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Data_ScienceUse_Cases/misc/Technical Test 2 (1)-checkpoint.ipynb
2023-11-30 14:55:26 +07:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Import Dependencies\n",
"In this task, the libraries I used are:\n",
"* Numpy\n",
"* Pandas\n",
"* Matplotlib"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# File\n",
"For this task, I have snipped specific columns in the \"Optimization Plant\" and make another csv to be read."
]
},
{
"cell_type": "code",
"execution_count": 39,
"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>GUDANG</th>\n",
" <th>BIAYA HANDLING @ CARTON</th>\n",
" <th>ONGKIR-BDG @ CARTON</th>\n",
" <th>ONGKIR-YOGYA @ CARTON</th>\n",
" <th>ONGKIR-MLG @ CARTON</th>\n",
" <th>ONGKIR-JKT @ CARTON</th>\n",
" <th>INDEX_MARGIN AA</th>\n",
" <th>INDEX_MARGIN BB</th>\n",
" <th>INDEX_MARGIN_CC</th>\n",
" <th>Capacity AA</th>\n",
" <th>Capacity BB</th>\n",
" <th>Capacity CC</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>PLANT1</td>\n",
" <td>1,000</td>\n",
" <td>2,500</td>\n",
" <td>1,500</td>\n",
" <td>2,000</td>\n",
" <td>2,000</td>\n",
" <td>50,000</td>\n",
" <td>45,000</td>\n",
" <td>60,000</td>\n",
" <td>15,000</td>\n",
" <td>20,000</td>\n",
" <td>12,000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>PLANT2</td>\n",
" <td>1,100</td>\n",
" <td>2,400</td>\n",
" <td>1,000</td>\n",
" <td>2,000</td>\n",
" <td>2,500</td>\n",
" <td>50,000</td>\n",
" <td>50,000</td>\n",
" <td>70,000</td>\n",
" <td>22,000</td>\n",
" <td>15,000</td>\n",
" <td>15,000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>PLANT3</td>\n",
" <td>1,200</td>\n",
" <td>1,000</td>\n",
" <td>3,000</td>\n",
" <td>2,000</td>\n",
" <td>3,000</td>\n",
" <td>60,000</td>\n",
" <td>45,000</td>\n",
" <td>55,000</td>\n",
" <td>20,000</td>\n",
" <td>10,000</td>\n",
" <td>27,000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" GUDANG BIAYA HANDLING @ CARTON ONGKIR-BDG @ CARTON ONGKIR-YOGYA @ CARTON \\\n",
"0 PLANT1 1,000 2,500 1,500 \n",
"1 PLANT2 1,100 2,400 1,000 \n",
"2 PLANT3 1,200 1,000 3,000 \n",
"\n",
" ONGKIR-MLG @ CARTON ONGKIR-JKT @ CARTON INDEX_MARGIN AA INDEX_MARGIN BB \\\n",
"0 2,000 2,000 50,000 45,000 \n",
"1 2,000 2,500 50,000 50,000 \n",
"2 2,000 3,000 60,000 45,000 \n",
"\n",
" INDEX_MARGIN_CC Capacity AA Capacity BB Capacity CC \n",
"0 60,000 15,000 20,000 12,000 \n",
"1 70,000 22,000 15,000 15,000 \n",
"2 55,000 20,000 10,000 27,000 "
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Read csv\n",
"df = pd.read_csv(\"C:\\\\Users\\\\Asus\\\\Documents\\\\ForCalculation.csv\")\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Get Margin Value\n",
"Based on our dataframe, we get our AA, BB, and CC margins respectively."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"getAA = df['INDEX_MARGIN AA'].values\n",
"getBB = df['INDEX_MARGIN BB'].values\n",
"getCC = df['INDEX_MARGIN_CC'].values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Note #1\n",
"Since our dataframe contains comma in each supposed number, I converted it to become int to be processed later."
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [],
"source": [
"# Plant 1 - Index Margin [AA, BB, CC]\n",
"M_P1_AA = int(getAA[0].replace(',', ''))\n",
"M_P1_BB = int(getBB[0].replace(',', ''))\n",
"M_P1_CC = int(getCC[0].replace(',', ''))\n",
"\n",
"# Plant 2 - Index Margin [AA, BB, CC]\n",
"M_P2_AA = int(getAA[1].replace(',', ''))\n",
"M_P2_BB = int(getBB[1].replace(',', ''))\n",
"M_P2_CC = int(getCC[1].replace(',', ''))\n",
"\n",
"# Plant 3 - Index Margin [AA, BB, CC]\n",
"M_P3_AA = int(getAA[2].replace(',', ''))\n",
"M_P3_BB = int(getBB[2].replace(',', ''))\n",
"M_P3_CC = int(getCC[2].replace(',', ''))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Get Cost Value\n",
"I extracted the values from **handling fees** and **Ongkir** respectively."
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"getCost = df['BIAYA HANDLING @ CARTON'].values\n",
"getO_BDG = df['ONGKIR-BDG @ CARTON'].values\n",
"getO_YGY = df['ONGKIR-YOGYA @ CARTON'].values\n",
"getO_MLG = df['ONGKIR-MLG @ CARTON'].values\n",
"getO_JKT = df['ONGKIR-JKT @ CARTON'].values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Note #2\n",
"Based on the example, the formula of Cost is **Handling fee** + **Ongkir**"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
"# Cost Plant 1 - All Product - Location [Bandung, Yogya, Malang, Jakarta]\n",
"CP1_BDG = int(getCost[0].replace(',', ''))+int(getO_BDG[0].replace(',', ''))\n",
"CP1_YGY = int(getCost[0].replace(',', ''))+int(getO_YGY[0].replace(',', ''))\n",
"CP1_MLG = int(getCost[0].replace(',', ''))+int(getO_MLG[0].replace(',', ''))\n",
"CP1_JKT = int(getCost[0].replace(',', ''))+int(getO_JKT[0].replace(',', ''))\n",
"\n",
"# Cost Plant 2 - All Product - Location [Bandung, Yogya, Malang, Jakarta]\n",
"CP2_BDG = int(getCost[1].replace(',', ''))+int(getO_BDG[1].replace(',', ''))\n",
"CP2_YGY = int(getCost[1].replace(',', ''))+int(getO_YGY[1].replace(',', ''))\n",
"CP2_MLG = int(getCost[1].replace(',', ''))+int(getO_MLG[1].replace(',', ''))\n",
"CP2_JKT = int(getCost[1].replace(',', ''))+int(getO_JKT[1].replace(',', ''))\n",
"\n",
"# Cost Plant 3 - All Product - Location [Bandung, Yogya, Malang, Jakarta]\n",
"CP3_BDG = int(getCost[2].replace(',', ''))+int(getO_BDG[2].replace(',', ''))\n",
"CP3_YGY = int(getCost[2].replace(',', ''))+int(getO_YGY[2].replace(',', ''))\n",
"CP3_MLG = int(getCost[2].replace(',', ''))+int(getO_MLG[2].replace(',', ''))\n",
"CP3_JKT = int(getCost[2].replace(',', ''))+int(getO_JKT[2].replace(',', ''))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Get Nett Margin Value\n",
"Based from the example, the formula for Nett Margin is **Margin** - **Cost**."
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [],
"source": [
"# Nett Margin Plant 1 - AA - Location [Bandung, Yogya, Malang, Jakarta]\n",
"NM_P1_AA_BDG = M_P1_AA - CP1_BDG\n",
"NM_P1_AA_YGY = M_P1_AA - CP1_YGY\n",
"NM_P1_AA_MLG = M_P1_AA - CP1_MLG\n",
"NM_P1_AA_JKT = M_P1_AA - CP1_JKT\n",
"\n",
"# Nett Margin Plant 1 - BB - Location [Bandung, Yogya, Malang, Jakarta]\n",
"NM_P1_BB_BDG = M_P1_BB - CP1_BDG\n",
"NM_P1_BB_YGY = M_P1_BB - CP1_YGY\n",
"NM_P1_BB_MLG = M_P1_BB - CP1_MLG\n",
"NM_P1_BB_JKT = M_P1_BB - CP1_JKT\n",
"\n",
"# Nett Margin Plant 1 - CC - Location [Bandung, Yogya, Malang, Jakarta]\n",
"NM_P1_CC_BDG = M_P1_CC - CP1_BDG\n",
"NM_P1_CC_YGY = M_P1_CC - CP1_YGY\n",
"NM_P1_CC_MLG = M_P1_CC - CP1_MLG\n",
"NM_P1_CC_JKT = M_P1_CC - CP1_JKT\n",
"\n",
"# Nett Margin Plant 2 - AA - Location [Bandung, Yogya, Malang, Jakarta]\n",
"NM_P2_AA_BDG = M_P2_AA - CP2_BDG\n",
"NM_P2_AA_YGY = M_P2_AA - CP2_YGY\n",
"NM_P2_AA_MLG = M_P2_AA - CP2_MLG\n",
"NM_P2_AA_JKT = M_P2_AA - CP2_JKT\n",
"\n",
"# Nett Margin Plant 2 - BB - Location [Bandung, Yogya, Malang, Jakarta]\n",
"NM_P2_BB_BDG = M_P2_BB - CP2_BDG\n",
"NM_P2_BB_YGY = M_P2_BB - CP2_YGY\n",
"NM_P2_BB_MLG = M_P2_BB - CP2_MLG\n",
"NM_P2_BB_JKT = M_P2_BB - CP2_JKT\n",
"\n",
"# Nett Margin Plant 2 - CC - Location [Bandung, Yogya, Malang, Jakarta]\n",
"NM_P2_CC_BDG = M_P2_CC - CP2_BDG\n",
"NM_P2_CC_YGY = M_P2_CC - CP2_YGY\n",
"NM_P2_CC_MLG = M_P2_CC - CP2_MLG\n",
"NM_P2_CC_JKT = M_P2_CC - CP2_JKT\n",
"\n",
"# Nett Margin Plant 3 - AA - Location [Bandung, Yogya, Malang, Jakarta]\n",
"NM_P3_AA_BDG = M_P3_AA - CP3_BDG\n",
"NM_P3_AA_YGY = M_P3_AA - CP3_YGY\n",
"NM_P3_AA_MLG = M_P3_AA - CP3_MLG\n",
"NM_P3_AA_JKT = M_P3_AA - CP3_JKT\n",
"\n",
"# Nett Margin Plant 3 - BB - Location [Bandung, Yogya, Malang, Jakarta]\n",
"NM_P3_BB_BDG = M_P3_BB - CP3_BDG\n",
"NM_P3_BB_YGY = M_P3_BB - CP3_YGY\n",
"NM_P3_BB_MLG = M_P3_BB - CP3_MLG\n",
"NM_P3_BB_JKT = M_P3_BB - CP3_JKT\n",
"\n",
"# Nett Margin Plant 3 - CC - Location [Bandung, Yogya, Malang, Jakarta]\n",
"NM_P3_CC_BDG = M_P3_CC - CP3_BDG\n",
"NM_P3_CC_YGY = M_P3_CC - CP3_YGY\n",
"NM_P3_CC_MLG = M_P3_CC - CP3_MLG\n",
"NM_P3_CC_JKT = M_P3_CC - CP3_JKT"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Nett Margin per Location\n",
"This is to determine which location has the **highest** nett margin."
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [],
"source": [
"# Net Margin Bandung\n",
"NM_BDG = NM_P1_AA_BDG + NM_P1_BB_BDG + NM_P1_CC_BDG + NM_P2_AA_BDG + NM_P2_BB_BDG + NM_P2_CC_BDG + NM_P3_AA_BDG + NM_P3_BB_BDG + NM_P3_CC_BDG\n",
"\n",
"# Net Margin Yogyakarta\n",
"NM_YGY = NM_P1_AA_YGY + NM_P1_BB_YGY + NM_P1_CC_YGY + NM_P2_AA_YGY + NM_P2_BB_YGY + NM_P2_CC_YGY + NM_P3_AA_YGY + NM_P3_BB_YGY + NM_P3_CC_YGY\n",
"\n",
"# Net Margin Malang\n",
"NM_MLG = NM_P1_AA_MLG + NM_P1_BB_MLG + NM_P1_CC_MLG + NM_P2_AA_MLG + NM_P2_BB_MLG + NM_P2_CC_MLG + NM_P3_AA_MLG + NM_P3_BB_MLG + NM_P3_CC_MLG\n",
"\n",
"# Net Margin Jakarta\n",
"NM_JKT = NM_P1_AA_JKT + NM_P1_BB_JKT + NM_P1_CC_JKT + NM_P2_AA_JKT + NM_P2_BB_JKT + NM_P2_CC_JKT + NM_P3_AA_JKT + NM_P3_BB_JKT + NM_P3_CC_JKT"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Nett Margin per Location Visualization"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x504 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"loc = ['Bandung', 'Yogya', 'Malang', 'Jakarta']\n",
"\n",
"data = [NM_BDG, NM_YGY, NM_MLG, NM_JKT]\n",
"wp = { 'linewidth' : 1, 'edgecolor' : \"black\" }\n",
"\n",
"explode = (0.1, 0.2, 0.1, 0.0)\n",
"colors = (\"burlywood\", \"darkorange\", \"bisque\", \"antiquewhite\")\n",
"\n",
"def func(pct, allvalues):\n",
" absolute = int(pct / 100.*np.sum(allvalues))\n",
" return \"{:.1f}%\\n({:d})\".format(pct, absolute)\n",
"\n",
"fig, ax = plt.subplots(figsize =(10, 7))\n",
"wedges, texts, autotexts = ax.pie(data, \n",
" autopct = lambda pct: func(pct, data),\n",
" explode = explode, \n",
" labels = loc,\n",
" shadow = True,\n",
" colors = colors,\n",
" startangle = 90,\n",
" wedgeprops = wp,\n",
" textprops = dict(color =\"black\"))\n",
"ax.set_title(\"Net Margin per Location\")\n",
"plt.show() "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Nett Margin per Product\n",
"This is to determine which product has the **highest** nett margin."
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {},
"outputs": [],
"source": [
"# Net Margin AA\n",
"NM_AA = NM_P1_AA_BDG+NM_P1_AA_YGY+NM_P1_AA_MLG+NM_P1_AA_JKT+NM_P2_AA_BDG+NM_P2_AA_YGY+NM_P2_AA_MLG+NM_P2_AA_JKT+NM_P3_AA_BDG+NM_P3_AA_YGY+NM_P3_AA_MLG+NM_P3_AA_JKT\n",
"\n",
"# Net Margin BB\n",
"NM_BB = NM_P1_BB_BDG+NM_P1_BB_YGY+NM_P1_BB_MLG+NM_P1_BB_JKT+NM_P2_BB_BDG+NM_P2_BB_YGY+NM_P2_BB_MLG+NM_P2_BB_JKT+NM_P3_BB_BDG+NM_P3_BB_YGY+NM_P3_BB_MLG+NM_P3_BB_JKT\n",
"\n",
"# Net Margin CC\n",
"NM_CC = NM_P1_CC_BDG+NM_P1_CC_YGY+NM_P1_CC_MLG+NM_P1_CC_JKT+NM_P2_CC_BDG+NM_P2_CC_YGY+NM_P2_CC_MLG+NM_P2_CC_JKT+NM_P3_CC_BDG+NM_P3_CC_YGY+NM_P3_CC_MLG+NM_P3_CC_JKT"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Nett Margin per Location Visualization"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x504 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"loc = ['AA', 'BB', 'CC']\n",
"data = [NM_AA, NM_BB, NM_CC]\n",
"\n",
"wp = { 'linewidth' : 1, 'edgecolor' : \"black\" }\n",
"explode = (0.1, 0.0, 0.2)\n",
"colors = (\"burlywood\",\"bisque\",\"darkorange\")\n",
"\n",
"def func(pct, allvalues):\n",
" absolute = int(pct / 100.*np.sum(allvalues))\n",
" return \"{:.1f}%\\n({:d})\".format(pct, absolute)\n",
"\n",
"fig, ax = plt.subplots(figsize =(10, 7))\n",
"wedges, texts, autotexts = ax.pie(data, \n",
" autopct = lambda pct: func(pct, data),\n",
" explode = explode, \n",
" labels = loc,\n",
" shadow = True,\n",
" colors = colors,\n",
" startangle = 90,\n",
" wedgeprops = wp,\n",
" textprops = dict(color =\"black\"))\n",
"ax.set_title(\"Net Margin per Product\")\n",
"plt.show() "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Nett Margin per Plant\n",
"This is to determine which plant has the **highest** nett margin."
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [],
"source": [
"# Net Margin Plant 1\n",
"NM_P1 = NM_P1_AA_BDG+NM_P1_AA_YGY+NM_P1_AA_MLG+NM_P1_AA_JKT+NM_P1_BB_BDG+NM_P1_BB_YGY+NM_P1_BB_MLG+NM_P1_BB_JKT+NM_P1_CC_BDG+NM_P1_CC_YGY+NM_P1_CC_MLG+NM_P1_CC_JKT\n",
"\n",
"# Net Margin Plant 2\n",
"NM_P2 = NM_P2_AA_BDG+NM_P2_AA_YGY+NM_P2_AA_MLG+NM_P2_AA_JKT+NM_P2_BB_BDG+NM_P2_BB_YGY+NM_P2_BB_MLG+NM_P2_BB_JKT+NM_P2_CC_BDG+NM_P2_CC_YGY+NM_P2_CC_MLG+NM_P2_CC_JKT\n",
"\n",
"# Net Margin Plant 3\n",
"NM_P3 = NM_P3_AA_BDG+NM_P3_AA_YGY+NM_P3_AA_MLG+NM_P3_AA_JKT+NM_P3_BB_BDG+NM_P3_BB_YGY+NM_P3_BB_MLG+NM_P3_BB_JKT+NM_P3_CC_BDG+NM_P3_CC_YGY+NM_P3_CC_MLG+NM_P3_CC_JKT"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Nett Margin per Location Visualization"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x504 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"loc = ['Plant 1', 'Plant 2', 'Plant 3']\n",
"data = [NM_P1, NM_P2, NM_P3]\n",
"\n",
"wp = { 'linewidth' : 1, 'edgecolor' : \"black\" }\n",
"explode = (0.0, 0.2, 0.1)\n",
"colors = (\"bisque\",\"darkorange\",\"burlywood\")\n",
"\n",
"def func(pct, allvalues):\n",
" absolute = int(pct / 100.*np.sum(allvalues))\n",
" return \"{:.1f}%\\n({:d})\".format(pct, absolute)\n",
"\n",
"fig, ax = plt.subplots(figsize =(10, 7))\n",
"wedges, texts, autotexts = ax.pie(data, \n",
" autopct = lambda pct: func(pct, data),\n",
" explode = explode, \n",
" labels = loc,\n",
" shadow = True,\n",
" colors = colors,\n",
" startangle = 90,\n",
" wedgeprops = wp,\n",
" textprops = dict(color =\"black\"))\n",
"ax.set_title(\"Net Margin per Plant\")\n",
"plt.show() "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Note #3\n",
"The calculations made, is referenced by the *Excel Case*, where it says **Nett Margin: Index Margin - Biaya handling - Ongkir**.\n",
"\n",
"I also took notes from the *example* Excel document, and I referenced how to get the numbers by looking at the formula bar.\n",
"\n",
"I admit I got confused in the **\"Distributed\"** column, I have got no clue why some cell is written, not with formula so I could not understand where it was coming from.\n",
"\n",
"And also there are **yellow highlight in the document**, indicating that it is an important indicator to be calculated. But I have not yet understand why some cells are highlighted as well."
]
}
],
"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": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}