iris-Multiple bivariate KDE plots.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple bivariate KDE plots\n",
    "* https://seaborn.pydata.org/examples/multiple_joint_kde.html\n",
    "* [확률 밀도 함수 - 위키백과, 우리 모두의 백과사전](https://ko.wikipedia.org/wiki/%ED%99%95%EB%A5%A0_%EB%B0%80%EB%8F%84_%ED%95%A8%EC%88%98)\n",
    "* [Kernel density estimation - Wikipedia](https://en.wikipedia.org/wiki/Kernel_density_estimation)\n",
    "* [Iris flower data set - Wikipedia](https://en.wikipedia.org/wiki/Iris_flower_data_set)\n",
    "* [로널드 피셔 - 위키백과, 우리 모두의 백과사전](https://ko.wikipedia.org/wiki/%EB%A1%9C%EB%84%90%EB%93%9C_%ED%94%BC%EC%85%94)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.set(style=\"darkgrid\")\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 5)\n"
     ]
    }
   ],
   "source": [
    "iris = sns.load_dataset(\"iris\")\n",
    "print(iris.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50, 5)\n",
      "(50, 5)\n"
     ]
    }
   ],
   "source": [
    "# Subset the iris dataset by species\n",
    "setosa = iris.query(\"species == 'setosa'\")\n",
    "print(setosa.shape)\n",
    "virginica = iris.query(\"species == 'virginica'\")\n",
    "print(virginica.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50.00000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.00600</td>\n",
       "      <td>3.428000</td>\n",
       "      <td>1.462000</td>\n",
       "      <td>0.246000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.35249</td>\n",
       "      <td>0.379064</td>\n",
       "      <td>0.173664</td>\n",
       "      <td>0.105386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.30000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.80000</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.00000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.20000</td>\n",
       "      <td>3.675000</td>\n",
       "      <td>1.575000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.80000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count      50.00000    50.000000     50.000000    50.000000\n",
       "mean        5.00600     3.428000      1.462000     0.246000\n",
       "std         0.35249     0.379064      0.173664     0.105386\n",
       "min         4.30000     2.300000      1.000000     0.100000\n",
       "25%         4.80000     3.200000      1.400000     0.200000\n",
       "50%         5.00000     3.400000      1.500000     0.200000\n",
       "75%         5.20000     3.675000      1.575000     0.300000\n",
       "max         5.80000     4.400000      1.900000     0.600000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "setosa.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50.00000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.58800</td>\n",
       "      <td>2.974000</td>\n",
       "      <td>5.552000</td>\n",
       "      <td>2.02600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.63588</td>\n",
       "      <td>0.322497</td>\n",
       "      <td>0.551895</td>\n",
       "      <td>0.27465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.90000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>1.40000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.22500</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.50000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.550000</td>\n",
       "      <td>2.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.90000</td>\n",
       "      <td>3.175000</td>\n",
       "      <td>5.875000</td>\n",
       "      <td>2.30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.90000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.50000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count      50.00000    50.000000     50.000000     50.00000\n",
       "mean        6.58800     2.974000      5.552000      2.02600\n",
       "std         0.63588     0.322497      0.551895      0.27465\n",
       "min         4.90000     2.200000      4.500000      1.40000\n",
       "25%         6.22500     2.800000      5.100000      1.80000\n",
       "50%         6.50000     3.000000      5.550000      2.00000\n",
       "75%         6.90000     3.175000      5.875000      2.30000\n",
       "max         7.90000     3.800000      6.900000      2.50000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "virginica.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1881a630>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(setosa[\"sepal_length\"])\n",
    "sns.kdeplot(setosa[\"sepal_width\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a197ebcc0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=setosa, x=\"sepal_width\", y=\"sepal_length\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(3.8, 4.5, 'setosa')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(8, 8))\n",
    "ax.set_aspect(\"equal\")\n",
    "\n",
    "ax = sns.kdeplot(setosa[\"sepal_width\"], setosa[\"sepal_length\"], \n",
    "            shade=True, shade_lowest=False)\n",
    "sns.scatterplot(data=setosa, x=\"sepal_width\", y=\"sepal_length\")\n",
    "ax = sns.kdeplot(virginica[\"sepal_width\"], virginica[\"sepal_length\"], \n",
    "            shade=True, shade_lowest=False)\n",
    "sns.scatterplot(data=virginica, x=\"sepal_width\", y=\"sepal_length\")\n",
    "\n",
    "ax.text(2.5, 8.2, \"virginica\", size=16, color=red)\n",
    "ax.text(3.8, 4.5, \"setosa\", size=16, color=blue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(3.8, 4.5, 'setosa')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set up the figure\n",
    "f, ax = plt.subplots(figsize=(8, 8))\n",
    "ax.set_aspect(\"equal\")\n",
    "\n",
    "# Draw the two density plots\n",
    "ax = sns.kdeplot(setosa.sepal_width, setosa.sepal_length,\n",
    "                 cmap=\"Reds\", shade=True, shade_lowest=False)\n",
    "ax = sns.kdeplot(virginica.sepal_width, virginica.sepal_length,\n",
    "                 cmap=\"Blues\", shade=True, shade_lowest=False)\n",
    "\n",
    "# Add labels to the plot\n",
    "red = sns.color_palette(\"Reds\")[-2]\n",
    "blue = sns.color_palette(\"Blues\")[-2]\n",
    "ax.text(2.5, 8.2, \"virginica\", size=16, color=blue)\n",
    "ax.text(3.8, 4.5, \"setosa\", size=16, color=red)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}