{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Learning how to understand a project management dataset and build models in Python for further analysis\n", "Today we will be exploring a synthetically generated dataset based on the PERIL database. Learn more about the PERIL dataset and it's creator Tom Kendrick [here](http://www.failureproofprojects.com/peril.php). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To accomplish our task of understanding the data, we will need to use some Python libraries. Python libraries can be summarized as code other people have written and shared that we can use to save us time. We can import and use that code with a small import statement. Some of the packages we will use today include [Pandas](https://towardsdatascience.com/a-quick-introduction-to-the-pandas-python-library-f1b678f34673), [Scipy](https://www.scipy.org/getting-started.html), [Numpy](https://docs.scipy.org/doc/numpy-1.15.0/user/whatisnumpy.html), and [MatPlotLib](https://matplotlib.org/). If you want to learn more about how these different packages work with examples, click [here](http://cs231n.github.io/python-numpy-tutorial/). If you would like to see the code that imports these packages - click the show code button below. " ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "#Importing python libraries which help us speed our coding up.\n", "#The packages we're importing are below.\n", "import pandas as pd\n", "import random\n", "from pandas.plotting import scatter_matrix\n", "import scipy as sc\n", "import numpy as np\n", "import matplotlib\n", "import matplotlib.pyplot as plt #Can abbreviate specific parts of the packages so it's easier to type\n", "import sklearn as sk\n", "import seaborn as sns\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn import metrics\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.model_selection import train_test_split\n", "from sklearn import datasets, linear_model" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "#We will now read the data in and store it in a dataframe called dataset\n", "dataset = pd.read_excel('Peril_Dataset.xlsx', header = 0)\n", "dataset.insert(loc = 0, column = 'Project Number', value = np.arange(len(dataset)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Understanding our data\n", "Let's run some code and see how many rows and columns we have. Below - you will see the result as (Number of Rows, Number of Columns)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(5000, 13)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### What do the first 10 rows of our data look like? " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Project NumberParameterCategorySub catImpactTRLDescriptionRegion NumericRegionProjectYearMonthCost
00ResourceMoneyLimitation185Did not have sufficient resources and the cont...0AmericasIT/Solution2010October700560
11ResourceOutsourcingDelayed start174Contractor setup delayed by a week0AmericasProd. Dev.2002May347922
22ResourceOutsourcingLate or poor output135Contractor did not spend time on the project b...0AmericasIT/Solution2002August919828
33ResourceOutsourcingLate or poor output137Contractors failed to show up as committed1AsiaIT/Solution2014July521495
44ResourceOutsourcingLate or poor output133Third-party vendor inadvertently introduced a ...0AmericasIT/Solution2010November322010
55ResourceOutsourcingLate or poor output167Outsouced staff lacked the necessary skillset3AfricaIT/Solution2014June498896
66ResourcePeopleLate start155Planning delayed due to staff being still tied...0AmericasProd. Dev.2015April592785
77ResourcePeopleLoss164Chef quit two days before the café was schedul...3AfricaIT/Solution2017October578928
88ResourcePeopleMotivation115Work at customer site had to be done by union ...3AfricaIT/Solution2003July318846
99ResourcePeopleQueuing166Critical task assigned to a heavily booked expert0AmericasIT/Solution2010June1109120
\n", "
" ], "text/plain": [ " Project Number Parameter Category Sub cat Impact TRL \\\n", "0 0 Resource Money Limitation 18 5 \n", "1 1 Resource Outsourcing Delayed start 17 4 \n", "2 2 Resource Outsourcing Late or poor output 13 5 \n", "3 3 Resource Outsourcing Late or poor output 13 7 \n", "4 4 Resource Outsourcing Late or poor output 13 3 \n", "5 5 Resource Outsourcing Late or poor output 16 7 \n", "6 6 Resource People Late start 15 5 \n", "7 7 Resource People Loss 16 4 \n", "8 8 Resource People Motivation 11 5 \n", "9 9 Resource People Queuing 16 6 \n", "\n", " Description Region Numeric \\\n", "0 Did not have sufficient resources and the cont... 0 \n", "1 Contractor setup delayed by a week 0 \n", "2 Contractor did not spend time on the project b... 0 \n", "3 Contractors failed to show up as committed 1 \n", "4 Third-party vendor inadvertently introduced a ... 0 \n", "5 Outsouced staff lacked the necessary skillset 3 \n", "6 Planning delayed due to staff being still tied... 0 \n", "7 Chef quit two days before the café was schedul... 3 \n", "8 Work at customer site had to be done by union ... 3 \n", "9 Critical task assigned to a heavily booked expert 0 \n", "\n", " Region Project Year Month Cost \n", "0 Americas IT/Solution 2010 October 700560 \n", "1 Americas Prod. Dev. 2002 May 347922 \n", "2 Americas IT/Solution 2002 August 919828 \n", "3 Asia IT/Solution 2014 July 521495 \n", "4 Americas IT/Solution 2010 November 322010 \n", "5 Africa IT/Solution 2014 June 498896 \n", "6 Americas Prod. Dev. 2015 April 592785 \n", "7 Africa IT/Solution 2017 October 578928 \n", "8 Africa IT/Solution 2003 July 318846 \n", "9 Americas IT/Solution 2010 June 1109120 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### What are our column names?\n", "##### As you can see above - we have 12 columns with different values\n", "Project Number: A unique number to identify each project. \n", "Parameter: The parameter of the project that was impacted. This dataset categorizes the risk in these three main groups of Scope, Resource, and Schedule. \n", "Category: The category of the risk. We have the following categories in the main parameters - Change(Scope), Defect(Scope), People(Resource), Money(Resource), Outsourcing(Resource), Delay(Schedule), Dependency(Schedule), Estimates(Schedule) \n", "Sub cat: The sub-category of the risk are the following - Creep, Dependency, Gap, Hardware, Software, Integration, Limitation, Late Start, Loss, Motivation, Queuing, Temp Loss, Decision, Information, Parts, Infrastructure, Legal, Project, Deadline, Judgement, and Learning \n", "\n", "
\n", "Below is the mapping for the Parameter -> Category -> Sub category:\n", "
\n", "\n", "* Scope -> Change -> Creep\n", "* Scope -> Change -> Dependency\n", "* Scope -> Change -> Gap \n", "
\n", "\n", "* Scope -> Defect -> Hardware\n", "* Scope -> Defect -> Software\n", "* Scope -> Defect -> Integration \n", "
\n", "\n", "* Resource -> Money -> Limitation \n", "
\n", "* Resource -> People -> Late Start\n", "* Resource -> People -> Loss\n", "* Resource -> People -> Motivation\n", "* Resource -> People -> Queuing\n", "* Resource -> People -> Temp Loss \n", "
\n", "* Schedule -> Delay -> Decision\n", "* Schedule -> Delay -> Hardware\n", "* Schedule -> Delay -> Information\n", "* Schedule -> Delay -> Parts \n", "
\n", "* Schedule -> Dependency -> Infrastructure\n", "* Schedule -> Dependency -> Legal\n", "* Schedule -> Dependency -> Project \n", "
\n", "* Schedule -> Estimates -> Deadline\n", "* Schedule -> Estimates -> Judgement\n", "* Schedule -> Estimates -> Learning \n", "
\n", "\n", "Impact: The impact to the project by number of weeks. \n", "TRL: The technology readiness level of the project. You can find more information [here](http://acqnotes.com/acqnote/tasks/technology-readiness-level). \n", "Description: The description of the issue. \n", "Region Numeric: A numeric value representing the region (0 = Americas, 1 = Asia, 2 = Eur/ME, 3 = Africa). \n", "Region: What region of the world did the issue occur in. \n", "Project: What type of project did the issue occur in. (Prod. Dev., or IT/Solution)\n", "Year: The year the issue occurred in. \n", "Cost: The cost, in USD, of the impact on the project. \n", "Month: The month the project failure occurred in. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Now let's get a breakdown of what are the counts of the values in each column\n", "Here we can see that there are three different types of parameters that each project is grouped into that we have: Scope, Resource, and Schedule. Below the chart are the raw counts of the projects in those parameter groups. " ] }, { "cell_type": "code", "execution_count": 129, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 129, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize = (3,10))\n", "plot_parameter = dataset['Parameter'].value_counts().plot(kind='bar')\n", "plot_parameter.set_xlabel(\"Parameter\")\n", "plot_parameter.set_ylabel(\"Count (Number of Projects)\")\n", "plot_parameter" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Scope 1991\n", "Resource 1579\n", "Schedule 1430\n", "Name: Parameter, dtype: int64" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset[\"Parameter\"].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What is the breakdown of the different Categories that we have? We find the following categories: Change, People, Delay, Defect, Outsourcing, Estimates, Dependency, and Money. Below the chart are the raw counts of the projects in those categories. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_category = dataset['Category'].value_counts().plot(kind='bar')\n", "plot_category.set_xlabel(\"Category\")\n", "plot_category.set_ylabel(\"Count (Number of Projects)\")\n", "plot_category" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Change 1459\n", "People 943\n", "Delay 763\n", "Defect 532\n", "Outsourcing 465\n", "Estimates 369\n", "Dependency 298\n", "Money 171\n", "Name: Category, dtype: int64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset[\"Category\"].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What is the breakdown of the sub-categories? Here we find the following: Gap, Creep, Late or poor output, Hardware, Parts, Loss, Temp loss, Queueing, Software, Learning, Limitation, Information, Judgement, Decision, Dependency, Infrastructure, Project, Late start, Delayed start, Integration, Deadline, Motivation, Legal, Project, Infrastructure, and Late or poor output. Below the chart are the raw counts of the projects in those sub-categories. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_subcat = dataset['Sub cat'].value_counts().plot(kind='bar')\n", "plot_subcat.set_xlabel(\"Sub-Category\")\n", "plot_subcat.set_ylabel(\"Count (Number of Projects)\")\n", "plot_subcat" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Gap 768\n", "Creep 576\n", "Late or poor output 380\n", "Hardware 373\n", "Parts 315\n", "Loss 298\n", "Temp loss 256\n", "Queuing 250\n", "Software 249\n", "Learning 173\n", "Limitation 171\n", "Information 161\n", "Judgment 138\n", "Decision 134\n", "Dependency 115\n", "Infrastructure 113\n", "Project 95\n", "Late start 93\n", "Delayed start 76\n", "Integration 63\n", "Deadline 58\n", "Motivation 51\n", "Legal 46\n", "Project 37\n", "Infrastructure 7\n", "Late or poor output 4\n", "Name: Sub cat, dtype: int64" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset[\"Sub cat\"].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here you can see that the Region column has four separate regions and the count of values in those regions is provided below in the table. " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Americas 2872\n", "Eur/ME 945\n", "Africa 636\n", "Asia 547\n", "Name: Region, dtype: int64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset[\"Region\"].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What years do we have in our dataset? 2001-2019" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2010 578\n", "2007 435\n", "2019 364\n", "2015 341\n", "2002 267\n", "2013 235\n", "2003 233\n", "2005 226\n", "2012 225\n", "2006 223\n", "2004 223\n", "2018 221\n", "2001 219\n", "2016 212\n", "2011 205\n", "2009 199\n", "2008 199\n", "2017 198\n", "2014 197\n", "Name: Year, dtype: int64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset[\"Year\"].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What is the distribution of the Impact levels in our data? " ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_impact = dataset['Impact'].value_counts().plot(kind='bar')\n", "plot_impact.set_xlabel(\"Impact Level\")\n", "plot_impact.set_ylabel(\"Count (Number of Projects)\")\n", "plot_impact" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analysis\n", "#### Below we'll start looking at analyzing projects from different perspectives to understand which parameters, categories, sub-categories, TRL levels, regions, project types, and dates produced the highest costs and impacts. \n", "\n", "The Column Names and Values that can be inputted below: \n", "\n", "##### The Project Number\n", "* A number between 0-4999. \n", "\n", "##### Parameter \n", "* Scope\n", "* Resource\n", "* Schedule\n", "\n", "##### Category\n", "* Change\n", "* Defect\n", "* Delay\n", "* Dependency\n", "* Estimates\n", "* Money\n", "* Outsourcing \n", "* People\n", "\n", "##### Sub cat \n", "* Creep\n", "* Dependency\n", "* Gap\n", "* Hardware\n", "* Software\n", "* Integration\n", "* Limitation\n", "* Late Start\n", "* Loss\n", "* Motivation\n", "* Queuing\n", "* Temp Loss\n", "* Decision\n", "* Information\n", "* Parts\n", "* Infrastructure\n", "* Legal\n", "* Project\n", "* Deadline\n", "* Judgement\n", "* Learning \n", "\n", "##### Impact \n", "* Numbers between 1-26 in weeks\n", "\n", "##### TRL \n", "* Numbers between 1-9\n", "\n", "##### Description \n", "\n", "##### Region Numeric\n", "* 0: Americas\n", "* 1: Asia\n", "* 2: Eur/ME\n", "* 3: Africa\n", "\n", "##### Region \n", "* Americas\n", "* Asia\n", "* Eur/ME\n", "* Africa\n", "\n", "##### Project \n", "* IT/Solution\n", "* Prod. Dev.\n", "\n", "##### Year \n", "* Years from 2001-2019\n", "\n", "##### Cost \n", "* Number between 10787 and 2543242\n", "\n", "##### Month \n", "* Any month in the calendar year (January - December)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__What is the cost distribution in our data? We find that we have a median between \"500,000\" and \"1,000,000\" and the cost values are skewed to the left.__" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "boxplot = dataset.boxplot(column=['Cost'], vert = False)\n", "boxplot.set_xlabel(\"Cost (in USD)\")\n", "boxplot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we want to understand which projects contribute to the outliers, let's filter the data to projects where the cost was greater than $2,000,000. Let's also sort the data so we can see the projects with the highest cost failures first. " ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Project NumberParameterCategorySub catImpactTRLDescriptionRegion NumericRegionProjectYearMonthCost
38423842ResourcePeopleLate start263Change Management Lead took unexpected medical...1AsiaIT/Solution2003November2562560
49404940ScopeChangeGap268Chip failed and had to be refabricated with ch...2Eur/MEIT/Solution2018January2548442
37053705ResourceOutsourcingDelayed start262Project had to wait until all data collection ...2Eur/MEProd. Dev.2001October2543034
49564956ScopeChangeGap266The scope of the project was poorly defined0AmericasIT/Solution2010September2535416
49384938ScopeChangeGap263Ship sinks with critical part; forced to repla...0AmericasProd. Dev.2010February2533232
40774077ScheduleDependencyProject265Client's insisted on the use of \"the latest te...0AmericasIT/Solution2010May2526342
49914991ResourcePeopleLoss262To expand business, unrealistic commitments we...3AfricaProd. Dev.2004May2504268
47884788ResourcePeopleLoss252Contract lab failed to inform us that they had...3AfricaProd. Dev.2002December2469325
45494549ResourceMoneyLimitation264\"Minor\" changes added and accepted late in pr...0AmericasIT/Solution2002January2437292
45524552ResourceMoneyLimitation267Server technology chage was necessitated to su...2Eur/MEProd. Dev.2015December2425748
41334133ResourceMoneyLimitation246Shipping documents not filled in correctly0AmericasIT/Solution2006March2383512
41164116ScopeChangeGap256Key contributor out ill at critical time.0AmericasIT/Solution2019December2371375
35803580ScheduleDelayParts242Team leader reassigned to another project and ...0AmericasIT/Solution2014November2366784
49254925ScopeChangeCreep252Outsourcing partner in Switzerland did unneede...0AmericasProd. Dev.2010November2360300
33943394ScheduleDependencyInfrastructure266Need to upgrade detected only at project end2Eur/MEProd. Dev.2003March2357602
42514251ResourceOutsourcingLate or poor output262Late in project, the budget and staff are cut ...0AmericasProd. Dev.2017March2350920
48434843ResourceOutsourcingLate start254Internal supplier problem; needed components d...3AfricaProd. Dev.2013June2326750
37303730ResourcePeopleQueuing246Planning delayed due to staff being still tied...0AmericasProd. Dev.2015August2324688
45984598ResourceOutsourcingLate or poor output256Product owner having unique knowledge left the...0AmericasIT/Solution2010February2316300
47974797ScopeChangeCreep247Late test results came back negative3AfricaIT/Solution2011January2309472
35233523ScopeChangeGap248Several employees had to take a leave of absen...1AsiaProd. Dev.2011August2301120
49334933ScopeChangeGap251Negotiation and training on a new tool took mo...0AmericasProd. Dev.2015September2292350
37403740ResourcePeopleTemp loss235Development plans failed to include all of the...0AmericasIT/Solution2016January2286108
46414641ResourcePeopleTemp loss245Delay in hiring more engineers due to cost cut...3AfricaIT/Solution2010June2286048
47534753ScopeChangeGap253Components do not work as documented. Replace...0AmericasIT/Solution2015September2285325
43934393ResourcePeopleLoss253Software modules in system do not work togethe...0AmericasProd. Dev.2008April2276500
45454545ScopeChangeGap231Escalated late changes from Sponsor0AmericasIT/Solution2008January2276126
45954595ResourceOutsourcingDelayed start255Market change due to Improvements in technolog...0AmericasIT/Solution2015May2265925
47254725ScopeChangeCreep239User specifications not defined until late in ...0AmericasIT/Solution2010July2259957
48754875ScheduleDelayHardware259Issues discovered late in testing0AmericasProd. Dev.2015May2257500
..........................................
49054905ScheduleEstimatesJudgment256Lack of testing of older WWW browser versions ...0AmericasProd. Dev.2015November2161575
48644864ResourcePeopleQueuing231Lack of understanding by the manager of the sy...2Eur/MEProd. Dev.2007April2157676
40834083ScheduleEstimatesLearning225Project delayed due to recent/last minute chan...1AsiaProd. Dev.2014March2150148
45994599ResourceOutsourcingLate or poor output251Poor communications resulted in never truly un...0AmericasIT/Solution2010March2149800
47474747ScopeChangeGap263Product developed for multiple platforms, but ...0AmericasProd. Dev.2010July2127450
48834883ScheduleDelayParts244Apartments were sold before construction begin...2Eur/MEIT/Solution2007December2124000
41604160ScheduleDelayParts247Cable required had been left off the implement...2Eur/MEIT/Solution2006April2106312
40004000ResourcePeopleLoss269Client requested \"15 minute\" change accepted a...1AsiaProd. Dev.2003November2098902
47794779ScopeDefectSoftware233Too few disc drives, and insufficient \"owned\" ...0AmericasProd. Dev.2015August2095208
35183518ScheduleDelayDecision242Poor Fit/Gap analysis1AsiaProd. Dev.2006November2094336
44374437ResourceMoneyLimitation265None of the developers had experience with the...2Eur/MEProd. Dev.2009July2093078
43164316ScopeChangeGap239Lack of spare parts and expertise0AmericasIT/Solution2011August2092333
37263726ResourcePeopleLoss249Project start delayed; staffing and buy-in lat...1AsiaIT/Solution2011June2090328
49474947ScopeChangeGap234Inexperienced staff0AmericasProd. Dev.2015April2088676
49864986ScopeDefectSoftware256Initial code release was a major failure0AmericasIT/Solution2010July2085275
38313831ScheduleDelayHardware224After more than 2/3 of the original budget had...2Eur/MEProd. Dev.2003March2085204
48354835ResourceOutsourcingLate or poor output247Parallel tasks require resources beyond what i...2Eur/MEIT/Solution2001July2082432
46824682ScheduleDelayParts263Supplier was unable to make a sample correctly2Eur/MEIT/Solution2004January2081560
41834183ScopeChangeGap259Scope not defined well; huge software delays0AmericasIT/Solution2003July2075050
33453345ResourcePeopleTemp loss248Scope expanded during the course of the project1AsiaIT/Solution2017December2055624
43184318ResourcePeopleTemp loss222Move out of old location required, but new spa...2Eur/MEIT/Solution2017November2052248
35573557ResourcePeopleQueuing243Inadequate procedures for acceptance, quality,...1AsiaProd. Dev.2015May2052144
38573857ResourcePeopleLoss257Team was staffed by 3 successive new hires in ...2Eur/MEIT/Solution2010March2050425
35563556ResourcePeopleQueuing251A pool of unqualified, poorly performing, marg...1AsiaProd. Dev.2013September2041850
38983898ResourceOutsourcingLate or poor output215Errors found in data and critrical reports1AsiaIT/Solution2011March2040171
38783878ResourceOutsourcingLate or poor output228Late stakeholders redefine requiremnts2Eur/MEIT/Solution2017April2035968
32353235ScopeDefectHardware219Conversion from legacy system to SAP has multi...2Eur/MEIT/Solution2017April2033829
35773577ScheduleDelayInformation223Project team was not aware that the new releas...2Eur/MEIT/Solution2016January2023054
44534453ScopeChangeCreep244Unexpectedly high demand requires outlisde help0AmericasProd. Dev.2010April2018520
33293329ResourcePeopleMotivation248Lack of head count from a 10% company-wide lay...0AmericasIT/Solution2003March2002488
\n", "

72 rows × 13 columns

\n", "
" ], "text/plain": [ " Project Number Parameter Category Sub cat Impact TRL \\\n", "3842 3842 Resource People Late start 26 3 \n", "4940 4940 Scope Change Gap 26 8 \n", "3705 3705 Resource Outsourcing Delayed start 26 2 \n", "4956 4956 Scope Change Gap 26 6 \n", "4938 4938 Scope Change Gap 26 3 \n", "4077 4077 Schedule Dependency Project 26 5 \n", "4991 4991 Resource People Loss 26 2 \n", "4788 4788 Resource People Loss 25 2 \n", "4549 4549 Resource Money Limitation 26 4 \n", "4552 4552 Resource Money Limitation 26 7 \n", "4133 4133 Resource Money Limitation 24 6 \n", "4116 4116 Scope Change Gap 25 6 \n", "3580 3580 Schedule Delay Parts 24 2 \n", "4925 4925 Scope Change Creep 25 2 \n", "3394 3394 Schedule Dependency Infrastructure 26 6 \n", "4251 4251 Resource Outsourcing Late or poor output 26 2 \n", "4843 4843 Resource Outsourcing Late start 25 4 \n", "3730 3730 Resource People Queuing 24 6 \n", "4598 4598 Resource Outsourcing Late or poor output 25 6 \n", "4797 4797 Scope Change Creep 24 7 \n", "3523 3523 Scope Change Gap 24 8 \n", "4933 4933 Scope Change Gap 25 1 \n", "3740 3740 Resource People Temp loss 23 5 \n", "4641 4641 Resource People Temp loss 24 5 \n", "4753 4753 Scope Change Gap 25 3 \n", "4393 4393 Resource People Loss 25 3 \n", "4545 4545 Scope Change Gap 23 1 \n", "4595 4595 Resource Outsourcing Delayed start 25 5 \n", "4725 4725 Scope Change Creep 23 9 \n", "4875 4875 Schedule Delay Hardware 25 9 \n", "... ... ... ... ... ... ... \n", "4905 4905 Schedule Estimates Judgment 25 6 \n", "4864 4864 Resource People Queuing 23 1 \n", "4083 4083 Schedule Estimates Learning 22 5 \n", "4599 4599 Resource Outsourcing Late or poor output 25 1 \n", "4747 4747 Scope Change Gap 26 3 \n", "4883 4883 Schedule Delay Parts 24 4 \n", "4160 4160 Schedule Delay Parts 24 7 \n", "4000 4000 Resource People Loss 26 9 \n", "4779 4779 Scope Defect Software 23 3 \n", "3518 3518 Schedule Delay Decision 24 2 \n", "4437 4437 Resource Money Limitation 26 5 \n", "4316 4316 Scope Change Gap 23 9 \n", "3726 3726 Resource People Loss 24 9 \n", "4947 4947 Scope Change Gap 23 4 \n", "4986 4986 Scope Defect Software 25 6 \n", "3831 3831 Schedule Delay Hardware 22 4 \n", "4835 4835 Resource Outsourcing Late or poor output 24 7 \n", "4682 4682 Schedule Delay Parts 26 3 \n", "4183 4183 Scope Change Gap 25 9 \n", "3345 3345 Resource People Temp loss 24 8 \n", "4318 4318 Resource People Temp loss 22 2 \n", "3557 3557 Resource People Queuing 24 3 \n", "3857 3857 Resource People Loss 25 7 \n", "3556 3556 Resource People Queuing 25 1 \n", "3898 3898 Resource Outsourcing Late or poor output 21 5 \n", "3878 3878 Resource Outsourcing Late or poor output 22 8 \n", "3235 3235 Scope Defect Hardware 21 9 \n", "3577 3577 Schedule Delay Information 22 3 \n", "4453 4453 Scope Change Creep 24 4 \n", "3329 3329 Resource People Motivation 24 8 \n", "\n", " Description Region Numeric \\\n", "3842 Change Management Lead took unexpected medical... 1 \n", "4940 Chip failed and had to be refabricated with ch... 2 \n", "3705 Project had to wait until all data collection ... 2 \n", "4956 The scope of the project was poorly defined 0 \n", "4938 Ship sinks with critical part; forced to repla... 0 \n", "4077 Client's insisted on the use of \"the latest te... 0 \n", "4991 To expand business, unrealistic commitments we... 3 \n", "4788 Contract lab failed to inform us that they had... 3 \n", "4549 \"Minor\" changes added and accepted late in pr... 0 \n", "4552 Server technology chage was necessitated to su... 2 \n", "4133 Shipping documents not filled in correctly 0 \n", "4116 Key contributor out ill at critical time. 0 \n", "3580 Team leader reassigned to another project and ... 0 \n", "4925 Outsourcing partner in Switzerland did unneede... 0 \n", "3394 Need to upgrade detected only at project end 2 \n", "4251 Late in project, the budget and staff are cut ... 0 \n", "4843 Internal supplier problem; needed components d... 3 \n", "3730 Planning delayed due to staff being still tied... 0 \n", "4598 Product owner having unique knowledge left the... 0 \n", "4797 Late test results came back negative 3 \n", "3523 Several employees had to take a leave of absen... 1 \n", "4933 Negotiation and training on a new tool took mo... 0 \n", "3740 Development plans failed to include all of the... 0 \n", "4641 Delay in hiring more engineers due to cost cut... 3 \n", "4753 Components do not work as documented. Replace... 0 \n", "4393 Software modules in system do not work togethe... 0 \n", "4545 Escalated late changes from Sponsor 0 \n", "4595 Market change due to Improvements in technolog... 0 \n", "4725 User specifications not defined until late in ... 0 \n", "4875 Issues discovered late in testing 0 \n", "... ... ... \n", "4905 Lack of testing of older WWW browser versions ... 0 \n", "4864 Lack of understanding by the manager of the sy... 2 \n", "4083 Project delayed due to recent/last minute chan... 1 \n", "4599 Poor communications resulted in never truly un... 0 \n", "4747 Product developed for multiple platforms, but ... 0 \n", "4883 Apartments were sold before construction begin... 2 \n", "4160 Cable required had been left off the implement... 2 \n", "4000 Client requested \"15 minute\" change accepted a... 1 \n", "4779 Too few disc drives, and insufficient \"owned\" ... 0 \n", "3518 Poor Fit/Gap analysis 1 \n", "4437 None of the developers had experience with the... 2 \n", "4316 Lack of spare parts and expertise 0 \n", "3726 Project start delayed; staffing and buy-in lat... 1 \n", "4947 Inexperienced staff 0 \n", "4986 Initial code release was a major failure 0 \n", "3831 After more than 2/3 of the original budget had... 2 \n", "4835 Parallel tasks require resources beyond what i... 2 \n", "4682 Supplier was unable to make a sample correctly 2 \n", "4183 Scope not defined well; huge software delays 0 \n", "3345 Scope expanded during the course of the project 1 \n", "4318 Move out of old location required, but new spa... 2 \n", "3557 Inadequate procedures for acceptance, quality,... 1 \n", "3857 Team was staffed by 3 successive new hires in ... 2 \n", "3556 A pool of unqualified, poorly performing, marg... 1 \n", "3898 Errors found in data and critrical reports 1 \n", "3878 Late stakeholders redefine requiremnts 2 \n", "3235 Conversion from legacy system to SAP has multi... 2 \n", "3577 Project team was not aware that the new releas... 2 \n", "4453 Unexpectedly high demand requires outlisde help 0 \n", "3329 Lack of head count from a 10% company-wide lay... 0 \n", "\n", " Region Project Year Month Cost \n", "3842 Asia IT/Solution 2003 November 2562560 \n", "4940 Eur/ME IT/Solution 2018 January 2548442 \n", "3705 Eur/ME Prod. Dev. 2001 October 2543034 \n", "4956 Americas IT/Solution 2010 September 2535416 \n", "4938 Americas Prod. Dev. 2010 February 2533232 \n", "4077 Americas IT/Solution 2010 May 2526342 \n", "4991 Africa Prod. Dev. 2004 May 2504268 \n", "4788 Africa Prod. Dev. 2002 December 2469325 \n", "4549 Americas IT/Solution 2002 January 2437292 \n", "4552 Eur/ME Prod. Dev. 2015 December 2425748 \n", "4133 Americas IT/Solution 2006 March 2383512 \n", "4116 Americas IT/Solution 2019 December 2371375 \n", "3580 Americas IT/Solution 2014 November 2366784 \n", "4925 Americas Prod. Dev. 2010 November 2360300 \n", "3394 Eur/ME Prod. Dev. 2003 March 2357602 \n", "4251 Americas Prod. Dev. 2017 March 2350920 \n", "4843 Africa Prod. Dev. 2013 June 2326750 \n", "3730 Americas Prod. Dev. 2015 August 2324688 \n", "4598 Americas IT/Solution 2010 February 2316300 \n", "4797 Africa IT/Solution 2011 January 2309472 \n", "3523 Asia Prod. Dev. 2011 August 2301120 \n", "4933 Americas Prod. Dev. 2015 September 2292350 \n", "3740 Americas IT/Solution 2016 January 2286108 \n", "4641 Africa IT/Solution 2010 June 2286048 \n", "4753 Americas IT/Solution 2015 September 2285325 \n", "4393 Americas Prod. Dev. 2008 April 2276500 \n", "4545 Americas IT/Solution 2008 January 2276126 \n", "4595 Americas IT/Solution 2015 May 2265925 \n", "4725 Americas IT/Solution 2010 July 2259957 \n", "4875 Americas Prod. Dev. 2015 May 2257500 \n", "... ... ... ... ... ... \n", "4905 Americas Prod. Dev. 2015 November 2161575 \n", "4864 Eur/ME Prod. Dev. 2007 April 2157676 \n", "4083 Asia Prod. Dev. 2014 March 2150148 \n", "4599 Americas IT/Solution 2010 March 2149800 \n", "4747 Americas Prod. Dev. 2010 July 2127450 \n", "4883 Eur/ME IT/Solution 2007 December 2124000 \n", "4160 Eur/ME IT/Solution 2006 April 2106312 \n", "4000 Asia Prod. Dev. 2003 November 2098902 \n", "4779 Americas Prod. Dev. 2015 August 2095208 \n", "3518 Asia Prod. Dev. 2006 November 2094336 \n", "4437 Eur/ME Prod. Dev. 2009 July 2093078 \n", "4316 Americas IT/Solution 2011 August 2092333 \n", "3726 Asia IT/Solution 2011 June 2090328 \n", "4947 Americas Prod. Dev. 2015 April 2088676 \n", "4986 Americas IT/Solution 2010 July 2085275 \n", "3831 Eur/ME Prod. Dev. 2003 March 2085204 \n", "4835 Eur/ME IT/Solution 2001 July 2082432 \n", "4682 Eur/ME IT/Solution 2004 January 2081560 \n", "4183 Americas IT/Solution 2003 July 2075050 \n", "3345 Asia IT/Solution 2017 December 2055624 \n", "4318 Eur/ME IT/Solution 2017 November 2052248 \n", "3557 Asia Prod. Dev. 2015 May 2052144 \n", "3857 Eur/ME IT/Solution 2010 March 2050425 \n", "3556 Asia Prod. Dev. 2013 September 2041850 \n", "3898 Asia IT/Solution 2011 March 2040171 \n", "3878 Eur/ME IT/Solution 2017 April 2035968 \n", "3235 Eur/ME IT/Solution 2017 April 2033829 \n", "3577 Eur/ME IT/Solution 2016 January 2023054 \n", "4453 Americas Prod. Dev. 2010 April 2018520 \n", "3329 Americas IT/Solution 2003 March 2002488 \n", "\n", "[72 rows x 13 columns]" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset_high_cost = dataset[dataset['Cost']>2000000]\n", "dataset_high_cost = dataset_high_cost.sort_values(['Cost'], ascending = False)\n", "dataset_high_cost" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We find 72 rows where the cost value is greater than $2,000,000. Let's plot the parameters, categories, and sub-categories off these project failures to get a better sense of what types of projects comprise these high cost failures. " ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,'The Count of High Cost Project Failures by Parameter')" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_high_cost = dataset_high_cost['Parameter'].value_counts().plot(kind='bar')\n", "plt.xlabel('Parameter')\n", "plt.ylabel('Count (of Project Failures)')\n", "plt.title('The Count of High Cost Project Failures by Parameter')" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,'The Count of High Cost Project Failures by Category')" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_high_cost_by_category = dataset_high_cost['Category'].value_counts().plot(kind='bar')\n", "plt.xlabel('Category')\n", "plt.ylabel('Count (of Project Failures)')\n", "plt.title('The Count of High Cost Project Failures by Category')" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,'The Count of High Cost Project Failures by Sub-Category')" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_high_cost_by_subcat = dataset_high_cost['Sub cat'].value_counts().plot(kind='bar')\n", "plt.xlabel('Sub-Category')\n", "plt.ylabel('Count (of Project Failures)')\n", "plt.title('The Count of High Cost Project Failures by Sub-Category')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's plot these high cost project failures to see which one has the most. " ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,'The Count of High Cost Project Failures by Region')" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_high_cost_by_region = dataset_high_cost['Region'].value_counts().plot(kind='bar')\n", "plt.xlabel('Region')\n", "plt.ylabel('Count (of Project Failures)')\n", "plt.title('The Count of High Cost Project Failures by Region')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Let's say we want to look at the data for the projects in the Americas region - we can run the following code. Let's also sort the data so we can see the projects with the highest cost failures first. If you want to see the data for a difference region then change 'Americas' to a different region (copy and paste the region name from above). For example you can copy and paste the line below to see the data for just the Europe/Middle East region:__\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Copy and paste the code below:
\n", "region_filter = dataset.loc[dataset['Region'] == 'Eur/ME']
\n", "region_filter = region_filter.sort_values(['Cost'], ascending = False)
\n", "region_filter.head(50) " ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Project NumberParameterCategorySub catImpactTRLDescriptionRegion NumericRegionProjectYearMonthCost
49564956ScopeChangeGap266The scope of the project was poorly defined0AmericasIT/Solution2010September2535416
49384938ScopeChangeGap263Ship sinks with critical part; forced to repla...0AmericasProd. Dev.2010February2533232
40774077ScheduleDependencyProject265Client's insisted on the use of \"the latest te...0AmericasIT/Solution2010May2526342
45494549ResourceMoneyLimitation264\"Minor\" changes added and accepted late in pr...0AmericasIT/Solution2002January2437292
41334133ResourceMoneyLimitation246Shipping documents not filled in correctly0AmericasIT/Solution2006March2383512
41164116ScopeChangeGap256Key contributor out ill at critical time.0AmericasIT/Solution2019December2371375
35803580ScheduleDelayParts242Team leader reassigned to another project and ...0AmericasIT/Solution2014November2366784
49254925ScopeChangeCreep252Outsourcing partner in Switzerland did unneede...0AmericasProd. Dev.2010November2360300
42514251ResourceOutsourcingLate or poor output262Late in project, the budget and staff are cut ...0AmericasProd. Dev.2017March2350920
37303730ResourcePeopleQueuing246Planning delayed due to staff being still tied...0AmericasProd. Dev.2015August2324688
45984598ResourceOutsourcingLate or poor output256Product owner having unique knowledge left the...0AmericasIT/Solution2010February2316300
49334933ScopeChangeGap251Negotiation and training on a new tool took mo...0AmericasProd. Dev.2015September2292350
37403740ResourcePeopleTemp loss235Development plans failed to include all of the...0AmericasIT/Solution2016January2286108
47534753ScopeChangeGap253Components do not work as documented. Replace...0AmericasIT/Solution2015September2285325
43934393ResourcePeopleLoss253Software modules in system do not work togethe...0AmericasProd. Dev.2008April2276500
45454545ScopeChangeGap231Escalated late changes from Sponsor0AmericasIT/Solution2008January2276126
45954595ResourceOutsourcingDelayed start255Market change due to Improvements in technolog...0AmericasIT/Solution2015May2265925
47254725ScopeChangeCreep239User specifications not defined until late in ...0AmericasIT/Solution2010July2259957
48754875ScheduleDelayHardware259Issues discovered late in testing0AmericasProd. Dev.2015May2257500
33373337ResourcePeopleQueuing256Building permit was issued late due to roof da...0AmericasIT/Solution2002June2227875
49704970ScopeChangeGap232Defects discovered in late testing0AmericasIT/Solution2010February2227527
34693469ScopeDefectIntegration263Program Manager squeezes in additional functio...0AmericasIT/Solution2014January2226432
38823882ScheduleDelayInformation255Foundation was inadaquate and needed upgrading0AmericasIT/Solution2003February2200725
44234423ScopeChangeGap227Partner software is incompatible with current ...0AmericasProd. Dev.2015November2180992
48424842ResourceOutsourcingLate or poor output268The Project Lead did not hold the customer/bus...0AmericasIT/Solution2015June2164214
49054905ScheduleEstimatesJudgment256Lack of testing of older WWW browser versions ...0AmericasProd. Dev.2015November2161575
45994599ResourceOutsourcingLate or poor output251Poor communications resulted in never truly un...0AmericasIT/Solution2010March2149800
47474747ScopeChangeGap263Product developed for multiple platforms, but ...0AmericasProd. Dev.2010July2127450
47794779ScopeDefectSoftware233Too few disc drives, and insufficient \"owned\" ...0AmericasProd. Dev.2015August2095208
43164316ScopeChangeGap239Lack of spare parts and expertise0AmericasIT/Solution2011August2092333
49474947ScopeChangeGap234Inexperienced staff0AmericasProd. Dev.2015April2088676
49864986ScopeDefectSoftware256Initial code release was a major failure0AmericasIT/Solution2010July2085275
41834183ScopeChangeGap259Scope not defined well; huge software delays0AmericasIT/Solution2003July2075050
44534453ScopeChangeCreep244Unexpectedly high demand requires outlisde help0AmericasProd. Dev.2010April2018520
33293329ResourcePeopleMotivation248Lack of head count from a 10% company-wide lay...0AmericasIT/Solution2003March2002488
47164716ScheduleEstimatesJudgment214Changing product definition and poor estimatio...0AmericasIT/Solution2015October1999221
42564256ResourceOutsourcingLate or poor output239Resources thin and tasks queued in backlogs.0AmericasProd. Dev.2002July1994169
40354035ScopeChangeGap226The project team agreed to new requirements; u...0AmericasIT/Solution2015September1987920
44004400ScheduleDependencyProject242Controlled Document rejected by Approvers0AmericasIT/Solution2010September1985688
37343734ResourcePeopleTemp loss224Programmers were volunteers, and ultimately al...0AmericasProd. Dev.2015July1975996
36153615ScopeChangeCreep269Developer added a new routine that was not ful...0AmericasProd. Dev.2008October1974674
46604660ScheduleDelayDecision201Key resource tied to a previous project for tw...0AmericasIT/Solution2015March1973680
45054505ScheduleDelayParts253Had only 1/3 of the units needed on time0AmericasIT/Solution2015October1972475
38383838ScopeChangeCreep267New application works, but data collected is n...0AmericasIT/Solution2005December1961206
46504650ResourcePeopleTemp loss219Project team members reassigned. Not replaced...0AmericasIT/Solution2010May1955856
49944994ScheduleDelayParts247Freelancer hired was a nutjob, and lacked t...0AmericasIT/Solution2015September1952040
40094009ResourceOutsourcingDelayed start251Upgrade to software required. Planning and tr...0AmericasProd. Dev.2016April1933950
35603560ResourcePeopleQueuing262Six weeks in, a redesign to the most fundament...0AmericasIT/Solution2011July1928602
44184418ScopeChangeGap231Contributor couldn't work due to injury and ba...0AmericasIT/Solution2010May1926181
45004500ScheduleDelayHardware256Needed system delivered to wrong building; los...0AmericasIT/Solution2015September1923300
\n", "
" ], "text/plain": [ " Project Number Parameter Category Sub cat Impact TRL \\\n", "4956 4956 Scope Change Gap 26 6 \n", "4938 4938 Scope Change Gap 26 3 \n", "4077 4077 Schedule Dependency Project 26 5 \n", "4549 4549 Resource Money Limitation 26 4 \n", "4133 4133 Resource Money Limitation 24 6 \n", "4116 4116 Scope Change Gap 25 6 \n", "3580 3580 Schedule Delay Parts 24 2 \n", "4925 4925 Scope Change Creep 25 2 \n", "4251 4251 Resource Outsourcing Late or poor output 26 2 \n", "3730 3730 Resource People Queuing 24 6 \n", "4598 4598 Resource Outsourcing Late or poor output 25 6 \n", "4933 4933 Scope Change Gap 25 1 \n", "3740 3740 Resource People Temp loss 23 5 \n", "4753 4753 Scope Change Gap 25 3 \n", "4393 4393 Resource People Loss 25 3 \n", "4545 4545 Scope Change Gap 23 1 \n", "4595 4595 Resource Outsourcing Delayed start 25 5 \n", "4725 4725 Scope Change Creep 23 9 \n", "4875 4875 Schedule Delay Hardware 25 9 \n", "3337 3337 Resource People Queuing 25 6 \n", "4970 4970 Scope Change Gap 23 2 \n", "3469 3469 Scope Defect Integration 26 3 \n", "3882 3882 Schedule Delay Information 25 5 \n", "4423 4423 Scope Change Gap 22 7 \n", "4842 4842 Resource Outsourcing Late or poor output 26 8 \n", "4905 4905 Schedule Estimates Judgment 25 6 \n", "4599 4599 Resource Outsourcing Late or poor output 25 1 \n", "4747 4747 Scope Change Gap 26 3 \n", "4779 4779 Scope Defect Software 23 3 \n", "4316 4316 Scope Change Gap 23 9 \n", "4947 4947 Scope Change Gap 23 4 \n", "4986 4986 Scope Defect Software 25 6 \n", "4183 4183 Scope Change Gap 25 9 \n", "4453 4453 Scope Change Creep 24 4 \n", "3329 3329 Resource People Motivation 24 8 \n", "4716 4716 Schedule Estimates Judgment 21 4 \n", "4256 4256 Resource Outsourcing Late or poor output 23 9 \n", "4035 4035 Scope Change Gap 22 6 \n", "4400 4400 Schedule Dependency Project 24 2 \n", "3734 3734 Resource People Temp loss 22 4 \n", "3615 3615 Scope Change Creep 26 9 \n", "4660 4660 Schedule Delay Decision 20 1 \n", "4505 4505 Schedule Delay Parts 25 3 \n", "3838 3838 Scope Change Creep 26 7 \n", "4650 4650 Resource People Temp loss 21 9 \n", "4994 4994 Schedule Delay Parts 24 7 \n", "4009 4009 Resource Outsourcing Delayed start 25 1 \n", "3560 3560 Resource People Queuing 26 2 \n", "4418 4418 Scope Change Gap 23 1 \n", "4500 4500 Schedule Delay Hardware 25 6 \n", "\n", " Description Region Numeric \\\n", "4956 The scope of the project was poorly defined 0 \n", "4938 Ship sinks with critical part; forced to repla... 0 \n", "4077 Client's insisted on the use of \"the latest te... 0 \n", "4549 \"Minor\" changes added and accepted late in pr... 0 \n", "4133 Shipping documents not filled in correctly 0 \n", "4116 Key contributor out ill at critical time. 0 \n", "3580 Team leader reassigned to another project and ... 0 \n", "4925 Outsourcing partner in Switzerland did unneede... 0 \n", "4251 Late in project, the budget and staff are cut ... 0 \n", "3730 Planning delayed due to staff being still tied... 0 \n", "4598 Product owner having unique knowledge left the... 0 \n", "4933 Negotiation and training on a new tool took mo... 0 \n", "3740 Development plans failed to include all of the... 0 \n", "4753 Components do not work as documented. Replace... 0 \n", "4393 Software modules in system do not work togethe... 0 \n", "4545 Escalated late changes from Sponsor 0 \n", "4595 Market change due to Improvements in technolog... 0 \n", "4725 User specifications not defined until late in ... 0 \n", "4875 Issues discovered late in testing 0 \n", "3337 Building permit was issued late due to roof da... 0 \n", "4970 Defects discovered in late testing 0 \n", "3469 Program Manager squeezes in additional functio... 0 \n", "3882 Foundation was inadaquate and needed upgrading 0 \n", "4423 Partner software is incompatible with current ... 0 \n", "4842 The Project Lead did not hold the customer/bus... 0 \n", "4905 Lack of testing of older WWW browser versions ... 0 \n", "4599 Poor communications resulted in never truly un... 0 \n", "4747 Product developed for multiple platforms, but ... 0 \n", "4779 Too few disc drives, and insufficient \"owned\" ... 0 \n", "4316 Lack of spare parts and expertise 0 \n", "4947 Inexperienced staff 0 \n", "4986 Initial code release was a major failure 0 \n", "4183 Scope not defined well; huge software delays 0 \n", "4453 Unexpectedly high demand requires outlisde help 0 \n", "3329 Lack of head count from a 10% company-wide lay... 0 \n", "4716 Changing product definition and poor estimatio... 0 \n", "4256 Resources thin and tasks queued in backlogs. 0 \n", "4035 The project team agreed to new requirements; u... 0 \n", "4400 Controlled Document rejected by Approvers 0 \n", "3734 Programmers were volunteers, and ultimately al... 0 \n", "3615 Developer added a new routine that was not ful... 0 \n", "4660 Key resource tied to a previous project for tw... 0 \n", "4505 Had only 1/3 of the units needed on time 0 \n", "3838 New application works, but data collected is n... 0 \n", "4650 Project team members reassigned. Not replaced... 0 \n", "4994 Freelancer hired was a nutjob, and lacked t... 0 \n", "4009 Upgrade to software required. Planning and tr... 0 \n", "3560 Six weeks in, a redesign to the most fundament... 0 \n", "4418 Contributor couldn't work due to injury and ba... 0 \n", "4500 Needed system delivered to wrong building; los... 0 \n", "\n", " Region Project Year Month Cost \n", "4956 Americas IT/Solution 2010 September 2535416 \n", "4938 Americas Prod. Dev. 2010 February 2533232 \n", "4077 Americas IT/Solution 2010 May 2526342 \n", "4549 Americas IT/Solution 2002 January 2437292 \n", "4133 Americas IT/Solution 2006 March 2383512 \n", "4116 Americas IT/Solution 2019 December 2371375 \n", "3580 Americas IT/Solution 2014 November 2366784 \n", "4925 Americas Prod. Dev. 2010 November 2360300 \n", "4251 Americas Prod. Dev. 2017 March 2350920 \n", "3730 Americas Prod. Dev. 2015 August 2324688 \n", "4598 Americas IT/Solution 2010 February 2316300 \n", "4933 Americas Prod. Dev. 2015 September 2292350 \n", "3740 Americas IT/Solution 2016 January 2286108 \n", "4753 Americas IT/Solution 2015 September 2285325 \n", "4393 Americas Prod. Dev. 2008 April 2276500 \n", "4545 Americas IT/Solution 2008 January 2276126 \n", "4595 Americas IT/Solution 2015 May 2265925 \n", "4725 Americas IT/Solution 2010 July 2259957 \n", "4875 Americas Prod. Dev. 2015 May 2257500 \n", "3337 Americas IT/Solution 2002 June 2227875 \n", "4970 Americas IT/Solution 2010 February 2227527 \n", "3469 Americas IT/Solution 2014 January 2226432 \n", "3882 Americas IT/Solution 2003 February 2200725 \n", "4423 Americas Prod. Dev. 2015 November 2180992 \n", "4842 Americas IT/Solution 2015 June 2164214 \n", "4905 Americas Prod. Dev. 2015 November 2161575 \n", "4599 Americas IT/Solution 2010 March 2149800 \n", "4747 Americas Prod. Dev. 2010 July 2127450 \n", "4779 Americas Prod. Dev. 2015 August 2095208 \n", "4316 Americas IT/Solution 2011 August 2092333 \n", "4947 Americas Prod. Dev. 2015 April 2088676 \n", "4986 Americas IT/Solution 2010 July 2085275 \n", "4183 Americas IT/Solution 2003 July 2075050 \n", "4453 Americas Prod. Dev. 2010 April 2018520 \n", "3329 Americas IT/Solution 2003 March 2002488 \n", "4716 Americas IT/Solution 2015 October 1999221 \n", "4256 Americas Prod. Dev. 2002 July 1994169 \n", "4035 Americas IT/Solution 2015 September 1987920 \n", "4400 Americas IT/Solution 2010 September 1985688 \n", "3734 Americas Prod. Dev. 2015 July 1975996 \n", "3615 Americas Prod. Dev. 2008 October 1974674 \n", "4660 Americas IT/Solution 2015 March 1973680 \n", "4505 Americas IT/Solution 2015 October 1972475 \n", "3838 Americas IT/Solution 2005 December 1961206 \n", "4650 Americas IT/Solution 2010 May 1955856 \n", "4994 Americas IT/Solution 2015 September 1952040 \n", "4009 Americas Prod. Dev. 2016 April 1933950 \n", "3560 Americas IT/Solution 2011 July 1928602 \n", "4418 Americas IT/Solution 2010 May 1926181 \n", "4500 Americas IT/Solution 2015 September 1923300 " ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "region_filter = dataset.loc[dataset['Region'] == 'Americas']\n", "region_filter = region_filter.sort_values(['Cost'], ascending = False)\n", "region_filter.head(50) #Let's look at the first 50 values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__What if we want to see the data for projects in the Americas that had a category of money? Let's also sort the data so we can see the projects with the highest cost failures first. See below for the code to do so, you can change the category that you would like by copying and pasting the category you want from above into the code below.__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For example, if I want to look at the project in the Americas where the category is People then I could do:
\n", "category_filter = category_filter.sort_values(['Cost'], ascending = False)
\n", "dataset.loc[(dataset['Category'] == 'People') & (dataset['Region'] == 'Americas')]" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Project NumberParameterCategorySub catImpactTRLDescriptionRegion NumericRegionProjectYearMonthCost
45494549ResourceMoneyLimitation264\"Minor\" changes added and accepted late in pr...0AmericasIT/Solution2002January2437292
41334133ResourceMoneyLimitation246Shipping documents not filled in correctly0AmericasIT/Solution2006March2383512
40824082ResourceMoneyLimitation241Components that could have been purchased were...0AmericasIT/Solution2017December1864824
44344434ResourceMoneyLimitation266Key analyst is scheduled to work on another pr...0AmericasProd. Dev.2015November1809678
45464546ResourceMoneyLimitation225Project schedule was revised user interface re...0AmericasProd. Dev.2010July1724844
44354435ResourceMoneyLimitation196Architect who knew how to integrate all compon...0AmericasProd. Dev.2010February1678745
10771077ResourceMoneyLimitation216Sponsor fails to provide money0AmericasProd. Dev.2007November1646505
11321132ResourceMoneyLimitation223IT department was underfunded and had insuffie...0AmericasIT/Solution2019January1594516
40724072ResourceMoneyLimitation269Delay in getting material when a fermentation ...0AmericasIT/Solution2014February1552278
14801480ResourceMoneyLimitation216Internal resources were not available on a ful...0AmericasIT/Solution2007December1486023
11381138ResourceMoneyLimitation215Staffing was not sufficient to complete all as...0AmericasProd. Dev.2019July1464645
43694369ResourceMoneyLimitation245Online badge printing requirement completely m...0AmericasProd. Dev.2001June1388904
44734473ResourceMoneyLimitation168The key person responsible for major marketing...0AmericasIT/Solution2015December1346976
11001100ResourceMoneyLimitation206Development team had insufficient resources as...0AmericasProd. Dev.2017August1342380
17091709ResourceMoneyLimitation176Chronically understaffed business environment0AmericasProd. Dev.2016September1339753
42924292ResourceMoneyLimitation167New equipment critical to the success of the p...0AmericasIT/Solution2010December1262176
20492049ResourceMoneyLimitation204Contractor setup delayed by a week0AmericasProd. Dev.2019August1229220
45534553ResourceMoneyLimitation236Corporate merger delayed project0AmericasIT/Solution2006February1213710
531531ResourceMoneyLimitation156Lack of funds for travel at launch0AmericasProd. Dev.2014September1160100
43874387ResourceMoneyLimitation148fittings and assemblies were not up to project...0AmericasProd. Dev.2008July1135526
43004300ResourceMoneyLimitation152Errors in input data had to be fixed and repor...0AmericasIT/Solution2007August1133325
937937ResourceMoneyLimitation155Without funding, all work ceased, contracts we...0AmericasProd. Dev.2001March1124565
10781078ResourceMoneyLimitation224Project needs $150K per month in supplies but ...0AmericasProd. Dev.2007August1105742
41344134ResourceMoneyLimitation177Dependency on other project not discovered unt...0AmericasIT/Solution2013August1035181
15211521ResourceMoneyLimitation154Due to a layoff no writer was immediately avai...0AmericasProd. Dev.2009January1028115
19631963ResourceMoneyLimitation225Did not have sufficient resources and the co...0AmericasIT/Solution2019January1005026
21692169ResourceMoneyLimitation224Lack of money for sufficient equipment0AmericasProd. Dev.2019November981112
32933293ResourceMoneyLimitation154Project has funding, but limited staff delays ...0AmericasIT/Solution2005April957585
13281328ResourceMoneyLimitation206Insufficient resources assigned to the project0AmericasProd. Dev.2007September930900
813813ResourceMoneyLimitation186lack of staff availability due to company down...0AmericasIT/Solution2007February917766
44454445ResourceMoneyLimitation172Patch to software required for project work; e...0AmericasIT/Solution2002May885836
20142014ResourceMoneyLimitation136Major infrastructure project cut back; delayed...0AmericasIT/Solution2007July883350
14001400ResourceMoneyLimitation163The travel budget for the project was cut and ...0AmericasProd. Dev.2003March849680
7676ResourceMoneyLimitation156Project budget was dramatically cut0AmericasIT/Solution2009May832665
27692769ResourceMoneyLimitation204Delays due to bidding issues and protests0AmericasIT/Solution2010August822300
43614361ResourceMoneyLimitation152Details of requirements were submitted incorre...0AmericasProd. Dev.2002June820110
24052405ResourceMoneyLimitation165Unexpectedly high demand requires outlisde help0AmericasIT/Solution2013December809664
20482048ResourceMoneyLimitation227Did not have sufficient resources and the cont...0AmericasIT/Solution2019April804584
13291329ResourceMoneyLimitation117Project Budget was restricted to the bare mini...0AmericasProd. Dev.2009June785147
356356ResourceMoneyLimitation135Development team had insufficient resources as...0AmericasProd. Dev.2005September779298
355355ResourceMoneyLimitation135Part of the project team ran of money three mo...0AmericasProd. Dev.2009July777777
40044004ResourceMoneyLimitation108Contract had no penalties for missing deadline...0AmericasIT/Solution2005August759950
18761876ResourceMoneyLimitation156Layoffs in mid-project0AmericasIT/Solution2013March758550
102102ResourceMoneyLimitation116Project had limited funding and limited staffing0AmericasIT/Solution2012April717541
7373ResourceMoneyLimitation105Failed to allocate enough resources and missed...0AmericasIT/Solution2002May708760
14531453ResourceMoneyLimitation124Lack of money for sufficient equipment0AmericasProd. Dev.2007August701112
00ResourceMoneyLimitation185Did not have sufficient resources and the cont...0AmericasIT/Solution2010October700560
28542854ResourceMoneyLimitation204Unexpectedly high demand requires outlisde help0AmericasIT/Solution2005July692300
719719ResourceMoneyLimitation114Client slashed the original budget0AmericasIT/Solution2014December673464
14051405ResourceMoneyLimitation154Key consultant was let go at a critical time f...0AmericasIT/Solution2014February664230
\n", "
" ], "text/plain": [ " Project Number Parameter Category Sub cat Impact TRL \\\n", "4549 4549 Resource Money Limitation 26 4 \n", "4133 4133 Resource Money Limitation 24 6 \n", "4082 4082 Resource Money Limitation 24 1 \n", "4434 4434 Resource Money Limitation 26 6 \n", "4546 4546 Resource Money Limitation 22 5 \n", "4435 4435 Resource Money Limitation 19 6 \n", "1077 1077 Resource Money Limitation 21 6 \n", "1132 1132 Resource Money Limitation 22 3 \n", "4072 4072 Resource Money Limitation 26 9 \n", "1480 1480 Resource Money Limitation 21 6 \n", "1138 1138 Resource Money Limitation 21 5 \n", "4369 4369 Resource Money Limitation 24 5 \n", "4473 4473 Resource Money Limitation 16 8 \n", "1100 1100 Resource Money Limitation 20 6 \n", "1709 1709 Resource Money Limitation 17 6 \n", "4292 4292 Resource Money Limitation 16 7 \n", "2049 2049 Resource Money Limitation 20 4 \n", "4553 4553 Resource Money Limitation 23 6 \n", "531 531 Resource Money Limitation 15 6 \n", "4387 4387 Resource Money Limitation 14 8 \n", "4300 4300 Resource Money Limitation 15 2 \n", "937 937 Resource Money Limitation 15 5 \n", "1078 1078 Resource Money Limitation 22 4 \n", "4134 4134 Resource Money Limitation 17 7 \n", "1521 1521 Resource Money Limitation 15 4 \n", "1963 1963 Resource Money Limitation 22 5 \n", "2169 2169 Resource Money Limitation 22 4 \n", "3293 3293 Resource Money Limitation 15 4 \n", "1328 1328 Resource Money Limitation 20 6 \n", "813 813 Resource Money Limitation 18 6 \n", "4445 4445 Resource Money Limitation 17 2 \n", "2014 2014 Resource Money Limitation 13 6 \n", "1400 1400 Resource Money Limitation 16 3 \n", "76 76 Resource Money Limitation 15 6 \n", "2769 2769 Resource Money Limitation 20 4 \n", "4361 4361 Resource Money Limitation 15 2 \n", "2405 2405 Resource Money Limitation 16 5 \n", "2048 2048 Resource Money Limitation 22 7 \n", "1329 1329 Resource Money Limitation 11 7 \n", "356 356 Resource Money Limitation 13 5 \n", "355 355 Resource Money Limitation 13 5 \n", "4004 4004 Resource Money Limitation 10 8 \n", "1876 1876 Resource Money Limitation 15 6 \n", "102 102 Resource Money Limitation 11 6 \n", "73 73 Resource Money Limitation 10 5 \n", "1453 1453 Resource Money Limitation 12 4 \n", "0 0 Resource Money Limitation 18 5 \n", "2854 2854 Resource Money Limitation 20 4 \n", "719 719 Resource Money Limitation 11 4 \n", "1405 1405 Resource Money Limitation 15 4 \n", "\n", " Description Region Numeric \\\n", "4549 \"Minor\" changes added and accepted late in pr... 0 \n", "4133 Shipping documents not filled in correctly 0 \n", "4082 Components that could have been purchased were... 0 \n", "4434 Key analyst is scheduled to work on another pr... 0 \n", "4546 Project schedule was revised user interface re... 0 \n", "4435 Architect who knew how to integrate all compon... 0 \n", "1077 Sponsor fails to provide money 0 \n", "1132 IT department was underfunded and had insuffie... 0 \n", "4072 Delay in getting material when a fermentation ... 0 \n", "1480 Internal resources were not available on a ful... 0 \n", "1138 Staffing was not sufficient to complete all as... 0 \n", "4369 Online badge printing requirement completely m... 0 \n", "4473 The key person responsible for major marketing... 0 \n", "1100 Development team had insufficient resources as... 0 \n", "1709 Chronically understaffed business environment 0 \n", "4292 New equipment critical to the success of the p... 0 \n", "2049 Contractor setup delayed by a week 0 \n", "4553 Corporate merger delayed project 0 \n", "531 Lack of funds for travel at launch 0 \n", "4387 fittings and assemblies were not up to project... 0 \n", "4300 Errors in input data had to be fixed and repor... 0 \n", "937 Without funding, all work ceased, contracts we... 0 \n", "1078 Project needs $150K per month in supplies but ... 0 \n", "4134 Dependency on other project not discovered unt... 0 \n", "1521 Due to a layoff no writer was immediately avai... 0 \n", "1963 Did not have sufficient resources and the co... 0 \n", "2169 Lack of money for sufficient equipment 0 \n", "3293 Project has funding, but limited staff delays ... 0 \n", "1328 Insufficient resources assigned to the project 0 \n", "813 lack of staff availability due to company down... 0 \n", "4445 Patch to software required for project work; e... 0 \n", "2014 Major infrastructure project cut back; delayed... 0 \n", "1400 The travel budget for the project was cut and ... 0 \n", "76 Project budget was dramatically cut 0 \n", "2769 Delays due to bidding issues and protests 0 \n", "4361 Details of requirements were submitted incorre... 0 \n", "2405 Unexpectedly high demand requires outlisde help 0 \n", "2048 Did not have sufficient resources and the cont... 0 \n", "1329 Project Budget was restricted to the bare mini... 0 \n", "356 Development team had insufficient resources as... 0 \n", "355 Part of the project team ran of money three mo... 0 \n", "4004 Contract had no penalties for missing deadline... 0 \n", "1876 Layoffs in mid-project 0 \n", "102 Project had limited funding and limited staffing 0 \n", "73 Failed to allocate enough resources and missed... 0 \n", "1453 Lack of money for sufficient equipment 0 \n", "0 Did not have sufficient resources and the cont... 0 \n", "2854 Unexpectedly high demand requires outlisde help 0 \n", "719 Client slashed the original budget 0 \n", "1405 Key consultant was let go at a critical time f... 0 \n", "\n", " Region Project Year Month Cost \n", "4549 Americas IT/Solution 2002 January 2437292 \n", "4133 Americas IT/Solution 2006 March 2383512 \n", "4082 Americas IT/Solution 2017 December 1864824 \n", "4434 Americas Prod. Dev. 2015 November 1809678 \n", "4546 Americas Prod. Dev. 2010 July 1724844 \n", "4435 Americas Prod. Dev. 2010 February 1678745 \n", "1077 Americas Prod. Dev. 2007 November 1646505 \n", "1132 Americas IT/Solution 2019 January 1594516 \n", "4072 Americas IT/Solution 2014 February 1552278 \n", "1480 Americas IT/Solution 2007 December 1486023 \n", "1138 Americas Prod. Dev. 2019 July 1464645 \n", "4369 Americas Prod. Dev. 2001 June 1388904 \n", "4473 Americas IT/Solution 2015 December 1346976 \n", "1100 Americas Prod. Dev. 2017 August 1342380 \n", "1709 Americas Prod. Dev. 2016 September 1339753 \n", "4292 Americas IT/Solution 2010 December 1262176 \n", "2049 Americas Prod. Dev. 2019 August 1229220 \n", "4553 Americas IT/Solution 2006 February 1213710 \n", "531 Americas Prod. Dev. 2014 September 1160100 \n", "4387 Americas Prod. Dev. 2008 July 1135526 \n", "4300 Americas IT/Solution 2007 August 1133325 \n", "937 Americas Prod. Dev. 2001 March 1124565 \n", "1078 Americas Prod. Dev. 2007 August 1105742 \n", "4134 Americas IT/Solution 2013 August 1035181 \n", "1521 Americas Prod. Dev. 2009 January 1028115 \n", "1963 Americas IT/Solution 2019 January 1005026 \n", "2169 Americas Prod. Dev. 2019 November 981112 \n", "3293 Americas IT/Solution 2005 April 957585 \n", "1328 Americas Prod. Dev. 2007 September 930900 \n", "813 Americas IT/Solution 2007 February 917766 \n", "4445 Americas IT/Solution 2002 May 885836 \n", "2014 Americas IT/Solution 2007 July 883350 \n", "1400 Americas Prod. Dev. 2003 March 849680 \n", "76 Americas IT/Solution 2009 May 832665 \n", "2769 Americas IT/Solution 2010 August 822300 \n", "4361 Americas Prod. Dev. 2002 June 820110 \n", "2405 Americas IT/Solution 2013 December 809664 \n", "2048 Americas IT/Solution 2019 April 804584 \n", "1329 Americas Prod. Dev. 2009 June 785147 \n", "356 Americas Prod. Dev. 2005 September 779298 \n", "355 Americas Prod. Dev. 2009 July 777777 \n", "4004 Americas IT/Solution 2005 August 759950 \n", "1876 Americas IT/Solution 2013 March 758550 \n", "102 Americas IT/Solution 2012 April 717541 \n", "73 Americas IT/Solution 2002 May 708760 \n", "1453 Americas Prod. Dev. 2007 August 701112 \n", "0 Americas IT/Solution 2010 October 700560 \n", "2854 Americas IT/Solution 2005 July 692300 \n", "719 Americas IT/Solution 2014 December 673464 \n", "1405 Americas IT/Solution 2014 February 664230 " ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "category_filter = dataset.loc[(dataset['Category'] == 'Money') & (dataset['Region'] == 'Americas')]\n", "category_filter = category_filter.sort_values(['Cost'], ascending = False)\n", "category_filter.head(50) #Let's look at the first 50 rows where the region is the Americas and the category is Money\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__How can we visualize this filtered data to understand how TRL level effects the Impact value in projects in the Americas, in the Money Category?__ " ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
TRLImpact
0114.75
1213.50
2312.45
3414.17
4514.71
5615.54
6711.78
7810.75
8922.00
\n", "
" ], "text/plain": [ " TRL Impact\n", "0 1 14.75\n", "1 2 13.50\n", "2 3 12.45\n", "3 4 14.17\n", "4 5 14.71\n", "5 6 15.54\n", "6 7 11.78\n", "7 8 10.75\n", "8 9 22.00" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "g = category_filter.groupby('TRL', as_index = False)['Impact'].mean()\n", "g['Impact'] = g['Impact'].round(2)\n", "g" ] }, { "cell_type": "code", "execution_count": 128, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 128, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAJcCAYAAADTvl9SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzt3Xm4bmVdP/73h0lAUFFQUYSjlbM5kfZ1KFMzFeeh1DScvmRpaulVZINYZuSvrMxvkjmgOZQaTqCpmUPmBBomigMiCsioIqAmgvfvj3ttec5273P2GTjrPue8Xte1r72fZ02f9ax7rfV+7rWeZ1drLQAAI9ll7gIAAJYTUACA4QgoAMBwBBQAYDgCCgAwHAEFABiOgLIdqap7VNVZc9exKap7VVV9q6o+MXc9y1XVu6rq8BmX/5yqevlWnN8ZVXXvrTW/OVXVMVX1R3PXsaiqDq6qS6tq1w2M06rqJ7dlXRtTVV+oqrtPfz+/qo7dRsu9R1V9divO77VVddTWmh9jW3NAqaoPTCeZq12VBW0r0/o8ee46Fm2rk8t0Ur50+vlBVV228PiY6aDyw+nxJdPB7QnL5rHWg/DdkvxikoNaa3faCrWvm5a9VO8ZVXXk5s6vtXa/1tqrt7Cmo6rqtZu5/Be01jarHVbVsVX1/M2ZdhOWUVV1elV97qpczkpaa09prf3ptl7uhrTWvtZa26e1dkWybY8jK7T9S6vq02uZtrV2s9baf17VNa6w3A+01m61OdNW1ZOr6gNbuaTF+X94ej1vtez546fn73ZVLXtzVNW1qupvq+pr07Y/rapeVFXXWcO0V+lreVVZU0CpqnVJ7p6kJXnQVVFIVe12VcyXHzedlPdpre2T5HVJXrj0uLX2lGm0r0/Dr5Hkt5P8Y1XdbDMWd0iSM1pr39nUCTfSJq411ffoJH9cVffdxOlZm59Lct0kN6mqn9lWC91QDwW97U8/t92WC94B96kvJvm1pQdVdd0kd0zyzdkqWkFV7ZnkP5LcPMl90o/Ld0lycZJDZyxto7aozbTWNvqT5I+T/FeSFyU5fuH5n01ybpJdF557aJL/mf7eJcmRSb6c5BtJ3pjk2tOwdemB50lJvpbkQ9Pzb5rm+e0kH0pyq4V5XyfJO9I3yolJnp/kwwvDb57kvemN6wtJfnkD6/SBJE+e/r5HkrOS/G6S85Ock+QhSe6f3oC/meQ5C9MeleTNSf4lySVJPpXktgvDl9b5kiSfS/LQZcv+v0lOXRh+hyT/lOSHSb6X5NIkv7tCzUt1PifJhUnOSPKr07CfSXJekt0Wxn94kpM3sm2PTfL8lZaz7Lnzkzxy4XFL8pMbmfeTkvxvkiumdXrewvqfNr2ub09yg2XzfWqSLyX5ygrzXGo3i+t5YpJnrzZ9+o584tSmTkxyl5XawfT4idO2+VaSdyc5ZGHYrRba13nTdrhvksuS/GBax09P4z4+yenTNv7K0nZaYX2OSvLaZet2ePo+cWGSP1hluiOmZV42Lfcd0/NnJHl2kv+Z1vdfkuy5MN0Dkpyc5KIkH0ny0xvZhq9MD7HHJXnJCvvQ86f5XJq+b15nGn9pH123lv0zvR2+NMk7k3wnyb2zrG0mefBU+8Xp+9d9p+efkCv3p9OT/PrCNPsnOX5a328m+c8ku6ywns9L8nfT37tPNbxwerxXejveb2Eb7Zbkz9Lb9v9O6/+ShTb4lPQ2+K0k/y9JrfL63inJR6f6zknykiR7rDLuj5a9wrCfSvL+9OPshenHk2suDD8ryT2mv5+f5Njp73unv4HIBsb9lyRvmF7fx6cf158zbYMLk/xzkv1WqXm9+U/z/p0kn0lvn29IcrUVprtN1j92XDg9/9okL07yrqmejya58cJ0t0zy79O2/nySh2+gbX84yR+l72u7TM89M8nfpZ+D7jY9t+e0zHOSnJ1+Htxjcf3Szx0XJPl6kl9bWMae0/hnph8z/j7T/jjVd7+Fca82tZdbr1DrU6Z5772B9fnDXHnM+WySB23ktVy1tmn470+vw9npx+yWaX9Ocq1pW1wwrf/vZ2rjSZ6cfu5+8bQdjk5v37dYmPeBSb6b5DobPP5saODCzE5L8pvpyfIHSa63MOzLSX5x4fGbkhy5sLE/luSg6cX/hyRvWLazvSbJ1ZPsNT3/xCT7TuP/TRZOsOk7wj8n2Tu9IZ6ZKaBM8zgz/WC1W/pJ/8IsBJwVDq6LAeXy9CC2+7QxLkjy+qmWW00b+CbT+EdNr8MjpvGfnX4S2n0a/sgkN0jfkX8l/WB34MKws9MDRSX5yUwnwWlD33sD22GpzhdNr8/PT/O+2TT8c1m/wb8lybM2sm2PzQYCyrQOD0oPT7dfGGejAWUa7/FZP0Tec9oud5jW4e8yhdOF+b43ybWX2sSy+S21m92m1++u6Q39XitNP/3+VpLHTdM8enp8nRXawUPS2/otpnH/MMlHpmH7ph+gnpW+Y++b5M4L7eG1CzVePf0kurRdDszq7fBH0y6s2z9Otd82yfezsGOvYdudkeQT6e3v2ukn7qdMw+6QHjTvnGTX9CB0RlY4QUzj7z2tx/3Tw+6FWTh5Tq/daUl+Isk109vfF9MP2rul79uvWsv+Oa3Lt6ftucv0Gv9o/dJP5N9Ov1y4S5IbJrn5NOywqYZK3ye+m+QO07A/T3JM+n66e3pP8I+FhfR2+Znp77ukH9c+vjDs08u20W7L28+yNnx8+kH84PRjyX1XeY3vmP5Gb7dp3qcmeeYq46637GXDbprkXkn2SO/x+q8kf7kwfEsCymVJHji97nulH+/+a9oGeyZ5RZJ/WqXmlQLKx5JcPz3MfnH567cw7pOTfGDZc6+d2s2h0/b8l1y5/+ybfmz9ten1vGN6YLvZKvP/cPrx6T8yncPS32z+TNYPKC9ID+EHTK/tx5M8d2H9Lk/y3KmeB6Ufk68xDX9J+nF4v/Rej3cm+dNp2HOSvG6hnocn+e9Van1zklds5Fj7y+nHml2SPCY9jFxvA6/lhmp7QHogukX6vvuGrB9QXp/+pmXfJDdJPw4cvrCsy5P8RvpxZq8kL0vyZwvLflaSt2xofVpbQ0BJv4fgB0n2nx5/PslvLwx/fpJXLjSQ7+TKE+6pmU4c0+MDp3kt7Ywt00l/lWVfaxrnmtOK/mCxsWWhByU9CPznsun/YakhrTDvD2T9gPK9TD1B03q0TCeg6blPJnnI9PdRST62MGyX9JPX3VdZ1slJHjz9/e4kz1hlvDOytoBy9YXn3pjkj6a/fy9Tg08/OX03UzDawDyPzcoB5Yfpqff76cn7mcvG2dyA8opM70ynx/tM23XdwnzvuYH5LbWbi9KDxqlJnr6srnsuPH5ckk8sm8dHkzx+hXbwriRPWrZdv5t+merRWf3gcVR+PKBclH7A+bGQtdq0C+t20MLwTyR51CZsuzOSPHbh8QuTHDP9/dJMB6CF4V9I8vOrzP+x6SfX3dLD5EVZ6A2cXrs/WHj8V0netfD4gZneYGQj++e0Lq9Zbf2mcf96Y+1tGvetmfaxJH+S5G0ba6u5spfkOuk9oM9JP5nuk9678uJl22hjAeVuy/bRI9dY+zOzyoE767f9pZ9nrzLuI5KcuPB4SwLKfywb/qXFNpPkRunHiZV6plYKKI9aePyiLOuZWxi2WkA5ZuHxg5KcMv39q0nev2z8V2T1XsilgPL49B6nWyU5dRq2GFC+muQ+C9MdluS0hfW7NOtfRfhmeoDaZWpThywMu3uSLy28bhcn2Weh3f7OKrW+P8v29TW0pVOSHLbSa7mG2l6ThWNFeu9nm9rg7unnoZsuDH9qkn9fWNbpy2q5a/qb+KVelpOTPGxj67CWe1AOT/Ke1tqF0+PXT89l4fHDpptnH5bkU621r07DDknylqq6qKouSj+ZXJHkegvTn7n0R1XtWlVHV9WXq+ri9INt0rtpD0g/UJ650rTTsu68tKxpeb+antTX4httuvEtPawkvdsrC8/ts9KyW2s/TN/xbjCtx69V1ckLddx6WoekN8ovr7GmlXyrrX8/x1eXlpu+8z6wqvZJT9P/2Vo7ZzOX8/XW2rXSk/WL099Fbg03SK85SdJauzT9Xc4NF8Y5c/lEK9i/tbZfa+0WrbUXLxu2OP16y5t8ddnylhyS5G8Xtts309+V3zCbsN2m7fMr6d2y51TVCVV187VMOzl34e/vZv12tyXTH5LkWcv2kRvlyvaz3OFJ3thau7y19v30d0yHLxtn+T6y2j6zlv1zQ9t91de/qu5XVR+rqm9O871/rtzf/r/0d3fvmW72XfGG6tba95KclN4D83NJPpj+rvmu03Mf3EBtK1nTNqyqm043ZZ47HfNesFD7avZvrV1r+vnLaT7Xr6o3VtXZ03yOXcN81mr5djk4yTsWtuNn0k9e113j/K7K9n3XZW3sV9LfGG/Im5P8UvpJ9jUrDD8w6x9Dlh8/Llw4dyzWdP30YP/phXqOz/Q6tdbOTH8D8tCqunb6vSWvX6XGb2xsParq8VW1uKybZ/U2sMHa0o8Jq51rr5veYbCh12S9NtNa+6/0UHO3qrp1ehs6YUPrk/QT/qqqaq/0E92uVbXUKK6W5FpVddvW2qdba5+rqq8muV96t9LiC3xmkidOxS2f97ql2heefkz6deal63rXTH+XXOnv5C5Pv1z0xWn8Gy1b1gdba7+4oXXain607KraZarr61V1SHoX/b2SfLS1dkVVnZy+Dkt1/sQq82yrPL9ov6q6+kJIOTg9Kae1dnZVfTT9PqDHpb9j3iKtte9X1e8l+UJVPaS19tYtnOXX0w8kSZKqunr6u9azFxe7hctYnH695U0OTvJvK0x3Zno35OuWD5i266PXsLz+RGvvTvLuaR96fnqbuPvGS98km/o6La3fn21sxKo6KD2U3qmqHj49vXeSPatq/4U3LJuy7I3tnxtanxX3m+mN0b+md+u/rbX2g6p6a6b9rbV2SXp38rOmT2u8v6pObK29b4VlfDB9nW+ffv/MB9NPXHdKv6a+qTWvxUuT/HeSR7fWLqmqZ6b3fmyqv0jvxbhNa+2bVfWIJH+5hum+k75dk/zohsblnwpZvo5nJXlMa+3jm1Hnptic9v2+1tr9NmkhrV1aVe9Jv6/rxiuMck76MeQL0+ODs/7xajXnpV8eu1lr7bxVxnl1ek/lPumXus9dZbx/T/8wwN6tte8uH1hVN0lvS/dKvzR5RVWdkivPO8tfy43Vdk76OW3J4rn2/PSOhkNy5bl4+Wuy0rZ7Tfq6XpT+xuf7K4yzno31oDxkKuSWSW43/dwi/UazX1sY7/VJnp7+zuNNC88fk+TPpoN7quqAqnrwBpa3b/pO9o30neYFSwOmhHpckqOqau/pHeliDccnuWlVPa6qdp9+fqaqbrGRddxcd6yqh0079DOnuj+W3r3f0gNVqn8899YL0708ybOr6o7V/eTS65PeaG6yhmU/r6r2qP69Bg/I+q/5a9Jv2LpN+vXFLdZauyy9+/6Plw3ao6r2XPhZyycvXp/kCVV1u+nk8oL0HeqMrVHrCt6Z3i4eU1W7VdWvpLfn41cY95gkvz+dyFJV16yqR07Djk9y/ap6ZlVdrar2rao7T8POS7JuCqqpqutV1YOm8PX99C7gK7L1rbW9LPnHJE+pqjtPbe/qVXVYVe27wriPSz/43CxX7vs3TT85rRbUNmRL989XpLebe1XVLlV1w+kYsEf6m6YLklxeVfdLfyeaJKmqB0z7WKV3p1+R1bfFB9OPKZ+b2vwH0rurv9Jau2CVaTZ1Gyy371TXpdP6/MYWzOc7Sb5dVTdKv09kLT6fZN+q+qWq2j1X3kuxIcckeUFVHZz0T75U1VXx6c7zkhw01bUWb09yq2lfX2pjd6q1ffrw99IvW63Ui/eG9HCwf1UdkH5j7Ua/VmA6Z708yd9M576qqoOq6j4Lox2Xfk/Y07Jy782SY9N7jt5cVTeb5rV/Vf1RVf1SesBZOu9U9Y++L/barvdarqG2NyZ50rSsvad1XlqvH6T3Or2gqvapqhunf9JzY6/JP6WH78dsZF1/ZGMB5fD0m9y+1lo7d+kn/eaaX60rPz70hvT7Fv5j2Turv01vNO+pqkvST+B3zupek95VdHb6DXcfWzb8aem9Kuemr+wb0k8AS++U7pPkUenvms9Nf1dxVX1vy9vSuw+XbsB8WGvtB621z6WfzD+a3ihuk35DWaY635R+9//r0++2fmv6/SJJv6HvD6t3ua12gDl3WubX0z8t8ZTW2ucXhr8l06W1thkf7d2AVyY5uKoeuPDcZ9O78Zd+nrDShIumd65/lP6u95z0d8WP2op1Ll/eN9JD3LPSg+/vJnnASj0ArbW3pLeZf67eTX5Kes/gUvv6xfT7Ks5Nvw7/C9OkSwHxG1X1qfT96lnp2+ib6ZcIfvMqWL1XJLnl1F422rPVWjsp/Qbwl6S3odPSr7+v5PAkf7+430/7/jH58cs8G7Wl+2dr7RPp7euv02+W/WD69fNL0t8cvXFap8ekH3OW/FT6u89L0/fJv2+tfWCVxXwk/V6Upd6Sz6Vfp1+t9yTpx7hHVP+OqOWXGtfi2VPNl6QHyH/ZjHkkPVgs3Uj89vT9a6Naa99K8lvp7+TPTm+vq72LX/Ki9B7I903H9Y+k31i6tb03fT87r67swV9Va+3b6T1ej00/tpybfkzdaBtrrZ29Uk//5HlJPp1+Ket/0m+S/fO1rED6ceCr6Zdyvp3kPeltcmm530k/Bxw8/V6tvv9N7907Lb09L51Pr5l+r9H/pF+K/0T6ut98qnPJSq/lqrW11t6R3iPzoWm6pddmqdfjN9N7YL6Svi++OhsJHdOb0M8kuay19pENjbtk6YaV7VJV/UWS67fWNvmAuYXLPSr9prvHbsvlrlVVfTn9o5b/Pncto6uqDyV5eWttTYkeYGuqqj9JcnBr7fFz17KaqrpN+iecrjbdc7m583lN+g20R61l/O3qq+6r6uZV9dNTd9Sd0r9nY6tcxthRVL9foKV/dI4NmLoub5L+LgBgm6r+LbBPSP8Y7lCq6qHTrQTXSf8uk7dtYTi5Sfo9pq9c6zTbVUBJv856XPq11jemX0p526wVDaT6Vxm/NMlTt6Qh7Qyqf2Pkuendkx+euRxgJ1NVv5H+JXFvW+slj23sqenfOfOl9EudT93cGVXVn6dfJntBa+1ra55ue77EAwDsmLa3HhQAYCewo/3jJ9gk+++/f1u3bt3cZcCKPvnJT17YWjtg7jpgDgIKO7V169blpJNOmrsMWFH1L8GEnZJLPADAcAQUAGA4AgoAMBwBBQAYjoACAAxHQAEAhiOgAADDEVAAgOEIKADAcAQUAGA4AgoAMBwBBQAYjoACAAxHQAEAhiOgAADDEVAAgOEIKADAcAQUAGA4AgoAMBwBBQAYjoACAAxHQAEAhiOgAADDEVAAgOHsNncBANu7dUeesFnTnXH0YVu5Ethx6EEBAIYjoAAAwxFQAIDhCCgAwHAEFABgOAIKADAcAQUAGI6AAgAMR0ABAIYjoAAAwxFQAIDhCCgAwHAEFABgOAIKADAcAQUAGI6AAgAMR0ABAIYjoAAAwxFQAIDhCCgAwHAEFABgOAIKADAcAQUAGI6AAgAMR0ABAIYjoAAAwxFQAIDhCCgAwHAEFABgOAIKADAcAQUAGI6AAgAMR0ABAIYjoAAAwxFQAIDhCCgAwHAEFABgOAIKADAcAQUAGI6AAgAMR0ABAIYjoAAAwxFQAIDhCChsd6rqRlX1/qo6tao+W1XPmJ6/dlW9t6q+NP3eb+5aAdg8Agrbo8uTPKu1doskP5vkqVV1yyRHJnlfa+2nkrxvegzAdkhAYbvTWjuntfap6e9Lkpya5IZJHpzk1dNor07ykHkqBGBLCShs16pqXZLbJ/l4kuu11s5JeohJct1Vpjmiqk6qqpMuuOCCbVUqAJtAQGG7VVX7JPnXJM9srV281ulaay9rrR3aWjv0gAMOuOoKBGCzCShsl6pq9/Rw8rrW2nHT0+dV1YHT8AOTnD9XfQBsGQGF7U5VVZJXJDm1tfaihUFvT3L49PfhSd62rWsDYOvYbe4CYDPcNcnjknymqk6enntOkqOTvLGqnpTka0keOVN9AGwhAYXtTmvtw0lqlcH32pa1AHDVcIkHABiOgAIADEdAAQCGI6AAAMMRUACA4QgoAMBwBBQAYDgCCgAwHAEFABiOgAIADEdAAQCGI6AAAMMRUACA4QgoAMBwBBQAYDgCCgAwHAEFABiOgAIADEdAAQCGI6AAAMMRUACA4QgoAMBwBBQAYDgCCgAwHAEFABiOgAIADEdAAQCGI6AAAMMRUACA4QgoAMBwBBQAYDgCCgAwHAEFABiOgAIADEdAAQCGI6AAAMMRUACA4QgoAMBwBBQAYDgCCgAwHAEFABiOgAIADEdAAQCGI6AAAMMRUACA4QgoAMBwBBQAYDgCCgAwHAEFABiOgAIADEdAAQCGI6AAAMMRUACA4QgoAMBwBBQAYDgCCgAwHAEFABiOgAIADEdAAQCGI6AAAMMRUACA4QgoAMBwBBQAYDgCCgAwHAEFABiOgAIADEdAAQCGI6AAAMMRUACA4QgoAMBwBBQAYDgCCgAwHAEFABiOgAIADEdAAQCGI6AAAMMRUACA4QgoAMBwBBQAYDgCCgAwHAEFABiOgAIADEdAAQCGI6AAAMMRUACA4QgoAMBwBBQAYDgCCgAwHAEFABiOgAIADEdAAQCGI6AAAMMRUACA4QgoAMBwBBQAYDgCCgAwHAEFABiOgAIADEdAAQCGs9vcBQA7n3VHnrBZ051x9GFbuRJgVHpQAIDhCCgAwHAEFABgOAIKADAcAQUAGI6AAgAMR0ABAIYjoAAAwxFQAIDhCChsl6rqlVV1flWdsvDcUVV1dlWdPP3cf84aAdh8Agrbq2OT3HeF5/+6tXa76eed27gmALYSAYXtUmvtQ0m+OXcdAFw1BBR2NE+rqv+ZLgHtt9IIVXVEVZ1UVSddcMEF27o+ANZAQGFH8tIkP5HkdknOSfJXK43UWntZa+3Q1tqhBxxwwLasD4A1ElDYYbTWzmutXdFa+2GSf0xyp7lrAmDzCCjsMKrqwIWHD01yymrjAjC23eYuADZHVb0hyT2S7F9VZyV5bpJ7VNXtkrQkZyT59dkKBGCLCChsl1prj17h6Vds80IAuEoIKLAB6448YbOmO+Pow7ZyJQA7FwEF2CRCG7AtuEkWABiOgAIADEdAAQCGI6AAAMMRUACA4QgoAMBwBBQAYDgCCgAwHAEFABiOgAIADEdAAQCG43/xwE7E/9EBthd6UACA4QgoAMBwBBQAYDgCCgAwHAEFABiOgAIADEdAAQCGI6AAAMMRUACA4QgoAMBwBBQAYDgCCgAwHAEFABiOgAIADEdAAQCGs9vcBcCObt2RJ2zWdGccfdhWrgRg+6EHBQAYjoACAAzHJR5mU1UHJXlUkrsnuUGS7yU5JckJSd7VWvvhjOUBMCMBhVlU1auS3DDJ8Un+Isn5SfZMctMk903yB1V1ZGvtQ/NVCcBcBBTm8lettVNWeP6UJMdV1R5JDt7GNQEwCPegMIuVwklV7VdVPz0Nv6y1dtq2rwyAEQgozKqqPlBV16iqayf5dJJXVdWL5q4LgHkJKMztmq21i5M8LMmrWmt3THLvmWsCYGYCCnPbraoOTPLL6TfMAoCAwuz+JMm7k5zWWjuxqm6S5Esz1wTAzHyKh7m9r7X2pqUHrbXTq+rZcxYEwPz0oDC3d1TVNZYeVNUtk7xjxnoAGICAwtxekB5S9qmqOyZ5U5LHzlwTADNziYdZtdZOqKrdk7wnyb5JHtJacw8KwE5OQGEWVfV3SdrCU9dIcnqS36qqtNaePk9lAIxAQGEuJy17/MlZqgBgSAIKs2itvXrp76raK8nBrbUvzFgSAANxkyyzqqoHJjk5yb9Nj29XVW+ftyoA5iagMLejktwpyUVJ0lo7OcmN5ywIgPkJKMzt8tbat5c911YcE4CdhntQmNspVfWYJLtW1U8leXqSj8xcEwAz04PC3H4rya2SfD/J65N8O8kzZ60IgNnpQWFWrbXvJvmDqnpBa+07c9cDwBj0oDCrqrpLVX0uyanT49tW1d/PXBYAM9ODwtz+OskvJXl7krTWPl1VPzdvSWNad+QJmzzNGUcfdhVUAnDV04PC7FprZy576opZCgFgGHpQmNuZVXWXJK2q9kj/FM+pM9cEwMz0oDC3pyR5apIbJjkrye2mxwDsxPSgMIuq2q+19q3W2oVJfnXuegAYi4DCXL5QVRekfynbfyX5SGvtizPXBMAgXOJhFq216yZ5aHo4uUuS46rqvKp6W1X97rzVATA3PSjMZuox+WKSY6vqJ5LcP8kzktwnyQvnrA2AeQkozGL65M5dkvyfJDdKcnqSjyV5bJJPzVgaAAMQUJjLh9ODyIuSvHX6ynsASCKgMJ8bpPeg3CXJU6pqt/TA8tEkH22tnT5ncQDMS0BhFq21c5McN/2kqvZO8sQkz0ty4yS7zlcdAHMTUJhFVV0z/f6TpV6U2yc5Lck70j/ZA8BOTEBhLqel3xT7kSR/muQTrbXvzVsSAKMQUJhFa+2AuWsAYFy+qA0AGI4eFGC7te7IEzZrujOOPmwrVwJsbXpQAIDh6EFhVlV1QJL/m2RdFtpja+2Jc9UEwPwEFOb2tiT/meTfk1wxcy0ADEJAYW57t9Z+b+4iABiLe1CY2/FVdf+5iwBgLAIKc3tGekj5XlVdXFWXVNXFcxcFwLxc4mFWrbV9564BgPEIKMyiqm7eWvt8Vd1hpeGttU9t65oAGIeAwlx+J8kRSf5qhWEtyT23bTkAjERAYRattSOm378wdy0AjMdNssyiqu62keHXqKpbb6t6ABiLHhTm8vCqemGSf0vyySQXJNkzyU8m+YUkhyR51nzlATAnAYVZtNZ+u6r2S/KIJI9McmCS7yU5Nck/tNY+PGd9AMxLQGE2rbVvJfnH6QcAfsQ9KADAcAQUAGA4AgqzqqqrreU5AHYuAgpz++ganwNgJ+ImWWZRVddPcsMke1XV7ZPUNOgaSfaerTAAhiCgMJdfSvKrewtfAAAMK0lEQVT4JAelf939UkC5OMlzZqoJgEEIKMyitfbqJK+uqoe31v517noAGIt7UJjbHavqWksPqmq/qnr+nAUBMD8Bhbndr7V20dKD6cvb7j9jPQAMwCUe5rZrVV2ttfb9JKmqvZL4mDHbzLojT9is6c44+rCtXAmwSEBhbq9N8r6qelWSluSJSV49b0kAzE1AYVattRdW1WeS3Cv9kzx/2lp798xlATAzAYXZtdbeleRdc9cBwDjcJMusqupnq+rEqrq0qi6rqiuq6uK56wJgXgIKc3tJkkcn+VKSvZI8OcnfzVoRALNziYfZtdZOq6pdW2tXJHlVVX1k7poAmJeAwty+W1V7JDm5ql6Y5JwkV5+5JgBm5hIPc3tcejt8WpLvJLlRkofPWhEAs9ODwqxaa1+delDWJTkuyRdaa5fNWxUAcxNQmFVVHZbkmCRfTv8elBtX1a9PHz0GYCcloDC3v0ryC62105Kkqn4iyQnxvSgAOzX3oDC385fCyeT0JOfPVQwAY9CDwtw+W1XvTPLG9P/F88gkJ1bVw5KktXbcnMUBMA89KMxtzyTnJfn5JPdIckGSayd5YJIHrDZRVb2yqs6vqlMWnrt2Vb23qr40/d7vqi0dgKuKHhRm1Vp7wmZOemz6t9C+ZuG5I5O8r7V2dFUdOT3+vS2rEIA5CCjMqqpunOS30j9m/KP22Fp70Iama619qKrWLXv6wem9MEny6iQfiIACsF0SUJjbW5O8Isk7kvxwC+d1vdbaOUnSWjunqq670khVdUSSI5Lk4IMP3sJFAnBVEFCY2/+21l68LRfYWntZkpclyaGHHtq25bIBWBsBhbn9bVU9N8l7knx/6cnW2qc2Y17nVdWBU+/JgfFxZYDtloDC3G6T/v947pkrL/G06fGmenuSw5McPf1+29YoEIBtT0Bhbg9NcpNN/f87VfWG9Bti96+qs5I8Nz2YvLGqnpTka+nfqQLAdkhAYW6fTnKtbOLlmNbao1cZdK8trgiA2QkozO16ST5fVSdm/XtQNvgxYwB2bAIKc3vu3AUAMB4BhVm11j44dw0AjEdAYRZVdUn6p3V+bFCS1lq7xjYuCYCBCCjMorW279w1ADAu/80YABiOgAIADEdAAQCGI6AAAMMRUACA4QgoAMBwBBQAYDgCCgAwHAEFABiOgAIADEdAAQCGI6AAAMMRUACA4QgoAMBwBBQAYDgCCgAwHAEFABiOgAIADEdAAQCGI6AAAMMRUACA4QgoAMBwBBQAYDgCCgAwHAEFABiOgAIADEdAAQCGI6AAAMMRUACA4QgoAMBwBBQAYDgCCgAwHAEFABiOgAIADEdAAQCGI6AAAMMRUACA4QgoAMBwBBQAYDgCCgAwHAEFABiOgAIADEdAAQCGI6AAAMMRUACA4QgoAMBwBBQAYDgCCgAwHAEFABiOgAIADEdAAQCGI6AAAMMRUACA4QgoAMBwBBQAYDgCCgAwHAEFABiOgAIADEdAAQCGI6AAAMMRUACA4QgoAMBwBBQAYDgCCgAwHAEFABiOgAIADEdAAQCGI6AAAMMRUACA4QgoAMBwBBQAYDgCCgAwHAEFABiOgAIADEdAAQCGI6AAAMMRUACA4QgoAMBwBBQAYDgCCgAwHAEFABiOgAIADEdAAQCGI6AAAMMRUACA4QgoAMBwBBQAYDgCCgAwHAEFABiOgAIADEdAAQCGI6AAAMMRUACA4QgoAMBwBBQAYDgCCgAwHAEFABiOgAIADEdAAQCGI6AAAMMRUACA4QgoAMBwBBQAYDgCCgAwnN3mLgC2tqo6I8klSa5Icnlr7dB5KwJgUwko7Kh+obV24dxFALB5XOIBAIYjoLAjakneU1WfrKojlg+sqiOq6qSqOumCCy6YoTwANkZAYUd019baHZLcL8lTq+rnFge21l7WWju0tXboAQccME+FAGyQgMIOp7X29en3+UnekuRO81YEwKYSUNihVNXVq2rfpb+T3CfJKfNWBcCm8ikedjTXS/KWqkp6+359a+3f5i0JgE0loLBDaa2dnuS2c9cBwJZxiQcAGI6AAgAMR0ABAIYjoAAAwxFQAIDhCCgAwHAEFABgOAIKADAcAQUAGI6AAgAMR0ABAIYjoAAAwxFQAIDhCCgAwHAEFABgOAIKADAcAQUAGI6AAgAMR0ABAIYjoAAAwxFQAIDhCCgAwHAEFABgOAIKADAcAQUAGI6AAgAMR0ABAIYjoAAAwxFQAIDhCCgAwHAEFABgOAIKADAcAQUAGI6AAgAMR0ABAIYjoAAAwxFQAIDhCCgAwHAEFABgOAIKADAcAQUAGI6AAgAMR0ABAIYjoAAAwxFQAIDhCCgAwHAEFABgOAIKADAcAQUAGI6AAgAMR0ABAIYjoAAAwxFQAIDhCCgAwHAEFABgOAIKADAcAQUAGI6AAgAMR0ABAIYjoAAAwxFQAIDhCCgAwHAEFABgOAIKADAcAQUAGI6AAgAMR0ABAIYjoAAAwxFQAIDhCCgAwHAEFABgOAIKADAcAQUAGI6AAgAMR0ABAIYjoAAAwxFQAIDhCCgAwHAEFABgOAIKADAcAQUAGI6AAgAMR0ABAIYjoAAAwxFQAIDhCCgAwHAEFABgOAIKADAcAQUAGI6AAgAMR0ABAIYjoAAAwxFQAIDhCCgAwHAEFABgOAIKADAcAQUAGI6AAgAMR0ABAIYjoAAAwxFQAIDhCCgAwHAEFABgOAIKADAcAQUAGI6AAgAMR0ABAIYjoAAAwxFQAIDhCCjscKrqvlX1hao6raqOnLseADadgMIOpap2TfL/ktwvyS2TPLqqbjlvVQBsKgGFHc2dkpzWWju9tXZZkn9O8uCZawJgE1Vrbe4aYKupqkckuW9r7cnT48cluXNr7WkL4xyR5Ijp4c2SfGGbF3ql/ZNcOOPyF41Syyh1JPPXckhr7YAZlw+z2W3uAmArqxWeWy+Ft9ZeluRl26acDauqk1prh85dRzJOLaPUkYxVC+xsXOJhR3NWkhstPD4oyddnqgWAzSSgsKM5MclPVdWNq2qPJI9K8vaZawJgE7nEww6ltXZ5VT0tybuT7Jrkla21z85c1oYMcalpMkoto9SRjFUL7FTcJAsADMclHgBgOAIKADAcAQVmUFU3qqr3V9WpVfXZqnrGzPXsWlX/XVXHz1zHtarqzVX1+em1+T8z1fHb03Y5pareUFV7zlEH7MwEFJjH5Ume1Vq7RZKfTfLUmb+S/xlJTp1x+Uv+Nsm/tdZunuS2maGmqrphkqcnObS1duv0m60fta3rgJ2dgAIzaK2d01r71PT3Jekn4hvOUUtVHZTksCQvn2P5C3VcI8nPJXlFkrTWLmutXTRTObsl2auqdkuyd3yXDmxzAgrMrKrWJbl9ko/PVMLfJPndJD+caflLbpLkgiSvmi43vbyqrr6ti2itnZ3kL5N8Lck5Sb7dWnvPtq4DdnYCCsyoqvZJ8q9Jntlau3iG5T8gyfmttU9u62WvYLckd0jy0tba7ZN8J8mR27qIqtov/R9M3jjJDZJcvaoeu63rgJ2dgAIzqard08PJ61prx81Uxl2TPKiqzkj/z8/3rKrXzlTLWUnOaq0t9SS9OT2wbGv3TvKV1toFrbUfJDkuyV1mqAN2agIKzKCqKv1ei1Nbay+aq47W2u+31g5qra1LvxH0P1prs/QWtNbOTXJmVd1seupeST43QylfS/KzVbX3tJ3ulTFuIIadiq+6h3ncNcnjknymqk6enntOa+2dM9Y0gt9K8rrp/yidnuQJ27qA1trHq+rNST6V/mmr/46vvIdtzlfdAwDDcYkHABiOgAIADEdAAQCGI6AAAMMRUACA4fiYMbAmVXWdJO+bHl4/yRXpX02f9H/s9+n0Y8pXkjyutXbR9DX+x0//dA9gzfSgAGvSWvtGa+12rbXbJTkmyV8vPP7O9Petk3wzyVNnLRbY7gkowNb20cz0n5mBHYeAAmw1VbVr+lfDv33uWoDtm4ACbA17TV/Z/40k107y3pnrAbZzAgqwNXxvuhflkCR7xD0owBYSUICtprX27SRPT/Lsqtp97nqA7ZeAAmxVrbX/Tv/I8aOmp25WVWct/DxyxvKA7YT/ZgwADEcPCgAwHAEFABiOgAIADEdAAQCGI6AAAMMRUACA4QgoAMBw/n/SSNxkGUrP2wAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize = (3,10))\n", "h = plt.bar(g['TRL'],g['Impact'])\n", "plt.xlabel('TRL')\n", "plt.ylabel('Impact (in Weeks)')\n", "plt.title('Average Impact by TRL for Projects in the Americas with a Failure in the Money Category')\n", "h" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Now that we can we graph this data easily and analyze the data for our understanding? Let's start with understanding how the average cost per failure varies by region.__" ] }, { "cell_type": "code", "execution_count": 125, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
RegionCost
0Africa744616.68
1Americas752808.41
2Asia724886.88
3Eur/ME778307.72
\n", "
" ], "text/plain": [ " Region Cost\n", "0 Africa 744616.68\n", "1 Americas 752808.41\n", "2 Asia 724886.88\n", "3 Eur/ME 778307.72" ] }, "execution_count": 125, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y = dataset.groupby('Region', as_index = False)['Cost'].mean()\n", "y['Cost'] = y['Cost'].round(2)\n", "y" ] }, { "cell_type": "code", "execution_count": 126, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 126, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize = (3,10))\n", "a = plt.bar(y['Region'],y['Cost'])\n", "plt.xlabel('Region')\n", "plt.ylabel('Average Cost (in USD)')\n", "plt.title('Average Cost (in USD) per Region')\n", "a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__What is the Average Cost Per Year for all the Projects?__" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
YearCost
02001766136.24
12002775338.76
22003751868.87
32004760491.31
42005714075.49
52006715143.45
62007711551.25
72008729581.87
82009782631.19
92010801956.27
102011759503.60
112012705645.49
122013701452.11
132014733437.70
142015875305.61
152016729610.30
162017770409.96
172018764912.75
182019699717.54
\n", "
" ], "text/plain": [ " Year Cost\n", "0 2001 766136.24\n", "1 2002 775338.76\n", "2 2003 751868.87\n", "3 2004 760491.31\n", "4 2005 714075.49\n", "5 2006 715143.45\n", "6 2007 711551.25\n", "7 2008 729581.87\n", "8 2009 782631.19\n", "9 2010 801956.27\n", "10 2011 759503.60\n", "11 2012 705645.49\n", "12 2013 701452.11\n", "13 2014 733437.70\n", "14 2015 875305.61\n", "15 2016 729610.30\n", "16 2017 770409.96\n", "17 2018 764912.75\n", "18 2019 699717.54" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "z = dataset.groupby('Year', as_index = False)['Cost'].mean()\n", "z['Cost'] = z['Cost'].round(2)\n", "z.sort_values(by = ['Year'])\n", "z" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "b = plt.plot(z['Year'],z['Cost'])\n", "plt.xlabel('Year')\n", "plt.ylabel('Average Cost (in USD)')\n", "plt.title('Average Cost (in USD) over Time for Projects in all the Regions')\n", "plt.xticks(np.arange(2001, 2020, 2)) \n", "b" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
YearCost
02001753027.31
12002759695.25
22003730311.18
32004734516.43
42005694079.68
52006704339.55
62007697654.34
72008724117.90
82009800630.01
92010841943.56
102011768046.24
112012743514.16
122013646923.40
132014752774.03
142015901210.34
152016682162.81
162017802036.48
172018741020.30
182019666180.41
\n", "
" ], "text/plain": [ " Year Cost\n", "0 2001 753027.31\n", "1 2002 759695.25\n", "2 2003 730311.18\n", "3 2004 734516.43\n", "4 2005 694079.68\n", "5 2006 704339.55\n", "6 2007 697654.34\n", "7 2008 724117.90\n", "8 2009 800630.01\n", "9 2010 841943.56\n", "10 2011 768046.24\n", "11 2012 743514.16\n", "12 2013 646923.40\n", "13 2014 752774.03\n", "14 2015 901210.34\n", "15 2016 682162.81\n", "16 2017 802036.48\n", "17 2018 741020.30\n", "18 2019 666180.41" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y = region_filter.groupby('Year', as_index = False)['Cost'].mean()\n", "y['Cost'] = y['Cost'].round(2)\n", "y = y.sort_values(by = ['Year'])\n", "y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Now let's look at the Average Cost Per Year for Projects in the Americas.__" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "a = plt.plot(y['Year'],y['Cost'])\n", "plt.xlabel('Year')\n", "plt.ylabel('Average Cost (in USD)')\n", "plt.title('Average Cost (in USD) over Time for Projects in the Americas')\n", "plt.xticks(np.arange(2001, 2020, 2)) \n", "a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Let's take this one step further - what if we look at the average cost for projects in all the regions but the Americas? First we'll filter the data to only include projects in the Asia, Eur/ME, and Africa regions. Let's also sort the data so we can see the projects with the highest cost failures first.__" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Project NumberParameterCategorySub catImpactTRLDescriptionRegion NumericRegionProjectYearMonthCost
38423842ResourcePeopleLate start263Change Management Lead took unexpected medical...1AsiaIT/Solution2003November2562560
49404940ScopeChangeGap268Chip failed and had to be refabricated with ch...2Eur/MEIT/Solution2018January2548442
37053705ResourceOutsourcingDelayed start262Project had to wait until all data collection ...2Eur/MEProd. Dev.2001October2543034
49914991ResourcePeopleLoss262To expand business, unrealistic commitments we...3AfricaProd. Dev.2004May2504268
47884788ResourcePeopleLoss252Contract lab failed to inform us that they had...3AfricaProd. Dev.2002December2469325
45524552ResourceMoneyLimitation267Server technology chage was necessitated to su...2Eur/MEProd. Dev.2015December2425748
33943394ScheduleDependencyInfrastructure266Need to upgrade detected only at project end2Eur/MEProd. Dev.2003March2357602
48434843ResourceOutsourcingLate start254Internal supplier problem; needed components d...3AfricaProd. Dev.2013June2326750
47974797ScopeChangeCreep247Late test results came back negative3AfricaIT/Solution2011January2309472
35233523ScopeChangeGap248Several employees had to take a leave of absen...1AsiaProd. Dev.2011August2301120
46414641ResourcePeopleTemp loss245Delay in hiring more engineers due to cost cut...3AfricaIT/Solution2010June2286048
48694869ResourcePeopleTemp loss255Some of the cross functional teams were not in...3AfricaIT/Solution2010October2242050
47564756ScopeChangeGap252Insufficient length of anchor bolts protruding...2Eur/MEIT/Solution2018August2237275
45684568ResourcePeopleQueuing255Insufficient initial project scope was defined...2Eur/MEIT/Solution2005April2218325
46754675ScheduleDelayInformation254Key parts would be delivered late2Eur/MEIT/Solution2018September2206475
36173617ScopeChangeCreep251New database wouldn’t work with the proposed p...2Eur/MEIT/Solution2015May2204675
38973897ScopeChangeCreep269Multiple projects depend on lone expert scientist1AsiaProd. Dev.2006August2194530
48644864ResourcePeopleQueuing231Lack of understanding by the manager of the sy...2Eur/MEProd. Dev.2007April2157676
40834083ScheduleEstimatesLearning225Project delayed due to recent/last minute chan...1AsiaProd. Dev.2014March2150148
48834883ScheduleDelayParts244Apartments were sold before construction begin...2Eur/MEIT/Solution2007December2124000
41604160ScheduleDelayParts247Cable required had been left off the implement...2Eur/MEIT/Solution2006April2106312
40004000ResourcePeopleLoss269Client requested \"15 minute\" change accepted a...1AsiaProd. Dev.2003November2098902
35183518ScheduleDelayDecision242Poor Fit/Gap analysis1AsiaProd. Dev.2006November2094336
44374437ResourceMoneyLimitation265None of the developers had experience with the...2Eur/MEProd. Dev.2009July2093078
37263726ResourcePeopleLoss249Project start delayed; staffing and buy-in lat...1AsiaIT/Solution2011June2090328
38313831ScheduleDelayHardware224After more than 2/3 of the original budget had...2Eur/MEProd. Dev.2003March2085204
48354835ResourceOutsourcingLate or poor output247Parallel tasks require resources beyond what i...2Eur/MEIT/Solution2001July2082432
46824682ScheduleDelayParts263Supplier was unable to make a sample correctly2Eur/MEIT/Solution2004January2081560
33453345ResourcePeopleTemp loss248Scope expanded during the course of the project1AsiaIT/Solution2017December2055624
43184318ResourcePeopleTemp loss222Move out of old location required, but new spa...2Eur/MEIT/Solution2017November2052248
35573557ResourcePeopleQueuing243Inadequate procedures for acceptance, quality,...1AsiaProd. Dev.2015May2052144
38573857ResourcePeopleLoss257Team was staffed by 3 successive new hires in ...2Eur/MEIT/Solution2010March2050425
35563556ResourcePeopleQueuing251A pool of unqualified, poorly performing, marg...1AsiaProd. Dev.2013September2041850
38983898ResourceOutsourcingLate or poor output215Errors found in data and critrical reports1AsiaIT/Solution2011March2040171
38783878ResourceOutsourcingLate or poor output228Late stakeholders redefine requiremnts2Eur/MEIT/Solution2017April2035968
32353235ScopeDefectHardware219Conversion from legacy system to SAP has multi...2Eur/MEIT/Solution2017April2033829
35773577ScheduleDelayInformation223Project team was not aware that the new releas...2Eur/MEIT/Solution2016January2023054
48164816ScopeChangeCreep203Inadequate staffing2Eur/MEProd. Dev.2018March1997540
45174517ScopeChangeCreep209Needed wiring not available on time2Eur/MEProd. Dev.2002February1992660
44494449ResourcePeopleQueuing223Work at customer site had to be done by union ...2Eur/MEProd. Dev.2002June1991176
46374637ResourcePeopleQueuing219Contract lab failed to inform us that they had...2Eur/MEIT/Solution2015April1977528
48154815ScopeChangeCreep205Client slashed the original budget3AfricaProd. Dev.2018February1974940
38003800ScheduleDelayParts244Did not have the correct people validate requi...1AsiaIT/Solution2004February1971720
31913191ResourcePeopleTemp loss219Redesign required late in printer project to m...2Eur/MEProd. Dev.2003October1971123
44774477ResourceOutsourcingLate or poor output227All students did not complete training as sche...2Eur/MEIT/Solution2019October1968626
46694669ScheduleDelayHardware239Decisions are being delayed without apparent r...3AfricaProd. Dev.2013June1964062
43264326ResourcePeopleQueuing256Approvals held up at project completion to wai...2Eur/MEIT/Solution2003June1960725
35743574ScheduleDelayHardware267In creating a new system, there was too little...1AsiaProd. Dev.2002August1952574
44054405ScopeChangeCreep245Delay in patient recruitment for clinical trials2Eur/MEIT/Solution2008January1948032
36733673ScopeDefectHardware224No understanding of the scope and not enough s...1AsiaProd. Dev.2001March1934790
\n", "
" ], "text/plain": [ " Project Number Parameter Category Sub cat Impact TRL \\\n", "3842 3842 Resource People Late start 26 3 \n", "4940 4940 Scope Change Gap 26 8 \n", "3705 3705 Resource Outsourcing Delayed start 26 2 \n", "4991 4991 Resource People Loss 26 2 \n", "4788 4788 Resource People Loss 25 2 \n", "4552 4552 Resource Money Limitation 26 7 \n", "3394 3394 Schedule Dependency Infrastructure 26 6 \n", "4843 4843 Resource Outsourcing Late start 25 4 \n", "4797 4797 Scope Change Creep 24 7 \n", "3523 3523 Scope Change Gap 24 8 \n", "4641 4641 Resource People Temp loss 24 5 \n", "4869 4869 Resource People Temp loss 25 5 \n", "4756 4756 Scope Change Gap 25 2 \n", "4568 4568 Resource People Queuing 25 5 \n", "4675 4675 Schedule Delay Information 25 4 \n", "3617 3617 Scope Change Creep 25 1 \n", "3897 3897 Scope Change Creep 26 9 \n", "4864 4864 Resource People Queuing 23 1 \n", "4083 4083 Schedule Estimates Learning 22 5 \n", "4883 4883 Schedule Delay Parts 24 4 \n", "4160 4160 Schedule Delay Parts 24 7 \n", "4000 4000 Resource People Loss 26 9 \n", "3518 3518 Schedule Delay Decision 24 2 \n", "4437 4437 Resource Money Limitation 26 5 \n", "3726 3726 Resource People Loss 24 9 \n", "3831 3831 Schedule Delay Hardware 22 4 \n", "4835 4835 Resource Outsourcing Late or poor output 24 7 \n", "4682 4682 Schedule Delay Parts 26 3 \n", "3345 3345 Resource People Temp loss 24 8 \n", "4318 4318 Resource People Temp loss 22 2 \n", "3557 3557 Resource People Queuing 24 3 \n", "3857 3857 Resource People Loss 25 7 \n", "3556 3556 Resource People Queuing 25 1 \n", "3898 3898 Resource Outsourcing Late or poor output 21 5 \n", "3878 3878 Resource Outsourcing Late or poor output 22 8 \n", "3235 3235 Scope Defect Hardware 21 9 \n", "3577 3577 Schedule Delay Information 22 3 \n", "4816 4816 Scope Change Creep 20 3 \n", "4517 4517 Scope Change Creep 20 9 \n", "4449 4449 Resource People Queuing 22 3 \n", "4637 4637 Resource People Queuing 21 9 \n", "4815 4815 Scope Change Creep 20 5 \n", "3800 3800 Schedule Delay Parts 24 4 \n", "3191 3191 Resource People Temp loss 21 9 \n", "4477 4477 Resource Outsourcing Late or poor output 22 7 \n", "4669 4669 Schedule Delay Hardware 23 9 \n", "4326 4326 Resource People Queuing 25 6 \n", "3574 3574 Schedule Delay Hardware 26 7 \n", "4405 4405 Scope Change Creep 24 5 \n", "3673 3673 Scope Defect Hardware 22 4 \n", "\n", " Description Region Numeric \\\n", "3842 Change Management Lead took unexpected medical... 1 \n", "4940 Chip failed and had to be refabricated with ch... 2 \n", "3705 Project had to wait until all data collection ... 2 \n", "4991 To expand business, unrealistic commitments we... 3 \n", "4788 Contract lab failed to inform us that they had... 3 \n", "4552 Server technology chage was necessitated to su... 2 \n", "3394 Need to upgrade detected only at project end 2 \n", "4843 Internal supplier problem; needed components d... 3 \n", "4797 Late test results came back negative 3 \n", "3523 Several employees had to take a leave of absen... 1 \n", "4641 Delay in hiring more engineers due to cost cut... 3 \n", "4869 Some of the cross functional teams were not in... 3 \n", "4756 Insufficient length of anchor bolts protruding... 2 \n", "4568 Insufficient initial project scope was defined... 2 \n", "4675 Key parts would be delivered late 2 \n", "3617 New database wouldn’t work with the proposed p... 2 \n", "3897 Multiple projects depend on lone expert scientist 1 \n", "4864 Lack of understanding by the manager of the sy... 2 \n", "4083 Project delayed due to recent/last minute chan... 1 \n", "4883 Apartments were sold before construction begin... 2 \n", "4160 Cable required had been left off the implement... 2 \n", "4000 Client requested \"15 minute\" change accepted a... 1 \n", "3518 Poor Fit/Gap analysis 1 \n", "4437 None of the developers had experience with the... 2 \n", "3726 Project start delayed; staffing and buy-in lat... 1 \n", "3831 After more than 2/3 of the original budget had... 2 \n", "4835 Parallel tasks require resources beyond what i... 2 \n", "4682 Supplier was unable to make a sample correctly 2 \n", "3345 Scope expanded during the course of the project 1 \n", "4318 Move out of old location required, but new spa... 2 \n", "3557 Inadequate procedures for acceptance, quality,... 1 \n", "3857 Team was staffed by 3 successive new hires in ... 2 \n", "3556 A pool of unqualified, poorly performing, marg... 1 \n", "3898 Errors found in data and critrical reports 1 \n", "3878 Late stakeholders redefine requiremnts 2 \n", "3235 Conversion from legacy system to SAP has multi... 2 \n", "3577 Project team was not aware that the new releas... 2 \n", "4816 Inadequate staffing 2 \n", "4517 Needed wiring not available on time 2 \n", "4449 Work at customer site had to be done by union ... 2 \n", "4637 Contract lab failed to inform us that they had... 2 \n", "4815 Client slashed the original budget 3 \n", "3800 Did not have the correct people validate requi... 1 \n", "3191 Redesign required late in printer project to m... 2 \n", "4477 All students did not complete training as sche... 2 \n", "4669 Decisions are being delayed without apparent r... 3 \n", "4326 Approvals held up at project completion to wai... 2 \n", "3574 In creating a new system, there was too little... 1 \n", "4405 Delay in patient recruitment for clinical trials 2 \n", "3673 No understanding of the scope and not enough s... 1 \n", "\n", " Region Project Year Month Cost \n", "3842 Asia IT/Solution 2003 November 2562560 \n", "4940 Eur/ME IT/Solution 2018 January 2548442 \n", "3705 Eur/ME Prod. Dev. 2001 October 2543034 \n", "4991 Africa Prod. Dev. 2004 May 2504268 \n", "4788 Africa Prod. Dev. 2002 December 2469325 \n", "4552 Eur/ME Prod. Dev. 2015 December 2425748 \n", "3394 Eur/ME Prod. Dev. 2003 March 2357602 \n", "4843 Africa Prod. Dev. 2013 June 2326750 \n", "4797 Africa IT/Solution 2011 January 2309472 \n", "3523 Asia Prod. Dev. 2011 August 2301120 \n", "4641 Africa IT/Solution 2010 June 2286048 \n", "4869 Africa IT/Solution 2010 October 2242050 \n", "4756 Eur/ME IT/Solution 2018 August 2237275 \n", "4568 Eur/ME IT/Solution 2005 April 2218325 \n", "4675 Eur/ME IT/Solution 2018 September 2206475 \n", "3617 Eur/ME IT/Solution 2015 May 2204675 \n", "3897 Asia Prod. Dev. 2006 August 2194530 \n", "4864 Eur/ME Prod. Dev. 2007 April 2157676 \n", "4083 Asia Prod. Dev. 2014 March 2150148 \n", "4883 Eur/ME IT/Solution 2007 December 2124000 \n", "4160 Eur/ME IT/Solution 2006 April 2106312 \n", "4000 Asia Prod. Dev. 2003 November 2098902 \n", "3518 Asia Prod. Dev. 2006 November 2094336 \n", "4437 Eur/ME Prod. Dev. 2009 July 2093078 \n", "3726 Asia IT/Solution 2011 June 2090328 \n", "3831 Eur/ME Prod. Dev. 2003 March 2085204 \n", "4835 Eur/ME IT/Solution 2001 July 2082432 \n", "4682 Eur/ME IT/Solution 2004 January 2081560 \n", "3345 Asia IT/Solution 2017 December 2055624 \n", "4318 Eur/ME IT/Solution 2017 November 2052248 \n", "3557 Asia Prod. Dev. 2015 May 2052144 \n", "3857 Eur/ME IT/Solution 2010 March 2050425 \n", "3556 Asia Prod. Dev. 2013 September 2041850 \n", "3898 Asia IT/Solution 2011 March 2040171 \n", "3878 Eur/ME IT/Solution 2017 April 2035968 \n", "3235 Eur/ME IT/Solution 2017 April 2033829 \n", "3577 Eur/ME IT/Solution 2016 January 2023054 \n", "4816 Eur/ME Prod. Dev. 2018 March 1997540 \n", "4517 Eur/ME Prod. Dev. 2002 February 1992660 \n", "4449 Eur/ME Prod. Dev. 2002 June 1991176 \n", "4637 Eur/ME IT/Solution 2015 April 1977528 \n", "4815 Africa Prod. Dev. 2018 February 1974940 \n", "3800 Asia IT/Solution 2004 February 1971720 \n", "3191 Eur/ME Prod. Dev. 2003 October 1971123 \n", "4477 Eur/ME IT/Solution 2019 October 1968626 \n", "4669 Africa Prod. Dev. 2013 June 1964062 \n", "4326 Eur/ME IT/Solution 2003 June 1960725 \n", "3574 Asia Prod. Dev. 2002 August 1952574 \n", "4405 Eur/ME IT/Solution 2008 January 1948032 \n", "3673 Asia Prod. Dev. 2001 March 1934790 " ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "region_filter_no_americas = dataset.loc[dataset['Region'] != 'Americas']\n", "region_filter_no_americas = region_filter_no_americas.sort_values(['Cost'], ascending = False)\n", "region_filter_no_americas.head(50) #Let's look at the first 50 values" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
YearCost
02001779125.99
12002795394.55
22003774372.07
32004784885.11
42005733375.79
52006727068.51
62007744882.14
72008734674.51
82009757989.94
92010730288.23
102011749011.00
112012669423.28
122013756446.87
132014713904.06
142015806226.34
152016789172.05
162017742397.90
172018785768.02
182019792031.09
\n", "
" ], "text/plain": [ " Year Cost\n", "0 2001 779125.99\n", "1 2002 795394.55\n", "2 2003 774372.07\n", "3 2004 784885.11\n", "4 2005 733375.79\n", "5 2006 727068.51\n", "6 2007 744882.14\n", "7 2008 734674.51\n", "8 2009 757989.94\n", "9 2010 730288.23\n", "10 2011 749011.00\n", "11 2012 669423.28\n", "12 2013 756446.87\n", "13 2014 713904.06\n", "14 2015 806226.34\n", "15 2016 789172.05\n", "16 2017 742397.90\n", "17 2018 785768.02\n", "18 2019 792031.09" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "c = region_filter_no_americas.groupby('Year', as_index = False)['Cost'].mean()\n", "c['Cost'] = c['Cost'].round(2)\n", "c = c.sort_values(by = ['Year'])\n", "c" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "l = plt.plot(c['Year'],c['Cost'])\n", "plt.xlabel('Year')\n", "plt.ylabel('Average Cost (in USD)')\n", "plt.title('Average Cost (in USD) over Time for Projects not in the Americas')\n", "plt.xticks(np.arange(2001, 2020, 2)) \n", "l" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Now that we've looked at the cost breakdown over time per region - let's look at the difference in the average Impact (in weeks) per region.__" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
RegionImpact
0Africa13.53
1Americas13.51
2Asia13.29
3Eur/ME13.41
\n", "
" ], "text/plain": [ " Region Impact\n", "0 Africa 13.53\n", "1 Americas 13.51\n", "2 Asia 13.29\n", "3 Eur/ME 13.41" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = dataset.groupby('Region', as_index = False)['Impact'].mean()\n", "d['Impact'] = d['Impact'].round(2)\n", "d" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "__Let's also take a look at how TRL values affect Impact.__ " ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
TRLImpact
0113.70
1213.13
2313.45
3413.37
4513.76
5613.83
6713.43
7812.82
8912.29
\n", "
" ], "text/plain": [ " TRL Impact\n", "0 1 13.70\n", "1 2 13.13\n", "2 3 13.45\n", "3 4 13.37\n", "4 5 13.76\n", "5 6 13.83\n", "6 7 13.43\n", "7 8 12.82\n", "8 9 12.29" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "e = dataset.groupby('TRL', as_index = False)['Impact'].mean()\n", "e['Impact'] = e['Impact'].round(2)\n", "e" ] }, { "cell_type": "code", "execution_count": 127, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 127, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize = (3,10))\n", "f = plt.bar(e['TRL'],e['Impact'])\n", "plt.xlabel('TRL')\n", "plt.ylabel('Average Impact (in Weeks)')\n", "plt.title('Average Impact (in Weeks) per TRL')\n", "plt.yticks(np.arange(0, 13, 3)) \n", "f" ] } ], "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": "3.7.0" } }, "nbformat": 4, "nbformat_minor": 2 }