From 385ee2117f2d6129bf1858c4a451c91f84764a98 Mon Sep 17 00:00:00 2001 From: Dustin Smith Date: Fri, 18 Mar 2022 22:22:36 +0700 Subject: [PATCH] removed old files after gitignore update --- ..._budgeting_cloud_tutorial-checkpoint.ipynb | 1376 ----------------- 1 file changed, 1376 deletions(-) delete mode 100644 notebooks/.ipynb_checkpoints/capital_budgeting_cloud_tutorial-checkpoint.ipynb diff --git a/notebooks/.ipynb_checkpoints/capital_budgeting_cloud_tutorial-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/capital_budgeting_cloud_tutorial-checkpoint.ipynb deleted file mode 100644 index 41ccd16..0000000 --- a/notebooks/.ipynb_checkpoints/capital_budgeting_cloud_tutorial-checkpoint.ipynb +++ /dev/null @@ -1,1376 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "application/vnd.databricks.v1+cell": { - "inputWidgets": {}, - "nuid": "ef2a21f8-6a38-4c33-a035-c6fca231b6de", - "showTitle": false, - "title": "" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
" - ] - }, - "metadata": { - "application/vnd.databricks.v1+output": { - "addedWidgets": {}, - "arguments": {}, - "data": "
", - "datasetInfos": [], - "metadata": {}, - "removedWidgets": [], - "type": "html" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "import operator\n", - "import numpy as np\n", - "import numpy_financial as npf\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "\n", - "pd.set_option('display.float_format', lambda x: '%.2f' % x)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "application/vnd.databricks.v1+cell": { - "inputWidgets": {}, - "nuid": "bd195b60-c465-4e63-8575-c6d2586aab14", - "showTitle": false, - "title": "" - } - }, - "source": [ - "# Scenario\n", - "Many in industry have taken the leap from on premise to cloud based infrastructure. In the following scenario, we will examine this real world problem using Monte Carlo simulation for standard line items of an on premise to cloud migration. For you inidividual case, you may have other cost to consider as well. For the cloud provider, we are using Google and Databricks for analytics platform. Here I have rolled up their cost into one line item but you may want to break them out for a more robust analysis. Additionally, Databricks provided the numbers below for this scenario. Many thanks to them.\n", - "\n", - "In the case laid out below, we have that\n", - "1. `data_center_thb` is the cost data center per year in Thai Baht.\n", - "2. `servers` are the number of servers we maintain.\n", - "3. `software` is the cost of enterprise licenses we pay for.\n", - "4. `engineers_thb` is the cost of all our engineers who would have responsibilities covered by migrating cloud.\n", - "5. `pro_service` is the professional service fee from GCP and Databricks for transitioning from on premise to cloud.\n", - "6. `hurdle_rate` is the business risk of capital rate; however, we can use a distribution for this value to look different rates.\n", - "\n", - "This notebook can be modified for traditional applications such as manufactoring or other tech style evaluations. This isn't solely for analyzing on premise to cloud but just the example used here today.\n", - "\n", - "### Info\n", - "In this scenario, we are based in Thailand so local cost will be in THB and others will be USD. All THB cost will be indicated with THB, and during the analysis, we will convert to USD for the final results." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "application/vnd.databricks.v1+cell": { - "inputWidgets": {}, - "nuid": "0c34e68c-0963-4e46-965e-c6320060c264", - "showTitle": false, - "title": "" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
" - ] - }, - "metadata": { - "application/vnd.databricks.v1+output": { - "addedWidgets": {}, - "arguments": {}, - "data": "
", - "datasetInfos": [], - "metadata": {}, - "removedWidgets": [], - "type": "html" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "data_center_thb = 33000000 # 500k-1.3M USD; we will take 1M USD\n", - "infra_per_server_thb = 1163250 # 1.41M annual per server would 1.16M THB\n", - "servers = 200\n", - "software = 468000\n", - "engineers_thb = 2227500 * 7 # 7 engineers at ~2.2M THB a year; full time for cloud replaceable costs\n", - "tax_rate = .2 # corporate tax rate incurred on all positive revenues\n", - "hurdle_rate = .1 # risk adjusted hurdle rate can compute by using the function below instead of this static value\n", - "year_one_thb = 33 # THB for year one (known)\n", - "# working with your vendors, you should be profile your current usage\n", - "pro_service = -350000 # migration professional service fee\n", - "platform = -944000 # contract rate for 5 years based on 4.635PB 4.72M but credits would probably be 4.85M\n", - "vm = -601000\n", - "storage = -875000" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "application/vnd.databricks.v1+cell": { - "inputWidgets": {}, - "nuid": "7736d85b-395e-4334-ad93-c3a30a5809c7", - "showTitle": false, - "title": "" - } - }, - "source": [ - "## Standard Distributions\n", - "Common distributions to use during capital budgeting Monte Carlo are `triangular`, `uniform`, and `normal`. However, others may be used with care. If one uses a normal distribution, remember the tails trail off to negative and positive infinity which may or may not make sense for your scenario. Even though the probability may be small of selection beyond 3 standard deviations, it can happen in large number of simulations. With normal distributions, it might be good to define lower and upper bounds to assign to anything selected beyond a certain range. Another option is to use distributions that have support and only needing to bound at one extreme.\n", - "\n", - "In the distributions below, I demonstrate the use of `uniform`, `triangular`, and `normal`, but dont feel limited to these. If you know one of your cost has a `beta` distribution, use it. If you are a US user with US cost, you will not need the exchange rate throughout the this demo. You can either remove it or set it to 1 since dividing by will not change the end result." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "application/vnd.databricks.v1+cell": { - "inputWidgets": {}, - "nuid": "98bc01cc-8348-4392-a33d-1d10c395a8a8", - "showTitle": false, - "title": "" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
" - ] - }, - "metadata": { - "application/vnd.databricks.v1+output": { - "addedWidgets": {}, - "arguments": {}, - "data": "
", - "datasetInfos": [], - "metadata": {}, - "removedWidgets": [], - "type": "html" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "def hardware_thb():\n", - " \"\"\"\n", - " Cost of the hardware per server in THB.\n", - " \"\"\"\n", - " return np.random.uniform(900000, 1200000)\n", - " \n", - " \n", - "def data_growth():\n", - " \"\"\"\n", - " Data growth of the cluster.\n", - " \"\"\"\n", - " return np.random.triangular(1.03, 1.075, 1.1)\n", - "\n", - "\n", - "def software_increase():\n", - " \"\"\"\n", - " Percentage increase of software licenses yearly.\n", - " \"\"\"\n", - " return np.random.triangular(1.005, 1.025, 1.05)\n", - "\n", - "\n", - "def engineers_increase():\n", - " \"\"\"\n", - " Percentage increase of the salary of the engineers per year.\n", - " \"\"\"\n", - " return np.random.triangular(1.05, 1.085, 1.12)\n", - "\n", - "\n", - "def data_center_cost_increase():\n", - " \"\"\"\n", - " Data center shared space cost increase yearly.\n", - " \"\"\"\n", - " return np.random.triangular(1.005, 1.01, 1.035)\n", - "\n", - "\n", - "def exchange_thb():\n", - " \"\"\"\n", - " THB to USD exchange rate flucuation with a mean of 32 and SD of 2. In this case, we will bound the return\n", - " values to a minimum of 28 and a maximum of 38.\n", - " \"\"\"\n", - " thb = np.random.normal(32, 2)\n", - " if thb < 28:\n", - " return 30\n", - " if thb > 36:\n", - " return 36\n", - " return thb\n", - "\n", - "\n", - "def year_one_dual_cost():\n", - " \"\"\"\n", - " During the first year transition phase, we will experience partial on premise and cloud cost depending\n", - " on the speed of migration.\n", - " \"\"\"\n", - " return np.random.triangular(.25, .35, .5)\n", - "\n", - " \n", - "def platform_5year_contract_growth():\n", - " \"\"\"\n", - " The growth rate for the contract platform cost.\n", - " \"\"\"\n", - " return np.random.triangular(1.035, 1.05, 1.07)\n", - " \n", - " \n", - "def platform_vms_growth():\n", - " \"\"\"\n", - " Platform cost and vms cost dont increase 1:1 with an increase in data. \n", - " Estimates according to DB was between 25-50%.\n", - " \"\"\"\n", - " return np.random.triangular(0.25, 0.375, 0.5)\n", - " \n", - " \n", - "def risk_adjusted_hurdle():\n", - " \"\"\"\n", - " We cannot account for every risk profile in this simulation; therefore, we can capture some of this additional\n", - " risk by considering hurdle rate which incorporates a higher return needed.\n", - " \"\"\"\n", - " rate = np.random.normal(.1, .025) # mean 10% std 2.5%\n", - " # will take a normal distribution with hard cuts at 10% and 25% as opposed to a triangular\n", - " if rate < .1:\n", - " return .1\n", - " if rate > .25:\n", - " return .25\n", - " return rate" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "application/vnd.databricks.v1+cell": { - "inputWidgets": {}, - "nuid": "5b594e0b-a67b-42f6-afa4-3902b032feae", - "showTitle": false, - "title": "" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
" - ] - }, - "metadata": { - "application/vnd.databricks.v1+output": { - "addedWidgets": {}, - "arguments": {}, - "data": "
", - "datasetInfos": [], - "metadata": {}, - "removedWidgets": [], - "type": "html" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "def create_data():\n", - " # if you have more line items to account for, update the indices needed here.\n", - " # setup dataframe\n", - " df = pd.DataFrame([], columns=[f\"year_{i}\" for i in range(0, 11)])\n", - " df.loc[:, \"index\"] = [\"data_center\", \"infrastructure\", \"engineers\", \"software\", \"platform\", \"cloud_vms\", \"cloud_storage\", \"cashflows\", \"after_tax\"]\n", - " df.set_index(\"index\", inplace=True)\n", - " df.fillna(0, inplace=True)\n", - " # initialize year_0\n", - " df.loc[\"cashflows\", \"year_0\"] = pro_service\n", - " df.loc[\"after_tax\", \"year_0\"] = pro_service\n", - " \n", - " # cloud/prem\n", - " premise = [\"data_center\", \"infrastructure\", \"engineers\", \"software\"]\n", - " cloud = [\"platform\", \"cloud_vms\", \"cloud_storage\"]\n", - " platform = -944000 \n", - " \n", - " # this the spend for 5 years\n", - " # if data growth causes higher spend, we need to add that in if it occurs.\n", - " # extras built into first contract to cover additional dbu spend\n", - " track_platform = 4850000\n", - "\n", - " for i in range(1, 11):\n", - " # not the cleanest but just doing first year value setting and checking for cycle update params\n", - " if i == 1: \n", - " hardware = infra_per_server_thb / year_one_thb / 5 * servers \n", - " engineer_pay = engineers_thb / year_one_thb\n", - " # to demo the split example, I am using 0.5 hardcode but you can use the distribution sampling.\n", - " # dual_cost = year_one_dual_cost() \n", - " dual_cost = 0.5 # using 0.5 here to mimic the example online\n", - " track_platform += platform\n", - " elif i == 2:\n", - " # distribution selection starts here; year one is known\n", - " growth = data_growth()\n", - " growth_others = 1 + (growth - 1) * platform_vms_growth()\n", - " track_platform += platform * growth_others\n", - " engineer_pay = engineers_thb * engineers_increase() / exchange_thb()\n", - " elif i == 6:\n", - " # hardware upgrade cycle split cost over 5 years\n", - " # platform contract renewal\n", - " rate = exchange_thb()\n", - " engineer_pay = engineers_thb * engineers_increase() / rate\n", - " hardware = hardware_thb() / rate / 5 * servers \n", - " data_increase = data_growth()\n", - " # contract\n", - " platform = -944000 * platform_5year_contract_growth()\n", - " track_platform = -platform * 4\n", - " else:\n", - " growth = data_growth()\n", - " growth_others = 1 + (growth - 1) * platform_vms_growth()\n", - " track_platform += platform * growth_others\n", - " # if data growth is higher than usage agreement, we would need to cover that additional cost\n", - " # for out of contract dbus\n", - " platform = platform if track_platform >= 0 else platform + track_platform\n", - " engineer_pay = engineers_thb * engineers_increase() / exchange_thb()\n", - " \n", - " # populate the data\n", - " df.loc[\"data_center\", f\"year_{i}\"] = data_center_thb / year_one_thb if i == 1 else df.loc[\"data_center\", f\"year_{i - 1}\"] * data_center_cost_increase()\n", - " df.loc[\"infrastructure\", f\"year_{i}\"] = hardware\n", - " df.loc[\"engineers\", f\"year_{i}\"] = engineer_pay \n", - " df.loc[\"software\", f\"year_{i}\"] = software if i == 1 else df.loc[\"software\", f\"year_{i - 1}\"] * software_increase()\n", - " df.loc[\"platform\", f\"year_{i}\"] = platform \n", - " df.loc[\"cloud_vms\", f\"year_{i}\"] = vm if i == 1 else df.loc[\"cloud_vms\", f\"year_{i - 1}\"] * growth_others \n", - " df.loc[\"cloud_storage\", f\"year_{i}\"] = storage if i == 1 else df.loc[\"cloud_storage\", f\"year_{i - 1}\"] * growth\n", - " df.loc[\"cashflows\", f\"year_{i}\"] = df.loc[premise, f\"year_{i}\"].sum() * (1 - dual_cost) + df.loc[cloud, f\"year_{i}\"].sum() * dual_cost if i == 1 else df.loc[premise, f\"year_{i}\"].sum() + df.loc[cloud, f\"year_{i}\"].sum()\n", - " df.loc[\"after_tax\", f\"year_{i}\"] = df.loc[\"cashflows\", f\"year_{i}\"] if df.loc[\"cashflows\", f\"year_{i}\"] <= 0 else df.loc[\"cashflows\", f\"year_{i}\"] * 0.8\n", - " \n", - " return df\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "application/vnd.databricks.v1+cell": { - "inputWidgets": {}, - "nuid": "255734fe-b90c-4538-b7e9-11b7c400befe", - "showTitle": false, - "title": "" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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year_0year_1year_2year_3year_4year_5year_6year_7year_8year_9year_10
index
data_center01000000.001022813.131042250.581061510.671090529.071113402.091138886.461154365.941184270.241196028.30
infrastructure01410000.001410000.001410000.001410000.001410000.001343811.671343811.671343811.671343811.671343811.67
engineers0472500.00514661.29590307.11526933.57510638.82575329.88553775.35558519.71503268.92503015.77
software0468000.00474855.01487516.97500622.92509316.22524894.70542898.66550845.80569464.38582023.90
platform0-944000.00-944000.00-944000.00-944000.00-944000.00-994763.38-994763.38-994763.38-994763.38-1082504.75
cloud_vms0-601000.00-622188.83-638608.88-650894.58-672389.49-694594.25-706406.47-723941.20-740923.95-757903.08
cloud_storage0-875000.00-959316.03-1026023.77-1097079.00-1185203.69-1280407.13-1339105.42-1444266.50-1539606.05-1625614.88
cashflows-350000465250.00896824.57921442.01807093.58718890.93587673.58539096.88444572.04325521.83158856.94
after_tax-350000372200.00717459.66737153.60645674.87575112.74470138.87431277.50355657.63260417.47127085.55
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year_0year_1year_2year_3year_4year_5year_6year_7year_8year_9year_10
index
data_center01000000.001022813.131042250.581061510.671090529.071113402.091138886.461154365.941184270.241196028.30
infrastructure01410000.001410000.001410000.001410000.001410000.001343811.671343811.671343811.671343811.671343811.67
engineers0472500.00514661.29590307.11526933.57510638.82575329.88553775.35558519.71503268.92503015.77
software0468000.00474855.01487516.97500622.92509316.22524894.70542898.66550845.80569464.38582023.90
platform0-944000.00-944000.00-944000.00-944000.00-944000.00-994763.38-994763.38-994763.38-994763.38-1082504.75
cloud_vms0-601000.00-622188.83-638608.88-650894.58-672389.49-694594.25-706406.47-723941.20-740923.95-757903.08
cloud_storage0-875000.00-959316.03-1026023.77-1097079.00-1185203.69-1280407.13-1339105.42-1444266.50-1539606.05-1625614.88
cashflows-350000465250.00896824.57921442.01807093.58718890.93587673.58539096.88444572.04325521.83158856.94
after_tax-350000372200.00717459.66737153.60645674.87575112.74470138.87431277.50355657.63260417.47127085.55
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", - "datasetInfos": [], - "metadata": {}, - "removedWidgets": [], - "textData": null, - "type": "htmlSandbox" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# take a look at the dataframe and see how each call changes the results\n", - "create_data()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "application/vnd.databricks.v1+cell": { - "inputWidgets": {}, - "nuid": "fb19c3db-de15-4aec-a510-7fa4fabed5ec", - "showTitle": false, - "title": "" - } - }, - "source": [ - "# 25000 Simulation\n", - "This is a bit slow at 25k but not bad at 10k. we can always use the multiple processing package to speed up the run time." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "application/vnd.databricks.v1+cell": { - "inputWidgets": {}, - "nuid": "22b5f52e-8921-4120-99ce-64c4221572dd", - "showTitle": false, - "title": "" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
" - ] - }, - "metadata": { - "application/vnd.databricks.v1+output": { - "addedWidgets": {}, - "arguments": {}, - "data": "
", - "datasetInfos": [], - "metadata": {}, - "removedWidgets": [], - "type": "html" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# collect data for before and after tax npv and irr for years 3, 5, 10\n", - "# you can run NPV, IRR for both before and after tax\n", - "# in this case, I will just be using after tax but feel free to use whatever method you want to use\n", - "\n", - "# npv_irr_10 = []\n", - "npv_irr_after_tax_10 = []\n", - "# npv_irr_5 = []\n", - "npv_irr_after_tax_5 = []\n", - "# npv_irr_3 = []\n", - "npv_irr_after_tax_3 = []\n", - "for _iter in range(1, 25001):\n", - " df = create_data()\n", - " # for a static hurdle rate, risk = hurdle_rate\n", - " risk = risk_adjusted_hurdle()\n", - "# npv_irr_10.append(\n", - "# (\n", - "# round(npf.npv(risk, df.loc[\"cashflows\", :].values[1:11]) + df.loc[\"cashflows\", \"year_0\"], 2),\n", - "# round(npf.irr(df.loc[\"cashflows\", :].values[0:11]), 2) * 100\n", - "# )\n", - "# )\n", - " npv_irr_after_tax_10.append(\n", - " (\n", - " round(npf.npv(risk, df.loc[\"after_tax\", :].values[1:11]) + df.loc[\"after_tax\", \"year_0\"], 2),\n", - " round(npf.irr(df.loc[\"after_tax\", :].values[0:11]), 2) * 100\n", - " )\n", - " )\n", - "# npv_irr_5.append(\n", - "# (\n", - "# round(npf.npv(risk, df.loc[\"cashflows\", :].values[1:6]) + df.loc[\"cashflows\", \"year_0\"], 2),\n", - "# round(npf.irr(df.loc[\"cashflows\", :].values[0:6]), 2) * 100\n", - "# )\n", - "# )\n", - " npv_irr_after_tax_5.append(\n", - " (\n", - " round(npf.npv(risk, df.loc[\"after_tax\", :].values[1:6]) + df.loc[\"after_tax\", \"year_0\"], 2),\n", - " round(npf.irr(df.loc[\"after_tax\", :].values[0:6]), 2) * 100\n", - " )\n", - " )\n", - "# npv_irr_3.append(\n", - "# (\n", - "# round(npf.npv(risk, df.loc[\"cashflows\", :].values[1:4]) + df.loc[\"cashflows\", \"year_0\"], 2),\n", - "# round(npf.irr(df.loc[\"cashflows\", :].values[0:4]), 2) * 100\n", - "# )\n", - "# )\n", - " npv_irr_after_tax_3.append(\n", - " (\n", - " round(npf.npv(risk, df.loc[\"after_tax\", :].values[1:4]) + df.loc[\"after_tax\", \"year_0\"], 2),\n", - " round(npf.irr(df.loc[\"after_tax\", :].values[0:4]), 2) * 100\n", - " )\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "application/vnd.databricks.v1+cell": { - "inputWidgets": {}, - "nuid": "050cfa6b-23b8-459a-a7de-8a2ec90d83da", - "showTitle": false, - "title": "" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
" - ] - }, - "metadata": { - "application/vnd.databricks.v1+output": { - "addedWidgets": {}, - "arguments": {}, - "data": "
", - "datasetInfos": [], - "metadata": {}, - "removedWidgets": [], - "type": "html" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "def get_results_plot(npv_irr_array, num_years, before_tax=True):\n", - " \"\"\"\n", - " Plot the distributions of NPV for the given years and factor in tax or not.\n", - " :param npv_irr_array: array containing a tuple of npv, irr\n", - " :param num_years: in our case, I only look at 3, 5, 10\n", - " :para before_tax: if you want after tax calculations, you would specify that here.\n", - " \"\"\"\n", - " npv = list(map(operator.itemgetter(0), npv_irr_array))\n", - " irr = list(map(operator.itemgetter(1), npv_irr_array))\n", - "\n", - " if before_tax:\n", - " naming = \"before\"\n", - " else:\n", - " naming = \"after\"\n", - "\n", - " plt.figure(figsize=(15, 10))\n", - " plt.xlabel(\"NPV in USD\")\n", - " plt.ylabel(\"Frequency\")\n", - " plt.title(f\"{num_years} Year {naming} Tax NPV\")\n", - " N, bins, patches = plt.hist(npv, bins=85, density=True, stacked=True)\n", - " \n", - " # pick 2 colors for NPV < 0 and > 0; red and green/black looked bad in my opinion\n", - " for i in range(0, len(bins) - 1):\n", - " if patches[i].xy[0] < 0:\n", - " patches[i].set_facecolor(\"black\")\n", - " else:\n", - " patches[i].set_facecolor(\"blue\")\n", - " \n", - " plt.show()\n", - " return npv, irr" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "application/vnd.databricks.v1+cell": { - "inputWidgets": {}, - "nuid": "d891a664-af10-42ce-93b4-ca871ca2b07f", - "showTitle": false, - "title": "" - } - }, - "source": [ - "# Before Tax NPV" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "application/vnd.databricks.v1+cell": { - "inputWidgets": {}, - "nuid": "3319fbfe-0f27-4991-ad50-829e6c3a7c6f", - "showTitle": false, - "title": "" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
" - ] - }, - "metadata": { - "application/vnd.databricks.v1+output": { - "addedWidgets": {}, - "arguments": {}, - "data": "
", - "datasetInfos": [], - "metadata": {}, - "removedWidgets": [], - "type": "html" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# npv_3, irr_3 = get_results_plot(npv_irr_3, \"3\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "application/vnd.databricks.v1+cell": { - "inputWidgets": {}, - "nuid": "bae39e7d-4b4c-4ffd-900a-2cf4b871f58d", - "showTitle": false, - "title": "" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
" - ] - }, - "metadata": { - "application/vnd.databricks.v1+output": { - "addedWidgets": {}, - "arguments": {}, - "data": "
", - "datasetInfos": [], - "metadata": {}, - "removedWidgets": [], - "type": "html" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# npv_5, irr_5 = get_results_plot(npv_irr_5, \"5\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "application/vnd.databricks.v1+cell": { - "inputWidgets": {}, - "nuid": "1e330563-b860-40b6-9578-49aba56a50af", - "showTitle": false, - "title": "" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
" - ] - }, - "metadata": { - "application/vnd.databricks.v1+output": { - "addedWidgets": {}, - "arguments": {}, - "data": "
", - "datasetInfos": [], - "metadata": {}, - "removedWidgets": [], - "type": "html" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "# npv_10, irr_10 = get_results_plot(npv_irr_10, \"10\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "application/vnd.databricks.v1+cell": { - "inputWidgets": {}, - "nuid": "315a899f-5f05-4d7c-93c0-4e7cd7d31696", - "showTitle": false, - "title": "" - } - }, - "source": [ - "# After Tax NPV" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "application/vnd.databricks.v1+cell": { - "inputWidgets": {}, - "nuid": "c870903c-a8eb-4d5b-8a4a-2a6f9f0e3a6c", - "showTitle": false, - "title": "" - } - }, - "outputs": [ - { - "data": { - "image/png": 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" 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", - "datasetInfos": [], - "metadata": { - "imageDimensions": { - "height": 604, - "width": 878 - } - }, - "removedWidgets": [], - "type": "image" - }, - "image/png": { - "height": 604, - "width": 878 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "npv_3_after, irr_3_after = get_results_plot(npv_irr_after_tax_3, \"3\", before_tax=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "application/vnd.databricks.v1+cell": { - "inputWidgets": {}, - "nuid": "e4265903-ebb3-491d-a03e-bc024a714cab", - "showTitle": false, - "title": "" - } - }, - "outputs": [ - { - "data": { - "image/png": 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" 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", - "datasetInfos": [], - "metadata": { - "imageDimensions": { - "height": 604, - "width": 878 - } - }, - "removedWidgets": [], - "type": "image" - }, - "image/png": { - "height": 604, - "width": 878 - } - }, - "output_type": "display_data" - } - ], - "source": [ - "npv_5_after, irr_5_after = get_results_plot(npv_irr_after_tax_5, \"5\", before_tax=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "application/vnd.databricks.v1+cell": { - "inputWidgets": {}, - "nuid": "4b22440b-d1a6-4e36-95bf-7263a1881da8", - "showTitle": false, - "title": "" - } - }, - "outputs": [ - { - "data": { - "image/png": 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" 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", 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", - "datasetInfos": [], - "metadata": {}, - "removedWidgets": [], - "type": "html" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "print(np.mean(npv_10_after))\n", - "print(np.median(npv_10_after))\n", - "print(np.min(npv_10_after))\n", - "print(np.max(npv_10_after))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "application/vnd.databricks.v1+cell": { - "inputWidgets": {}, - "nuid": "ade2c1fa-25c9-4e16-aab2-eafa15327b6b", - "showTitle": false, - "title": "" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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", - "datasetInfos": [], - "metadata": {}, - "removedWidgets": [], - "type": "html" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "print(np.mean(npv_5_after))\n", - "print(np.median(npv_5_after))\n", - "print(np.min(npv_5_after))\n", - "print(np.max(npv_5_after))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "application/vnd.databricks.v1+cell": { - "inputWidgets": {}, - "nuid": "74379578-5ae2-4a45-b47f-07b690e3badc", - "showTitle": false, - "title": "" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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", - "datasetInfos": [], - "metadata": {}, - "removedWidgets": [], - "type": "html" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "print(np.mean(npv_3_after))\n", - "print(np.median(npv_3_after))\n", - "print(np.min(npv_3_after))\n", - "print(np.max(npv_3_after))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "application/vnd.databricks.v1+cell": { - "inputWidgets": {}, - "nuid": "203543f3-d98f-45a1-b083-9fd066bb09ba", - "showTitle": false, - "title": "" - } - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "application/vnd.databricks.v1+notebook": { - "dashboards": [], - "language": "python", - "notebookMetadata": { - "pythonIndentUnit": 2 - }, - "notebookName": "capital_budgeting_cloud_tutorial", - "notebookOrigID": 2461542657148852, - "widgets": {} - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -}