Treemap.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import squarify\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.set_option('display.max_colwidth', 0)\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.options.display.max_seq_items = 2000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dataframe td {\n",
       "    white-space: nowrap;\n",
       "}\n",
       "</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<style>\n",
    ".dataframe td {\n",
    "    white-space: nowrap;\n",
    "}\n",
    "</style>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Data\n",
    "### Replenishments number by locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8,099 records\n"
     ]
    },
    {
     "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>DAY</th>\n",
       "      <th>SKU</th>\n",
       "      <th>Location</th>\n",
       "      <th>Alley</th>\n",
       "      <th>AlleyCell</th>\n",
       "      <th>Max</th>\n",
       "      <th>Min</th>\n",
       "      <th>WEEK-1</th>\n",
       "      <th>WEEK-2</th>\n",
       "      <th>WEEK-3</th>\n",
       "      <th>WEEK-4</th>\n",
       "      <th>WEEK-5</th>\n",
       "      <th>Capacity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FRI</td>\n",
       "      <td>2354</td>\n",
       "      <td>A0617402</td>\n",
       "      <td>A06</td>\n",
       "      <td>A0617</td>\n",
       "      <td>80.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FRI</td>\n",
       "      <td>5028</td>\n",
       "      <td>A0703503</td>\n",
       "      <td>A07</td>\n",
       "      <td>A0703</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>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FRI</td>\n",
       "      <td>8972</td>\n",
       "      <td>A0527401</td>\n",
       "      <td>A05</td>\n",
       "      <td>A0527</td>\n",
       "      <td>120.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FRI</td>\n",
       "      <td>10116</td>\n",
       "      <td>A0742501</td>\n",
       "      <td>A07</td>\n",
       "      <td>A0742</td>\n",
       "      <td>144.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FRI</td>\n",
       "      <td>11259</td>\n",
       "      <td>A0608303</td>\n",
       "      <td>A06</td>\n",
       "      <td>A0608</td>\n",
       "      <td>200.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   DAY    SKU  Location Alley AlleyCell    Max    Min  WEEK-1  WEEK-2  WEEK-3  \\\n",
       "0  FRI   2354  A0617402   A06     A0617   80.0   60.0     0.0     1.0     1.0   \n",
       "1  FRI   5028  A0703503   A07     A0703    0.0    0.0     0.0     0.0     1.0   \n",
       "2  FRI   8972  A0527401   A05     A0527  120.0   90.0     1.0     2.0     0.0   \n",
       "3  FRI  10116  A0742501   A07     A0742  144.0  108.0     0.0     1.0     0.0   \n",
       "4  FRI  11259  A0608303   A06     A0608  200.0  150.0     1.0     3.0     2.0   \n",
       "\n",
       "   WEEK-4  WEEK-5  Capacity  \n",
       "0     0.0     0.0        20  \n",
       "1     0.0     0.0         1  \n",
       "2     1.0     0.0        30  \n",
       "3     0.0     0.0        36  \n",
       "4     0.0     0.0        50  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_repl = pd.read_csv('In/Replenishment.csv', sep =',', index_col = 0)\n",
    "print('{:,} records'.format(len(df_repl)))\n",
    "df_repl.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot Split of Replenishment\n",
    "### Process the Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4,325 records\n"
     ]
    },
    {
     "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>Alley</th>\n",
       "      <th>WEEK-2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A06</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A05</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A07</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A06</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A05</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Alley  WEEK-2\n",
       "0   A06     1.0\n",
       "2   A05     2.0\n",
       "3   A07     1.0\n",
       "4   A06     3.0\n",
       "5   A05     1.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter by Week Number\n",
    "wk = 'WEEK-2'\n",
    "df_plot = df_repl[df_repl[wk]>0][['Alley', wk]].copy()\n",
    "print('{:,} records'.format(len(df_plot)))\n",
    "df_plot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['A06', 'A05', 'A07', 'A04', 'A02', 'R04', 'A08', 'A03', 'A01', 'R05', 'A09', 'A10', 'H02', 'B03', 'A11', 'R06']\n"
     ]
    }
   ],
   "source": [
    "# Unique Alleys\n",
    "print(list(df_plot.Alley.unique()))\n",
    "dict_color = dict(zip(['A05', 'A06', 'A04', 'A07', 'A02', 'R04', 'A01', 'A08', \n",
    "                       'R05', 'A03', 'A09', 'A10', 'H02', 'B03', 'A11', 'R06']\n",
    "                      , ['blue', 'red', 'yellow', 'orange', 'brown', 'cyan', 'tab:blue', 'tab:red', \n",
    " 'lightblue', 'tab:orange', 'grey', 'tab:green', 'purple', 'darkblue', 'white', 'magenta']))"
   ]
  },
  {
   "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>Alley</th>\n",
       "      <th>WEEK-2</th>\n",
       "      <th>%</th>\n",
       "      <th>LABEL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A01</td>\n",
       "      <td>127.0</td>\n",
       "      <td>0.57</td>\n",
       "      <td>A01\\n0.57%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A02</td>\n",
       "      <td>1238.0</td>\n",
       "      <td>5.52</td>\n",
       "      <td>A02\\n5.52%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A03</td>\n",
       "      <td>1077.0</td>\n",
       "      <td>4.80</td>\n",
       "      <td>A03\\n4.8%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A04</td>\n",
       "      <td>3020.0</td>\n",
       "      <td>13.46</td>\n",
       "      <td>A04\\n13.46%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A05</td>\n",
       "      <td>899.0</td>\n",
       "      <td>4.01</td>\n",
       "      <td>A05\\n4.01%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Alley  WEEK-2      %        LABEL\n",
       "0   A01   127.0   0.57   A01\\n0.57%\n",
       "1   A02  1238.0   5.52   A02\\n5.52%\n",
       "2   A03  1077.0   4.80    A03\\n4.8%\n",
       "3   A04  3020.0  13.46  A04\\n13.46%\n",
       "4   A05   899.0   4.01   A05\\n4.01%"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Processing\n",
    "df_plot = pd.DataFrame(df_plot.groupby(['Alley'])[wk].sum())\n",
    "df_plot['%'] = (100*df_plot[wk]/df_plot[wk].sum()).round(2)\n",
    "df_plot.reset_index(inplace = True)\n",
    "df_plot['LABEL'] = df_plot[['Alley','%']].apply(lambda t: t['Alley'] +'\\n' + str(t['%']) + '%', axis = 1)\n",
    "df_plot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkIAAAHjCAYAAADPD3DWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "plt.figure(figsize=(10, 8))\n",
    "tree_map = squarify.plot(sizes = df_plot[wk], color = df_plot['Alley'].map(dict_color)\n",
    "            , label = df_plot['LABEL']\n",
    "            , edgecolor=\"#222222\", text_kwargs={'fontsize':10})\n",
    "plt.axis('on')\n",
    "plt.title('Number of Replenishments per Picking Cell' +'\\n', fontsize=12)\n",
    "tree_map.axes.xaxis.set_visible(False)\n",
    "tree_map.axes.yaxis.set_visible(False)\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}