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Infrastructure, Economic Development, Solid Waste Management, Materials.
2.0" report — 2018 As a reminder, these levels are to be compared to the 0.73kg per day per person estimated for MSW, which is therefore dwarfed by industrial and agricultural waste. The spoil tips, a landmark of all mining countries, are a testimony to the huge volume of waste material produced
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outside MSW. The spoil tips in Lievin France — Photo by Hemmer — License CC BY-SA 3.0 We will however focus on MSW, given its relevance for infrastructure as well as the availability of data. Wealth and propensity to emit waste are clearly correlated. We notice a few outliers, such as the USA in
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the High Income category, which holds the record for waste generation, both in absolute and relative terms. China, on the other hand, seems to generate somewhat less waste than other large economies. For the World Bank, this (astonishing) result is imputable to the large share of rural population
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in that country. But it might also result from issues with the data. Computed by the author based on data from the World Bank If we remove the log-log scale, the major challenge associated with moving up the income scale becomes even more apparent. Computed by the author based on World Bank data.
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We have displayed three different regression models, which suggest different paths in this relationship between GDP and waste. The model in small dots and in the middle represents the one used by the World Bank (with slightly different coefficients), as a basis to the forecast we present further
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down. The World Bank’s long term forecast is summarized in the chart below (broken down by income category). The total would reach 2.6Gt by 2030 and 3.4Gt by 2050. Source: World Bank” What a Waste 2.0" report — 2018 The expected growth in solid waste is quite consistent with the overall trend for
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material consumption, although these two estimates come from different sources (albeit with one shared input: the GDP forecast comes from the OECD in both cases). After all, it is quite logical to see solid waste generation driven by how much material was used in the first place. Apparently, there
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are indications that some rich countries might be able to contain or even decrease the volume of solid waste they produce, as suggested by the OECD in another report issued in 2019. But it is not clear whether the data is reliable enough to reach a conclusion: more work would be warranted to
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analyze this further. Here an excerpt from that report. “Most OECD countries (14 of the 20 for which data was available) reduced the amount of municipal waste generated per capita between 2000 and 2015. (…). In some cases, these decreases may be attributed in part to changes or uncertainties in the
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reporting of data (for example, Estonia, Poland). In other cases, decreases in MSW generation may be a result of policy measures. For example, the decrease observed in MSW generation were attributed to the introduction of volume-based pricing in Korea and broader waste reduction policies in the
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Netherlands. At least ten OECD countries experienced an increase in MSW generation per capita: (…) Norway saw an increase of almost 20% over this period, which was attributed to increased consumption in that country. (…) Israel has the highest per capita waste generation and experienced only a
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moderate reduction (-2%) during the 2000–15 period.” If we stick to the growth rates displayed above, the challenge is even more vertiginous than the numbers suggest. Not only the world -in particular the emerging world- will have to cope with that growth, but it will do so from a starting point
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that is already far from acceptable. It is one thing to perform a service and having to accommodate a surging demand. It is a much larger problem when the current demand is not satisfactorily addressed already. To illustrate my assertion about the low starting point, I could use two charts from the
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World Bank’s study. The first one shows the abysmal performance in collection in low and middle income countries, despite the fact that collection should be a very basic service. Source: World Bank” What a Waste 2.0" report — 2018 The next one is the share of waste that is not recovered in some
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fashion. Source: World Bank” What a Waste 2.0" report — 2018 These charts are quite telling about the work left to do. But even in developed countries, the performance is far from optimal, as evidenced in this chart from the OECD. Source: OECD environment at a glance 2020 Among the rich economies,
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the US is of particular concern: it is the country with the highest volume and rate of waste generation, whereas the recovery rate remains low, and has made limited progress since 2000. Yet, as another illustration of the lack of interest in the subject, the major infrastructure plan currently in
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discussion between the Senate and the White House hardly mentions the topic. I suppose I don’t need to belabor the point about the inefficacy of our recycling system, even in the developed world that could afford much better. I regularly go to the recycling facility near my home, since curbside
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collection is only partial where I live! Once at the facility, I have to take out each piece of plastic, one by one, look for the triangular symbol, figure out the number embossed, before I toss the stuff in the -hopefully- right bucket. “Wait, was that HDPE (number 2) or PS (number 6)?” I am
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amazed at how dysfunctional this whole system is, in which we end-consumers are given the responsibility to become plastic experts. Not only that, but also infrastructure experts, since we have got to know which plastic can be recycled locally, if we want to avoid “wishcycling”. And of course, we
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should all have a master’s degree in supply chains and know, before purchasing a packaged item, which package will be recycled in reality rather than in theory. The “recyclable” sign on the package has an informative value close to zero, because recyclability depends on so many circumstances (is
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there local recycling capacity for that specific product? is there demand at the moment for the recycled product? does it lose too much of its quality or attractiveness when recycled? will it be thrown away because of its inevitable contamination by food or other embedded materials?). Some of the
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difficulties, seen from the recyclers’ perspective, are explained in this survey commissioned and analyzed by McKinsey . How have we come to that point of malfunction would deserve a whole discussion in itself. In that regard, an episode of Thoughline on NPR back in 2019 provided an interesting
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analysis of that debacle. The Litter Myth : Throughline There is more waste in the world today than at any time in history, and the responsibility for keeping the environment…www.npr.org It is after observing that inherent flaw in the economy’s transformation process that the notion of the circular
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economy (as opposed to linear) emerged. The Ellen MacArthur foundation has become a reference in that field. The European Union has an action plan. The phrase is everywhere… ad nauseam. The concept is seducing, because it makes sense, is easy to understand and perfectly suited for sleek
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infographics. But honestly, and maybe I am over-critical here, despite the occasional inspiring experiment here and there, it seems we have a hard time getting beyond the catchphrase and tackling the fundamental issues. Admittedly, some initiatives deserve praise. For example, the Ellen MacArthur
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foundation’s Global Commitment. Notwithstanding the very variable degree of transparency among signatories, the initiative reveals the difficulty to achieve significant objectives such as the “post-consumer recycled content”, an application of the important concept of Extended Producer
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Responsibility (EPR). Despite its appeal, the notion of circular economy is not exempt from flaws. First, even in the words of one of its creators, Kenneth Boulding, an expert in systems theory, full circularity is not possible, since at least an input of energy will be required to go full circle
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(ha! physics!). the earth has become a single spaceship, without unlimited reservoirs of anything, either for extraction or for pollution, and in which, therefore, man must find his place in a cyclical ecological system which is capable of continuous reproduction of material form even though it
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cannot escape having inputs of energy. (Quotation from Kovacic, Strand, Völker-The circular economy in Europe). Note: the highlight is mine Second, and more relevantly to this publication, circularity hardly addresses the fundamental problem in the emerging world: a circle has by definition a fixed
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radius, whereas the world GDP is expected to expand 2.5x in the next thirty years, which growth, notwithstanding my limited geometry skills, will be difficult to contain within a circle! In other words, circularity is alright for a humming mature economy. In the emerging world, however, the spiral
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would be a more appropriate shape. For example, if an emerging country decides to solve the housing issue in its megalopolis, that will require considerable amounts of building material. As already discussed in part 2 of this essay, the pressure is already mounting on such elementary materials as
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sand. The usual response to this concern comes directly from the circular economy universe: let’s use recycled construction material. And this recycling is already practiced, at least to some extent, albeit often by downcycling. When a Building Comes Down, Where Do Its Materials Go? To understand
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buildings, consider cities. They are evolving, iterative systems whose peripheries and hinterlands are…www.metropolismag.com But if a country needs to increase its housing capacity, it means that not enough buildings existed in the first place. Therefore there is not much initial stock to retrieve
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the recycled material from. Second, by definition again, or should I say “by construction”!- building material stays stocked for decades (in… buildings) and most of it is therefore not available for recycling, as the country grows. While recycling can help reduce the material bill, additional
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extractions will be inevitable. Third, while strict rules and mandates might help, circularity is much more likely to self-sustain if it makes economic sense, i.e. if economic value drives the recycling process. Most metals experience relatively high levels of recycling because of the economic
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value in doing so. Source: UNEP-IRP Resource efficiency: Potential and economic implications (2017) The fundamental question to address is therefore: how do we instill economic value to make circular economies sustainable? My thesis is that it will come from the rarefaction of primary materials we
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have discussed earlier, on the one hand, and the unsustainability of the amount of waste produced on the other hand. Regardless of whether this thesis holds or not, and while our economies are striving to become circular in a way or another, or as close as possible to that shape, we will need a
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massive catch-up in our capacity to manage solid waste. Yet, in that regard, investment remains slow. It is difficult to get consolidated numbers. So we have to use proxies. For example, the low volume of related PPPs (as tracked by the World Bank’s PPIAF) in emerging countries. On the positive
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side, there is an undeniable upswing in volumes and number of projects, as evidenced in the chart below (omitting 2020, an abnormal year). Source: World Bank — PPIAF — Annual PPI Database Global report 2019 But this is a number to take with some nuances after: comparing it to the volume of PPP
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flows in other sectors (see chart below), considering that only five countries have concentrated 90% of these flows, in which China ranks preeminently. Moreover, these PPPs have focused mainly on Waste-To-Energy. More diversification in the type and location of projects would certainly be
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warranted. Computed by author based on data from the PPI Database This is for PPPs only, which have their limitations. Starting with an inherent complexity that can make them ill-suited for projects of small amplitude or filled with specific risks (the MSW sector ticks both boxes!). Looking at a
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few proxies again (such as the World Bank’s total commitments in the sector), it is unlikely the public sector is compensating for the private sector’s hesitancy. So what are the obstacles to a genuine push in that sector? Listening to experts -or reading them-, here are the main arguments I
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encountered: the willingness-to-pay is low, and insufficient to maintain more ambitious projects afloat. in the same vein, projects are heavily reliant on payments from public authorities, with all the associated risks. when an economic model is found (e.g. waste-to-energy), it often requires a
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certain specificity in the quality and quantity of waste, which can make it vulnerable to changes in its conditions. The breakeven point remains high for properly managed projects. it is essentially a local issue, managed and regulated locally, and is marred by insufficient economies of scale and
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poor management, regulation and payment capacity. the projects are environmentally challenging, for example with regard to the emissions resulting from the incineration of plastics, moreover in a sometimes rapidly changing regulatory environment, which change-in-law provisions imperfectly address,
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if at all. dumpsites or trash collection can be a playground for mafia-type organizations and/or an ancillary source of revenue for some local politicians. implementing projects in MSW have strong social implications, given the -mostly destitute- people who live from and on solid waste facilities.
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It takes a lot of effort and courage to structure projects that address this social component (see this interesting example in Mexico). unavailability of land in dense cities and plain old NIMBY! More fundamentally, the issue is that nobody pays much attention. Trash is trash. Who cares where it
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goes? It takes disasters such as the 2000 Payatas landslide near Manila to raise awareness, at least temporarily. Nonthaburi landfill (Thailand) — Picture Thibaud Saintin — License CC BY-NC-ND 2.0 For all these reasons, solid waste management is hardly on the investors’ radar, especially where it
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is needed, namely in the emerging world. We do see some increasing appetite driven by Waste-To-Energy projects, such as this one that recently closed its financing in Côte d’Ivoire (dealing with agricultural waste). And these projects, benefiting from the tailwinds of the global push for energy
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transition, can indeed present tremendous opportunities, especially if they improve the whole chain, from collection to recovery. We also see some budding initiatives around recycling, for example this one in the Philippines dealing with sustainable aviation fuel made from plastics. The economic
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model, however, remains shaky, or at least uncertain, for many of these projects, unless -until- some additional value is introduced (e.g. premium for renewable energy, when eligible). Unfortunately, mechanisms that would capture the immense intrinsic value of abating the scourge of trash are
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largely missing, at least for the moment. When the world starts looking frenetically for material that is otherwise hard or costly to extract, that will likely change. Moreover, countries, especially cities, will have no choice but to find an economically viable solution, if they want to avoid
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being overwhelmed, with all the social and political implications this would entail. The 2015 crisis in Beirut certainly had its idiosyncrasies. But it may also be a harbinger of what awaits many cities around the world, if the numbers shown above hold true. Today's global reality is that we
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already don’t know what to do with all our trash (see this article on the current status of trash exports after the related Basel convention was implemented). So the tipping point as of which we start changing our social and economic approaches to material and waste might come sooner than we think.
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Internet of Things.
Table of Contents Challenges of Manually Collecting Data The Benefits of an Automated Approach Implementing an Automated KPI Approach with OMH Caution on KPI Implementation Conclusion In today’s manufacturing environment, achieving operational excellence is a constant pursuit. Key Performance
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Indicators (KPIs) serve as navigation to guide organizations toward their production goals. However, manual data collection and reporting methods can introduce errors and inefficiencies that hinder the ability to make informed decisions. In this blog, we will discuss the transformative power of
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automation through the capabilities of the Open Manufacturing Hub (OMH). Manufacturers can revolutionize the way they monitor, analyze, and optimize their operations. Challenges of Manually Collecting Data Determining Overall Equipment Effectiveness (OEE) is critical to production efficiency.
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Manual data collection (using paper or Excel) is inadequate due to the complexity of automation. Operators focus on smooth operation, not data accuracy, which leads to errors. Software-based solutions provide accuracy, real-time insight, efficiency, consistency and scalability. Integration with
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systems such as ERP and MES improves data sharing and decision-making. In modern manufacturing, software-based OEE calculation is essential for accurate and efficient assessment. Choosing the right time period is critical for accurate production metrics. Very short periods of time, such as a day or
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a shift, produce skewed results due to insufficient data and undue focus on isolated events. Calculating metrics over a longer time span, such as a month, smoothes out productivity and downtime fluctuations and provides more meaningful insights. The Benefits of an Automated Approach Automated
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Approach offers efficiency, accuracy, and improved decision-making. Therefore, the shift from manual methods is crucial for navigating complex environments. Let’s delve into the compelling advantages of embracing automation. Precision through Accurate Data Capture Manual data entry is prone to
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human errors that can distort the integrity of KPIs. Automated systems eliminate the risk of inaccuracies by consistently and precisely capturing data. This results in reliable measurements that form the foundation for strategic decision-making. Consistency in Data Collection Automation ensures
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that data is collected consistently across different shifts, operators, and production cycles. This consistency allows for accurate historical comparisons and facilitates the identification of trends and patterns. Optimizing Human Resources Shifting from manual data entry to automated solutions
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frees up human resources for more valuable tasks. Skilled employees can focus on problem-solving, process improvement, and innovative initiatives that drive manufacturing excellence. Proactive Optimization Automated systems provide real-time monitoring of KPIs. Deviations from desired values
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trigger alerts, enabling immediate corrective actions. This proactive approach prevents production disruptions and helps maintain optimal efficiency. Empowering Real-Time Decision-Making Manufacturing leaders can access real-time data on KPI performance from anywhere. This empowers them to make
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informed decisions promptly, based on accurate and up-to-date information. Efficient Reporting and Communication Automated systems generate detailed reports on KPI performance, offering insights into production trends and areas that require attention. Additionally, automated notifications keep
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stakeholders informed about critical developments, ensuring swift responses to issues. Implementing an Automated KPI Approach with OMH The Open Manufacturing Hub (OMH) presented by EMQ is a reference architecture for building powerful and scalable Industrial IoT applications that keep your KPIs in
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control. Automating KPIs processes by using OMH is a strategic move for boosting efficiency and informed decision-making. This technology-driven approach optimizes operations and cultivates competitiveness in modern business environments. Selecting Appropriate KPIs Organizations align KPI selection
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with strategic objectives, leveraging the capabilities of the OMH to focus on metrics like efficiency, quality, downtime, and production yield. Leveraging the IT and OT Connectivity of OMH OMH’s IIoT capabilities form the backbone of the automated KPI approach. Its connectivity with machinery,
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sensors, and data streams ensures comprehensive and real-time data collection. Seamless Integration with Industrial System The OMH seamlessly integrates with existing manufacturing systems, promoting a harmonious exchange of data. Integration with ERP and MES systems ensures an unobstructed flow of
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information. Customization and Configuration The OMH can be tailored to capture data that accurately reflects the intricacies of production processes, enhancing the accuracy and relevance of KPI measurements. Unified Namespace Architecture The scalable unified namespace architecture of the OMH
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ensures that as manufacturing operations grow and evolve, the system can seamlessly accommodate increasing data volume and complexity. Data Governance and Security The centralized nature of the OMH enhances data governance and security. Access control and data protection measures can be implemented
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more effectively, safeguarding the integrity and confidentiality of the data used for KPI calculations. Caution on KPI Implementation Calculating the KPI score for an entire manufacturing site lacks practicality. While it provides an overview of the site’s performance, it does not identify
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actionable areas for improvement. To effectively identify weaknesses and drive improvement, a more detailed approach is required. For example, When calculating OEE, start by examining a single pilot machine and collecting comparable metrics. Gradually expand this methodology to individual machines
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for a more accurate assessment and systematic improvement process. Reducing KPI analysis to a single value may seem convenient for comparing different production systems or facilities. However, such comparisons often ignore the unique conditions and requirements of each facility, leading to
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inaccurate conclusions. These valid comparisons are only useful when evaluating identical machines producing the same product using identical methods. Caution should be exercised when making such comparisons. Instead, use KPI to monitor changes in a machine’s productivity over time and fine-tune
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processes accordingly. Conclusion As the manufacturing landscape evolves, the pursuit of excellence remains unwavering. Embracing the automated KPI approach through the OMH bridges the gap between innovation and operational effectiveness. This integration provides benefits such as accurate data
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collection, consistent measurement, optimal resource utilization, proactive optimization, real-time insights, and efficient reporting. The OMH enables companies to confidently navigate the complexities of modern manufacturing and inspire a culture of continuous improvement and excellence.
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Numpy, Data Cleaning.
Introduction If you had your data in a NumPy numeric array and you want to observe missing values and want to remove them quickly, in that case, you don’t have to convert the array to pandas series to deal with it! We can do these within NumPy itself. Here’s how we do it [1]. In the realm of data
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science and analytics, the quality of your data can make or break your insights. Garbage in, garbage out, as the saying goes. Before you can derive meaningful conclusions or build robust models, you must ensure that your data is clean, consistent, and ready for analysis. This is where NumPy, one of
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the fundamental libraries in Python, shines. NumPy, short for Numerical Python, is a powerful library for numerical computing that provides support for arrays, matrices, and mathematical functions. While it’s widely known for its capabilities in numerical operations and scientific computing, NumPy
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also offers robust tools for data cleaning tasks. In this comprehensive guide, we’ll explore how NumPy can be leveraged for data cleaning to ensure your datasets are primed for analysis. Sections Understanding Data Cleaning NumPy’s Role in Data Cleaning Identifying Missing Values Removing Rows or
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Columns with Missing Values Removing Outliers Removing Duplicates Conclusion Section 1- Understanding Data Cleaning Data cleaning involves identifying and correcting errors, inconsistencies, and inaccuracies in your dataset. This process is crucial because real-world data is often messy and may
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contain missing values, outliers, duplicates, or formatting issues. Failing to address these issues can lead to biased analyses and erroneous conclusions. Section 2- NumPy’s Role in Data Cleaning NumPy provides several features and functions that are invaluable for data cleaning tasks: Array
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Operations: NumPy’s array operations allow for efficient manipulation of data. You can perform element-wise operations, slicing, indexing, and reshaping of arrays, making it easy to clean and preprocess data. Handling Missing Values: NumPy offers functions such as numpy.nan for representing missing
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values and numpy.isnan() for detecting them. You can use these functions to identify missing values in your dataset and handle them accordingly, whether by imputation or removal. Dealing with Outliers: Outliers can skew your analysis and model performance. NumPy provides statistical functions like
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numpy.percentile() and numpy.mean() that help identify outliers based on threshold values or statistical measures. You can then choose to remove or transform these outliers as necessary. Removing Duplicates: NumPy’s numpy.unique() function is handy for identifying and removing duplicate entries
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from arrays or datasets. By eliminating duplicates, you can ensure that your analyses are based on unique observations. Data Transformation: NumPy offers a variety of mathematical and statistical functions for transforming data. Whether you need to scale values, apply logarithmic transformations,
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or normalize data, NumPy has you covered. Section 3- Identifying Missing Values NumPy provides functions to check for missing values in a numeric array, represented as NaN (Not a Number). # Create a NumPy array with missing values data = np.array([1, 2, np.nan, 4, np.nan, 6]) # Check for missing
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values has_missing = np.isnan(data) print(has_missing) import numpy as np data = np.array([1, 2, np.nan, 4, 5]) mean_value = np.nanmean(data) # Compute mean ignoring NaN data[np.isnan(data)] = mean_value # Replace NaN with mean Section 4- Removing Rows or Columns with Missing Values We can use
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np.isnan to get a boolean matrix with True for the indices where there is a missing value. And when we pass it to np.any, it will return a 1D array with True for the index where any row item is True. And finally, we ~ (not), and pass the Boolean to the original Matrix, which will remove the rows
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with missing values. # Create a 2D array with missing values data = np.array([[1, 2, 3], [4, np.nan, 6], [7, 8, 9]]) # Remove rows with any missing values cleaned_data = data[~np.any(np.isnan(data), axis=1)] print(cleaned_data) # Result: [[1,2,3],[7,8,9]] Section 5-Removing Outliers: data =
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np.array([1, 2, 3, 100, 101, 102]) threshold = np.percentile(data, 95) # Get 95th percentile cleaned_data = data[data <= threshold] # Remove outliers above threshold Section 6- Removing Duplicates: data = np.array([1, 2, 3, 2, 4, 5, 1]) unique_values = np.unique(data) # Get unique values Conclusion
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NumPy is not just for numerical computations; it’s also a powerful tool for data cleaning and preprocessing. By leveraging NumPy’s array operations, statistical functions, and data manipulation capabilities, you can streamline the process of cleaning and preparing your datasets for analysis.
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Incorporating NumPy into your data cleaning workflow will help you ensure the integrity and reliability of your analyses, paving the way for more accurate insights and better decision-making. Please Follow and 👏 Clap for the story courses teach to see latest updates on this story 🚀 Elevate Your
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| 8,827 |
Numpy, Data Cleaning.
Data Skills with Coursesteach! 🚀 Ready to dive into Python, Machine Learning, Data Science, Statistics, Linear Algebra, Computer Vision, and Research? Coursesteach has you covered! 🔍 Python, 🤖 ML, 📊 Stats, ➕ Linear Algebra, 👁️🗨️ Computer Vision, 🔬 Research — all in one place! Don’t Miss Out on
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| 8,828 |
Numpy, Data Cleaning.
This Exclusive Opportunity to Enhance Your Skill Set! Enroll Today 🌟 at Machine Learning libraries Course 🔍 Explore Tools, Python libraries for ML, Slides, Source Code, Free online Courses and More! Stay tuned for our upcoming articles because we reach end to end ,where we will explore specific
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| 8,829 |
Numpy, Data Cleaning.
topics related to Machine Learning libraries in more detail! Remember, learning is a continuous process. So keep learning and keep creating and Sharing with others!💻✌️ 📚GitHub Repository Ready to dive into data science and AI but unsure how to start? I’m here to help! Offering personalized research
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| 8,830 |
Numpy, Data Cleaning.
supervision and long-term mentoring. Let’s chat on Skype: themushtaq48 or email me at [email protected]. Let’s kickstart your journey together! Contribution: We would love your help in making coursesteach community even better! If you want to contribute in some courses , or if you have any
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| 8,831 |
Statistics, Statistical Analysis, Random Variable, Discrete Random Variable, Mathematics.
Image- Umang Bhalla Topics Covered- Expected Values of R.V. , Mean , Variance , Correlation Coefficient. Prerequisite- Random Variables , Joint Random Variables , Mean , Variance Here I am going to assume that readers already know basic definition of mean, standard deviation and variance. The
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