{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 构建一个df数据集\n",
    "iris = load_iris()\n",
    "iris_df = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
    "iris_df['label'] = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>label</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>0</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>0</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>0</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>0</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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   label  \n",
       "0      0  \n",
       "1      0  \n",
       "2      0  \n",
       "3      0  \n",
       "4      0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a282a57b8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 401x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用sns绘制散点图\n",
    "sns.lmplot(data=iris_df, x='sepal length (cm)', y='petal length (cm)', hue='label', fit_reg=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 使用plt绘制散点图\n",
    "plt.figure(figsize=(20,8))\n",
    "plt.scatter(iris_df['sepal length (cm)'], iris_df['petal length (cm)'], c=iris_df['label'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据集划分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# random_state 随机种子相同，随机划分的结果相同 \n",
    "x_train, x_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2)"
   ]
  },
  {
   "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}