{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.3 预备知识"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "coins = ['比特币', '以太坊', '柚子币']\n",
    "coins_ser = pd.Series(coins)\n",
    "print(type(coins_ser))\n",
    "print(coins_ser)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "coins_ser.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "coins_ser.values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "coin_dict = {'BTH': '比特币',\n",
    "              'ETH': '以太坊',\n",
    "              'EOS': '柚子币'}\n",
    "\n",
    "coin_dict_s = pd.Series(coin_dict)\n",
    "print(coin_dict_s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "coin_dict_s.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('iloc:', coin_dict_s.iloc[1])\n",
    "print('loc:', coin_dict_s.loc['ETH'])\n",
    "print('[]:', coin_dict_s['ETH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('iloc:\\n', coin_dict_s.iloc[ [0, 2] ])\n",
    "print()\n",
    "print('loc:\\n', coin_dict_s.loc[ ['BTH', 'EOS'] ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "coin1 = pd.Series({'Name': '比特币',\n",
    "                   'Year': 2017,\n",
    "                   'MaxPrice': 19798.68})\n",
    "\n",
    "coin2 = pd.Series({'Name': '以太坊',\n",
    "                   'Year': 2017,\n",
    "                   'MaxPrice': 1440.00})\n",
    "\n",
    "coin3 = pd.Series({'Name': '柚子币',\n",
    "                   'Year': 2018,\n",
    "                   'MaxPrice': 15.68})\n",
    "\n",
    "df = pd.DataFrame([coin1, coin2, coin3], index=['BTH', 'ETH', 'EOS'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 注意在jupyter中使用print和不使用print的区别\n",
    "# print(df)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 行索引\n",
    "print('loc:')\n",
    "print(df.loc['BTH'])\n",
    "\n",
    "print()\n",
    "\n",
    "print('iloc:')\n",
    "print(df.iloc[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 列索引\n",
    "print(df['Year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 获取不连续的列数据\n",
    "df[['Name', 'MaxPrice']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['MaxPrice/100'] = df['MaxPrice'] / 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 加载csv文件数据\n",
    "reprot_2015_df = pd.read_csv('./2015.csv')\n",
    "print('2015年数据预览：')\n",
    "reprot_2015_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "reprot_2015_df[reprot_2015_df['Happiness Rank'] <= 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "top5_df = reprot_2015_df[reprot_2015_df['Happiness Rank'] <= 5]\n",
    "top5_df.to_csv('top5.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.4 ECharts介绍及体验"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pyecharts体验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyecharts.charts import Bar\n",
    "from pyecharts import options as opts\n",
    "\n",
    "bar = (\n",
    "    Bar()\n",
    "    .add_xaxis(['衬衣', '毛衣', '领带', '风衣', '高跟鞋', '袜子'])\n",
    "    .add_yaxis('商家A', [114, 55, 27, 101, 125, 27])\n",
    "    .add_yaxis('商家B', [57, 134, 137, 129, 145, 60])\n",
    "    .set_global_opts(title_opts=opts.TitleOpts(title='某商场销售情况'))\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "bar.render_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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