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genya0407/heatmap.ipynb

Created November 27, 2017 13:24
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heatmap.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import seaborn as sns\n",
"import pandas as pd\n",
"sns.set()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame(\n",
" [\n",
" {'lv': 1, 'kin1': 100, 'kin2': 10, 'kin3': 100},\n",
" {'lv': 1, 'kin1': 100, 'kin2': 10, 'kin3': 100},\n",
" {'lv': 2, 'kin1': 200, 'kin2': 10, 'kin3': 100},\n",
" {'lv': 2, 'kin1': 34, 'kin2': 10, 'kin3': 100},\n",
" {'lv': 3, 'kin1': 200, 'kin2': 10, 'kin3': 100},\n",
" {'lv': 3, 'kin1': 200, 'kin2': 10, 'kin3': 100},\n",
" {'lv': 4, 'kin1': 45, 'kin2': 10, 'kin3': 100},\n",
" {'lv': 4, 'kin1': 67, 'kin2': 10, 'kin3': 100},\n",
" {'lv': 1, 'kin1': 5000, 'kin2': 10, 'kin3': 100 },\n",
" ]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>kin1</th>\n",
" <th>kin2</th>\n",
" <th>kin3</th>\n",
" <th>lv</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>100</td>\n",
" <td>10</td>\n",
" <td>100</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>100</td>\n",
" <td>10</td>\n",
" <td>100</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>200</td>\n",
" <td>10</td>\n",
" <td>100</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>34</td>\n",
" <td>10</td>\n",
" <td>100</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>200</td>\n",
" <td>10</td>\n",
" <td>100</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>200</td>\n",
" <td>10</td>\n",
" <td>100</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>45</td>\n",
" <td>10</td>\n",
" <td>100</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>67</td>\n",
" <td>10</td>\n",
" <td>100</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>5000</td>\n",
" <td>10</td>\n",
" <td>100</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" kin1 kin2 kin3 lv\n",
"0 100 10 100 1\n",
"1 100 10 100 1\n",
"2 200 10 100 2\n",
"3 34 10 100 2\n",
"4 200 10 100 3\n",
"5 200 10 100 3\n",
"6 45 10 100 4\n",
"7 67 10 100 4\n",
"8 5000 10 100 1"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df_sorted = df.sort_values(by='lv')"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>kin1</th>\n",
" <th>kin2</th>\n",
" <th>kin3</th>\n",
" <th>lv</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>100</td>\n",
" <td>10</td>\n",
" <td>100</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>100</td>\n",
" <td>10</td>\n",
" <td>100</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>5000</td>\n",
" <td>10</td>\n",
" <td>100</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>200</td>\n",
" <td>10</td>\n",
" <td>100</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>34</td>\n",
" <td>10</td>\n",
" <td>100</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>200</td>\n",
" <td>10</td>\n",
" <td>100</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>200</td>\n",
" <td>10</td>\n",
" <td>100</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>45</td>\n",
" <td>10</td>\n",
" <td>100</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>67</td>\n",
" <td>10</td>\n",
" <td>100</td>\n",
" <td>4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" kin1 kin2 kin3 lv\n",
"0 100 10 100 1\n",
"1 100 10 100 1\n",
"8 5000 10 100 1\n",
"2 200 10 100 2\n",
"3 34 10 100 2\n",
"4 200 10 100 3\n",
"5 200 10 100 3\n",
"6 45 10 100 4\n",
"7 67 10 100 4"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_sorted"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x116360b70>"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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HHVq9erUkyXEcBYNB1dbWKj09XZKUmZmpqqoqHT16VBkZGbIsS6NHj1Z7e7saGhq6XXup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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1163061d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.heatmap(df)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11634f668>"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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A4JplWX0+Ijly5IhGjBih0tJS/fKXv9TWrVtVXFys3NxclZaWKhwO6+jRo6qtrVVVVZXK\nyspUUlKiwsJCSep2bCSEIgDAPZ+LI4J77rlHa9askSSFw2H5/X7V1tYqLS1NkpSRkaHKykpVV1cr\nPT1dlmVpzJgx6ujoUGNjY7djr/Q2AABwxatOMSkpSYFAQM3NzVq9erVyc3MVDoe7XpeUlKSmpiY1\nNzcrEAhc8rqmpqZux0bS61A8e/Zsb4cCANBvPvnkEy1btkwLFy7UggULLlkTDIVCCgaDCgQCCoVC\nl5xPTk7udmwkPYZiXV3dJUdOTk7XfwMAEA2ffvqpHnzwQT3++OPKysqSJE2bNk3Hjh2TJJWXlys1\nNVUpKSmqqKiQbduqr6+XbdsaOXJkt2MjscLhcLi7X9x1111KTEzU9ddfr3A4rOPHj+uWW26RZVna\nt29fr97MuYaPev3G0Td8ybD37Pb2gS5hSPDFxw90CcZLCI7y7Nr/tat3udCdlEeX9fi7oqIivfLK\nK5o4cWLXuY0bN6qoqEjt7e2aOHGiioqK5Pf7tXv3bpWXl8u2beXn5ys1NVV1dXXavHnzZWN70mMo\nnj17VgUFBVq6dKnuuOMOZWdna//+/Vf1RglF7xGK3iMUo4NQ9J6XofjXp64uH/63f8vN7sdK3Onx\nPsVRo0bpqaee0s6dO/XOO+9EsyYAQIwx5ClvkTfaxMXFaePGjV1TqAAAdMuy+n4MIr16ok1mZqYy\nMzO9rgUAgAHFY94AAK5ZvsHV8fUVN+8DAOCgUwQAuDbIlgb7jFAEALhmypcME4oAANcMyUTWFAEA\n6ESnCABwz5BWkVAEALjGLRkAABiGThEA4Johs6eEIgCgHxiSikyfAgDgoFMEALhmSKNIKAIA3DNl\n9ymhCABwzZTHvLGmCACAg04RAOCeGY0inSIAAJ3oFAEArpmypkgoAgBcIxQBAOhkyGIcoQgAcM2U\nTtGQbAcAwD1CEQAAB9OnAADXTJk+JRQBAO6ZkYm9nz61bVsNDQ2ybdvLegAAMcjyWX0+BpOIobhh\nwwZJ0ltvvaV58+Zp1apV+uY3v6mampqoFAcAiBGW1fdjEIk4ffrxxx9Lknbt2qW9e/dq/Pjxamho\nUF5eng4cOBCVAgEAiJZeTZ/6/X6NHz9ekjR69GimUAEARooYis3NzcrMzNTp06dVVlam1tZWFRYW\nasyYMdGqDwAQAwyZPY08fXro0CG1tbXp+PHjSkxMlGVZmjx5srKysqJVHwAgBgyZWzISEhI0Y8aM\nrp+XLl3qaUEAgBg0yHaR9hX3KQIAXDOlU+QxbwAAOOgUAQDumdEo0ikCANCJThEA4Jopa4qEIgDA\ntcH2DNO+IhQBAO7RKQIAcJEp06dstAEAwEGnCABwz4xGkU4RAIBOdIoAANfYfdoL/mGJXl4eADBY\nGLLRhk4RAOAau08BADAMoQgAcM9n9f3ohbfeekvZ2dmSpJMnT2rp0qV64IEHVFBQINu2JUl79uxR\nVlaWlixZorfffjvi2B7fhouPAAAASRenT/t6XMnevXu1adMmtba2SpKKi4uVm5ur0tJShcNhHT16\nVLW1taqqqlJZWZlKSkpUWFjY49hICEUAwKA2btw47d69u+vn2tpapaWlSZIyMjJUWVmp6upqpaen\ny7IsjRkzRh0dHWpsbOx2bCSEIgDAPcvFcQXz5s1TXNxn+0LD4XBXh5mUlKSmpiY1NzcrEAh0jek8\n393YSNh9CgBwLZq7T32+z/q5UCikYDCoQCCgUCh0yfnk5ORux0a8dv+XCwCAd6ZNm6Zjx45JksrL\ny5WamqqUlBRVVFTItm3V19fLtm2NHDmy27GR0CkCANyL4hNt1q1bp82bN6ukpEQTJ07UvHnz5Pf7\nlZqaqsWLF8u2bW3ZsqXHsZFY4XA47FXhrf/6/15dGg7L5x/oEoxnt7cPdAlDgi8+fqBLMF5CcJRn\n12548/U+v3b0rLv6rQ636BQBAO7xRBsAAMxCpwgAcG1IPvu0sbFRHi5BAgAwoCJ2ii+++KI++eQT\nzZ49W3l5eRo2bJhaWlpUUFCg22+/PVo1AgAGu6HwfYqlpaXav3+/cnJy9PTTT2vChAlqaGjQypUr\nCUUAQBdTpk8jhmJ8fLyGDx+upKQkjR07VpI0evRoY948AKCfGJILEUPx7rvvVk5OjiZPnqyHH35Y\ns2bN0ptvvqmZM2dGqz4AQAywDJk+veLN+1VVVaqoqNA///lPjRgxQl/5yld011139eri3LzvPW7e\n9x4370cHN+97z8ub9z/9S+Rvn4jk2v83eJbjrnhLRlpaWtfXbgAAYDLuUwQAuDcU1hQBAOgNUzZg\nEooAAPcIRQAALjJl9ykPBAcAwEEoAgDgYPoUAOAea4oAADgIRQAALuKWDAAAOrH7FAAAs9ApAgBc\nsywzeixvQzHyF3AAADCo0CkCANxjow0AABex+xQAgE7sPgUAwCx0igAA15g+BQCgkyGhyPQpAAAO\nOkUAgHvcvA8AwEUWu08BADALnSIAwD1DNtoQigAA17glAwCAToZstIn4Lpqbm6NVBwAAAy5iKN5x\nxx0qKyuLVi0AgBhl+aw+H4NJxFC85ZZb9N5772nZsmWqqqqKVk0AAAyIiGuKw4YN05YtW/TOO+/o\n2Wef1datWzVz5kyNHTtWy5Yti1aNAIDBbihstAmHw5Kk6dOna/fu3WpqatJf/vIX1dXVRaU4AEBs\nGBK7TzMzMy/5OTk5WXfffbenBQEAYpAhu08jhuKiRYuiVQcAIJYNsg0zfWVGtAMA0A8IRQAAHDzR\nBgDg2pDYaAMAQK8MhY02AAD0Bp0iAACdDOkUzXgXAAD0A0IRAAAH06cAANcG27dd9BWhCABwj402\nAABcZBmy0YZQBAC4Z0inaIU7vx8KAIAhzox+FwCAfkAoAgDgIBQBAHAQigAAOAhFAAAchCIAAA5C\nEQAAx5AKxUOHDumnP/1p18/l5eV6/vnnr/i6P/zhD8rLy/OyNGNc7Wfc1NSkH/3oR/rud7+rxYsX\n669//Ws0yox5V/s5nzt3Tjk5OfrOd76j5cuXq6GhIRplGun/fvYwy5B+ok1GRsYVxxQVFamiokJT\np06NQkXmudJn/Ktf/UozZ87U8uXL9cEHHygvL08vvfRSlKozx5U+5xdeeEFf+tKXtGrVKh06dEh7\n9+7Vpk2bolQdEDuGZCg2NjZq5cqVuu+++3Ty5EktWbJEeXl5+vznP69Tp05p+vTpKiwslCSlpKRo\nzpw5veoo8ZnefsbLly9XQkKCJKmjo0PDhg0b4Mpjy9V8zh0dHZKk+vp6BYPBAa48tr3wwgtKTEzU\nqlWr1NbWpnvvvVdHjhzp+ltG7BpS06eSdPbsWeXk5Cg/P19+v7/r/Icffqht27aprKxM5eXlOnPm\njCRp/vz5sgx5pl+0XM1nHAwGlZiYqDNnzujxxx/XY489NoCVx5ar/Vv2+/1atmyZDhw4oLlz5w5U\n2Ua455579MorrygcDuvo0aOaPXs2gWiIIReKb775ptra2mTb9iXnx40bp0AgIL/fr+uuu06tra0D\nVGHsu9rP+MSJE1q+fLkeffRRpaWlDUTJMakvf8v79u3Tc889p0ceeSTa5RolGAxq6tSpqq6u1ksv\nvaSsrKyBLgn9ZMiF4re+9S395Cc/0aZNm3T+/Pmu83SD/edqPuP3339fa9as0ZNPPqk777wzmmXG\nvKv5nJ955hkdPnxYkpSUlHRJZ4m+uf/++/Wb3/xGLS0tuvnmmwe6HPSTIReKkjRp0iTde++9Ki4u\nHuhSjNXbz/jJJ59UW1ubtm3bpuzsbOXk5ESpQjP09nO+77779Lvf/U7Z2dl67LHHtH379ihVaK60\ntDT9/e9/V2Zm5kCXgn7EV0cBAOAYkp0iAADdIRQBAHAQigAAOAhFAAAchCIAAA5CEQAAB6EIAIDj\nfwBbWAYCJ0waWAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1164372e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.heatmap(df_sorted)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df_dropped = df_sorted.drop('lv', axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>kin1</th>\n",
" <th>kin2</th>\n",
" <th>kin3</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>100</td>\n",
" <td>10</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>100</td>\n",
" <td>10</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>5000</td>\n",
" <td>10</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>200</td>\n",
" <td>10</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>34</td>\n",
" <td>10</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>200</td>\n",
" <td>10</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>200</td>\n",
" <td>10</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>45</td>\n",
" <td>10</td>\n",
" <td>100</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>67</td>\n",
" <td>10</td>\n",
" <td>100</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" kin1 kin2 kin3\n",
"0 100 10 100\n",
"1 100 10 100\n",
"8 5000 10 100\n",
"2 200 10 100\n",
"3 34 10 100\n",
"4 200 10 100\n",
"5 200 10 100\n",
"6 45 10 100\n",
"7 67 10 100"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_dropped"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.LineCollection at 0x1167fab00>"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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qalJzc7MikUi39zU1NfU4Npp+heLly5fV1tbWn7cAAGDkH//4hx5++GFlZ2fr3nvv7bYm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\n/wFbKrnC8SCUNAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1167facf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = sns.heatmap(df_dropped, yticklabels=list(df_sorted['lv']))\n",
"ax.hlines([2, 4, 6], *ax.get_xlim())"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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