38.64,2011 600 | Radial Velocity,2,3.93,0.017,43.53,2009 601 | Radial Velocity,2,567.0,0.33,43.53,2009 602 | Radial Velocity,1,1311.0,1.26,33.29,2002 603 | Radial Velocity,1,1294.0,2.1,26.52,2002 604 | Radial Velocity,1,118.45,0.65,37.89,2003 605 | Radial Velocity,2,7.126816,1.39,19.72,1998 606 | Radial Velocity,2,4270.0,2.6,19.72,2005 607 | Radial Velocity,1,1319.0,13.0,54.92,2010 608 | Radial Velocity,1,225.7,0.21,29.94,2010 609 | Radial Velocity,1,5501.0,10.39,29.2,2012 610 | Radial Velocity,1,2093.3,,,2012 611 | Radial Velocity,1,3.8335,0.06,81.1,2007 612 | Radial Velocity,1,672.1,11.1,289.02,2012 613 | Radial Velocity,1,2209.0,1.06,45.23,2012 614 | Radial Velocity,1,3724.7,1.45,48.43,2011 615 | Radial Velocity,1,456.1,3.09,52.88,2007 616 | Radial Velocity,1,3999.0,1.9,50.45,2011 617 | Radial Velocity,1,572.38,7.75,41.95,1999 618 | Radial Velocity,1,26.73,0.71,94.07,2006 619 | Radial Velocity,1,141.6,1.08,108.46,2007 620 | Radial Velocity,1,192.0,6.575,,2013 621 | Radial Velocity,1,501.75,6.9,,2009 622 | Radial Velocity,1,745.7,5.3,307.69,2011 623 | Radial Velocity,1,2443.0,2.54,54.92,2009 624 | Radial Velocity,1,8.7836,0.0265,9.42,2008 625 | Radial Velocity,1,3.369,0.76,50.2,2004 626 | Radial Velocity,2,345.72,1.42,44.8,2009 627 | Radial Velocity,2,9017.8,,44.8,2011 628 | Radial Velocity,1,57.0,0.47,23.8,2010 629 | Radial Velocity,1,16.2,1.25,223.21,2010 630 | Radial Velocity,3,6.673855,3.88,52.88,2005 631 | Radial Velocity,3,147.73,1.28,52.88,2006 632 | Radial Velocity,3,952.0,0.57,52.88,2009 633 | Radial Velocity,1,41.397,0.298,11.03,2010 634 | Radial Velocity,3,8.1352,0.036,25.87,2011 635 | Radial Velocity,3,32.03,0.41,25.87,2011 636 | Radial Velocity,3,431.7,0.527,25.87,2011 637 | Imaging,1,,,11.43,2010 638 | Radial Velocity,1,124.6,9.18,149.25,2013 639 | Radial Velocity,1,17337.5,9.0,23.98,2009 640 | Radial Velocity,1,511.098,8.82,31.33,2002 641 | Imaging,1,,,131.93,2010 642 | Radial Velocity,1,111.7,2.1,14.9,2009 643 | Radial Velocity,1,5.0505,1.068,44.46,2013 644 | Radial Velocity,1,311.288,1.94,17.24,1999 645 | Imaging,4,170000.0,,39.94,2008 646 | Imaging,4,69000.0,,39.94,2008 647 | Imaging,4,37000.0,,39.94,2008 648 | Imaging,4,18000.0,,39.94,2010 649 | Transit,1,1.217514,,262.0,2012 650 | Transit,1,4.1137912,,124.22,2012 651 | Transit,1,2.7033904,1.47,178.0,2013 652 | Transit,1,7.8457,,222.0,2014 653 | Transit,1,2.47063,,213.0,2006 654 | Transit,1,2.204737,,320.0,2008 655 | Transit,1,4.8878162,,38.0,2008 656 | Transit,1,3.21346,,550.0,2009 657 | Transit,1,3.54846,,,2009 658 | Transit,1,3.234723,,,2009 659 | Transit,1,4.885525,,,2009 660 | Transit,1,3.52254,,1330.0,2010 661 | Transit,3,19.24,,650.0,2010 662 | Transit,3,38.91,,650.0,2010 663 | Transit,3,1.592851,,650.0,2010 664 | Transit,2,0.837495,,173.0,2011 665 | Transit,2,45.29485,,173.0,2011 666 | Transit,6,10.3039,,613.0,2010 667 | Transit,6,13.0241,,613.0,2010 668 | Transit,6,22.6845,,613.0,2010 669 | Transit,6,31.9996,,613.0,2010 670 | Transit,6,46.6888,,613.0,2010 671 | Transit,6,118.3807,,613.0,2010 672 | Transit,1,4.4379637,,600.0,2011 673 | Transit,1,1.7635892,,,2011 674 | Transit,1,6.790123,,980.0,2011 675 | Transit,1,4.942782,,,2011 676 | Transit,1,228.776,,61.0,2011 677 | Transit,1,1.4857108,,800.0,2011 678 | Transit,3,3.504725,,,2011 679 | Transit,3,7.64159,,,2011 680 | Transit,3,14.85888,,,2011 681 | Transit,2,9.2869944,,2119.0,2011 682 | Transit Timing Variations,2,160.0,,2119.0,2011 683 | Transit,5,3.6961219,,290.0,2011 684 | Transit,5,10.854092,,290.0,2011 685 | Transit,5,77.61184,,290.0,2011 686 | Transit,5,6.098493,,290.0,2011 687 | Transit,5,19.57706,,290.0,2011 688 | Transit,1,2.785755,,108.0,2011 689 | Transit,1,289.8623,,190.0,2011 690 | Transit,2,7.1073,,800.0,2011 691 | Transit,2,10.7421,,800.0,2011 692 | Transit,2,8.1453,,1200.0,2011 693 | Transit,2,12.3335,,1200.0,2011 694 | Transit,3,6.2385,,,2011 695 | Transit,3,12.7204,,,2011 696 | Radial Velocity,3,123.0,,,2014 697 | Transit,2,12.2829,,,2011 698 | Transit,2,17.2513,,,2011 699 | Transit,2,15.3348,,,2011 700 | Transit,2,31.3309,,,2011 701 | Transit,2,5.9123,,,2011 702 | Transit,2,8.9858,,,2011 703 | Transit,2,10.3376,,1400.0,2011 704 | Transit,2,13.2907,,1400.0,2011 705 | Transit,3,29.33434,,1400.0,2012 706 | Transit,3,60.323105,,1400.0,2012 707 | Transit,3,143.34394,,1400.0,2012 708 | Transit,2,20.8613,,2100.0,2011 709 | Transit,2,42.6318,,2100.0,2011 710 | Transit,5,5.90124,,303.0,2011 711 | Transit,5,8.7522,,303.0,2011 712 | Transit,5,22.7802,,303.0,2012 713 | Transit,5,2.896,,303.0,2012 714 | Transit,5,0.74296,,303.0,2012 715 | Transit,5,5.66793,,,2011 716 | Transit,5,13.17562,,,2011 717 | Transit,5,21.77596,,,2011 718 | Transit,5,31.7844,,,2011 719 | Transit,5,41.02902,,,2011 720 | Transit,1,288.822,,1499.0,2011 721 | Transit,1,131.458,,1645.0,2011 722 | Transit,2,13.83989,,470.0,2012 723 | Transit,2,16.23855,,470.0,2012 724 | Transit,3,13.367308,,66.0,2013 725 | Transit,3,21.301886,,66.0,2013 726 | Transit,3,39.792187,,66.0,2013 727 | Transit,1,105.599,,600.0,2012 728 | Transit,1,21.0874,,1200.0,2011 729 | Transit,1,6.87349,,2700.0,2010 730 | Transit,1,1.855558,,770.0,2011 731 | Transit,3,1.2137672,,38.7,2011 732 | Transit,3,0.45328509,,38.7,2011 733 | Transit,3,1.865169,,38.7,2011 734 | Transit,1,3.024095,,1950.0,2011 735 | Transit,1,3.24674,,2250.0,2011 736 | Transit,1,2.455239,,333.0,2011 737 | Transit,2,33.60134,,855.0,2012 738 | Transit Timing Variations,2,57.011,,855.0,2012 739 | Transit,2,49.532,,1500.0,2012 740 | Transit,2,303.137,,1500.0,2012 741 | Transit,4,4.7779803,,,2012 742 | Transit,4,9.6739283,,,2012 743 | Transit,4,42.8961,,,2014 744 | Radial Velocity,4,982.0,,,2014 745 | Transit,2,7.2037945,,,2012 746 | Transit,2,10.9129343,,,2012 747 | Transit,2,7.81254,,,2012 748 | Transit,2,9.37647,,,2012 749 | Transit,3,45.154,,,2012 750 | Transit,3,85.312,,,2012 751 | Transit Timing Variations,3,,,,2014 752 | Transit,2,7.8773565,,,2012 753 | Transit,2,16.3850021,,,2012 754 | Transit,2,18.6489525,,,2012 755 | Transit,2,38.5583038,,,2012 756 | Transit,2,8.0109434,,,2012 757 | Transit,2,12.0717249,,,2012 758 | Transit,2,27.9481449,,,2012 759 | Transit,2,42.1516418,,,2012 760 | Transit,2,10.5016,,,2012 761 | Transit,2,21.40239,,,2012 762 | Transit,2,5.7293196,,,2012 763 | Transit,2,11.6092567,,,2012 764 | Transit,2,10.2184954,,,2012 765 | Transit,2,15.5741568,,,2012 766 | Transit,2,11.8681707,,,2012 767 | Transit,2,17.9801235,,,2012 768 | Transit,3,7.1316185,,,2012 769 | Transit,3,8.9193459,,,2012 770 | Transit,3,11.9016171,,,2012 771 | Transit,1,59.87756,,,2013 772 | Transit,5,5.714932,,368.0,2013 773 | Transit,5,12.4417,,368.0,2013 774 | Transit,5,18.16406,,368.0,2013 775 | Transit,5,122.3874,,368.0,2013 776 | Transit,5,267.291,,368.0,2013 777 | Transit,1,9.4341505,,200.0,2013 778 | Transit,1,138.317,,1000.0,2012 779 | Transit,3,2.15491,,,2012 780 | Transit,3,5.859944,,,2012 781 | Transit,3,8.13123,,,2012 782 | Transit,1,17.815815,,1107.0,2013 783 | Transit,1,15.7259,,1107.0,2013 784 | Transit,3,5.398763,,135.0,2012 785 | Transit,3,9.605085,,135.0,2012 786 | Radial Velocity,3,580.0,0.947,135.0,2012 787 | Transit,2,13.722341,,,2013 788 | Transit,2,242.4613,,,2013 789 | Orbital Brightness Modulation,2,0.240104,,1180.0,2011 790 | Orbital Brightness Modulation,2,0.342887,,1180.0,2011 791 | Transit,1,3.90512,,800.0,2010 792 | Transit,1,7.340718,,1330.0,2013 793 | Transit,1,8.884924,,1140.0,2013 794 | Orbital Brightness Modulation,1,1.54492875,,,2013 795 | Transit,1,3.57878087,,570.0,2013 796 | Transit,1,0.355,,,2013 797 | Transit,2,13.485,,,2012 798 | Transit,2,27.402,,,2012 799 | Transit,2,7.053,,,2012 800 | Transit,2,9.522,,,2012 801 | Transit,2,5.955,,,2012 802 | Transit,2,12.04,,,2012 803 | Transit,2,26.444,,,2012 804 | Transit,2,51.538,,,2012 805 | Transit,2,9.77,,,2012 806 | Transit,2,20.09,,,2012 807 | Transit,2,8.726,,,2012 808 | Transit,2,12.883,,,2012 809 | Transit,2,8.306,,,2012 810 | Transit,2,12.513,,,2012 811 | Transit,1,282.5255,,,2013 812 | Transit,2,114.73635,,,2013 813 | Transit,2,191.2318,,,2013 814 | Transit,2,10.95416,,339.0,2013 815 | Transit Timing Variations,2,22.3395,,339.0,2013 816 | Transit,4,3.743208,,,2013 817 | Transit,4,10.423648,,,2013 818 | Transit,4,22.342989,,,2013 819 | Transit,4,54.32031,,,2013 820 | Transit,7,7.008151,,780.0,2013 821 | Transit,7,8.719375,,780.0,2013 822 | Transit,7,59.73667,,780.0,2013 823 | Transit,7,91.93913,,780.0,2013 824 | Transit,7,124.9144,,780.0,2013 825 | Transit,7,210.60697,,780.0,2013 826 | Transit,7,331.60059,,780.0,2013 827 | Transit,1,6.24658,,1030.0,2013 828 | Transit,2,13.749,,,2013 829 | Transit,2,26.723,,,2013 830 | Transit,2,4.72674,,,2014 831 | Radial Velocity,2,1460.0,,,2014 832 | Transit,2,2.50806,,,2014 833 | Radial Velocity,2,820.3,,,2014 834 | Transit,1,11.5231,,,2014 835 | Transit,1,16.2385,,,2014 836 | Transit,2,2.58664,,,2014 837 | Radial Velocity,2,789.0,,,2014 838 | Transit,1,1.54168,,,2014 839 | Transit,1,4.60358,,,2014 840 | Transit,3,6.88705,,,2014 841 | Transit,3,12.8159,,,2014 842 | Transit,3,35.3331,,,2014 843 | Transit,5,5.28696,,,2014 844 | Transit,5,7.07142,,,2014 845 | Transit,5,10.3117,,,2014 846 | Transit,5,16.1457,,,2013 847 | Transit,5,27.4536,,,2014 848 | Transit,2,15.9654,,,2014 849 | Transit,2,179.612,,,2014 850 | Transit,2,5.4122,,,2013 851 | Transit,4,6.16486,,,2014 852 | Transit,4,13.5708,,,2014 853 | Transit,4,23.9802,,,2014 854 | Transit,4,43.8445,,,2014 855 | Transit,2,6.48163,,,2014 856 | Transit,2,21.2227,,,2014 857 | Transit,2,4.754,,,2014 858 | Transit,2,8.92507,,,2014 859 | Transit,2,8.041,,,2013 860 | Transit,2,11.776,,,2013 861 | Transit,2,15.09,,,2013 862 | Transit,2,22.804,,,2013 863 | Transit,3,27.50868,,,2013 864 | Transit,2,16.092,,,2014 865 | Transit,2,25.5169,,,2014 866 | Transit,2,13.78164,,,2014 867 | Transit,2,23.08933,,,2014 868 | Transit,2,22.951,,,2013 869 | Transit,2,42.882,,,2013 870 | Transit,2,36.855,,,2013 871 | Transit,2,49.412,,,2013 872 | Transit,2,23.654,,,2013 873 | Transit,2,50.447,,,2013 874 | Transit,2,31.884,,,2013 875 | Transit,2,48.648,,,2013 876 | Transit,2,17.324,,,2013 877 | Transit,2,33.006,,,2013 878 | Transit,2,35.736,,,2013 879 | Transit,2,54.414,,,2013 880 | Transit,2,24.806,,,2013 881 | Transit,2,44.347,,,2013 882 | Transit,2,5.487,,,2013 883 | Transit,2,8.291,,,2013 884 | Transit,2,10.416,,,2013 885 | Transit,2,13.084,,,2013 886 | Transit,2,34.921,,,2013 887 | Transit,2,71.312,,,2013 888 | Transit,2,17.849,,,2013 889 | Transit,2,26.136,,,2013 890 | Transit,2,42.994,,,2013 891 | Transit,2,88.505,,,2013 892 | Transit,2,2.42629,,,2014 893 | Transit,2,4.62332,,,2014 894 | Transit,2,0.66931,,,2014 895 | Radial Velocity,2,3000.0,,,2014 896 | Transit,1,2.46502,,,2014 897 | Transit,1,68.9584,,,2014 898 | Transit,1,17.833648,,132.0,2013 899 | Transit,1,3.00516,,,2013 900 | Transit,1,1.72086123,,1056.0,2014 901 | Transit,1,66.262,,,2014 902 | Imaging,1,40000.0,,,2011 903 | Transit,1,3.91405,,2000.0,2007 904 | Microlensing,1,,,,2008 905 | Microlensing,1,,,,2008 906 | Microlensing,1,,,,2009 907 | Microlensing,1,,,3600.0,2013 908 | Microlensing,1,2780.0,,,2011 909 | Microlensing,1,,,,2010 910 | Microlensing,1,1970.0,,,2010 911 | Microlensing,1,,,2300.0,2012 912 | Microlensing,1,,,2800.0,2012 913 | Microlensing,1,,,7720.0,2012 914 | Microlensing,1,,,7560.0,2013 915 | Radial Velocity,1,677.8,19.8,,2007 916 | Radial Velocity,1,6.958,0.34,,2014 917 | Radial Velocity,1,5.118,0.4,,2014 918 | Radial Velocity,1,121.71,1.54,,2014 919 | Microlensing,1,,,,2004 920 | Microlensing,1,3600.0,,,2005 921 | Microlensing,1,3300.0,,,2006 922 | Microlensing,1,3500.0,,,2005 923 | Microlensing,2,1825.0,,,2008 924 | Microlensing,2,5100.0,,,2008 925 | Microlensing,1,,,,2009 926 | Microlensing,1,,,2570.0,2012 927 | Microlensing,2,,,4080.0,2012 928 | Microlensing,2,,,4080.0,2012 929 | Microlensing,1,,,1760.0,2013 930 | Microlensing,1,,,4970.0,2013 931 | Transit,1,2.4855335,,,2008 932 | Transit,1,3.101278,,,2004 933 | Transit,1,1.2119189,,,2002 934 | Transit,1,4.0161,,,2004 935 | Transit,1,1.4324752,,600.0,2004 936 | Transit,1,1.689868,,2500.0,2004 937 | Transit,1,3.9791,,,2007 938 | Transit,1,3.67724,,,2007 939 | Imaging,1,730000.0,,,2006 940 | Transit,1,3.1606296,,1200.0,2013 941 | Radial Velocity,1,4.4264,,,2012 942 | Radial Velocity,1,2.1451,,,2012 943 | Pulsar Timing,3,25.262,,,1992 944 | Pulsar Timing,3,66.5419,,,1992 945 | Pulsar Timing,3,98.2114,,,1994 946 | Pulsar Timing,1,36525.0,,,2003 947 | Pulsar Timing,1,0.09070629,,1200.0,2011 948 | Transit,1,1.420033,,,2010 949 | Transit,1,1.3371182,,,2011 950 | Imaging,1,,,135.0,2013 951 | Imaging,1,,,120.0,2013 952 | Imaging,1,,,,2010 953 | Transit,1,4.2,,8500.0,2006 954 | Transit,1,1.796,,8500.0,2006 955 | Transit,1,3.030065,,152.3,2004 956 | Transit,1,1.30618581,,228.0,2007 957 | Transit,1,3.553945,,492.0,2007 958 | Transit,1,1.4822446,,360.0,2011 959 | Imaging,1,,,,2008 960 | Pulsation Timing Variations,1,1170.0,,,2007 961 | Transit,1,2.5199449,,408.0,2007 962 | Transit,1,2.152226,,147.0,2007 963 | Transit,1,1.846835,,220.0,2007 964 | Transit,1,1.33823187,,300.0,2007 965 | Transit,1,1.62843142,,300.0,2008 966 | Transit,1,3.361006,,,2009 967 | Transit,1,4.954658,,140.0,2008 968 | Transit,1,8.158715,,87.0,2010 969 | Transit,1,3.0927616,,90.0,2008 970 | Transit,1,3.7224747,,121.7,2008 971 | Transit,1,1.091423,,,2008 972 | Transit,1,4.353011,,155.0,2009 973 | Transit,1,2.243752,,160.0,2008 974 | Transit,1,3.7520656,,,2009 975 | Transit,1,3.1186009,,,2009 976 | Transit,1,3.7354417,,400.0,2009 977 | Transit,1,0.94145299,,105.49,2009 978 | Transit,1,0.78884,,250.0,2009 979 | Transit,1,4.322482,,242.0,2010 980 | Transit,1,3.5327313,,300.0,2010 981 | Transit,1,2.9444256,,,2010 982 | Transit,1,2.34121242,,,2010 983 | Transit,1,3.764825,,,2010 984 | Transit,1,2.7566004,,250.0,2010 985 | Transit,1,3.408821,,,2010 986 | Transit,1,3.922727,,70.0,2010 987 | Transit,1,3.4059096,,360.0,2010 988 | Transit,1,2.718659,,,2010 989 | Transit,1,1.2198669,,116.14,2010 990 | Transit,1,4.3176782,,120.0,2010 991 | Transit,1,3.161575,,,2011 992 | Transit,1,1.5373653,,450.0,2011 993 | Transit,1,3.577469,,343.0,2010 994 | Transit,1,6.871815,,110.0,2010 995 | Transit,1,4.055259,,230.0,2011 996 | Transit,1,3.052401,,180.0,2010 997 | Transit,1,4.9816872,,160.0,2012 998 | Transit,1,0.813475,,80.0,2011 999 | Transit,1,2.4238039,,,2011 1000 | Transit,1,3.1260876,,,2011 1001 | Transit,1,1.43037,,,2011 1002 | Transit,1,4.1591399,,200.0,2012 1003 | Transit,1,2.143634,,,2011 1004 | Transit,1,2.7817387,,170.0,2012 1005 | Transit,1,1.9550959,,230.0,2011 1006 | Transit,1,1.7497798,,140.0,2012 1007 | Transit,1,3.6936411,,200.0,2012 1008 | Transit,1,4.465633,,330.0,2012 1009 | Transit,1,4.617101,,255.0,2012 1010 | Transit,1,2.838971,,455.0,2012 1011 | Transit,1,5.01718,,300.0,2012 1012 | Transit,1,7.919585,,125.0,2012 1013 | Transit,1,4.3050011,,400.0,2012 1014 | Transit,1,3.8559,,480.0,2012 1015 | Transit,1,4.411953,,160.0,2012 1016 | Transit,1,4.37809,,330.0,2012 1017 | Transit,1,1.5732918,,350.0,2012 1018 | Transit,1,2.3114243,,310.0,2013 1019 | Transit,1,4.086052,,380.0,2012 1020 | Transit,1,4.61442,,225.0,2012 1021 | Transit,1,2.9036747,,345.0,2012 1022 | Transit,1,2.2167421,,340.0,2012 1023 | Transit,1,2.484193,,260.0,2013 1024 | Transit,1,1.3600309,,93.0,2012 1025 | Transit,1,2.17517632,,550.0,2012 1026 | Transit,1,3.6623866,,240.0,2012 1027 | Transit,1,3.0678504,,60.0,2012 1028 | Transit,1,0.925542,,470.0,2014 1029 | Imaging,1,,,19.2,2011 1030 | Transit,1,3.352057,,3200.0,2012 1031 | Imaging,1,,,10.1,2012 1032 | Transit,1,3.94150685,,172.0,2006 1033 | Transit,1,2.615864,,148.0,2007 1034 | Transit,1,3.1915239,,174.0,2007 1035 | Transit,1,4.1250828,,293.0,2008 1036 | Transit,1,4.187757,,260.0,2008 1037 | -------------------------------------------------------------------------------- /datasets/tips.csv: -------------------------------------------------------------------------------- 1 | "total_bill","tip","sex","smoker","day","time","size" 2 | 16.99,1.01,"Female","No","Sun","Dinner",2 3 | 10.34,1.66,"Male","No","Sun","Dinner",3 4 | 21.01,3.5,"Male","No","Sun","Dinner",3 5 | 23.68,3.31,"Male","No","Sun","Dinner",2 6 | 24.59,3.61,"Female","No","Sun","Dinner",4 7 | 25.29,4.71,"Male","No","Sun","Dinner",4 8 | 8.77,2,"Male","No","Sun","Dinner",2 9 | 26.88,3.12,"Male","No","Sun","Dinner",4 10 | 15.04,1.96,"Male","No","Sun","Dinner",2 11 | 14.78,3.23,"Male","No","Sun","Dinner",2 12 | 10.27,1.71,"Male","No","Sun","Dinner",2 13 | 35.26,5,"Female","No","Sun","Dinner",4 14 | 15.42,1.57,"Male","No","Sun","Dinner",2 15 | 18.43,3,"Male","No","Sun","Dinner",4 16 | 14.83,3.02,"Female","No","Sun","Dinner",2 17 | 21.58,3.92,"Male","No","Sun","Dinner",2 18 | 10.33,1.67,"Female","No","Sun","Dinner",3 19 | 16.29,3.71,"Male","No","Sun","Dinner",3 20 | 16.97,3.5,"Female","No","Sun","Dinner",3 21 | 20.65,3.35,"Male","No","Sat","Dinner",3 22 | 17.92,4.08,"Male","No","Sat","Dinner",2 23 | 20.29,2.75,"Female","No","Sat","Dinner",2 24 | 15.77,2.23,"Female","No","Sat","Dinner",2 25 | 39.42,7.58,"Male","No","Sat","Dinner",4 26 | 19.82,3.18,"Male","No","Sat","Dinner",2 27 | 17.81,2.34,"Male","No","Sat","Dinner",4 28 | 13.37,2,"Male","No","Sat","Dinner",2 29 | 12.69,2,"Male","No","Sat","Dinner",2 30 | 21.7,4.3,"Male","No","Sat","Dinner",2 31 | 19.65,3,"Female","No","Sat","Dinner",2 32 | 9.55,1.45,"Male","No","Sat","Dinner",2 33 | 18.35,2.5,"Male","No","Sat","Dinner",4 34 | 15.06,3,"Female","No","Sat","Dinner",2 35 | 20.69,2.45,"Female","No","Sat","Dinner",4 36 | 17.78,3.27,"Male","No","Sat","Dinner",2 37 | 24.06,3.6,"Male","No","Sat","Dinner",3 38 | 16.31,2,"Male","No","Sat","Dinner",3 39 | 16.93,3.07,"Female","No","Sat","Dinner",3 40 | 18.69,2.31,"Male","No","Sat","Dinner",3 41 | 31.27,5,"Male","No","Sat","Dinner",3 42 | 16.04,2.24,"Male","No","Sat","Dinner",3 43 | 17.46,2.54,"Male","No","Sun","Dinner",2 44 | 13.94,3.06,"Male","No","Sun","Dinner",2 45 | 9.68,1.32,"Male","No","Sun","Dinner",2 46 | 30.4,5.6,"Male","No","Sun","Dinner",4 47 | 18.29,3,"Male","No","Sun","Dinner",2 48 | 22.23,5,"Male","No","Sun","Dinner",2 49 | 32.4,6,"Male","No","Sun","Dinner",4 50 | 28.55,2.05,"Male","No","Sun","Dinner",3 51 | 18.04,3,"Male","No","Sun","Dinner",2 52 | 12.54,2.5,"Male","No","Sun","Dinner",2 53 | 10.29,2.6,"Female","No","Sun","Dinner",2 54 | 34.81,5.2,"Female","No","Sun","Dinner",4 55 | 9.94,1.56,"Male","No","Sun","Dinner",2 56 | 25.56,4.34,"Male","No","Sun","Dinner",4 57 | 19.49,3.51,"Male","No","Sun","Dinner",2 58 | 38.01,3,"Male","Yes","Sat","Dinner",4 59 | 26.41,1.5,"Female","No","Sat","Dinner",2 60 | 11.24,1.76,"Male","Yes","Sat","Dinner",2 61 | 48.27,6.73,"Male","No","Sat","Dinner",4 62 | 20.29,3.21,"Male","Yes","Sat","Dinner",2 63 | 13.81,2,"Male","Yes","Sat","Dinner",2 64 | 11.02,1.98,"Male","Yes","Sat","Dinner",2 65 | 18.29,3.76,"Male","Yes","Sat","Dinner",4 66 | 17.59,2.64,"Male","No","Sat","Dinner",3 67 | 20.08,3.15,"Male","No","Sat","Dinner",3 68 | 16.45,2.47,"Female","No","Sat","Dinner",2 69 | 3.07,1,"Female","Yes","Sat","Dinner",1 70 | 20.23,2.01,"Male","No","Sat","Dinner",2 71 | 15.01,2.09,"Male","Yes","Sat","Dinner",2 72 | 12.02,1.97,"Male","No","Sat","Dinner",2 73 | 17.07,3,"Female","No","Sat","Dinner",3 74 | 26.86,3.14,"Female","Yes","Sat","Dinner",2 75 | 25.28,5,"Female","Yes","Sat","Dinner",2 76 | 14.73,2.2,"Female","No","Sat","Dinner",2 77 | 10.51,1.25,"Male","No","Sat","Dinner",2 78 | 17.92,3.08,"Male","Yes","Sat","Dinner",2 79 | 27.2,4,"Male","No","Thur","Lunch",4 80 | 22.76,3,"Male","No","Thur","Lunch",2 81 | 17.29,2.71,"Male","No","Thur","Lunch",2 82 | 19.44,3,"Male","Yes","Thur","Lunch",2 83 | 16.66,3.4,"Male","No","Thur","Lunch",2 84 | 10.07,1.83,"Female","No","Thur","Lunch",1 85 | 32.68,5,"Male","Yes","Thur","Lunch",2 86 | 15.98,2.03,"Male","No","Thur","Lunch",2 87 | 34.83,5.17,"Female","No","Thur","Lunch",4 88 | 13.03,2,"Male","No","Thur","Lunch",2 89 | 18.28,4,"Male","No","Thur","Lunch",2 90 | 24.71,5.85,"Male","No","Thur","Lunch",2 91 | 21.16,3,"Male","No","Thur","Lunch",2 92 | 28.97,3,"Male","Yes","Fri","Dinner",2 93 | 22.49,3.5,"Male","No","Fri","Dinner",2 94 | 5.75,1,"Female","Yes","Fri","Dinner",2 95 | 16.32,4.3,"Female","Yes","Fri","Dinner",2 96 | 22.75,3.25,"Female","No","Fri","Dinner",2 97 | 40.17,4.73,"Male","Yes","Fri","Dinner",4 98 | 27.28,4,"Male","Yes","Fri","Dinner",2 99 | 12.03,1.5,"Male","Yes","Fri","Dinner",2 100 | 21.01,3,"Male","Yes","Fri","Dinner",2 101 | 12.46,1.5,"Male","No","Fri","Dinner",2 102 | 11.35,2.5,"Female","Yes","Fri","Dinner",2 103 | 15.38,3,"Female","Yes","Fri","Dinner",2 104 | 44.3,2.5,"Female","Yes","Sat","Dinner",3 105 | 22.42,3.48,"Female","Yes","Sat","Dinner",2 106 | 20.92,4.08,"Female","No","Sat","Dinner",2 107 | 15.36,1.64,"Male","Yes","Sat","Dinner",2 108 | 20.49,4.06,"Male","Yes","Sat","Dinner",2 109 | 25.21,4.29,"Male","Yes","Sat","Dinner",2 110 | 18.24,3.76,"Male","No","Sat","Dinner",2 111 | 14.31,4,"Female","Yes","Sat","Dinner",2 112 | 14,3,"Male","No","Sat","Dinner",2 113 | 7.25,1,"Female","No","Sat","Dinner",1 114 | 38.07,4,"Male","No","Sun","Dinner",3 115 | 23.95,2.55,"Male","No","Sun","Dinner",2 116 | 25.71,4,"Female","No","Sun","Dinner",3 117 | 17.31,3.5,"Female","No","Sun","Dinner",2 118 | 29.93,5.07,"Male","No","Sun","Dinner",4 119 | 10.65,1.5,"Female","No","Thur","Lunch",2 120 | 12.43,1.8,"Female","No","Thur","Lunch",2 121 | 24.08,2.92,"Female","No","Thur","Lunch",4 122 | 11.69,2.31,"Male","No","Thur","Lunch",2 123 | 13.42,1.68,"Female","No","Thur","Lunch",2 124 | 14.26,2.5,"Male","No","Thur","Lunch",2 125 | 15.95,2,"Male","No","Thur","Lunch",2 126 | 12.48,2.52,"Female","No","Thur","Lunch",2 127 | 29.8,4.2,"Female","No","Thur","Lunch",6 128 | 8.52,1.48,"Male","No","Thur","Lunch",2 129 | 14.52,2,"Female","No","Thur","Lunch",2 130 | 11.38,2,"Female","No","Thur","Lunch",2 131 | 22.82,2.18,"Male","No","Thur","Lunch",3 132 | 19.08,1.5,"Male","No","Thur","Lunch",2 133 | 20.27,2.83,"Female","No","Thur","Lunch",2 134 | 11.17,1.5,"Female","No","Thur","Lunch",2 135 | 12.26,2,"Female","No","Thur","Lunch",2 136 | 18.26,3.25,"Female","No","Thur","Lunch",2 137 | 8.51,1.25,"Female","No","Thur","Lunch",2 138 | 10.33,2,"Female","No","Thur","Lunch",2 139 | 14.15,2,"Female","No","Thur","Lunch",2 140 | 16,2,"Male","Yes","Thur","Lunch",2 141 | 13.16,2.75,"Female","No","Thur","Lunch",2 142 | 17.47,3.5,"Female","No","Thur","Lunch",2 143 | 34.3,6.7,"Male","No","Thur","Lunch",6 144 | 41.19,5,"Male","No","Thur","Lunch",5 145 | 27.05,5,"Female","No","Thur","Lunch",6 146 | 16.43,2.3,"Female","No","Thur","Lunch",2 147 | 8.35,1.5,"Female","No","Thur","Lunch",2 148 | 18.64,1.36,"Female","No","Thur","Lunch",3 149 | 11.87,1.63,"Female","No","Thur","Lunch",2 150 | 9.78,1.73,"Male","No","Thur","Lunch",2 151 | 7.51,2,"Male","No","Thur","Lunch",2 152 | 14.07,2.5,"Male","No","Sun","Dinner",2 153 | 13.13,2,"Male","No","Sun","Dinner",2 154 | 17.26,2.74,"Male","No","Sun","Dinner",3 155 | 24.55,2,"Male","No","Sun","Dinner",4 156 | 19.77,2,"Male","No","Sun","Dinner",4 157 | 29.85,5.14,"Female","No","Sun","Dinner",5 158 | 48.17,5,"Male","No","Sun","Dinner",6 159 | 25,3.75,"Female","No","Sun","Dinner",4 160 | 13.39,2.61,"Female","No","Sun","Dinner",2 161 | 16.49,2,"Male","No","Sun","Dinner",4 162 | 21.5,3.5,"Male","No","Sun","Dinner",4 163 | 12.66,2.5,"Male","No","Sun","Dinner",2 164 | 16.21,2,"Female","No","Sun","Dinner",3 165 | 13.81,2,"Male","No","Sun","Dinner",2 166 | 17.51,3,"Female","Yes","Sun","Dinner",2 167 | 24.52,3.48,"Male","No","Sun","Dinner",3 168 | 20.76,2.24,"Male","No","Sun","Dinner",2 169 | 31.71,4.5,"Male","No","Sun","Dinner",4 170 | 10.59,1.61,"Female","Yes","Sat","Dinner",2 171 | 10.63,2,"Female","Yes","Sat","Dinner",2 172 | 50.81,10,"Male","Yes","Sat","Dinner",3 173 | 15.81,3.16,"Male","Yes","Sat","Dinner",2 174 | 7.25,5.15,"Male","Yes","Sun","Dinner",2 175 | 31.85,3.18,"Male","Yes","Sun","Dinner",2 176 | 16.82,4,"Male","Yes","Sun","Dinner",2 177 | 32.9,3.11,"Male","Yes","Sun","Dinner",2 178 | 17.89,2,"Male","Yes","Sun","Dinner",2 179 | 14.48,2,"Male","Yes","Sun","Dinner",2 180 | 9.6,4,"Female","Yes","Sun","Dinner",2 181 | 34.63,3.55,"Male","Yes","Sun","Dinner",2 182 | 34.65,3.68,"Male","Yes","Sun","Dinner",4 183 | 23.33,5.65,"Male","Yes","Sun","Dinner",2 184 | 45.35,3.5,"Male","Yes","Sun","Dinner",3 185 | 23.17,6.5,"Male","Yes","Sun","Dinner",4 186 | 40.55,3,"Male","Yes","Sun","Dinner",2 187 | 20.69,5,"Male","No","Sun","Dinner",5 188 | 20.9,3.5,"Female","Yes","Sun","Dinner",3 189 | 30.46,2,"Male","Yes","Sun","Dinner",5 190 | 18.15,3.5,"Female","Yes","Sun","Dinner",3 191 | 23.1,4,"Male","Yes","Sun","Dinner",3 192 | 15.69,1.5,"Male","Yes","Sun","Dinner",2 193 | 19.81,4.19,"Female","Yes","Thur","Lunch",2 194 | 28.44,2.56,"Male","Yes","Thur","Lunch",2 195 | 15.48,2.02,"Male","Yes","Thur","Lunch",2 196 | 16.58,4,"Male","Yes","Thur","Lunch",2 197 | 7.56,1.44,"Male","No","Thur","Lunch",2 198 | 10.34,2,"Male","Yes","Thur","Lunch",2 199 | 43.11,5,"Female","Yes","Thur","Lunch",4 200 | 13,2,"Female","Yes","Thur","Lunch",2 201 | 13.51,2,"Male","Yes","Thur","Lunch",2 202 | 18.71,4,"Male","Yes","Thur","Lunch",3 203 | 12.74,2.01,"Female","Yes","Thur","Lunch",2 204 | 13,2,"Female","Yes","Thur","Lunch",2 205 | 16.4,2.5,"Female","Yes","Thur","Lunch",2 206 | 20.53,4,"Male","Yes","Thur","Lunch",4 207 | 16.47,3.23,"Female","Yes","Thur","Lunch",3 208 | 26.59,3.41,"Male","Yes","Sat","Dinner",3 209 | 38.73,3,"Male","Yes","Sat","Dinner",4 210 | 24.27,2.03,"Male","Yes","Sat","Dinner",2 211 | 12.76,2.23,"Female","Yes","Sat","Dinner",2 212 | 30.06,2,"Male","Yes","Sat","Dinner",3 213 | 25.89,5.16,"Male","Yes","Sat","Dinner",4 214 | 48.33,9,"Male","No","Sat","Dinner",4 215 | 13.27,2.5,"Female","Yes","Sat","Dinner",2 216 | 28.17,6.5,"Female","Yes","Sat","Dinner",3 217 | 12.9,1.1,"Female","Yes","Sat","Dinner",2 218 | 28.15,3,"Male","Yes","Sat","Dinner",5 219 | 11.59,1.5,"Male","Yes","Sat","Dinner",2 220 | 7.74,1.44,"Male","Yes","Sat","Dinner",2 221 | 30.14,3.09,"Female","Yes","Sat","Dinner",4 222 | 12.16,2.2,"Male","Yes","Fri","Lunch",2 223 | 13.42,3.48,"Female","Yes","Fri","Lunch",2 224 | 8.58,1.92,"Male","Yes","Fri","Lunch",1 225 | 15.98,3,"Female","No","Fri","Lunch",3 226 | 13.42,1.58,"Male","Yes","Fri","Lunch",2 227 | 16.27,2.5,"Female","Yes","Fri","Lunch",2 228 | 10.09,2,"Female","Yes","Fri","Lunch",2 229 | 20.45,3,"Male","No","Sat","Dinner",4 230 | 13.28,2.72,"Male","No","Sat","Dinner",2 231 | 22.12,2.88,"Female","Yes","Sat","Dinner",2 232 | 24.01,2,"Male","Yes","Sat","Dinner",4 233 | 15.69,3,"Male","Yes","Sat","Dinner",3 234 | 11.61,3.39,"Male","No","Sat","Dinner",2 235 | 10.77,1.47,"Male","No","Sat","Dinner",2 236 | 15.53,3,"Male","Yes","Sat","Dinner",2 237 | 10.07,1.25,"Male","No","Sat","Dinner",2 238 | 12.6,1,"Male","Yes","Sat","Dinner",2 239 | 32.83,1.17,"Male","Yes","Sat","Dinner",2 240 | 35.83,4.67,"Female","No","Sat","Dinner",3 241 | 29.03,5.92,"Male","No","Sat","Dinner",3 242 | 27.18,2,"Female","Yes","Sat","Dinner",2 243 | 22.67,2,"Male","Yes","Sat","Dinner",2 244 | 17.82,1.75,"Male","No","Sat","Dinner",2 245 | 18.78,3,"Female","No","Thur","Dinner",2 246 | -------------------------------------------------------------------------------- /datasets/titanic.csv: -------------------------------------------------------------------------------- 1 | survived,pclass,sex,age,sibsp,parch,fare,embarked,class,who,adult_male,deck,embark_town,alive,alone 2 | 0,3,male,22.0,1,0,7.25,S,Third,man,True,,Southampton,no,False 3 | 1,1,female,38.0,1,0,71.2833,C,First,woman,False,C,Cherbourg,yes,False 4 | 1,3,female,26.0,0,0,7.925,S,Third,woman,False,,Southampton,yes,True 5 | 1,1,female,35.0,1,0,53.1,S,First,woman,False,C,Southampton,yes,False 6 | 0,3,male,35.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 7 | 0,3,male,,0,0,8.4583,Q,Third,man,True,,Queenstown,no,True 8 | 0,1,male,54.0,0,0,51.8625,S,First,man,True,E,Southampton,no,True 9 | 0,3,male,2.0,3,1,21.075,S,Third,child,False,,Southampton,no,False 10 | 1,3,female,27.0,0,2,11.1333,S,Third,woman,False,,Southampton,yes,False 11 | 1,2,female,14.0,1,0,30.0708,C,Second,child,False,,Cherbourg,yes,False 12 | 1,3,female,4.0,1,1,16.7,S,Third,child,False,G,Southampton,yes,False 13 | 1,1,female,58.0,0,0,26.55,S,First,woman,False,C,Southampton,yes,True 14 | 0,3,male,20.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 15 | 0,3,male,39.0,1,5,31.275,S,Third,man,True,,Southampton,no,False 16 | 0,3,female,14.0,0,0,7.8542,S,Third,child,False,,Southampton,no,True 17 | 1,2,female,55.0,0,0,16.0,S,Second,woman,False,,Southampton,yes,True 18 | 0,3,male,2.0,4,1,29.125,Q,Third,child,False,,Queenstown,no,False 19 | 1,2,male,,0,0,13.0,S,Second,man,True,,Southampton,yes,True 20 | 0,3,female,31.0,1,0,18.0,S,Third,woman,False,,Southampton,no,False 21 | 1,3,female,,0,0,7.225,C,Third,woman,False,,Cherbourg,yes,True 22 | 0,2,male,35.0,0,0,26.0,S,Second,man,True,,Southampton,no,True 23 | 1,2,male,34.0,0,0,13.0,S,Second,man,True,D,Southampton,yes,True 24 | 1,3,female,15.0,0,0,8.0292,Q,Third,child,False,,Queenstown,yes,True 25 | 1,1,male,28.0,0,0,35.5,S,First,man,True,A,Southampton,yes,True 26 | 0,3,female,8.0,3,1,21.075,S,Third,child,False,,Southampton,no,False 27 | 1,3,female,38.0,1,5,31.3875,S,Third,woman,False,,Southampton,yes,False 28 | 0,3,male,,0,0,7.225,C,Third,man,True,,Cherbourg,no,True 29 | 0,1,male,19.0,3,2,263.0,S,First,man,True,C,Southampton,no,False 30 | 1,3,female,,0,0,7.8792,Q,Third,woman,False,,Queenstown,yes,True 31 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 32 | 0,1,male,40.0,0,0,27.7208,C,First,man,True,,Cherbourg,no,True 33 | 1,1,female,,1,0,146.5208,C,First,woman,False,B,Cherbourg,yes,False 34 | 1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True 35 | 0,2,male,66.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 36 | 0,1,male,28.0,1,0,82.1708,C,First,man,True,,Cherbourg,no,False 37 | 0,1,male,42.0,1,0,52.0,S,First,man,True,,Southampton,no,False 38 | 1,3,male,,0,0,7.2292,C,Third,man,True,,Cherbourg,yes,True 39 | 0,3,male,21.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 40 | 0,3,female,18.0,2,0,18.0,S,Third,woman,False,,Southampton,no,False 41 | 1,3,female,14.0,1,0,11.2417,C,Third,child,False,,Cherbourg,yes,False 42 | 0,3,female,40.0,1,0,9.475,S,Third,woman,False,,Southampton,no,False 43 | 0,2,female,27.0,1,0,21.0,S,Second,woman,False,,Southampton,no,False 44 | 0,3,male,,0,0,7.8958,C,Third,man,True,,Cherbourg,no,True 45 | 1,2,female,3.0,1,2,41.5792,C,Second,child,False,,Cherbourg,yes,False 46 | 1,3,female,19.0,0,0,7.8792,Q,Third,woman,False,,Queenstown,yes,True 47 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 48 | 0,3,male,,1,0,15.5,Q,Third,man,True,,Queenstown,no,False 49 | 1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True 50 | 0,3,male,,2,0,21.6792,C,Third,man,True,,Cherbourg,no,False 51 | 0,3,female,18.0,1,0,17.8,S,Third,woman,False,,Southampton,no,False 52 | 0,3,male,7.0,4,1,39.6875,S,Third,child,False,,Southampton,no,False 53 | 0,3,male,21.0,0,0,7.8,S,Third,man,True,,Southampton,no,True 54 | 1,1,female,49.0,1,0,76.7292,C,First,woman,False,D,Cherbourg,yes,False 55 | 1,2,female,29.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False 56 | 0,1,male,65.0,0,1,61.9792,C,First,man,True,B,Cherbourg,no,False 57 | 1,1,male,,0,0,35.5,S,First,man,True,C,Southampton,yes,True 58 | 1,2,female,21.0,0,0,10.5,S,Second,woman,False,,Southampton,yes,True 59 | 0,3,male,28.5,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True 60 | 1,2,female,5.0,1,2,27.75,S,Second,child,False,,Southampton,yes,False 61 | 0,3,male,11.0,5,2,46.9,S,Third,child,False,,Southampton,no,False 62 | 0,3,male,22.0,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True 63 | 1,1,female,38.0,0,0,80.0,,First,woman,False,B,,yes,True 64 | 0,1,male,45.0,1,0,83.475,S,First,man,True,C,Southampton,no,False 65 | 0,3,male,4.0,3,2,27.9,S,Third,child,False,,Southampton,no,False 66 | 0,1,male,,0,0,27.7208,C,First,man,True,,Cherbourg,no,True 67 | 1,3,male,,1,1,15.2458,C,Third,man,True,,Cherbourg,yes,False 68 | 1,2,female,29.0,0,0,10.5,S,Second,woman,False,F,Southampton,yes,True 69 | 0,3,male,19.0,0,0,8.1583,S,Third,man,True,,Southampton,no,True 70 | 1,3,female,17.0,4,2,7.925,S,Third,woman,False,,Southampton,yes,False 71 | 0,3,male,26.0,2,0,8.6625,S,Third,man,True,,Southampton,no,False 72 | 0,2,male,32.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 73 | 0,3,female,16.0,5,2,46.9,S,Third,woman,False,,Southampton,no,False 74 | 0,2,male,21.0,0,0,73.5,S,Second,man,True,,Southampton,no,True 75 | 0,3,male,26.0,1,0,14.4542,C,Third,man,True,,Cherbourg,no,False 76 | 1,3,male,32.0,0,0,56.4958,S,Third,man,True,,Southampton,yes,True 77 | 0,3,male,25.0,0,0,7.65,S,Third,man,True,F,Southampton,no,True 78 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 79 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 80 | 1,2,male,0.83,0,2,29.0,S,Second,child,False,,Southampton,yes,False 81 | 1,3,female,30.0,0,0,12.475,S,Third,woman,False,,Southampton,yes,True 82 | 0,3,male,22.0,0,0,9.0,S,Third,man,True,,Southampton,no,True 83 | 1,3,male,29.0,0,0,9.5,S,Third,man,True,,Southampton,yes,True 84 | 1,3,female,,0,0,7.7875,Q,Third,woman,False,,Queenstown,yes,True 85 | 0,1,male,28.0,0,0,47.1,S,First,man,True,,Southampton,no,True 86 | 1,2,female,17.0,0,0,10.5,S,Second,woman,False,,Southampton,yes,True 87 | 1,3,female,33.0,3,0,15.85,S,Third,woman,False,,Southampton,yes,False 88 | 0,3,male,16.0,1,3,34.375,S,Third,man,True,,Southampton,no,False 89 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 90 | 1,1,female,23.0,3,2,263.0,S,First,woman,False,C,Southampton,yes,False 91 | 0,3,male,24.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 92 | 0,3,male,29.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 93 | 0,3,male,20.0,0,0,7.8542,S,Third,man,True,,Southampton,no,True 94 | 0,1,male,46.0,1,0,61.175,S,First,man,True,E,Southampton,no,False 95 | 0,3,male,26.0,1,2,20.575,S,Third,man,True,,Southampton,no,False 96 | 0,3,male,59.0,0,0,7.25,S,Third,man,True,,Southampton,no,True 97 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 98 | 0,1,male,71.0,0,0,34.6542,C,First,man,True,A,Cherbourg,no,True 99 | 1,1,male,23.0,0,1,63.3583,C,First,man,True,D,Cherbourg,yes,False 100 | 1,2,female,34.0,0,1,23.0,S,Second,woman,False,,Southampton,yes,False 101 | 0,2,male,34.0,1,0,26.0,S,Second,man,True,,Southampton,no,False 102 | 0,3,female,28.0,0,0,7.8958,S,Third,woman,False,,Southampton,no,True 103 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 104 | 0,1,male,21.0,0,1,77.2875,S,First,man,True,D,Southampton,no,False 105 | 0,3,male,33.0,0,0,8.6542,S,Third,man,True,,Southampton,no,True 106 | 0,3,male,37.0,2,0,7.925,S,Third,man,True,,Southampton,no,False 107 | 0,3,male,28.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 108 | 1,3,female,21.0,0,0,7.65,S,Third,woman,False,,Southampton,yes,True 109 | 1,3,male,,0,0,7.775,S,Third,man,True,,Southampton,yes,True 110 | 0,3,male,38.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 111 | 1,3,female,,1,0,24.15,Q,Third,woman,False,,Queenstown,yes,False 112 | 0,1,male,47.0,0,0,52.0,S,First,man,True,C,Southampton,no,True 113 | 0,3,female,14.5,1,0,14.4542,C,Third,child,False,,Cherbourg,no,False 114 | 0,3,male,22.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 115 | 0,3,female,20.0,1,0,9.825,S,Third,woman,False,,Southampton,no,False 116 | 0,3,female,17.0,0,0,14.4583,C,Third,woman,False,,Cherbourg,no,True 117 | 0,3,male,21.0,0,0,7.925,S,Third,man,True,,Southampton,no,True 118 | 0,3,male,70.5,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 119 | 0,2,male,29.0,1,0,21.0,S,Second,man,True,,Southampton,no,False 120 | 0,1,male,24.0,0,1,247.5208,C,First,man,True,B,Cherbourg,no,False 121 | 0,3,female,2.0,4,2,31.275,S,Third,child,False,,Southampton,no,False 122 | 0,2,male,21.0,2,0,73.5,S,Second,man,True,,Southampton,no,False 123 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 124 | 0,2,male,32.5,1,0,30.0708,C,Second,man,True,,Cherbourg,no,False 125 | 1,2,female,32.5,0,0,13.0,S,Second,woman,False,E,Southampton,yes,True 126 | 0,1,male,54.0,0,1,77.2875,S,First,man,True,D,Southampton,no,False 127 | 1,3,male,12.0,1,0,11.2417,C,Third,child,False,,Cherbourg,yes,False 128 | 0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 129 | 1,3,male,24.0,0,0,7.1417,S,Third,man,True,,Southampton,yes,True 130 | 1,3,female,,1,1,22.3583,C,Third,woman,False,F,Cherbourg,yes,False 131 | 0,3,male,45.0,0,0,6.975,S,Third,man,True,,Southampton,no,True 132 | 0,3,male,33.0,0,0,7.8958,C,Third,man,True,,Cherbourg,no,True 133 | 0,3,male,20.0,0,0,7.05,S,Third,man,True,,Southampton,no,True 134 | 0,3,female,47.0,1,0,14.5,S,Third,woman,False,,Southampton,no,False 135 | 1,2,female,29.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False 136 | 0,2,male,25.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 137 | 0,2,male,23.0,0,0,15.0458,C,Second,man,True,,Cherbourg,no,True 138 | 1,1,female,19.0,0,2,26.2833,S,First,woman,False,D,Southampton,yes,False 139 | 0,1,male,37.0,1,0,53.1,S,First,man,True,C,Southampton,no,False 140 | 0,3,male,16.0,0,0,9.2167,S,Third,man,True,,Southampton,no,True 141 | 0,1,male,24.0,0,0,79.2,C,First,man,True,B,Cherbourg,no,True 142 | 0,3,female,,0,2,15.2458,C,Third,woman,False,,Cherbourg,no,False 143 | 1,3,female,22.0,0,0,7.75,S,Third,woman,False,,Southampton,yes,True 144 | 1,3,female,24.0,1,0,15.85,S,Third,woman,False,,Southampton,yes,False 145 | 0,3,male,19.0,0,0,6.75,Q,Third,man,True,,Queenstown,no,True 146 | 0,2,male,18.0,0,0,11.5,S,Second,man,True,,Southampton,no,True 147 | 0,2,male,19.0,1,1,36.75,S,Second,man,True,,Southampton,no,False 148 | 1,3,male,27.0,0,0,7.7958,S,Third,man,True,,Southampton,yes,True 149 | 0,3,female,9.0,2,2,34.375,S,Third,child,False,,Southampton,no,False 150 | 0,2,male,36.5,0,2,26.0,S,Second,man,True,F,Southampton,no,False 151 | 0,2,male,42.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 152 | 0,2,male,51.0,0,0,12.525,S,Second,man,True,,Southampton,no,True 153 | 1,1,female,22.0,1,0,66.6,S,First,woman,False,C,Southampton,yes,False 154 | 0,3,male,55.5,0,0,8.05,S,Third,man,True,,Southampton,no,True 155 | 0,3,male,40.5,0,2,14.5,S,Third,man,True,,Southampton,no,False 156 | 0,3,male,,0,0,7.3125,S,Third,man,True,,Southampton,no,True 157 | 0,1,male,51.0,0,1,61.3792,C,First,man,True,,Cherbourg,no,False 158 | 1,3,female,16.0,0,0,7.7333,Q,Third,woman,False,,Queenstown,yes,True 159 | 0,3,male,30.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 160 | 0,3,male,,0,0,8.6625,S,Third,man,True,,Southampton,no,True 161 | 0,3,male,,8,2,69.55,S,Third,man,True,,Southampton,no,False 162 | 0,3,male,44.0,0,1,16.1,S,Third,man,True,,Southampton,no,False 163 | 1,2,female,40.0,0,0,15.75,S,Second,woman,False,,Southampton,yes,True 164 | 0,3,male,26.0,0,0,7.775,S,Third,man,True,,Southampton,no,True 165 | 0,3,male,17.0,0,0,8.6625,S,Third,man,True,,Southampton,no,True 166 | 0,3,male,1.0,4,1,39.6875,S,Third,child,False,,Southampton,no,False 167 | 1,3,male,9.0,0,2,20.525,S,Third,child,False,,Southampton,yes,False 168 | 1,1,female,,0,1,55.0,S,First,woman,False,E,Southampton,yes,False 169 | 0,3,female,45.0,1,4,27.9,S,Third,woman,False,,Southampton,no,False 170 | 0,1,male,,0,0,25.925,S,First,man,True,,Southampton,no,True 171 | 0,3,male,28.0,0,0,56.4958,S,Third,man,True,,Southampton,no,True 172 | 0,1,male,61.0,0,0,33.5,S,First,man,True,B,Southampton,no,True 173 | 0,3,male,4.0,4,1,29.125,Q,Third,child,False,,Queenstown,no,False 174 | 1,3,female,1.0,1,1,11.1333,S,Third,child,False,,Southampton,yes,False 175 | 0,3,male,21.0,0,0,7.925,S,Third,man,True,,Southampton,no,True 176 | 0,1,male,56.0,0,0,30.6958,C,First,man,True,A,Cherbourg,no,True 177 | 0,3,male,18.0,1,1,7.8542,S,Third,man,True,,Southampton,no,False 178 | 0,3,male,,3,1,25.4667,S,Third,man,True,,Southampton,no,False 179 | 0,1,female,50.0,0,0,28.7125,C,First,woman,False,C,Cherbourg,no,True 180 | 0,2,male,30.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 181 | 0,3,male,36.0,0,0,0.0,S,Third,man,True,,Southampton,no,True 182 | 0,3,female,,8,2,69.55,S,Third,woman,False,,Southampton,no,False 183 | 0,2,male,,0,0,15.05,C,Second,man,True,,Cherbourg,no,True 184 | 0,3,male,9.0,4,2,31.3875,S,Third,child,False,,Southampton,no,False 185 | 1,2,male,1.0,2,1,39.0,S,Second,child,False,F,Southampton,yes,False 186 | 1,3,female,4.0,0,2,22.025,S,Third,child,False,,Southampton,yes,False 187 | 0,1,male,,0,0,50.0,S,First,man,True,A,Southampton,no,True 188 | 1,3,female,,1,0,15.5,Q,Third,woman,False,,Queenstown,yes,False 189 | 1,1,male,45.0,0,0,26.55,S,First,man,True,,Southampton,yes,True 190 | 0,3,male,40.0,1,1,15.5,Q,Third,man,True,,Queenstown,no,False 191 | 0,3,male,36.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 192 | 1,2,female,32.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True 193 | 0,2,male,19.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 194 | 1,3,female,19.0,1,0,7.8542,S,Third,woman,False,,Southampton,yes,False 195 | 1,2,male,3.0,1,1,26.0,S,Second,child,False,F,Southampton,yes,False 196 | 1,1,female,44.0,0,0,27.7208,C,First,woman,False,B,Cherbourg,yes,True 197 | 1,1,female,58.0,0,0,146.5208,C,First,woman,False,B,Cherbourg,yes,True 198 | 0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 199 | 0,3,male,42.0,0,1,8.4042,S,Third,man,True,,Southampton,no,False 200 | 1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True 201 | 0,2,female,24.0,0,0,13.0,S,Second,woman,False,,Southampton,no,True 202 | 0,3,male,28.0,0,0,9.5,S,Third,man,True,,Southampton,no,True 203 | 0,3,male,,8,2,69.55,S,Third,man,True,,Southampton,no,False 204 | 0,3,male,34.0,0,0,6.4958,S,Third,man,True,,Southampton,no,True 205 | 0,3,male,45.5,0,0,7.225,C,Third,man,True,,Cherbourg,no,True 206 | 1,3,male,18.0,0,0,8.05,S,Third,man,True,,Southampton,yes,True 207 | 0,3,female,2.0,0,1,10.4625,S,Third,child,False,G,Southampton,no,False 208 | 0,3,male,32.0,1,0,15.85,S,Third,man,True,,Southampton,no,False 209 | 1,3,male,26.0,0,0,18.7875,C,Third,man,True,,Cherbourg,yes,True 210 | 1,3,female,16.0,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True 211 | 1,1,male,40.0,0,0,31.0,C,First,man,True,A,Cherbourg,yes,True 212 | 0,3,male,24.0,0,0,7.05,S,Third,man,True,,Southampton,no,True 213 | 1,2,female,35.0,0,0,21.0,S,Second,woman,False,,Southampton,yes,True 214 | 0,3,male,22.0,0,0,7.25,S,Third,man,True,,Southampton,no,True 215 | 0,2,male,30.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 216 | 0,3,male,,1,0,7.75,Q,Third,man,True,,Queenstown,no,False 217 | 1,1,female,31.0,1,0,113.275,C,First,woman,False,D,Cherbourg,yes,False 218 | 1,3,female,27.0,0,0,7.925,S,Third,woman,False,,Southampton,yes,True 219 | 0,2,male,42.0,1,0,27.0,S,Second,man,True,,Southampton,no,False 220 | 1,1,female,32.0,0,0,76.2917,C,First,woman,False,D,Cherbourg,yes,True 221 | 0,2,male,30.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 222 | 1,3,male,16.0,0,0,8.05,S,Third,man,True,,Southampton,yes,True 223 | 0,2,male,27.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 224 | 0,3,male,51.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 225 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 226 | 1,1,male,38.0,1,0,90.0,S,First,man,True,C,Southampton,yes,False 227 | 0,3,male,22.0,0,0,9.35,S,Third,man,True,,Southampton,no,True 228 | 1,2,male,19.0,0,0,10.5,S,Second,man,True,,Southampton,yes,True 229 | 0,3,male,20.5,0,0,7.25,S,Third,man,True,,Southampton,no,True 230 | 0,2,male,18.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 231 | 0,3,female,,3,1,25.4667,S,Third,woman,False,,Southampton,no,False 232 | 1,1,female,35.0,1,0,83.475,S,First,woman,False,C,Southampton,yes,False 233 | 0,3,male,29.0,0,0,7.775,S,Third,man,True,,Southampton,no,True 234 | 0,2,male,59.0,0,0,13.5,S,Second,man,True,,Southampton,no,True 235 | 1,3,female,5.0,4,2,31.3875,S,Third,child,False,,Southampton,yes,False 236 | 0,2,male,24.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 237 | 0,3,female,,0,0,7.55,S,Third,woman,False,,Southampton,no,True 238 | 0,2,male,44.0,1,0,26.0,S,Second,man,True,,Southampton,no,False 239 | 1,2,female,8.0,0,2,26.25,S,Second,child,False,,Southampton,yes,False 240 | 0,2,male,19.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 241 | 0,2,male,33.0,0,0,12.275,S,Second,man,True,,Southampton,no,True 242 | 0,3,female,,1,0,14.4542,C,Third,woman,False,,Cherbourg,no,False 243 | 1,3,female,,1,0,15.5,Q,Third,woman,False,,Queenstown,yes,False 244 | 0,2,male,29.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 245 | 0,3,male,22.0,0,0,7.125,S,Third,man,True,,Southampton,no,True 246 | 0,3,male,30.0,0,0,7.225,C,Third,man,True,,Cherbourg,no,True 247 | 0,1,male,44.0,2,0,90.0,Q,First,man,True,C,Queenstown,no,False 248 | 0,3,female,25.0,0,0,7.775,S,Third,woman,False,,Southampton,no,True 249 | 1,2,female,24.0,0,2,14.5,S,Second,woman,False,,Southampton,yes,False 250 | 1,1,male,37.0,1,1,52.5542,S,First,man,True,D,Southampton,yes,False 251 | 0,2,male,54.0,1,0,26.0,S,Second,man,True,,Southampton,no,False 252 | 0,3,male,,0,0,7.25,S,Third,man,True,,Southampton,no,True 253 | 0,3,female,29.0,1,1,10.4625,S,Third,woman,False,G,Southampton,no,False 254 | 0,1,male,62.0,0,0,26.55,S,First,man,True,C,Southampton,no,True 255 | 0,3,male,30.0,1,0,16.1,S,Third,man,True,,Southampton,no,False 256 | 0,3,female,41.0,0,2,20.2125,S,Third,woman,False,,Southampton,no,False 257 | 1,3,female,29.0,0,2,15.2458,C,Third,woman,False,,Cherbourg,yes,False 258 | 1,1,female,,0,0,79.2,C,First,woman,False,,Cherbourg,yes,True 259 | 1,1,female,30.0,0,0,86.5,S,First,woman,False,B,Southampton,yes,True 260 | 1,1,female,35.0,0,0,512.3292,C,First,woman,False,,Cherbourg,yes,True 261 | 1,2,female,50.0,0,1,26.0,S,Second,woman,False,,Southampton,yes,False 262 | 0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 263 | 1,3,male,3.0,4,2,31.3875,S,Third,child,False,,Southampton,yes,False 264 | 0,1,male,52.0,1,1,79.65,S,First,man,True,E,Southampton,no,False 265 | 0,1,male,40.0,0,0,0.0,S,First,man,True,B,Southampton,no,True 266 | 0,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,no,True 267 | 0,2,male,36.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 268 | 0,3,male,16.0,4,1,39.6875,S,Third,man,True,,Southampton,no,False 269 | 1,3,male,25.0,1,0,7.775,S,Third,man,True,,Southampton,yes,False 270 | 1,1,female,58.0,0,1,153.4625,S,First,woman,False,C,Southampton,yes,False 271 | 1,1,female,35.0,0,0,135.6333,S,First,woman,False,C,Southampton,yes,True 272 | 0,1,male,,0,0,31.0,S,First,man,True,,Southampton,no,True 273 | 1,3,male,25.0,0,0,0.0,S,Third,man,True,,Southampton,yes,True 274 | 1,2,female,41.0,0,1,19.5,S,Second,woman,False,,Southampton,yes,False 275 | 0,1,male,37.0,0,1,29.7,C,First,man,True,C,Cherbourg,no,False 276 | 1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True 277 | 1,1,female,63.0,1,0,77.9583,S,First,woman,False,D,Southampton,yes,False 278 | 0,3,female,45.0,0,0,7.75,S,Third,woman,False,,Southampton,no,True 279 | 0,2,male,,0,0,0.0,S,Second,man,True,,Southampton,no,True 280 | 0,3,male,7.0,4,1,29.125,Q,Third,child,False,,Queenstown,no,False 281 | 1,3,female,35.0,1,1,20.25,S,Third,woman,False,,Southampton,yes,False 282 | 0,3,male,65.0,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 283 | 0,3,male,28.0,0,0,7.8542,S,Third,man,True,,Southampton,no,True 284 | 0,3,male,16.0,0,0,9.5,S,Third,man,True,,Southampton,no,True 285 | 1,3,male,19.0,0,0,8.05,S,Third,man,True,,Southampton,yes,True 286 | 0,1,male,,0,0,26.0,S,First,man,True,A,Southampton,no,True 287 | 0,3,male,33.0,0,0,8.6625,C,Third,man,True,,Cherbourg,no,True 288 | 1,3,male,30.0,0,0,9.5,S,Third,man,True,,Southampton,yes,True 289 | 0,3,male,22.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 290 | 1,2,male,42.0,0,0,13.0,S,Second,man,True,,Southampton,yes,True 291 | 1,3,female,22.0,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True 292 | 1,1,female,26.0,0,0,78.85,S,First,woman,False,,Southampton,yes,True 293 | 1,1,female,19.0,1,0,91.0792,C,First,woman,False,B,Cherbourg,yes,False 294 | 0,2,male,36.0,0,0,12.875,C,Second,man,True,D,Cherbourg,no,True 295 | 0,3,female,24.0,0,0,8.85,S,Third,woman,False,,Southampton,no,True 296 | 0,3,male,24.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 297 | 0,1,male,,0,0,27.7208,C,First,man,True,,Cherbourg,no,True 298 | 0,3,male,23.5,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True 299 | 0,1,female,2.0,1,2,151.55,S,First,child,False,C,Southampton,no,False 300 | 1,1,male,,0,0,30.5,S,First,man,True,C,Southampton,yes,True 301 | 1,1,female,50.0,0,1,247.5208,C,First,woman,False,B,Cherbourg,yes,False 302 | 1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True 303 | 1,3,male,,2,0,23.25,Q,Third,man,True,,Queenstown,yes,False 304 | 0,3,male,19.0,0,0,0.0,S,Third,man,True,,Southampton,no,True 305 | 1,2,female,,0,0,12.35,Q,Second,woman,False,E,Queenstown,yes,True 306 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 307 | 1,1,male,0.92,1,2,151.55,S,First,child,False,C,Southampton,yes,False 308 | 1,1,female,,0,0,110.8833,C,First,woman,False,,Cherbourg,yes,True 309 | 1,1,female,17.0,1,0,108.9,C,First,woman,False,C,Cherbourg,yes,False 310 | 0,2,male,30.0,1,0,24.0,C,Second,man,True,,Cherbourg,no,False 311 | 1,1,female,30.0,0,0,56.9292,C,First,woman,False,E,Cherbourg,yes,True 312 | 1,1,female,24.0,0,0,83.1583,C,First,woman,False,C,Cherbourg,yes,True 313 | 1,1,female,18.0,2,2,262.375,C,First,woman,False,B,Cherbourg,yes,False 314 | 0,2,female,26.0,1,1,26.0,S,Second,woman,False,,Southampton,no,False 315 | 0,3,male,28.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 316 | 0,2,male,43.0,1,1,26.25,S,Second,man,True,,Southampton,no,False 317 | 1,3,female,26.0,0,0,7.8542,S,Third,woman,False,,Southampton,yes,True 318 | 1,2,female,24.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False 319 | 0,2,male,54.0,0,0,14.0,S,Second,man,True,,Southampton,no,True 320 | 1,1,female,31.0,0,2,164.8667,S,First,woman,False,C,Southampton,yes,False 321 | 1,1,female,40.0,1,1,134.5,C,First,woman,False,E,Cherbourg,yes,False 322 | 0,3,male,22.0,0,0,7.25,S,Third,man,True,,Southampton,no,True 323 | 0,3,male,27.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 324 | 1,2,female,30.0,0,0,12.35,Q,Second,woman,False,,Queenstown,yes,True 325 | 1,2,female,22.0,1,1,29.0,S,Second,woman,False,,Southampton,yes,False 326 | 0,3,male,,8,2,69.55,S,Third,man,True,,Southampton,no,False 327 | 1,1,female,36.0,0,0,135.6333,C,First,woman,False,C,Cherbourg,yes,True 328 | 0,3,male,61.0,0,0,6.2375,S,Third,man,True,,Southampton,no,True 329 | 1,2,female,36.0,0,0,13.0,S,Second,woman,False,D,Southampton,yes,True 330 | 1,3,female,31.0,1,1,20.525,S,Third,woman,False,,Southampton,yes,False 331 | 1,1,female,16.0,0,1,57.9792,C,First,woman,False,B,Cherbourg,yes,False 332 | 1,3,female,,2,0,23.25,Q,Third,woman,False,,Queenstown,yes,False 333 | 0,1,male,45.5,0,0,28.5,S,First,man,True,C,Southampton,no,True 334 | 0,1,male,38.0,0,1,153.4625,S,First,man,True,C,Southampton,no,False 335 | 0,3,male,16.0,2,0,18.0,S,Third,man,True,,Southampton,no,False 336 | 1,1,female,,1,0,133.65,S,First,woman,False,,Southampton,yes,False 337 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 338 | 0,1,male,29.0,1,0,66.6,S,First,man,True,C,Southampton,no,False 339 | 1,1,female,41.0,0,0,134.5,C,First,woman,False,E,Cherbourg,yes,True 340 | 1,3,male,45.0,0,0,8.05,S,Third,man,True,,Southampton,yes,True 341 | 0,1,male,45.0,0,0,35.5,S,First,man,True,,Southampton,no,True 342 | 1,2,male,2.0,1,1,26.0,S,Second,child,False,F,Southampton,yes,False 343 | 1,1,female,24.0,3,2,263.0,S,First,woman,False,C,Southampton,yes,False 344 | 0,2,male,28.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 345 | 0,2,male,25.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 346 | 0,2,male,36.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 347 | 1,2,female,24.0,0,0,13.0,S,Second,woman,False,F,Southampton,yes,True 348 | 1,2,female,40.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True 349 | 1,3,female,,1,0,16.1,S,Third,woman,False,,Southampton,yes,False 350 | 1,3,male,3.0,1,1,15.9,S,Third,child,False,,Southampton,yes,False 351 | 0,3,male,42.0,0,0,8.6625,S,Third,man,True,,Southampton,no,True 352 | 0,3,male,23.0,0,0,9.225,S,Third,man,True,,Southampton,no,True 353 | 0,1,male,,0,0,35.0,S,First,man,True,C,Southampton,no,True 354 | 0,3,male,15.0,1,1,7.2292,C,Third,child,False,,Cherbourg,no,False 355 | 0,3,male,25.0,1,0,17.8,S,Third,man,True,,Southampton,no,False 356 | 0,3,male,,0,0,7.225,C,Third,man,True,,Cherbourg,no,True 357 | 0,3,male,28.0,0,0,9.5,S,Third,man,True,,Southampton,no,True 358 | 1,1,female,22.0,0,1,55.0,S,First,woman,False,E,Southampton,yes,False 359 | 0,2,female,38.0,0,0,13.0,S,Second,woman,False,,Southampton,no,True 360 | 1,3,female,,0,0,7.8792,Q,Third,woman,False,,Queenstown,yes,True 361 | 1,3,female,,0,0,7.8792,Q,Third,woman,False,,Queenstown,yes,True 362 | 0,3,male,40.0,1,4,27.9,S,Third,man,True,,Southampton,no,False 363 | 0,2,male,29.0,1,0,27.7208,C,Second,man,True,,Cherbourg,no,False 364 | 0,3,female,45.0,0,1,14.4542,C,Third,woman,False,,Cherbourg,no,False 365 | 0,3,male,35.0,0,0,7.05,S,Third,man,True,,Southampton,no,True 366 | 0,3,male,,1,0,15.5,Q,Third,man,True,,Queenstown,no,False 367 | 0,3,male,30.0,0,0,7.25,S,Third,man,True,,Southampton,no,True 368 | 1,1,female,60.0,1,0,75.25,C,First,woman,False,D,Cherbourg,yes,False 369 | 1,3,female,,0,0,7.2292,C,Third,woman,False,,Cherbourg,yes,True 370 | 1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True 371 | 1,1,female,24.0,0,0,69.3,C,First,woman,False,B,Cherbourg,yes,True 372 | 1,1,male,25.0,1,0,55.4417,C,First,man,True,E,Cherbourg,yes,False 373 | 0,3,male,18.0,1,0,6.4958,S,Third,man,True,,Southampton,no,False 374 | 0,3,male,19.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 375 | 0,1,male,22.0,0,0,135.6333,C,First,man,True,,Cherbourg,no,True 376 | 0,3,female,3.0,3,1,21.075,S,Third,child,False,,Southampton,no,False 377 | 1,1,female,,1,0,82.1708,C,First,woman,False,,Cherbourg,yes,False 378 | 1,3,female,22.0,0,0,7.25,S,Third,woman,False,,Southampton,yes,True 379 | 0,1,male,27.0,0,2,211.5,C,First,man,True,C,Cherbourg,no,False 380 | 0,3,male,20.0,0,0,4.0125,C,Third,man,True,,Cherbourg,no,True 381 | 0,3,male,19.0,0,0,7.775,S,Third,man,True,,Southampton,no,True 382 | 1,1,female,42.0,0,0,227.525,C,First,woman,False,,Cherbourg,yes,True 383 | 1,3,female,1.0,0,2,15.7417,C,Third,child,False,,Cherbourg,yes,False 384 | 0,3,male,32.0,0,0,7.925,S,Third,man,True,,Southampton,no,True 385 | 1,1,female,35.0,1,0,52.0,S,First,woman,False,,Southampton,yes,False 386 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 387 | 0,2,male,18.0,0,0,73.5,S,Second,man,True,,Southampton,no,True 388 | 0,3,male,1.0,5,2,46.9,S,Third,child,False,,Southampton,no,False 389 | 1,2,female,36.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True 390 | 0,3,male,,0,0,7.7292,Q,Third,man,True,,Queenstown,no,True 391 | 1,2,female,17.0,0,0,12.0,C,Second,woman,False,,Cherbourg,yes,True 392 | 1,1,male,36.0,1,2,120.0,S,First,man,True,B,Southampton,yes,False 393 | 1,3,male,21.0,0,0,7.7958,S,Third,man,True,,Southampton,yes,True 394 | 0,3,male,28.0,2,0,7.925,S,Third,man,True,,Southampton,no,False 395 | 1,1,female,23.0,1,0,113.275,C,First,woman,False,D,Cherbourg,yes,False 396 | 1,3,female,24.0,0,2,16.7,S,Third,woman,False,G,Southampton,yes,False 397 | 0,3,male,22.0,0,0,7.7958,S,Third,man,True,,Southampton,no,True 398 | 0,3,female,31.0,0,0,7.8542,S,Third,woman,False,,Southampton,no,True 399 | 0,2,male,46.0,0,0,26.0,S,Second,man,True,,Southampton,no,True 400 | 0,2,male,23.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 401 | 1,2,female,28.0,0,0,12.65,S,Second,woman,False,,Southampton,yes,True 402 | 1,3,male,39.0,0,0,7.925,S,Third,man,True,,Southampton,yes,True 403 | 0,3,male,26.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 404 | 0,3,female,21.0,1,0,9.825,S,Third,woman,False,,Southampton,no,False 405 | 0,3,male,28.0,1,0,15.85,S,Third,man,True,,Southampton,no,False 406 | 0,3,female,20.0,0,0,8.6625,S,Third,woman,False,,Southampton,no,True 407 | 0,2,male,34.0,1,0,21.0,S,Second,man,True,,Southampton,no,False 408 | 0,3,male,51.0,0,0,7.75,S,Third,man,True,,Southampton,no,True 409 | 1,2,male,3.0,1,1,18.75,S,Second,child,False,,Southampton,yes,False 410 | 0,3,male,21.0,0,0,7.775,S,Third,man,True,,Southampton,no,True 411 | 0,3,female,,3,1,25.4667,S,Third,woman,False,,Southampton,no,False 412 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 413 | 0,3,male,,0,0,6.8583,Q,Third,man,True,,Queenstown,no,True 414 | 1,1,female,33.0,1,0,90.0,Q,First,woman,False,C,Queenstown,yes,False 415 | 0,2,male,,0,0,0.0,S,Second,man,True,,Southampton,no,True 416 | 1,3,male,44.0,0,0,7.925,S,Third,man,True,,Southampton,yes,True 417 | 0,3,female,,0,0,8.05,S,Third,woman,False,,Southampton,no,True 418 | 1,2,female,34.0,1,1,32.5,S,Second,woman,False,,Southampton,yes,False 419 | 1,2,female,18.0,0,2,13.0,S,Second,woman,False,,Southampton,yes,False 420 | 0,2,male,30.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 421 | 0,3,female,10.0,0,2,24.15,S,Third,child,False,,Southampton,no,False 422 | 0,3,male,,0,0,7.8958,C,Third,man,True,,Cherbourg,no,True 423 | 0,3,male,21.0,0,0,7.7333,Q,Third,man,True,,Queenstown,no,True 424 | 0,3,male,29.0,0,0,7.875,S,Third,man,True,,Southampton,no,True 425 | 0,3,female,28.0,1,1,14.4,S,Third,woman,False,,Southampton,no,False 426 | 0,3,male,18.0,1,1,20.2125,S,Third,man,True,,Southampton,no,False 427 | 0,3,male,,0,0,7.25,S,Third,man,True,,Southampton,no,True 428 | 1,2,female,28.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False 429 | 1,2,female,19.0,0,0,26.0,S,Second,woman,False,,Southampton,yes,True 430 | 0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 431 | 1,3,male,32.0,0,0,8.05,S,Third,man,True,E,Southampton,yes,True 432 | 1,1,male,28.0,0,0,26.55,S,First,man,True,C,Southampton,yes,True 433 | 1,3,female,,1,0,16.1,S,Third,woman,False,,Southampton,yes,False 434 | 1,2,female,42.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False 435 | 0,3,male,17.0,0,0,7.125,S,Third,man,True,,Southampton,no,True 436 | 0,1,male,50.0,1,0,55.9,S,First,man,True,E,Southampton,no,False 437 | 1,1,female,14.0,1,2,120.0,S,First,child,False,B,Southampton,yes,False 438 | 0,3,female,21.0,2,2,34.375,S,Third,woman,False,,Southampton,no,False 439 | 1,2,female,24.0,2,3,18.75,S,Second,woman,False,,Southampton,yes,False 440 | 0,1,male,64.0,1,4,263.0,S,First,man,True,C,Southampton,no,False 441 | 0,2,male,31.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 442 | 1,2,female,45.0,1,1,26.25,S,Second,woman,False,,Southampton,yes,False 443 | 0,3,male,20.0,0,0,9.5,S,Third,man,True,,Southampton,no,True 444 | 0,3,male,25.0,1,0,7.775,S,Third,man,True,,Southampton,no,False 445 | 1,2,female,28.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True 446 | 1,3,male,,0,0,8.1125,S,Third,man,True,,Southampton,yes,True 447 | 1,1,male,4.0,0,2,81.8583,S,First,child,False,A,Southampton,yes,False 448 | 1,2,female,13.0,0,1,19.5,S,Second,child,False,,Southampton,yes,False 449 | 1,1,male,34.0,0,0,26.55,S,First,man,True,,Southampton,yes,True 450 | 1,3,female,5.0,2,1,19.2583,C,Third,child,False,,Cherbourg,yes,False 451 | 1,1,male,52.0,0,0,30.5,S,First,man,True,C,Southampton,yes,True 452 | 0,2,male,36.0,1,2,27.75,S,Second,man,True,,Southampton,no,False 453 | 0,3,male,,1,0,19.9667,S,Third,man,True,,Southampton,no,False 454 | 0,1,male,30.0,0,0,27.75,C,First,man,True,C,Cherbourg,no,True 455 | 1,1,male,49.0,1,0,89.1042,C,First,man,True,C,Cherbourg,yes,False 456 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 457 | 1,3,male,29.0,0,0,7.8958,C,Third,man,True,,Cherbourg,yes,True 458 | 0,1,male,65.0,0,0,26.55,S,First,man,True,E,Southampton,no,True 459 | 1,1,female,,1,0,51.8625,S,First,woman,False,D,Southampton,yes,False 460 | 1,2,female,50.0,0,0,10.5,S,Second,woman,False,,Southampton,yes,True 461 | 0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 462 | 1,1,male,48.0,0,0,26.55,S,First,man,True,E,Southampton,yes,True 463 | 0,3,male,34.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 464 | 0,1,male,47.0,0,0,38.5,S,First,man,True,E,Southampton,no,True 465 | 0,2,male,48.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 466 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 467 | 0,3,male,38.0,0,0,7.05,S,Third,man,True,,Southampton,no,True 468 | 0,2,male,,0,0,0.0,S,Second,man,True,,Southampton,no,True 469 | 0,1,male,56.0,0,0,26.55,S,First,man,True,,Southampton,no,True 470 | 0,3,male,,0,0,7.725,Q,Third,man,True,,Queenstown,no,True 471 | 1,3,female,0.75,2,1,19.2583,C,Third,child,False,,Cherbourg,yes,False 472 | 0,3,male,,0,0,7.25,S,Third,man,True,,Southampton,no,True 473 | 0,3,male,38.0,0,0,8.6625,S,Third,man,True,,Southampton,no,True 474 | 1,2,female,33.0,1,2,27.75,S,Second,woman,False,,Southampton,yes,False 475 | 1,2,female,23.0,0,0,13.7917,C,Second,woman,False,D,Cherbourg,yes,True 476 | 0,3,female,22.0,0,0,9.8375,S,Third,woman,False,,Southampton,no,True 477 | 0,1,male,,0,0,52.0,S,First,man,True,A,Southampton,no,True 478 | 0,2,male,34.0,1,0,21.0,S,Second,man,True,,Southampton,no,False 479 | 0,3,male,29.0,1,0,7.0458,S,Third,man,True,,Southampton,no,False 480 | 0,3,male,22.0,0,0,7.5208,S,Third,man,True,,Southampton,no,True 481 | 1,3,female,2.0,0,1,12.2875,S,Third,child,False,,Southampton,yes,False 482 | 0,3,male,9.0,5,2,46.9,S,Third,child,False,,Southampton,no,False 483 | 0,2,male,,0,0,0.0,S,Second,man,True,,Southampton,no,True 484 | 0,3,male,50.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 485 | 1,3,female,63.0,0,0,9.5875,S,Third,woman,False,,Southampton,yes,True 486 | 1,1,male,25.0,1,0,91.0792,C,First,man,True,B,Cherbourg,yes,False 487 | 0,3,female,,3,1,25.4667,S,Third,woman,False,,Southampton,no,False 488 | 1,1,female,35.0,1,0,90.0,S,First,woman,False,C,Southampton,yes,False 489 | 0,1,male,58.0,0,0,29.7,C,First,man,True,B,Cherbourg,no,True 490 | 0,3,male,30.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 491 | 1,3,male,9.0,1,1,15.9,S,Third,child,False,,Southampton,yes,False 492 | 0,3,male,,1,0,19.9667,S,Third,man,True,,Southampton,no,False 493 | 0,3,male,21.0,0,0,7.25,S,Third,man,True,,Southampton,no,True 494 | 0,1,male,55.0,0,0,30.5,S,First,man,True,C,Southampton,no,True 495 | 0,1,male,71.0,0,0,49.5042,C,First,man,True,,Cherbourg,no,True 496 | 0,3,male,21.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 497 | 0,3,male,,0,0,14.4583,C,Third,man,True,,Cherbourg,no,True 498 | 1,1,female,54.0,1,0,78.2667,C,First,woman,False,D,Cherbourg,yes,False 499 | 0,3,male,,0,0,15.1,S,Third,man,True,,Southampton,no,True 500 | 0,1,female,25.0,1,2,151.55,S,First,woman,False,C,Southampton,no,False 501 | 0,3,male,24.0,0,0,7.7958,S,Third,man,True,,Southampton,no,True 502 | 0,3,male,17.0,0,0,8.6625,S,Third,man,True,,Southampton,no,True 503 | 0,3,female,21.0,0,0,7.75,Q,Third,woman,False,,Queenstown,no,True 504 | 0,3,female,,0,0,7.6292,Q,Third,woman,False,,Queenstown,no,True 505 | 0,3,female,37.0,0,0,9.5875,S,Third,woman,False,,Southampton,no,True 506 | 1,1,female,16.0,0,0,86.5,S,First,woman,False,B,Southampton,yes,True 507 | 0,1,male,18.0,1,0,108.9,C,First,man,True,C,Cherbourg,no,False 508 | 1,2,female,33.0,0,2,26.0,S,Second,woman,False,,Southampton,yes,False 509 | 1,1,male,,0,0,26.55,S,First,man,True,,Southampton,yes,True 510 | 0,3,male,28.0,0,0,22.525,S,Third,man,True,,Southampton,no,True 511 | 1,3,male,26.0,0,0,56.4958,S,Third,man,True,,Southampton,yes,True 512 | 1,3,male,29.0,0,0,7.75,Q,Third,man,True,,Queenstown,yes,True 513 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 514 | 1,1,male,36.0,0,0,26.2875,S,First,man,True,E,Southampton,yes,True 515 | 1,1,female,54.0,1,0,59.4,C,First,woman,False,,Cherbourg,yes,False 516 | 0,3,male,24.0,0,0,7.4958,S,Third,man,True,,Southampton,no,True 517 | 0,1,male,47.0,0,0,34.0208,S,First,man,True,D,Southampton,no,True 518 | 1,2,female,34.0,0,0,10.5,S,Second,woman,False,F,Southampton,yes,True 519 | 0,3,male,,0,0,24.15,Q,Third,man,True,,Queenstown,no,True 520 | 1,2,female,36.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False 521 | 0,3,male,32.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 522 | 1,1,female,30.0,0,0,93.5,S,First,woman,False,B,Southampton,yes,True 523 | 0,3,male,22.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 524 | 0,3,male,,0,0,7.225,C,Third,man,True,,Cherbourg,no,True 525 | 1,1,female,44.0,0,1,57.9792,C,First,woman,False,B,Cherbourg,yes,False 526 | 0,3,male,,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True 527 | 0,3,male,40.5,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 528 | 1,2,female,50.0,0,0,10.5,S,Second,woman,False,,Southampton,yes,True 529 | 0,1,male,,0,0,221.7792,S,First,man,True,C,Southampton,no,True 530 | 0,3,male,39.0,0,0,7.925,S,Third,man,True,,Southampton,no,True 531 | 0,2,male,23.0,2,1,11.5,S,Second,man,True,,Southampton,no,False 532 | 1,2,female,2.0,1,1,26.0,S,Second,child,False,,Southampton,yes,False 533 | 0,3,male,,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True 534 | 0,3,male,17.0,1,1,7.2292,C,Third,man,True,,Cherbourg,no,False 535 | 1,3,female,,0,2,22.3583,C,Third,woman,False,,Cherbourg,yes,False 536 | 0,3,female,30.0,0,0,8.6625,S,Third,woman,False,,Southampton,no,True 537 | 1,2,female,7.0,0,2,26.25,S,Second,child,False,,Southampton,yes,False 538 | 0,1,male,45.0,0,0,26.55,S,First,man,True,B,Southampton,no,True 539 | 1,1,female,30.0,0,0,106.425,C,First,woman,False,,Cherbourg,yes,True 540 | 0,3,male,,0,0,14.5,S,Third,man,True,,Southampton,no,True 541 | 1,1,female,22.0,0,2,49.5,C,First,woman,False,B,Cherbourg,yes,False 542 | 1,1,female,36.0,0,2,71.0,S,First,woman,False,B,Southampton,yes,False 543 | 0,3,female,9.0,4,2,31.275,S,Third,child,False,,Southampton,no,False 544 | 0,3,female,11.0,4,2,31.275,S,Third,child,False,,Southampton,no,False 545 | 1,2,male,32.0,1,0,26.0,S,Second,man,True,,Southampton,yes,False 546 | 0,1,male,50.0,1,0,106.425,C,First,man,True,C,Cherbourg,no,False 547 | 0,1,male,64.0,0,0,26.0,S,First,man,True,,Southampton,no,True 548 | 1,2,female,19.0,1,0,26.0,S,Second,woman,False,,Southampton,yes,False 549 | 1,2,male,,0,0,13.8625,C,Second,man,True,,Cherbourg,yes,True 550 | 0,3,male,33.0,1,1,20.525,S,Third,man,True,,Southampton,no,False 551 | 1,2,male,8.0,1,1,36.75,S,Second,child,False,,Southampton,yes,False 552 | 1,1,male,17.0,0,2,110.8833,C,First,man,True,C,Cherbourg,yes,False 553 | 0,2,male,27.0,0,0,26.0,S,Second,man,True,,Southampton,no,True 554 | 0,3,male,,0,0,7.8292,Q,Third,man,True,,Queenstown,no,True 555 | 1,3,male,22.0,0,0,7.225,C,Third,man,True,,Cherbourg,yes,True 556 | 1,3,female,22.0,0,0,7.775,S,Third,woman,False,,Southampton,yes,True 557 | 0,1,male,62.0,0,0,26.55,S,First,man,True,,Southampton,no,True 558 | 1,1,female,48.0,1,0,39.6,C,First,woman,False,A,Cherbourg,yes,False 559 | 0,1,male,,0,0,227.525,C,First,man,True,,Cherbourg,no,True 560 | 1,1,female,39.0,1,1,79.65,S,First,woman,False,E,Southampton,yes,False 561 | 1,3,female,36.0,1,0,17.4,S,Third,woman,False,,Southampton,yes,False 562 | 0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 563 | 0,3,male,40.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 564 | 0,2,male,28.0,0,0,13.5,S,Second,man,True,,Southampton,no,True 565 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 566 | 0,3,female,,0,0,8.05,S,Third,woman,False,,Southampton,no,True 567 | 0,3,male,24.0,2,0,24.15,S,Third,man,True,,Southampton,no,False 568 | 0,3,male,19.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 569 | 0,3,female,29.0,0,4,21.075,S,Third,woman,False,,Southampton,no,False 570 | 0,3,male,,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True 571 | 1,3,male,32.0,0,0,7.8542,S,Third,man,True,,Southampton,yes,True 572 | 1,2,male,62.0,0,0,10.5,S,Second,man,True,,Southampton,yes,True 573 | 1,1,female,53.0,2,0,51.4792,S,First,woman,False,C,Southampton,yes,False 574 | 1,1,male,36.0,0,0,26.3875,S,First,man,True,E,Southampton,yes,True 575 | 1,3,female,,0,0,7.75,Q,Third,woman,False,,Queenstown,yes,True 576 | 0,3,male,16.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 577 | 0,3,male,19.0,0,0,14.5,S,Third,man,True,,Southampton,no,True 578 | 1,2,female,34.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True 579 | 1,1,female,39.0,1,0,55.9,S,First,woman,False,E,Southampton,yes,False 580 | 0,3,female,,1,0,14.4583,C,Third,woman,False,,Cherbourg,no,False 581 | 1,3,male,32.0,0,0,7.925,S,Third,man,True,,Southampton,yes,True 582 | 1,2,female,25.0,1,1,30.0,S,Second,woman,False,,Southampton,yes,False 583 | 1,1,female,39.0,1,1,110.8833,C,First,woman,False,C,Cherbourg,yes,False 584 | 0,2,male,54.0,0,0,26.0,S,Second,man,True,,Southampton,no,True 585 | 0,1,male,36.0,0,0,40.125,C,First,man,True,A,Cherbourg,no,True 586 | 0,3,male,,0,0,8.7125,C,Third,man,True,,Cherbourg,no,True 587 | 1,1,female,18.0,0,2,79.65,S,First,woman,False,E,Southampton,yes,False 588 | 0,2,male,47.0,0,0,15.0,S,Second,man,True,,Southampton,no,True 589 | 1,1,male,60.0,1,1,79.2,C,First,man,True,B,Cherbourg,yes,False 590 | 0,3,male,22.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 591 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 592 | 0,3,male,35.0,0,0,7.125,S,Third,man,True,,Southampton,no,True 593 | 1,1,female,52.0,1,0,78.2667,C,First,woman,False,D,Cherbourg,yes,False 594 | 0,3,male,47.0,0,0,7.25,S,Third,man,True,,Southampton,no,True 595 | 0,3,female,,0,2,7.75,Q,Third,woman,False,,Queenstown,no,False 596 | 0,2,male,37.0,1,0,26.0,S,Second,man,True,,Southampton,no,False 597 | 0,3,male,36.0,1,1,24.15,S,Third,man,True,,Southampton,no,False 598 | 1,2,female,,0,0,33.0,S,Second,woman,False,,Southampton,yes,True 599 | 0,3,male,49.0,0,0,0.0,S,Third,man,True,,Southampton,no,True 600 | 0,3,male,,0,0,7.225,C,Third,man,True,,Cherbourg,no,True 601 | 1,1,male,49.0,1,0,56.9292,C,First,man,True,A,Cherbourg,yes,False 602 | 1,2,female,24.0,2,1,27.0,S,Second,woman,False,,Southampton,yes,False 603 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 604 | 0,1,male,,0,0,42.4,S,First,man,True,,Southampton,no,True 605 | 0,3,male,44.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 606 | 1,1,male,35.0,0,0,26.55,C,First,man,True,,Cherbourg,yes,True 607 | 0,3,male,36.0,1,0,15.55,S,Third,man,True,,Southampton,no,False 608 | 0,3,male,30.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 609 | 1,1,male,27.0,0,0,30.5,S,First,man,True,,Southampton,yes,True 610 | 1,2,female,22.0,1,2,41.5792,C,Second,woman,False,,Cherbourg,yes,False 611 | 1,1,female,40.0,0,0,153.4625,S,First,woman,False,C,Southampton,yes,True 612 | 0,3,female,39.0,1,5,31.275,S,Third,woman,False,,Southampton,no,False 613 | 0,3,male,,0,0,7.05,S,Third,man,True,,Southampton,no,True 614 | 1,3,female,,1,0,15.5,Q,Third,woman,False,,Queenstown,yes,False 615 | 0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 616 | 0,3,male,35.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 617 | 1,2,female,24.0,1,2,65.0,S,Second,woman,False,,Southampton,yes,False 618 | 0,3,male,34.0,1,1,14.4,S,Third,man,True,,Southampton,no,False 619 | 0,3,female,26.0,1,0,16.1,S,Third,woman,False,,Southampton,no,False 620 | 1,2,female,4.0,2,1,39.0,S,Second,child,False,F,Southampton,yes,False 621 | 0,2,male,26.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 622 | 0,3,male,27.0,1,0,14.4542,C,Third,man,True,,Cherbourg,no,False 623 | 1,1,male,42.0,1,0,52.5542,S,First,man,True,D,Southampton,yes,False 624 | 1,3,male,20.0,1,1,15.7417,C,Third,man,True,,Cherbourg,yes,False 625 | 0,3,male,21.0,0,0,7.8542,S,Third,man,True,,Southampton,no,True 626 | 0,3,male,21.0,0,0,16.1,S,Third,man,True,,Southampton,no,True 627 | 0,1,male,61.0,0,0,32.3208,S,First,man,True,D,Southampton,no,True 628 | 0,2,male,57.0,0,0,12.35,Q,Second,man,True,,Queenstown,no,True 629 | 1,1,female,21.0,0,0,77.9583,S,First,woman,False,D,Southampton,yes,True 630 | 0,3,male,26.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 631 | 0,3,male,,0,0,7.7333,Q,Third,man,True,,Queenstown,no,True 632 | 1,1,male,80.0,0,0,30.0,S,First,man,True,A,Southampton,yes,True 633 | 0,3,male,51.0,0,0,7.0542,S,Third,man,True,,Southampton,no,True 634 | 1,1,male,32.0,0,0,30.5,C,First,man,True,B,Cherbourg,yes,True 635 | 0,1,male,,0,0,0.0,S,First,man,True,,Southampton,no,True 636 | 0,3,female,9.0,3,2,27.9,S,Third,child,False,,Southampton,no,False 637 | 1,2,female,28.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True 638 | 0,3,male,32.0,0,0,7.925,S,Third,man,True,,Southampton,no,True 639 | 0,2,male,31.0,1,1,26.25,S,Second,man,True,,Southampton,no,False 640 | 0,3,female,41.0,0,5,39.6875,S,Third,woman,False,,Southampton,no,False 641 | 0,3,male,,1,0,16.1,S,Third,man,True,,Southampton,no,False 642 | 0,3,male,20.0,0,0,7.8542,S,Third,man,True,,Southampton,no,True 643 | 1,1,female,24.0,0,0,69.3,C,First,woman,False,B,Cherbourg,yes,True 644 | 0,3,female,2.0,3,2,27.9,S,Third,child,False,,Southampton,no,False 645 | 1,3,male,,0,0,56.4958,S,Third,man,True,,Southampton,yes,True 646 | 1,3,female,0.75,2,1,19.2583,C,Third,child,False,,Cherbourg,yes,False 647 | 1,1,male,48.0,1,0,76.7292,C,First,man,True,D,Cherbourg,yes,False 648 | 0,3,male,19.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 649 | 1,1,male,56.0,0,0,35.5,C,First,man,True,A,Cherbourg,yes,True 650 | 0,3,male,,0,0,7.55,S,Third,man,True,,Southampton,no,True 651 | 1,3,female,23.0,0,0,7.55,S,Third,woman,False,,Southampton,yes,True 652 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 653 | 1,2,female,18.0,0,1,23.0,S,Second,woman,False,,Southampton,yes,False 654 | 0,3,male,21.0,0,0,8.4333,S,Third,man,True,,Southampton,no,True 655 | 1,3,female,,0,0,7.8292,Q,Third,woman,False,,Queenstown,yes,True 656 | 0,3,female,18.0,0,0,6.75,Q,Third,woman,False,,Queenstown,no,True 657 | 0,2,male,24.0,2,0,73.5,S,Second,man,True,,Southampton,no,False 658 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 659 | 0,3,female,32.0,1,1,15.5,Q,Third,woman,False,,Queenstown,no,False 660 | 0,2,male,23.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 661 | 0,1,male,58.0,0,2,113.275,C,First,man,True,D,Cherbourg,no,False 662 | 1,1,male,50.0,2,0,133.65,S,First,man,True,,Southampton,yes,False 663 | 0,3,male,40.0,0,0,7.225,C,Third,man,True,,Cherbourg,no,True 664 | 0,1,male,47.0,0,0,25.5875,S,First,man,True,E,Southampton,no,True 665 | 0,3,male,36.0,0,0,7.4958,S,Third,man,True,,Southampton,no,True 666 | 1,3,male,20.0,1,0,7.925,S,Third,man,True,,Southampton,yes,False 667 | 0,2,male,32.0,2,0,73.5,S,Second,man,True,,Southampton,no,False 668 | 0,2,male,25.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 669 | 0,3,male,,0,0,7.775,S,Third,man,True,,Southampton,no,True 670 | 0,3,male,43.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 671 | 1,1,female,,1,0,52.0,S,First,woman,False,C,Southampton,yes,False 672 | 1,2,female,40.0,1,1,39.0,S,Second,woman,False,,Southampton,yes,False 673 | 0,1,male,31.0,1,0,52.0,S,First,man,True,B,Southampton,no,False 674 | 0,2,male,70.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 675 | 1,2,male,31.0,0,0,13.0,S,Second,man,True,,Southampton,yes,True 676 | 0,2,male,,0,0,0.0,S,Second,man,True,,Southampton,no,True 677 | 0,3,male,18.0,0,0,7.775,S,Third,man,True,,Southampton,no,True 678 | 0,3,male,24.5,0,0,8.05,S,Third,man,True,,Southampton,no,True 679 | 1,3,female,18.0,0,0,9.8417,S,Third,woman,False,,Southampton,yes,True 680 | 0,3,female,43.0,1,6,46.9,S,Third,woman,False,,Southampton,no,False 681 | 1,1,male,36.0,0,1,512.3292,C,First,man,True,B,Cherbourg,yes,False 682 | 0,3,female,,0,0,8.1375,Q,Third,woman,False,,Queenstown,no,True 683 | 1,1,male,27.0,0,0,76.7292,C,First,man,True,D,Cherbourg,yes,True 684 | 0,3,male,20.0,0,0,9.225,S,Third,man,True,,Southampton,no,True 685 | 0,3,male,14.0,5,2,46.9,S,Third,child,False,,Southampton,no,False 686 | 0,2,male,60.0,1,1,39.0,S,Second,man,True,,Southampton,no,False 687 | 0,2,male,25.0,1,2,41.5792,C,Second,man,True,,Cherbourg,no,False 688 | 0,3,male,14.0,4,1,39.6875,S,Third,child,False,,Southampton,no,False 689 | 0,3,male,19.0,0,0,10.1708,S,Third,man,True,,Southampton,no,True 690 | 0,3,male,18.0,0,0,7.7958,S,Third,man,True,,Southampton,no,True 691 | 1,1,female,15.0,0,1,211.3375,S,First,child,False,B,Southampton,yes,False 692 | 1,1,male,31.0,1,0,57.0,S,First,man,True,B,Southampton,yes,False 693 | 1,3,female,4.0,0,1,13.4167,C,Third,child,False,,Cherbourg,yes,False 694 | 1,3,male,,0,0,56.4958,S,Third,man,True,,Southampton,yes,True 695 | 0,3,male,25.0,0,0,7.225,C,Third,man,True,,Cherbourg,no,True 696 | 0,1,male,60.0,0,0,26.55,S,First,man,True,,Southampton,no,True 697 | 0,2,male,52.0,0,0,13.5,S,Second,man,True,,Southampton,no,True 698 | 0,3,male,44.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 699 | 1,3,female,,0,0,7.7333,Q,Third,woman,False,,Queenstown,yes,True 700 | 0,1,male,49.0,1,1,110.8833,C,First,man,True,C,Cherbourg,no,False 701 | 0,3,male,42.0,0,0,7.65,S,Third,man,True,F,Southampton,no,True 702 | 1,1,female,18.0,1,0,227.525,C,First,woman,False,C,Cherbourg,yes,False 703 | 1,1,male,35.0,0,0,26.2875,S,First,man,True,E,Southampton,yes,True 704 | 0,3,female,18.0,0,1,14.4542,C,Third,woman,False,,Cherbourg,no,False 705 | 0,3,male,25.0,0,0,7.7417,Q,Third,man,True,,Queenstown,no,True 706 | 0,3,male,26.0,1,0,7.8542,S,Third,man,True,,Southampton,no,False 707 | 0,2,male,39.0,0,0,26.0,S,Second,man,True,,Southampton,no,True 708 | 1,2,female,45.0,0,0,13.5,S,Second,woman,False,,Southampton,yes,True 709 | 1,1,male,42.0,0,0,26.2875,S,First,man,True,E,Southampton,yes,True 710 | 1,1,female,22.0,0,0,151.55,S,First,woman,False,,Southampton,yes,True 711 | 1,3,male,,1,1,15.2458,C,Third,man,True,,Cherbourg,yes,False 712 | 1,1,female,24.0,0,0,49.5042,C,First,woman,False,C,Cherbourg,yes,True 713 | 0,1,male,,0,0,26.55,S,First,man,True,C,Southampton,no,True 714 | 1,1,male,48.0,1,0,52.0,S,First,man,True,C,Southampton,yes,False 715 | 0,3,male,29.0,0,0,9.4833,S,Third,man,True,,Southampton,no,True 716 | 0,2,male,52.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 717 | 0,3,male,19.0,0,0,7.65,S,Third,man,True,F,Southampton,no,True 718 | 1,1,female,38.0,0,0,227.525,C,First,woman,False,C,Cherbourg,yes,True 719 | 1,2,female,27.0,0,0,10.5,S,Second,woman,False,E,Southampton,yes,True 720 | 0,3,male,,0,0,15.5,Q,Third,man,True,,Queenstown,no,True 721 | 0,3,male,33.0,0,0,7.775,S,Third,man,True,,Southampton,no,True 722 | 1,2,female,6.0,0,1,33.0,S,Second,child,False,,Southampton,yes,False 723 | 0,3,male,17.0,1,0,7.0542,S,Third,man,True,,Southampton,no,False 724 | 0,2,male,34.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 725 | 0,2,male,50.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 726 | 1,1,male,27.0,1,0,53.1,S,First,man,True,E,Southampton,yes,False 727 | 0,3,male,20.0,0,0,8.6625,S,Third,man,True,,Southampton,no,True 728 | 1,2,female,30.0,3,0,21.0,S,Second,woman,False,,Southampton,yes,False 729 | 1,3,female,,0,0,7.7375,Q,Third,woman,False,,Queenstown,yes,True 730 | 0,2,male,25.0,1,0,26.0,S,Second,man,True,,Southampton,no,False 731 | 0,3,female,25.0,1,0,7.925,S,Third,woman,False,,Southampton,no,False 732 | 1,1,female,29.0,0,0,211.3375,S,First,woman,False,B,Southampton,yes,True 733 | 0,3,male,11.0,0,0,18.7875,C,Third,child,False,,Cherbourg,no,True 734 | 0,2,male,,0,0,0.0,S,Second,man,True,,Southampton,no,True 735 | 0,2,male,23.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 736 | 0,2,male,23.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 737 | 0,3,male,28.5,0,0,16.1,S,Third,man,True,,Southampton,no,True 738 | 0,3,female,48.0,1,3,34.375,S,Third,woman,False,,Southampton,no,False 739 | 1,1,male,35.0,0,0,512.3292,C,First,man,True,B,Cherbourg,yes,True 740 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 741 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 742 | 1,1,male,,0,0,30.0,S,First,man,True,D,Southampton,yes,True 743 | 0,1,male,36.0,1,0,78.85,S,First,man,True,C,Southampton,no,False 744 | 1,1,female,21.0,2,2,262.375,C,First,woman,False,B,Cherbourg,yes,False 745 | 0,3,male,24.0,1,0,16.1,S,Third,man,True,,Southampton,no,False 746 | 1,3,male,31.0,0,0,7.925,S,Third,man,True,,Southampton,yes,True 747 | 0,1,male,70.0,1,1,71.0,S,First,man,True,B,Southampton,no,False 748 | 0,3,male,16.0,1,1,20.25,S,Third,man,True,,Southampton,no,False 749 | 1,2,female,30.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True 750 | 0,1,male,19.0,1,0,53.1,S,First,man,True,D,Southampton,no,False 751 | 0,3,male,31.0,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 752 | 1,2,female,4.0,1,1,23.0,S,Second,child,False,,Southampton,yes,False 753 | 1,3,male,6.0,0,1,12.475,S,Third,child,False,E,Southampton,yes,False 754 | 0,3,male,33.0,0,0,9.5,S,Third,man,True,,Southampton,no,True 755 | 0,3,male,23.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 756 | 1,2,female,48.0,1,2,65.0,S,Second,woman,False,,Southampton,yes,False 757 | 1,2,male,0.67,1,1,14.5,S,Second,child,False,,Southampton,yes,False 758 | 0,3,male,28.0,0,0,7.7958,S,Third,man,True,,Southampton,no,True 759 | 0,2,male,18.0,0,0,11.5,S,Second,man,True,,Southampton,no,True 760 | 0,3,male,34.0,0,0,8.05,S,Third,man,True,,Southampton,no,True 761 | 1,1,female,33.0,0,0,86.5,S,First,woman,False,B,Southampton,yes,True 762 | 0,3,male,,0,0,14.5,S,Third,man,True,,Southampton,no,True 763 | 0,3,male,41.0,0,0,7.125,S,Third,man,True,,Southampton,no,True 764 | 1,3,male,20.0,0,0,7.2292,C,Third,man,True,,Cherbourg,yes,True 765 | 1,1,female,36.0,1,2,120.0,S,First,woman,False,B,Southampton,yes,False 766 | 0,3,male,16.0,0,0,7.775,S,Third,man,True,,Southampton,no,True 767 | 1,1,female,51.0,1,0,77.9583,S,First,woman,False,D,Southampton,yes,False 768 | 0,1,male,,0,0,39.6,C,First,man,True,,Cherbourg,no,True 769 | 0,3,female,30.5,0,0,7.75,Q,Third,woman,False,,Queenstown,no,True 770 | 0,3,male,,1,0,24.15,Q,Third,man,True,,Queenstown,no,False 771 | 0,3,male,32.0,0,0,8.3625,S,Third,man,True,,Southampton,no,True 772 | 0,3,male,24.0,0,0,9.5,S,Third,man,True,,Southampton,no,True 773 | 0,3,male,48.0,0,0,7.8542,S,Third,man,True,,Southampton,no,True 774 | 0,2,female,57.0,0,0,10.5,S,Second,woman,False,E,Southampton,no,True 775 | 0,3,male,,0,0,7.225,C,Third,man,True,,Cherbourg,no,True 776 | 1,2,female,54.0,1,3,23.0,S,Second,woman,False,,Southampton,yes,False 777 | 0,3,male,18.0,0,0,7.75,S,Third,man,True,,Southampton,no,True 778 | 0,3,male,,0,0,7.75,Q,Third,man,True,F,Queenstown,no,True 779 | 1,3,female,5.0,0,0,12.475,S,Third,child,False,,Southampton,yes,True 780 | 0,3,male,,0,0,7.7375,Q,Third,man,True,,Queenstown,no,True 781 | 1,1,female,43.0,0,1,211.3375,S,First,woman,False,B,Southampton,yes,False 782 | 1,3,female,13.0,0,0,7.2292,C,Third,child,False,,Cherbourg,yes,True 783 | 1,1,female,17.0,1,0,57.0,S,First,woman,False,B,Southampton,yes,False 784 | 0,1,male,29.0,0,0,30.0,S,First,man,True,D,Southampton,no,True 785 | 0,3,male,,1,2,23.45,S,Third,man,True,,Southampton,no,False 786 | 0,3,male,25.0,0,0,7.05,S,Third,man,True,,Southampton,no,True 787 | 0,3,male,25.0,0,0,7.25,S,Third,man,True,,Southampton,no,True 788 | 1,3,female,18.0,0,0,7.4958,S,Third,woman,False,,Southampton,yes,True 789 | 0,3,male,8.0,4,1,29.125,Q,Third,child,False,,Queenstown,no,False 790 | 1,3,male,1.0,1,2,20.575,S,Third,child,False,,Southampton,yes,False 791 | 0,1,male,46.0,0,0,79.2,C,First,man,True,B,Cherbourg,no,True 792 | 0,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 793 | 0,2,male,16.0,0,0,26.0,S,Second,man,True,,Southampton,no,True 794 | 0,3,female,,8,2,69.55,S,Third,woman,False,,Southampton,no,False 795 | 0,1,male,,0,0,30.6958,C,First,man,True,,Cherbourg,no,True 796 | 0,3,male,25.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 797 | 0,2,male,39.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 798 | 1,1,female,49.0,0,0,25.9292,S,First,woman,False,D,Southampton,yes,True 799 | 1,3,female,31.0,0,0,8.6833,S,Third,woman,False,,Southampton,yes,True 800 | 0,3,male,30.0,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True 801 | 0,3,female,30.0,1,1,24.15,S,Third,woman,False,,Southampton,no,False 802 | 0,2,male,34.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 803 | 1,2,female,31.0,1,1,26.25,S,Second,woman,False,,Southampton,yes,False 804 | 1,1,male,11.0,1,2,120.0,S,First,child,False,B,Southampton,yes,False 805 | 1,3,male,0.42,0,1,8.5167,C,Third,child,False,,Cherbourg,yes,False 806 | 1,3,male,27.0,0,0,6.975,S,Third,man,True,,Southampton,yes,True 807 | 0,3,male,31.0,0,0,7.775,S,Third,man,True,,Southampton,no,True 808 | 0,1,male,39.0,0,0,0.0,S,First,man,True,A,Southampton,no,True 809 | 0,3,female,18.0,0,0,7.775,S,Third,woman,False,,Southampton,no,True 810 | 0,2,male,39.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 811 | 1,1,female,33.0,1,0,53.1,S,First,woman,False,E,Southampton,yes,False 812 | 0,3,male,26.0,0,0,7.8875,S,Third,man,True,,Southampton,no,True 813 | 0,3,male,39.0,0,0,24.15,S,Third,man,True,,Southampton,no,True 814 | 0,2,male,35.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 815 | 0,3,female,6.0,4,2,31.275,S,Third,child,False,,Southampton,no,False 816 | 0,3,male,30.5,0,0,8.05,S,Third,man,True,,Southampton,no,True 817 | 0,1,male,,0,0,0.0,S,First,man,True,B,Southampton,no,True 818 | 0,3,female,23.0,0,0,7.925,S,Third,woman,False,,Southampton,no,True 819 | 0,2,male,31.0,1,1,37.0042,C,Second,man,True,,Cherbourg,no,False 820 | 0,3,male,43.0,0,0,6.45,S,Third,man,True,,Southampton,no,True 821 | 0,3,male,10.0,3,2,27.9,S,Third,child,False,,Southampton,no,False 822 | 1,1,female,52.0,1,1,93.5,S,First,woman,False,B,Southampton,yes,False 823 | 1,3,male,27.0,0,0,8.6625,S,Third,man,True,,Southampton,yes,True 824 | 0,1,male,38.0,0,0,0.0,S,First,man,True,,Southampton,no,True 825 | 1,3,female,27.0,0,1,12.475,S,Third,woman,False,E,Southampton,yes,False 826 | 0,3,male,2.0,4,1,39.6875,S,Third,child,False,,Southampton,no,False 827 | 0,3,male,,0,0,6.95,Q,Third,man,True,,Queenstown,no,True 828 | 0,3,male,,0,0,56.4958,S,Third,man,True,,Southampton,no,True 829 | 1,2,male,1.0,0,2,37.0042,C,Second,child,False,,Cherbourg,yes,False 830 | 1,3,male,,0,0,7.75,Q,Third,man,True,,Queenstown,yes,True 831 | 1,1,female,62.0,0,0,80.0,,First,woman,False,B,,yes,True 832 | 1,3,female,15.0,1,0,14.4542,C,Third,child,False,,Cherbourg,yes,False 833 | 1,2,male,0.83,1,1,18.75,S,Second,child,False,,Southampton,yes,False 834 | 0,3,male,,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True 835 | 0,3,male,23.0,0,0,7.8542,S,Third,man,True,,Southampton,no,True 836 | 0,3,male,18.0,0,0,8.3,S,Third,man,True,,Southampton,no,True 837 | 1,1,female,39.0,1,1,83.1583,C,First,woman,False,E,Cherbourg,yes,False 838 | 0,3,male,21.0,0,0,8.6625,S,Third,man,True,,Southampton,no,True 839 | 0,3,male,,0,0,8.05,S,Third,man,True,,Southampton,no,True 840 | 1,3,male,32.0,0,0,56.4958,S,Third,man,True,,Southampton,yes,True 841 | 1,1,male,,0,0,29.7,C,First,man,True,C,Cherbourg,yes,True 842 | 0,3,male,20.0,0,0,7.925,S,Third,man,True,,Southampton,no,True 843 | 0,2,male,16.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 844 | 1,1,female,30.0,0,0,31.0,C,First,woman,False,,Cherbourg,yes,True 845 | 0,3,male,34.5,0,0,6.4375,C,Third,man,True,,Cherbourg,no,True 846 | 0,3,male,17.0,0,0,8.6625,S,Third,man,True,,Southampton,no,True 847 | 0,3,male,42.0,0,0,7.55,S,Third,man,True,,Southampton,no,True 848 | 0,3,male,,8,2,69.55,S,Third,man,True,,Southampton,no,False 849 | 0,3,male,35.0,0,0,7.8958,C,Third,man,True,,Cherbourg,no,True 850 | 0,2,male,28.0,0,1,33.0,S,Second,man,True,,Southampton,no,False 851 | 1,1,female,,1,0,89.1042,C,First,woman,False,C,Cherbourg,yes,False 852 | 0,3,male,4.0,4,2,31.275,S,Third,child,False,,Southampton,no,False 853 | 0,3,male,74.0,0,0,7.775,S,Third,man,True,,Southampton,no,True 854 | 0,3,female,9.0,1,1,15.2458,C,Third,child,False,,Cherbourg,no,False 855 | 1,1,female,16.0,0,1,39.4,S,First,woman,False,D,Southampton,yes,False 856 | 0,2,female,44.0,1,0,26.0,S,Second,woman,False,,Southampton,no,False 857 | 1,3,female,18.0,0,1,9.35,S,Third,woman,False,,Southampton,yes,False 858 | 1,1,female,45.0,1,1,164.8667,S,First,woman,False,,Southampton,yes,False 859 | 1,1,male,51.0,0,0,26.55,S,First,man,True,E,Southampton,yes,True 860 | 1,3,female,24.0,0,3,19.2583,C,Third,woman,False,,Cherbourg,yes,False 861 | 0,3,male,,0,0,7.2292,C,Third,man,True,,Cherbourg,no,True 862 | 0,3,male,41.0,2,0,14.1083,S,Third,man,True,,Southampton,no,False 863 | 0,2,male,21.0,1,0,11.5,S,Second,man,True,,Southampton,no,False 864 | 1,1,female,48.0,0,0,25.9292,S,First,woman,False,D,Southampton,yes,True 865 | 0,3,female,,8,2,69.55,S,Third,woman,False,,Southampton,no,False 866 | 0,2,male,24.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 867 | 1,2,female,42.0,0,0,13.0,S,Second,woman,False,,Southampton,yes,True 868 | 1,2,female,27.0,1,0,13.8583,C,Second,woman,False,,Cherbourg,yes,False 869 | 0,1,male,31.0,0,0,50.4958,S,First,man,True,A,Southampton,no,True 870 | 0,3,male,,0,0,9.5,S,Third,man,True,,Southampton,no,True 871 | 1,3,male,4.0,1,1,11.1333,S,Third,child,False,,Southampton,yes,False 872 | 0,3,male,26.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 873 | 1,1,female,47.0,1,1,52.5542,S,First,woman,False,D,Southampton,yes,False 874 | 0,1,male,33.0,0,0,5.0,S,First,man,True,B,Southampton,no,True 875 | 0,3,male,47.0,0,0,9.0,S,Third,man,True,,Southampton,no,True 876 | 1,2,female,28.0,1,0,24.0,C,Second,woman,False,,Cherbourg,yes,False 877 | 1,3,female,15.0,0,0,7.225,C,Third,child,False,,Cherbourg,yes,True 878 | 0,3,male,20.0,0,0,9.8458,S,Third,man,True,,Southampton,no,True 879 | 0,3,male,19.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 880 | 0,3,male,,0,0,7.8958,S,Third,man,True,,Southampton,no,True 881 | 1,1,female,56.0,0,1,83.1583,C,First,woman,False,C,Cherbourg,yes,False 882 | 1,2,female,25.0,0,1,26.0,S,Second,woman,False,,Southampton,yes,False 883 | 0,3,male,33.0,0,0,7.8958,S,Third,man,True,,Southampton,no,True 884 | 0,3,female,22.0,0,0,10.5167,S,Third,woman,False,,Southampton,no,True 885 | 0,2,male,28.0,0,0,10.5,S,Second,man,True,,Southampton,no,True 886 | 0,3,male,25.0,0,0,7.05,S,Third,man,True,,Southampton,no,True 887 | 0,3,female,39.0,0,5,29.125,Q,Third,woman,False,,Queenstown,no,False 888 | 0,2,male,27.0,0,0,13.0,S,Second,man,True,,Southampton,no,True 889 | 1,1,female,19.0,0,0,30.0,S,First,woman,False,B,Southampton,yes,True 890 | 0,3,female,,1,2,23.45,S,Third,woman,False,,Southampton,no,False 891 | 1,1,male,26.0,0,0,30.0,C,First,man,True,C,Cherbourg,yes,True 892 | 0,3,male,32.0,0,0,7.75,Q,Third,man,True,,Queenstown,no,True 893 | -------------------------------------------------------------------------------- /img/BP.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/milaan9/12_Python_Seaborn_Module/92d2863a362f089e14b009fe28114f014542cfc8/img/BP.png -------------------------------------------------------------------------------- /img/SCC.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/milaan9/12_Python_Seaborn_Module/92d2863a362f089e14b009fe28114f014542cfc8/img/SCC.png -------------------------------------------------------------------------------- /img/dnld_rep.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/milaan9/12_Python_Seaborn_Module/92d2863a362f089e14b009fe28114f014542cfc8/img/dnld_rep.png --------------------------------------------------------------------------------