You don't need a pivot table if you can pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>Date</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Age</th>\n",
       "      <th>Age Group</th>\n",
       "      <th>Customer Gender</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>Product Category</th>\n",
       "      <th>Sub Category</th>\n",
       "      <th>Product</th>\n",
       "      <th>Frame Size</th>\n",
       "      <th>Order Quantity</th>\n",
       "      <th>Unit Cost</th>\n",
       "      <th>Unit Price</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11/26/2013</td>\n",
       "      <td>November</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>11019.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Youth (&lt;25)</td>\n",
       "      <td>M</td>\n",
       "      <td>Canada</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>950</td>\n",
       "      <td>590.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11/26/2015</td>\n",
       "      <td>November</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>11019.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Youth (&lt;25)</td>\n",
       "      <td>M</td>\n",
       "      <td>Canada</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>950</td>\n",
       "      <td>590.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3/23/2014</td>\n",
       "      <td>March</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>11039.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>M</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1035.0</td>\n",
       "      <td>2401</td>\n",
       "      <td>1366.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3/23/2016</td>\n",
       "      <td>March</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>11039.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>M</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>2088</td>\n",
       "      <td>1188.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5/15/2014</td>\n",
       "      <td>May</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>11046.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>Adults (35-64)</td>\n",
       "      <td>F</td>\n",
       "      <td>Australia</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>Hitch Rack - 4-Bike</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>418</td>\n",
       "      <td>238.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date     Month    Year  Customer ID  Customer Age       Age Group  \\\n",
       "0  11/26/2013  November  2013.0      11019.0          19.0     Youth (<25)   \n",
       "1  11/26/2015  November  2015.0      11019.0          19.0     Youth (<25)   \n",
       "2   3/23/2014     March  2014.0      11039.0          49.0  Adults (35-64)   \n",
       "3   3/23/2016     March  2016.0      11039.0          49.0  Adults (35-64)   \n",
       "4   5/15/2014       May  2014.0      11046.0          47.0  Adults (35-64)   \n",
       "\n",
       "  Customer Gender    Country             State Product Category Sub Category  \\\n",
       "0               M     Canada  British Columbia      Accessories   Bike Racks   \n",
       "1               M     Canada  British Columbia      Accessories   Bike Racks   \n",
       "2               M  Australia   New South Wales      Accessories   Bike Racks   \n",
       "3               M  Australia   New South Wales      Accessories   Bike Racks   \n",
       "4               F  Australia   New South Wales      Accessories   Bike Racks   \n",
       "\n",
       "               Product  Frame Size  Order Quantity  Unit Cost  Unit Price  \\\n",
       "0  Hitch Rack - 4-Bike         NaN             8.0       45.0       120.0   \n",
       "1  Hitch Rack - 4-Bike         NaN             8.0       45.0       120.0   \n",
       "2  Hitch Rack - 4-Bike         NaN            23.0       45.0       120.0   \n",
       "3  Hitch Rack - 4-Bike         NaN            20.0       45.0       120.0   \n",
       "4  Hitch Rack - 4-Bike         NaN             4.0       45.0       120.0   \n",
       "\n",
       "     Cost  Revenue  Profit  \n",
       "0   360.0      950   590.0  \n",
       "1   360.0      950   590.0  \n",
       "2  1035.0     2401  1366.0  \n",
       "3   900.0     2088  1188.0  \n",
       "4   180.0      418   238.0  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "worksheet = pd.read_csv(r\"C:\\Users\\Alexander\\Desktop\\Springboard\\PivotTables.csv\")\n",
    "worksheet.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>Product Category</th>\n",
       "      <th>Sub Category</th>\n",
       "      <th>Year</th>\n",
       "      <th>Revenue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>2401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>2088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Accessories</td>\n",
       "      <td>Bike Racks</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>418</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product Category Sub Category    Year  Revenue\n",
       "0      Accessories   Bike Racks  2013.0      950\n",
       "1      Accessories   Bike Racks  2015.0      950\n",
       "2      Accessories   Bike Racks  2014.0     2401\n",
       "3      Accessories   Bike Racks  2016.0     2088\n",
       "4      Accessories   Bike Racks  2014.0      418"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivotsubset = worksheet[[\"Product Category\", \"Sub Category\", \"Year\", \"Revenue\"]]\n",
    "pivotsubset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"6\" halign=\"left\">Revenue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>2011.0</th>\n",
       "      <th>2012.0</th>\n",
       "      <th>2013.0</th>\n",
       "      <th>2014.0</th>\n",
       "      <th>2015.0</th>\n",
       "      <th>2016.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product Category</th>\n",
       "      <th>Sub Category</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 rowspan=\"8\" valign=\"top\">Accessories</th>\n",
       "      <th>Bike Racks</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112605.0</td>\n",
       "      <td>152966.0</td>\n",
       "      <td>107813.0</td>\n",
       "      <td>144416.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bike Stands</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95396.0</td>\n",
       "      <td>83148.0</td>\n",
       "      <td>88822.0</td>\n",
       "      <td>76709.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bottles and Cages</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>292535.0</td>\n",
       "      <td>422000.0</td>\n",
       "      <td>285144.0</td>\n",
       "      <td>409495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cleaners</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42840.0</td>\n",
       "      <td>58013.0</td>\n",
       "      <td>41559.0</td>\n",
       "      <td>56409.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fenders</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>284577.0</td>\n",
       "      <td>347236.0</td>\n",
       "      <td>277261.0</td>\n",
       "      <td>336659.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Helmets</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1285031.0</td>\n",
       "      <td>1629103.0</td>\n",
       "      <td>1250303.0</td>\n",
       "      <td>1576644.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hydration Packs</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>237491.0</td>\n",
       "      <td>264521.0</td>\n",
       "      <td>230850.0</td>\n",
       "      <td>257544.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tires and Tubes</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1033740.0</td>\n",
       "      <td>1336605.0</td>\n",
       "      <td>1004202.0</td>\n",
       "      <td>1296355.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Bikes</th>\n",
       "      <th>Mountain Bikes</th>\n",
       "      <td>2198270.0</td>\n",
       "      <td>2182853.0</td>\n",
       "      <td>3732841.0</td>\n",
       "      <td>2983026.0</td>\n",
       "      <td>5581524.0</td>\n",
       "      <td>4445012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Road Bikes</th>\n",
       "      <td>6766618.0</td>\n",
       "      <td>6993130.0</td>\n",
       "      <td>4836352.0</td>\n",
       "      <td>3017127.0</td>\n",
       "      <td>7279822.0</td>\n",
       "      <td>4470012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Touring Bikes</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1289594.0</td>\n",
       "      <td>1611090.0</td>\n",
       "      <td>1937737.0</td>\n",
       "      <td>2457126.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">Clothing</th>\n",
       "      <th>Caps</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>117068.0</td>\n",
       "      <td>161372.0</td>\n",
       "      <td>113612.0</td>\n",
       "      <td>156725.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gloves</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>191818.0</td>\n",
       "      <td>250571.0</td>\n",
       "      <td>185339.0</td>\n",
       "      <td>243691.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jerseys</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>956093.0</td>\n",
       "      <td>1129894.0</td>\n",
       "      <td>927556.0</td>\n",
       "      <td>1100199.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shorts</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>447464.0</td>\n",
       "      <td>435603.0</td>\n",
       "      <td>433788.0</td>\n",
       "      <td>423855.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Socks</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32888.0</td>\n",
       "      <td>41825.0</td>\n",
       "      <td>31942.0</td>\n",
       "      <td>40516.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vests</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>251704.0</td>\n",
       "      <td>228624.0</td>\n",
       "      <td>246717.0</td>\n",
       "      <td>222018.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      Revenue                        \\\n",
       "Year                                   2011.0     2012.0     2013.0   \n",
       "Product Category Sub Category                                         \n",
       "Accessories      Bike Racks               NaN        NaN   112605.0   \n",
       "                 Bike Stands              NaN        NaN    95396.0   \n",
       "                 Bottles and Cages        NaN        NaN   292535.0   \n",
       "                 Cleaners                 NaN        NaN    42840.0   \n",
       "                 Fenders                  NaN        NaN   284577.0   \n",
       "                 Helmets                  NaN        NaN  1285031.0   \n",
       "                 Hydration Packs          NaN        NaN   237491.0   \n",
       "                 Tires and Tubes          NaN        NaN  1033740.0   \n",
       "Bikes            Mountain Bikes     2198270.0  2182853.0  3732841.0   \n",
       "                 Road Bikes         6766618.0  6993130.0  4836352.0   \n",
       "                 Touring Bikes            NaN        NaN  1289594.0   \n",
       "Clothing         Caps                     NaN        NaN   117068.0   \n",
       "                 Gloves                   NaN        NaN   191818.0   \n",
       "                 Jerseys                  NaN        NaN   956093.0   \n",
       "                 Shorts                   NaN        NaN   447464.0   \n",
       "                 Socks                    NaN        NaN    32888.0   \n",
       "                 Vests                    NaN        NaN   251704.0   \n",
       "\n",
       "                                                                     \n",
       "Year                                   2014.0     2015.0     2016.0  \n",
       "Product Category Sub Category                                        \n",
       "Accessories      Bike Racks          152966.0   107813.0   144416.0  \n",
       "                 Bike Stands          83148.0    88822.0    76709.0  \n",
       "                 Bottles and Cages   422000.0   285144.0   409495.0  \n",
       "                 Cleaners             58013.0    41559.0    56409.0  \n",
       "                 Fenders             347236.0   277261.0   336659.0  \n",
       "                 Helmets            1629103.0  1250303.0  1576644.0  \n",
       "                 Hydration Packs     264521.0   230850.0   257544.0  \n",
       "                 Tires and Tubes    1336605.0  1004202.0  1296355.0  \n",
       "Bikes            Mountain Bikes     2983026.0  5581524.0  4445012.0  \n",
       "                 Road Bikes         3017127.0  7279822.0  4470012.0  \n",
       "                 Touring Bikes      1611090.0  1937737.0  2457126.0  \n",
       "Clothing         Caps                161372.0   113612.0   156725.0  \n",
       "                 Gloves              250571.0   185339.0   243691.0  \n",
       "                 Jerseys            1129894.0   927556.0  1100199.0  \n",
       "                 Shorts              435603.0   433788.0   423855.0  \n",
       "                 Socks                41825.0    31942.0    40516.0  \n",
       "                 Vests               228624.0   246717.0   222018.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivot1 = pd.pivot_table(pivotsubset, index = [\"Product Category\", \"Sub Category\"],\n",
    "                       values=[\"Revenue\"], columns=[\"Year\"],aggfunc=np.sum)\n",
    "pivot1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State\n",
       "Hessen                 2073391\n",
       "Saarland               2029545\n",
       "Nordrhein-Westfalen    1804900\n",
       "Hamburg                1559005\n",
       "Bayern                 1341985\n",
       "Brandenburg             169770\n",
       "Name: Revenue, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "worksheet.head()\n",
    "df = worksheet.loc[worksheet.Country == \"Germany\"]\n",
    "df = df.groupby([\"State\"]).Revenue.sum().sort_values(ascending=False)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"6\" halign=\"left\">Revenue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>2011.0</th>\n",
       "      <th>2012.0</th>\n",
       "      <th>2013.0</th>\n",
       "      <th>2014.0</th>\n",
       "      <th>2015.0</th>\n",
       "      <th>2016.0</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th>State</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 rowspan=\"6\" valign=\"top\">Germany</th>\n",
       "      <th>Bayern</th>\n",
       "      <td>117195.0</td>\n",
       "      <td>120829.0</td>\n",
       "      <td>193034.0</td>\n",
       "      <td>267226.0</td>\n",
       "      <td>278047.0</td>\n",
       "      <td>365654.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brandenburg</th>\n",
       "      <td>7268.0</td>\n",
       "      <td>11249.0</td>\n",
       "      <td>34355.0</td>\n",
       "      <td>33486.0</td>\n",
       "      <td>48520.0</td>\n",
       "      <td>34892.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hamburg</th>\n",
       "      <td>124618.0</td>\n",
       "      <td>137850.0</td>\n",
       "      <td>277548.0</td>\n",
       "      <td>281576.0</td>\n",
       "      <td>368779.0</td>\n",
       "      <td>368634.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hessen</th>\n",
       "      <td>230224.0</td>\n",
       "      <td>230652.0</td>\n",
       "      <td>316229.0</td>\n",
       "      <td>386423.0</td>\n",
       "      <td>409265.0</td>\n",
       "      <td>500598.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nordrhein-Westfalen</th>\n",
       "      <td>155142.0</td>\n",
       "      <td>141564.0</td>\n",
       "      <td>288261.0</td>\n",
       "      <td>365008.0</td>\n",
       "      <td>395595.0</td>\n",
       "      <td>459330.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saarland</th>\n",
       "      <td>199156.0</td>\n",
       "      <td>193044.0</td>\n",
       "      <td>343277.0</td>\n",
       "      <td>368789.0</td>\n",
       "      <td>430885.0</td>\n",
       "      <td>494394.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Revenue                                          \\\n",
       "Year                           2011.0    2012.0    2013.0    2014.0    2015.0   \n",
       "Country State                                                                   \n",
       "Germany Bayern               117195.0  120829.0  193034.0  267226.0  278047.0   \n",
       "        Brandenburg            7268.0   11249.0   34355.0   33486.0   48520.0   \n",
       "        Hamburg              124618.0  137850.0  277548.0  281576.0  368779.0   \n",
       "        Hessen               230224.0  230652.0  316229.0  386423.0  409265.0   \n",
       "        Nordrhein-Westfalen  155142.0  141564.0  288261.0  365008.0  395595.0   \n",
       "        Saarland             199156.0  193044.0  343277.0  368789.0  430885.0   \n",
       "\n",
       "                                       \n",
       "Year                           2016.0  \n",
       "Country State                          \n",
       "Germany Bayern               365654.0  \n",
       "        Brandenburg           34892.0  \n",
       "        Hamburg              368634.0  \n",
       "        Hessen               500598.0  \n",
       "        Nordrhein-Westfalen  459330.0  \n",
       "        Saarland             494394.0  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"pivotSub2 = worksheet[[\"Country\", \"State\", \"Revenue\", \"Year\"]]\n",
    "pivotsub2 = pd.pivot_table(pivotSub2, index=[\"Country\", \"State\"], \n",
    "                            values=[\"Revenue\"], columns=[\"Year\"], \n",
    "                            aggfunc=np.sum)\n",
    "pivotsub2.query('Country == [\"Germany\"]')\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>Revenue</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Saarland</td>\n",
       "      <td>295</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Saarland</td>\n",
       "      <td>590</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Hessen</td>\n",
       "      <td>3125</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Nordrhein-Westfalen</td>\n",
       "      <td>209</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Hessen</td>\n",
       "      <td>558</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112754</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Hamburg</td>\n",
       "      <td>1190</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112854</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Saarland</td>\n",
       "      <td>840</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112940</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Hessen</td>\n",
       "      <td>1488</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112942</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Saarland</td>\n",
       "      <td>892</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113026</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Hessen</td>\n",
       "      <td>1190</td>\n",
       "      <td>2013.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2254 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Country                State  Revenue    Year\n",
       "48      Germany             Saarland      295  2013.0\n",
       "62      Germany             Saarland      590  2013.0\n",
       "94      Germany               Hessen     3125  2013.0\n",
       "136     Germany  Nordrhein-Westfalen      209  2013.0\n",
       "254     Germany               Hessen      558  2013.0\n",
       "...         ...                  ...      ...     ...\n",
       "112754  Germany              Hamburg     1190  2013.0\n",
       "112854  Germany             Saarland      840  2013.0\n",
       "112940  Germany               Hessen     1488  2013.0\n",
       "112942  Germany             Saarland      892  2013.0\n",
       "113026  Germany               Hessen     1190  2013.0\n",
       "\n",
       "[2254 rows x 4 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"pivotSub2 = worksheet[[\"Country\", \"State\", \"Revenue\", \"Year\"]]\n",
    "pivotSub2 = pivotSub2.loc[(pivotSub2.Year == 2013) & (pivotSub2.Country == \"Germany\")]\n",
    "pivotSub2\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State\n",
       "Saarland               343277\n",
       "Hessen                 316229\n",
       "Nordrhein-Westfalen    288261\n",
       "Hamburg                277548\n",
       "Bayern                 193034\n",
       "Brandenburg             34355\n",
       "Name: Revenue, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = worksheet.loc[(worksheet.Country == \"Germany\") & (worksheet.Year == 2013)]\n",
    "df = df.groupby([\"State\"]).Revenue.sum().sort_values(ascending=False)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>Revenue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frame Size</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38.0</th>\n",
       "      <td>7934102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40.0</th>\n",
       "      <td>1810719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42.0</th>\n",
       "      <td>8385348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44.0</th>\n",
       "      <td>6487682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46.0</th>\n",
       "      <td>7231838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48.0</th>\n",
       "      <td>8197548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50.0</th>\n",
       "      <td>1875166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52.0</th>\n",
       "      <td>5957787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54.0</th>\n",
       "      <td>1600407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56.0</th>\n",
       "      <td>3158805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58.0</th>\n",
       "      <td>3111467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60.0</th>\n",
       "      <td>1744136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62.0</th>\n",
       "      <td>4287129</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Revenue\n",
       "Frame Size         \n",
       "38.0        7934102\n",
       "40.0        1810719\n",
       "42.0        8385348\n",
       "44.0        6487682\n",
       "46.0        7231838\n",
       "48.0        8197548\n",
       "50.0        1875166\n",
       "52.0        5957787\n",
       "54.0        1600407\n",
       "56.0        3158805\n",
       "58.0        3111467\n",
       "60.0        1744136\n",
       "62.0        4287129"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"third = worksheet[[\"Frame Size\", \"Revenue\"]]\n",
    "third = pd.pivot_table(third, index=[\"Frame Size\"], \n",
    "                            values=[\"Revenue\"], \n",
    "                            aggfunc=np.sum)\n",
    "third\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Frame Size\n",
       "42.0    8385348\n",
       "48.0    8197548\n",
       "38.0    7934102\n",
       "46.0    7231838\n",
       "44.0    6487682\n",
       "52.0    5957787\n",
       "62.0    4287129\n",
       "56.0    3158805\n",
       "58.0    3111467\n",
       "50.0    1875166\n",
       "40.0    1810719\n",
       "60.0    1744136\n",
       "54.0    1600407\n",
       "Name: Revenue, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "third = worksheet[[\"Frame Size\", \"Revenue\"]]\n",
    "third = third.groupby([\"Frame Size\"]).Revenue.sum().sort_values(ascending=False)\n",
    "third"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age Group\n",
       "Seniors (64+)             308042\n",
       "Youth (<25)             11723199\n",
       "Young Adults (25-34)    30655614\n",
       "Adults (35-64)          42584153\n",
       "Name: Revenue, dtype: int64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fourth = worksheet[[\"Age Group\", \"Revenue\"]]\n",
    "fourth = fourth.groupby([\"Age Group\"]).Revenue.sum().sort_values()\n",
    "fourth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is where the exercises move to plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aecc673d60>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "first = worksheet[[\"Country\", \"Revenue\", \"Year\"]]\n",
    "first = first.groupby([\"Year\", \"Country\"]).Revenue.sum()\n",
    "first = first.unstack(level=\"Country\")\n",
    "first\n",
    "sb.lineplot(data=first)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aecd596220>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "second = worksheet[[\"Product Category\", \"Revenue\", \"Year\"]]\n",
    "second = second.groupby([\"Year\", \"Product Category\"]).Revenue.sum()\n",
    "second = second.unstack(level=\"Product Category\")\n",
    "second\n",
    "sb.lineplot(data=second)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aecd6c94f0>"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "third = worksheet[[\"Age Group\", \"Revenue\", \"Year\"]]\n",
    "third = third.groupby([\"Year\", \"Age Group\"]).Revenue.sum()\n",
    "third = third.unstack(level=\"Age Group\")\n",
    "third\n",
    "sb.lineplot(data=third)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1aecd4c0790>"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fourth = worksheet[[\"Frame Size\", \"Revenue\", \"Year\"]]\n",
    "fourth = fourth.groupby([\"Year\", \"Frame Size\"]).Revenue.sum()\n",
    "fourth = fourth.unstack(level=\"Frame Size\")\n",
    "fourth = fourth.dropna()\n",
    "sb.barplot(data=fourth)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'fourth = worksheet[[\"Frame Size\", \"Revenue\", \"Year\"]]\\nfourth = fourth.groupby([\"Frame Size\"]).Revenue.sum()\\nfourth = fourth.sort_values(ascending=False)\\nfourth.columns = [\"Frame Size\", \"Revenue\"]\\nfourth'"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"fourth = worksheet[[\"Frame Size\", \"Revenue\", \"Year\"]]\n",
    "fourth = fourth.groupby([\"Frame Size\"]).Revenue.sum()\n",
    "fourth = fourth.sort_values(ascending=False)\n",
    "fourth.columns = [\"Frame Size\", \"Revenue\"]\n",
    "fourth\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}