{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Xorbits Pandas DataFrame\n",
    "\n",
    "This notebook goes over how to load data from a [xorbits.pandas](https://doc.xorbits.io/en/latest/reference/pandas/frame.html) DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install xorbits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xorbits.pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"example_data/mlb_teams_2012.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Team</th>\n",
       "      <th>\"Payroll (millions)\"</th>\n",
       "      <th>\"Wins\"</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nationals</td>\n",
       "      <td>81.34</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Reds</td>\n",
       "      <td>82.20</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yankees</td>\n",
       "      <td>197.96</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Giants</td>\n",
       "      <td>117.62</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Braves</td>\n",
       "      <td>83.31</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Team   \"Payroll (millions)\"   \"Wins\"\n",
       "0  Nationals                  81.34       98\n",
       "1       Reds                  82.20       97\n",
       "2    Yankees                 197.96       95\n",
       "3     Giants                 117.62       94\n",
       "4     Braves                  83.31       94"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.document_loaders import XorbitsLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "loader = XorbitsLoader(df, page_content_column=\"Team\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "version_minor": 0
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      "text/plain": [
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      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[Document(page_content='Nationals', metadata={' \"Payroll (millions)\"': 81.34, ' \"Wins\"': 98}),\n",
       " Document(page_content='Reds', metadata={' \"Payroll (millions)\"': 82.2, ' \"Wins\"': 97}),\n",
       " Document(page_content='Yankees', metadata={' \"Payroll (millions)\"': 197.96, ' \"Wins\"': 95}),\n",
       " Document(page_content='Giants', metadata={' \"Payroll (millions)\"': 117.62, ' \"Wins\"': 94}),\n",
       " Document(page_content='Braves', metadata={' \"Payroll (millions)\"': 83.31, ' \"Wins\"': 94}),\n",
       " Document(page_content='Athletics', metadata={' \"Payroll (millions)\"': 55.37, ' \"Wins\"': 94}),\n",
       " Document(page_content='Rangers', metadata={' \"Payroll (millions)\"': 120.51, ' \"Wins\"': 93}),\n",
       " Document(page_content='Orioles', metadata={' \"Payroll (millions)\"': 81.43, ' \"Wins\"': 93}),\n",
       " Document(page_content='Rays', metadata={' \"Payroll (millions)\"': 64.17, ' \"Wins\"': 90}),\n",
       " Document(page_content='Angels', metadata={' \"Payroll (millions)\"': 154.49, ' \"Wins\"': 89}),\n",
       " Document(page_content='Tigers', metadata={' \"Payroll (millions)\"': 132.3, ' \"Wins\"': 88}),\n",
       " Document(page_content='Cardinals', metadata={' \"Payroll (millions)\"': 110.3, ' \"Wins\"': 88}),\n",
       " Document(page_content='Dodgers', metadata={' \"Payroll (millions)\"': 95.14, ' \"Wins\"': 86}),\n",
       " Document(page_content='White Sox', metadata={' \"Payroll (millions)\"': 96.92, ' \"Wins\"': 85}),\n",
       " Document(page_content='Brewers', metadata={' \"Payroll (millions)\"': 97.65, ' \"Wins\"': 83}),\n",
       " Document(page_content='Phillies', metadata={' \"Payroll (millions)\"': 174.54, ' \"Wins\"': 81}),\n",
       " Document(page_content='Diamondbacks', metadata={' \"Payroll (millions)\"': 74.28, ' \"Wins\"': 81}),\n",
       " Document(page_content='Pirates', metadata={' \"Payroll (millions)\"': 63.43, ' \"Wins\"': 79}),\n",
       " Document(page_content='Padres', metadata={' \"Payroll (millions)\"': 55.24, ' \"Wins\"': 76}),\n",
       " Document(page_content='Mariners', metadata={' \"Payroll (millions)\"': 81.97, ' \"Wins\"': 75}),\n",
       " Document(page_content='Mets', metadata={' \"Payroll (millions)\"': 93.35, ' \"Wins\"': 74}),\n",
       " Document(page_content='Blue Jays', metadata={' \"Payroll (millions)\"': 75.48, ' \"Wins\"': 73}),\n",
       " Document(page_content='Royals', metadata={' \"Payroll (millions)\"': 60.91, ' \"Wins\"': 72}),\n",
       " Document(page_content='Marlins', metadata={' \"Payroll (millions)\"': 118.07, ' \"Wins\"': 69}),\n",
       " Document(page_content='Red Sox', metadata={' \"Payroll (millions)\"': 173.18, ' \"Wins\"': 69}),\n",
       " Document(page_content='Indians', metadata={' \"Payroll (millions)\"': 78.43, ' \"Wins\"': 68}),\n",
       " Document(page_content='Twins', metadata={' \"Payroll (millions)\"': 94.08, ' \"Wins\"': 66}),\n",
       " Document(page_content='Rockies', metadata={' \"Payroll (millions)\"': 78.06, ' \"Wins\"': 64}),\n",
       " Document(page_content='Cubs', metadata={' \"Payroll (millions)\"': 88.19, ' \"Wins\"': 61}),\n",
       " Document(page_content='Astros', metadata={' \"Payroll (millions)\"': 60.65, ' \"Wins\"': 55})]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loader.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "page_content='Nationals' metadata={' \"Payroll (millions)\"': 81.34, ' \"Wins\"': 98}\n",
      "page_content='Reds' metadata={' \"Payroll (millions)\"': 82.2, ' \"Wins\"': 97}\n",
      "page_content='Yankees' metadata={' \"Payroll (millions)\"': 197.96, ' \"Wins\"': 95}\n",
      "page_content='Giants' metadata={' \"Payroll (millions)\"': 117.62, ' \"Wins\"': 94}\n",
      "page_content='Braves' metadata={' \"Payroll (millions)\"': 83.31, ' \"Wins\"': 94}\n",
      "page_content='Athletics' metadata={' \"Payroll (millions)\"': 55.37, ' \"Wins\"': 94}\n",
      "page_content='Rangers' metadata={' \"Payroll (millions)\"': 120.51, ' \"Wins\"': 93}\n",
      "page_content='Orioles' metadata={' \"Payroll (millions)\"': 81.43, ' \"Wins\"': 93}\n",
      "page_content='Rays' metadata={' \"Payroll (millions)\"': 64.17, ' \"Wins\"': 90}\n",
      "page_content='Angels' metadata={' \"Payroll (millions)\"': 154.49, ' \"Wins\"': 89}\n",
      "page_content='Tigers' metadata={' \"Payroll (millions)\"': 132.3, ' \"Wins\"': 88}\n",
      "page_content='Cardinals' metadata={' \"Payroll (millions)\"': 110.3, ' \"Wins\"': 88}\n",
      "page_content='Dodgers' metadata={' \"Payroll (millions)\"': 95.14, ' \"Wins\"': 86}\n",
      "page_content='White Sox' metadata={' \"Payroll (millions)\"': 96.92, ' \"Wins\"': 85}\n",
      "page_content='Brewers' metadata={' \"Payroll (millions)\"': 97.65, ' \"Wins\"': 83}\n",
      "page_content='Phillies' metadata={' \"Payroll (millions)\"': 174.54, ' \"Wins\"': 81}\n",
      "page_content='Diamondbacks' metadata={' \"Payroll (millions)\"': 74.28, ' \"Wins\"': 81}\n",
      "page_content='Pirates' metadata={' \"Payroll (millions)\"': 63.43, ' \"Wins\"': 79}\n",
      "page_content='Padres' metadata={' \"Payroll (millions)\"': 55.24, ' \"Wins\"': 76}\n",
      "page_content='Mariners' metadata={' \"Payroll (millions)\"': 81.97, ' \"Wins\"': 75}\n",
      "page_content='Mets' metadata={' \"Payroll (millions)\"': 93.35, ' \"Wins\"': 74}\n",
      "page_content='Blue Jays' metadata={' \"Payroll (millions)\"': 75.48, ' \"Wins\"': 73}\n",
      "page_content='Royals' metadata={' \"Payroll (millions)\"': 60.91, ' \"Wins\"': 72}\n",
      "page_content='Marlins' metadata={' \"Payroll (millions)\"': 118.07, ' \"Wins\"': 69}\n",
      "page_content='Red Sox' metadata={' \"Payroll (millions)\"': 173.18, ' \"Wins\"': 69}\n",
      "page_content='Indians' metadata={' \"Payroll (millions)\"': 78.43, ' \"Wins\"': 68}\n",
      "page_content='Twins' metadata={' \"Payroll (millions)\"': 94.08, ' \"Wins\"': 66}\n",
      "page_content='Rockies' metadata={' \"Payroll (millions)\"': 78.06, ' \"Wins\"': 64}\n",
      "page_content='Cubs' metadata={' \"Payroll (millions)\"': 88.19, ' \"Wins\"': 61}\n",
      "page_content='Astros' metadata={' \"Payroll (millions)\"': 60.65, ' \"Wins\"': 55}\n"
     ]
    }
   ],
   "source": [
    "# Use lazy load for larger table, which won't read the full table into memory\n",
    "for i in loader.lazy_load():\n",
    "    print(i)"
   ]
  }
 ],
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