StockX EDA (Cleaner Version).ipynb

{
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  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Sneaker Name</th>\n",
       "      <th>Sale Price</th>\n",
       "      <th>Retail Price</th>\n",
       "      <th>Release Date</th>\n",
       "      <th>Shoe Size</th>\n",
       "      <th>Buyer Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>Yeezy</td>\n",
       "      <td>Adidas-Yeezy-Boost-350-Low-V2-Beluga</td>\n",
       "      <td>1097.0</td>\n",
       "      <td>220</td>\n",
       "      <td>2016-09-24</td>\n",
       "      <td>11.0</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>Yeezy</td>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Core-Black-Copper</td>\n",
       "      <td>685.0</td>\n",
       "      <td>220</td>\n",
       "      <td>2016-11-23</td>\n",
       "      <td>11.0</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>Yeezy</td>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Core-Black-Green</td>\n",
       "      <td>690.0</td>\n",
       "      <td>220</td>\n",
       "      <td>2016-11-23</td>\n",
       "      <td>11.0</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>Yeezy</td>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Core-Black-Red</td>\n",
       "      <td>1075.0</td>\n",
       "      <td>220</td>\n",
       "      <td>2016-11-23</td>\n",
       "      <td>11.5</td>\n",
       "      <td>Kentucky</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>Yeezy</td>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Core-Black-Red-2017</td>\n",
       "      <td>828.0</td>\n",
       "      <td>220</td>\n",
       "      <td>2017-02-11</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Rhode Island</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order Date   Brand                                   Sneaker Name  \\\n",
       "0 2017-09-01   Yeezy           Adidas-Yeezy-Boost-350-Low-V2-Beluga   \n",
       "1 2017-09-01   Yeezy    Adidas-Yeezy-Boost-350-V2-Core-Black-Copper   \n",
       "2 2017-09-01   Yeezy     Adidas-Yeezy-Boost-350-V2-Core-Black-Green   \n",
       "3 2017-09-01   Yeezy       Adidas-Yeezy-Boost-350-V2-Core-Black-Red   \n",
       "4 2017-09-01   Yeezy  Adidas-Yeezy-Boost-350-V2-Core-Black-Red-2017   \n",
       "\n",
       "   Sale Price  Retail Price Release Date  Shoe Size  Buyer Region  \n",
       "0      1097.0           220   2016-09-24       11.0    California  \n",
       "1       685.0           220   2016-11-23       11.0    California  \n",
       "2       690.0           220   2016-11-23       11.0    California  \n",
       "3      1075.0           220   2016-11-23       11.5      Kentucky  \n",
       "4       828.0           220   2017-02-11       11.0  Rhode Island  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "import plotly.tools as tls\n",
    "import plotly.figure_factory as ff \n",
    "from plotly.grid_objs import Grid, Column \n",
    "import colorlover as cl\n",
    "\n",
    "#####################################################\n",
    "#####################################################\n",
    "\n",
    "                ##### STOCK X #####\n",
    "                     # DATA #\n",
    "                #### CHALLENGE ####\n",
    "            \n",
    "#####################################################\n",
    "#####################################################\n",
    "\n",
    "path = 'https://s3.amazonaws.com/stockx-sneaker-analysis/wp-content/uploads/2019/02/StockX-Data-Contest-2019.xlsx'\n",
    "\n",
    "df = pd.read_excel(path, sheet_name=1)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Off-White dataframe : (27794, 8)\n",
      "Shape of Yeezy dataframe : (72162, 8)\n"
     ]
    }
   ],
   "source": [
    "off_white = df.copy()\n",
    "off_white = off_white[off_white['Brand'].isin(['Off-White'])]\n",
    "\n",
    "yeezy = df.copy()\n",
    "yeezy = yeezy[yeezy['Brand'].isin([' Yeezy'])]\n",
    "\n",
    "print('Shape of Off-White dataframe : {}'.format(off_white.shape))\n",
    "print('Shape of Yeezy dataframe : {}'.format(yeezy.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#gonna do some simple subtraction!\n",
    "def profit_after_fees(x,y):\n",
    "    '''\n",
    "    Taking into account StockX's seller fees at 9.5% maximum and the additional 3% transaction fees.\n",
    "    Should be 12.5% max \n",
    "    https://help.stockx.com/selling-on-stockx/what-are-stockx-selling-fees\n",
    "    '''\n",
    "    profit = x - y\n",
    "    return round((profit * .875),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Sneaker Name</th>\n",
       "      <th>Sale Price</th>\n",
       "      <th>Retail Price</th>\n",
       "      <th>Release Date</th>\n",
       "      <th>Shoe Size</th>\n",
       "      <th>Buyer Region</th>\n",
       "      <th>Profit After Fees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>Yeezy</td>\n",
       "      <td>Adidas-Yeezy-Boost-350-Low-V2-Beluga</td>\n",
       "      <td>1097.0</td>\n",
       "      <td>220</td>\n",
       "      <td>2016-09-24</td>\n",
       "      <td>11.0</td>\n",
       "      <td>California</td>\n",
       "      <td>767.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>Yeezy</td>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Core-Black-Copper</td>\n",
       "      <td>685.0</td>\n",
       "      <td>220</td>\n",
       "      <td>2016-11-23</td>\n",
       "      <td>11.0</td>\n",
       "      <td>California</td>\n",
       "      <td>406.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>Yeezy</td>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Core-Black-Green</td>\n",
       "      <td>690.0</td>\n",
       "      <td>220</td>\n",
       "      <td>2016-11-23</td>\n",
       "      <td>11.0</td>\n",
       "      <td>California</td>\n",
       "      <td>411.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>Yeezy</td>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Core-Black-Red</td>\n",
       "      <td>1075.0</td>\n",
       "      <td>220</td>\n",
       "      <td>2016-11-23</td>\n",
       "      <td>11.5</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>748.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>Yeezy</td>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Core-Black-Red-2017</td>\n",
       "      <td>828.0</td>\n",
       "      <td>220</td>\n",
       "      <td>2017-02-11</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>532.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order Date   Brand                                   Sneaker Name  \\\n",
       "0 2017-09-01   Yeezy           Adidas-Yeezy-Boost-350-Low-V2-Beluga   \n",
       "1 2017-09-01   Yeezy    Adidas-Yeezy-Boost-350-V2-Core-Black-Copper   \n",
       "2 2017-09-01   Yeezy     Adidas-Yeezy-Boost-350-V2-Core-Black-Green   \n",
       "3 2017-09-01   Yeezy       Adidas-Yeezy-Boost-350-V2-Core-Black-Red   \n",
       "4 2017-09-01   Yeezy  Adidas-Yeezy-Boost-350-V2-Core-Black-Red-2017   \n",
       "\n",
       "   Sale Price  Retail Price Release Date  Shoe Size  Buyer Region  \\\n",
       "0      1097.0           220   2016-09-24       11.0    California   \n",
       "1       685.0           220   2016-11-23       11.0    California   \n",
       "2       690.0           220   2016-11-23       11.0    California   \n",
       "3      1075.0           220   2016-11-23       11.5      Kentucky   \n",
       "4       828.0           220   2017-02-11       11.0  Rhode Island   \n",
       "\n",
       "   Profit After Fees  \n",
       "0             767.38  \n",
       "1             406.88  \n",
       "2             411.25  \n",
       "3             748.12  \n",
       "4             532.00  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yeezy['Profit After Fees'] = yeezy.apply(lambda x: profit_after_fees(x['Sale Price'],x['Retail Price']), axis=1)\n",
    "yeezy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Sneaker Name</th>\n",
       "      <th>Sale Price</th>\n",
       "      <th>Retail Price</th>\n",
       "      <th>Release Date</th>\n",
       "      <th>Shoe Size</th>\n",
       "      <th>Buyer Region</th>\n",
       "      <th>Profit After Fees</th>\n",
       "      <th>Favorite</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>Yeezy</td>\n",
       "      <td>Adidas-Yeezy-Boost-350-Low-V2-Beluga</td>\n",
       "      <td>1097.0</td>\n",
       "      <td>220</td>\n",
       "      <td>2016-09-24</td>\n",
       "      <td>11.0</td>\n",
       "      <td>California</td>\n",
       "      <td>767.38</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>Yeezy</td>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Core-Black-Copper</td>\n",
       "      <td>685.0</td>\n",
       "      <td>220</td>\n",
       "      <td>2016-11-23</td>\n",
       "      <td>11.0</td>\n",
       "      <td>California</td>\n",
       "      <td>406.88</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>Yeezy</td>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Core-Black-Green</td>\n",
       "      <td>690.0</td>\n",
       "      <td>220</td>\n",
       "      <td>2016-11-23</td>\n",
       "      <td>11.0</td>\n",
       "      <td>California</td>\n",
       "      <td>411.25</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>Yeezy</td>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Core-Black-Red</td>\n",
       "      <td>1075.0</td>\n",
       "      <td>220</td>\n",
       "      <td>2016-11-23</td>\n",
       "      <td>11.5</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>748.12</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>Yeezy</td>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Core-Black-Red-2017</td>\n",
       "      <td>828.0</td>\n",
       "      <td>220</td>\n",
       "      <td>2017-02-11</td>\n",
       "      <td>11.0</td>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>532.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order Date   Brand                                   Sneaker Name  \\\n",
       "0 2017-09-01   Yeezy           Adidas-Yeezy-Boost-350-Low-V2-Beluga   \n",
       "1 2017-09-01   Yeezy    Adidas-Yeezy-Boost-350-V2-Core-Black-Copper   \n",
       "2 2017-09-01   Yeezy     Adidas-Yeezy-Boost-350-V2-Core-Black-Green   \n",
       "3 2017-09-01   Yeezy       Adidas-Yeezy-Boost-350-V2-Core-Black-Red   \n",
       "4 2017-09-01   Yeezy  Adidas-Yeezy-Boost-350-V2-Core-Black-Red-2017   \n",
       "\n",
       "   Sale Price  Retail Price Release Date  Shoe Size  Buyer Region  \\\n",
       "0      1097.0           220   2016-09-24       11.0    California   \n",
       "1       685.0           220   2016-11-23       11.0    California   \n",
       "2       690.0           220   2016-11-23       11.0    California   \n",
       "3      1075.0           220   2016-11-23       11.5      Kentucky   \n",
       "4       828.0           220   2017-02-11       11.0  Rhode Island   \n",
       "\n",
       "   Profit After Fees  Favorite  \n",
       "0             767.38       115  \n",
       "1             406.88        58  \n",
       "2             411.25        58  \n",
       "3             748.12         8  \n",
       "4             532.00         3  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yeezy['Favorite'] = yeezy.groupby(['Buyer Region','Sneaker Name'])['Sneaker Name'].transform('count')\n",
    "yeezy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Buyer Region</th>\n",
       "      <th>Sneaker Name</th>\n",
       "      <th>Favorite</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Adidas-Yeezy-Boost-350-Low-Pirate-Black-2016</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Adidas-Yeezy-Boost-350-Low-V2-Beluga</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Beluga-2pt0</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Blue-Tint</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Core-Black-Red</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Buyer Region                                  Sneaker Name  Favorite\n",
       "0      Alabama  Adidas-Yeezy-Boost-350-Low-Pirate-Black-2016         1\n",
       "1      Alabama          Adidas-Yeezy-Boost-350-Low-V2-Beluga         4\n",
       "2      Alabama         Adidas-Yeezy-Boost-350-V2-Beluga-2pt0        47\n",
       "3      Alabama           Adidas-Yeezy-Boost-350-V2-Blue-Tint        47\n",
       "4      Alabama      Adidas-Yeezy-Boost-350-V2-Core-Black-Red         1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = yeezy.groupby(['Buyer Region','Sneaker Name'], as_index=False)['Favorite'].max()\n",
    "z.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "    }\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>Sneaker Name</th>\n",
       "      <th>Adidas-Yeezy-Boost-350-Low-Moonrock</th>\n",
       "      <th>Adidas-Yeezy-Boost-350-Low-Oxford-Tan</th>\n",
       "      <th>Adidas-Yeezy-Boost-350-Low-Pirate-Black-2015</th>\n",
       "      <th>Adidas-Yeezy-Boost-350-Low-Pirate-Black-2016</th>\n",
       "      <th>Adidas-Yeezy-Boost-350-Low-Turtledove</th>\n",
       "      <th>Adidas-Yeezy-Boost-350-Low-V2-Beluga</th>\n",
       "      <th>Adidas-Yeezy-Boost-350-V2-Beluga-2pt0</th>\n",
       "      <th>Adidas-Yeezy-Boost-350-V2-Blue-Tint</th>\n",
       "      <th>Adidas-Yeezy-Boost-350-V2-Core-Black-Copper</th>\n",
       "      <th>Adidas-Yeezy-Boost-350-V2-Core-Black-Green</th>\n",
       "      <th>Adidas-Yeezy-Boost-350-V2-Core-Black-Red</th>\n",
       "      <th>Adidas-Yeezy-Boost-350-V2-Core-Black-Red-2017</th>\n",
       "      <th>Adidas-Yeezy-Boost-350-V2-Core-Black-White</th>\n",
       "      <th>Adidas-Yeezy-Boost-350-V2-Cream-White</th>\n",
       "      <th>Adidas-Yeezy-Boost-350-V2-Semi-Frozen-Yellow</th>\n",
       "      <th>Adidas-Yeezy-Boost-350-V2-Sesame</th>\n",
       "      <th>Adidas-Yeezy-Boost-350-V2-Zebra</th>\n",
       "      <th>adidas-Yeezy-Boost-350-V2-Butter</th>\n",
       "      <th>adidas-Yeezy-Boost-350-V2-Static</th>\n",
       "      <th>adidas-Yeezy-Boost-350-V2-Static-Reflective</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Buyer Region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>25.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>2146.0</td>\n",
       "      <td>1682.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>1653.0</td>\n",
       "      <td>587.0</td>\n",
       "      <td>1045.0</td>\n",
       "      <td>1737.0</td>\n",
       "      <td>1810.0</td>\n",
       "      <td>923.0</td>\n",
       "      <td>696.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sneaker Name  Adidas-Yeezy-Boost-350-Low-Moonrock  \\\n",
       "Buyer Region                                        \n",
       "Alabama                                       0.0   \n",
       "Alaska                                        0.0   \n",
       "Arizona                                       1.0   \n",
       "Arkansas                                      1.0   \n",
       "California                                   25.0   \n",
       "\n",
       "Sneaker Name  Adidas-Yeezy-Boost-350-Low-Oxford-Tan  \\\n",
       "Buyer Region                                          \n",
       "Alabama                                         0.0   \n",
       "Alaska                                          0.0   \n",
       "Arizona                                         0.0   \n",
       "Arkansas                                        0.0   \n",
       "California                                     16.0   \n",
       "\n",
       "Sneaker Name  Adidas-Yeezy-Boost-350-Low-Pirate-Black-2015  \\\n",
       "Buyer Region                                                 \n",
       "Alabama                                                0.0   \n",
       "Alaska                                                 0.0   \n",
       "Arizona                                                1.0   \n",
       "Arkansas                                               0.0   \n",
       "California                                            28.0   \n",
       "\n",
       "Sneaker Name  Adidas-Yeezy-Boost-350-Low-Pirate-Black-2016  \\\n",
       "Buyer Region                                                 \n",
       "Alabama                                                1.0   \n",
       "Alaska                                                 0.0   \n",
       "Arizona                                                1.0   \n",
       "Arkansas                                               0.0   \n",
       "California                                            38.0   \n",
       "\n",
       "Sneaker Name  Adidas-Yeezy-Boost-350-Low-Turtledove  \\\n",
       "Buyer Region                                          \n",
       "Alabama                                         0.0   \n",
       "Alaska                                          0.0   \n",
       "Arizona                                         1.0   \n",
       "Arkansas                                        0.0   \n",
       "California                                     22.0   \n",
       "\n",
       "Sneaker Name  Adidas-Yeezy-Boost-350-Low-V2-Beluga  \\\n",
       "Buyer Region                                         \n",
       "Alabama                                        4.0   \n",
       "Alaska                                         0.0   \n",
       "Arizona                                        5.0   \n",
       "Arkansas                                       1.0   \n",
       "California                                   115.0   \n",
       "\n",
       "Sneaker Name  Adidas-Yeezy-Boost-350-V2-Beluga-2pt0  \\\n",
       "Buyer Region                                          \n",
       "Alabama                                        47.0   \n",
       "Alaska                                          4.0   \n",
       "Arizona                                       147.0   \n",
       "Arkansas                                       20.0   \n",
       "California                                   2146.0   \n",
       "\n",
       "Sneaker Name  Adidas-Yeezy-Boost-350-V2-Blue-Tint  \\\n",
       "Buyer Region                                        \n",
       "Alabama                                      47.0   \n",
       "Alaska                                        5.0   \n",
       "Arizona                                     139.0   \n",
       "Arkansas                                     18.0   \n",
       "California                                 1682.0   \n",
       "\n",
       "Sneaker Name  Adidas-Yeezy-Boost-350-V2-Core-Black-Copper  \\\n",
       "Buyer Region                                                \n",
       "Alabama                                               0.0   \n",
       "Alaska                                                0.0   \n",
       "Arizona                                               1.0   \n",
       "Arkansas                                              1.0   \n",
       "California                                           58.0   \n",
       "\n",
       "Sneaker Name  Adidas-Yeezy-Boost-350-V2-Core-Black-Green  \\\n",
       "Buyer Region                                               \n",
       "Alabama                                              0.0   \n",
       "Alaska                                               0.0   \n",
       "Arizona                                              2.0   \n",
       "Arkansas                                             0.0   \n",
       "California                                          58.0   \n",
       "\n",
       "Sneaker Name  Adidas-Yeezy-Boost-350-V2-Core-Black-Red  \\\n",
       "Buyer Region                                             \n",
       "Alabama                                            1.0   \n",
       "Alaska                                             1.0   \n",
       "Arizona                                            2.0   \n",
       "Arkansas                                           0.0   \n",
       "California                                        82.0   \n",
       "\n",
       "Sneaker Name  Adidas-Yeezy-Boost-350-V2-Core-Black-Red-2017  \\\n",
       "Buyer Region                                                  \n",
       "Alabama                                                 1.0   \n",
       "Alaska                                                  1.0   \n",
       "Arizona                                                10.0   \n",
       "Arkansas                                                2.0   \n",
       "California                                            250.0   \n",
       "\n",
       "Sneaker Name  Adidas-Yeezy-Boost-350-V2-Core-Black-White  \\\n",
       "Buyer Region                                               \n",
       "Alabama                                              5.0   \n",
       "Alaska                                               0.0   \n",
       "Arizona                                              6.0   \n",
       "Arkansas                                             1.0   \n",
       "California                                         142.0   \n",
       "\n",
       "Sneaker Name  Adidas-Yeezy-Boost-350-V2-Cream-White  \\\n",
       "Buyer Region                                          \n",
       "Alabama                                        51.0   \n",
       "Alaska                                          9.0   \n",
       "Arizona                                       152.0   \n",
       "Arkansas                                       26.0   \n",
       "California                                   1653.0   \n",
       "\n",
       "Sneaker Name  Adidas-Yeezy-Boost-350-V2-Semi-Frozen-Yellow  \\\n",
       "Buyer Region                                                 \n",
       "Alabama                                               36.0   \n",
       "Alaska                                                 5.0   \n",
       "Arizona                                               62.0   \n",
       "Arkansas                                               7.0   \n",
       "California                                           587.0   \n",
       "\n",
       "Sneaker Name  Adidas-Yeezy-Boost-350-V2-Sesame  \\\n",
       "Buyer Region                                     \n",
       "Alabama                                   24.0   \n",
       "Alaska                                     3.0   \n",
       "Arizona                                   81.0   \n",
       "Arkansas                                  10.0   \n",
       "California                              1045.0   \n",
       "\n",
       "Sneaker Name  Adidas-Yeezy-Boost-350-V2-Zebra  \\\n",
       "Buyer Region                                    \n",
       "Alabama                                  71.0   \n",
       "Alaska                                    4.0   \n",
       "Arizona                                 142.0   \n",
       "Arkansas                                 12.0   \n",
       "California                             1737.0   \n",
       "\n",
       "Sneaker Name  adidas-Yeezy-Boost-350-V2-Butter  \\\n",
       "Buyer Region                                     \n",
       "Alabama                                   48.0   \n",
       "Alaska                                     4.0   \n",
       "Arizona                                  164.0   \n",
       "Arkansas                                  26.0   \n",
       "California                              1810.0   \n",
       "\n",
       "Sneaker Name  adidas-Yeezy-Boost-350-V2-Static  \\\n",
       "Buyer Region                                     \n",
       "Alabama                                   23.0   \n",
       "Alaska                                     5.0   \n",
       "Arizona                                   59.0   \n",
       "Arkansas                                  13.0   \n",
       "California                               923.0   \n",
       "\n",
       "Sneaker Name  adidas-Yeezy-Boost-350-V2-Static-Reflective  \n",
       "Buyer Region                                               \n",
       "Alabama                                              16.0  \n",
       "Alaska                                                0.0  \n",
       "Arizona                                              29.0  \n",
       "Arkansas                                              3.0  \n",
       "California                                          696.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xz = z.pivot('Buyer Region','Sneaker Name','Favorite').fillna(0)\n",
    "xz.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "xz['Favorite Sneaker'] = xz[['Adidas-Yeezy-Boost-350-Low-Moonrock',\n",
    "       'Adidas-Yeezy-Boost-350-Low-Oxford-Tan',\n",
    "       'Adidas-Yeezy-Boost-350-Low-Pirate-Black-2015',\n",
    "       'Adidas-Yeezy-Boost-350-Low-Pirate-Black-2016',\n",
    "       'Adidas-Yeezy-Boost-350-Low-Turtledove',\n",
    "       'Adidas-Yeezy-Boost-350-Low-V2-Beluga',\n",
    "       'Adidas-Yeezy-Boost-350-V2-Beluga-2pt0',\n",
    "       'Adidas-Yeezy-Boost-350-V2-Blue-Tint',\n",
    "       'Adidas-Yeezy-Boost-350-V2-Core-Black-Copper',\n",
    "       'Adidas-Yeezy-Boost-350-V2-Core-Black-Green',\n",
    "       'Adidas-Yeezy-Boost-350-V2-Core-Black-Red',\n",
    "       'Adidas-Yeezy-Boost-350-V2-Core-Black-Red-2017',\n",
    "       'Adidas-Yeezy-Boost-350-V2-Core-Black-White',\n",
    "       'Adidas-Yeezy-Boost-350-V2-Cream-White',\n",
    "       'Adidas-Yeezy-Boost-350-V2-Semi-Frozen-Yellow',\n",
    "       'Adidas-Yeezy-Boost-350-V2-Sesame', 'Adidas-Yeezy-Boost-350-V2-Zebra',\n",
    "       'adidas-Yeezy-Boost-350-V2-Butter', 'adidas-Yeezy-Boost-350-V2-Static',\n",
    "       'adidas-Yeezy-Boost-350-V2-Static-Reflective']].max(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Sneaker</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Zebra</td>\n",
       "      <td>Alabama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Cream-White</td>\n",
       "      <td>Alaska</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>adidas-Yeezy-Boost-350-V2-Butter</td>\n",
       "      <td>Arizona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Cream-White</td>\n",
       "      <td>Arkansas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Beluga-2pt0</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Sneaker       State\n",
       "0        Adidas-Yeezy-Boost-350-V2-Zebra     Alabama\n",
       "1  Adidas-Yeezy-Boost-350-V2-Cream-White      Alaska\n",
       "2       adidas-Yeezy-Boost-350-V2-Butter     Arizona\n",
       "3  Adidas-Yeezy-Boost-350-V2-Cream-White    Arkansas\n",
       "4  Adidas-Yeezy-Boost-350-V2-Beluga-2pt0  California"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = []\n",
    "for i in range(0,len(xz)):\n",
    "    p = (xz == xz['Favorite Sneaker'][i]).idxmax(axis=1)[i]\n",
    "    l.append(p)\n",
    "\n",
    "new_df = pd.DataFrame(l,columns={'Sneaker'})\n",
    "new_df.head()\n",
    "new_df['State'] = xz.index\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "yeezy_copy = yeezy.copy()\n",
    "yeezy_copy = yeezy_copy['Buyer Region'].value_counts().reset_index().rename(columns={'index': 'State', 'Buyer Region': 'Total Sales'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Sneaker</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Zebra</td>\n",
       "      <td>Alabama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Cream-White</td>\n",
       "      <td>Alaska</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>adidas-Yeezy-Boost-350-V2-Butter</td>\n",
       "      <td>Arizona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Cream-White</td>\n",
       "      <td>Arkansas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Beluga-2pt0</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Sneaker       State\n",
       "0        Adidas-Yeezy-Boost-350-V2-Zebra     Alabama\n",
       "1  Adidas-Yeezy-Boost-350-V2-Cream-White      Alaska\n",
       "2       adidas-Yeezy-Boost-350-V2-Butter     Arizona\n",
       "3  Adidas-Yeezy-Boost-350-V2-Cream-White    Arkansas\n",
       "4  Adidas-Yeezy-Boost-350-V2-Beluga-2pt0  California"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yeezy_df = new_df.copy()\n",
    "yeezy_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IDs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  IDs\n",
       "0  AL\n",
       "1  AK\n",
       "2  AZ\n",
       "3  AR\n",
       "4  CA"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "ids = [\n",
    "'AL',\n",
    "'AK',    \n",
    "'AZ',\n",
    "'AR',\n",
    "'CA',\n",
    "'CO',\n",
    "'CT',\n",
    "'DE',\n",
    "'DC',\n",
    "'FL',\n",
    "'GA',\n",
    "'HI',\n",
    "'ID',\n",
    "'IL',\n",
    "'IN',\n",
    "'IA',    \n",
    "'KS',\n",
    "'KY',\n",
    "'LA',\n",
    "'ME',\n",
    "'MD',\n",
    "'MA',\n",
    "'MI',\n",
    "'MN',\n",
    "'MS',\n",
    "'MO',\n",
    "'MT',\n",
    "'NE',\n",
    "'NV',\n",
    "'NH',\n",
    "'NJ',\n",
    "'NM',\n",
    "'NY',\n",
    "'NC',\n",
    "'ND',\n",
    "'OH',\n",
    "'OK',\n",
    "'OR',\n",
    "'PA',\n",
    "'RI',\n",
    "'SC',\n",
    "'SD',\n",
    "'TN',\n",
    "'TX',\n",
    "'UT',\n",
    "'VT',\n",
    "'VA',\n",
    "'WA',\n",
    "'WV',\n",
    "'WI',\n",
    "'WY']\n",
    "ids_l = pd.DataFrame(ids,columns={'IDs'})\n",
    "ids_l.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sneaker</th>\n",
       "      <th>State</th>\n",
       "      <th>IDs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Zebra</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Cream-White</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>adidas-Yeezy-Boost-350-V2-Butter</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Cream-White</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>AR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Beluga-2pt0</td>\n",
       "      <td>California</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Sneaker       State IDs\n",
       "0        Adidas-Yeezy-Boost-350-V2-Zebra     Alabama  AL\n",
       "1  Adidas-Yeezy-Boost-350-V2-Cream-White      Alaska  AK\n",
       "2       adidas-Yeezy-Boost-350-V2-Butter     Arizona  AZ\n",
       "3  Adidas-Yeezy-Boost-350-V2-Cream-White    Arkansas  AR\n",
       "4  Adidas-Yeezy-Boost-350-V2-Beluga-2pt0  California  CA"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ids_l['State'] = xz.index\n",
    "ids_l.head()\n",
    "yeezy_df = pd.merge(yeezy_df,ids_l, on='State', how='inner', sort=True)\n",
    "yeezy_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IDs</th>\n",
       "      <th>Sneaker</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Zebra</td>\n",
       "      <td>Alabama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AK</td>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Cream-White</td>\n",
       "      <td>Alaska</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AZ</td>\n",
       "      <td>adidas-Yeezy-Boost-350-V2-Butter</td>\n",
       "      <td>Arizona</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AR</td>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Cream-White</td>\n",
       "      <td>Arkansas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Beluga-2pt0</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  IDs                                Sneaker       State\n",
       "0  AL        Adidas-Yeezy-Boost-350-V2-Zebra     Alabama\n",
       "1  AK  Adidas-Yeezy-Boost-350-V2-Cream-White      Alaska\n",
       "2  AZ       adidas-Yeezy-Boost-350-V2-Butter     Arizona\n",
       "3  AR  Adidas-Yeezy-Boost-350-V2-Cream-White    Arkansas\n",
       "4  CA  Adidas-Yeezy-Boost-350-V2-Beluga-2pt0  California"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dropped_dc = yeezy_df.copy()\n",
    "dropped_dc = dropped_dc.set_index('IDs')\n",
    "dropped_dc = dropped_dc.drop(index='DC')\n",
    "dropped_dc = dropped_dc.reset_index()\n",
    "dropped_dc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Population</th>\n",
       "      <th>IDs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4887871</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>737438</td>\n",
       "      <td>AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7171646</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3013825</td>\n",
       "      <td>AR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>39557045</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Population IDs\n",
       "0     4887871  AL\n",
       "1      737438  AK\n",
       "2     7171646  AZ\n",
       "3     3013825  AR\n",
       "4    39557045  CA"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#divide each sales amount by population of State = percapita\n",
    "#Find yeezys per capita\n",
    "\n",
    "IDs = dropped_dc['IDs'].values\n",
    "pops = [\n",
    "    4887871,\n",
    "    737438,\n",
    "    7171646,\n",
    "    3013825,\n",
    "    39557045,\n",
    "    5695564,\n",
    "    3572665,\n",
    "    967171,\n",
    "    21299325,\n",
    "    10519475,\n",
    "    1420491,\n",
    "    1754208,\n",
    "    12741080,\n",
    "    6691878,\n",
    "    3156145,\n",
    "    2911505,\n",
    "    4468402,\n",
    "    4659978,\n",
    "    1338404,\n",
    "    6042718,\n",
    "    6902149,\n",
    "    9998915,\n",
    "    5611179,\n",
    "    2986530,\n",
    "    6126452,\n",
    "    1062305,\n",
    "    1929268,\n",
    "    3034392,\n",
    "    1356458,\n",
    "    9032873,\n",
    "    2095428,\n",
    "    19542209,\n",
    "    10383620,\n",
    "    760077,\n",
    "    11689442,\n",
    "    3943079,\n",
    "    4190793,\n",
    "    12807060,\n",
    "    1057315,\n",
    "    5084127,\n",
    "    882235,\n",
    "    6770010,\n",
    "    28701845,\n",
    "    3161105,\n",
    "    626299,\n",
    "    8517685,\n",
    "    7535591,\n",
    "    1805832,\n",
    "    5813568,\n",
    "    577737\n",
    "]\n",
    "\n",
    "pops = pd.DataFrame(pops,columns={'Population'})\n",
    "pops['IDs'] = IDs\n",
    "pops.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sneaker</th>\n",
       "      <th>State</th>\n",
       "      <th>IDs</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Zebra</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>4887871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Cream-White</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>AK</td>\n",
       "      <td>737438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>adidas-Yeezy-Boost-350-V2-Butter</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>AZ</td>\n",
       "      <td>7171646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Cream-White</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>AR</td>\n",
       "      <td>3013825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adidas-Yeezy-Boost-350-V2-Beluga-2pt0</td>\n",
       "      <td>California</td>\n",
       "      <td>CA</td>\n",
       "      <td>39557045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Sneaker       State IDs  Population\n",
       "0        Adidas-Yeezy-Boost-350-V2-Zebra     Alabama  AL     4887871\n",
       "1  Adidas-Yeezy-Boost-350-V2-Cream-White      Alaska  AK      737438\n",
       "2       adidas-Yeezy-Boost-350-V2-Butter     Arizona  AZ     7171646\n",
       "3  Adidas-Yeezy-Boost-350-V2-Cream-White    Arkansas  AR     3013825\n",
       "4  Adidas-Yeezy-Boost-350-V2-Beluga-2pt0  California  CA    39557045"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yeezy_df = pd.merge(yeezy_df, pops, how='inner', on='IDs')\n",
    "yeezy_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(51, 18)\n"
     ]
    }
   ],
   "source": [
    "months = np.datetime_as_string(yeezy['Order Date'].values,unit='M')\n",
    "months.shape\n",
    "yeezy['Order Month'] = months\n",
    "yeezy['Count'] = np.ones(yeezy.shape[0])\n",
    "monthly_df = yeezy.groupby(['Buyer Region', 'Order Month'], as_index=False).sum().pivot('Buyer Region','Order Month','Count').fillna(0).cumsum(axis=1)\n",
    "print(monthly_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>2017-09</th>\n",
       "      <th>2017-10</th>\n",
       "      <th>2017-11</th>\n",
       "      <th>2017-12</th>\n",
       "      <th>2018-01</th>\n",
       "      <th>2018-02</th>\n",
       "      <th>2018-03</th>\n",
       "      <th>2018-04</th>\n",
       "      <th>2018-05</th>\n",
       "      <th>2018-06</th>\n",
       "      <th>2018-07</th>\n",
       "      <th>2018-08</th>\n",
       "      <th>2018-09</th>\n",
       "      <th>2018-10</th>\n",
       "      <th>2018-11</th>\n",
       "      <th>2018-12</th>\n",
       "      <th>2019-01</th>\n",
       "      <th>2019-02</th>\n",
       "      <th>IDs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>304.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>316.0</td>\n",
       "      <td>357.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>487.0</td>\n",
       "      <td>518.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>703.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>974.0</td>\n",
       "      <td>1005.0</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>AR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>94.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>920.0</td>\n",
       "      <td>2266.0</td>\n",
       "      <td>2907.0</td>\n",
       "      <td>3361.0</td>\n",
       "      <td>3762.0</td>\n",
       "      <td>4044.0</td>\n",
       "      <td>4417.0</td>\n",
       "      <td>4946.0</td>\n",
       "      <td>5966.0</td>\n",
       "      <td>6471.0</td>\n",
       "      <td>6954.0</td>\n",
       "      <td>7634.0</td>\n",
       "      <td>9250.0</td>\n",
       "      <td>10955.0</td>\n",
       "      <td>12526.0</td>\n",
       "      <td>13113.0</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        State  2017-09  2017-10  2017-11  2017-12  2018-01  2018-02  2018-03  \\\n",
       "0     Alabama      6.0      8.0     25.0     51.0     67.0     78.0     84.0   \n",
       "1      Alaska      0.0      0.0      1.0      6.0     10.0     10.0     10.0   \n",
       "2     Arizona      3.0      9.0     59.0    139.0    196.0    228.0    266.0   \n",
       "3    Arkansas      0.0      1.0      6.0     13.0     21.0     23.0     29.0   \n",
       "4  California     94.0    206.0    920.0   2266.0   2907.0   3361.0   3762.0   \n",
       "\n",
       "   2018-04  2018-05  2018-06  2018-07  2018-08  2018-09  2018-10  2018-11  \\\n",
       "0     96.0    104.0    117.0    134.0    156.0    168.0    184.0    241.0   \n",
       "1     10.0     11.0     12.0     16.0     17.0     19.0     21.0     25.0   \n",
       "2    290.0    316.0    357.0    440.0    487.0    518.0    571.0    703.0   \n",
       "3     35.0     38.0     42.0     54.0     61.0     68.0     79.0     95.0   \n",
       "4   4044.0   4417.0   4946.0   5966.0   6471.0   6954.0   7634.0   9250.0   \n",
       "\n",
       "   2018-12  2019-01  2019-02 IDs  \n",
       "0    304.0    352.0    375.0  AL  \n",
       "1     37.0     39.0     41.0  AK  \n",
       "2    870.0    974.0   1005.0  AZ  \n",
       "3    117.0    136.0    141.0  AR  \n",
       "4  10955.0  12526.0  13113.0  CA  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yeezy_monthly = monthly_df.reset_index().rename(columns={'Buyer Region': 'State'})\n",
    "yeezy_monthly = pd.merge(yeezy_monthly, ids_l, how='inner', on='State')\n",
    "yeezy_monthly.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50, 19)\n"
     ]
    }
   ],
   "source": [
    "yeezy_monthly = yeezy_monthly.set_index(['IDs'])\n",
    "yeezy_monthly = yeezy_monthly.drop('DC')\n",
    "print(yeezy_monthly.shape)\n",
    "yeezy_monthly = yeezy_monthly.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>IDs</th>\n",
       "      <th>State</th>\n",
       "      <th>2017-09</th>\n",
       "      <th>2017-10</th>\n",
       "      <th>2017-11</th>\n",
       "      <th>2017-12</th>\n",
       "      <th>2018-01</th>\n",
       "      <th>2018-02</th>\n",
       "      <th>2018-03</th>\n",
       "      <th>2018-04</th>\n",
       "      <th>...</th>\n",
       "      <th>2018-06</th>\n",
       "      <th>2018-07</th>\n",
       "      <th>2018-08</th>\n",
       "      <th>2018-09</th>\n",
       "      <th>2018-10</th>\n",
       "      <th>2018-11</th>\n",
       "      <th>2018-12</th>\n",
       "      <th>2019-01</th>\n",
       "      <th>2019-02</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>...</td>\n",
       "      <td>117.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>304.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>375.0</td>\n",
       "      <td>4887871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AK</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>737438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AZ</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>196.0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>...</td>\n",
       "      <td>357.0</td>\n",
       "      <td>440.0</td>\n",
       "      <td>487.0</td>\n",
       "      <td>518.0</td>\n",
       "      <td>571.0</td>\n",
       "      <td>703.0</td>\n",
       "      <td>870.0</td>\n",
       "      <td>974.0</td>\n",
       "      <td>1005.0</td>\n",
       "      <td>7171646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AR</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>...</td>\n",
       "      <td>42.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>136.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>3013825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>California</td>\n",
       "      <td>94.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>920.0</td>\n",
       "      <td>2266.0</td>\n",
       "      <td>2907.0</td>\n",
       "      <td>3361.0</td>\n",
       "      <td>3762.0</td>\n",
       "      <td>4044.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4946.0</td>\n",
       "      <td>5966.0</td>\n",
       "      <td>6471.0</td>\n",
       "      <td>6954.0</td>\n",
       "      <td>7634.0</td>\n",
       "      <td>9250.0</td>\n",
       "      <td>10955.0</td>\n",
       "      <td>12526.0</td>\n",
       "      <td>13113.0</td>\n",
       "      <td>39557045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  IDs       State  2017-09  2017-10  2017-11  2017-12  2018-01  2018-02  \\\n",
       "0  AL     Alabama      6.0      8.0     25.0     51.0     67.0     78.0   \n",
       "1  AK      Alaska      0.0      0.0      1.0      6.0     10.0     10.0   \n",
       "2  AZ     Arizona      3.0      9.0     59.0    139.0    196.0    228.0   \n",
       "3  AR    Arkansas      0.0      1.0      6.0     13.0     21.0     23.0   \n",
       "4  CA  California     94.0    206.0    920.0   2266.0   2907.0   3361.0   \n",
       "\n",
       "   2018-03  2018-04     ...      2018-06  2018-07  2018-08  2018-09  2018-10  \\\n",
       "0     84.0     96.0     ...        117.0    134.0    156.0    168.0    184.0   \n",
       "1     10.0     10.0     ...         12.0     16.0     17.0     19.0     21.0   \n",
       "2    266.0    290.0     ...        357.0    440.0    487.0    518.0    571.0   \n",
       "3     29.0     35.0     ...         42.0     54.0     61.0     68.0     79.0   \n",
       "4   3762.0   4044.0     ...       4946.0   5966.0   6471.0   6954.0   7634.0   \n",
       "\n",
       "   2018-11  2018-12  2019-01  2019-02  Population  \n",
       "0    241.0    304.0    352.0    375.0     4887871  \n",
       "1     25.0     37.0     39.0     41.0      737438  \n",
       "2    703.0    870.0    974.0   1005.0     7171646  \n",
       "3     95.0    117.0    136.0    141.0     3013825  \n",
       "4   9250.0  10955.0  12526.0  13113.0    39557045  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yeezy_cap = yeezy.groupby(['Buyer Region', 'Order Month'], as_index=False).sum().pivot('Buyer Region', 'Order Month', 'Count').fillna(0).cumsum(axis=1)\n",
    "yeezy_cap = yeezy_cap.reset_index().rename(columns={'Buyer Region': 'State'})\n",
    "yeezy_cap = pd.merge(yeezy_cap, ids_l,how='inner',on='State')\n",
    "yeezy_cap = yeezy_cap.set_index(['IDs'])\n",
    "yeezy_cap = yeezy_cap.drop('DC')\n",
    "yeezy_cap = yeezy_cap.reset_index()\n",
    "yeezy_cap = pd.merge(yeezy_cap, pops, on='IDs', how='inner')\n",
    "yeezy_cap.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IDs</th>\n",
       "      <th>State</th>\n",
       "      <th>2017-09</th>\n",
       "      <th>2017-10</th>\n",
       "      <th>2017-11</th>\n",
       "      <th>2017-12</th>\n",
       "      <th>2018-01</th>\n",
       "      <th>2018-02</th>\n",
       "      <th>2018-03</th>\n",
       "      <th>2018-04</th>\n",
       "      <th>...</th>\n",
       "      <th>2018-06</th>\n",
       "      <th>2018-07</th>\n",
       "      <th>2018-08</th>\n",
       "      <th>2018-09</th>\n",
       "      <th>2018-10</th>\n",
       "      <th>2018-11</th>\n",
       "      <th>2018-12</th>\n",
       "      <th>2019-01</th>\n",
       "      <th>2019-02</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>1.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.4</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>6.2</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.7</td>\n",
       "      <td>4887871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AK</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>5.6</td>\n",
       "      <td>737438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AZ</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>6.8</td>\n",
       "      <td>7.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.8</td>\n",
       "      <td>12.1</td>\n",
       "      <td>13.6</td>\n",
       "      <td>14.0</td>\n",
       "      <td>7171646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AR</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>...</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.6</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3013825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>California</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>7.3</td>\n",
       "      <td>8.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>10.2</td>\n",
       "      <td>...</td>\n",
       "      <td>12.5</td>\n",
       "      <td>15.1</td>\n",
       "      <td>16.4</td>\n",
       "      <td>17.6</td>\n",
       "      <td>19.3</td>\n",
       "      <td>23.4</td>\n",
       "      <td>27.7</td>\n",
       "      <td>31.7</td>\n",
       "      <td>33.1</td>\n",
       "      <td>39557045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  IDs       State  2017-09  2017-10  2017-11  2017-12  2018-01  2018-02  \\\n",
       "0  AL     Alabama      0.1      0.2      0.5      1.0      1.4      1.6   \n",
       "1  AK      Alaska      0.0      0.0      0.1      0.8      1.4      1.4   \n",
       "2  AZ     Arizona      0.0      0.1      0.8      1.9      2.7      3.2   \n",
       "3  AR    Arkansas      0.0      0.0      0.2      0.4      0.7      0.8   \n",
       "4  CA  California      0.2      0.5      2.3      5.7      7.3      8.5   \n",
       "\n",
       "   2018-03  2018-04     ...      2018-06  2018-07  2018-08  2018-09  2018-10  \\\n",
       "0      1.7      2.0     ...          2.4      2.7      3.2      3.4      3.8   \n",
       "1      1.4      1.4     ...          1.6      2.2      2.3      2.6      2.8   \n",
       "2      3.7      4.0     ...          5.0      6.1      6.8      7.2      8.0   \n",
       "3      1.0      1.2     ...          1.4      1.8      2.0      2.3      2.6   \n",
       "4      9.5     10.2     ...         12.5     15.1     16.4     17.6     19.3   \n",
       "\n",
       "   2018-11  2018-12  2019-01  2019-02  Population  \n",
       "0      4.9      6.2      7.2      7.7     4887871  \n",
       "1      3.4      5.0      5.3      5.6      737438  \n",
       "2      9.8     12.1     13.6     14.0     7171646  \n",
       "3      3.2      3.9      4.5      4.7     3013825  \n",
       "4     23.4     27.7     31.7     33.1    39557045  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "months = ['2017-09', '2017-10', '2017-11', '2017-12', '2018-01', '2018-02',\n",
    "       '2018-03', '2018-04', '2018-05', '2018-06', '2018-07', '2018-08',\n",
    "       '2018-09', '2018-10', '2018-11', '2018-12', '2019-01', '2019-02']\n",
    "\n",
    "# I want to represent each value as : YEEZYS OWNED PER 100,000 IN STATE\n",
    "for month in months:\n",
    "    yeezy_cap[month] = (yeezy_cap[month].divide(yeezy_cap['Population']))\n",
    "    yeezy_cap[month] = yeezy_cap[month].multiply(100000)\n",
    "    yeezy_cap[month] = round(yeezy_cap[month],1)\n",
    "yeezy_cap.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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style=\"background-color:hsl(139.96969696969697, 60.12121212121212%, 37.81818181818182%);height:20px;width:20px;margin-bottom:0px;display:inline-block;\"></div><div style=\"background-color:hsl(140.4949494949495, 62.02020202020203%, 36.96969696969697%);height:20px;width:20px;margin-bottom:0px;display:inline-block;\"></div><div style=\"background-color:hsl(141.02020202020202, 63.91919191919192%, 36.121212121212125%);height:20px;width:20px;margin-bottom:0px;display:inline-block;\"></div><div style=\"background-color:hsl(141.54545454545456, 65.81818181818183%, 35.272727272727266%);height:20px;width:20px;margin-bottom:0px;display:inline-block;\"></div><div style=\"background-color:hsl(142.07070707070707, 67.71717171717172%, 34.42424242424242%);height:20px;width:20px;margin-bottom:0px;display:inline-block;\"></div><div style=\"background-color:hsl(142.59595959595958, 69.6161616161616%, 33.57575757575758%);height:20px;width:20px;margin-bottom:0px;display:inline-block;\"></div><div style=\"background-color:hsl(143.12121212121212, 71.51515151515152%, 32.72727272727273%);height:20px;width:20px;margin-bottom:0px;display:inline-block;\"></div><div style=\"background-color:hsl(143.64646464646464, 73.41414141414141%, 31.878787878787882%);height:20px;width:20px;margin-bottom:0px;display:inline-block;\"></div><div style=\"background-color:hsl(144.17171717171718, 75.31313131313132%, 31.030303030303028%);height:20px;width:20px;margin-bottom:0px;display:inline-block;\"></div><div style=\"background-color:hsl(144.6969696969697, 77.21212121212122%, 30.18181818181818%);height:20px;width:20px;margin-bottom:0px;display:inline-block;\"></div><div style=\"background-color:hsl(145.22222222222223, 79.1111111111111%, 29.333333333333336%);height:20px;width:20px;margin-bottom:0px;display:inline-block;\"></div><div style=\"background-color:hsl(145.74747474747474, 81.01010101010101%, 28.484848484848484%);height:20px;width:20px;margin-bottom:0px;display:inline-block;\"></div><div style=\"background-color:hsl(146.27272727272728, 82.9090909090909%, 27.63636363636364%);height:20px;width:20px;margin-bottom:0px;display:inline-block;\"></div><div style=\"background-color:hsl(146.7979797979798, 84.80808080808082%, 26.787878787878785%);height:20px;width:20px;margin-bottom:0px;display:inline-block;\"></div><div style=\"background-color:hsl(147.32323232323233, 86.7070707070707%, 25.93939393939394%);height:20px;width:20px;margin-bottom:0px;display:inline-block;\"></div><div style=\"background-color:hsl(147.84848484848484, 88.60606060606062%, 25.090909090909086%);height:20px;width:20px;margin-bottom:0px;display:inline-block;\"></div><div style=\"background-color:hsl(148.37373737373738, 90.5050505050505%, 24.242424242424242%);height:20px;width:20px;margin-bottom:0px;display:inline-block;\"></div><div style=\"background-color:hsl(148.8989898989899, 92.40404040404039%, 23.393939393939398%);height:20px;width:20px;margin-bottom:0px;display:inline-block;\"></div><div style=\"background-color:hsl(149.42424242424244, 94.30303030303031%, 22.545454545454543%);height:20px;width:20px;margin-bottom:0px;display:inline-block;\"></div><div style=\"background-color:hsl(149.94949494949495, 96.20202020202021%, 21.6969696969697%);height:20px;width:20px;margin-bottom:0px;display:inline-block;\"></div><div style=\"background-color:hsl(150.4747474747475, 98.1010101010101%, 20.848484848484844%);height:20px;width:20px;margin-bottom:0px;display:inline-block;\"></div><div style=\"background-color:hsl(151.0, 100.0%, 20.0%);height:20px;width:20px;margin-bottom:0px;display:inline-block;\"></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def convert_colorscale_format(colorscale):\n",
    "    plotly_colorscale = []\n",
    "    for index, sec_value in enumerate(np.linspace(0, 1, len(colorscale))):\n",
    "        plotly_colorscale.append([sec_value, colorscale[index]])\n",
    "    return plotly_colorscale\n",
    "\n",
    "x = cl.scales['5']['seq']['YlGn']\n",
    "x100 = cl.to_hsl(cl.interp(x,100))\n",
    "from IPython.display import HTML\n",
    "test = convert_colorscale_format(x100)\n",
    "HTML(cl.to_html(x100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "yeezy_cap['Population'] = yeezy_cap['Population'].astype(str)\n",
    "\n",
    "yeezy_cap['text'] = 'Yeezys Resold per 100,000' + '<br>' +\\\n",
    "     yeezy_cap['State'] + '<br>' +\\\n",
    "    'State Population : ' + yeezy_cap['Population'] + '<br>' +\\\n",
    "    'Favorite Sneaker : ' + yeezy_df['Sneaker']\n",
    "\n",
    "columns = []\n",
    "cols = yeezy_cap.columns\n",
    "for col in cols: \n",
    "    column_name = '{}'.format(col)\n",
    "    a_columns = Column(list(yeezy_cap[col]), column_name)\n",
    "    columns.append(a_columns)\n",
    "\n",
    "grid3 = Grid(columns)\n",
    "grid_url = py.grid_ops.upload(grid3,'yeezy_gridfinal_show', auto_open=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://plot.ly/~Ruwai/74/'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## THIS IS WHERE THE PLOTLY MAGIC HAPPENS\n",
    "\n",
    "months = ['2017-09', '2017-10', '2017-11', '2017-12', '2018-01', '2018-02',\n",
    "       '2018-03', '2018-04', '2018-05', '2018-06', '2018-07', '2018-08',\n",
    "       '2018-09', '2018-10', '2018-11', '2018-12', '2019-01', '2019-02']\n",
    "\n",
    "\n",
    "layout = dict(\n",
    "    title='Yeezys Resold Per Capita by State <br> Interactive Choropleth Between <br> (09/2017 - 02/2019) <br> (Hover for Details)',\n",
    "    font=dict(family='Helvetica',size=15),\n",
    "    showlegend=True,\n",
    "    geo = dict(\n",
    "        scope = 'usa',\n",
    "        projection = dict(type='albers usa'),\n",
    "        showlakes = True,\n",
    "        lakecolor = 'rgb(255,255,255)',\n",
    "        ),\n",
    "    updatemenus = [dict(\n",
    "        type='buttons',\n",
    "        showactive=False,\n",
    "        x=0.05,\n",
    "        y=1,\n",
    "        xanchor='right',\n",
    "        yanchor='top',\n",
    "        pad=dict(t=0,r=10),\n",
    "        buttons=[dict(\n",
    "            label='Play',\n",
    "            method='animate',\n",
    "            args=[None,\n",
    "                 dict(fromcurrent=True,\n",
    "                     frame=dict(\n",
    "                         duration=500,redraw=True),\n",
    "                     transition=dict(\n",
    "                         duration=500, easing='elastic-out')\n",
    "                     )]\n",
    "        )]\n",
    "    )],\n",
    ")\n",
    "\n",
    "figure = {\n",
    "    'data': [],\n",
    "    'layout': layout,\n",
    "    'frames': [],   \n",
    "}\n",
    "\n",
    "sliders = dict(\n",
    "        active=0,\n",
    "        x = 0.1,\n",
    "        y = 0,\n",
    "        yanchor = 'top',\n",
    "        xachor = 'left',\n",
    "        currentvalue = dict(\n",
    "            font = dict(\n",
    "                size=20\n",
    "            ),\n",
    "            prefix = 'Year - Month: ',\n",
    "            xanchor='right',\n",
    "            visible = True,\n",
    "        ),\n",
    "        pad = dict(\n",
    "            b=10,\n",
    "            t=50\n",
    "        ),\n",
    "        transition = dict(\n",
    "            duration=300,\n",
    "            easing = 'elastic-in-out'\n",
    "        ),\n",
    "        steps = [],\n",
    "        args = [dict(\n",
    "            transition=dict(duration=500, easing='elastic-in-out')\n",
    "        )],\n",
    "        initialValue = months[0],\n",
    "        plotlycommand = 'animate',\n",
    "        values = months,\n",
    "        visible=True\n",
    "        )\n",
    "\n",
    "# for month in months:\n",
    "data_dict = {\n",
    "    'type': 'choropleth',\n",
    "    'colorscale': test,\n",
    "    'zsrc': grid3.get_column_reference('{}'.format(months[0])),\n",
    "    'locationssrc': grid3.get_column_reference('IDs'),\n",
    "    'textsrc': grid3.get_column_reference('text'),\n",
    "    'locationmode': 'USA-states',\n",
    "    'marker': {\n",
    "        'line': {\n",
    "            'color': 'rgb(255,255,255)',\n",
    "            'width': 2\n",
    "        }\n",
    "    },\n",
    "    'colorbar': {\n",
    "        'title': {\n",
    "            'text': 'Yeezys Resold Per Capita <br> Per 100,000',\n",
    "            'font': {\n",
    "                'size': 20\n",
    "            }\n",
    "        },\n",
    "        'showticksuffix': 'first',\n",
    "        'ticksuffix': ' Yeezys Resold Per 100,000',\n",
    "    },\n",
    "    'name': months[0]}\n",
    "figure['data'].append(data_dict)\n",
    "\n",
    "for month in months:\n",
    "    frame = {\n",
    "            'data': [], \n",
    "            'name': str(month)\n",
    "    }\n",
    "#     for month in months:\n",
    "    data_dict = {\n",
    "        'type': 'choropleth',\n",
    "        'colorscale': test,\n",
    "        'zsrc': grid3.get_column_reference('{}'.format(month)),\n",
    "        'locationssrc': grid3.get_column_reference('IDs'),\n",
    "        'textsrc': grid3.get_column_reference('text'),\n",
    "        'locationmode': 'USA-states',\n",
    "        'marker': {\n",
    "            'line': {\n",
    "                'color': 'rgb(255,255,255)',\n",
    "                'width': 2\n",
    "            }\n",
    "        },\n",
    "        'colorbar': {\n",
    "            'title': {\n",
    "                'text': 'Yeezys Resold Per Capita <br> Per 100,000',\n",
    "                'font': {\n",
    "                    'size': 20\n",
    "            }\n",
    "        },\n",
    "            'showticksuffix': 'first',\n",
    "            'ticksuffix': ' Yeezys Resold Per 100,000',\n",
    "        },\n",
    "        'name': month\n",
    "    }\n",
    "\n",
    "    frame['data'].append(data_dict)\n",
    "        \n",
    "    figure['frames'].append(frame)\n",
    "    \n",
    "    slider_step = {'args':[\n",
    "        [month],\n",
    "        {'frame': {'duration':300, 'redraw':True},\n",
    "         'mode': 'immediate',\n",
    "         'transition': {'duration':300}}\n",
    "        ],\n",
    "        'label': month,\n",
    "        'method':'animate'}\n",
    "    sliders['steps'].append(slider_step)\n",
    "\n",
    "figure['layout']['sliders'] = [sliders]\n",
    "\n",
    "final = py.create_animations(figure,filename='yeezys_final_WOW',auto_open=False)\n",
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#okay so i'm going to create another chart on the comparison of \n",
    "#standardized average sales?\n",
    "#or maybe some simple linear regression to predict sales?\n",
    "#i'm down\n",
    "#we've gotta do some train test splits\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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