Module 1, Lesson 1 - Introduction to data as a science.ipynb

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    "# 1. Introduction to data as a science\n",
    "\n",
    "_A __data scientist__ is a researcher who answers a research question using data_, and can lead the development of the research process. They may design the methods to acquire primary or secondary sources of data that inform the research process, monitor and ensure ethical responsibilities, curate the research data and results, or communicate the process and results to stakeholders. Coding is incidental to that process, and it is possible to be a data scientist without programming at all.\n",
    "\n",
    "When researchers look to answer questions, they begin an investigative process:\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<ol>\n",
    "    <li><b><i>Question</i></b>: Identify a research question or problem;</li>\n",
    "    <li><b><i>Ethics</i></b>: Consider the ethical framework to inform the research process;</li>\n",
    "    <li><b><i>Pre-publish</i></b>: Develop, and publish, a research protocol;</li>\n",
    "    <li><b><i>Collect</i></b>: Collect appropriate data;</li>\n",
    "    <li><b><i>Curate</i></b>: Restructure data into a useable format;</li>\n",
    "    <li><b><i>Analyse</i></b>: Analyse that data;</li>\n",
    "    <li><b><i>Prepare</i></b>: Formulate recommendations and conclusions;</li>\n",
    "    <li><b><i>Present</i></b>: Present and publish the complete study, from data to results;</li>\n",
    "</ol>\n",
    "</div>\n",
    " \n",
    "Ordinarily, when teaching data science, everyone - from teachers to students - prefers to focus on the last three points because analysis and presentation are more fun and require less frustration with messy data or ethical dilemmas. Working data scientists will point out that the bulk of their time is taken up with the first five points.\n",
    "\n",
    "The objective of this syllabus is to ensure you have a comprehensive grasp of a data-driven research process. _Data as a Science_ guides learners to confidence in the ethics, curation, analysis, and presentation of data, integrating each of these topics into each lesson:\n",
    "\n",
    " - __Ethics__: The social and behavioural challenges a research question, and data collection process, pose to answering that question;\n",
    " - __Curation__: The requirements for data collection and management arising from a research question;\n",
    " - __Analysis__: The techniques required to explore and analysis data collected to answer a research question;\n",
    " - __Presentation__: The methods and approaches to present the results of analysis so as to answer a question and promote a response;\n",
    "\n",
    "The course is inspired by the [Sloyd model](https://en.wikipedia.org/wiki/Sloyd) of technical training. Each lesson is a discrete mini-research exercise, integrating all the functional techniques of research, building on the techniques of the previous lesson, and providing a functional and holistic understanding of the scientific method as it applies to data. \n",
    "\n",
    "Each lesson should feel only slightly more challenging than the one before, permitting you to gain in confidence and skill without needing to absorb everything at once. Education is not about learning an algorithm and applying it to abstract, arbitrary data. Algorithms or statistical techniques will not be taught separately from the research process. \n",
    "\n",
    "Each lesson starts with a research question, and progresses by teaching a complete, and practical, set of skills allowing you to learn at a pace which suites your current understanding. Case-studies and tutorials are drawn from public health, economics and social issues, and the course is accessible to anyone with an interest in data.\n",
    "\n",
    "Research is not a set of discrete topics. Issues of analysis and presentation will affect choices made for curation. Just when you think your data are neat and tidy, you'll discover an error that you need to correct, and necessitating redoing your analysis. Ethical dilemmas will appear during analysis that require _ad hoc_ solutions.\n",
    "\n",
    "On a small project, one person may have to grapple with all these issues alone. Where a project may span multiple countries, or a vast research question, teams will need to communicate and collaborate to ensure there is a complete custodial chain validating all decisions.\n",
    "\n",
    "Each lesson teaches a complete, and practical, set of skills allowing you to learn at your own pace and in an order which suites your current understanding. Case-studies and tutorials are drawn from public health but the course is accessible to anyone with an interest in data. Course materials, case studies and guided tutorials are presented in Jupyter Notebooks permitting you to test running code and gain hands-on understanding of the techniques discussed.\n",
    "\n",
    "Our first steps, in this lesson, are to prepare our work surface and lay out the tools - both philosophical and technical - that we will use.\n",
    "\n",
    "First, though, we start with our research question for this lesson:\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Research question:</b>\n",
    "<i>The war in Yemen has caused tremendous hardship and suffering to that region's civilian population. Aid- and civil-rights organisations are concerned that cholera is spreading. International health response organisations want to assess the scale of the problem. Using information published by a credible international research organisation, map reported incidents of cholera over time and assess whether there is a growing regional cholera epidemic.</i>\n",
    "</div>\n",
    "\n",
    "---\n",
    "\n",
    "## 1.1 Ethics: a foundation for reasoning\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<i>Identify concepts in ethical reasoning which may influence our analysis and results from data.</i>\n",
    "</div>\n",
    "\n",
    "_Polygal_ was a gel made from beet and apple pectin. Administered to a severely wounded patient, it was supposed to reduce bleeding. To test this hypothesis, [Sigmund Rascher](https://en.wikipedia.org/wiki/Sigmund_Rascher) administered a tablet to human subjects who were then shot or - without anesthesia - had their limbs amputated.\n",
    "\n",
    "During the Second World War, and under the direction of senior Nazi officers, medical experiments of quite unusual violence were conducted on prisoners of war and civilians regarded by the Nazi regime as sub-human. After the war, twenty medical doctors were tried for war crimes and crimes against humanity at the [Doctor's Trial](https://en.wikipedia.org/wiki/Doctors%27_trial) held in Nuremberg from 1946 to 1949.\n",
    "\n",
    "Throughout the trial, expert witnesses, prosecutors and judges proposed key tests as part of a universal code which could be used to assess whether a medical experiment could be regarded as ethical. This became known as the [Nuremberg Code](https://archive.hhs.gov/ohrp/references/nurcode.htm). Key to that was the very first test:\n",
    "\n",
    " > _The voluntary consent of the human subject is absolutely essential._\n",
    " > \n",
    " > _This means that the person involved should have legal capacity to give consent; should be so situated as to be able to exercise free power of choice, without the intervention of any element of force, fraud, deceit, duress, over-reaching, or other ulterior form of constraint or coercion; and should have sufficient knowledge and comprehension of the elements of the subject matter involved, as to enable him to make an understanding and enlightened decision. This latter element requires that, before the acceptance of an affirmative decision by the experimental subject, there should be made known to him the nature, duration, and purpose of the experiment; the method and means by which it is to be conducted; all inconveniences and hazards reasonably to be expected; and the effects upon his health or person, which may possibly come from his participation in the experiment._\n",
    " >\n",
    " > _The duty and responsibility for ascertaining the quality of the consent rests upon each individual who initiates, directs or engages in the experiment. It is a personal duty and responsibility which may not be delegated to another with impunity._ \n",
    "\n",
    "It would be decades before such ethical principles would be more generally adopted into research practice, and only after great human cost.\n",
    "\n",
    "The [Tuskegee syphilis experiment](https://en.wikipedia.org/wiki/Tuskegee_syphilis_experiment) is regarded as \"arguably the most infamous biomedical research study in US history\" and led to significantly greater fear by minority Americans of participating in medical research \\cite{katz_tuskegee_2006}.\n",
    "\n",
    "Between 1932 and 1972, the US Public Health Service conducted a clinical study on 600 poor, semi-literate African-American farmworkers from Alabama. The experiment was not designed to cure, but to observe the full progression of untreated syphilis. And, while the experiment began before the development of antibiotics, even after penicillin became the standard treatment in 1947, subjects were deliberately denied treatment. Researchers even went out of their way to prevent those subjects drafted to serve in the military in the Second World War from receiving treatment.\n",
    "\n",
    "It wasn't until 1978 that the US [_Belmont Report_](https://www.hhs.gov/ohrp/regulations-and-policy/belmont-report/index.html) was issued which offered specific ethical principles when conducting medical experiments, and only in 1991 was a universal set of rules adopted across the US.\n",
    "\n",
    "Despite these hard-won lessons, there remain numerous investors and researchers who see ethical considerations as placing a burden on their objectives. In 2017, for example, citing frustration with the US Food and Drug Administration Institutional Review Board, [Rational Vaccines](https://khn.org/news/offshore-rush-for-herpes-vaccine-roils-debate-over-u-s-safety-rules/) began human-subject testing of their controversial herpes vaccine on the Caribbean island of St Kitts, deliberately placing themselves outside of US jurisdiction.\n",
    "\n",
    "Far from being settled, the philosophical concerns and controversies raised by ethics are all around us \\cite{baggini_ethics_2007}.\n",
    "\n",
    "### 1.1.1 The context of _right_ and _wrong_\n",
    "\n",
    "Computers cannot make decisions. Their output is an absolute function of the data provided as input, and the algorithms applied to analyse that input. The aid of computers in decision-making does not override human responsibility and accountability.\n",
    "\n",
    "It should be expected that both data and algorithms should stand up to scrutiny so as to justify any and all decisions made as a result of their output. \"Computer says no,\" is not an unquestionable statement.\n",
    "\n",
    "In any community, people interact with their environment and each other to produce their livelihoods. The process by which they acquire and accumulate things, and how their interactions affect each other and their environment, lead to asymmetrical changes \\cite{bowles_economy_2017}. Over time, these changes accumulate in different ways to different people leading to highly diverse and complex societies.\n",
    "\n",
    "Economists study these changes and attempt to understand the causes and effects which underpin them, as well as the mechanisms by which economic change may be realised. The question of what sorts of change are _good_ or _bad_ - especially where they give rise to incredible disparities in wealth and opportunity - is one which data and analysis can do little to answer.\n",
    "\n",
    "Facts can tell you what has happened, not what we should do about it. Yet we collect data and analysis with the objective of furthering a particular objective.\n",
    "\n",
    "That objective may have unforeseen consequences. \n",
    "\n",
    "For example, combustion engines which burn fossil fuels have revolutionised transport and manufacturing, and produced incredible wealth, yet a consequence has been a catastrophic change to our environment through immediate effects, like pollution, and long-term effects, like [climate change](http://www.worldbank.org/en/news/feature/2012/11/18/Climate-change-report-warns-dramatically-warmer-world-this-century).\n",
    "\n",
    "The process by which we examine and explain why what we consider to be _right_ or _wrong_ is considered right or wrong in matters of human conduct belongs to the study of ethics \\cite{folse_fundamental_2005}.\n",
    "\n",
    "<br>\n",
    "<div class=\"well\">\n",
    "<b><i>Case-study</i></b>: In 1947 <a href=\"https://en.wikipedia.org/wiki/Kurt_Blome\">Kurt Blome</a> - Deputy Reich Health Leader, a high-ranking Nazi scientist - was acquitted of war crimes at the Nuremberg trials on the strength of intervention by the United States. Within two months, he was being debriefed by the US military who wished to learn everything he knew about biological warfare.\n",
    "<br><br>\n",
    "<i>Do you feel the US was \"right\" or \"wrong\" to offer Blome immunity from prosecution in exchange for what he knew?</i>\n",
    "<br><br>\n",
    "There were numerous experiments conducted by the Nazis that raise ethical dilemmas, including: immersing prisoners in freezing water to observe the result and test hypothermia revival techniques; high altitude pressure and decompression experiments; sulfanilamide tests for treating gangrene and other bacterial infections.\n",
    "<br><br>\n",
    "<i>Do you feel it would be \"right\" or \"wrong\" to use these data in your experiments?</i>\n",
    "<br><br>\n",
    "    <a href=\"http://www.loyno.edu/~folse/ethics.html\">Further reading</a>.\n",
    "</div>\n",
    "\n",
    "### 1.1.2 The weight of intent and what we choose to know\n",
    "\n",
    "Our actions - as data scientists - are intended to persuade people to act or think other than the way they currently do based on nothing more than the strength of our analysis, and informed by data.\n",
    "\n",
    "Whereas everything else we do - data curation, analysis and presentation - can describe human behaviour as it _is_, ethics provides a theoretical framework to describe the world as it _should_, or _should not_ be. It gives us full range to describe an ideal outcome, and to consider all that we know and do not know which may impede or confound our desired result:\n",
    "\n",
    "<table>\n",
    "  <tr>\n",
    "    <td style=\"border-style:solid; border-width:1px; text-align:center; background-color:#bbdaff;font-size:1.25em;padding:10px 5px;\">unknown<br>knowns</td>\n",
    "    <td style=\"border-style:solid; border-width:1px; text-align:center; background-color:#ecf4ff;font-size:1.25em;padding:10px 5px;\">known<br>knowns</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "    <td style=\"border-style:solid; border-width:1px; text-align:center; background-color:#ecf4ff;font-size:1.25em;padding:10px 5px;\">unknown<br>unknowns</td>\n",
    "    <td style=\"border-style:solid; border-width:1px; text-align:center; background-color:#bbdaff;font-size:1.25em;padding:10px 5px;\">known<br>unknowns</td>\n",
    "  </tr>\n",
    "</table>\n",
    "\n",
    "Each of these aspects can be dangerous, but what we insist on not knowing - the _unknown-knowns_; that which we do not like to know or do not want to know - constitute our inherent bias and can compromise any analysis irretrievably.\n",
    "\n",
    "<br>\n",
    "<div class=\"well\">\n",
    "<b><i>Case-study</i></b>: A Nigerian man travels to a conference in the United States. After one of the sessions, he goes to the bathroom to wash his hands. The electronic automated soap dispensor does not recognise his hands beneath the sensor. A white American sees his confusion and places his hands beneath the device. Soap is dispensed. The Nigerian man tries again. It still does not recognise him.\n",
    "<br><br>\n",
    "<i>How would something like this happen? Is it an ethical concern? Where would you place it in terms of the known vs unknown matrix above?</i>\n",
    "<br><br>\n",
    "<a href=\"http://www.iflscience.com/technology/this-racist-soap-dispenser-reveals-why-diversity-in-tech-is-muchneeded/\">Further reading</a>.\n",
    "</div>\n",
    "\n",
    "When we consider ethical outcomes, we use the terms _good_ or _bad_ to describe judgements about people or things, and we use _right_ or _wrong_ to refer to the outcome of specific actions. Understand, though, that - while right and wrong may sometimes be obvious - we are often stuck in ethical dilemmas.\n",
    "\n",
    "How we consider whether an action is right or wrong comes down to the tension between what was intended by an action, and what the consequences of that action were. Are only intensions important? Or should we only consider outcomes? And how absolutely do you want to judge this chain: a _good_ motivation, leading to a _good_ intention, performing a _good_ action, resulting in only the _right_ consequences. How do we evaluate this against what it may be impossible to know at the time, even if that information will become available after a decision is made?\n",
    "\n",
    "We also need to consider competing interests in right and wrong outcomes. A positive outcome for the individual making the decision may have a negative consequence for numerous others. Conversely, an altruistic person may act only for the benefit of others even to their own detriment.\n",
    "\n",
    "Ethical problems do not always require a call to facts to justify a particular decision, but they do have a number of characteristics:\n",
    "\n",
    " - _Public_: the process by which we arrive at an ethical choice is known to all participants;\n",
    " - _Informal_: the process cannot always be codified into law like a legal system;\n",
    " - _Rational_: despite the informality, the logic used must be accessible and defensible;\n",
    " - _Impartial_: any decision must not favour any group or person;\n",
    "\n",
    "Rather than imposing a specific set of rules to be obeyed, ethics provides a framework in which we may consider whether what we are setting out to achieve conforms to our values, and whether the process by which arrive at our decision can be validated and inspected by others.\n",
    "\n",
    "No matter how sophisticated our automated machines become, unless our intention is to construct a society \"of machines, for machines\", people will always be needed to decide on what ethical considerations must be taken into account.\n",
    "\n",
    "---\n",
    "\n",
    "## 1.2 Curation: keeping track of what we know\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<i>Understand the process of data curation, and the custodial duty of data science. Learn some essential tools used by data scientists.</i>\n",
    "</div>\n",
    "\n",
    "Ensuring we know what we know would seem to be the simplest of our ethical responsibilities.\n",
    "\n",
    "As a project grows in complexity, and as the number of people involved gets larger, sharing what we know in a format accessible to all gets more complex. Even if everyone starts out the same, the diversity of requirements across any project will lead individuals to specialise. Unique grammars may be used to describe the data each collects, and these grammars will become opaque to others.\n",
    "\n",
    "Before we even begin, we need a grammar to describe and organise the data we collect, and we need a mechanism to ensure that both the grammar and data are accessible to anyone who requires them.\n",
    "\n",
    "### 1.2.1 Data about data\n",
    "\n",
    "A grammar which describes our source data is known as _metadata_.\n",
    "\n",
    "Consider simply this online textbook. It has a title, author and url. But - hidden amidst all your other resources - how will you know what this book is about? That's where a solid metadata system comes in; it improves your ability to understand the _aboutness_ of an object.\n",
    "\n",
    "There are numerous metadata systems, known as _grammars_, available, from the general, to the specialised.\n",
    "\n",
    "As an introduction, let's consider citations.\n",
    "\n",
    "If you've spent any time in research, you'll know the importance of citing your sources. You'll also know the wide range of ways in which you could present your references. [BibTeX](https://www.ctan.org/pkg/bibtex) is a standardised structure to format lists of references. It separates out the specific bibliographic information according to a defined grammar:\n",
    "\n",
    "    @book{DataScience,\n",
    "        title = \"Data as a Science\",\n",
    "        author = \"Gavin Chait\",\n",
    "        year = \"2018\",\n",
    "        publisher = \"Whythawk.com\",\n",
    "        institution = \"Whythawk\",\n",
    "        url = \"https://data-as-a-science.com/\"\n",
    "    }\n",
    "\n",
    "The term `@book` is an _entry type_ which describes the type of information that the entry contains. You can have a range of such types: article, book, manual, techreport, and so on.\n",
    "\n",
    "Each entry contains a number of _fields_. Some are compulsory, depending on the type, and some are optional. A book must contain an _author_, _title_, _publisher_ and _year_. An article requires an _author_, _title_, _journal_, _year_ and _volume_.\n",
    "\n",
    "The term `DataScience` must be unique and references this book object. You can store a list of such objects in a standard text file with the extension `.bib` instead of `.txt`. The software I used to write this textbook automatically looks up my citations and places them in the __References__ section at the bottom of this page.\n",
    "\n",
    "<br>\n",
    "<div class=\"well\">\n",
    "<b><i>Exercise</i></b>: Read through the <a href=\"https://en.wikipedia.org/wiki/BibTeX\">Wikipedia BibTeX</a> entry, then pick any book or article from your library and create a BibTeX object for it.\n",
    "</div>\n",
    "\n",
    "One of the more ubiquitous general metadata systems used to describe web resources is [Dublin Core](http://dublincore.org/). \n",
    "\n",
    "Some of the terms will be immediately obvious (title, subject, publisher, language, description, date, creator) while others will be unclear (isFormatOf, isReferencedBy, isReplacedBy). The purpose of the metadata grammar is not only to describe the referenced object, but also to connect it to other datasets and describe each object's relationship to other objects.\n",
    "\n",
    "Throughout this book, we will be using live data examples. As I introduce each, I will also provide a Dublin Core-compliant reference to each so that you can add the resource to your own repository and keep track of what it is and where it comes from. This is the complete [Dublin Core reference](http://dublincore.org/documents/dcmi-terms/).\n",
    "\n",
    "<br>\n",
    "<div class=\"well\">\n",
    "<b><i>Case-study</i></b>: Responding to serious public health issues requires constant monitoring. <a href=\"https://data.humdata.org/\">The Humanitarian Data Exchange</a> is provided by <a href=\"https://www.unocha.org/\">UNOCHA</a> as their central repository for public data sharing. You can explore the site and discover datasets.\n",
    "<br><br>\n",
    "    Our <b>research question</b> for this lesson requires us to assess a respectable public data source referencing Yemen's cholera incident rate. Let's look at <a href=\"https://data.humdata.org/dataset/yemen-cholera-outbreak-daily-epidemiology-update\">Yemen: Cholera Outbreak Epidemiology Update Data</a>. The dataset page contains a number of features useful when providing a centralised platform to share data:\n",
    "<ul>\n",
    "    <li><i>Metadata</i>: including Source, Contributor, Date of Dataset, Expected Update Frequency, Location, Visibility, License, Methdology, Comments, Tags;</li>\n",
    "    <li><i>Data and resources</i>: both for download and to explore online;</li>\n",
    "    <li><i>Contact</i>: importantly, if the data are entirely unclear, there's a way to contact the data provider;</li>\n",
    "</ul>\n",
    "Consider the metadata in full:\n",
    "<table>\n",
    "  <tr>\n",
    "      <td style=\"text-align:left;\">Source</td>\n",
    "      <td style=\"text-align:left;\">World Health Organisation (WHO)</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "      <td style=\"text-align:left;\">Contributor</td>\n",
    "      <td style=\"text-align:left;\">HDX</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "      <td style=\"text-align:left;\">Date of Dataset</td>\n",
    "      <td style=\"text-align:left;\">Nov 08, 2017</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "      <td style=\"text-align:left;\">Expected Update Frequency</td>\n",
    "      <td style=\"text-align:left;\">Never (last update, 18 February 2018)</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "      <td style=\"text-align:left;\">Location</td>\n",
    "      <td style=\"text-align:left;\">Yemen</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "      <td style=\"text-align:left;\">Visibility</td>\n",
    "      <td style=\"text-align:left;\">Public</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "      <td style=\"text-align:left;\">License</td>\n",
    "      <td style=\"text-align:left;\">Creative Commons Attribution for Intergovernmental Organisations</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "      <td style=\"text-align:left;\">Methodology</td>\n",
    "      <td style=\"text-align:left;\">Registry</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "      <td style=\"text-align:left;\">Caveats / Comments</td>\n",
    "      <td style=\"text-align:left;\">\n",
    "    The data contains figures from epi bulletins, weekly epi bulletins and daily bulletins.\n",
    "    Starting 19 June 2017, the unit used for the attack rate changed from per 10,000 to per 1,000. Previous data were adjusted to the new unit by dividing by 10.\n",
    "    Starting 2 July 2017, the data included figures for a Moklla. These data were not mapped into any of the govenorates in the Yemen CODs.\n",
    "    Starting 6 July, the data included figures for Say'on. These data were not mapped into any of the govenorates in the Yemen CODs.\n",
    "    Governorate level updates were discontinued in this dataset. The last governorate level update was on 18 February 2018. The governorate level updates were discontinued in this dataset because the governorate level data is published as an image since that date.\n",
    "</td>\n",
    "  </tr>\n",
    "  <tr>\n",
    "      <td style=\"text-align:left;\">Tags</td>\n",
    "      <td style=\"text-align:left;\">ATTACK RATE CASE FATALITY RATE CASE FATALITY RATIO CASES CFR CHOLERA CONFLICT DEATHS HEALTH WAR</td>\n",
    "  </tr>\n",
    "</table>\n",
    "On the <a href=\"https://data.humdata.org/dataset?sort=pageviews_last_14_days+desc\">search results page</a> you can see a funnel icon next to the search bar. When you click on it, you can filter your results by these metadata:\n",
    "<img src=\"images/humdata-filter.JPG\" alt=\"HDS Filter\">\n",
    "</div>\n",
    "\n",
    "While it does happen that data-series end, it is unusual for a data-series to be ended because it has been replaced by a visualisation of that data. An image does not permit us to explore and investigate for ourselves. While these data meet the standards we require for research sources, the lack of continuity is certainly problematic.\n",
    "\n",
    "This research question and case study was originally prepared while the data was still being updated. Given that there is no way to know when you will be studying this lesson, for the sake of our question, let us assume that you are performing this analyis in February 2018, prior to the ending of this series.\n",
    "\n",
    "### 1.2.2 Tools for data curation, analysis and presentation\n",
    "\n",
    "One thing we haven't done yet is look at the data itself. Before we can do that, we need a toolkit to explore it. The two most important for this course are [Jupyter Notebook](http://jupyter.org/) and the [Python](https://www.python.org/) programming language.\n",
    "\n",
    "#### Jupyter Notebook\n",
    "\n",
    "If you're studying this syllabus via a taught course, then may be provided with a login to an online Jupyter Hub. If you are self-studying, or if you want to continue using these tools beyond the scope of the course, then the easiest way to install everything you need is with [Anaconda](https://www.anaconda.com/), a comprehensive data science platform.\n",
    "\n",
    " > An [installation guide](#1.7.2-Install-a-local-version-of-Anaconda-and-Jupyter-Notebook) can be found below in __1.7 Extras and options__, describing the process as well as the additional Python packages you'll need.\n",
    "\n",
    "Jupyter Notebook and Anaconda provide a complete development environment. This textbook was written in Jupyter and, as you'll soon discover, you can write and run code in it as well. This flexibility, including the ability to export notebooks in a variety of formats (from HTML to LaTeX), has made Jupyter an important data science learning and research tool.\n",
    "\n",
    "Jupyter is a key part of this syllabus and it is important you follow along in your own notebooks to gain confidence. While it is certainly straightforward to copy and paste, reading the code and trying to work out what it does is critical to your understanding.\n",
    "\n",
    "#### Python\n",
    "\n",
    "Python is an _interpreted_ programming language that is relatively easy to learn. _Interpreted_ means that we don't have to compile the code before we run it. This capacity to explore code and functionality _live_ has made it one of the most widely-used data science tools, with libraries that allow you to go from data exploration and visualisation, to sophisticated machine-learning.\n",
    "\n",
    "This textbook will not provide a primer in Python. Instead, each time any new code is introduced, a brief set of definitions and explanations will be provided. There are also thousands of free online resources to guide you. One you can start with is [LearnPython.org](http://www.learnpython.org/). The _Learn the Basics_ section will be more than sufficient to follow along with the code we'll be using in this course.\n",
    "\n",
    "If you find this first analysis exercise too difficult, I have presented a much slower, much more detailed version in my [Introduction to Python](https://github.com/whythawk/intro-to-python) course. Doing that course now will bring you to exactly the same place as this lesson, but with far greater emphasis on learning fundamental Python coding skills.\n",
    "\n",
    "For starters, though, a quick demonstration of both Jupyter Notebooks and the simplicity of Python:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello World!\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello World!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can create different types of \"cells\" in Jupyter by selecting from the dropdown menu in the toolbar: ![Jupyter toolbar](images/jupyter-toolbar.JPG \"Jupyter toolbar\")\n",
    "\n",
    "These appear one beneath the other on a page. If you select \"Code\", you can write Python script, and selecting \"Markdown\" allows you to write a [Markdown](http://commonmark.org/help/) version of HTML (the text in this notebook). Of the other symbols, \"+\" will create a new cell, and \"Run\" will run that cell (or \"ctrl-enter\" on Windows).\n",
    "\n",
    "The important libraries we'll be using each need to be imported and we do that with the `import` command. As I introduce new programming concepts, I'll explain them inside a box. I'll also provide references where you can learn more. A very useful resource is \"Python for Data Analysis\" by Wes McKinney \\cite{mckinney_python_2013}.\n",
    "\n",
    "For now, we're going to dive into some data analysis.\n",
    "\n",
    "---\n",
    "\n",
    "## 1.3 Analysis: exploring data\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<i>Investigate and manipulate data to learn its metadata, shape and robustness.</i>\n",
    "</div>\n",
    "\n",
    "There are a number of tools used by data scientists to understand and analyse data. We'll get to those, but one of the fundamentals is simply exploring a new dataset.\n",
    "\n",
    "### 1.3.1 Initial exploration\n",
    "\n",
    "Usually, in data courses, you're presented with a nice, clean dataset and run some algorithms on it and get some answers. That isn't helpful to you. Except for data you collect, you're unlikely to know the shape and contents of a dataset you import from others, no matter how good their research.\n",
    "\n",
    "Especially for large datasets, it can be difficult to know how many unique terms you may be working with and how they relate to each other.\n",
    "\n",
    "In [Section 1.2.1](#1.2.1-Data-about-data) on data curation, we reviewed the [Yemen: Cholera Outbreak Epidemiology Update Data](https://data.humdata.org/dataset/yemen-cholera-outbreak-daily-epidemiology-update). We could view this in a spreadsheet, like Excel, but we're going to use Pandas, a Python library that makes working with large structured data simple.\n",
    "\n",
    "Open your own Jupyter Notebook and follow along with the code: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Governorate</th>\n",
       "      <th>Cases</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>CFR (%)</th>\n",
       "      <th>Attack Rate (per 1000)</th>\n",
       "      <th>COD Gov English</th>\n",
       "      <th>COD Gov Arabic</th>\n",
       "      <th>COD Gov Pcode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-02-18</td>\n",
       "      <td>Amran</td>\n",
       "      <td>103965</td>\n",
       "      <td>176</td>\n",
       "      <td>0.17</td>\n",
       "      <td>89.582</td>\n",
       "      <td>Amran</td>\n",
       "      <td>عمران</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-02-18</td>\n",
       "      <td>Al Mahwit</td>\n",
       "      <td>62887</td>\n",
       "      <td>151</td>\n",
       "      <td>0.24</td>\n",
       "      <td>86.122</td>\n",
       "      <td>Al Mahwit</td>\n",
       "      <td>المحويت</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-02-18</td>\n",
       "      <td>Al Dhale'e</td>\n",
       "      <td>47136</td>\n",
       "      <td>81</td>\n",
       "      <td>0.17</td>\n",
       "      <td>64.438</td>\n",
       "      <td>Al Dhale'e</td>\n",
       "      <td>الضالع</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-02-18</td>\n",
       "      <td>Hajjah</td>\n",
       "      <td>121287</td>\n",
       "      <td>422</td>\n",
       "      <td>0.35</td>\n",
       "      <td>52.060</td>\n",
       "      <td>Hajjah</td>\n",
       "      <td>حجة</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-02-18</td>\n",
       "      <td>Sana'a</td>\n",
       "      <td>76250</td>\n",
       "      <td>123</td>\n",
       "      <td>0.16</td>\n",
       "      <td>51.859</td>\n",
       "      <td>Sana'a</td>\n",
       "      <td>صنعاء</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018-02-18</td>\n",
       "      <td>Dhamar</td>\n",
       "      <td>103214</td>\n",
       "      <td>161</td>\n",
       "      <td>0.16</td>\n",
       "      <td>51.292</td>\n",
       "      <td>Dhamar</td>\n",
       "      <td>ذمار</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2018-02-18</td>\n",
       "      <td>Abyan</td>\n",
       "      <td>28243</td>\n",
       "      <td>35</td>\n",
       "      <td>0.12</td>\n",
       "      <td>49.477</td>\n",
       "      <td>Abyan</td>\n",
       "      <td>أبين</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2018-02-18</td>\n",
       "      <td>Al Hudaydah</td>\n",
       "      <td>155908</td>\n",
       "      <td>282</td>\n",
       "      <td>0.18</td>\n",
       "      <td>48.147</td>\n",
       "      <td>Al Hudaydah</td>\n",
       "      <td>الحديدة</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2018-02-18</td>\n",
       "      <td>Al Bayda</td>\n",
       "      <td>30568</td>\n",
       "      <td>36</td>\n",
       "      <td>0.12</td>\n",
       "      <td>40.253</td>\n",
       "      <td>Al Bayda</td>\n",
       "      <td>البيضاء</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2018-02-18</td>\n",
       "      <td>Amanat Al Asimah</td>\n",
       "      <td>103184</td>\n",
       "      <td>71</td>\n",
       "      <td>0.07</td>\n",
       "      <td>36.489</td>\n",
       "      <td>Amanat Al Asimah</td>\n",
       "      <td>أمانة العاصمة</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date       Governorate   Cases  Deaths  CFR (%)  \\\n",
       "0  2018-02-18             Amran  103965     176     0.17   \n",
       "1  2018-02-18         Al Mahwit   62887     151     0.24   \n",
       "2  2018-02-18        Al Dhale'e   47136      81     0.17   \n",
       "3  2018-02-18            Hajjah  121287     422     0.35   \n",
       "4  2018-02-18            Sana'a   76250     123     0.16   \n",
       "5  2018-02-18            Dhamar  103214     161     0.16   \n",
       "6  2018-02-18             Abyan   28243      35     0.12   \n",
       "7  2018-02-18       Al Hudaydah  155908     282     0.18   \n",
       "8  2018-02-18          Al Bayda   30568      36     0.12   \n",
       "9  2018-02-18  Amanat Al Asimah  103184      71     0.07   \n",
       "\n",
       "   Attack Rate (per 1000)   COD Gov English COD Gov Arabic  COD Gov Pcode  \n",
       "0                  89.582             Amran          عمران           29.0  \n",
       "1                  86.122         Al Mahwit        المحويت           27.0  \n",
       "2                  64.438        Al Dhale'e         الضالع           30.0  \n",
       "3                  52.060            Hajjah            حجة           17.0  \n",
       "4                  51.859            Sana'a          صنعاء           23.0  \n",
       "5                  51.292            Dhamar           ذمار           20.0  \n",
       "6                  49.477             Abyan           أبين           12.0  \n",
       "7                  48.147       Al Hudaydah        الحديدة           18.0  \n",
       "8                  40.253          Al Bayda        البيضاء           14.0  \n",
       "9                  36.489  Amanat Al Asimah  أمانة العاصمة           13.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comments to code are not executed and are flagged with this '#' symbol.\n",
    "# First we'll import the pandas library.\n",
    "# We use 'as' so that we can reference it as 'pd', which is shorter to type.\n",
    "import pandas as pd\n",
    "\n",
    "# In Python, we can declare our variables by simply naming them, like below\n",
    "# 'data_url' is a variable name and the url is the text reference we're assigning to it\n",
    "data_url = \"https://docs.google.com/spreadsheets/d/1P0ob0sfz3xqG8u_dxT98YcVTMwzPSnya_qx6MbX-_Z8/pub?gid=0&single=true&output=csv\"\n",
    "\n",
    "# We import our data as a 'dataframe' using this simple instruction.\n",
    "# How did I know it was a CSV (Comma Separated Value) file? If you look at the end of the urls (above),\n",
    "# you'll see 'output=csv'. A variable in Python can be anything. Here our variable is a Pandas dataframe type.\n",
    "data = pd.read_csv(data_url)\n",
    "\n",
    "# Lets see what that looks like (I limit the number of rows printed by using '[:10]', \n",
    "# and Python is '0' indexed, meaning the first item starts at '0'):\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the first ten rows of the Pandas dataframe. You can think of a dataframe as being like a spreadsheet, or database table, allowing you to do bulk operations, or searches and filters, on the overall data.\n",
    "\n",
    "The same ten rows in the source file looks like this:\n",
    "\n",
    "    Date,Governorate,Cases,Deaths,CFR (%),Attack Rate (per 1000),COD Gov English,COD Gov Arabic,COD Gov Pcode\n",
    "    2017-11-08,Amran,\"89,729\",170,0.19,77.315,Amran,عمران,29\n",
    "    2017-11-08,Al Mahwit,\"52,920\",145,0.27,72.472,Al Mahwit,المحويت,27\n",
    "    2017-11-08,Al Dhale'e,\"46,721\",81,0.17,63.870,Al Dhale'e,الضالع,30\n",
    "    2017-11-08,Abyan,\"27,957\",35,0.13,48.976,Abyan,أبين,12\n",
    "    2017-11-08,Sana'a,\"66,086\",122,0.18,44.946,Sana'a,صنعاء,23\n",
    "    2017-11-08,Hajjah,\"100,850\",414,0.41,43.288,Hajjah,حجة,17\n",
    "    2017-11-08,Dhamar,\"84,741\",157,0.19,42.112,Dhamar,ذمار,20\n",
    "    2017-11-08,Al Hudaydah,\"131,827\",268,0.20,40.710,Al Hudaydah,الحديدة,18\n",
    "    2017-11-08,Al Bayda,\"25,905\",31,0.12,34.113,Al Bayda,البيضاء,14\n",
    "    2017-11-08,Amanat Al Asimah,\"87,578\",68,0.08,30.970,Amanat Al Asimah,أمانة العاصمة,13\n",
    "\n",
    "All the data are separated into columns by commas, hence \"comma separated values\". Numbers - where commas are used to separate 1,000s - are surrounded by inverted commas (\"\") to _escape_ the commas (prevent them being interpreted as a column marker). This is the simplest mechanism for sharing data in a way that doesn't require us to all use a specific software package to open the data file. Various efforts have been made to standardise CSV files but that is likely to remain a work-in-progress.\n",
    "\n",
    "The top bolded row of the dataframe contains the terms which describe the data in each column. Not all of those terms will be familiar, and - even when familiar - the units may not be obvious. These _headers_ are another form of metadata.\n",
    "\n",
    "In [Section 1.2.1](#1.2.1-Data-about-data) the data about the overall dataset is called _descriptive metadata_. Now we need information about the data within each dataset. That is called _**structural metadata**_; a grammar describing the structure and definitions of the data in a table.\n",
    "\n",
    "Sometimes the data you're working with has no further information and you need to experiment with similar data to assess what the terms mean, or what units are being used, or to gap-fill missing data. Sometimes there's someone to ask. Sometimes you get a structural metadata definition to work with.\n",
    "\n",
    "This process, of researching a dataset, of exploration and tremendous frustration, is known as _munging_ or _data wrangling_.\n",
    "\n",
    "In this case, the publisher has helpfully provided another table containing the definitions for the structural metadata."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Date</td>\n",
       "      <td>Date when the figures were reported.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Governorate</td>\n",
       "      <td>The Governorate name as reported in the WHO ep...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cases</td>\n",
       "      <td>Number of cases recorded in the governorate si...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Deaths</td>\n",
       "      <td>Number of deaths recorded in the governorate s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CFR (%)</td>\n",
       "      <td>The case fatality rate in governorate since 27...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Attack Rate (per 1000)</td>\n",
       "      <td>The attack rate per 1,000 of the population in...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>COD Gov English</td>\n",
       "      <td>The English name for the governorate according...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>COD Gov Arabic</td>\n",
       "      <td>The Arabic name for the governorate according ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>COD Gov Pcode</td>\n",
       "      <td>The PCODE name for the governorate according t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Bulletin Type</td>\n",
       "      <td>The type of bulletin from which the data was e...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Bulletin URL</td>\n",
       "      <td>The URL of the bulletin from which the data wa...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Column                                        Description\n",
       "0                     Date               Date when the figures were reported.\n",
       "1              Governorate  The Governorate name as reported in the WHO ep...\n",
       "2                    Cases  Number of cases recorded in the governorate si...\n",
       "3                   Deaths  Number of deaths recorded in the governorate s...\n",
       "4                  CFR (%)  The case fatality rate in governorate since 27...\n",
       "5   Attack Rate (per 1000)  The attack rate per 1,000 of the population in...\n",
       "6          COD Gov English  The English name for the governorate according...\n",
       "7           COD Gov Arabic  The Arabic name for the governorate according ...\n",
       "8            COD Gov Pcode  The PCODE name for the governorate according t...\n",
       "9            Bulletin Type  The type of bulletin from which the data was e...\n",
       "10            Bulletin URL  The URL of the bulletin from which the data wa..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First we set the url for the metadata table\n",
    "metadata_url = \"https://docs.google.com/spreadsheets/d/1P0ob0sfz3xqG8u_dxT98YcVTMwzPSnya_qx6MbX-_Z8/pub?gid=771626114&single=true&output=csv\"\n",
    "# Import it from CSV\n",
    "metadata = pd.read_csv(metadata_url)\n",
    "# Show the metadata:\n",
    "metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The column widths are too narrow to read the full text. There are two ways we can widen them. The first is to adjust the output style of the dataframe. The second is to pull out the text from each cell and iterate through a list. The first is easier (one line), but the second is an opportunity to demonstrate how to work with dataframes.\n",
    "\n",
    "We can explore each of these metadata terms, but rows 2 to 5 would appear the most relevant. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_fa3351ae_c616_11eb_a7af_3052cb21a1b4row0_col1,#T_fa3351ae_c616_11eb_a7af_3052cb21a1b4row1_col1,#T_fa3351ae_c616_11eb_a7af_3052cb21a1b4row2_col1,#T_fa3351ae_c616_11eb_a7af_3052cb21a1b4row3_col1{\n",
       "            width:  400px;\n",
       "            text-align:  left;\n",
       "        }</style><table id=\"T_fa3351ae_c616_11eb_a7af_3052cb21a1b4\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Column</th>        <th class=\"col_heading level0 col1\" >Description</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_fa3351ae_c616_11eb_a7af_3052cb21a1b4level0_row0\" class=\"row_heading level0 row0\" >2</th>\n",
       "                        <td id=\"T_fa3351ae_c616_11eb_a7af_3052cb21a1b4row0_col0\" class=\"data row0 col0\" >Cases</td>\n",
       "                        <td id=\"T_fa3351ae_c616_11eb_a7af_3052cb21a1b4row0_col1\" class=\"data row0 col1\" >Number of cases recorded in the governorate since 27 April 2017.</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fa3351ae_c616_11eb_a7af_3052cb21a1b4level0_row1\" class=\"row_heading level0 row1\" >3</th>\n",
       "                        <td id=\"T_fa3351ae_c616_11eb_a7af_3052cb21a1b4row1_col0\" class=\"data row1 col0\" >Deaths</td>\n",
       "                        <td id=\"T_fa3351ae_c616_11eb_a7af_3052cb21a1b4row1_col1\" class=\"data row1 col1\" >Number of deaths recorded in the governorate since 27 April 2017.</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fa3351ae_c616_11eb_a7af_3052cb21a1b4level0_row2\" class=\"row_heading level0 row2\" >4</th>\n",
       "                        <td id=\"T_fa3351ae_c616_11eb_a7af_3052cb21a1b4row2_col0\" class=\"data row2 col0\" >CFR (%)</td>\n",
       "                        <td id=\"T_fa3351ae_c616_11eb_a7af_3052cb21a1b4row2_col1\" class=\"data row2 col1\" >The case fatality rate in governorate since 27 April 2017.</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fa3351ae_c616_11eb_a7af_3052cb21a1b4level0_row3\" class=\"row_heading level0 row3\" >5</th>\n",
       "                        <td id=\"T_fa3351ae_c616_11eb_a7af_3052cb21a1b4row3_col0\" class=\"data row3 col0\" >Attack Rate (per 1000)</td>\n",
       "                        <td id=\"T_fa3351ae_c616_11eb_a7af_3052cb21a1b4row3_col1\" class=\"data row3 col1\" >The attack rate per 1,000 of the population in the governorate since 27 April 2017.</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1c2eddf5c08>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First, the one-line solution.\n",
    "# Note: it's 2 up-to 6, i.e. up-to-but-not-including\n",
    "metadata[2:6].style.set_properties(subset=[\"Description\"], **{\"width\": \"400px\", \"text-align\": \"left\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The second approach is two lines, and requires some new coding skills. We address an individual cell from a specific dataframe column as follows:\n",
    "\n",
    "    dataframe.column_name[row_number]\n",
    "    \n",
    "We have four terms and it would be tedious to type out each term we're interested in this way, so we'll use a loop. Python uses whitespace indentation to structure its code.\n",
    "\n",
    "    for variable in list:\n",
    "        print(variable)\n",
    "\n",
    "This will loop through the list of variables you have, giving the name `variable` to each item. Everything indented (using either a tab, or four spaces to indent) will be executed in order in the loop. In this case, the loop prints the variable.\n",
    "\n",
    "We're also going to use two other code terms:\n",
    "\n",
    " - `'{}{}'.format(var1, var2)` - used to add variables to text; {} braces will be replaced in the order the variables are provided;\n",
    " - `range` - a way to create a numerical list (e.g. `range(2,6)` creates a list of integers like this `[2,3,4,5]`);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 - Number of cases recorded in the governorate since 27 April 2017.\n",
      "3 - Number of deaths recorded in the governorate since 27 April 2017.\n",
      "4 - The case fatality rate in governorate since 27 April 2017.\n",
      "5 - The attack rate per 1,000 of the population in the governorate since 27 April 2017.\n"
     ]
    }
   ],
   "source": [
    "for i in range(2, 6):\n",
    "    print(F\"{i} - {metadata.Description[i]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unless you work in epidemiology, \"attack rate\" may still be unfamiliar. The US Centers for Disease Control and Prevention has a self-study course \\cite{dicker_principles_2012} which covers the principles of epidemiology and contains this definition: \"In the outbreak setting, the term attack rate is often used as a synonym for risk. It is the risk of getting the disease during a specified period, such as the duration of an outbreak.\"\n",
    "\n",
    "An \"Attack rate (per 1000)\" implies the rate of new infections per 1,000 people in a particular population.\n",
    "\n",
    "There are two more things to find out: how many governorates are there in Yemen, and over what period do we have data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Governorates: 26\n",
      "['Amran' 'Al Mahwit' \"Al Dhale'e\" 'Hajjah' \"Sana'a\" 'Dhamar' 'Abyan'\n",
      " 'Al Hudaydah' 'Al Bayda' 'Amanat Al Asimah' 'Raymah' 'Al Jawf' 'Lahj'\n",
      " 'Aden' 'Ibb' 'Taizz' 'Marib' \"Sa'ada\" 'Al Maharah' 'Shabwah' 'Moklla'\n",
      " \"Say'on\" 'Al-Hudaydah' 'Al_Jawf' \"Ma'areb\" 'AL Mahrah']\n"
     ]
    }
   ],
   "source": [
    "# Get the unique governorates from the 'Governorate' column:\n",
    "# Note the way we address the column and call for 'unique()'\n",
    "governorates = data.Governorate.unique()\n",
    "print(F\"Number of Governorates: {len(governorates)}\")\n",
    "print(governorates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting on 2017-05-22, ending on 2018-02-18; with 136 periods.\n"
     ]
    }
   ],
   "source": [
    "# We can do the same for the dates, but we also want to know the start and end\n",
    "# Note the alternative way to address a dataframe column which permits use of names \n",
    "# with spaces or none-alphabetic characters\n",
    "date_list = data[\"Date\"].unique()\n",
    "print(F\"Starting on {min(date_list)}, ending on {max(date_list)}; with {len(date_list)} periods.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now summarise what we have learned: _data covering a daily update of cholera infection and fatality rates for 136 days, starting on 22 May 2017 till 18 Feburary 2018 for the 26 governorates in Yemen._\n",
    "\n",
    "Mostly this confirms what was in the description on HDX, but we also have some updates and additional data to consider.\n",
    "\n",
    "Before we go any further, it is helpful to check that the data presented are in the format we expect. Are those integers and floats defined that way, or are they being interpreted as text (because, for example, someone left commas in the data ... which, when we checked the source data, is exactly what they did)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2914 entries, 0 to 2913\n",
      "Data columns (total 9 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   Date                    2914 non-null   object \n",
      " 1   Governorate             2914 non-null   object \n",
      " 2   Cases                   2914 non-null   object \n",
      " 3   Deaths                  2914 non-null   int64  \n",
      " 4   CFR (%)                 2914 non-null   float64\n",
      " 5   Attack Rate (per 1000)  2914 non-null   float64\n",
      " 6   COD Gov English         2713 non-null   object \n",
      " 7   COD Gov Arabic          2713 non-null   object \n",
      " 8   COD Gov Pcode           2713 non-null   float64\n",
      "dtypes: float64(3), int64(1), object(5)\n",
      "memory usage: 205.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# This will give us a quick summary of the data, including value types and the number or rows with valid data\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Immediately we notice a problem. Our \"Dates\" are not of a date-type, and \"Cases\" are not integers. This will cause us problems as we get deeper into our analysis. Thankfully, conversion is quick and easy.\n",
    "\n",
    "Pandas will attempt to figure out what type of object each value is and assign it appropriately. If it can't figure it out, then the default is to convert it to a text string. There are a number of ways in which numbers and dates can be formatted confusingly, and the most common for integers is when commas are included.\n",
    "\n",
    "Ordinarily - especially if we intend to download each update of these data and use the series regularly - we can specify transformations on load. We didn't know about these problems when we started so we'll fix it directly now.\n",
    "\n",
    "These sorts of transformations - converting dates, integers and floats - are common requirements and it's useful to get a grasp of them at the beginning. In later modules, we'll consider more complex transformations and gap-filling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2914 entries, 0 to 2913\n",
      "Data columns (total 9 columns):\n",
      " #   Column                  Non-Null Count  Dtype         \n",
      "---  ------                  --------------  -----         \n",
      " 0   Date                    2914 non-null   datetime64[ns]\n",
      " 1   Governorate             2914 non-null   object        \n",
      " 2   Cases                   2914 non-null   int64         \n",
      " 3   Deaths                  2914 non-null   int64         \n",
      " 4   CFR (%)                 2914 non-null   float64       \n",
      " 5   Attack Rate (per 1000)  2914 non-null   float64       \n",
      " 6   COD Gov English         2713 non-null   object        \n",
      " 7   COD Gov Arabic          2713 non-null   object        \n",
      " 8   COD Gov Pcode           2713 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(3), int64(2), object(3)\n",
      "memory usage: 205.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# Removing commas for an entire column and converting to integers\n",
    "data[\"Cases\"] = [int(x.replace(\",\",\"\")) for x in data[\"Cases\"]]\n",
    "# And converting to date is even simpler\n",
    "data[\"Date\"] = pd.to_datetime(data[\"Date\"])\n",
    "# And let's check the overview again\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code used to transform \"Cases\" (the bit to the right of = and between the []) is called a [_list comprehension_](https://www.datacamp.com/community/tutorials/python-list-comprehension). These are very efficient, taking little time to execute.\n",
    "\n",
    "The time it takes code to run is not a major concern right now, with only 2,913 rows, but it becomes a major factor once we work with larger datasets and is something addressed in later modules.\n",
    "\n",
    "Our data are a time-series and our analysis will focus on attempting to understand what is happening and where. We're continuing to explore the shape of it and assess how we can best present the human story carried by that data.\n",
    "\n",
    "We know that the cholera epidemic is getting worse, since more governorates were added in since the time-series began. To get a rough sense of how the disease and humanitarian response has progressed, we will limit our table only to the columns we're interested in, and create two slices at the start and end of the series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                       Date  Cases  Deaths  CFR (%)  Attack Rate (per 1000)\n",
      "Governorate                                                                \n",
      "Al Mahwit        2017-05-22   2486      34      1.4                    3.27\n",
      "Sana'a           2017-05-22   3815      39      1.0                    3.05\n",
      "Amanat Al Asimah 2017-05-22   9216      33      0.4                    2.79\n",
      "Amran            2017-05-22   3743      45      1.2                    2.45\n",
      "Hajjah           2017-05-22   4664      42      0.9                    2.10\n",
      "Al Bayda         2017-05-22   1498       6      0.4                    1.95\n",
      "Al Dhale'e       2017-05-22   1401       8      0.6                    1.86\n",
      "Abyan            2017-05-22   1068      10      0.9                    1.75\n",
      "Raymah           2017-05-22    549       4      0.7                    0.87\n",
      "Dhamar           2017-05-22   1617      33      2.0                    0.76\n",
      "Taizz            2017-05-22   1791      23      1.3                    0.59\n",
      "Aden             2017-05-22    489      12      2.5                    0.51\n",
      "Ibb              2017-05-22   1378      37      2.7                    0.45\n",
      "Al-Hudaydah      2017-05-22   1397      32      2.3                    0.42\n",
      "Al_Jawf          2017-05-22    189       3      1.6                    0.29\n",
      "Lahj             2017-05-22    168       0      0.0                    0.16\n",
      "Ma'areb          2017-05-22      2       0      0.0                    0.01\n",
      "                       Date   Cases  Deaths  CFR (%)  Attack Rate (per 1000)\n",
      "Governorate                                                                 \n",
      "Amran            2018-02-18  103965     176     0.17                  89.582\n",
      "Al Mahwit        2018-02-18   62887     151     0.24                  86.122\n",
      "Al Dhale'e       2018-02-18   47136      81     0.17                  64.438\n",
      "Hajjah           2018-02-18  121287     422     0.35                  52.060\n",
      "Sana'a           2018-02-18   76250     123     0.16                  51.859\n",
      "Dhamar           2018-02-18  103214     161     0.16                  51.292\n",
      "Abyan            2018-02-18   28243      35     0.12                  49.477\n",
      "Al Hudaydah      2018-02-18  155908     282     0.18                  48.147\n",
      "Al Bayda         2018-02-18   30568      36     0.12                  40.253\n",
      "Amanat Al Asimah 2018-02-18  103184      71     0.07                  36.489\n",
      "Raymah           2018-02-18   17841     120     0.67                  29.403\n",
      "Al Jawf          2018-02-18   16018      22     0.14                  27.685\n",
      "Lahj             2018-02-18   24342      22     0.09                  24.128\n",
      "Aden             2018-02-18   20966      62     0.30                  22.715\n",
      "Ibb              2018-02-18   67129     288     0.43                  22.701\n",
      "Taizz            2018-02-18   63696     188     0.30                  21.244\n",
      "Marib            2018-02-18    7288       7     0.10                  20.327\n",
      "Sa'ada           2018-02-18   10706       5     0.05                  11.828\n",
      "Al Maharah       2018-02-18    1168       1     0.09                   7.866\n",
      "Shabwah          2018-02-18    1399       3     0.21                   2.315\n",
      "Moklla           2018-02-18     569       2     0.35                   1.290\n",
      "Say'on           2018-02-18      22       0     0.00                   0.058\n"
     ]
    }
   ],
   "source": [
    "# First, we limit our original data only to the columns we will use,\n",
    "# and we sort the table according to the attack rate:\n",
    "data_slice = data[[\"Date\", \"Governorate\", \"Cases\", \"Deaths\", \"CFR (%)\", \"Attack Rate (per 1000)\"]\n",
    "                 ].sort_values(\"Attack Rate (per 1000)\", ascending=False)\n",
    "# Now we create our two slices, and set the index to Governorate\n",
    "ds_start = data_slice.loc[data_slice.Date == \"2017-05-22\"].set_index(\"Governorate\")\n",
    "ds_end = data_slice.loc[data_slice.Date == \"2018-02-18\"].set_index(\"Governorate\")\n",
    "# And print\n",
    "print(ds_start)\n",
    "print(ds_end)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's a great deal of data to process here, but the most important is that the attack rate has risen exponentially, and cholera has spread to more areas.\n",
    "\n",
    "However, there are also a few errors in the data. Note that _Al Jawf_ appears twice (as \"Al Jawf\" and as \"Al_Jawf\"). It is essential to remember that computers are morons. They can only do exactly what you tell them to do. Different spellings, or even different capitalisations, of words are different words.\n",
    "\n",
    "You may have hoped that the data munging part was complete, but we need to fix this. We should also account for the introduction of \"Moklla\" and \"Say'on\" which are two districts in the governorate of \"Hadramaut\" so that we do only have a list of governorates (and you may have picked this up if you'd read through the comments in the metadata earlier).\n",
    "\n",
    "We can now filter our dataframe by the groups of governorates we need to correct. This introduces a few new concepts in Python. Our first step is to create a _dictionary_ containing the terms we want to search for, and the terms we want to standardise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A dictionary is a set of key: value pairs - the 'key' is a term used to index a specific value;\n",
    "# the 'value' can be any Python object. Here it is a list of terms we want to search for:\n",
    "fix = {\"Hadramaut\": [\"Moklla\",\"Say'on\"],\n",
    "       \"Al Hudaydah\": [\"Al Hudaydah\", \"Al-Hudaydah\"], \n",
    "       \"Al Jawf\": [\"Al Jawf\", \"Al_Jawf\"], \n",
    "       \"Al Maharah\": [\"Al Maharah\", \"AL Mahrah\"], \n",
    "       \"Marib\": [\"Marib\", \"Ma'areb\"]\n",
    "      }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we will define a _function_. This is similar to the libraries we have been using, such as Pandas. A function encapsulates some code into a reusable object so that we don't need to repeat ourselves and can call it whenever we want.\n",
    "\n",
    "We notice that the terms \"Al Hudaydah\" and \"Al-Hudaydah\" are equivalent. Our function needs to combine these terms into one agreed-upon term (the `key` in our dictionary above). If both terms are used in one time period (such as for \"Moklla\" and \"Say'on\") we need the function to recalculate the averages for \"CFR (%)\" and \"Attack Rate (per 1000)\" as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fix_governorates(data, fix_govs):\n",
    "    \"\"\"\n",
    "    Rename terms in a dataframe based on a key-value dictionary.\n",
    "    \n",
    "    This is our function _fix_governorates_; note that we must pass it\n",
    "    two variables, known as arguments.\n",
    "    \n",
    "    Args:\n",
    "        data - the dataframe we want to fix;\n",
    "        fix_govs - a dictionary of the governorates we need to correct.\n",
    "    \n",
    "    The function will perform the following task:\n",
    "        For a given dataframe, date list, and dictionary of Governorates\n",
    "        loop through the keys in the dictionary and combine the list \n",
    "        of associated governorates into a new dataframe.\n",
    "        Return a new, corrected, dataframe.\n",
    "        \n",
    "    Returns:\n",
    "        Pandas dataframe.\n",
    "    \"\"\"\n",
    "    # Create an empty list for each of the new dataframes we'll create\n",
    "    new_frames = []\n",
    "    # And an empty list for all the governorates we'll need to remove later\n",
    "    remove = []\n",
    "    # Create our list of dates\n",
    "    date_list = data[\"Date\"].unique()\n",
    "    # Loop through each of the governorates we need to fix\n",
    "    for key in fix_govs.keys():\n",
    "        # Create a filtered dataframe containing only the governorates to fix\n",
    "        ds = data.loc[data.Governorate.isin(fix_govs[key])]\n",
    "        # New entries for the new dataframe\n",
    "        new_rows = {\"Date\": [],\n",
    "                    \"Cases\": [],\n",
    "                    \"Deaths\": [],\n",
    "                    \"CFR (%)\": [],\n",
    "                    \"Attack Rate (per 1000)\": []\n",
    "                   }\n",
    "        # Divisor for averages (i.e. there could be more than 2 govs to fix)\n",
    "        num = len(fix_govs[key])\n",
    "        # Add the governorate values to the remove list\n",
    "        remove.extend(fix_govs[key])\n",
    "        # For each date, generate new values\n",
    "        for d in date_list:\n",
    "            # Data in the dataframe is stored as a Timestamp value\n",
    "            r = ds[ds[\"Date\"] == pd.Timestamp(d)]\n",
    "            new_rows[\"Date\"].append(pd.Timestamp(d))\n",
    "            new_rows[\"Cases\"].append(r.Cases.sum())\n",
    "            new_rows[\"Deaths\"].append(r.Deaths.sum())\n",
    "            new_rows[\"CFR (%)\"].append(r[\"CFR (%)\"].sum()/num)\n",
    "            new_rows[\"Attack Rate (per 1000)\"].append(r[\"Attack Rate (per 1000)\"].sum()/num)\n",
    "        # Create a new dataframe from the combined data\n",
    "        new_rows = pd.DataFrame(new_rows)\n",
    "        # And assign the values to the key governorate\n",
    "        new_rows[\"Governorate\"] = key\n",
    "        # Add the new dataframe to our list of new frames\n",
    "        new_frames.append(new_rows)\n",
    "    # Get an inverse filtered dataframe from what we had before\n",
    "    ds = data.loc[~data.Governorate.isin(remove)]\n",
    "    new_frames.append(ds)\n",
    "    # Return a new contatenated dataframe with all our corrected data\n",
    "    return pd.concat(new_frames, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can run our function on our data and reproduce the two tables from before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                       Date  Cases  Deaths  CFR (%)  Attack Rate (per 1000)\n",
      "Governorate                                                                \n",
      "Al Mahwit        2017-05-22   2486      34     1.40                   3.270\n",
      "Sana'a           2017-05-22   3815      39     1.00                   3.050\n",
      "Amanat Al Asimah 2017-05-22   9216      33     0.40                   2.790\n",
      "Amran            2017-05-22   3743      45     1.20                   2.450\n",
      "Hajjah           2017-05-22   4664      42     0.90                   2.100\n",
      "Al Bayda         2017-05-22   1498       6     0.40                   1.950\n",
      "Al Dhale'e       2017-05-22   1401       8     0.60                   1.860\n",
      "Abyan            2017-05-22   1068      10     0.90                   1.750\n",
      "Raymah           2017-05-22    549       4     0.70                   0.870\n",
      "Dhamar           2017-05-22   1617      33     2.00                   0.760\n",
      "Taizz            2017-05-22   1791      23     1.30                   0.590\n",
      "Aden             2017-05-22    489      12     2.50                   0.510\n",
      "Ibb              2017-05-22   1378      37     2.70                   0.450\n",
      "Al Hudaydah      2017-05-22   1397      32     1.15                   0.210\n",
      "Lahj             2017-05-22    168       0     0.00                   0.160\n",
      "Al Jawf          2017-05-22    189       3     0.80                   0.145\n",
      "Marib            2017-05-22      2       0     0.00                   0.005\n",
      "Hadramaut        2017-05-22      0       0     0.00                   0.000\n",
      "Al Maharah       2017-05-22      0       0     0.00                   0.000\n",
      "                       Date   Cases  Deaths  CFR (%)  Attack Rate (per 1000)\n",
      "Governorate                                                                 \n",
      "Amran            2018-02-18  103965     176    0.170                 89.5820\n",
      "Al Mahwit        2018-02-18   62887     151    0.240                 86.1220\n",
      "Al Dhale'e       2018-02-18   47136      81    0.170                 64.4380\n",
      "Hajjah           2018-02-18  121287     422    0.350                 52.0600\n",
      "Sana'a           2018-02-18   76250     123    0.160                 51.8590\n",
      "Dhamar           2018-02-18  103214     161    0.160                 51.2920\n",
      "Abyan            2018-02-18   28243      35    0.120                 49.4770\n",
      "Al Bayda         2018-02-18   30568      36    0.120                 40.2530\n",
      "Amanat Al Asimah 2018-02-18  103184      71    0.070                 36.4890\n",
      "Raymah           2018-02-18   17841     120    0.670                 29.4030\n",
      "Lahj             2018-02-18   24342      22    0.090                 24.1280\n",
      "Al Hudaydah      2018-02-18  155908     282    0.090                 24.0735\n",
      "Aden             2018-02-18   20966      62    0.300                 22.7150\n",
      "Ibb              2018-02-18   67129     288    0.430                 22.7010\n",
      "Taizz            2018-02-18   63696     188    0.300                 21.2440\n",
      "Al Jawf          2018-02-18   16018      22    0.070                 13.8425\n",
      "Sa'ada           2018-02-18   10706       5    0.050                 11.8280\n",
      "Marib            2018-02-18    7288       7    0.050                 10.1635\n",
      "Al Maharah       2018-02-18    1168       1    0.045                  3.9330\n",
      "Shabwah          2018-02-18    1399       3    0.210                  2.3150\n",
      "Hadramaut        2018-02-18     591       2    0.175                  0.6740\n"
     ]
    }
   ],
   "source": [
    "data_slice = fix_governorates(data_slice, fix).sort_values(\"Attack Rate (per 1000)\", ascending=False)\n",
    "# Now we recreate our two slices, and set the index to Governorate\n",
    "ds_start = data_slice.loc[data_slice.Date == \"2017-05-22\"].set_index(\"Governorate\")\n",
    "ds_end = data_slice.loc[data_slice.Date == \"2018-02-18\"].set_index(\"Governorate\")\n",
    "# And print\n",
    "print(ds_start)\n",
    "print(ds_end)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also create a line chart to see how the number of cases has progressed over time. This will be our first use of _Matplotlib_, a fairly ubiquitous and powerful Python plotting library. Jupyter Notebook has some \"magic\" we can use in the line `%matplotlib inline` which permits us to draw charts directly in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c2eee7e348>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Matplotlib for additional customization\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# First we create a pivot table of the data we wish to plot. Here only the \"Cases\", although you\n",
    "# should experiment with the other columns as well.\n",
    "drawing = pd.pivot_table(data_slice, values=\"Cases\", index=[\"Date\"], columns=[\"Governorate\"])\n",
    "# Then we set a plot figure size and draw\n",
    "drawing.plot(figsize=(20,15), grid=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are not glamorous charts or tables. This last is what I call a _spaghetti chart_ because of the tangle of lines that make it difficult to track what is happening.\n",
    "\n",
    "However, they are useful methods for investigating what the data tell us and contextualising it against the events behind the data.\n",
    "\n",
    "Perhaps, given where we are, you feel some confidence that you could begin to piece together a story of what is happening in the Yemen cholera epidemic?\n",
    "\n",
    "### 1.3.2 Data and the trouble with accuracy\n",
    "\n",
    "Sitting at your computer in comfortable surroundings - whether in a quiet office, or the clatter and warmth of your favourite coffee shop - it is tempting to place confidence in a neat table of numbers and descriptions. You may have a sense that data are, in some reassuring way, _truthy_.\n",
    "\n",
    "They are not.\n",
    "\n",
    "All data are a reflection of the time when they were collected, the methodology that produced it, and the care with which that methodology was implemented. It is a sample of a moment in time and it is inherently imperfect.\n",
    "\n",
    "Medical data, produced by interviewing patient volunteers, is reliant on self-reported experiences and people - even when they're trying to be honest and reporting on something uncontroversial - have imperfect memories. Blood or tissue samples depend on the consistency with which those samples were acquired, and the chain which stretches from patient, to clinic, to courier, to laboratory, to data analyst. Anything can go wrong, from spillage to spoilage to contamination to overheating or freezing.\n",
    "\n",
    "Even data generated autonomously via sensors or computational sampling is based on what a human thought was important to measure, and implemented by people who had to interpret instructions on what to collect and apply it to the tools at hand. Sensors can be in the wrong place, pointing in the wrong direction, miscalibrated, or based on faulty assumptions from the start.\n",
    "\n",
    "Data carry the bias of the people who constructed the research and the hopes of those who wish to learn from it.\n",
    "\n",
    "> Data are inherently uncertain and any analysis must be absolutely cognizant of this. It is the reason we start with ethics. We must, from the outset, be truthful to ourselves.\n",
    "\n",
    "In future lessons we'll consider methods of assessing the uncertainty in our data and how much confidence we can have. For this lesson, we'll develop a theoretical understanding of the uncertainty and which data we can use to tell a story about events happening in Yemen.\n",
    "\n",
    "In the space of six months (from May to November 2017), Yemen went from 35,000 cholera cases to almost 1 million. Deaths now exceed 2,000 people per month and the attack rate per 1,000 has gone from an average of 1, to 30. This reads like an out-of-control disaster.\n",
    "\n",
    "At the same time, however, the fatality rate has dropped from 1% to 0.2%. \n",
    "\n",
    "Grounds for optimism, then? Somehow medical staff are getting on top of the illness even as infection spreads?\n",
    "\n",
    "Consider how these data are collected. Consider the environment in which it is being collected.\n",
    "\n",
    "<br>\n",
    "<div class=\"well\">\n",
    "<b><i>Background</i></b>: reading on what is happening in Yemen (December 2017):\n",
    "<br>\n",
    "<ul>\n",
    "    <li><a href=\"https://www.hrw.org/news/2017/12/07/yemen-coalition-blockade-imperils-civilians\">Yemen: Coalition Blockade Imperils Civilians</a> - Human Rights Watch, 7 December 2017</li>\n",
    "    <li><a href=\"https://www.theguardian.com/world/ng-interactive/2016/sep/16/how-saudi-arabias-airstrikes-have-hit-civilian-life-in-yemen\">What is happening in Yemen and how are Saudi Arabia's airstrikes affecting civilians</a> - Paul Torpey, Pablo Gutiérrez, Glenn Swann and Cath Levett, The Guardian, 16 September 2016</li>\n",
    "    <li><a href=\"http://www.bbc.co.uk/news/world-middle-east-39651265\">Saudi 'should be blacklisted' over Yemen hospital attacks</a> - BBC, 20 April 2017</li>\n",
    "    <li><a href=\"https://www.usip.org/sites/default/files/SR342_Process-Lessons-Learned-in-Yemens-National-Dialogue.pdf\">Process Lessons Learned in Yemen’s National Dialogue</a> - Erica Gaston, USIP, February 2014</li>\n",
    "</ul>\n",
    "According to <a href=\"https://www.unicef.org/media/media_101496.html\">UNICEF</a>, as of November 2017, \"More than 20 million people, including over 11 million children, are in need of urgent humanitarian assistance. At least 14.8 million are without basic healthcare and an outbreak of cholera has resulted in more than 900,000 suspected cases.\"\n",
    "</div>\n",
    "\n",
    "Cholera incidence data are being collected in an active war zone where genocide and human rights violations are committed daily. Hospital staff are stretched thin, and many have been killed. Hospitals themselves are being deliberately targeted in aerial bombardment. Islamic religious law requires a body to be buried as soon as possible, and this is even more important in a conflict zone to limit further spread of disease.\n",
    "\n",
    "The likelihood is that medical staff are overwhelmed, and that the living and ill must take precedence over the dead. They see as many people as they can, and it is a testament to their dedication and professionalism that these data continue to reach the WHO and UNICEF.\n",
    "\n",
    "There are human beings behind these data. They have suffered greatly to bring it to you.\n",
    "\n",
    "In other words, all we can be certain of is that the Cases and Deaths are the minimum likely, and that attack- and death rates are probably extremely inaccurate. The undercount in deaths may lead to a false sense that the death rate is falling relative to infection, but one shouldn't count on this.\n",
    "\n",
    "Despite these caveats, humanitarian organisations must use these data to prepare their relief response. Food, medication and aid workers must be readied for the moment when fighting drops sufficiently to get to Yemen. Journalists hope to stir public opinion in donor nations (and those outside nations active in the conflict), using these data to explain what is happening.\n",
    "\n",
    "The story we are working on must accept that the infection rate has only a reasonable approximation of what is happening, and that these data should be developed to reflect events.\n",
    "\n",
    "---\n",
    "\n",
    "## 1.4 Presentation: simplicity and letting data tell a story\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<i>Identify an appropriate chart and present data to illustrate its core characteristics</i>\n",
    "</div>\n",
    "\n",
    "A data scientist requires confidence across a broad range of expertise and against a rapidly-changing environment in which the tools and methods used to pursue our profession are in continual flux. Most of what we do is safely hidden from view.\n",
    "\n",
    "The one area where what we do rises to the awareness of the lay public is in the presentation of our results. It is also an area with continual development of new visualisation tools and techniques.\n",
    "\n",
    "This is to highlight that the presentation part of this course may date the fastest and you should take from it principles and approaches to presentation, and not necessarily the software tools.\n",
    "\n",
    "Presentation is everything from writing up academic findings for publication in a journal, to writing a financial and market report for a business, to producing journalism on a complex and fast-moving topic, to persuading donors and humanitarian agencies to take a particular health or environmental threat seriously.\n",
    "\n",
    "It is, first and foremost, about organising your thoughts to tell a consistent and compelling story.\n",
    "\n",
    "### 1.4.1 A language and approach to data-driven story-telling\n",
    "\n",
    "There are \"lies, damned lies, and statistics\", as Mark Twain used to say. Be very careful that you tell the story that is there, rather than one which reflects your own biases (cf. [Ethics](#1.1-Ethics:-a-foundation-for-reasoning)).\n",
    "\n",
    "According to Edward Tufte \\cite{tufte_beautiful_2006}, professor of statistics at Yale, graphical displays should:\n",
    "\n",
    " - Show the data;\n",
    " - Induce the viewer to think about the substance, rather than about the methodology, graphic design, the technology of graphic production, or something else;\n",
    " - Avoid distorting what the data have to say;\n",
    " - Present many numbers in a small space;\n",
    " - Make large datasets coherent;\n",
    " - Encourage the eye to compare different pieces of data;\n",
    " - Reveal the data at several levels of detail, from a broad overview to the fine structure;\n",
    " - Serve a reasonably clear purpose: description, exploration, tabulation, or decoration;\n",
    " - Be closely integrated with the statistical and verbal descriptions of a dataset;\n",
    " \n",
    "There are a lot of people with a great many opinions about what constitutes good visual practice. Manual Lima, in his Visual Complexity blog, has even come up with an [Information Visualisation Manifesto](http://www.visualcomplexity.com/vc/blog/?p=644).\n",
    "\n",
    "Any story has a beginning, a middle, and a conclusion. The story-telling form can vary but the best and most memorable stories have compelling narratives easily retold.\n",
    "\n",
    "Throwing data at a bunch of charts in the hopes that something will stick does not promote engagement anymore than randomly plunking at an instrument produces music.\n",
    "\n",
    "Storytelling does not just happen.\n",
    "\n",
    "Sun Tzu said, \"There are not more than five musical notes, yet the combinations of these five give rise to more melodies than can ever be heard.\"\n",
    "\n",
    "These are the fundamental chart-types that form the data scientist's toolkit:\n",
    "\n",
    " - Line chart;\n",
    " - Bar chart;\n",
    " - Stacked / area variations of bar and line;\n",
    " - Bubble-charts;\n",
    " - Text charts;\n",
    " - Choropleth maps;\n",
    " - Tree maps;\n",
    "\n",
    "Plus we can use small-multiple versions of any of the above to enhance comparisons. Small multiples are simple charts placed alongside each other in a way that encourages analysis while still telling an engaging story. The axes are the same throughout and extraneous chart guides (like dividers between the charts and the vertical axes) have been removed. The simple line-chart becomes both modern and information-dense when presented in this way.\n",
    "\n",
    "There are numerous special types of charts (such as [Chernoff Faces](https://en.wikipedia.org/wiki/Chernoff_face)) but you're unlikely to have these implemented in your charting software.  \n",
    "\n",
    "Here is a simple methodology for developing a visual story:\n",
    "\n",
    " - Write a flow-chart of the narrative encapsulating each of the components in a module;\n",
    " - Each module will encapsulate a single data-driven _thought_ and the type of chart will be imposed by the data:\n",
    "   - Time-series can be presented in line charts, or by small multiples of other plots;\n",
    "   - Geospatial data invites choropleths;\n",
    "   - Complex multivariate data can be presented in tree maps;\n",
    " - In all matters, be led by the data and by good sense;\n",
    " - Arrange those modules in a series of illustrations;\n",
    " - Revise and edit according to the rules in the previous points;\n",
    "\n",
    "Writing a narrative dashboard with multiple charts can be guided by George Orwell's rules from [_Politics and the English Language_](http://www.orwell.ru/library/essays/politics/english/e_polit/):\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<ol>\n",
    "    <li>Never use a pie chart; use a table instead.</li>\n",
    "    <li>Never use a complicated chart where a simple one will do.</li>\n",
    "    <li>Never clutter your data with unnecessary grids, ticks, labels or detail.</li>\n",
    "    <li>If it is possible to remove a chart without taking away from your story, always remove it.</li>\n",
    "    <li>Never mislead your reader through confusing or ambiguous axes or visualisations.</li>\n",
    "    <li>Break any of these rules sooner than draw anything outright barbarous.</li>\n",
    "<ol>\n",
    "</div>\n",
    "\n",
    "### 1.4.2 Telling the story of an epidemic in Yemen\n",
    "\n",
    "We have covered a great deal in this first lesson and now we come to the final section. Before we go further, we need two new libraries. _GeoPandas_ is almost identical to Pandas, but permits us to work with geospatial data (of which, more in a moment). _Seaborn_ is similar to Matplotlib (and is a simplified wrapper around Matplotlib) but looks better, is designed for statistical data, and is simpler to use.\n",
    "\n",
    "Our first step is to improve the line chart drawn at the end of [1.3.1](#1.3.1-Initial-exploration), our initial exploration.  I mentioned the notion of _small multiples_ earlier, and here is our first opportunity to draw it. Notice how much can be achieved in only a few lines of code, most of which (below) is about formatting the charts themselves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1c2ef9f4d08>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 21 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seaborn for plotting and styling\n",
    "import seaborn as sns\n",
    "\n",
    "# Everything you need to know about Seaborn FacetGrid\n",
    "# https://seaborn.pydata.org/generated/seaborn.FacetGrid.html#seaborn.FacetGrid\n",
    "sm = sns.FacetGrid(data_slice, col=\"Governorate\", col_wrap=5, height=3, aspect=2, margin_titles=True)\n",
    "sm = sm.map(plt.plot, \"Date\", \"Cases\")\n",
    "# And now format the plots with appropriate titles and font sizes\n",
    "# The backslash '\\' permits us to split a long line into two for better legibility\n",
    "sm.set_titles(\"{col_name}\", size=22).set_ylabels(size=20).set_yticklabels(size=15)\\\n",
    "                                    .set_xlabels(size=20).set_xticklabels(size=15, rotation=40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how, even with the condensed format, it is still straightforward to understand what is happening and the overall display makes for a compelling and engaging visual.\n",
    "\n",
    "Unfortunately, unless you know Yemen well, this story is incomplete. It is difficult to see where these changes are taking place, or how each governorate is related to the others in physical space. For that we need to plot our data onto a map.\n",
    "\n",
    "There are a number of limits for publishing data on maps:\n",
    "\n",
    " - A choropleth map is really a type of bar-chart where the height of the bars is reflected by a colour gradient in 2D-space;\n",
    " - Boundaries that make up regions, districts (or governorates) are of wildly different sizes and can mislead into prioritising size over colour scale;\n",
    "\n",
    "Despite these limitations, map-based charts are useful for grounding data in a physical place. When used in combination with other charts (such as the line-charts above) one can build a complete narrative.\n",
    "\n",
    "To draw a map we need a [shapefile](http://wiki.openstreetmap.org/wiki/Shapefiles). These are a collection of several files developed according to a standard created by [Esri](https://www.esri.com/en-us/home) that contain shapes defined by geographic points, polylines or polygons, as well as additional files with metadata or attributes.\n",
    "\n",
    "HDX has exactly what we need as [_Yemen - Administrative Boundaries_](https://data.humdata.org/dataset/yemen-admin-boundaries). Download the shapefile [zip files](https://data.humdata.org/dataset/6b2656e2-b915-4671-bfed-468d5edcd80a/resource/31161a5e-2111-4967-9f1e-4fceeb38a3a1/download/yem-administrative-divisions-shapefiles.zip) to a folder and unzip all the files.\n",
    "\n",
    "Now we're going to create a GeoPandas dataframe to open the shapefile, and then join this dataframe to our existing data so that we can draw maps. You will need to install the GeoPandas library, and you will find details on how to do so below in [1.7.2 Install a local version of Anaconda and Jupyter Notebook](#1.7.2-Install-a-local-version-of-Anaconda-and-Jupyter-Notebook)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ADM0_EN</th>\n",
       "      <th>ADM0_AR</th>\n",
       "      <th>ADM0_PCODE</th>\n",
       "      <th>ADM1_EN</th>\n",
       "      <th>ADM1_AR</th>\n",
       "      <th>ADM1_PCODE</th>\n",
       "      <th>ADM1REF_EN</th>\n",
       "      <th>ADM1ALT1AR</th>\n",
       "      <th>ADM1ALT1EN</th>\n",
       "      <th>ADM1ALT2AR</th>\n",
       "      <th>ADM1ALT2EN</th>\n",
       "      <th>date_</th>\n",
       "      <th>validOn</th>\n",
       "      <th>validTo</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>اليمن</td>\n",
       "      <td>YE</td>\n",
       "      <td>Ibb</td>\n",
       "      <td>اب</td>\n",
       "      <td>YE11</td>\n",
       "      <td>Ibb</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2019-02-01</td>\n",
       "      <td>2019-10-02</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((44.08076 14.38973, 44.08362 14.37772...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>اليمن</td>\n",
       "      <td>YE</td>\n",
       "      <td>Abyan</td>\n",
       "      <td>ابين</td>\n",
       "      <td>YE12</td>\n",
       "      <td>Abyan</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2019-02-01</td>\n",
       "      <td>2019-10-02</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((46.29563 14.26991, 46.29770 14.26687...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>اليمن</td>\n",
       "      <td>YE</td>\n",
       "      <td>Sana'a City</td>\n",
       "      <td>امانة العاصمه</td>\n",
       "      <td>YE13</td>\n",
       "      <td>Sanaa City</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2019-02-01</td>\n",
       "      <td>2019-10-02</td>\n",
       "      <td>None</td>\n",
       "      <td>POLYGON ((44.33380 15.60702, 44.35087 15.59940...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>اليمن</td>\n",
       "      <td>YE</td>\n",
       "      <td>Al Bayda</td>\n",
       "      <td>البيضاء</td>\n",
       "      <td>YE14</td>\n",
       "      <td>Al Bayda</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2019-02-01</td>\n",
       "      <td>2019-10-02</td>\n",
       "      <td>None</td>\n",
       "      <td>MULTIPOLYGON (((44.72676 14.15240, 44.72637 14...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>اليمن</td>\n",
       "      <td>YE</td>\n",
       "      <td>Ta'iz</td>\n",
       "      <td>تعز</td>\n",
       "      <td>YE15</td>\n",
       "      <td>Taiz</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2019-02-01</td>\n",
       "      <td>2019-10-02</td>\n",
       "      <td>None</td>\n",
       "      <td>MULTIPOLYGON (((43.41111 12.67158, 43.41124 12...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ADM0_EN ADM0_AR ADM0_PCODE      ADM1_EN        ADM1_AR ADM1_PCODE  \\\n",
       "0   Yemen   اليمن         YE          Ibb             اب       YE11   \n",
       "1   Yemen   اليمن         YE        Abyan           ابين       YE12   \n",
       "2   Yemen   اليمن         YE  Sana'a City  امانة العاصمه       YE13   \n",
       "3   Yemen   اليمن         YE     Al Bayda        البيضاء       YE14   \n",
       "4   Yemen   اليمن         YE        Ta'iz            تعز       YE15   \n",
       "\n",
       "   ADM1REF_EN ADM1ALT1AR ADM1ALT1EN ADM1ALT2AR ADM1ALT2EN       date_  \\\n",
       "0         Ibb       None       None       None       None  2019-02-01   \n",
       "1       Abyan       None       None       None       None  2019-02-01   \n",
       "2  Sanaa City       None       None       None       None  2019-02-01   \n",
       "3    Al Bayda       None       None       None       None  2019-02-01   \n",
       "4        Taiz       None       None       None       None  2019-02-01   \n",
       "\n",
       "      validOn validTo                                           geometry  \n",
       "0  2019-10-02    None  POLYGON ((44.08076 14.38973, 44.08362 14.37772...  \n",
       "1  2019-10-02    None  POLYGON ((46.29563 14.26991, 46.29770 14.26687...  \n",
       "2  2019-10-02    None  POLYGON ((44.33380 15.60702, 44.35087 15.59940...  \n",
       "3  2019-10-02    None  MULTIPOLYGON (((44.72676 14.15240, 44.72637 14...  \n",
       "4  2019-10-02    None  MULTIPOLYGON (((43.41111 12.67158, 43.41124 12...  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import our GeoPandas library\n",
    "import geopandas as gpd\n",
    "# Open the shapefile called \"yem_admbnda_adm1_govyem_cso_20191002.shp\" and note that - if you're \n",
    "# doing this on your home computer, you'll need to load the file from where-ever you saved it.\n",
    "# While you only need to load this file here, the other file data needed are cross-referenced, \n",
    "# so make sure to unzip all the files.\n",
    "shape_data = gpd.GeoDataFrame.from_file(\"data/yem_admbnda_adm1_govyem_cso_20191002.shp\")\n",
    "# You can list your shape_data exactly like a regular dataframe\n",
    "shape_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Turukawa\\.conda\\envs\\datascience\\lib\\site-packages\\pyproj\\crs\\crs.py:53: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can also draw this boundary on a map so you can see what we have\n",
    "# The 'geometry' column above has the coordinate data we need\n",
    "# The 'contextily' library allows us to draw on a map\n",
    "import contextily as ctx\n",
    "# We need to change the map projection so that it will fit the same\n",
    "# coordinate system - this is beyond the requirements of this class,\n",
    "# but read up on projection systems if you're interested.\n",
    "shape_data.to_crs(epsg=3857, inplace=True)\n",
    "# and create the chart axes\n",
    "ax = shape_data[\"geometry\"].plot(color=\"darkred\", alpha=0.25, figsize=(12,12))\n",
    "ctx.add_basemap(ax, source=ctx.providers.CartoDB.Positron)\n",
    "ax.set_axis_off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this late stage, a new complexity emerges. Since this case study was written, accepted spellings and names for the regions have changed. We can see this if we look for differences between the two dataframes we need to merge."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\"Al Dhale'e\", 'Al Hudaydah', 'Amanat Al Asimah', 'Hadramaut', 'Marib', \"Sa'ada\", 'Taizz']\n",
      "[\"Ad Dali'\", 'Al Hodeidah', 'Hadramawt', \"Ma'rib\", \"Sa'dah\", \"Sana'a City\", 'Socotra', \"Ta'iz\"]\n"
     ]
    }
   ],
   "source": [
    "map_governorates = shape_data.ADM1_EN.unique()\n",
    "data_governorates = data_slice.Governorate.unique()\n",
    "name_list = list(set(data_governorates) - set(map_governorates))\n",
    "name_list.sort()\n",
    "print(name_list)\n",
    "name_list = list(set(map_governorates) - set(data_governorates))\n",
    "name_list.sort()\n",
    "print(name_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should see that the names are mostly subtle spelling differences, except for one. \"Sana'a City\" is separate from the Sanaa Governorate, forming the \"Amanat Al Asemah\" governorate. In the map geometry file it is listed as \"Sana'a City\".\n",
    "\n",
    "So ... we need to rename everything again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "fix = {\"Ad Dali'\": [\"Al Dhale'e\"],\n",
    "       \"Al Hodeidah\": [\"Al Hudaydah\"], \n",
    "       \"Hadramawt\": [\"Hadramaut\"], \n",
    "       \"Ma'rib\": [\"Marib\"], \n",
    "       \"Sa'dah\": [\"Sa'ada\"],\n",
    "       \"Sana'a City\": [\"Amanat Al Asimah\"], \n",
    "       \"Ta'iz\": [\"Taizz\"]\n",
    "      }\n",
    "data_slice = fix_governorates(data_slice, fix).sort_values(\"Attack Rate (per 1000)\", ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can draw it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c2f5309508>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We have no data for Socotra island, so we can drop this row\n",
    "# It's name is listed in the ADM1_EN column\n",
    "shape_data = shape_data.loc[~shape_data.ADM1_EN.isin([\"Socotra\"])]\n",
    "# And now we can merge our existing data_slice to produce our map data\n",
    "map_data = pd.merge(shape_data, data_slice, how=\"outer\", left_on=\"ADM1_EN\", right_on=\"Governorate\", indicator=False)\n",
    "\n",
    "# Let's draw a map\n",
    "\n",
    "# First, define a figure, axis and plot size\n",
    "fig, ax = plt.subplots(figsize=(25,14))\n",
    "# We'll look at one specific date, the last entry in the series\n",
    "md = map_data.loc[map_data.Date == \"2018-02-18\"]\n",
    "# And plot - note that 'cmap' is simply a colour gradient used in the visualisation - we're using OrRd, orange-red\n",
    "md.plot(ax=ax, column='Cases', cmap='OrRd')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here we hit a fundamental limit of a map ... it would be nice to show a time-series of how events progressed.\n",
    "\n",
    "Well, remember the small multiple ... So, to end this first lesson, here's what a small multiple map looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This is a bit more complex than you may expect ... but think of it like this:\n",
    "# We're going to create a figure and then iterate over the time-series to progressively\n",
    "# add in new subplots. Since there are 125 dates - and that's rather a lot - we'll\n",
    "# deliberately limit this to the first date in each month, and the final date.\n",
    "\n",
    "# Create a datetime format data series\n",
    "date_list = pd.Series([pd.Timestamp(d) for d in map_data[\"Date\"].unique()])\n",
    "# Sort dates in place\n",
    "date_list.sort_values(inplace = True)\n",
    "dl = {}\n",
    "for d in date_list:\n",
    "    # A mechanism to get the last day of each year-month\n",
    "    k = \"{}-{}\".format(d.year, d.month)\n",
    "    dl[k] = d\n",
    "# Recover and sort the unique list of dates\n",
    "date_list = list(dl.values())\n",
    "date_list.sort()\n",
    "\n",
    "# Create our figure\n",
    "fig = plt.figure(figsize=(18, 10))\n",
    "# Set two check_sums, first_date and sub_count\n",
    "first_date = 0\n",
    "sub_count = 1\n",
    "# Loop through the dates, using \"enumerate\" to count the number of times we loop\n",
    "for i, d in enumerate(date_list):\n",
    "    # Convert the Numpy time format to a simpler Python format\n",
    "    check_date = pd.Timestamp(d).to_pydatetime()\n",
    "    # Check if we've seen this month before\n",
    "    if check_date.month == first_date:\n",
    "        # If we have, check if it's the last value in the loop\n",
    "        if i < len(date_list)-1:\n",
    "            # And skip the rest of this loop\n",
    "            continue\n",
    "    # Store the month we've just reached\n",
    "    first_date = check_date.month\n",
    "    # Get a dataframe for the subplot at this date\n",
    "    subplot = map_data.loc[map_data.Date == pd.Timestamp(d)]\n",
    "    # Add the appropriate subplot in a frame structured as 3 items in 4 rows \n",
    "    # (there are 10 time periods but we need a square coordinate grid)\n",
    "    ax = fig.add_subplot(3, 4, sub_count)\n",
    "    # Increment the count\n",
    "    sub_count+=1\n",
    "    # Do some visual fixes to ensure we don't distort the maps, and provide titles\n",
    "    ax.set_aspect('equal')\n",
    "    ax.set_axis_off()\n",
    "    ax.title.set_text(pd.Timestamp(d).date())\n",
    "    # And plot\n",
    "    subplot.plot(ax=ax, column='Cases', cmap='OrRd')"
   ]
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    "The two small-multiples - the line charts and the choropleth - permit us to answer our research question: _there is an increasing incidence and prevalence of cholera in Yemen, with the rate of increase occuring both in scale and in geographic range. The challenge for any aid response will not only be in addressing the scale of the problem, but also in distributing care over an increasingly large physical area._\n",
    "\n",
    "The visualisations themselves, along with the source data, are all part of our answer to our question.\n",
    "\n",
    "---\n",
    "\n",
    "That brings us to the end of this first lesson and this case-study. You can play around with the code, pick a different column to visualise (perhaps \"Deaths\"), and can learn more in the libraries about how to present these charts.\n",
    "\n",
    "Below you will find a summary, additional exercises, and some extras and options to extend your knowledge."
   ]
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    "## 1.5 Lesson summary\n",
    "\n",
    "[Ethical](#1.1-Ethics:-a-foundation-for-reasoning) problems do not always require a call to facts to justify a particular decision, but they do have a number of characteristics:\n",
    "\n",
    " - _Public_: the process by which we arrive at an ethical choice is known to all participants;\n",
    " - _Informal_: the process cannot always be codified into law like a legal system;\n",
    " - _Rational_: despite the informality, the logic used must be accessible and defensible;\n",
    " - _Impartial_: any decision must not favour any group or person;\n",
    "\n",
    "[Curation](#1.2-Curation:-keeping-track-of-what-we-know) requires us to keep a grammar to describe and organise the data we collect, and we need a mechanism to ensure that both the grammar and data are accessible to anyone who require them.\n",
    "\n",
    "Data about the overall dataset is called descriptive metadata. Structural metadata is a grammar describing the structure and definitions of the data in a table.\n",
    "\n",
    "[Analysis](#1.3-Analysis:-exploring-data) starts with data exploration, and the requirement to wrangle data into a format which permits that exploration. Data are inherently uncertain and any analysis must be absolutely cognizant of this. It is the reason we start with ethics. We must, from the outset, be truthful to ourselves.\n",
    "\n",
    "[Presentation](#1.4-Presentation:-simplicity-and-letting-data-tell-a-story) is about the development of data-driven stories.\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<ol>\n",
    "    <li>Never use a pie chart; use a table instead.</li>\n",
    "    <li>Never use a complicated chart where a simple one will do.</li>\n",
    "    <li>Never clutter your data with unnecessary grids, ticks, labels or detail.</li>\n",
    "    <li>If it is possible to remove a chart without taking away from your story, always remove it.</li>\n",
    "    <li>Never mislead your reader through confusing or ambiguous axes or visualisations.</li>\n",
    "    <li>Break any of these rules sooner than draw anything outright barbarous.</li>\n",
    "<ol>\n",
    "</div>\n",
    "\n",
    "## 1.6 Exercises\n",
    "\n",
    "The World Bank has a comprehensive open data website. A population series on [Crude Birth Rate](https://data.worldbank.org/indicator/SP.DYN.CBRT.IN) has data for every UN member-state. Go through the same process as in this lesson and tell a story. If you want to create a world map, you'll find help in the documentation for GeoPandas guiding you to source the necessary shapes.\n",
    "\n",
    "Numerous governments and international agencies run their own open data portals. Explore them for data and see what stories you can find:\n",
    "\n",
    " - [Data.gov.uk](https://data.gov.uk/)\n",
    " - [Data.govt.nz](https://www.data.govt.nz/)\n",
    " - [Opendata.go.tz](http://opendata.go.tz/)\n",
    " - [Datos.gob.mx](https://datos.gob.mx/)\n",
    " - [WHO.int](http://www.who.int/gho/database/en/)\n",
    "\n",
    "## 1.7 Extras and options\n",
    "\n",
    "### 1.7.1 Additional learning material\n",
    "\n",
    "There are a host of online tutorials and resources that will deepen your knowledge of the topics discussed in this lesson, and you can probably find these yourself. The documentation for the libraries we use are also a critical resource and familiarising yourself with how to read and use these will repay itself massively:\n",
    "\n",
    " - [Anaconda](https://docs.anaconda.com/)\n",
    " - [Jupyter Notebook](http://jupyter.org/documentation.html)\n",
    " - [Pandas](https://pandas.pydata.org/pandas-docs/stable/)\n",
    " - [Numpy](https://docs.scipy.org/doc/numpy-1.13.0/user/)\n",
    " - [Matplotlib](https://matplotlib.org/contents.html)\n",
    " - [GeoPandas](http://geopandas.org/)\n",
    " - [Seaborn](https://seaborn.pydata.org/tutorial.html)\n",
    "\n",
    "### 1.7.2 Install a local version of Anaconda and Jupyter Notebook\n",
    "\n",
    "[Download Anaconda](https://www.anaconda.com/download/), choosing the version for your operating system, and ensuring that you get the Python 3.* version. If you have a relatively recent computer, you should be fine with the 64-bit version.\n",
    "\n",
    "[Follow these instructions](https://docs.anaconda.com/anaconda/install/windows) for the Windows installation. Then start the Anaconda Navigator and select Environments from the menu on the left: ![Jupyter menu](images/jupyter-menu.JPG)\n",
    "Anaconda allows you to create isolated development environments. The important thing to understand about these is that it keeps your programming software contained to its own \"play-area\", making it easy to extend and delete. Create a new environment by clicking on the \"Create\" button at the bottom of the screen: ![Jupyter environments](images/jupyter-environments.JPG \"Jupyter environments\")\n",
    "Name your environment anything you like, but you can call it \"datascience\" to make it easy to find. Now you need to install the packages we need during this course. In the filter bar, choose \"Not installed\" and then search for, and select, the following packages: Pandas, Numpy, Matplotlib, and Seaborn. That's all we'll need for now: ![Jupyter packages](images/jupyter-packages.JPG \"Jupyter packages\")\n",
    "You'll see an \"apply\" button on the bottom right of the Anaconda screen. Click that and follow the instructions to install everything you need (and don't be surprised by all the other dependencies required for this library).\n",
    "\n",
    "The last two packages need to be installed manually. Left-click on the \"arrow\" next to \"datascience\" and choose \"Open Terminal\": ![Jupyter terminal](images/jupyter-terminal.JPG \"Jupyter terminal\")\n",
    "A new window will open with a command prompt. Type in each of the following (one-by-one) and enter. Follow the instructions \n",
    "\n",
    "    conda install -c conda-forge geopandas\n",
    "    conda install -c conda-forge folium\n",
    "  \n",
    "Return to Home and click to install Jupyter Notebook. Once installed, launch it: ![Jupyter launch](images/jupyter-launch.JPG \"Launch Jupyter Notebook\")\n",
    "If you get stuck, [read the docs](https://docs.anaconda.com/) or search for solutions on [Stack Overflow](https://stackoverflow.com/)."
   ]
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    "# References\n",
    "\n",
    "(<a id=\"cit-katz_tuskegee_2006\" href=\"#call-katz_tuskegee_2006\">Katz, Kegeles <em>et al.</em>, 2006</a>) Katz Ralph, Kegeles Steven, Kressin Nancy <em>et al.</em>, ``_The Tuskegee Legacy Project: Willingness of Minorities to Participate in Biomedical Research_'', Journal of Health Care for the Poor and Underserved, vol. 17, number 4, pp. 698--715, November 2006.  [online](http://muse.jhu.edu/article/206217)\n",
    "\n",
    "(<a id=\"cit-baggini_ethics_2007\" href=\"#call-baggini_ethics_2007\">Baggini and Fosl, 2007</a>) Julian Baggini and Peter Fosl, ``_The Ethics Toolkit_'',  2007.  [online](http://www.blackwellpublishing.com/)\n",
    "\n",
    "(<a id=\"cit-bowles_economy_2017\" href=\"#call-bowles_economy_2017\">Bowles, Carlin <em>et al.</em>, 2017</a>) Samuel Bowles, Wendy Carlin and Margaret Stevens, ``_The Economy_'',  2017.  [online](http://www.core-econ.org/the-economy/)\n",
    "\n",
    "(<a id=\"cit-folse_fundamental_2005\" href=\"#call-folse_fundamental_2005\">Folse, 2005</a>) Henry Folse, ``_Some Fundamental Concepts in Ethics_'',  2005.  [online](http://people.loyno.edu/~folse/ethics.html)\n",
    "\n",
    "(<a id=\"cit-mckinney_python_2013\" href=\"#call-mckinney_python_2013\">McKinney, 2013</a>) Wes McKinney, ``_Python for Data Analysis_'',  2013.  [online](http://wesmckinney.com/)\n",
    "\n",
    "(<a id=\"cit-dicker_principles_2012\" href=\"#call-dicker_principles_2012\">Dicker, Coronado <em>et al.</em>, 2012</a>) Richard Dicker, Fátima Coronado, Denise Koo <em>et al.</em>, ``_Principles of Epidemiology in Public Health Practice: An Introduction to Applied Epidemiology and Biostatistics_'',  2012.  [online](https://www.cdc.gov/ophss/csels/dsepd/ss1978/)\n",
    "\n",
    "(<a id=\"cit-tufte_beautiful_2006\" href=\"#call-tufte_beautiful_2006\">Tufte, 2006</a>) Edward Tufte, ``_Beautiful Evidence_'',  2006.  [online](https://www.edwardtufte.com/tufte/books_be)\n",
    "\n"
   ]
  }
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