{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the Plotly Express Data Sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotly express comes with several data sets pre-loaded which we can use for testing and exploration purposes. In this recipe we will learn to load such data sets.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting ready \n",
    "1. Import the `plotly` module and cerify that your version is 4.14 (this is when the data sets were included for the first time) or newer \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5.23.0'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotly.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to do it \n",
    "1. Import the `plotly.express.data` submodule as `datasets`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express.data as datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Print the names of the data sets available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "carshare\n",
      "election\n",
      "election_geojson\n",
      "experiment\n",
      "gapminder\n",
      "iris\n",
      "medals_long\n",
      "medals_wide\n",
      "stocks\n",
      "tips\n",
      "wind\n"
     ]
    }
   ],
   "source": [
    "for name in dir(datasets):\n",
    "    if '__' not in name:\n",
    "        print(name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Import one of the available data sets and check its type using the function `type`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = datasets.stocks()\n",
    "type(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is a pandas `DataFrame`!\n",
    "\n",
    "4. Use the method `head` to inspect the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>FB</th>\n",
       "      <th>NFLX</th>\n",
       "      <th>MSFT</th>\n",
       "    </tr>\n",
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       "      <td>2018-01-01</td>\n",
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       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-08</td>\n",
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       "      <td>1.011943</td>\n",
       "      <td>1.061881</td>\n",
       "      <td>0.959968</td>\n",
       "      <td>1.053526</td>\n",
       "      <td>1.015988</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-15</td>\n",
       "      <td>1.032008</td>\n",
       "      <td>1.019771</td>\n",
       "      <td>1.053240</td>\n",
       "      <td>0.970243</td>\n",
       "      <td>1.049860</td>\n",
       "      <td>1.020524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-22</td>\n",
       "      <td>1.066783</td>\n",
       "      <td>0.980057</td>\n",
       "      <td>1.140676</td>\n",
       "      <td>1.016858</td>\n",
       "      <td>1.307681</td>\n",
       "      <td>1.066561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-29</td>\n",
       "      <td>1.008773</td>\n",
       "      <td>0.917143</td>\n",
       "      <td>1.163374</td>\n",
       "      <td>1.018357</td>\n",
       "      <td>1.273537</td>\n",
       "      <td>1.040708</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date      GOOG      AAPL      AMZN        FB      NFLX      MSFT\n",
       "0  2018-01-01  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000\n",
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       "2  2018-01-15  1.032008  1.019771  1.053240  0.970243  1.049860  1.020524\n",
       "3  2018-01-22  1.066783  0.980057  1.140676  1.016858  1.307681  1.066561\n",
       "4  2018-01-29  1.008773  0.917143  1.163374  1.018357  1.273537  1.040708"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the `head` method receives an argument `n` (which defaults to 5) which represents the number of rows of the `DataFrame` to be show. Let's display the first 10 rows by calling `head(10)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <th>AMZN</th>\n",
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       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-08</td>\n",
       "      <td>1.018172</td>\n",
       "      <td>1.011943</td>\n",
       "      <td>1.061881</td>\n",
       "      <td>0.959968</td>\n",
       "      <td>1.053526</td>\n",
       "      <td>1.015988</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-15</td>\n",
       "      <td>1.032008</td>\n",
       "      <td>1.019771</td>\n",
       "      <td>1.053240</td>\n",
       "      <td>0.970243</td>\n",
       "      <td>1.049860</td>\n",
       "      <td>1.020524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-22</td>\n",
       "      <td>1.066783</td>\n",
       "      <td>0.980057</td>\n",
       "      <td>1.140676</td>\n",
       "      <td>1.016858</td>\n",
       "      <td>1.307681</td>\n",
       "      <td>1.066561</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-29</td>\n",
       "      <td>1.008773</td>\n",
       "      <td>0.917143</td>\n",
       "      <td>1.163374</td>\n",
       "      <td>1.018357</td>\n",
       "      <td>1.273537</td>\n",
       "      <td>1.040708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018-02-05</td>\n",
       "      <td>0.941528</td>\n",
       "      <td>0.893771</td>\n",
       "      <td>1.089868</td>\n",
       "      <td>0.942521</td>\n",
       "      <td>1.188009</td>\n",
       "      <td>0.999887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018-02-12</td>\n",
       "      <td>0.993259</td>\n",
       "      <td>0.985314</td>\n",
       "      <td>1.178621</td>\n",
       "      <td>0.949211</td>\n",
       "      <td>1.326349</td>\n",
       "      <td>1.043202</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018-02-19</td>\n",
       "      <td>1.022282</td>\n",
       "      <td>1.002857</td>\n",
       "      <td>1.220365</td>\n",
       "      <td>0.980947</td>\n",
       "      <td>1.361636</td>\n",
       "      <td>1.066561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-02-26</td>\n",
       "      <td>0.978852</td>\n",
       "      <td>1.006914</td>\n",
       "      <td>1.220569</td>\n",
       "      <td>0.945250</td>\n",
       "      <td>1.433640</td>\n",
       "      <td>1.055108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018-03-05</td>\n",
       "      <td>1.052448</td>\n",
       "      <td>1.028457</td>\n",
       "      <td>1.284549</td>\n",
       "      <td>0.991330</td>\n",
       "      <td>1.578361</td>\n",
       "      <td>1.094682</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
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       "         date      GOOG      AAPL      AMZN        FB      NFLX      MSFT\n",
       "0  2018-01-01  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000\n",
       "1  2018-01-08  1.018172  1.011943  1.061881  0.959968  1.053526  1.015988\n",
       "2  2018-01-15  1.032008  1.019771  1.053240  0.970243  1.049860  1.020524\n",
       "3  2018-01-22  1.066783  0.980057  1.140676  1.016858  1.307681  1.066561\n",
       "4  2018-01-29  1.008773  0.917143  1.163374  1.018357  1.273537  1.040708\n",
       "5  2018-02-05  0.941528  0.893771  1.089868  0.942521  1.188009  0.999887\n",
       "6  2018-02-12  0.993259  0.985314  1.178621  0.949211  1.326349  1.043202\n",
       "7  2018-02-19  1.022282  1.002857  1.220365  0.980947  1.361636  1.066561\n",
       "8  2018-02-26  0.978852  1.006914  1.220569  0.945250  1.433640  1.055108\n",
       "9  2018-03-05  1.052448  1.028457  1.284549  0.991330  1.578361  1.094682"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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