Pareto Analysis.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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "144,339 order lines for 59,372 orders\n"
     ]
    },
    {
     "data": {
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       "</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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import\n",
    "df = pd.read_excel('In/' + '1-2017.xlsx')\n",
    "print(\"{:,} order lines for {:,} orders\".format(len(df), df.ORDER_NUMBER.nunique()))\n",
    "df.head()"
   ]
  },
  {
   "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": [
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       "</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_par80['%SKU'].min()\n",
    "perc_sum80 = df_par80['%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",
    "# 10% SKU\n",
    "df_sku5 = df_par[df_par['%SKU'] > 5].copy()\n",
    "perc_sku5 = df_sku5['%SKU'].min()\n",
    "perc_sum5 = df_sku5['%CumSum'].min()\n",
    "\n",
    "print(\"Pareto Analysis for {:,} unique SKU\".format(len(df_par)))\n",
    "df_par.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>464</th>\n",
       "      <td>363345</td>\n",
       "      <td>56.0</td>\n",
       "      <td>125213.0</td>\n",
       "      <td>75.795712</td>\n",
       "      <td>9.560033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>325496</td>\n",
       "      <td>56.0</td>\n",
       "      <td>125269.0</td>\n",
       "      <td>75.829611</td>\n",
       "      <td>9.580592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>330664</td>\n",
       "      <td>56.0</td>\n",
       "      <td>125325.0</td>\n",
       "      <td>75.863509</td>\n",
       "      <td>9.601151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>330869</td>\n",
       "      <td>55.0</td>\n",
       "      <td>125380.0</td>\n",
       "      <td>75.896803</td>\n",
       "      <td>9.621711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>346552</td>\n",
       "      <td>55.0</td>\n",
       "      <td>125435.0</td>\n",
       "      <td>75.930096</td>\n",
       "      <td>9.642270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4859</th>\n",
       "      <td>299866</td>\n",
       "      <td>1.0</td>\n",
       "      <td>165194.0</td>\n",
       "      <td>99.997579</td>\n",
       "      <td>99.917763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4860</th>\n",
       "      <td>299636</td>\n",
       "      <td>1.0</td>\n",
       "      <td>165195.0</td>\n",
       "      <td>99.998184</td>\n",
       "      <td>99.938322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4861</th>\n",
       "      <td>299631</td>\n",
       "      <td>1.0</td>\n",
       "      <td>165196.0</td>\n",
       "      <td>99.998789</td>\n",
       "      <td>99.958882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4862</th>\n",
       "      <td>299630</td>\n",
       "      <td>1.0</td>\n",
       "      <td>165197.0</td>\n",
       "      <td>99.999395</td>\n",
       "      <td>99.979441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4863</th>\n",
       "      <td>1189</td>\n",
       "      <td>1.0</td>\n",
       "      <td>165198.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4400 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         SKU   BOX    CumSum     %CumSum        %SKU\n",
       "464   363345  56.0  125213.0   75.795712    9.560033\n",
       "465   325496  56.0  125269.0   75.829611    9.580592\n",
       "466   330664  56.0  125325.0   75.863509    9.601151\n",
       "467   330869  55.0  125380.0   75.896803    9.621711\n",
       "468   346552  55.0  125435.0   75.930096    9.642270\n",
       "...      ...   ...       ...         ...         ...\n",
       "4859  299866   1.0  165194.0   99.997579   99.917763\n",
       "4860  299636   1.0  165195.0   99.998184   99.938322\n",
       "4861  299631   1.0  165196.0   99.998789   99.958882\n",
       "4862  299630   1.0  165197.0   99.999395   99.979441\n",
       "4863    1189   1.0  165198.0  100.000000  100.000000\n",
       "\n",
       "[4400 rows x 5 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_par.tail(4400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18ae7ebc6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_par.plot(x='%SKU', y='%CumSum', figsize = (20,7.5))\n",
    "plt.xlabel('Percentage of SKU (%)',fontsize=15)\n",
    "plt.ylabel('Percentage of Boxes Ordered (%)',fontsize=15)\n",
    "plt.title('Pareto Analysis using Cumulative Sum of Boxes Prepared (%) = f(%SKU)', fontsize = 15)\n",
    "# 5% SKU\n",
    "ax.axhline(perc_sum5 , color=\"black\", linestyle=\"--\", linewidth = 1.0)\n",
    "ax.axvline(perc_sku5, color=\"black\", linestyle=\"--\", linewidth = 1.0)\n",
    "# 80% Volume\n",
    "ax.axhline(perc_sum80 , color=\"red\", linestyle=\"--\", linewidth = 1.0)\n",
    "ax.axvline(perc_sku80, color=\"red\", linestyle=\"--\", linewidth = 1.0)\n",
    "# 20% SKU\n",
    "ax.axhline(perc_sum20 , color=\"blue\", linestyle=\"--\", linewidth = 1.0)\n",
    "ax.axvline(perc_sku20, color=\"blue\", linestyle=\"--\", linewidth = 1.0)\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
}