{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.2 数据可视化的目标和作用"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 安斯库姆四重奏"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "anscombe_df = sns.load_dataset('anscombe')\n",
    "anscombe_df\n",
    "# anscombe_df[anscombe_df['dataset'] == 'II']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "anscombe_df.groupby('dataset').agg([np.mean, np.var])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(style=\"ticks\")\n",
    "sns.lmplot(x=\"x\", y=\"y\", col=\"dataset\", hue=\"dataset\", \n",
    "           data=anscombe_df, col_wrap=2, ci=None, palette=\"muted\", \n",
    "           height=4, scatter_kws={\"s\": 50, \"alpha\": 1})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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