{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# I/O：CSV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "Example："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>uid</th>\n",
       "      <th>name</th>\n",
       "      <th>company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>246000b6-0256-476b-b125-0f0b95719a1d</td>\n",
       "      <td>Steve Jobs</td>\n",
       "      <td>Apple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2155499d-3e0b-4e8a-965f-acd3f31ac2fc</td>\n",
       "      <td>Bill Gates</td>\n",
       "      <td>Microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5f51767f-f743-43d1-aeca-5eaaad5e47ab</td>\n",
       "      <td>Mark Zuckerberg</td>\n",
       "      <td>Facebook</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>e86c9b7e-53ba-41bf-98e2-6979f1d462cf</td>\n",
       "      <td>Larry Page</td>\n",
       "      <td>Google</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5aa2d881-8721-4eba-a73d-e5f0a19a5ede</td>\n",
       "      <td>Sergey Brin</td>\n",
       "      <td>Yandex</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    uid             name    company\n",
       "0  246000b6-0256-476b-b125-0f0b95719a1d       Steve Jobs      Apple\n",
       "1  2155499d-3e0b-4e8a-965f-acd3f31ac2fc       Bill Gates  Microsoft\n",
       "2  5f51767f-f743-43d1-aeca-5eaaad5e47ab  Mark Zuckerberg   Facebook\n",
       "3  e86c9b7e-53ba-41bf-98e2-6979f1d462cf       Larry Page     Google\n",
       "4  5aa2d881-8721-4eba-a73d-e5f0a19a5ede      Sergey Brin     Yandex"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pathlib\n",
    "import uuid\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "csvfile = pathlib.Path(r\"~/Desktop/pandas.csv\").expanduser()\n",
    "txtfile = pathlib.Path(r\"~/Desktop/pandas.txt\").expanduser()\n",
    "data = dict(\n",
    "    uid=[uuid.uuid4() for _ in range(5)],\n",
    "    name=[\"Steve Jobs\", \"Bill Gates\", \"Mark Zuckerberg\", \"Larry Page\", \"Sergey Brin\"],\n",
    "    company=[\"Apple\", \"Microsoft\", \"Facebook\", \"Google\", \"Yandex\"],\n",
    ")\n",
    "df = pd.DataFrame(data)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df.to_csv(csvfile, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0muid\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcompany\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m246000\u001b[0m\u001b[0mb6\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;36m256\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m476\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mb125\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0mf0b95719a1d\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mSteve\u001b[0m \u001b[0mJobs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mApple\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m2155499\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m3e0\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m4e8\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m965\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0macd3f31ac2fc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mBill\u001b[0m \u001b[0mGates\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mMicrosoft\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0mf51767f\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mf743\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m43\u001b[0m\u001b[0md1\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0maeca\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0meaaad5e47ab\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mMark\u001b[0m \u001b[0mZuckerberg\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mFacebook\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0me86c9b7e\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m53\u001b[0m\u001b[0mba\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m41\u001b[0m\u001b[0mbf\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m98e2\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m6979\u001b[0m\u001b[0mf1d462cf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mLarry\u001b[0m \u001b[0mPage\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mGoogle\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0maa2d881\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m8721\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0meba\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0ma73d\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0me5f0a19a5ede\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mSergey\u001b[0m \u001b[0mBrin\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mYandex\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "%pycat ~/Desktop/pandas.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df.to_csv(txtfile, index=False, sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0muid\u001b[0m     \u001b[0mname\u001b[0m    \u001b[0mcompany\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m246000\u001b[0m\u001b[0mb6\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;36m256\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m476\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mb125\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0mf0b95719a1d\u001b[0m    \u001b[0mSteve\u001b[0m \u001b[0mJobs\u001b[0m      \u001b[0mApple\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m2155499\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m3e0\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m4e8\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m965\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0macd3f31ac2fc\u001b[0m    \u001b[0mBill\u001b[0m \u001b[0mGates\u001b[0m      \u001b[0mMicrosoft\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0mf51767f\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mf743\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m43\u001b[0m\u001b[0md1\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0maeca\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0meaaad5e47ab\u001b[0m    \u001b[0mMark\u001b[0m \u001b[0mZuckerberg\u001b[0m \u001b[0mFacebook\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0me86c9b7e\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m53\u001b[0m\u001b[0mba\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m41\u001b[0m\u001b[0mbf\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m98e2\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m6979\u001b[0m\u001b[0mf1d462cf\u001b[0m    \u001b[0mLarry\u001b[0m \u001b[0mPage\u001b[0m      \u001b[0mGoogle\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0maa2d881\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m8721\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0meba\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0ma73d\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0me5f0a19a5ede\u001b[0m    \u001b[0mSergey\u001b[0m \u001b[0mBrin\u001b[0m     \u001b[0mYandex\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "%pycat ~/Desktop/pandas.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## read"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### sep & delimeter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                    uid             name    company\n",
      "0  246000b6-0256-476b-b125-0f0b95719a1d       Steve Jobs      Apple\n",
      "1  2155499d-3e0b-4e8a-965f-acd3f31ac2fc       Bill Gates  Microsoft\n",
      "2  5f51767f-f743-43d1-aeca-5eaaad5e47ab  Mark Zuckerberg   Facebook\n",
      "3  e86c9b7e-53ba-41bf-98e2-6979f1d462cf       Larry Page     Google\n",
      "4  5aa2d881-8721-4eba-a73d-e5f0a19a5ede      Sergey Brin     Yandex\n"
     ]
    }
   ],
   "source": [
    "import pathlib\n",
    "\n",
    "txtfile = pathlib.Path(r\"~/Desktop/pandas.txt\").expanduser()\n",
    "\n",
    "df = pd.read_csv(txtfile, sep=\"\\t\")\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### usecols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              name    company\n",
      "0       Steve Jobs      Apple\n",
      "1       Bill Gates  Microsoft\n",
      "2  Mark Zuckerberg   Facebook\n",
      "3       Larry Page     Google\n",
      "4      Sergey Brin     Yandex\n"
     ]
    }
   ],
   "source": [
    "import pathlib\n",
    "csvfile = pathlib.Path(r\"~/Desktop/pandas.csv\").expanduser()\n",
    "\n",
    "df = pd.read_csv(csvfile, usecols=[\"name\", \"company\"])\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### skiprows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import pathlib\n",
    "\n",
    "csvfile = pathlib.Path(r\"~/Desktop/pandas.csv\").expanduser()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>uid</th>\n",
       "      <th>name</th>\n",
       "      <th>company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2155499d-3e0b-4e8a-965f-acd3f31ac2fc</td>\n",
       "      <td>Bill Gates</td>\n",
       "      <td>Microsoft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>e86c9b7e-53ba-41bf-98e2-6979f1d462cf</td>\n",
       "      <td>Larry Page</td>\n",
       "      <td>Google</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5aa2d881-8721-4eba-a73d-e5f0a19a5ede</td>\n",
       "      <td>Sergey Brin</td>\n",
       "      <td>Yandex</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    uid         name    company\n",
       "0  2155499d-3e0b-4e8a-965f-acd3f31ac2fc   Bill Gates  Microsoft\n",
       "1  e86c9b7e-53ba-41bf-98e2-6979f1d462cf   Larry Page     Google\n",
       "2  5aa2d881-8721-4eba-a73d-e5f0a19a5ede  Sergey Brin     Yandex"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(csvfile, skiprows=[1, 3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>246000b6-0256-476b-b125-0f0b95719a1d</th>\n",
       "      <th>Steve Jobs</th>\n",
       "      <th>Apple</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5f51767f-f743-43d1-aeca-5eaaad5e47ab</td>\n",
       "      <td>Mark Zuckerberg</td>\n",
       "      <td>Facebook</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5aa2d881-8721-4eba-a73d-e5f0a19a5ede</td>\n",
       "      <td>Sergey Brin</td>\n",
       "      <td>Yandex</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   246000b6-0256-476b-b125-0f0b95719a1d       Steve Jobs     Apple\n",
       "0  5f51767f-f743-43d1-aeca-5eaaad5e47ab  Mark Zuckerberg  Facebook\n",
       "1  5aa2d881-8721-4eba-a73d-e5f0a19a5ede      Sergey Brin    Yandex"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(csvfile, skiprows=lambda x: x % 2 == 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>uid</th>\n",
       "      <th>name</th>\n",
       "      <th>company</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>246000b6-0256-476b-b125-0f0b95719a1d</td>\n",
       "      <td>Steve Jobs</td>\n",
       "      <td>Apple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2155499d-3e0b-4e8a-965f-acd3f31ac2fc</td>\n",
       "      <td>Bill Gates</td>\n",
       "      <td>Microsoft</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    uid        name    company\n",
       "0  246000b6-0256-476b-b125-0f0b95719a1d  Steve Jobs      Apple\n",
       "1  2155499d-3e0b-4e8a-965f-acd3f31ac2fc  Bill Gates  Microsoft"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(csvfile, nrows=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## write"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "from io import StringIO\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "data = StringIO(\n",
    "    \"\"\"a,b,c\\n\n",
    "    1,\"Hello, world\",3\\n\n",
    "    \"\"\"\n",
    ")\n",
    "\n",
    "df = pd.read_csv(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"a\",\"b\",\"c\"\n",
      "\"1\",\"Hello, world\",\"3\"\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(df.to_csv(index=False, quoting=csv.QUOTE_ALL))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"a\",\"b\",\"c\"\n",
      "1,\"Hello, world\",3\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(df.to_csv(index=False, quoting=csv.QUOTE_NONNUMERIC))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"a\",\"b\",\"c\"\n",
      "\"1\",\"Hello, world\",\"3\"\n",
      "\n"
     ]
    }
   ],
   "source": [
    "csv_string = df.to_csv(index=False, quoting=csv.QUOTE_ALL)\n",
    "print(csv_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "type: <class 'str'>\n"
     ]
    }
   ],
   "source": [
    "print(f\"type: {type(csv_string)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# I/O：Excel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "如果未安装 `openpyxl`，则可以通过取消如下命令的注释后执行安装："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# !pip install openpyxl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## read"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   id       name    category\n",
      "0   0      Apple        Tech\n",
      "1   1     Google        Tech\n",
      "2   2  Microsoft        Tech\n",
      "3   3   Facebook      Social\n",
      "4   4     Amazon  E-Commerce\n"
     ]
    }
   ],
   "source": [
    "import pathlib\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "excel_file = pathlib.Path(\"../../data\") / \"excel_io_example.xlsx\"\n",
    "\n",
    "df = pd.read_excel(excel_file, engine=\"openpyxl\")\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## write"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        NAME    CATEGORY\n",
      "0      Apple        Tech\n",
      "1     Google        Tech\n",
      "2  Microsoft        Tech\n",
      "3   Facebook      Social\n",
      "4     Amazon  E-Commerce\n"
     ]
    }
   ],
   "source": [
    "excel_output = (\n",
    "    pathlib.Path(\"~/Desktop\").expanduser() / \"excel_io_example_output.xlsx\"\n",
    ")\n",
    "\n",
    "df.columns = df.columns.str.upper()\n",
    "df = df.drop(\"ID\", axis=1)\n",
    "print(df)\n",
    "df.to_excel(excel_output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# I/O：Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# generate fake data\n",
    "\n",
    "import pathlib\n",
    "import sqlite3\n",
    "\n",
    "DB_PATH = pathlib.Path(\"~/Desktop/pandas.db\").expanduser()\n",
    "\n",
    "db = sqlite3.connect(DB_PATH)\n",
    "\n",
    "\n",
    "def generate_fake_data(conn: sqlite3.Connection):\n",
    "    conn.execute(\n",
    "        \"\"\"\n",
    "        CREATE TABLE IF NOT EXISTS items (\n",
    "            ID int not null,        -- 自增ID\n",
    "            `NAME` VARCHAR(10),     -- 商品名\n",
    "            PRICE float(2),         -- 价格\n",
    "            `NUMBER` int(10)        -- 数量\n",
    "        );\n",
    "        \"\"\"\n",
    "    )\n",
    "\n",
    "    data = [\n",
    "        (0, \"Apple\", 6.99, 4),\n",
    "        (1, \"Orange\", 4.92, 10),\n",
    "        (2, \"Peach\", 11.00, 3),\n",
    "        (3, \"Banana\", 2.33, 7),\n",
    "        (4, \"Purple\", 7.7, 8),\n",
    "    ]\n",
    "    has_data = conn.execute(\"SELECT COUNT(0) FROM items\").fetchone()[0]\n",
    "    if not has_data:\n",
    "        conn.executemany(\n",
    "            \"INSERT INTO ITEMS(ID, NAME, PRICE, NUMBER) VALUES(?, ?, ?, ?)\", data\n",
    "        )\n",
    "        conn.commit()\n",
    "\n",
    "\n",
    "generate_fake_data(conn=db)\n",
    "db.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## read"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "SQL query example：\n",
    "```sql\n",
    "SELECT s.name,\n",
    "       s.category\n",
    "FROM (\n",
    "         SELECT `name`,\n",
    "                case\n",
    "                    WHEN number % 2 == 0 THEN 0\n",
    "                    WHEN number % 3 >= 1 THEN 1\n",
    "                    WHEN number % 4 != 0 THEN 3\n",
    "                    ELSE number\n",
    "                    END as category\n",
    "         FROM items\n",
    "     ) s\n",
    "WHERE s.category > 0;\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>name</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Peach</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Banana</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     name  category\n",
       "2   Peach         3\n",
       "3  Banana         1"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pathlib\n",
    "import sqlite3\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "DB_PATH = pathlib.Path(\"~/Desktop/pandas.db\").expanduser()\n",
    "\n",
    "db = sqlite3.connect(DB_PATH)\n",
    "data = pd.read_sql_query(\"SELECT * FROM items\", db)\n",
    "data.columns = data.columns.str.lower()\n",
    "\n",
    "\n",
    "def case_when(number):\n",
    "    if number % 2 == 0:\n",
    "        return 0\n",
    "    elif number % 3 >= 1:\n",
    "        return 1\n",
    "    elif number % 4 != 0:\n",
    "        return 3\n",
    "    else:\n",
    "        return number\n",
    "\n",
    "\n",
    "data[\"category\"] = data[\"number\"].map(case_when)\n",
    "data.loc[data[\"category\"] > 0, [\"name\", \"category\"]]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## write"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   id        name  price  number\n",
      "0   5      Cherry     15      20\n",
      "1   6  BlackBerry     20      11\n",
      "2   7        Corn      3     100\n"
     ]
    }
   ],
   "source": [
    "items = [\n",
    "    dict(id=5, name=\"Cherry\", price=15, number=20),\n",
    "    dict(id=6, name=\"BlackBerry\", price=20, number=11),\n",
    "    dict(id=7, name=\"Corn\", price=3, number=100),\n",
    "]\n",
    "\n",
    "df = pd.DataFrame(items)\n",
    "df.to_sql(name=\"items\", con=db, if_exists=\"append\", index=False)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 'Apple', 6.99, 4), (1, 'Orange', 4.92, 10), (2, 'Peach', 11.0, 3), (3, 'Banana', 2.33, 7), (4, 'Purple', 7.7, 8), (5, 'Cherry', 15.0, 20), (6, 'BlackBerry', 20.0, 11), (7, 'Corn', 3.0, 100)]\n"
     ]
    }
   ],
   "source": [
    "rows = db.execute(\"SELECT * FROM items\").fetchall()\n",
    "print(rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  }
 ],
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  "interpreter": {
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  },
  "kernelspec": {
   "display_name": "Python 3.9.0 ('pandas-startup')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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