07-Intro2Python-ExtraMaterial3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import packages\n",
    "import pandas as pd\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<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>State</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Category</th>\n",
       "      <th>City</th>\n",
       "      <th>Country</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>...</th>\n",
       "      <th>Region</th>\n",
       "      <th>Row ID</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Number of Records</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Houston</td>\n",
       "      <td>United States</td>\n",
       "      <td>DP-13000</td>\n",
       "      <td>Darren Powers</td>\n",
       "      <td>2013-01-03</td>\n",
       "      <td>CA-2011-103800</td>\n",
       "      <td>77095</td>\n",
       "      <td>...</td>\n",
       "      <td>Central</td>\n",
       "      <td>7981</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>2013-01-07</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>5.5512</td>\n",
       "      <td>2</td>\n",
       "      <td>16.448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Naperville</td>\n",
       "      <td>United States</td>\n",
       "      <td>PO-19195</td>\n",
       "      <td>Phillina Ober</td>\n",
       "      <td>2013-01-04</td>\n",
       "      <td>CA-2011-112326</td>\n",
       "      <td>60540</td>\n",
       "      <td>...</td>\n",
       "      <td>Central</td>\n",
       "      <td>742</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>2013-01-08</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1</td>\n",
       "      <td>-5.4870</td>\n",
       "      <td>2</td>\n",
       "      <td>3.540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Naperville</td>\n",
       "      <td>United States</td>\n",
       "      <td>PO-19195</td>\n",
       "      <td>Phillina Ober</td>\n",
       "      <td>2013-01-04</td>\n",
       "      <td>CA-2011-112326</td>\n",
       "      <td>60540</td>\n",
       "      <td>...</td>\n",
       "      <td>Central</td>\n",
       "      <td>740</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>2013-01-08</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>4.2717</td>\n",
       "      <td>3</td>\n",
       "      <td>11.784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Naperville</td>\n",
       "      <td>United States</td>\n",
       "      <td>PO-19195</td>\n",
       "      <td>Phillina Ober</td>\n",
       "      <td>2013-01-04</td>\n",
       "      <td>CA-2011-112326</td>\n",
       "      <td>60540</td>\n",
       "      <td>...</td>\n",
       "      <td>Central</td>\n",
       "      <td>741</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>2013-01-08</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>-64.7748</td>\n",
       "      <td>3</td>\n",
       "      <td>272.736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>Art</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>United States</td>\n",
       "      <td>MB-18085</td>\n",
       "      <td>Mick Brown</td>\n",
       "      <td>2013-01-05</td>\n",
       "      <td>CA-2011-141817</td>\n",
       "      <td>19143</td>\n",
       "      <td>...</td>\n",
       "      <td>East</td>\n",
       "      <td>1760</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>2013-01-12</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>4.8840</td>\n",
       "      <td>3</td>\n",
       "      <td>19.536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9989</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>Fasteners</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Loveland</td>\n",
       "      <td>United States</td>\n",
       "      <td>JM-15580</td>\n",
       "      <td>Jill Matthias</td>\n",
       "      <td>2016-12-30</td>\n",
       "      <td>CA-2014-156720</td>\n",
       "      <td>80538</td>\n",
       "      <td>...</td>\n",
       "      <td>West</td>\n",
       "      <td>5092</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.6048</td>\n",
       "      <td>3</td>\n",
       "      <td>3.024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9990</th>\n",
       "      <td>California</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Fairfield</td>\n",
       "      <td>United States</td>\n",
       "      <td>EB-13975</td>\n",
       "      <td>Erica Bern</td>\n",
       "      <td>2016-12-30</td>\n",
       "      <td>CA-2014-115427</td>\n",
       "      <td>94533</td>\n",
       "      <td>...</td>\n",
       "      <td>West</td>\n",
       "      <td>1297</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>4.5188</td>\n",
       "      <td>2</td>\n",
       "      <td>13.904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9991</th>\n",
       "      <td>California</td>\n",
       "      <td>Binders</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Fairfield</td>\n",
       "      <td>United States</td>\n",
       "      <td>EB-13975</td>\n",
       "      <td>Erica Bern</td>\n",
       "      <td>2016-12-30</td>\n",
       "      <td>CA-2014-115427</td>\n",
       "      <td>94533</td>\n",
       "      <td>...</td>\n",
       "      <td>West</td>\n",
       "      <td>1298</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>6.4750</td>\n",
       "      <td>2</td>\n",
       "      <td>20.720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9992</th>\n",
       "      <td>New York</td>\n",
       "      <td>Phones</td>\n",
       "      <td>Technology</td>\n",
       "      <td>New York City</td>\n",
       "      <td>United States</td>\n",
       "      <td>PO-18865</td>\n",
       "      <td>Patrick O'Donnell</td>\n",
       "      <td>2016-12-30</td>\n",
       "      <td>CA-2014-143259</td>\n",
       "      <td>10009</td>\n",
       "      <td>...</td>\n",
       "      <td>East</td>\n",
       "      <td>908</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.7279</td>\n",
       "      <td>7</td>\n",
       "      <td>90.930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9993</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>Appliances</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Columbus</td>\n",
       "      <td>United States</td>\n",
       "      <td>CC-12430</td>\n",
       "      <td>Chuck Clark</td>\n",
       "      <td>2016-12-30</td>\n",
       "      <td>CA-2014-126221</td>\n",
       "      <td>47201</td>\n",
       "      <td>...</td>\n",
       "      <td>Central</td>\n",
       "      <td>646</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>2017-01-05</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>56.5110</td>\n",
       "      <td>2</td>\n",
       "      <td>209.300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9994 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             State Sub-Category         Category           City  \\\n",
       "0            Texas        Paper  Office Supplies        Houston   \n",
       "1         Illinois      Binders  Office Supplies     Naperville   \n",
       "2         Illinois       Labels  Office Supplies     Naperville   \n",
       "3         Illinois      Storage  Office Supplies     Naperville   \n",
       "4     Pennsylvania          Art  Office Supplies   Philadelphia   \n",
       "...            ...          ...              ...            ...   \n",
       "9989      Colorado    Fasteners  Office Supplies       Loveland   \n",
       "9990    California      Binders  Office Supplies      Fairfield   \n",
       "9991    California      Binders  Office Supplies      Fairfield   \n",
       "9992      New York       Phones       Technology  New York City   \n",
       "9993       Indiana   Appliances  Office Supplies       Columbus   \n",
       "\n",
       "            Country Customer ID      Customer Name Order Date        Order ID  \\\n",
       "0     United States    DP-13000      Darren Powers 2013-01-03  CA-2011-103800   \n",
       "1     United States    PO-19195      Phillina Ober 2013-01-04  CA-2011-112326   \n",
       "2     United States    PO-19195      Phillina Ober 2013-01-04  CA-2011-112326   \n",
       "3     United States    PO-19195      Phillina Ober 2013-01-04  CA-2011-112326   \n",
       "4     United States    MB-18085         Mick Brown 2013-01-05  CA-2011-141817   \n",
       "...             ...         ...                ...        ...             ...   \n",
       "9989  United States    JM-15580      Jill Matthias 2016-12-30  CA-2014-156720   \n",
       "9990  United States    EB-13975         Erica Bern 2016-12-30  CA-2014-115427   \n",
       "9991  United States    EB-13975         Erica Bern 2016-12-30  CA-2014-115427   \n",
       "9992  United States    PO-18865  Patrick O'Donnell 2016-12-30  CA-2014-143259   \n",
       "9993  United States    CC-12430        Chuck Clark 2016-12-30  CA-2014-126221   \n",
       "\n",
       "      Postal Code  ...   Region Row ID      Segment  Ship Date  \\\n",
       "0           77095  ...  Central   7981     Consumer 2013-01-07   \n",
       "1           60540  ...  Central    742  Home Office 2013-01-08   \n",
       "2           60540  ...  Central    740  Home Office 2013-01-08   \n",
       "3           60540  ...  Central    741  Home Office 2013-01-08   \n",
       "4           19143  ...     East   1760     Consumer 2013-01-12   \n",
       "...           ...  ...      ...    ...          ...        ...   \n",
       "9989        80538  ...     West   5092     Consumer 2017-01-03   \n",
       "9990        94533  ...     West   1297    Corporate 2017-01-03   \n",
       "9991        94533  ...     West   1298    Corporate 2017-01-03   \n",
       "9992        10009  ...     East    908     Consumer 2017-01-03   \n",
       "9993        47201  ...  Central    646  Home Office 2017-01-05   \n",
       "\n",
       "           Ship Mode Discount Number of Records   Profit  Quantity    Sales  \n",
       "0     Standard Class      0.2                 1   5.5512         2   16.448  \n",
       "1     Standard Class      0.8                 1  -5.4870         2    3.540  \n",
       "2     Standard Class      0.2                 1   4.2717         3   11.784  \n",
       "3     Standard Class      0.2                 1 -64.7748         3  272.736  \n",
       "4     Standard Class      0.2                 1   4.8840         3   19.536  \n",
       "...              ...      ...               ...      ...       ...      ...  \n",
       "9989  Standard Class      0.2                 1  -0.6048         3    3.024  \n",
       "9990  Standard Class      0.2                 1   4.5188         2   13.904  \n",
       "9991  Standard Class      0.2                 1   6.4750         2   20.720  \n",
       "9992  Standard Class      0.0                 1   2.7279         7   90.930  \n",
       "9993  Standard Class      0.0                 1  56.5110         2  209.300  \n",
       "\n",
       "[9994 rows x 22 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read the dataset\n",
    "df = pd.read_excel('./datasets/store.xlsx')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9994 entries, 0 to 9993\n",
      "Data columns (total 22 columns):\n",
      " #   Column             Non-Null Count  Dtype         \n",
      "---  ------             --------------  -----         \n",
      " 0   State              9994 non-null   object        \n",
      " 1   Sub-Category       9994 non-null   object        \n",
      " 2   Category           9994 non-null   object        \n",
      " 3   City               9994 non-null   object        \n",
      " 4   Country            9994 non-null   object        \n",
      " 5   Customer ID        9994 non-null   object        \n",
      " 6   Customer Name      9994 non-null   object        \n",
      " 7   Order Date         9994 non-null   datetime64[ns]\n",
      " 8   Order ID           9994 non-null   object        \n",
      " 9   Postal Code        9994 non-null   int64         \n",
      " 10  Product ID         9994 non-null   object        \n",
      " 11  Product Name       9994 non-null   object        \n",
      " 12  Region             9994 non-null   object        \n",
      " 13  Row ID             9994 non-null   int64         \n",
      " 14  Segment            9994 non-null   object        \n",
      " 15  Ship Date          9994 non-null   datetime64[ns]\n",
      " 16  Ship Mode          9994 non-null   object        \n",
      " 17  Discount           9994 non-null   float64       \n",
      " 18  Number of Records  9994 non-null   int64         \n",
      " 19  Profit             9994 non-null   float64       \n",
      " 20  Quantity           9994 non-null   int64         \n",
      " 21  Sales              9994 non-null   float64       \n",
      "dtypes: datetime64[ns](2), float64(3), int64(4), object(13)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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   "source": [
    "df2 = df[['Category', 'Sales']].groupby('Category').sum()\n",
    "#df2\n",
    "fig = px.pie(df2,\n",
    "             values='Sales',\n",
    "             names=df2.index,\n",
    "             title='Ventas'\n",
    "            )\n",
    "fig.show()\n",
    "#fig.write_html('./outputs/pie.html')"
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  },
  {
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   "metadata": {},
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   "source": [
    "fig = px.sunburst(df,\n",
    "                  path=['Region', 'Category', 'Sub-Category'],\n",
    "                  values='Sales',\n",
    "                  width=600,\n",
    "                  height=600\n",
    "                )\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Row ID</th>\n",
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       "      <th>Sales</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>12771539</td>\n",
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       "      <td>328449.1030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Furniture</td>\n",
       "      <td>Furnishings</td>\n",
       "      <td>51880430</td>\n",
       "      <td>4842624</td>\n",
       "      <td>132.40</td>\n",
       "      <td>957</td>\n",
       "      <td>13059.1436</td>\n",
       "      <td>3563</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
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       "      <td>83.35</td>\n",
       "      <td>319</td>\n",
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       "      <td>1241</td>\n",
       "      <td>206965.5320</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Appliances</td>\n",
       "      <td>25250538</td>\n",
       "      <td>2407754</td>\n",
       "      <td>77.60</td>\n",
       "      <td>466</td>\n",
       "      <td>18138.0054</td>\n",
       "      <td>1729</td>\n",
       "      <td>107532.1610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Art</td>\n",
       "      <td>43329658</td>\n",
       "      <td>3920109</td>\n",
       "      <td>59.60</td>\n",
       "      <td>796</td>\n",
       "      <td>6527.7870</td>\n",
       "      <td>3000</td>\n",
       "      <td>27118.7920</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Binders</td>\n",
       "      <td>83626398</td>\n",
       "      <td>7633935</td>\n",
       "      <td>567.00</td>\n",
       "      <td>1523</td>\n",
       "      <td>30221.7633</td>\n",
       "      <td>5974</td>\n",
       "      <td>203412.7330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Envelopes</td>\n",
       "      <td>13325731</td>\n",
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       "      <td>20.40</td>\n",
       "      <td>254</td>\n",
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       "      <td>906</td>\n",
       "      <td>16476.4020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Fasteners</td>\n",
       "      <td>12506063</td>\n",
       "      <td>1017287</td>\n",
       "      <td>17.80</td>\n",
       "      <td>217</td>\n",
       "      <td>949.5182</td>\n",
       "      <td>914</td>\n",
       "      <td>3024.2800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>19552985</td>\n",
       "      <td>1817254</td>\n",
       "      <td>25.00</td>\n",
       "      <td>364</td>\n",
       "      <td>5546.2540</td>\n",
       "      <td>1400</td>\n",
       "      <td>12486.3120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>76299221</td>\n",
       "      <td>6886090</td>\n",
       "      <td>102.60</td>\n",
       "      <td>1370</td>\n",
       "      <td>34053.5693</td>\n",
       "      <td>5178</td>\n",
       "      <td>78479.2060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>46248720</td>\n",
       "      <td>4126649</td>\n",
       "      <td>63.20</td>\n",
       "      <td>846</td>\n",
       "      <td>21278.8264</td>\n",
       "      <td>3158</td>\n",
       "      <td>223843.6080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Supplies</td>\n",
       "      <td>10633558</td>\n",
       "      <td>993554</td>\n",
       "      <td>14.60</td>\n",
       "      <td>190</td>\n",
       "      <td>-1189.0995</td>\n",
       "      <td>647</td>\n",
       "      <td>46673.5380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Technology</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>44468434</td>\n",
       "      <td>3928080</td>\n",
       "      <td>60.80</td>\n",
       "      <td>775</td>\n",
       "      <td>41936.6357</td>\n",
       "      <td>2976</td>\n",
       "      <td>167380.3180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Technology</td>\n",
       "      <td>Copiers</td>\n",
       "      <td>3873477</td>\n",
       "      <td>355661</td>\n",
       "      <td>11.00</td>\n",
       "      <td>68</td>\n",
       "      <td>55617.8249</td>\n",
       "      <td>234</td>\n",
       "      <td>149528.0300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Technology</td>\n",
       "      <td>Machines</td>\n",
       "      <td>6364668</td>\n",
       "      <td>561428</td>\n",
       "      <td>35.20</td>\n",
       "      <td>115</td>\n",
       "      <td>3384.7569</td>\n",
       "      <td>440</td>\n",
       "      <td>189238.6310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Technology</td>\n",
       "      <td>Phones</td>\n",
       "      <td>47897175</td>\n",
       "      <td>4395985</td>\n",
       "      <td>137.40</td>\n",
       "      <td>889</td>\n",
       "      <td>44515.7306</td>\n",
       "      <td>3289</td>\n",
       "      <td>330007.0540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Category Sub-Category  Postal Code   Row ID  Discount  \\\n",
       "0         Furniture    Bookcases     12771539  1156535     48.14   \n",
       "1         Furniture       Chairs     34936229  3121503    105.00   \n",
       "2         Furniture  Furnishings     51880430  4842624    132.40   \n",
       "3         Furniture       Tables     18607828  1572664     83.35   \n",
       "4   Office Supplies   Appliances     25250538  2407754     77.60   \n",
       "5   Office Supplies          Art     43329658  3920109     59.60   \n",
       "6   Office Supplies      Binders     83626398  7633935    567.00   \n",
       "7   Office Supplies    Envelopes     13325731  1207903     20.40   \n",
       "8   Office Supplies    Fasteners     12506063  1017287     17.80   \n",
       "9   Office Supplies       Labels     19552985  1817254     25.00   \n",
       "10  Office Supplies        Paper     76299221  6886090    102.60   \n",
       "11  Office Supplies      Storage     46248720  4126649     63.20   \n",
       "12  Office Supplies     Supplies     10633558   993554     14.60   \n",
       "13       Technology  Accessories     44468434  3928080     60.80   \n",
       "14       Technology      Copiers      3873477   355661     11.00   \n",
       "15       Technology     Machines      6364668   561428     35.20   \n",
       "16       Technology       Phones     47897175  4395985    137.40   \n",
       "\n",
       "    Number of Records      Profit  Quantity        Sales  \n",
       "0                 228  -3472.5560       868  114879.9963  \n",
       "1                 617  26590.1663      2356  328449.1030  \n",
       "2                 957  13059.1436      3563   91705.1640  \n",
       "3                 319 -17725.4811      1241  206965.5320  \n",
       "4                 466  18138.0054      1729  107532.1610  \n",
       "5                 796   6527.7870      3000   27118.7920  \n",
       "6                1523  30221.7633      5974  203412.7330  \n",
       "7                 254   6964.1767       906   16476.4020  \n",
       "8                 217    949.5182       914    3024.2800  \n",
       "9                 364   5546.2540      1400   12486.3120  \n",
       "10               1370  34053.5693      5178   78479.2060  \n",
       "11                846  21278.8264      3158  223843.6080  \n",
       "12                190  -1189.0995       647   46673.5380  \n",
       "13                775  41936.6357      2976  167380.3180  \n",
       "14                 68  55617.8249       234  149528.0300  \n",
       "15                115   3384.7569       440  189238.6310  \n",
       "16                889  44515.7306      3289  330007.0540  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.groupby(['Category', 'Sub-Category']).sum(numeric_only=True)\n",
    "df2 = df2.reset_index()\n",
    "df2"
   ]
  },
  {
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   "source": [
    "fig = px.sunburst(df2,\n",
    "                  path=['Category', 'Sub-Category'],\n",
    "                  values='Sales',\n",
    "                  width=800,\n",
    "                  height=800\n",
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            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "sum of Profit"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "State"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2 = (df.groupby('State')\n",
    "         .sum(numeric_only=True)\n",
    "         .sort_values('Profit')\n",
    "         .reset_index()\n",
    "       )\n",
    "fig = px.histogram(df2,\n",
    "                   x='Profit',\n",
    "                   y='State',\n",
    "                   histfunc='sum',\n",
    "                   height=600\n",
    "                  )\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       [-159.298908, 70.760123],\n",
       "       [-158.975769, 70.798462],\n",
       "       [-158.658106, 70.787508],\n",
       "       [-158.033735, 70.831323],\n",
       "       [-157.420318, 70.979201],\n",
       "       [-156.812377, 71.285909],\n",
       "       [-156.565915, 71.351633],\n",
       "       [-156.522099, 71.296863],\n",
       "       [-155.585543, 71.170894],\n",
       "       [-155.508865, 71.083263],\n",
       "       [-155.832005, 70.968247],\n",
       "       [-155.979882, 70.96277],\n",
       "       [-155.974405, 70.809416],\n",
       "       [-155.503388, 70.858708],\n",
       "       [-155.476004, 70.940862],\n",
       "       [-155.262403, 71.017539],\n",
       "       [-155.191203, 70.973724],\n",
       "       [-155.032372, 71.148986],\n",
       "       [-154.566832, 70.990155],\n",
       "       [-154.643509, 70.869662],\n",
       "       [-154.353231, 70.8368],\n",
       "       [-154.183446, 70.7656],\n",
       "       [-153.931507, 70.880616],\n",
       "       [-153.487874, 70.886093],\n",
       "       [-153.235935, 70.924431],\n",
       "       [-152.589656, 70.886093],\n",
       "       [-152.26104, 70.842277],\n",
       "       [-152.419871, 70.606769],\n",
       "       [-151.817408, 70.546523],\n",
       "       [-151.773592, 70.486276],\n",
       "       [-151.187559, 70.382214],\n",
       "       [-151.182082, 70.431507],\n",
       "       [-150.760358, 70.49723],\n",
       "       [-150.355064, 70.491753],\n",
       "       [-150.349588, 70.436984],\n",
       "       [-150.114079, 70.431507],\n",
       "       [-149.867617, 70.508184],\n",
       "       [-149.462323, 70.519138],\n",
       "       [-149.177522, 70.486276],\n",
       "       [-148.78866, 70.404122],\n",
       "       [-148.607921, 70.420553],\n",
       "       [-148.350504, 70.305537],\n",
       "       [-148.202627, 70.349353],\n",
       "       [-147.961642, 70.316491],\n",
       "       [-147.786379, 70.245291]]],\n",
       "     [[[-152.94018, 58.026237],\n",
       "       [-152.945657, 57.982421],\n",
       "       [-153.290705, 58.048145],\n",
       "       [-153.044242, 58.305561],\n",
       "       [-152.819688, 58.327469],\n",
       "       [-152.666333, 58.562977],\n",
       "       [-152.496548, 58.354853],\n",
       "       [-152.354148, 58.426053],\n",
       "       [-152.080301, 58.311038],\n",
       "       [-152.080301, 58.152206],\n",
       "       [-152.480117, 58.130299],\n",
       "       [-152.655379, 58.059098],\n",
       "       [-152.94018, 58.026237]]],\n",
       "     [[[-153.958891, 57.538789],\n",
       "       [-153.67409, 57.670236],\n",
       "       [-153.931507, 57.69762],\n",
       "       [-153.936983, 57.812636],\n",
       "       [-153.723383, 57.889313],\n",
       "       [-153.570028, 57.834544],\n",
       "       [-153.548121, 57.719528],\n",
       "       [-153.46049, 57.796205],\n",
       "       [-153.455013, 57.96599],\n",
       "       [-153.268797, 57.889313],\n",
       "       [-153.235935, 57.998852],\n",
       "       [-153.071627, 57.933129],\n",
       "       [-152.874457, 57.933129],\n",
       "       [-152.721103, 57.993375],\n",
       "       [-152.469163, 57.889313],\n",
       "       [-152.469163, 57.599035],\n",
       "       [-152.151501, 57.620943],\n",
       "       [-152.359625, 57.42925],\n",
       "       [-152.74301, 57.505928],\n",
       "       [-152.60061, 57.379958],\n",
       "       [-152.710149, 57.275896],\n",
       "       [-152.907319, 57.325188],\n",
       "       [-152.912796, 57.128019],\n",
       "       [-153.214027, 57.073249],\n",
       "       [-153.312612, 56.991095],\n",
       "       [-153.498828, 57.067772],\n",
       "       [-153.695998, 56.859649],\n",
       "       [-153.849352, 56.837741],\n",
       "       [-154.013661, 56.744633],\n",
       "       [-154.073907, 56.969187],\n",
       "       [-154.303938, 56.848695],\n",
       "       [-154.314892, 56.919895],\n",
       "       [-154.523016, 56.991095],\n",
       "       [-154.539447, 57.193742],\n",
       "       [-154.742094, 57.275896],\n",
       "       [-154.627078, 57.511404],\n",
       "       [-154.227261, 57.659282],\n",
       "       [-153.980799, 57.648328],\n",
       "       [-153.958891, 57.538789]]],\n",
       "     [[[-154.53397, 56.602232],\n",
       "       [-154.742094, 56.399586],\n",
       "       [-154.807817, 56.432447],\n",
       "       [-154.53397, 56.602232]]],\n",
       "     [[[-155.634835, 55.923092],\n",
       "       [-155.476004, 55.912138],\n",
       "       [-155.530773, 55.704014],\n",
       "       [-155.793666, 55.731399],\n",
       "       [-155.837482, 55.802599],\n",
       "       [-155.634835, 55.923092]]],\n",
       "     [[[-159.890418, 55.28229],\n",
       "       [-159.950664, 55.068689],\n",
       "       [-160.257373, 54.893427],\n",
       "       [-160.109495, 55.161797],\n",
       "       [-160.005433, 55.134412],\n",
       "       [-159.890418, 55.28229]]],\n",
       "     [[[-160.520266, 55.358967],\n",
       "       [-160.33405, 55.358967],\n",
       "       [-160.339527, 55.249428],\n",
       "       [-160.525743, 55.128935],\n",
       "       [-160.690051, 55.211089],\n",
       "       [-160.794113, 55.134412],\n",
       "       [-160.854359, 55.320628],\n",
       "       [-160.79959, 55.380875],\n",
       "       [-160.520266, 55.358967]]],\n",
       "     [[[-162.256456, 54.981058],\n",
       "       [-162.234548, 54.893427],\n",
       "       [-162.349564, 54.838658],\n",
       "       [-162.437195, 54.931766],\n",
       "       [-162.256456, 54.981058]]],\n",
       "     [[[-162.415287, 63.634624],\n",
       "       [-162.563165, 63.536039],\n",
       "       [-162.612457, 63.62367],\n",
       "       [-162.415287, 63.634624]]],\n",
       "     [[[-162.80415, 54.488133],\n",
       "       [-162.590549, 54.449795],\n",
       "       [-162.612457, 54.367641],\n",
       "       [-162.782242, 54.373118],\n",
       "       [-162.80415, 54.488133]]],\n",
       "     [[[-165.548097, 54.29644],\n",
       "       [-165.476897, 54.181425],\n",
       "       [-165.630251, 54.132132],\n",
       "       [-165.685021, 54.252625],\n",
       "       [-165.548097, 54.29644]]],\n",
       "     [[[-165.73979, 54.15404],\n",
       "       [-166.046499, 54.044501],\n",
       "       [-166.112222, 54.121178],\n",
       "       [-165.980775, 54.219763],\n",
       "       [-165.73979, 54.15404]]],\n",
       "     [[[-166.364161, 60.359413],\n",
       "       [-166.13413, 60.397752],\n",
       "       [-166.084837, 60.326552],\n",
       "       [-165.88219, 60.342983],\n",
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       "       [-166.062929, 59.745996],\n",
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       "       [-166.6161, 59.850058],\n",
       "       [-166.994009, 59.992458],\n",
       "       [-167.125456, 59.992458],\n",
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       "       [-167.421211, 60.206059],\n",
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       "       [-166.577762, 60.321075],\n",
       "       [-166.495608, 60.392275],\n",
       "       [-166.364161, 60.359413]]],\n",
       "     [[[-166.375115, 54.01164],\n",
       "       [-166.210807, 53.934962],\n",
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       "       [-166.375115, 54.01164]]],\n",
       "     [[[-168.790446, 53.157237],\n",
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       "       [-168.686384, 52.965544],\n",
       "       [-168.790446, 53.157237]]],\n",
       "     [[[-169.74891, 52.894344],\n",
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       "       [-169.74891, 52.894344]]],\n",
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       "       [-170.313035, 57.221127],\n",
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       "       [-170.669036, 52.697174]]],\n",
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      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "import json\n",
    "\n",
    "# define the geojson variable to use in the map with the States in the U.S.\n",
    "geojson = 'https://raw.githubusercontent.com/python-visualization/folium/master/examples/data/us-states.json'\n",
    "\n",
    "# como el geojson es un archivo json, podemos usar la librería requests para obtenerlo\n",
    "response = requests.get(geojson)\n",
    "data = response.json()\n",
    "data\n",
    "\n",
    "# note que el geojson tiene una lista de diccionarios, donde cada diccionario tiene la información de un estado y usa el id del estado para identificarlo, no el nombre del estado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Row ID</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Number of Records</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Texas</td>\n",
       "      <td>75747693</td>\n",
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       "      <td>3724</td>\n",
       "      <td>170188.0458</td>\n",
       "      <td>TX</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ohio</td>\n",
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       "      <td>469</td>\n",
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       "      <td>1759</td>\n",
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       "      <td>OH</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pennsylvania</td>\n",
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       "      <td>2768523</td>\n",
       "      <td>192.90</td>\n",
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       "      <td>-15559.9603</td>\n",
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       "      <td>PA</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Illinois</td>\n",
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       "      <td>1845</td>\n",
       "      <td>80166.1010</td>\n",
       "      <td>IL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>6994384</td>\n",
       "      <td>1219177</td>\n",
       "      <td>70.60</td>\n",
       "      <td>249</td>\n",
       "      <td>-7490.9122</td>\n",
       "      <td>983</td>\n",
       "      <td>55603.1640</td>\n",
       "      <td>NC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Colorado</td>\n",
       "      <td>14613828</td>\n",
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       "      <td>57.60</td>\n",
       "      <td>182</td>\n",
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       "      <td>693</td>\n",
       "      <td>32108.1180</td>\n",
       "      <td>CO</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Tennessee</td>\n",
       "      <td>6890574</td>\n",
       "      <td>1068141</td>\n",
       "      <td>53.30</td>\n",
       "      <td>183</td>\n",
       "      <td>-5341.6936</td>\n",
       "      <td>681</td>\n",
       "      <td>30661.8730</td>\n",
       "      <td>TN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>19102126</td>\n",
       "      <td>1019876</td>\n",
       "      <td>68.00</td>\n",
       "      <td>224</td>\n",
       "      <td>-3427.9246</td>\n",
       "      <td>862</td>\n",
       "      <td>35282.0010</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Florida</td>\n",
       "      <td>12640225</td>\n",
       "      <td>1958255</td>\n",
       "      <td>114.65</td>\n",
       "      <td>383</td>\n",
       "      <td>-3399.3017</td>\n",
       "      <td>1379</td>\n",
       "      <td>89473.7080</td>\n",
       "      <td>FL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>12072125</td>\n",
       "      <td>652996</td>\n",
       "      <td>35.80</td>\n",
       "      <td>124</td>\n",
       "      <td>-1190.4705</td>\n",
       "      <td>499</td>\n",
       "      <td>17431.1500</td>\n",
       "      <td>OR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>82001</td>\n",
       "      <td>5870</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1</td>\n",
       "      <td>100.1960</td>\n",
       "      <td>4</td>\n",
       "      <td>1603.1360</td>\n",
       "      <td>WY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>104012</td>\n",
       "      <td>33175</td>\n",
       "      <td>0.30</td>\n",
       "      <td>4</td>\n",
       "      <td>185.9216</td>\n",
       "      <td>18</td>\n",
       "      <td>1209.8240</td>\n",
       "      <td>WV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>406721</td>\n",
       "      <td>36796</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7</td>\n",
       "      <td>230.1497</td>\n",
       "      <td>30</td>\n",
       "      <td>919.9100</td>\n",
       "      <td>ND</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>South Dakota</td>\n",
       "      <td>686730</td>\n",
       "      <td>48858</td>\n",
       "      <td>0.00</td>\n",
       "      <td>12</td>\n",
       "      <td>394.8283</td>\n",
       "      <td>42</td>\n",
       "      <td>1315.5600</td>\n",
       "      <td>SD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Maine</td>\n",
       "      <td>34725</td>\n",
       "      <td>46198</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8</td>\n",
       "      <td>454.4862</td>\n",
       "      <td>35</td>\n",
       "      <td>1270.5300</td>\n",
       "      <td>ME</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Idaho</td>\n",
       "      <td>1752709</td>\n",
       "      <td>129427</td>\n",
       "      <td>1.80</td>\n",
       "      <td>21</td>\n",
       "      <td>826.7231</td>\n",
       "      <td>64</td>\n",
       "      <td>4382.4860</td>\n",
       "      <td>ID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Kansas</td>\n",
       "      <td>1603798</td>\n",
       "      <td>125372</td>\n",
       "      <td>0.00</td>\n",
       "      <td>24</td>\n",
       "      <td>836.4435</td>\n",
       "      <td>74</td>\n",
       "      <td>2914.3100</td>\n",
       "      <td>KS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>200160</td>\n",
       "      <td>17918</td>\n",
       "      <td>0.00</td>\n",
       "      <td>10</td>\n",
       "      <td>1059.5893</td>\n",
       "      <td>40</td>\n",
       "      <td>2865.0200</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>New Mexico</td>\n",
       "      <td>3241556</td>\n",
       "      <td>166251</td>\n",
       "      <td>2.20</td>\n",
       "      <td>37</td>\n",
       "      <td>1157.1161</td>\n",
       "      <td>151</td>\n",
       "      <td>4783.5220</td>\n",
       "      <td>NM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Iowa</td>\n",
       "      <td>1537707</td>\n",
       "      <td>142173</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30</td>\n",
       "      <td>1183.8119</td>\n",
       "      <td>112</td>\n",
       "      <td>4579.7600</td>\n",
       "      <td>IA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>93835</td>\n",
       "      <td>135645</td>\n",
       "      <td>0.30</td>\n",
       "      <td>27</td>\n",
       "      <td>1706.5028</td>\n",
       "      <td>127</td>\n",
       "      <td>7292.5240</td>\n",
       "      <td>NH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>South Carolina</td>\n",
       "      <td>1230068</td>\n",
       "      <td>194095</td>\n",
       "      <td>0.00</td>\n",
       "      <td>42</td>\n",
       "      <td>1769.0566</td>\n",
       "      <td>172</td>\n",
       "      <td>8481.7100</td>\n",
       "      <td>SC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Montana</td>\n",
       "      <td>892376</td>\n",
       "      <td>61438</td>\n",
       "      <td>1.00</td>\n",
       "      <td>15</td>\n",
       "      <td>1833.3285</td>\n",
       "      <td>56</td>\n",
       "      <td>5589.3520</td>\n",
       "      <td>MT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Nebraska</td>\n",
       "      <td>2588614</td>\n",
       "      <td>205384</td>\n",
       "      <td>0.00</td>\n",
       "      <td>38</td>\n",
       "      <td>2037.0942</td>\n",
       "      <td>136</td>\n",
       "      <td>7464.9300</td>\n",
       "      <td>NE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Louisiana</td>\n",
       "      <td>2972649</td>\n",
       "      <td>173625</td>\n",
       "      <td>0.00</td>\n",
       "      <td>42</td>\n",
       "      <td>2196.1023</td>\n",
       "      <td>156</td>\n",
       "      <td>9217.0300</td>\n",
       "      <td>LA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Vermont</td>\n",
       "      <td>59488</td>\n",
       "      <td>91049</td>\n",
       "      <td>0.00</td>\n",
       "      <td>11</td>\n",
       "      <td>2244.9783</td>\n",
       "      <td>50</td>\n",
       "      <td>8929.3700</td>\n",
       "      <td>VT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Utah</td>\n",
       "      <td>4462368</td>\n",
       "      <td>193525</td>\n",
       "      <td>3.20</td>\n",
       "      <td>53</td>\n",
       "      <td>2546.5335</td>\n",
       "      <td>219</td>\n",
       "      <td>11220.0560</td>\n",
       "      <td>UT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Mississippi</td>\n",
       "      <td>2077023</td>\n",
       "      <td>250903</td>\n",
       "      <td>0.00</td>\n",
       "      <td>53</td>\n",
       "      <td>3172.9762</td>\n",
       "      <td>221</td>\n",
       "      <td>10771.3400</td>\n",
       "      <td>MS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>3476337</td>\n",
       "      <td>223546</td>\n",
       "      <td>2.40</td>\n",
       "      <td>39</td>\n",
       "      <td>3316.7659</td>\n",
       "      <td>168</td>\n",
       "      <td>16729.1020</td>\n",
       "      <td>NV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Connecticut</td>\n",
       "      <td>531005</td>\n",
       "      <td>444411</td>\n",
       "      <td>0.60</td>\n",
       "      <td>82</td>\n",
       "      <td>3511.4918</td>\n",
       "      <td>281</td>\n",
       "      <td>13384.3570</td>\n",
       "      <td>CT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>4339309</td>\n",
       "      <td>304575</td>\n",
       "      <td>0.00</td>\n",
       "      <td>60</td>\n",
       "      <td>4008.6871</td>\n",
       "      <td>240</td>\n",
       "      <td>11678.1300</td>\n",
       "      <td>AR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>4863120</td>\n",
       "      <td>328549</td>\n",
       "      <td>0.00</td>\n",
       "      <td>66</td>\n",
       "      <td>4853.9560</td>\n",
       "      <td>247</td>\n",
       "      <td>19683.3900</td>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>2195669</td>\n",
       "      <td>243876</td>\n",
       "      <td>0.00</td>\n",
       "      <td>61</td>\n",
       "      <td>5786.8253</td>\n",
       "      <td>256</td>\n",
       "      <td>19510.6400</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Missouri</td>\n",
       "      <td>4298362</td>\n",
       "      <td>325246</td>\n",
       "      <td>0.00</td>\n",
       "      <td>66</td>\n",
       "      <td>6436.2105</td>\n",
       "      <td>252</td>\n",
       "      <td>22205.1500</td>\n",
       "      <td>MO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>268295</td>\n",
       "      <td>708877</td>\n",
       "      <td>2.10</td>\n",
       "      <td>135</td>\n",
       "      <td>6785.5016</td>\n",
       "      <td>491</td>\n",
       "      <td>28634.4340</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Maryland</td>\n",
       "      <td>2206740</td>\n",
       "      <td>556423</td>\n",
       "      <td>0.60</td>\n",
       "      <td>105</td>\n",
       "      <td>7031.1788</td>\n",
       "      <td>420</td>\n",
       "      <td>23705.5230</td>\n",
       "      <td>MD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>162878</td>\n",
       "      <td>305385</td>\n",
       "      <td>1.20</td>\n",
       "      <td>56</td>\n",
       "      <td>7285.6293</td>\n",
       "      <td>199</td>\n",
       "      <td>22627.9560</td>\n",
       "      <td>RI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>5899704</td>\n",
       "      <td>570981</td>\n",
       "      <td>0.00</td>\n",
       "      <td>110</td>\n",
       "      <td>8401.8004</td>\n",
       "      <td>463</td>\n",
       "      <td>32114.6100</td>\n",
       "      <td>WI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>New Jersey</td>\n",
       "      <td>1007934</td>\n",
       "      <td>653855</td>\n",
       "      <td>0.60</td>\n",
       "      <td>130</td>\n",
       "      <td>9772.9138</td>\n",
       "      <td>454</td>\n",
       "      <td>35764.3120</td>\n",
       "      <td>NJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>1896504</td>\n",
       "      <td>504370</td>\n",
       "      <td>0.60</td>\n",
       "      <td>96</td>\n",
       "      <td>9977.3748</td>\n",
       "      <td>367</td>\n",
       "      <td>27451.0690</td>\n",
       "      <td>DE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>4932224</td>\n",
       "      <td>316645</td>\n",
       "      <td>0.00</td>\n",
       "      <td>89</td>\n",
       "      <td>10823.1874</td>\n",
       "      <td>331</td>\n",
       "      <td>29863.1500</td>\n",
       "      <td>MN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Kentucky</td>\n",
       "      <td>5725336</td>\n",
       "      <td>698921</td>\n",
       "      <td>0.00</td>\n",
       "      <td>139</td>\n",
       "      <td>11199.6966</td>\n",
       "      <td>523</td>\n",
       "      <td>36591.7500</td>\n",
       "      <td>KY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>5685480</td>\n",
       "      <td>883590</td>\n",
       "      <td>0.00</td>\n",
       "      <td>184</td>\n",
       "      <td>16250.0433</td>\n",
       "      <td>705</td>\n",
       "      <td>49095.8400</td>\n",
       "      <td>GA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>6991602</td>\n",
       "      <td>728770</td>\n",
       "      <td>0.00</td>\n",
       "      <td>149</td>\n",
       "      <td>18382.9363</td>\n",
       "      <td>578</td>\n",
       "      <td>53555.3600</td>\n",
       "      <td>IN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>5124335</td>\n",
       "      <td>1218137</td>\n",
       "      <td>0.00</td>\n",
       "      <td>224</td>\n",
       "      <td>18597.9504</td>\n",
       "      <td>893</td>\n",
       "      <td>70636.7200</td>\n",
       "      <td>VA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Michigan</td>\n",
       "      <td>12356103</td>\n",
       "      <td>1200167</td>\n",
       "      <td>1.80</td>\n",
       "      <td>255</td>\n",
       "      <td>24463.1876</td>\n",
       "      <td>946</td>\n",
       "      <td>76269.6140</td>\n",
       "      <td>MI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Washington</td>\n",
       "      <td>49661687</td>\n",
       "      <td>2584458</td>\n",
       "      <td>32.40</td>\n",
       "      <td>506</td>\n",
       "      <td>33402.6517</td>\n",
       "      <td>1883</td>\n",
       "      <td>138641.2700</td>\n",
       "      <td>WA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>New York</td>\n",
       "      <td>11835721</td>\n",
       "      <td>5452096</td>\n",
       "      <td>62.40</td>\n",
       "      <td>1128</td>\n",
       "      <td>74038.5486</td>\n",
       "      <td>4224</td>\n",
       "      <td>310876.2710</td>\n",
       "      <td>NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>California</td>\n",
       "      <td>184382639</td>\n",
       "      <td>10137449</td>\n",
       "      <td>145.60</td>\n",
       "      <td>2001</td>\n",
       "      <td>76381.3871</td>\n",
       "      <td>7667</td>\n",
       "      <td>457687.6315</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   State  Postal Code    Row ID  Discount  Number of Records  \\\n",
       "0                  Texas     75747693   5085541    364.64                985   \n",
       "1                   Ohio     20579836   2355994    152.40                469   \n",
       "2           Pennsylvania     11190565   2768523    192.90                587   \n",
       "3               Illinois     29873772   2571481    191.90                492   \n",
       "4         North Carolina      6994384   1219177     70.60                249   \n",
       "5               Colorado     14613828    797002     57.60                182   \n",
       "6              Tennessee      6890574   1068141     53.30                183   \n",
       "7                Arizona     19102126   1019876     68.00                224   \n",
       "8                Florida     12640225   1958255    114.65                383   \n",
       "9                 Oregon     12072125    652996     35.80                124   \n",
       "10               Wyoming        82001      5870      0.20                  1   \n",
       "11         West Virginia       104012     33175      0.30                  4   \n",
       "12          North Dakota       406721     36796      0.00                  7   \n",
       "13          South Dakota       686730     48858      0.00                 12   \n",
       "14                 Maine        34725     46198      0.00                  8   \n",
       "15                 Idaho      1752709    129427      1.80                 21   \n",
       "16                Kansas      1603798    125372      0.00                 24   \n",
       "17  District of Columbia       200160     17918      0.00                 10   \n",
       "18            New Mexico      3241556    166251      2.20                 37   \n",
       "19                  Iowa      1537707    142173      0.00                 30   \n",
       "20         New Hampshire        93835    135645      0.30                 27   \n",
       "21        South Carolina      1230068    194095      0.00                 42   \n",
       "22               Montana       892376     61438      1.00                 15   \n",
       "23              Nebraska      2588614    205384      0.00                 38   \n",
       "24             Louisiana      2972649    173625      0.00                 42   \n",
       "25               Vermont        59488     91049      0.00                 11   \n",
       "26                  Utah      4462368    193525      3.20                 53   \n",
       "27           Mississippi      2077023    250903      0.00                 53   \n",
       "28                Nevada      3476337    223546      2.40                 39   \n",
       "29           Connecticut       531005    444411      0.60                 82   \n",
       "30              Arkansas      4339309    304575      0.00                 60   \n",
       "31              Oklahoma      4863120    328549      0.00                 66   \n",
       "32               Alabama      2195669    243876      0.00                 61   \n",
       "33              Missouri      4298362    325246      0.00                 66   \n",
       "34         Massachusetts       268295    708877      2.10                135   \n",
       "35              Maryland      2206740    556423      0.60                105   \n",
       "36          Rhode Island       162878    305385      1.20                 56   \n",
       "37             Wisconsin      5899704    570981      0.00                110   \n",
       "38            New Jersey      1007934    653855      0.60                130   \n",
       "39              Delaware      1896504    504370      0.60                 96   \n",
       "40             Minnesota      4932224    316645      0.00                 89   \n",
       "41              Kentucky      5725336    698921      0.00                139   \n",
       "42               Georgia      5685480    883590      0.00                184   \n",
       "43               Indiana      6991602    728770      0.00                149   \n",
       "44              Virginia      5124335   1218137      0.00                224   \n",
       "45              Michigan     12356103   1200167      1.80                255   \n",
       "46            Washington     49661687   2584458     32.40                506   \n",
       "47              New York     11835721   5452096     62.40               1128   \n",
       "48            California    184382639  10137449    145.60               2001   \n",
       "\n",
       "        Profit  Quantity        Sales    id  \n",
       "0  -25729.3563      3724  170188.0458    TX  \n",
       "1  -16971.3766      1759   78258.1360    OH  \n",
       "2  -15559.9603      2153  116511.9140    PA  \n",
       "3  -12607.8870      1845   80166.1010    IL  \n",
       "4   -7490.9122       983   55603.1640    NC  \n",
       "5   -6527.8579       693   32108.1180    CO  \n",
       "6   -5341.6936       681   30661.8730    TN  \n",
       "7   -3427.9246       862   35282.0010    AZ  \n",
       "8   -3399.3017      1379   89473.7080    FL  \n",
       "9   -1190.4705       499   17431.1500    OR  \n",
       "10    100.1960         4    1603.1360    WY  \n",
       "11    185.9216        18    1209.8240    WV  \n",
       "12    230.1497        30     919.9100    ND  \n",
       "13    394.8283        42    1315.5600    SD  \n",
       "14    454.4862        35    1270.5300    ME  \n",
       "15    826.7231        64    4382.4860    ID  \n",
       "16    836.4435        74    2914.3100    KS  \n",
       "17   1059.5893        40    2865.0200  None  \n",
       "18   1157.1161       151    4783.5220    NM  \n",
       "19   1183.8119       112    4579.7600    IA  \n",
       "20   1706.5028       127    7292.5240    NH  \n",
       "21   1769.0566       172    8481.7100    SC  \n",
       "22   1833.3285        56    5589.3520    MT  \n",
       "23   2037.0942       136    7464.9300    NE  \n",
       "24   2196.1023       156    9217.0300    LA  \n",
       "25   2244.9783        50    8929.3700    VT  \n",
       "26   2546.5335       219   11220.0560    UT  \n",
       "27   3172.9762       221   10771.3400    MS  \n",
       "28   3316.7659       168   16729.1020    NV  \n",
       "29   3511.4918       281   13384.3570    CT  \n",
       "30   4008.6871       240   11678.1300    AR  \n",
       "31   4853.9560       247   19683.3900    OK  \n",
       "32   5786.8253       256   19510.6400    AL  \n",
       "33   6436.2105       252   22205.1500    MO  \n",
       "34   6785.5016       491   28634.4340    MA  \n",
       "35   7031.1788       420   23705.5230    MD  \n",
       "36   7285.6293       199   22627.9560    RI  \n",
       "37   8401.8004       463   32114.6100    WI  \n",
       "38   9772.9138       454   35764.3120    NJ  \n",
       "39   9977.3748       367   27451.0690    DE  \n",
       "40  10823.1874       331   29863.1500    MN  \n",
       "41  11199.6966       523   36591.7500    KY  \n",
       "42  16250.0433       705   49095.8400    GA  \n",
       "43  18382.9363       578   53555.3600    IN  \n",
       "44  18597.9504       893   70636.7200    VA  \n",
       "45  24463.1876       946   76269.6140    MI  \n",
       "46  33402.6517      1883  138641.2700    WA  \n",
       "47  74038.5486      4224  310876.2710    NY  \n",
       "48  76381.3871      7667  457687.6315    CA  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Entonces, hay que crear una columna en el dataframe que tenga el id del estado para que podamos hacer el merge con el geojson\n",
    "df2['id'] = (df2['State']\n",
    "             .apply(lambda x: [item['id'] for item in data['features'] \n",
    "                                          if item['properties']['name'] == x][0]\n",
    "                                          if [item['id'] for item in data['features']\n",
    "                                          if item['properties']['name'] == x] else None)\n",
    "            )\n",
    "df2"
   ]
  },
  {
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             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create choropleth_mapbox\n",
    "fig = px.choropleth_mapbox(df2,\n",
    "                           geojson=geojson,\n",
    "                           locations='id',\n",
    "                           color='Profit',\n",
    "                           color_continuous_scale='viridis', # https://plotly.com/python/builtin-colorscales/\n",
    "                           mapbox_style='carto-positron',\n",
    "                           zoom=3,\n",
    "                           center={'lat': 37.0902, 'lon': -95.7129},\n",
    "                           opacity=0.75,\n",
    "                          )\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Geojson España:\n",
    "\n",
    "https://github.com/codeforgermany/click_that_hood/blob/main/public/data/spain-provinces.geojson"
   ]
  }
 ],
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