├── In
    └── Files.rar
├── Pareto Chart.ipynb
├── Treemap.ipynb
├── Sankey Chart.ipynb
└── Stacked Bar Plot.ipynb


/In/Files.rar:
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https://raw.githubusercontent.com/samirsaci/visuals_analysis/HEAD/In/Files.rar


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/Pareto Chart.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {
  7 |     "collapsed": true
  8 |    },
  9 |    "outputs": [],
 10 |    "source": [
 11 |     "import pandas as pd\n",
 12 |     "import matplotlib.pyplot as plt"
 13 |    ]
 14 |   },
 15 |   {
 16 |    "cell_type": "code",
 17 |    "execution_count": 2,
 18 |    "metadata": {
 19 |     "collapsed": true
 20 |    },
 21 |    "outputs": [],
 22 |    "source": [
 23 |     "pd.set_option('display.max_colwidth', 0)\n",
 24 |     "pd.set_option('display.max_columns', None)\n",
 25 |     "pd.options.display.max_seq_items = 2000"
 26 |    ]
 27 |   },
 28 |   {
 29 |    "cell_type": "code",
 30 |    "execution_count": 3,
 31 |    "metadata": {},
 32 |    "outputs": [
 33 |     {
 34 |      "data": {
 35 |       "text/html": [
 36 |        "<style>\n",
 37 |        ".dataframe td {\n",
 38 |        "    white-space: nowrap;\n",
 39 |        "}\n",
 40 |        "</style>"
 41 |       ],
 42 |       "text/plain": [
 43 |        "<IPython.core.display.HTML object>"
 44 |       ]
 45 |      },
 46 |      "metadata": {},
 47 |      "output_type": "display_data"
 48 |     }
 49 |    ],
 50 |    "source": [
 51 |     "%%html\n",
 52 |     "<style>\n",
 53 |     ".dataframe td {\n",
 54 |     "    white-space: nowrap;\n",
 55 |     "}\n",
 56 |     "</style>"
 57 |    ]
 58 |   },
 59 |   {
 60 |    "cell_type": "markdown",
 61 |    "metadata": {},
 62 |    "source": [
 63 |     "### Import Data"
 64 |    ]
 65 |   },
 66 |   {
 67 |    "cell_type": "code",
 68 |    "execution_count": 21,
 69 |    "metadata": {},
 70 |    "outputs": [
 71 |     {
 72 |      "name": "stdout",
 73 |      "output_type": "stream",
 74 |      "text": [
 75 |       "144,339 order lines\n"
 76 |      ]
 77 |     },
 78 |     {
 79 |      "data": {
 80 |       "text/html": [
 81 |        "<div>\n",
 82 |        "<style scoped>\n",
 83 |        "    .dataframe tbody tr th:only-of-type {\n",
 84 |        "        vertical-align: middle;\n",
 85 |        "    }\n",
 86 |        "\n",
 87 |        "    .dataframe tbody tr th {\n",
 88 |        "        vertical-align: top;\n",
 89 |        "    }\n",
 90 |        "\n",
 91 |        "    .dataframe thead th {\n",
 92 |        "        text-align: right;\n",
 93 |        "    }\n",
 94 |        "</style>\n",
 95 |        "<table border=\"1\" class=\"dataframe\">\n",
 96 |        "  <thead>\n",
 97 |        "    <tr style=\"text-align: right;\">\n",
 98 |        "      <th></th>\n",
 99 |        "      <th>DATE FORMAT</th>\n",
100 |        "      <th>ORDER_NUMBER</th>\n",
101 |        "      <th>SKU</th>\n",
102 |        "      <th>BOX</th>\n",
103 |        "    </tr>\n",
104 |        "  </thead>\n",
105 |        "  <tbody>\n",
106 |        "    <tr>\n",
107 |        "      <th>0</th>\n",
108 |        "      <td>2017-01-01</td>\n",
109 |        "      <td>835220</td>\n",
110 |        "      <td>290731</td>\n",
111 |        "      <td>1.0</td>\n",
112 |        "    </tr>\n",
113 |        "    <tr>\n",
114 |        "      <th>1</th>\n",
115 |        "      <td>2017-01-01</td>\n",
116 |        "      <td>835220</td>\n",
117 |        "      <td>373272</td>\n",
118 |        "      <td>1.0</td>\n",
119 |        "    </tr>\n",
120 |        "    <tr>\n",
121 |        "      <th>2</th>\n",
122 |        "      <td>2017-01-01</td>\n",
123 |        "      <td>835214</td>\n",
124 |        "      <td>362101</td>\n",
125 |        "      <td>1.0</td>\n",
126 |        "    </tr>\n",
127 |        "    <tr>\n",
128 |        "      <th>3</th>\n",
129 |        "      <td>2017-01-01</td>\n",
130 |        "      <td>835215</td>\n",
131 |        "      <td>215515</td>\n",
132 |        "      <td>1.0</td>\n",
133 |        "    </tr>\n",
134 |        "    <tr>\n",
135 |        "      <th>4</th>\n",
136 |        "      <td>2017-01-01</td>\n",
137 |        "      <td>835215</td>\n",
138 |        "      <td>287995</td>\n",
139 |        "      <td>1.0</td>\n",
140 |        "    </tr>\n",
141 |        "  </tbody>\n",
142 |        "</table>\n",
143 |        "</div>"
144 |       ],
145 |       "text/plain": [
146 |        "  DATE FORMAT  ORDER_NUMBER     SKU  BOX\n",
147 |        "0  2017-01-01        835220  290731  1.0\n",
148 |        "1  2017-01-01        835220  373272  1.0\n",
149 |        "2  2017-01-01        835214  362101  1.0\n",
150 |        "3  2017-01-01        835215  215515  1.0\n",
151 |        "4  2017-01-01        835215  287995  1.0"
152 |       ]
153 |      },
154 |      "execution_count": 21,
155 |      "metadata": {},
156 |      "output_type": "execute_result"
157 |     }
158 |    ],
159 |    "source": [
160 |     "# Import\n",
161 |     "df = pd.read_excel('In/1-2017.xlsx')\n",
162 |     "print(\"{:,} order lines\".format(len(df)))\n",
163 |     "df.head()"
164 |    ]
165 |   },
166 |   {
167 |    "cell_type": "markdown",
168 |    "metadata": {},
169 |    "source": [
170 |     "### Processing"
171 |    ]
172 |   },
173 |   {
174 |    "cell_type": "code",
175 |    "execution_count": 3,
176 |    "metadata": {},
177 |    "outputs": [
178 |     {
179 |      "name": "stdout",
180 |      "output_type": "stream",
181 |      "text": [
182 |       "Pareto Analysis for 4,864 unique SKU\n"
183 |      ]
184 |     },
185 |     {
186 |      "data": {
187 |       "text/html": [
188 |        "<div>\n",
189 |        "<style scoped>\n",
190 |        "    .dataframe tbody tr th:only-of-type {\n",
191 |        "        vertical-align: middle;\n",
192 |        "    }\n",
193 |        "\n",
194 |        "    .dataframe tbody tr th {\n",
195 |        "        vertical-align: top;\n",
196 |        "    }\n",
197 |        "\n",
198 |        "    .dataframe thead th {\n",
199 |        "        text-align: right;\n",
200 |        "    }\n",
201 |        "</style>\n",
202 |        "<table border=\"1\" class=\"dataframe\">\n",
203 |        "  <thead>\n",
204 |        "    <tr style=\"text-align: right;\">\n",
205 |        "      <th></th>\n",
206 |        "      <th>SKU</th>\n",
207 |        "      <th>BOX</th>\n",
208 |        "      <th>CumSum</th>\n",
209 |        "      <th>%CumSum</th>\n",
210 |        "      <th>%SKU</th>\n",
211 |        "    </tr>\n",
212 |        "  </thead>\n",
213 |        "  <tbody>\n",
214 |        "    <tr>\n",
215 |        "      <th>0</th>\n",
216 |        "      <td>359803</td>\n",
217 |        "      <td>4810.0</td>\n",
218 |        "      <td>4810.0</td>\n",
219 |        "      <td>2.911658</td>\n",
220 |        "      <td>0.020559</td>\n",
221 |        "    </tr>\n",
222 |        "    <tr>\n",
223 |        "      <th>1</th>\n",
224 |        "      <td>290478</td>\n",
225 |        "      <td>4132.0</td>\n",
226 |        "      <td>8942.0</td>\n",
227 |        "      <td>5.412898</td>\n",
228 |        "      <td>0.041118</td>\n",
229 |        "    </tr>\n",
230 |        "    <tr>\n",
231 |        "      <th>2</th>\n",
232 |        "      <td>366639</td>\n",
233 |        "      <td>4104.0</td>\n",
234 |        "      <td>13046.0</td>\n",
235 |        "      <td>7.897190</td>\n",
236 |        "      <td>0.061678</td>\n",
237 |        "    </tr>\n",
238 |        "    <tr>\n",
239 |        "      <th>3</th>\n",
240 |        "      <td>359958</td>\n",
241 |        "      <td>4062.0</td>\n",
242 |        "      <td>17108.0</td>\n",
243 |        "      <td>10.356058</td>\n",
244 |        "      <td>0.082237</td>\n",
245 |        "    </tr>\n",
246 |        "    <tr>\n",
247 |        "      <th>4</th>\n",
248 |        "      <td>253500</td>\n",
249 |        "      <td>3879.0</td>\n",
250 |        "      <td>20987.0</td>\n",
251 |        "      <td>12.704149</td>\n",
252 |        "      <td>0.102796</td>\n",
253 |        "    </tr>\n",
254 |        "  </tbody>\n",
255 |        "</table>\n",
256 |        "</div>"
257 |       ],
258 |       "text/plain": [
259 |        "      SKU     BOX   CumSum    %CumSum      %SKU\n",
260 |        "0  359803  4810.0   4810.0   2.911658  0.020559\n",
261 |        "1  290478  4132.0   8942.0   5.412898  0.041118\n",
262 |        "2  366639  4104.0  13046.0   7.897190  0.061678\n",
263 |        "3  359958  4062.0  17108.0  10.356058  0.082237\n",
264 |        "4  253500  3879.0  20987.0  12.704149  0.102796"
265 |       ]
266 |      },
267 |      "execution_count": 3,
268 |      "metadata": {},
269 |      "output_type": "execute_result"
270 |     }
271 |    ],
272 |    "source": [
273 |     "# BOX/SKU\n",
274 |     "df_par = pd.DataFrame(df.groupby(['SKU'])['BOX'].sum())\n",
275 |     "df_par.columns = ['BOX']\n",
276 |     "\n",
277 |     "# Sort Values\n",
278 |     "df_par.sort_values(['BOX'], ascending = False, inplace = True)\n",
279 |     "df_par.reset_index(inplace = True)\n",
280 |     "\n",
281 |     "# Cumulative Sum \n",
282 |     "df_par['CumSum'] = df_par['BOX'].cumsum()\n",
283 |     "\n",
284 |     "# % CumSum\n",
285 |     "df_par['%CumSum'] = (100 * df_par['CumSum']/df_par['BOX'].sum())\n",
286 |     "\n",
287 |     "# % SKU\n",
288 |     "df_par['%SKU'] = (100 * (df_par.index + 1).astype(float)/(df_par.index.max() + 1))\n",
289 |     "\n",
290 |     "# 80% Volume\n",
291 |     "df_par80 = df_par[df_par['%CumSum'] > 80].copy()\n",
292 |     "perc_sku80 = df_par[df_par['%CumSum'] > 80]['%SKU'].min()\n",
293 |     "perc_sum80 = df_par[df_par['%CumSum'] > 80]['%CumSum'].min()\n",
294 |     "\n",
295 |     "# 20% SKU\n",
296 |     "df_sku20 = df_par[df_par['%SKU'] > 20].copy()\n",
297 |     "perc_sku20 = df_sku20['%SKU'].min()\n",
298 |     "perc_sum20 = df_sku20['%CumSum'].min()\n",
299 |     "\n",
300 |     "# 5% SKU\n",
301 |     "df_sku5 = df_par[df_par['%SKU'] > 5].copy()\n",
302 |     "perc_sku5 = df_par[df_par['%SKU'] > 5]['%SKU'].min()\n",
303 |     "perc_sum5 = df_par[df_par['%SKU'] > 5]['%CumSum'].min()\n",
304 |     "\n",
305 |     "print(\"Pareto Analysis for {:,} unique SKU\".format(len(df_par)))\n",
306 |     "df_par.head()"
307 |    ]
308 |   },
309 |   {
310 |    "cell_type": "markdown",
311 |    "metadata": {},
312 |    "source": [
313 |     "### Pareto Chart Plot"
314 |    ]
315 |   },
316 |   {
317 |    "cell_type": "code",
318 |    "execution_count": 20,
319 |    "metadata": {},
320 |    "outputs": [
321 |     {
322 |      "data": {
323 |       "image/png": 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O4bQjxzgPnyRJUpWKiAdSSjO721d9LYAvPARzR3e/7/zr9s6VtvCGvfOndWfu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\nY3C9Hw9JkiSpmvgXvnjkuc1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SOSmlGcBo4GTgv4ALgOURcX5Jq65iDxWN/jlzytj8CpEk\nSZJUdXprAfwh8LaU0vaeDkgpJWBZYflcRJwJ2G/xAD34zKaO56cfZQCUJEmS1H/2GQBTSv++vydM\nKf3uwMupbSklHiq6/+8MA6AkSZKkfnTAE8FHxGDgBCCAx1JKzf1WVY1asWEnG3c0ATB66GCOmTA8\n54okSZIkVZMDCoAR8UrgZmBIYdkaEZellH7dn8XVmoee3dv987Qjx1BXFyV9v8Ouuaak568UV738\nqrxLKK3zr8u7AkmSJA0QB9oC+F/AR1JKN0dEPfBvwFfJBoPRAXqwKACWo/vn2Etnl/w9KsGsE2bl\nXUJpzbw87wokSZI0QPQ2CuhtETG1m12TyAaIIaXUCtxV2KaD8OAzmzuen3H0mNzqkCRJklSdemsB\nfBB4ICKuB/65aDTQW4EfRsT/AsOAvwYWlK7M6rezqYVlq7PpEyOyLqCltunmeUD1twTOf3w+UMUt\ngQtvyB5tCZQkSVIv9tkCmFK6FngJcDjweES0/4X5AbJ7AF8HvAL4PPC+EtZZ9Zau2kZbNv87x0wY\nzsjGwSV/z9VXX83qq68u+fvk7dr7ruXa+67Nu4zSufND2SJJkiT1otd7AFNKK4F3RcTZwHUR8X7g\ngymlLwBfKHWBtWLJC1s6ns84fHSOlUiSJEmqVvtsASyWUvptSulssta+myPipog4snSl1ZbFL2zt\neH7y5FE5ViJJkiSpWvUaACPidRHxmYj4XES8E/gf4ETgKeCRiLgmIoaWutBq92hRC+DJk20BlCRJ\nktT/ehsF9CrgFmAk0Ap8CrgtpbQjpfSPwBnAdOCxQjjUAWhubePx1ds71m0BlCRJklQKvd0D+H7g\nz1NKtwBExJeAJyPiyJTScymlFcCsiHg18DngOyWttko9sWY7Ta1tABw+ZihjhjXkXJEkSZKkatRb\nF9BtwJSi9aOBAHYUH5RS+iXw0n6trIYs7tT909Y/SZIkSaXRWwvgR4BvR8QcYBdwCnBVSmlj1wNT\nSqkE9dWEzgPAlO/+v2nLlpbtvfK0aM6ivEsorblbej9GkiRJopcAmFK6LSKmAGcDDcDDKaXl5Sis\nlixZ5QigkiRJkkqvL/MAbgC+X4ZaalJKicfXbOtYP2nSyByrkSRJklTNehsF9F0RUb8/J4yI4yLi\nVQdXVu1Yt20Pm3c2AzBiyCAOH1O+GTWWX3Qxyy+6uGzvl5fZd8xm9h2z8y6jdL56brZIkiRJveit\nBfDDwCcj4tvALSmlR7o7KCLGA28ELgNeA/y/fq2yij2+Zu/0D8cdMoKIKNt7716ypGzvlaelG6v8\nXsdV3f5nKUmSJL1Ib/cAnh4RlwIfAP4xIrYDS4H1wB5gDDAVOArYRDZJ/F+mlJ4vZdHVpLj75wmH\njsixEkmSJEnVri/3AN4M3BwRxwKvI5v8/TBgOLAGuAf4DXB3Sqn5YIopdDddCDyfUjo/IqYCNwHj\ngQeAd6WUmg7mPSrNE2uLA6D3/0mSJEkqnV4DYLuU0lPAUyWsBeBvyFoY24fC/AzwuZTSTRHxFeA9\nwJdLXENZPbbaAChJkiSpPHqbCL5sIuII4C3A1wrrAfwRcEvhkBuBt+VSXImklHii6B5AA6AkSZKk\nUqqYAAhcB3wMaCusjwc2p5RaCusrgcO7e2FEXBERCyNi4bp160peaH9ZvXU32/ZklzeycRCHjhqS\nc0WSJEmSqlmfu4CWUkScD6xNKT0QEeft7+tTStcD1wPMnDkz9W91pVM8AuiJh44s6wigAGNmzSrr\n++Xl4uOrfKqLM+bkXYEkSZIGiIoIgMArgbdGxJuBRrJ7AD8PjImIQYVWwCOAqhpddPm6vQHw2Inl\nHwF00ievLft75mHuK+bmXUJpvfULeVcgSZKkAaIiuoCmlP4hpXRESmkK2VyCP08p/QnwC+CSwmFz\ngO/lVGJJPLNxZ8fzoycMy7ESSZIkSbWgIgLgPvw98OGIeJLsnsCv51xPv3p2w94AOGX88LK//65H\nF7Pr0cVlf99yW7xhMYs3VPF1vvBQtkiSJEm9qJQuoB1SSncDdxeePw2cmWc9pbRiw46O50eNK38L\n4IpLssbVacuWlv29y+myOy8DYNGcRTlXUiLXn5c9zt2SaxmSJEmqfJXeAli1WtsSz23c1bF+9Hi7\ngEqSJEkqLQNgTlZv3U1TazbjxfjhDYxsHJxzRZIkSZKqnQEwJ88Ud/+09U+SJElSGRgAc5L3ADCS\nJEmSao8BMCcrigJgHgPASJIkSao9BsCcPLtxbxdQB4CRJEmSVA4VNw1ErSgeATSvFsApt9ySy/uW\n203n35R3CaV1xd15VyBJkqQBwgCYk1Vb9gbAyWOG5lLD0Bkn5/K+5Xby+Cq/zsmn512BJEmSBgi7\ngOZgd3Mr67c3AVAXcMjIITlXJEmSJKkWGABzsHrL7o7nh41qZFB9Pr+GVVdexaorr8rlvctp7r1z\nmXvv3LzLKJ3bP5gtkiRJUi8MgDl4oaj756Scun8CbJ4/n83z5+f2/uWy4IkFLHhiQd5llM6DN2aL\nJEmS1AsDYA5Wbd7bAjhpdGOOlUiSJEmqJQbAHFTCADCSJEmSao8BMAfPF7UATrYFUJIkSVKZGABz\nsKpC7gGUJEmSVFsMgDkoHgV08mgDoCRJkqTycCL4HKzfvqfj+cQc5wBsnD49t/cup2njpuVdQmlN\nOjXvCiRJkjRAGADLrK0tsXFHU8f62OGDc6tl6q1VPDVCkXkXzMu7hNJ67z15VyBJkqQBwi6gZbZ5\nVzNtKXs+snEQQwbV51uQJEmSpJphACyzjTv2dv8cP7whx0okSZIk1RoDYJlt2L63++e4nAPg0pOm\nsfSkKr8/DjjlxlM45cZT8i6jdOaOzhZJkiSpFwbAMiu+/2/c8PwGgJEkSZJUewyAZbahKADaBVSS\nJElSORkAy6xTC+AIA6AkSZKk8jEAltlGWwAlSZIk5cQAWGYbdlTOIDCSJEmSaosBsMyKp4EwAEqS\nJEkqp0F5F1BriqeBGJ/zKKCHXXNNru9fLle9/Kq8Syit86/LuwJJkiQNEAbAMqukQWDGXjo71/cv\nl1knzMq7hNKaeXneFUiSJGmAsAtoGaWU2LTTQWAkSZIk5cMAWEZbd7fQ3JoAGNZQT+Pg+lzr2XTz\nPDbdPC/XGsph/uPzmf/4/LzLKJ2FN2SLJEmS1Au7gJbR5qLWv7HD8m/9W3311UD1dwW99r5rgSru\nCnrnh7JHu4JKkiSpF7YAltHmnc0dz8cOH5xjJZIkSZJqkQGwjDbv2hsAxwzNvwVQkiRJUm0xAJZR\ncRfQ0UNtAZQkSZJUXgbAMtpS1AI4epgBUJIkSVJ5GQDLaMvO4i6gBkBJkiRJ5WUALKNO9wDaAihJ\nkiSpzJwGoow276ysQWCmLVuadwllsWjOorxLKK25W/KuQJIkSQOELYBltGVX0SAwtgBKkiRJKjMD\nYBlt9h5ASZIkSTkyAJZR53sA8+8Cuvyii1l+0cV5l1Fys++Yzew7ZuddRul89dxskSRJknrhPYBl\ntLUoAI4amv+PfveSJXmXUBZLN1b5vY6rHsm7AkmSJA0QtgCW0c6m1o7nw4fkHwAlSZIk1RYDYJmk\nlNjR1NKxPmxwfY7VSJIkSapFBsAy2dXcSkrZ88bBdQyq90cvSZIkqbxMIWWyfc/e1r/hDXb/lCRJ\nklR+BsAy2bnH+/8kSZIk5cskUibFLYDDGirj/r8xs2blXUJZXHx8lU91ccacvCuQJEnSAGEALJPi\nEUBHVEgL4KRPXpt3CWUx9xVz8y6htN76hbwrkCRJ0gBhF9Ay6TQCaIUEQEmSJEm1xQBYJjs6DQJT\nGV1Adz26mF2PLs67jJJbvGExizdU8XW+8FC2SJIkSb2wKapMKnEQmBWXXALAtGVLc66ktC678zIA\nFs1ZlHMlJXL9ednj3C25liFJkqTKZwtgmWyvwBZASZIkSbXFAFgmO4vuAayUFkBJkiRJtcUAWCbb\nK7ALqCRJkqTaYgAsk04tgHYBlSRJkpQDA2CZ7ChqAXQaCEmSJEl5MACWSfE0EJUyEbwkSZKk2mIS\nKZNOE8FXSBfQKbfckncJZXHT+TflXUJpXXF33hVIkiRpgDAAlkkltgAOnXFy3iWUxcnjq/w6J5+e\ndwWSJEkaIOwCWiY7m/beAzi0QloAJUmSJNUWA2CZ7G4uCoCDKyMArrryKlZdeVXeZZTc3HvnMvfe\nuXmXUTq3fzBbJEmSpF5URACMiCMj4hcRsSQiFkfE3xS2j4uIn0bEE4XHsXnXeqB2N7d1PG+skAC4\nef58Ns+fn3cZJbfgiQUseGJB3mWUzoM3ZoskSZLUi4oIgEAL8JGU0nTgbOB9ETEd+DhwV0rpeOCu\nwvqAtKsCWwAlSZIk1ZaKCIAppVUppQcLz7cBS4HDgQuB9qaNG4G35VJgPyjuAlopLYCSJEmSaktF\nBMBiETEFOB24Hzg0pbSqsGs1cGgPr7kiIhZGxMJ169aVp9D9kFJiT8veLqBDBlXcj12SJElSDaio\nJBIRI4AFwIdSSluL96WUEpC6e11K6fqU0syU0syJEyeWodL9Uxz+GgbVUVcXOVYjSZIkqVZVTACM\niMFk4e9/U0q3FjaviYhJhf2TgLV51XcwdhVNAdFo658kSZKknFTEjOQREcDXgaUppf8o2nU7MAf4\ndOHxezmUd9B2t1TmHICN06fnXUJZTBs3Le8SSmvSqXlXIEmSpAGiIgIg8ErgXcCiiHi4sO0TZMFv\nXkS8B3gGmJ1PeQenEqeAAJh6axVPjVBk3gXz8i6htN57T94VSJIkaYCoiACYUvo10NONca8tZy2l\n0GkE0EGVEwAlSZIk1RZvSCuDXZ2mgPBHLkmSJCkfppEyKG4BHFJBXUCXnjSNpSdV+f1xwCk3nsIp\nN56SdxmlM3d0tkiSJEm9MACWwZ6iewCHVlAAlCRJklRbDIBlsNsuoJIkSZIqgGmkDIqngaikUUAl\nSZIk1RYDYBnsaiqaBsJRQCVJkiTlxABYBnYBlSRJklQJTCNl0Ny6twWwYZA/ckmSJEn5qIiJ4Ktd\nS1vqeD6ovnIC4GHXXJN3CWVx1cuvyruE0jr/urwrkCRJ0gBhACyD4hbAwXWRYyWdjb10dt4llMWs\nE2blXUJpzbw87wokSZI0QFROc1QVKw6AldQCKEmSJKm2mEbKoKW1uAto5bQAbrp5Hptunpd3GSU3\n//H5zH98ft5llM7CG7JFkiRJ6oVdQMuguSgADq6rnMy9+uqrgervCnrtfdcCVdwV9M4PZY92BZUk\nSVIvKieNVLGWtuIuoJXTAihJkiSpthgAy6C5tTJHAZUkSZJUW0wjZdBSoaOASpIkSaotBsAyqNR5\nACVJkiTVFtNIGXSaB9B7ACVJkiTlxABYBsXTQAy2BVCSJElSTpwGogw6jQJaQfcATlu2NO8SymLR\nnEV5l1Bac7fkXYEkSZIGCJujyqDZFkBJkiRJFcA0UgbOAyhJkiSpEhgAy6DTPIB1lfMjX37RxSy/\n6OK8yyi52XfMZvYds/Muo3S+em62SJIkSb3wHsAyaKnQUUB3L1mSdwllsXRjld/ruOqRvCuQJEnS\nAFE5zVFVzHkAJUmSJFUC00gZdO4CWjktgJIkSZJqiwGwDDp3AfVHLkmSJCkfppEy6NwF1BZASZIk\nSfkwAJZBc3ELYAWNAipJkiSptjgKaBm0tFZmC+CYWbPyLqEsLj6+yqe6OGNO3hVIkiRpgDAAlkGl\nTgQ/6ZPX5l1CWcx9xdy8Syitt34h7wokSZI0QNgfsQyKRwG1C6gkSZKkvJhGyqD4HsBKagHc9ehi\ndj26OO8ySm7xhsUs3lDF1/nCQ9kiSZIk9cIuoGVQfA9gJU0DseKSSwCYtmxpzpWU1mV3XgbAojmL\ncq6kRK4/L3ucuyXXMiRJklT5KieNVLHm4nsAnQhekiRJUk4MgCXW2pZIexsAqTcASpIkScqJAbDE\nOs0BWB9EGAAlSZIk5cMAWGItbUVzADoCqCRJkqQcmUhKrKVLC6AkSZIk5cUAWGLNFToCqCRJkqTa\n4zQQJdbSVplzAAJMueWWvEsoi5vOvynvEkrrirvzrkCSJEkDhAGwxIrnAKy0ewCHzjg57xLK4uTx\nVX6dk0/PuwJJkiQNEJWVSKpQ11FAJUmSJCkvBsAS6zQKaIXdA7jqyqtYdeVVeZdRcnPvncvce+fm\nXUbp3P7BbJEkSZJ6UVmJpAoVtwAOqrBJ4DfPn8/m+fPzLqPkFjyxgAVPLMi7jNJ58MZskSRJknph\nACyxFkcBlSRJklQhTCQlVsmjgEqSJEmqLQbAEus0D2CFjQIqSZIkqbaYSEqs0zQQtgBKkiRJypEB\nsMSaO3UB9cctSZIkKT9OBF9inQaBqbBRQBunT8+7hLKYNm5a3iWU1qRT865AkiRJA4QBsMRaWit3\nEJipt1bx1AhF5l0wL+8SSuu99+RdgSRJkgYI+ySWWHMFTwQvSZIkqbaYSEqsuWVvC2CldQGVJEmS\nVFsMgCXWUsGDwCw9aRpLT6ry++OAU248hVNuPCXvMkpn7uhskSRJknpRWYmkCrUliELD3+AKuwdQ\nkiRJUm1xEJgSe8eZR/GOM4+itS2RUur9BZIkSZJUIgbAMqmvC8AWQEmSJEn5sQuoJEmSJNUIA6Ak\nSZIk1QgDoCRJkiTVCO8BrGGHXXNN3iWUxVUvvyrvEkrr/OvyrkCSJEkDRFT6yJQR8Ubg80A98LWU\n0qf3dfzMmTPTwoULy1KbJEmSJFWaiHggpTSzu30V3QU0IuqBLwFvAqYD74iI6flWJUmSJEkDU0UH\nQOBM4MmU0tMppSbgJuDCnGuqGptunsemm+flXUbJzX98PvMfn593GaWz8IZskSRJknpR6fcAHg48\nV7S+Ejir60ERcQVwBcBRRx1VnsqqwOqrrwZg7KWzc66ktK6971oAZp0wK+dKSuTOD2WPMy/PtQxJ\nkiRVvkpvAeyTlNL1KaWZKaWZEydOzLscSZIkSapIlR4AnweOLFo/orBNkiRJkrSfKj0A/h44PiKm\nRkQDcBlwe841SZIkSdKAVNH3AKaUWiLi/cCPyaaB+EZKaXHOZUmSJEnSgFTRARAgpfQD4Ad51yFJ\nkiRJA12ldwGVJEmSJPWTSCnlXUO/ioh1wDN519GNCcD6vItQ1fLzpVLy86VS8zOmUvLzpVKq1M/X\n0SmlbqdHqLoAWKkiYmFKaWbedag6+flSKfn5Uqn5GVMp+flSKQ3Ez5ddQCVJkiSpRhgAJUmSJKlG\nGADL5/q8C1BV8/OlUvLzpVLzM6ZS8vOlUhpwny/vAZQkSZKkGmELoCRJkiTVCAOgJEmSJNUIA2CJ\nRcQbI+KxiHgyIj6edz0a+CLiyIj4RUQsiYjFEfE3he3jIuKnEfFE4XFs3rVq4IqI+oh4KCLuLKxP\njYj7C99lN0dEQ941amCKiDERcUtELIuIpRHxcr+/1F8i4m8L/zY+GhHfjYhGv790MCLiGxGxNiIe\nLdrW7XdWZL5Q+Kz9ISLOyK/ynhkASygi6oEvAW8CpgPviIjp+ValKtACfCSlNB04G3hf4XP1ceCu\nlNLxwF2FdelA/Q2wtGj9M8DnUkrHAZuA9+RSlarB54EfpZROAk4l+5z5/aWDFhGHAx8EZqaUZgD1\nwGX4/aWD803gjV229fSd9Sbg+MJyBfDlMtW4XwyApXUm8GRK6emUUhNwE3BhzjVpgEsprUopPVh4\nvo3sj6fDyT5bNxYOuxF4Wy4FasCLiCOAtwBfK6wH8EfALYVD/HzpgETEaOBc4OsAKaWmlNJm/P5S\n/xkEDI2IQcAwYBV+f+kgpJTuATZ22dzTd9aFwLdS5rfAmIiYVJZC94MBsLQOB54rWl9Z2Cb1i4iY\nApwO3A8cmlJaVdi1Gjg0r7o04F0HfAxoK6yPBzanlFoK636X6UBNBdYBNxS6GH8tIobj95f6QUrp\neeCzwLNkwW8L8AB+f6n/9fSdNSD+9jcASgNURIwAFgAfSiltLd6XsvldnONF+y0izgfWppQeyLsW\nVaVBwBnAl1NKpwM76NLd0+8vHajCfVgXkv2PhsnAcF7cdU/qVwPxO8sAWFrPA0cWrR9R2CYdlIgY\nTBb+/jeldGth85r2bgaFx7V51acB7ZXAWyNiBVm39T8iu2drTKFLFfhdpgO3EliZUrq/sH4LWSD0\n+0v94XXA8pTSupRSM3Ar2Xea31/qbz19Zw2Iv/0NgKX1e+D4wuhTDWQ3It+ec00a4Ar3Y30dWJpS\n+o+iXbcDcwrP5wDfK3dtGvhSSv+QUjoipTSF7Dvr5ymlPwF+AVxSOMzPlw5ISmk18FxEnFjY9Fpg\nCX5/qX88C5wdEcMK/1a2f778/lJ/6+k763bgzwqjgZ4NbCnqKloxImu1VKlExJvJ7qepB76RUvpU\nvhVpoIuIc4BfAYvYe4/WJ8juA5wHHAU8A8xOKXW9aVnqs4g4D/hoSun8iDiGrEVwHPAQ8KcppT05\nlqcBKiJOIxtgqAF4Gric7H9I+/2lgxYR1wCXko2Y/RDw52T3YPn9pQMSEd8FzgMmAGuAq4Hb6OY7\nq/A/Hr5I1vV4J3B5SmlhDmXvkwFQkiRJkmqEXUAlSZIkqUYYACVJkiSpRhgAJUmSJKlGGAAlSZIk\nqUYYACVJkiSpRhgAJUlVrTAf08MRMado29si4umIWBcRV3fzmqsi4kXztkbERyPirj6+7/CI+GRE\nPBYRuyJiTUT8MiLeU3TMeRGRImJGl9f+ZWH71RExpfD8/G7eY0Zh33l9qUmSJAOgJKnazSab/+s7\nABExAfgf4KvA+4APR8Tr2w+OiCOAvwU+3M25vgqc0cfAtQC4gmxOqDcDHwQeLTzvUUS8G/gv4NMp\npWv68D6SJPXZoLwLkCSpxD4IfDul1FxYPxt4JqX0GYCIeA3wx8BPCvs/DXwtpfRk1xOllLZFxALg\nA8DdPb1hRBwPvIFscuD5RbtuLkwU3NPr3gF8Hfh8Sukf+nh9kiT1mQFQklS1IuI44BXA+4s2NwC7\nitZ3FrYREWcDrwNO2MdpFwC3RcS4lNLGHo4ZU3hc3XVHSin1UOtFwLeAr6aU/nYf7y9J0gGzC6gk\nqZq9FtgBPFK07WHglIh4TURMBS4GFhZa5j4P/FNKaes+znkfMBh41T6OeazwvtdFxOsjorGXOt8C\n3EQWAN/Xy7GSJB0wA6AkqZq9FFiaUmpr35BSehr4FPBz4GlgMfBd4F1kLYHf2NcJU0qbgWeBM/dx\nzFbgL4ATgR8DWyPinoj4ix66gH4a+APwFz21EEqS1B8MgJKkanYYsL7rxpTStcBEYEpK6S1AI/Av\nwN8AgyLiK4URQhdFxDndnHd94dw9Sil9Fzga+H9krXsnANdTGIymi5+QhdV39/G6JEk6IAZASVI1\nawT2dLcjpbQ+pfRMYfUfgN+klO4B/hI4lSywfYps4JYhXV6+p3DufUopbUgp3ZBS+jPgSOAG4LKI\nOLXLoX8H3AxcHxEXdNnXUnis7+Yt6rscI0nSPhkAJUnVbCN7B2TpVkRMAf4a+Fhh02uA/00pbUop\n3QQM4cWDwowpnLvPCqOQfq6welKX3W3AnwG/IAucryzat6Gwv7sWx0mFx7X7U4skqXYZACVJ1ewx\nYGovx3wW+FJKaUXRtmEAEVFPFgA77tuLiDrgKODxnk4YESMjYmg3u44vPK7puiOl1ARcBCwB7oiI\nkwvbdwEPABd2c74LyUYafdGUFZIkdcdpICRJ1ew3wFURMTGltK7rzoh4Ndm8gHOKNv8S+FBELAH+\nCNhGFiTbnQiMKJy7JycCt0fEN4B7yaaaOA34R7JRSH/d3YsK8wy+uXDuH0fEK1JKzwJzgTsj4iay\nAWuagfOB9wJ/XTzIjSRJ+2ILoCSpmt1N1lXzjV13FFryrgP+IaW0o2jXl4G7gP8B3gS8I6VUfB/h\nG8lGD31oH+/7FPA1sgnmvw38gOzewm8Ar00p9XjPXkppLfB6svv7fhwR41NKPyCbKuIIskFkbqUQ\nXFNKX9lHHZIkdRKONi1JqmYR8XnguMJon/1xvvuA76eU/rk/zidJUjkZACVJVS0ijiC7X++0lFKP\n9+318VxnAT8CphbmA5QkaUCxC6gkqaqllFaSzcU3qbdj+2AcWbfLzf1wLkmSys4WQEmSJEmqEbYA\nSpIkSVKNMABKkiRJUo0wAEqSJElSjTAASpIkSVKNMABKkiRJUo34/wGE/rqj7kAtOwAAAABJRU5E\nrkJggg==\n",
324 |       "text/plain": [
325 |        "<Figure size 1080x504 with 1 Axes>"
326 |       ]
327 |      },
328 |      "metadata": {},
329 |      "output_type": "display_data"
330 |     }
331 |    ],
332 |    "source": [
333 |     "ax = df_par.plot(x='%SKU', y='%CumSum', figsize = (15,7), color = 'tab:blue', legend = False, linewidth=3)\n",
334 |     "plt.xlabel('(%) SKU',fontsize=15)\n",
335 |     "plt.ylabel('(%) Boxes',fontsize=15)\n",
336 |     "plt.title('Pareto Analysis by Boxes Qty', fontsize = 15)\n",
337 |     "# 5% SKU\n",
338 |     "ax.axhline(perc_sum5 , color=\"tab:red\", linestyle=\"--\", linewidth = 2.0)\n",
339 |     "ax.axvline(perc_sku5, color=\"tab:red\", linestyle=\"--\", linewidth = 2.0)\n",
340 |     "# 80% Volume\n",
341 |     "ax.axhline(perc_sum80 , color=\"tab:green\", linestyle=\"--\", linewidth = 2.0)\n",
342 |     "ax.axvline(perc_sku80, color=\"tab:green\", linestyle=\"--\", linewidth = 2.0)\n",
343 |     "# 20% SKU\n",
344 |     "ax.axhline(perc_sum20 , color=\"tab:orange\", linestyle=\"--\", linewidth = 2.0)\n",
345 |     "ax.axvline(perc_sku20, color=\"tab:orange\", linestyle=\"--\", linewidth = 2.0)\n",
346 |     "plt.show()"
347 |    ]
348 |   },
349 |   {
350 |    "cell_type": "code",
351 |    "execution_count": null,
352 |    "metadata": {
353 |     "collapsed": true
354 |    },
355 |    "outputs": [],
356 |    "source": []
357 |   }
358 |  ],
359 |  "metadata": {
360 |   "kernelspec": {
361 |    "display_name": "Python 3",
362 |    "language": "python",
363 |    "name": "python3"
364 |   },
365 |   "language_info": {
366 |    "codemirror_mode": {
367 |     "name": "ipython",
368 |     "version": 3
369 |    },
370 |    "file_extension": ".py",
371 |    "mimetype": "text/x-python",
372 |    "name": "python",
373 |    "nbconvert_exporter": "python",
374 |    "pygments_lexer": "ipython3",
375 |    "version": "3.6.1"
376 |   }
377 |  },
378 |  "nbformat": 4,
379 |  "nbformat_minor": 2
380 | }
381 | 


--------------------------------------------------------------------------------
/Treemap.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {
  7 |     "collapsed": true
  8 |    },
  9 |    "outputs": [],
 10 |    "source": [
 11 |     "import numpy as np\n",
 12 |     "import pandas as pd\n",
 13 |     "import squarify\n",
 14 |     "import matplotlib\n",
 15 |     "import matplotlib.pyplot as plt"
 16 |    ]
 17 |   },
 18 |   {
 19 |    "cell_type": "code",
 20 |    "execution_count": 166,
 21 |    "metadata": {
 22 |     "collapsed": true
 23 |    },
 24 |    "outputs": [],
 25 |    "source": [
 26 |     "pd.set_option('display.max_colwidth', 0)\n",
 27 |     "pd.set_option('display.max_columns', None)\n",
 28 |     "pd.options.display.max_seq_items = 2000"
 29 |    ]
 30 |   },
 31 |   {
 32 |    "cell_type": "code",
 33 |    "execution_count": 167,
 34 |    "metadata": {},
 35 |    "outputs": [
 36 |     {
 37 |      "data": {
 38 |       "text/html": [
 39 |        "<style>\n",
 40 |        ".dataframe td {\n",
 41 |        "    white-space: nowrap;\n",
 42 |        "}\n",
 43 |        "</style>"
 44 |       ],
 45 |       "text/plain": [
 46 |        "<IPython.core.display.HTML object>"
 47 |       ]
 48 |      },
 49 |      "metadata": {},
 50 |      "output_type": "display_data"
 51 |     }
 52 |    ],
 53 |    "source": [
 54 |     "%%html\n",
 55 |     "<style>\n",
 56 |     ".dataframe td {\n",
 57 |     "    white-space: nowrap;\n",
 58 |     "}\n",
 59 |     "</style>"
 60 |    ]
 61 |   },
 62 |   {
 63 |    "cell_type": "markdown",
 64 |    "metadata": {},
 65 |    "source": [
 66 |     "## Import Data\n",
 67 |     "### Replenishments number by locations"
 68 |    ]
 69 |   },
 70 |   {
 71 |    "cell_type": "code",
 72 |    "execution_count": 4,
 73 |    "metadata": {},
 74 |    "outputs": [
 75 |     {
 76 |      "name": "stdout",
 77 |      "output_type": "stream",
 78 |      "text": [
 79 |       "8,099 records\n"
 80 |      ]
 81 |     },
 82 |     {
 83 |      "data": {
 84 |       "text/html": [
 85 |        "<div>\n",
 86 |        "<style scoped>\n",
 87 |        "    .dataframe tbody tr th:only-of-type {\n",
 88 |        "        vertical-align: middle;\n",
 89 |        "    }\n",
 90 |        "\n",
 91 |        "    .dataframe tbody tr th {\n",
 92 |        "        vertical-align: top;\n",
 93 |        "    }\n",
 94 |        "\n",
 95 |        "    .dataframe thead th {\n",
 96 |        "        text-align: right;\n",
 97 |        "    }\n",
 98 |        "</style>\n",
 99 |        "<table border=\"1\" class=\"dataframe\">\n",
100 |        "  <thead>\n",
101 |        "    <tr style=\"text-align: right;\">\n",
102 |        "      <th></th>\n",
103 |        "      <th>DAY</th>\n",
104 |        "      <th>SKU</th>\n",
105 |        "      <th>Location</th>\n",
106 |        "      <th>Alley</th>\n",
107 |        "      <th>AlleyCell</th>\n",
108 |        "      <th>Max</th>\n",
109 |        "      <th>Min</th>\n",
110 |        "      <th>WEEK-1</th>\n",
111 |        "      <th>WEEK-2</th>\n",
112 |        "      <th>WEEK-3</th>\n",
113 |        "      <th>WEEK-4</th>\n",
114 |        "      <th>WEEK-5</th>\n",
115 |        "      <th>Capacity</th>\n",
116 |        "    </tr>\n",
117 |        "  </thead>\n",
118 |        "  <tbody>\n",
119 |        "    <tr>\n",
120 |        "      <th>0</th>\n",
121 |        "      <td>FRI</td>\n",
122 |        "      <td>2354</td>\n",
123 |        "      <td>A0617402</td>\n",
124 |        "      <td>A06</td>\n",
125 |        "      <td>A0617</td>\n",
126 |        "      <td>80.0</td>\n",
127 |        "      <td>60.0</td>\n",
128 |        "      <td>0.0</td>\n",
129 |        "      <td>1.0</td>\n",
130 |        "      <td>1.0</td>\n",
131 |        "      <td>0.0</td>\n",
132 |        "      <td>0.0</td>\n",
133 |        "      <td>20</td>\n",
134 |        "    </tr>\n",
135 |        "    <tr>\n",
136 |        "      <th>1</th>\n",
137 |        "      <td>FRI</td>\n",
138 |        "      <td>5028</td>\n",
139 |        "      <td>A0703503</td>\n",
140 |        "      <td>A07</td>\n",
141 |        "      <td>A0703</td>\n",
142 |        "      <td>0.0</td>\n",
143 |        "      <td>0.0</td>\n",
144 |        "      <td>0.0</td>\n",
145 |        "      <td>0.0</td>\n",
146 |        "      <td>1.0</td>\n",
147 |        "      <td>0.0</td>\n",
148 |        "      <td>0.0</td>\n",
149 |        "      <td>1</td>\n",
150 |        "    </tr>\n",
151 |        "    <tr>\n",
152 |        "      <th>2</th>\n",
153 |        "      <td>FRI</td>\n",
154 |        "      <td>8972</td>\n",
155 |        "      <td>A0527401</td>\n",
156 |        "      <td>A05</td>\n",
157 |        "      <td>A0527</td>\n",
158 |        "      <td>120.0</td>\n",
159 |        "      <td>90.0</td>\n",
160 |        "      <td>1.0</td>\n",
161 |        "      <td>2.0</td>\n",
162 |        "      <td>0.0</td>\n",
163 |        "      <td>1.0</td>\n",
164 |        "      <td>0.0</td>\n",
165 |        "      <td>30</td>\n",
166 |        "    </tr>\n",
167 |        "    <tr>\n",
168 |        "      <th>3</th>\n",
169 |        "      <td>FRI</td>\n",
170 |        "      <td>10116</td>\n",
171 |        "      <td>A0742501</td>\n",
172 |        "      <td>A07</td>\n",
173 |        "      <td>A0742</td>\n",
174 |        "      <td>144.0</td>\n",
175 |        "      <td>108.0</td>\n",
176 |        "      <td>0.0</td>\n",
177 |        "      <td>1.0</td>\n",
178 |        "      <td>0.0</td>\n",
179 |        "      <td>0.0</td>\n",
180 |        "      <td>0.0</td>\n",
181 |        "      <td>36</td>\n",
182 |        "    </tr>\n",
183 |        "    <tr>\n",
184 |        "      <th>4</th>\n",
185 |        "      <td>FRI</td>\n",
186 |        "      <td>11259</td>\n",
187 |        "      <td>A0608303</td>\n",
188 |        "      <td>A06</td>\n",
189 |        "      <td>A0608</td>\n",
190 |        "      <td>200.0</td>\n",
191 |        "      <td>150.0</td>\n",
192 |        "      <td>1.0</td>\n",
193 |        "      <td>3.0</td>\n",
194 |        "      <td>2.0</td>\n",
195 |        "      <td>0.0</td>\n",
196 |        "      <td>0.0</td>\n",
197 |        "      <td>50</td>\n",
198 |        "    </tr>\n",
199 |        "  </tbody>\n",
200 |        "</table>\n",
201 |        "</div>"
202 |       ],
203 |       "text/plain": [
204 |        "   DAY    SKU  Location Alley AlleyCell    Max    Min  WEEK-1  WEEK-2  WEEK-3  \\\n",
205 |        "0  FRI   2354  A0617402   A06     A0617   80.0   60.0     0.0     1.0     1.0   \n",
206 |        "1  FRI   5028  A0703503   A07     A0703    0.0    0.0     0.0     0.0     1.0   \n",
207 |        "2  FRI   8972  A0527401   A05     A0527  120.0   90.0     1.0     2.0     0.0   \n",
208 |        "3  FRI  10116  A0742501   A07     A0742  144.0  108.0     0.0     1.0     0.0   \n",
209 |        "4  FRI  11259  A0608303   A06     A0608  200.0  150.0     1.0     3.0     2.0   \n",
210 |        "\n",
211 |        "   WEEK-4  WEEK-5  Capacity  \n",
212 |        "0     0.0     0.0        20  \n",
213 |        "1     0.0     0.0         1  \n",
214 |        "2     1.0     0.0        30  \n",
215 |        "3     0.0     0.0        36  \n",
216 |        "4     0.0     0.0        50  "
217 |       ]
218 |      },
219 |      "execution_count": 4,
220 |      "metadata": {},
221 |      "output_type": "execute_result"
222 |     }
223 |    ],
224 |    "source": [
225 |     "df_repl = pd.read_csv('In/Replenishment.csv', sep =',', index_col = 0)\n",
226 |     "print('{:,} records'.format(len(df_repl)))\n",
227 |     "df_repl.head()"
228 |    ]
229 |   },
230 |   {
231 |    "cell_type": "markdown",
232 |    "metadata": {},
233 |    "source": [
234 |     "## Plot Split of Replenishment\n",
235 |     "### Process the Dataframe"
236 |    ]
237 |   },
238 |   {
239 |    "cell_type": "code",
240 |    "execution_count": 17,
241 |    "metadata": {},
242 |    "outputs": [
243 |     {
244 |      "name": "stdout",
245 |      "output_type": "stream",
246 |      "text": [
247 |       "4,325 records\n"
248 |      ]
249 |     },
250 |     {
251 |      "data": {
252 |       "text/html": [
253 |        "<div>\n",
254 |        "<style scoped>\n",
255 |        "    .dataframe tbody tr th:only-of-type {\n",
256 |        "        vertical-align: middle;\n",
257 |        "    }\n",
258 |        "\n",
259 |        "    .dataframe tbody tr th {\n",
260 |        "        vertical-align: top;\n",
261 |        "    }\n",
262 |        "\n",
263 |        "    .dataframe thead th {\n",
264 |        "        text-align: right;\n",
265 |        "    }\n",
266 |        "</style>\n",
267 |        "<table border=\"1\" class=\"dataframe\">\n",
268 |        "  <thead>\n",
269 |        "    <tr style=\"text-align: right;\">\n",
270 |        "      <th></th>\n",
271 |        "      <th>Alley</th>\n",
272 |        "      <th>WEEK-2</th>\n",
273 |        "    </tr>\n",
274 |        "  </thead>\n",
275 |        "  <tbody>\n",
276 |        "    <tr>\n",
277 |        "      <th>0</th>\n",
278 |        "      <td>A06</td>\n",
279 |        "      <td>1.0</td>\n",
280 |        "    </tr>\n",
281 |        "    <tr>\n",
282 |        "      <th>2</th>\n",
283 |        "      <td>A05</td>\n",
284 |        "      <td>2.0</td>\n",
285 |        "    </tr>\n",
286 |        "    <tr>\n",
287 |        "      <th>3</th>\n",
288 |        "      <td>A07</td>\n",
289 |        "      <td>1.0</td>\n",
290 |        "    </tr>\n",
291 |        "    <tr>\n",
292 |        "      <th>4</th>\n",
293 |        "      <td>A06</td>\n",
294 |        "      <td>3.0</td>\n",
295 |        "    </tr>\n",
296 |        "    <tr>\n",
297 |        "      <th>5</th>\n",
298 |        "      <td>A05</td>\n",
299 |        "      <td>1.0</td>\n",
300 |        "    </tr>\n",
301 |        "  </tbody>\n",
302 |        "</table>\n",
303 |        "</div>"
304 |       ],
305 |       "text/plain": [
306 |        "  Alley  WEEK-2\n",
307 |        "0   A06     1.0\n",
308 |        "2   A05     2.0\n",
309 |        "3   A07     1.0\n",
310 |        "4   A06     3.0\n",
311 |        "5   A05     1.0"
312 |       ]
313 |      },
314 |      "execution_count": 17,
315 |      "metadata": {},
316 |      "output_type": "execute_result"
317 |     }
318 |    ],
319 |    "source": [
320 |     "# Filter by Week Number\n",
321 |     "wk = 'WEEK-2'\n",
322 |     "df_plot = df_repl[df_repl[wk]>0][['Alley', wk]].copy()\n",
323 |     "print('{:,} records'.format(len(df_plot)))\n",
324 |     "df_plot.head()"
325 |    ]
326 |   },
327 |   {
328 |    "cell_type": "code",
329 |    "execution_count": 18,
330 |    "metadata": {},
331 |    "outputs": [
332 |     {
333 |      "name": "stdout",
334 |      "output_type": "stream",
335 |      "text": [
336 |       "['A06', 'A05', 'A07', 'A04', 'A02', 'R04', 'A08', 'A03', 'A01', 'R05', 'A09', 'A10', 'H02', 'B03', 'A11', 'R06']\n"
337 |      ]
338 |     }
339 |    ],
340 |    "source": [
341 |     "# Unique Alleys\n",
342 |     "print(list(df_plot.Alley.unique()))\n",
343 |     "dict_color = dict(zip(['A05', 'A06', 'A04', 'A07', 'A02', 'R04', 'A01', 'A08', \n",
344 |     "                       'R05', 'A03', 'A09', 'A10', 'H02', 'B03', 'A11', 'R06']\n",
345 |     "                      , ['blue', 'red', 'yellow', 'orange', 'brown', 'cyan', 'tab:blue', 'tab:red', \n",
346 |     " 'lightblue', 'tab:orange', 'grey', 'tab:green', 'purple', 'darkblue', 'white', 'magenta']))"
347 |    ]
348 |   },
349 |   {
350 |    "cell_type": "code",
351 |    "execution_count": 19,
352 |    "metadata": {},
353 |    "outputs": [
354 |     {
355 |      "data": {
356 |       "text/html": [
357 |        "<div>\n",
358 |        "<style scoped>\n",
359 |        "    .dataframe tbody tr th:only-of-type {\n",
360 |        "        vertical-align: middle;\n",
361 |        "    }\n",
362 |        "\n",
363 |        "    .dataframe tbody tr th {\n",
364 |        "        vertical-align: top;\n",
365 |        "    }\n",
366 |        "\n",
367 |        "    .dataframe thead th {\n",
368 |        "        text-align: right;\n",
369 |        "    }\n",
370 |        "</style>\n",
371 |        "<table border=\"1\" class=\"dataframe\">\n",
372 |        "  <thead>\n",
373 |        "    <tr style=\"text-align: right;\">\n",
374 |        "      <th></th>\n",
375 |        "      <th>Alley</th>\n",
376 |        "      <th>WEEK-2</th>\n",
377 |        "      <th>%</th>\n",
378 |        "      <th>LABEL</th>\n",
379 |        "    </tr>\n",
380 |        "  </thead>\n",
381 |        "  <tbody>\n",
382 |        "    <tr>\n",
383 |        "      <th>0</th>\n",
384 |        "      <td>A01</td>\n",
385 |        "      <td>127.0</td>\n",
386 |        "      <td>0.57</td>\n",
387 |        "      <td>A01\\n0.57%</td>\n",
388 |        "    </tr>\n",
389 |        "    <tr>\n",
390 |        "      <th>1</th>\n",
391 |        "      <td>A02</td>\n",
392 |        "      <td>1238.0</td>\n",
393 |        "      <td>5.52</td>\n",
394 |        "      <td>A02\\n5.52%</td>\n",
395 |        "    </tr>\n",
396 |        "    <tr>\n",
397 |        "      <th>2</th>\n",
398 |        "      <td>A03</td>\n",
399 |        "      <td>1077.0</td>\n",
400 |        "      <td>4.80</td>\n",
401 |        "      <td>A03\\n4.8%</td>\n",
402 |        "    </tr>\n",
403 |        "    <tr>\n",
404 |        "      <th>3</th>\n",
405 |        "      <td>A04</td>\n",
406 |        "      <td>3020.0</td>\n",
407 |        "      <td>13.46</td>\n",
408 |        "      <td>A04\\n13.46%</td>\n",
409 |        "    </tr>\n",
410 |        "    <tr>\n",
411 |        "      <th>4</th>\n",
412 |        "      <td>A05</td>\n",
413 |        "      <td>899.0</td>\n",
414 |        "      <td>4.01</td>\n",
415 |        "      <td>A05\\n4.01%</td>\n",
416 |        "    </tr>\n",
417 |        "  </tbody>\n",
418 |        "</table>\n",
419 |        "</div>"
420 |       ],
421 |       "text/plain": [
422 |        "  Alley  WEEK-2      %        LABEL\n",
423 |        "0   A01   127.0   0.57   A01\\n0.57%\n",
424 |        "1   A02  1238.0   5.52   A02\\n5.52%\n",
425 |        "2   A03  1077.0   4.80    A03\\n4.8%\n",
426 |        "3   A04  3020.0  13.46  A04\\n13.46%\n",
427 |        "4   A05   899.0   4.01   A05\\n4.01%"
428 |       ]
429 |      },
430 |      "execution_count": 19,
431 |      "metadata": {},
432 |      "output_type": "execute_result"
433 |     }
434 |    ],
435 |    "source": [
436 |     "# Processing\n",
437 |     "df_plot = pd.DataFrame(df_plot.groupby(['Alley'])[wk].sum())\n",
438 |     "df_plot['%'] = (100*df_plot[wk]/df_plot[wk].sum()).round(2)\n",
439 |     "df_plot.reset_index(inplace = True)\n",
440 |     "df_plot['LABEL'] = df_plot[['Alley','%']].apply(lambda t: t['Alley'] +'\\n' + str(t['%']) + '%', axis = 1)\n",
441 |     "df_plot.head()"
442 |    ]
443 |   },
444 |   {
445 |    "cell_type": "code",
446 |    "execution_count": 22,
447 |    "metadata": {},
448 |    "outputs": [
449 |     {
450 |      "data": {
451 |       "image/png": 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DM1xW3RD/B8B/gRE4d51eibN46/no70ARAf0kEJGAisUJOPg/13f152qcCdLB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\nAAAASUVORK5CYII=\n",
452 |       "text/plain": [
453 |        "<Figure size 720x576 with 1 Axes>"
454 |       ]
455 |      },
456 |      "metadata": {},
457 |      "output_type": "display_data"
458 |     }
459 |    ],
460 |    "source": [
461 |     "# Plot\n",
462 |     "plt.figure(figsize=(10, 8))\n",
463 |     "tree_map = squarify.plot(sizes = df_plot[wk], color = df_plot['Alley'].map(dict_color)\n",
464 |     "            , label = df_plot['LABEL']\n",
465 |     "            , edgecolor=\"#222222\", text_kwargs={'fontsize':10})\n",
466 |     "plt.axis('on')\n",
467 |     "plt.title('Number of Replenishments per Picking Cell' +'\\n', fontsize=12)\n",
468 |     "tree_map.axes.xaxis.set_visible(False)\n",
469 |     "tree_map.axes.yaxis.set_visible(False)\n",
470 |     "plt.show()"
471 |    ]
472 |   }
473 |  ],
474 |  "metadata": {
475 |   "kernelspec": {
476 |    "display_name": "Python 3",
477 |    "language": "python",
478 |    "name": "python3"
479 |   },
480 |   "language_info": {
481 |    "codemirror_mode": {
482 |     "name": "ipython",
483 |     "version": 3
484 |    },
485 |    "file_extension": ".py",
486 |    "mimetype": "text/x-python",
487 |    "name": "python",
488 |    "nbconvert_exporter": "python",
489 |    "pygments_lexer": "ipython3",
490 |    "version": "3.6.1"
491 |   }
492 |  },
493 |  "nbformat": 4,
494 |  "nbformat_minor": 2
495 | }
496 | 


--------------------------------------------------------------------------------
/Sankey Chart.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {
  7 |     "collapsed": true
  8 |    },
  9 |    "outputs": [],
 10 |    "source": [
 11 |     "import pandas as pd\n",
 12 |     "import numpy as np\n",
 13 |     "import matplotlib.pyplot as plt\n",
 14 |     "from pySankey.sankey import sankey"
 15 |    ]
 16 |   },
 17 |   {
 18 |    "cell_type": "code",
 19 |    "execution_count": 2,
 20 |    "metadata": {
 21 |     "collapsed": true
 22 |    },
 23 |    "outputs": [],
 24 |    "source": [
 25 |     "pd.set_option('display.max_colwidth', 0)\n",
 26 |     "pd.set_option('display.max_columns', None)\n",
 27 |     "pd.options.display.max_seq_items = 2000"
 28 |    ]
 29 |   },
 30 |   {
 31 |    "cell_type": "code",
 32 |    "execution_count": 3,
 33 |    "metadata": {},
 34 |    "outputs": [
 35 |     {
 36 |      "data": {
 37 |       "text/html": [
 38 |        "<style>\n",
 39 |        ".dataframe td {\n",
 40 |        "    white-space: nowrap;\n",
 41 |        "}\n",
 42 |        "</style>"
 43 |       ],
 44 |       "text/plain": [
 45 |        "<IPython.core.display.HTML object>"
 46 |       ]
 47 |      },
 48 |      "metadata": {},
 49 |      "output_type": "display_data"
 50 |     }
 51 |    ],
 52 |    "source": [
 53 |     "%%html\n",
 54 |     "<style>\n",
 55 |     ".dataframe td {\n",
 56 |     "    white-space: nowrap;\n",
 57 |     "}\n",
 58 |     "</style>"
 59 |    ]
 60 |   },
 61 |   {
 62 |    "cell_type": "code",
 63 |    "execution_count": 4,
 64 |    "metadata": {
 65 |     "collapsed": true
 66 |    },
 67 |    "outputs": [],
 68 |    "source": [
 69 |     "colors = {\n",
 70 |     "    \"INDIA\": \"#f71b1b\",\n",
 71 |     "    \"BRAZIL\": \"#1b7ef7\",\n",
 72 |     "    \"JAPAN\": \"#f3f71b\",\n",
 73 |     "    \"GERMANY\": \"#12e23f\",\n",
 74 |     "    \"USA\": \"#f78c1b\"\n",
 75 |     "}"
 76 |    ]
 77 |   },
 78 |   {
 79 |    "cell_type": "markdown",
 80 |    "metadata": {
 81 |     "collapsed": true
 82 |    },
 83 |    "source": [
 84 |     "## Import Data"
 85 |    ]
 86 |   },
 87 |   {
 88 |    "cell_type": "markdown",
 89 |    "metadata": {},
 90 |    "source": [
 91 |     "### Production Quantity by From/To"
 92 |    ]
 93 |   },
 94 |   {
 95 |    "cell_type": "code",
 96 |    "execution_count": 5,
 97 |    "metadata": {},
 98 |    "outputs": [
 99 |     {
100 |      "name": "stdout",
101 |      "output_type": "stream",
102 |      "text": [
103 |       "25 lines\n"
104 |      ]
105 |     }
106 |    ],
107 |    "source": [
108 |     "# Production Quantity\n",
109 |     "df_prod = pd.read_csv('In/Sankey data.csv', index_col = 0, sep = ',')\n",
110 |     "print('{:,} lines'.format(len(df_prod)))"
111 |    ]
112 |   },
113 |   {
114 |    "cell_type": "markdown",
115 |    "metadata": {},
116 |    "source": [
117 |     "## Process Data Frame\n",
118 |     "### Color mapping"
119 |    ]
120 |   },
121 |   {
122 |    "cell_type": "code",
123 |    "execution_count": 6,
124 |    "metadata": {
125 |     "collapsed": true
126 |    },
127 |    "outputs": [],
128 |    "source": [
129 |     "colors = {\n",
130 |     "    \"INDIA\": \"#f71b1b\",\n",
131 |     "    \"BRAZIL\": \"#1b7ef7\",\n",
132 |     "    \"JAPAN\": \"#f3f71b\",\n",
133 |     "    \"GERMANY\": \"#12e23f\",\n",
134 |     "    \"USA\": \"#f78c1b\"\n",
135 |     "}"
136 |    ]
137 |   },
138 |   {
139 |    "cell_type": "markdown",
140 |    "metadata": {},
141 |    "source": [
142 |     "### Build the plot"
143 |    ]
144 |   },
145 |   {
146 |    "cell_type": "code",
147 |    "execution_count": 7,
148 |    "metadata": {},
149 |    "outputs": [
150 |     {
151 |      "data": {
152 |       "text/html": [
153 |        "<div>\n",
154 |        "<style scoped>\n",
155 |        "    .dataframe tbody tr th:only-of-type {\n",
156 |        "        vertical-align: middle;\n",
157 |        "    }\n",
158 |        "\n",
159 |        "    .dataframe tbody tr th {\n",
160 |        "        vertical-align: top;\n",
161 |        "    }\n",
162 |        "\n",
163 |        "    .dataframe thead th {\n",
164 |        "        text-align: right;\n",
165 |        "    }\n",
166 |        "</style>\n",
167 |        "<table border=\"1\" class=\"dataframe\">\n",
168 |        "  <thead>\n",
169 |        "    <tr style=\"text-align: right;\">\n",
170 |        "      <th></th>\n",
171 |        "      <th>index</th>\n",
172 |        "      <th>source</th>\n",
173 |        "      <th>target</th>\n",
174 |        "      <th>units</th>\n",
175 |        "    </tr>\n",
176 |        "  </thead>\n",
177 |        "  <tbody>\n",
178 |        "    <tr>\n",
179 |        "      <th>0</th>\n",
180 |        "      <td>12</td>\n",
181 |        "      <td>JAPAN</td>\n",
182 |        "      <td>JAPAN</td>\n",
183 |        "      <td>1500000.0</td>\n",
184 |        "    </tr>\n",
185 |        "    <tr>\n",
186 |        "      <th>1</th>\n",
187 |        "      <td>15</td>\n",
188 |        "      <td>BRAZIL</td>\n",
189 |        "      <td>USA</td>\n",
190 |        "      <td>1250000.0</td>\n",
191 |        "    </tr>\n",
192 |        "    <tr>\n",
193 |        "      <th>2</th>\n",
194 |        "      <td>18</td>\n",
195 |        "      <td>BRAZIL</td>\n",
196 |        "      <td>BRAZIL</td>\n",
197 |        "      <td>145000.0</td>\n",
198 |        "    </tr>\n",
199 |        "    <tr>\n",
200 |        "      <th>3</th>\n",
201 |        "      <td>20</td>\n",
202 |        "      <td>INDIA</td>\n",
203 |        "      <td>USA</td>\n",
204 |        "      <td>1550000.0</td>\n",
205 |        "    </tr>\n",
206 |        "    <tr>\n",
207 |        "      <th>4</th>\n",
208 |        "      <td>21</td>\n",
209 |        "      <td>INDIA</td>\n",
210 |        "      <td>GERMANY</td>\n",
211 |        "      <td>90000.0</td>\n",
212 |        "    </tr>\n",
213 |        "  </tbody>\n",
214 |        "</table>\n",
215 |        "</div>"
216 |       ],
217 |       "text/plain": [
218 |        "   index  source   target      units\n",
219 |        "0  12     JAPAN   JAPAN    1500000.0\n",
220 |        "1  15     BRAZIL  USA      1250000.0\n",
221 |        "2  18     BRAZIL  BRAZIL   145000.0 \n",
222 |        "3  20     INDIA   USA      1550000.0\n",
223 |        "4  21     INDIA   GERMANY  90000.0  "
224 |       ]
225 |      },
226 |      "execution_count": 7,
227 |      "metadata": {},
228 |      "output_type": "execute_result"
229 |     }
230 |    ],
231 |    "source": [
232 |     "# Keeping only positive values\n",
233 |     "df_plot = df_prod[df_prod['units']>0].reset_index()\n",
234 |     "df_plot.head()"
235 |    ]
236 |   },
237 |   {
238 |    "cell_type": "code",
239 |    "execution_count": 8,
240 |    "metadata": {},
241 |    "outputs": [
242 |     {
243 |      "data": {
244 |       "image/png": 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eKSLPAbgCwJtF5JOq+mMAUNVdIvJvnvPaALj9Cj4nIuOwa2QfAvByzxwE7wXwMQBtzsRk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\n",
245 |       "text/plain": [
246 |        "<Figure size 864x432 with 1 Axes>"
247 |       ]
248 |      },
249 |      "metadata": {},
250 |      "output_type": "display_data"
251 |     }
252 |    ],
253 |    "source": [
254 |     "# Plot\n",
255 |     "sankey(\n",
256 |     "    left = df_plot[\"source\"], right = df_plot[\"target\"], \n",
257 |     "    leftWeight= df_plot[\"units\"], rightWeight = df_plot[\"units\"], fontsize=12, colorDict=colors\n",
258 |     ")\n",
259 |     "plt.gcf().set_size_inches((12,6))\n",
260 |     "plt.title('Distribution of Production by Country')\n",
261 |     "plt.show()"
262 |    ]
263 |   }
264 |  ],
265 |  "metadata": {
266 |   "kernelspec": {
267 |    "display_name": "Python 3",
268 |    "language": "python",
269 |    "name": "python3"
270 |   },
271 |   "language_info": {
272 |    "codemirror_mode": {
273 |     "name": "ipython",
274 |     "version": 3
275 |    },
276 |    "file_extension": ".py",
277 |    "mimetype": "text/x-python",
278 |    "name": "python",
279 |    "nbconvert_exporter": "python",
280 |    "pygments_lexer": "ipython3",
281 |    "version": "3.6.1"
282 |   }
283 |  },
284 |  "nbformat": 4,
285 |  "nbformat_minor": 2
286 | }
287 | 


--------------------------------------------------------------------------------
/Stacked Bar Plot.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 30,
  6 |    "metadata": {
  7 |     "collapsed": true
  8 |    },
  9 |    "outputs": [],
 10 |    "source": [
 11 |     "import pandas as pd\n",
 12 |     "import matplotlib.pyplot as plt\n",
 13 |     "import datetime\n",
 14 |     "random.seed(1447)"
 15 |    ]
 16 |   },
 17 |   {
 18 |    "cell_type": "code",
 19 |    "execution_count": 2,
 20 |    "metadata": {
 21 |     "collapsed": true
 22 |    },
 23 |    "outputs": [],
 24 |    "source": [
 25 |     "pd.set_option('display.max_colwidth', 0)\n",
 26 |     "pd.set_option('display.max_columns', None)\n",
 27 |     "pd.options.display.max_seq_items = 2000"
 28 |    ]
 29 |   },
 30 |   {
 31 |    "cell_type": "code",
 32 |    "execution_count": 3,
 33 |    "metadata": {},
 34 |    "outputs": [
 35 |     {
 36 |      "data": {
 37 |       "text/html": [
 38 |        "<style>\n",
 39 |        ".dataframe td {\n",
 40 |        "    white-space: nowrap;\n",
 41 |        "}\n",
 42 |        "</style>"
 43 |       ],
 44 |       "text/plain": [
 45 |        "<IPython.core.display.HTML object>"
 46 |       ]
 47 |      },
 48 |      "metadata": {},
 49 |      "output_type": "display_data"
 50 |     }
 51 |    ],
 52 |    "source": [
 53 |     "%%html\n",
 54 |     "<style>\n",
 55 |     ".dataframe td {\n",
 56 |     "    white-space: nowrap;\n",
 57 |     "}\n",
 58 |     "</style>"
 59 |    ]
 60 |   },
 61 |   {
 62 |    "cell_type": "markdown",
 63 |    "metadata": {},
 64 |    "source": [
 65 |     "## Parameters\n",
 66 |     "### Shipments Data"
 67 |    ]
 68 |   },
 69 |   {
 70 |    "cell_type": "code",
 71 |    "execution_count": 25,
 72 |    "metadata": {},
 73 |    "outputs": [
 74 |     {
 75 |      "name": "stdout",
 76 |      "output_type": "stream",
 77 |      "text": [
 78 |       "10,000 lines\n"
 79 |      ]
 80 |     },
 81 |     {
 82 |      "data": {
 83 |       "text/html": [
 84 |        "<div>\n",
 85 |        "<style scoped>\n",
 86 |        "    .dataframe tbody tr th:only-of-type {\n",
 87 |        "        vertical-align: middle;\n",
 88 |        "    }\n",
 89 |        "\n",
 90 |        "    .dataframe tbody tr th {\n",
 91 |        "        vertical-align: top;\n",
 92 |        "    }\n",
 93 |        "\n",
 94 |        "    .dataframe thead th {\n",
 95 |        "        text-align: right;\n",
 96 |        "    }\n",
 97 |        "</style>\n",
 98 |        "<table border=\"1\" class=\"dataframe\">\n",
 99 |        "  <thead>\n",
100 |        "    <tr style=\"text-align: right;\">\n",
101 |        "      <th></th>\n",
102 |        "      <th>Test</th>\n",
103 |        "      <th>Order Time</th>\n",
104 |        "      <th>Order Date</th>\n",
105 |        "      <th>City</th>\n",
106 |        "      <th>Store</th>\n",
107 |        "      <th>#Shipment</th>\n",
108 |        "      <th>Order Amount</th>\n",
109 |        "      <th>Transmission OnTime</th>\n",
110 |        "      <th>Transmission</th>\n",
111 |        "      <th>Start PickPack</th>\n",
112 |        "      <th>...</th>\n",
113 |        "      <th>Store Arrival</th>\n",
114 |        "      <th>Store Arrival Date</th>\n",
115 |        "      <th>Delivery OnTime</th>\n",
116 |        "      <th>Delivery Time</th>\n",
117 |        "      <th>Extraction Time</th>\n",
118 |        "      <th>Last Status</th>\n",
119 |        "      <th>Reason Code</th>\n",
120 |        "      <th>Late</th>\n",
121 |        "      <th>Is Late</th>\n",
122 |        "      <th>LT</th>\n",
123 |        "    </tr>\n",
124 |        "  </thead>\n",
125 |        "  <tbody>\n",
126 |        "    <tr>\n",
127 |        "      <th>0</th>\n",
128 |        "      <td>1</td>\n",
129 |        "      <td>2021-05-02 17:00:00.000000</td>\n",
130 |        "      <td>2021-05-02</td>\n",
131 |        "      <td>CITY1</td>\n",
132 |        "      <td>CITY1/ST5</td>\n",
133 |        "      <td>2021-05-02/CITY1/ST5/1</td>\n",
134 |        "      <td>2759</td>\n",
135 |        "      <td>False</td>\n",
136 |        "      <td>2021-05-03 17:00:00.000000</td>\n",
137 |        "      <td>2021-05-04 07:00:00.000000</td>\n",
138 |        "      <td>...</td>\n",
139 |        "      <td>2021-05-06 16:36:04.552300</td>\n",
140 |        "      <td>2021-05-06</td>\n",
141 |        "      <td>False</td>\n",
142 |        "      <td>2021-05-07 16:30:00.000000</td>\n",
143 |        "      <td>2021-05-11 10:00:00</td>\n",
144 |        "      <td>Delivery Time</td>\n",
145 |        "      <td>Multi</td>\n",
146 |        "      <td>Late</td>\n",
147 |        "      <td>True</td>\n",
148 |        "      <td>4.979167</td>\n",
149 |        "    </tr>\n",
150 |        "    <tr>\n",
151 |        "      <th>1</th>\n",
152 |        "      <td>2</td>\n",
153 |        "      <td>2021-05-09 12:00:00.000000</td>\n",
154 |        "      <td>2021-05-09</td>\n",
155 |        "      <td>CITY2</td>\n",
156 |        "      <td>CITY2/ST9</td>\n",
157 |        "      <td>2021-05-09/CITY2/ST9/2</td>\n",
158 |        "      <td>1503</td>\n",
159 |        "      <td>True</td>\n",
160 |        "      <td>2021-05-09 13:19:07.490100</td>\n",
161 |        "      <td>2021-05-10 07:00:00.000000</td>\n",
162 |        "      <td>...</td>\n",
163 |        "      <td>2021-05-12 16:19:07.938647</td>\n",
164 |        "      <td>2021-05-12</td>\n",
165 |        "      <td>True</td>\n",
166 |        "      <td>2021-05-12 16:19:07.938647</td>\n",
167 |        "      <td>2021-05-11 10:00:00</td>\n",
168 |        "      <td>Takeoff</td>\n",
169 |        "      <td>NaN</td>\n",
170 |        "      <td>OnTime</td>\n",
171 |        "      <td>False</td>\n",
172 |        "      <td>3.179953</td>\n",
173 |        "    </tr>\n",
174 |        "    <tr>\n",
175 |        "      <th>2</th>\n",
176 |        "      <td>3</td>\n",
177 |        "      <td>2021-05-02 14:00:00.000000</td>\n",
178 |        "      <td>2021-05-02</td>\n",
179 |        "      <td>CITY2</td>\n",
180 |        "      <td>CITY2/ST1</td>\n",
181 |        "      <td>2021-05-02/CITY2/ST1/3</td>\n",
182 |        "      <td>5140</td>\n",
183 |        "      <td>True</td>\n",
184 |        "      <td>2021-05-02 15:32:13.222838</td>\n",
185 |        "      <td>2021-05-03 07:00:00.000000</td>\n",
186 |        "      <td>...</td>\n",
187 |        "      <td>2021-05-05 14:46:07.574411</td>\n",
188 |        "      <td>2021-05-05</td>\n",
189 |        "      <td>True</td>\n",
190 |        "      <td>2021-05-05 14:46:07.574411</td>\n",
191 |        "      <td>2021-05-11 10:00:00</td>\n",
192 |        "      <td>Delivery Time</td>\n",
193 |        "      <td>NaN</td>\n",
194 |        "      <td>OnTime</td>\n",
195 |        "      <td>False</td>\n",
196 |        "      <td>3.032032</td>\n",
197 |        "    </tr>\n",
198 |        "    <tr>\n",
199 |        "      <th>3</th>\n",
200 |        "      <td>4</td>\n",
201 |        "      <td>2021-05-11 11:00:00.000000</td>\n",
202 |        "      <td>2021-05-11</td>\n",
203 |        "      <td>CITY4</td>\n",
204 |        "      <td>CITY4/ST3</td>\n",
205 |        "      <td>2021-05-11/CITY4/ST3/4</td>\n",
206 |        "      <td>1677</td>\n",
207 |        "      <td>True</td>\n",
208 |        "      <td>2021-05-11 12:01:04.166619</td>\n",
209 |        "      <td>2021-05-12 07:00:00.000000</td>\n",
210 |        "      <td>...</td>\n",
211 |        "      <td>2021-05-14 15:46:42.418481</td>\n",
212 |        "      <td>2021-05-14</td>\n",
213 |        "      <td>True</td>\n",
214 |        "      <td>2021-05-14 15:46:42.418481</td>\n",
215 |        "      <td>2021-05-11 10:00:00</td>\n",
216 |        "      <td>Open</td>\n",
217 |        "      <td>NaN</td>\n",
218 |        "      <td>OnTime</td>\n",
219 |        "      <td>False</td>\n",
220 |        "      <td>3.199102</td>\n",
221 |        "    </tr>\n",
222 |        "    <tr>\n",
223 |        "      <th>4</th>\n",
224 |        "      <td>5</td>\n",
225 |        "      <td>2021-05-08 10:00:00.000000</td>\n",
226 |        "      <td>2021-05-08</td>\n",
227 |        "      <td>CITY4</td>\n",
228 |        "      <td>CITY4/ST1</td>\n",
229 |        "      <td>2021-05-08/CITY4/ST1/5</td>\n",
230 |        "      <td>4158</td>\n",
231 |        "      <td>True</td>\n",
232 |        "      <td>2021-05-08 10:56:14.062500</td>\n",
233 |        "      <td>2021-05-09 07:00:00.000000</td>\n",
234 |        "      <td>...</td>\n",
235 |        "      <td>2021-05-11 16:00:59.003211</td>\n",
236 |        "      <td>2021-05-11</td>\n",
237 |        "      <td>True</td>\n",
238 |        "      <td>2021-05-11 16:00:59.003211</td>\n",
239 |        "      <td>2021-05-11 10:00:00</td>\n",
240 |        "      <td>Start Clearance</td>\n",
241 |        "      <td>NaN</td>\n",
242 |        "      <td>OnTime</td>\n",
243 |        "      <td>False</td>\n",
244 |        "      <td>3.250683</td>\n",
245 |        "    </tr>\n",
246 |        "  </tbody>\n",
247 |        "</table>\n",
248 |        "<p>5 rows × 35 columns</p>\n",
249 |        "</div>"
250 |       ],
251 |       "text/plain": [
252 |        "   Test                  Order Time  Order Date   City      Store  \\\n",
253 |        "0     1  2021-05-02 17:00:00.000000  2021-05-02  CITY1  CITY1/ST5   \n",
254 |        "1     2  2021-05-09 12:00:00.000000  2021-05-09  CITY2  CITY2/ST9   \n",
255 |        "2     3  2021-05-02 14:00:00.000000  2021-05-02  CITY2  CITY2/ST1   \n",
256 |        "3     4  2021-05-11 11:00:00.000000  2021-05-11  CITY4  CITY4/ST3   \n",
257 |        "4     5  2021-05-08 10:00:00.000000  2021-05-08  CITY4  CITY4/ST1   \n",
258 |        "\n",
259 |        "                #Shipment  Order Amount  Transmission OnTime  \\\n",
260 |        "0  2021-05-02/CITY1/ST5/1          2759                False   \n",
261 |        "1  2021-05-09/CITY2/ST9/2          1503                 True   \n",
262 |        "2  2021-05-02/CITY2/ST1/3          5140                 True   \n",
263 |        "3  2021-05-11/CITY4/ST3/4          1677                 True   \n",
264 |        "4  2021-05-08/CITY4/ST1/5          4158                 True   \n",
265 |        "\n",
266 |        "                 Transmission              Start PickPack  ...  \\\n",
267 |        "0  2021-05-03 17:00:00.000000  2021-05-04 07:00:00.000000  ...   \n",
268 |        "1  2021-05-09 13:19:07.490100  2021-05-10 07:00:00.000000  ...   \n",
269 |        "2  2021-05-02 15:32:13.222838  2021-05-03 07:00:00.000000  ...   \n",
270 |        "3  2021-05-11 12:01:04.166619  2021-05-12 07:00:00.000000  ...   \n",
271 |        "4  2021-05-08 10:56:14.062500  2021-05-09 07:00:00.000000  ...   \n",
272 |        "\n",
273 |        "                Store Arrival Store Arrival Date Delivery OnTime  \\\n",
274 |        "0  2021-05-06 16:36:04.552300         2021-05-06           False   \n",
275 |        "1  2021-05-12 16:19:07.938647         2021-05-12            True   \n",
276 |        "2  2021-05-05 14:46:07.574411         2021-05-05            True   \n",
277 |        "3  2021-05-14 15:46:42.418481         2021-05-14            True   \n",
278 |        "4  2021-05-11 16:00:59.003211         2021-05-11            True   \n",
279 |        "\n",
280 |        "                Delivery Time      Extraction Time      Last Status  \\\n",
281 |        "0  2021-05-07 16:30:00.000000  2021-05-11 10:00:00    Delivery Time   \n",
282 |        "1  2021-05-12 16:19:07.938647  2021-05-11 10:00:00          Takeoff   \n",
283 |        "2  2021-05-05 14:46:07.574411  2021-05-11 10:00:00    Delivery Time   \n",
284 |        "3  2021-05-14 15:46:42.418481  2021-05-11 10:00:00             Open   \n",
285 |        "4  2021-05-11 16:00:59.003211  2021-05-11 10:00:00  Start Clearance   \n",
286 |        "\n",
287 |        "   Reason Code    Late Is Late        LT  \n",
288 |        "0        Multi    Late    True  4.979167  \n",
289 |        "1          NaN  OnTime   False  3.179953  \n",
290 |        "2          NaN  OnTime   False  3.032032  \n",
291 |        "3          NaN  OnTime   False  3.199102  \n",
292 |        "4          NaN  OnTime   False  3.250683  \n",
293 |        "\n",
294 |        "[5 rows x 35 columns]"
295 |       ]
296 |      },
297 |      "execution_count": 25,
298 |      "metadata": {},
299 |      "output_type": "execute_result"
300 |     }
301 |    ],
302 |    "source": [
303 |     "df_rep = pd.read_csv('In/stacked_bar.csv', sep=',', index_col = 0)\n",
304 |     "print('{:,} lines'.format(len(df_rep)))\n",
305 |     "df_rep.head()"
306 |    ]
307 |   },
308 |   {
309 |    "cell_type": "code",
310 |    "execution_count": 14,
311 |    "metadata": {
312 |     "collapsed": true
313 |    },
314 |    "outputs": [],
315 |    "source": [
316 |     "# PARAMS\n",
317 |     "EXTRACTION_DAY = '2021-05-11'\n",
318 |     "EXTRACTION_DATE = '{} 10:00:00'.format(EXTRACTION_DAY)\n",
319 |     "EXTRACTION_DAY = pd.to_datetime(EXTRACTION_DAY).date()"
320 |    ]
321 |   },
322 |   {
323 |    "cell_type": "code",
324 |    "execution_count": 22,
325 |    "metadata": {},
326 |    "outputs": [
327 |     {
328 |      "data": {
329 |       "text/html": [
330 |        "<div>\n",
331 |        "<style scoped>\n",
332 |        "    .dataframe tbody tr th:only-of-type {\n",
333 |        "        vertical-align: middle;\n",
334 |        "    }\n",
335 |        "\n",
336 |        "    .dataframe tbody tr th {\n",
337 |        "        vertical-align: top;\n",
338 |        "    }\n",
339 |        "\n",
340 |        "    .dataframe thead th {\n",
341 |        "        text-align: right;\n",
342 |        "    }\n",
343 |        "</style>\n",
344 |        "<table border=\"1\" class=\"dataframe\">\n",
345 |        "  <thead>\n",
346 |        "    <tr style=\"text-align: right;\">\n",
347 |        "      <th>Last Status</th>\n",
348 |        "      <th>Order Date</th>\n",
349 |        "      <th>Delivery Time</th>\n",
350 |        "      <th>End Clearance</th>\n",
351 |        "      <th>Leaving Airport</th>\n",
352 |        "      <th>Open</th>\n",
353 |        "      <th>Order Time</th>\n",
354 |        "      <th>Pickpack</th>\n",
355 |        "      <th>Start Clearance</th>\n",
356 |        "      <th>Start PickPack</th>\n",
357 |        "      <th>Takeoff</th>\n",
358 |        "    </tr>\n",
359 |        "  </thead>\n",
360 |        "  <tbody>\n",
361 |        "    <tr>\n",
362 |        "      <th>0</th>\n",
363 |        "      <td>2021-05-01</td>\n",
364 |        "      <td>531.0</td>\n",
365 |        "      <td>0.0</td>\n",
366 |        "      <td>0.0</td>\n",
367 |        "      <td>0.0</td>\n",
368 |        "      <td>0.0</td>\n",
369 |        "      <td>0.0</td>\n",
370 |        "      <td>0.0</td>\n",
371 |        "      <td>0.0</td>\n",
372 |        "      <td>0.0</td>\n",
373 |        "    </tr>\n",
374 |        "    <tr>\n",
375 |        "      <th>1</th>\n",
376 |        "      <td>2021-05-02</td>\n",
377 |        "      <td>506.0</td>\n",
378 |        "      <td>0.0</td>\n",
379 |        "      <td>0.0</td>\n",
380 |        "      <td>0.0</td>\n",
381 |        "      <td>0.0</td>\n",
382 |        "      <td>0.0</td>\n",
383 |        "      <td>0.0</td>\n",
384 |        "      <td>0.0</td>\n",
385 |        "      <td>0.0</td>\n",
386 |        "    </tr>\n",
387 |        "    <tr>\n",
388 |        "      <th>2</th>\n",
389 |        "      <td>2021-05-03</td>\n",
390 |        "      <td>508.0</td>\n",
391 |        "      <td>0.0</td>\n",
392 |        "      <td>0.0</td>\n",
393 |        "      <td>0.0</td>\n",
394 |        "      <td>0.0</td>\n",
395 |        "      <td>0.0</td>\n",
396 |        "      <td>0.0</td>\n",
397 |        "      <td>0.0</td>\n",
398 |        "      <td>0.0</td>\n",
399 |        "    </tr>\n",
400 |        "    <tr>\n",
401 |        "      <th>3</th>\n",
402 |        "      <td>2021-05-04</td>\n",
403 |        "      <td>523.0</td>\n",
404 |        "      <td>0.0</td>\n",
405 |        "      <td>1.0</td>\n",
406 |        "      <td>0.0</td>\n",
407 |        "      <td>0.0</td>\n",
408 |        "      <td>0.0</td>\n",
409 |        "      <td>0.0</td>\n",
410 |        "      <td>0.0</td>\n",
411 |        "      <td>0.0</td>\n",
412 |        "    </tr>\n",
413 |        "    <tr>\n",
414 |        "      <th>4</th>\n",
415 |        "      <td>2021-05-05</td>\n",
416 |        "      <td>516.0</td>\n",
417 |        "      <td>0.0</td>\n",
418 |        "      <td>6.0</td>\n",
419 |        "      <td>0.0</td>\n",
420 |        "      <td>0.0</td>\n",
421 |        "      <td>0.0</td>\n",
422 |        "      <td>0.0</td>\n",
423 |        "      <td>0.0</td>\n",
424 |        "      <td>0.0</td>\n",
425 |        "    </tr>\n",
426 |        "  </tbody>\n",
427 |        "</table>\n",
428 |        "</div>"
429 |       ],
430 |       "text/plain": [
431 |        "Last Status  Order Date  Delivery Time  End Clearance  Leaving Airport  Open  \\\n",
432 |        "0            2021-05-01          531.0            0.0              0.0   0.0   \n",
433 |        "1            2021-05-02          506.0            0.0              0.0   0.0   \n",
434 |        "2            2021-05-03          508.0            0.0              0.0   0.0   \n",
435 |        "3            2021-05-04          523.0            0.0              1.0   0.0   \n",
436 |        "4            2021-05-05          516.0            0.0              6.0   0.0   \n",
437 |        "\n",
438 |        "Last Status  Order Time  Pickpack  Start Clearance  Start PickPack  Takeoff  \n",
439 |        "0                   0.0       0.0              0.0             0.0      0.0  \n",
440 |        "1                   0.0       0.0              0.0             0.0      0.0  \n",
441 |        "2                   0.0       0.0              0.0             0.0      0.0  \n",
442 |        "3                   0.0       0.0              0.0             0.0      0.0  \n",
443 |        "4                   0.0       0.0              0.0             0.0      0.0  "
444 |       ]
445 |      },
446 |      "execution_count": 22,
447 |      "metadata": {},
448 |      "output_type": "execute_result"
449 |     }
450 |    ],
451 |    "source": [
452 |     "# Group by date\n",
453 |     "df_stat = pd.DataFrame(df_rep.groupby(['Order Date', 'Last Status'])['#Shipment'].nunique())\n",
454 |     "df_stat.columns = ['#Shipments']\n",
455 |     "df_stat = pd.pivot_table(df_stat.reset_index(), index = 'Order Date', columns = 'Last Status', \n",
456 |     "                         values = '#Shipments', aggfunc = np.sum).fillna(0)\n",
457 |     "\n",
458 |     "df_plot = df_stat.reset_index()\n",
459 |     "df_plot['Order Date'] = pd.to_datetime(df_plot['Order Date']).dt.date\n",
460 |     "df_plot = df_plot[df_plot['Order Date']<=EXTRACTION_DAY]\n",
461 |     "df_plot.head()"
462 |    ]
463 |   },
464 |   {
465 |    "cell_type": "markdown",
466 |    "metadata": {},
467 |    "source": [
468 |     "### Plot Number of Shipments per status"
469 |    ]
470 |   },
471 |   {
472 |    "cell_type": "code",
473 |    "execution_count": 29,
474 |    "metadata": {},
475 |    "outputs": [
476 |     {
477 |      "data": {
478 |       "image/png": 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1cjcbS5KaFhEcv9NVlQ5jCZc/PKwqj/cRwR3f2LTSYSzhqyOfM185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\nYII=\n",
479 |       "text/plain": [
480 |        "<Figure size 1080x576 with 1 Axes>"
481 |       ]
482 |      },
483 |      "metadata": {},
484 |      "output_type": "display_data"
485 |     }
486 |    ],
487 |    "source": [
488 |     "ax = df_plot.reset_index().plot.bar(figsize=(15, 8), edgecolor='black', x = 'Order Date',\n",
489 |     "                      y=df_stat.columns\n",
490 |     "                      , legend= True, fontsize = 14, stacked = True, colormap='Paired')\n",
491 |     "plt.ylabel('Number of Shipments', fontsize = 14)\n",
492 |     "plt.title('Shipments status report: {}'.format(EXTRACTION_DAY), fontsize = 14)\n",
493 |     "ax.legend(ncol=8)\n",
494 |     "plt.show()"
495 |    ]
496 |   }
497 |  ],
498 |  "metadata": {
499 |   "kernelspec": {
500 |    "display_name": "Python 3",
501 |    "language": "python",
502 |    "name": "python3"
503 |   },
504 |   "language_info": {
505 |    "codemirror_mode": {
506 |     "name": "ipython",
507 |     "version": 3
508 |    },
509 |    "file_extension": ".py",
510 |    "mimetype": "text/x-python",
511 |    "name": "python",
512 |    "nbconvert_exporter": "python",
513 |    "pygments_lexer": "ipython3",
514 |    "version": "3.6.1"
515 |   }
516 |  },
517 |  "nbformat": 4,
518 |  "nbformat_minor": 2
519 | }
520 | 


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