{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "daily-impression",
   "metadata": {},
   "source": [
    "# Load some data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "transparent-diversity",
   "metadata": {},
   "outputs": [],
   "source": [
    "import hotstepper as hs\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "biblical-gates",
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   Row ID        Order ID Order Date  Ship Date       Ship Mode Customer ID  \\\n",
       "0       1  CA-2016-152156 2016-11-08 2016-11-11    Second Class    CG-12520   \n",
       "1       2  CA-2016-152156 2016-11-08 2016-11-11    Second Class    CG-12520   \n",
       "2       3  CA-2016-138688 2016-06-12 2016-06-16    Second Class    DV-13045   \n",
       "3       4  US-2015-108966 2015-10-11 2015-10-18  Standard Class    SO-20335   \n",
       "4       5  US-2015-108966 2015-10-11 2015-10-18  Standard Class    SO-20335   \n",
       "\n",
       "     Customer Name    Segment        Country             City  ...  \\\n",
       "0      Claire Gute   Consumer  United States        Henderson  ...   \n",
       "1      Claire Gute   Consumer  United States        Henderson  ...   \n",
       "2  Darrin Van Huff  Corporate  United States      Los Angeles  ...   \n",
       "3   Sean O'Donnell   Consumer  United States  Fort Lauderdale  ...   \n",
       "4   Sean O'Donnell   Consumer  United States  Fort Lauderdale  ...   \n",
       "\n",
       "  Postal Code  Region       Product ID         Category Sub-Category  \\\n",
       "0       42420   South  FUR-BO-10001798        Furniture    Bookcases   \n",
       "1       42420   South  FUR-CH-10000454        Furniture       Chairs   \n",
       "2       90036    West  OFF-LA-10000240  Office Supplies       Labels   \n",
       "3       33311   South  FUR-TA-10000577        Furniture       Tables   \n",
       "4       33311   South  OFF-ST-10000760  Office Supplies      Storage   \n",
       "\n",
       "                                        Product Name     Sales  Quantity  \\\n",
       "0                  Bush Somerset Collection Bookcase  261.9600         2   \n",
       "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.9400         3   \n",
       "2  Self-Adhesive Address Labels for Typewriters b...   14.6200         2   \n",
       "3      Bretford CR4500 Series Slim Rectangular Table  957.5775         5   \n",
       "4                     Eldon Fold 'N Roll Cart System   22.3680         2   \n",
       "\n",
       "   Discount    Profit  \n",
       "0      0.00   41.9136  \n",
       "1      0.00  219.5820  \n",
       "2      0.00    6.8714  \n",
       "3      0.45 -383.0310  \n",
       "4      0.20    2.5164  \n",
       "\n",
       "[5 rows x 21 columns]"
      ],
      "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>Row ID</th>\n      <th>Order ID</th>\n      <th>Order Date</th>\n      <th>Ship Date</th>\n      <th>Ship Mode</th>\n      <th>Customer ID</th>\n      <th>Customer Name</th>\n      <th>Segment</th>\n      <th>Country</th>\n      <th>City</th>\n      <th>...</th>\n      <th>Postal Code</th>\n      <th>Region</th>\n      <th>Product ID</th>\n      <th>Category</th>\n      <th>Sub-Category</th>\n      <th>Product Name</th>\n      <th>Sales</th>\n      <th>Quantity</th>\n      <th>Discount</th>\n      <th>Profit</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>CA-2016-152156</td>\n      <td>2016-11-08</td>\n      <td>2016-11-11</td>\n      <td>Second Class</td>\n      <td>CG-12520</td>\n      <td>Claire Gute</td>\n      <td>Consumer</td>\n      <td>United States</td>\n      <td>Henderson</td>\n      <td>...</td>\n      <td>42420</td>\n      <td>South</td>\n      <td>FUR-BO-10001798</td>\n      <td>Furniture</td>\n      <td>Bookcases</td>\n      <td>Bush Somerset Collection Bookcase</td>\n      <td>261.9600</td>\n      <td>2</td>\n      <td>0.00</td>\n      <td>41.9136</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>CA-2016-152156</td>\n      <td>2016-11-08</td>\n      <td>2016-11-11</td>\n      <td>Second Class</td>\n      <td>CG-12520</td>\n      <td>Claire Gute</td>\n      <td>Consumer</td>\n      <td>United States</td>\n      <td>Henderson</td>\n      <td>...</td>\n      <td>42420</td>\n      <td>South</td>\n      <td>FUR-CH-10000454</td>\n      <td>Furniture</td>\n      <td>Chairs</td>\n      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n      <td>731.9400</td>\n      <td>3</td>\n      <td>0.00</td>\n      <td>219.5820</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>CA-2016-138688</td>\n      <td>2016-06-12</td>\n      <td>2016-06-16</td>\n      <td>Second Class</td>\n      <td>DV-13045</td>\n      <td>Darrin Van Huff</td>\n      <td>Corporate</td>\n      <td>United States</td>\n      <td>Los Angeles</td>\n      <td>...</td>\n      <td>90036</td>\n      <td>West</td>\n      <td>OFF-LA-10000240</td>\n      <td>Office Supplies</td>\n      <td>Labels</td>\n      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n      <td>14.6200</td>\n      <td>2</td>\n      <td>0.00</td>\n      <td>6.8714</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>US-2015-108966</td>\n      <td>2015-10-11</td>\n      <td>2015-10-18</td>\n      <td>Standard Class</td>\n      <td>SO-20335</td>\n      <td>Sean O'Donnell</td>\n      <td>Consumer</td>\n      <td>United States</td>\n      <td>Fort Lauderdale</td>\n      <td>...</td>\n      <td>33311</td>\n      <td>South</td>\n      <td>FUR-TA-10000577</td>\n      <td>Furniture</td>\n      <td>Tables</td>\n      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n      <td>957.5775</td>\n      <td>5</td>\n      <td>0.45</td>\n      <td>-383.0310</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>US-2015-108966</td>\n      <td>2015-10-11</td>\n      <td>2015-10-18</td>\n      <td>Standard Class</td>\n      <td>SO-20335</td>\n      <td>Sean O'Donnell</td>\n      <td>Consumer</td>\n      <td>United States</td>\n      <td>Fort Lauderdale</td>\n      <td>...</td>\n      <td>33311</td>\n      <td>South</td>\n      <td>OFF-ST-10000760</td>\n      <td>Office Supplies</td>\n      <td>Storage</td>\n      <td>Eldon Fold 'N Roll Cart System</td>\n      <td>22.3680</td>\n      <td>2</td>\n      <td>0.20</td>\n      <td>2.5164</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 21 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "df_data = pd.read_excel('https://raw.githubusercontent.com/TangleSpace/hotstepper-data/master/data/superstore.xls',\n",
    "                            parse_dates=['Order Date','Ship Date'])\n",
    "df_data.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sophisticated-pavilion",
   "metadata": {},
   "source": [
    "All we need as a start is to usually have the data in a Pandas Dataframe. Fun fact, if you set the field type of a datetime field within the dataframe, either with the parse_dates option when reading a file or explicitly using apply pd.to_datetime directly on the field, when you tell HotStepper to use that field as either a start or end step key, Hotstepper will automatically detect the type and set the use_datetime=True flag for you.\n",
    "\n",
    "For example,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "found-reynolds",
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "sales_orders = hs.Steps.read_dataframe(\n",
    "        df_data, #the dataframe we will read from\n",
    "        start='Order Date', #field name of the start keys\n",
    "        end='Ship Date', #field name of the end keys\n",
    "        weight='Profit') #field name of the weight or value of each step\n",
    "\n",
    "sales_orders.using_datetime()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "central-share",
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "    Metric                Value\n",
       "0    Count                 1434\n",
       "1     Mean               761.85\n",
       "2   Median                513.1\n",
       "3     Mode             -6927.35\n",
       "4      Std              1363.45\n",
       "5      Var           1859001.89\n",
       "6      Min             -6927.35\n",
       "7      25%               140.24\n",
       "8      75%              1195.82\n",
       "9      Max             11642.96\n",
       "10    Area          26750137.57\n",
       "11   Start  2014-01-03 00:00:00\n",
       "12     End  2018-01-05 00:00:00"
      ],
      "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>Metric</th>\n      <th>Value</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Count</td>\n      <td>1434</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Mean</td>\n      <td>761.85</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Median</td>\n      <td>513.1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Mode</td>\n      <td>-6927.35</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Std</td>\n      <td>1363.45</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Var</td>\n      <td>1859001.89</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Min</td>\n      <td>-6927.35</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>25%</td>\n      <td>140.24</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>75%</td>\n      <td>1195.82</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Max</td>\n      <td>11642.96</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Area</td>\n      <td>26750137.57</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Start</td>\n      <td>2014-01-03 00:00:00</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>End</td>\n      <td>2018-01-05 00:00:00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 4
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "ax = sales_orders.plot()\n",
    "sales_orders.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sudden-embassy",
   "metadata": {},
   "source": [
    "We can also load data into a Steps object a few more ways, let's go with a raw example and use the read_array method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "outer-canberra",
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1152x576 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "sales_orders_arr = hs.Steps.read_array(\n",
    "        start=df_data['Order Date'], #array of values for the start keys\n",
    "        end=df_data['Ship Date'],    #array of values for the end keys\n",
    "        weight=df_data['Profit'])    #array of values for the step weights or values\n",
    "\n",
    "ax = sales_orders_arr.plot()\n",
    "ax.axhline(0,color='r');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "disturbed-forth",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.8.5 64-bit (conda)",
   "metadata": {
    "interpreter": {
     "hash": "97ae724bfa85b9b34df7982b8bb8c7216f435b92902d749e4263f71162bea840"
    }
   }
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}