Pareto Chart.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "144,339 order lines\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>DATE FORMAT</th>\n",
       "      <th>ORDER_NUMBER</th>\n",
       "      <th>SKU</th>\n",
       "      <th>BOX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>835220</td>\n",
       "      <td>290731</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>835220</td>\n",
       "      <td>373272</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>835214</td>\n",
       "      <td>362101</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>835215</td>\n",
       "      <td>215515</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>835215</td>\n",
       "      <td>287995</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  DATE FORMAT  ORDER_NUMBER     SKU  BOX\n",
       "0  2017-01-01        835220  290731  1.0\n",
       "1  2017-01-01        835220  373272  1.0\n",
       "2  2017-01-01        835214  362101  1.0\n",
       "3  2017-01-01        835215  215515  1.0\n",
       "4  2017-01-01        835215  287995  1.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import\n",
    "df = pd.read_excel('In/1-2017.xlsx')\n",
    "print(\"{:,} order lines\".format(len(df)))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pareto Analysis for 4,864 unique SKU\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>SKU</th>\n",
       "      <th>BOX</th>\n",
       "      <th>CumSum</th>\n",
       "      <th>%CumSum</th>\n",
       "      <th>%SKU</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>359803</td>\n",
       "      <td>4810.0</td>\n",
       "      <td>4810.0</td>\n",
       "      <td>2.911658</td>\n",
       "      <td>0.020559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>290478</td>\n",
       "      <td>4132.0</td>\n",
       "      <td>8942.0</td>\n",
       "      <td>5.412898</td>\n",
       "      <td>0.041118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>366639</td>\n",
       "      <td>4104.0</td>\n",
       "      <td>13046.0</td>\n",
       "      <td>7.897190</td>\n",
       "      <td>0.061678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>359958</td>\n",
       "      <td>4062.0</td>\n",
       "      <td>17108.0</td>\n",
       "      <td>10.356058</td>\n",
       "      <td>0.082237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>253500</td>\n",
       "      <td>3879.0</td>\n",
       "      <td>20987.0</td>\n",
       "      <td>12.704149</td>\n",
       "      <td>0.102796</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      SKU     BOX   CumSum    %CumSum      %SKU\n",
       "0  359803  4810.0   4810.0   2.911658  0.020559\n",
       "1  290478  4132.0   8942.0   5.412898  0.041118\n",
       "2  366639  4104.0  13046.0   7.897190  0.061678\n",
       "3  359958  4062.0  17108.0  10.356058  0.082237\n",
       "4  253500  3879.0  20987.0  12.704149  0.102796"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# BOX/SKU\n",
    "df_par = pd.DataFrame(df.groupby(['SKU'])['BOX'].sum())\n",
    "df_par.columns = ['BOX']\n",
    "\n",
    "# Sort Values\n",
    "df_par.sort_values(['BOX'], ascending = False, inplace = True)\n",
    "df_par.reset_index(inplace = True)\n",
    "\n",
    "# Cumulative Sum \n",
    "df_par['CumSum'] = df_par['BOX'].cumsum()\n",
    "\n",
    "# % CumSum\n",
    "df_par['%CumSum'] = (100 * df_par['CumSum']/df_par['BOX'].sum())\n",
    "\n",
    "# % SKU\n",
    "df_par['%SKU'] = (100 * (df_par.index + 1).astype(float)/(df_par.index.max() + 1))\n",
    "\n",
    "# 80% Volume\n",
    "df_par80 = df_par[df_par['%CumSum'] > 80].copy()\n",
    "perc_sku80 = df_par[df_par['%CumSum'] > 80]['%SKU'].min()\n",
    "perc_sum80 = df_par[df_par['%CumSum'] > 80]['%CumSum'].min()\n",
    "\n",
    "# 20% SKU\n",
    "df_sku20 = df_par[df_par['%SKU'] > 20].copy()\n",
    "perc_sku20 = df_sku20['%SKU'].min()\n",
    "perc_sum20 = df_sku20['%CumSum'].min()\n",
    "\n",
    "# 5% SKU\n",
    "df_sku5 = df_par[df_par['%SKU'] > 5].copy()\n",
    "perc_sku5 = df_par[df_par['%SKU'] > 5]['%SKU'].min()\n",
    "perc_sum5 = df_par[df_par['%SKU'] > 5]['%CumSum'].min()\n",
    "\n",
    "print(\"Pareto Analysis for {:,} unique SKU\".format(len(df_par)))\n",
    "df_par.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pareto Chart Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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O4bQjxzgPnyRJUpWKiAdSSjO721d9LYAvPARzR3e/7/zr9s6VtvCGvfOndWfu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\nY3C9Hw9JkiSpmvgXvnjkuc1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SOSmlGcBo4GTgv4ALgOURcX5Jq65iDxWN/jlzytj8CpEk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\nrkJggg==\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_par.plot(x='%SKU', y='%CumSum', figsize = (15,7), color = 'tab:blue', legend = False, linewidth=3)\n",
    "plt.xlabel('(%) SKU',fontsize=15)\n",
    "plt.ylabel('(%) Boxes',fontsize=15)\n",
    "plt.title('Pareto Analysis by Boxes Qty', fontsize = 15)\n",
    "# 5% SKU\n",
    "ax.axhline(perc_sum5 , color=\"tab:red\", linestyle=\"--\", linewidth = 2.0)\n",
    "ax.axvline(perc_sku5, color=\"tab:red\", linestyle=\"--\", linewidth = 2.0)\n",
    "# 80% Volume\n",
    "ax.axhline(perc_sum80 , color=\"tab:green\", linestyle=\"--\", linewidth = 2.0)\n",
    "ax.axvline(perc_sku80, color=\"tab:green\", linestyle=\"--\", linewidth = 2.0)\n",
    "# 20% SKU\n",
    "ax.axhline(perc_sum20 , color=\"tab:orange\", linestyle=\"--\", linewidth = 2.0)\n",
    "ax.axvline(perc_sku20, color=\"tab:orange\", linestyle=\"--\", linewidth = 2.0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
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