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python for Data Science For Dummies 1

丛书名： For Dummies: Computers

2015年

作者： John Paul Mueller, Luca Massaron

ISBN: (数字)9781118844144

ISBN: (纸本)1118844181;9781118844182

Unleash the power of python for your data analysis projects with For Dummies! python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. python for Data Science For Dummies shows you how to take advantage of python programming to acquire, organize, process, and analyze large amounts of information and use basic statistics concepts to identify trends and patterns. Youll get familiar with the python development environment, manipulate data, design compelling visualizations, and solve scientific computing challenges as you work your way through this user-friendly guide. Covers the fundamentals of python data analysis programming and statistics to help you build a solid foundation in data science concepts like probability, random distributions, hypothesis testing, and regression models Explains objects, functions, modules, and libraries and their role in data analysis Walks you through some of the most widely-used libraries, including NumPy, SciPy, BeautifulSoup, Pandas, and MatPlobLib Whether youre new to data analysis or just new to python, python for Data Science For Dummies is your practical guide to getting a grip on data overload and doing interesting things with the oodles of information you uncover.

关键词： data science data scientists data mining data analysis data warehousing python python for data analysis python programming python programming language python data numpy scipy beautifulsoup pandas matploblib data visualization data science books data science book data analysis books data analysis book data science guide data science reference data analysis guide data science jobs big data big data tools big data solutions big data management learn about big data books on big data books on big data management choosing big data solutions how to manage big data what is big data networking data storage cloud computing data analytics data management database management

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Grid-enabling a problem solving environment: Implementation and everyday use

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Joint Workshop on Making Grids Works

作者： Georgiou, Konstantinos Kollias, Giorggos Gallopoulos, Efstratios Univ Patras CEID Patras 26500 Greece

ISBN: (纸本)9780387784472

We describe a simple, yet powerful API for accessing and using Grid resources from within Jylab, a novel, extensible scientific computing workbench consisting of a suite of open-source Java libraries scriptable through a Jython interpreter. The API provides a Java-based, python-scriptable interactive environment and aims to simplify Grid application development and use. We demonstrate the utilization of the API in the context of an application from Internet algorithmics, specifically creating an index of crawled Web pages and using it for link-based ranking calculations (PageRank) and search queries.

关键词： problem solving environment internet algoridinlics PageRank python programming language

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Animal representations in Margaret Atwood's novels:a study based on pan-indexicality model

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Language and Semiotic Studies 2023年第4期9卷 484-509页

作者： Jing Zhu Jiying Kang Chunyun Duan Soochow University SuzhouP.R.China Tsinghua University BejingP.R.China

Margaret Atwood is a Canadian author of more than thirty-five books and the winner of prestigious literary prizes,such as the Booker Prize,the Giller Prize,and the Governor General's *** influence on Canadian literature and contemporary literature as a whole is ***,little is known with respect to how Atwood represents animals covering the full range of her *** paper reports on the analysis of animal representations in Atwood's seventeen novels through python programming and close reading under the framework of a new semiotic research finding,a pan-indexicality model within the context of literature and the *** study investigates the frequencies of animal vocabulary in the seventeen novels,the changes of animal representations in her novels before 1990s and after 1990s,and the implication of the ever-changing animal representations during the fty *** paper concludes that nonhuman animal descriptions in Atwood's novels of 1970s and 1980s run at a high level and decrease in her novels of 1990s,while scientific animal descriptions increase in her novels of 2000s and *** animals in her novels of 1970s and 1980s are instrumentalized as a vehicle for indigenization and national individuation from the United States,and scientific animals in her novels of 2000s and 2010s are instrumentalized in the service of environmental *** study suggests that the pan-indexicality model can be employed to understand the meaning of signs in literature and the environment from the perspective of authorial intention,with reference to authors'encyclopedic knowledge,personal experience,social,and cultural background information.

关键词： pan-indexicality model animal representation ecocriticism Margaret Atwood's novels python programming

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Evaluating the Quality of LLM-Generated Explanations for Logical Errors in CS1 Student Programs 23

Evaluating the Quality of LLM-Generated Explanations for Log...

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16th Annual ACM India Compute Conference (COMPUTE)

作者： Balse, Rishabh Kumar, Viraj Prasad, Prajish Warriem, Jayakrishnan Madathil Indian Inst Technol Madras BS Programme Data Sci & Applicat Chennai Tamil Nadu India Indian Inst Sci Bangalore Karnataka India FLAME Univ Sch Comp & Data Sci Pune Maharashtra India Indian Inst Technol Madras BS Programme Data Sci & Applicat Natl Programme Technol Enhanced Learning NPTEL Chennai Tamil Nadu India

ISBN: (纸本)9798400708404

When students in CS1 (Introductory programming) write erroneous code, course staff can use automated tools to provide various types of helpful feedback. In this paper, we focus on syntactically correct student code containing logical errors. Tools that explain logical errors typically require course staff to invest greater effort than tools that detect such errors. To reduce this effort, prior work has investigated the use of Large Language Models (LLMs) such as GPT3 to generate explanations. Unfortunately, these explanations can be incomplete or incorrect, and therefore unhelpful if presented to students directly. Nevertheless, LLM-generated explanations may be of adequate quality for Teaching Assistants (TAs) to efficiently craft helpful explanations on their basis. We evaluate the quality of explanations generated by an LLM (GPT-3.5-turbo) in two ways, for 30 buggy student solutions across 6 code-writing problems. First, in a study with 5 undergraduate TAs, we compare TA perception of LLM-generated and peer-generated explanation quality. TAs were unaware which explanations were LLM-generated, but they found them to be comparable in quality to peer-generated explanations. Second, we performed a detailed manual analysis of LLM-generated explanations for all 30 buggy solutions. We found at least one incorrect statement in 15/30 explanations (50%). However, in 28/30 cases (93%), the LLM-generated explanation correctly identified at least one logical error. Our results suggest that for large CS1 courses, TAs with adequate training to detect erroneous statements may be able to extract value from such explanations.

关键词： Large language models (LLMs) GPT-3.5-Turbo Explanation Logical Errors python programming

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Day Ahead Load Forecasting Model Using Gaussian Naive Bayes with Ensemble Empirical Mode Decomposition

Day Ahead Load Forecasting Model Using Gaussian Naive Bayes ...

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IEEE Region 10 Symposium (TENSYMP) - Good Technologies for Creating Future

作者： Angeles, Emerie R. Badreldin, Mohammed D. Santos, Austine James C. Conrado, F. Ostia, Jr. Mapua Univ Sch Elect Elect & Comp Engn Pasig Philippines Mapua Univ Sch Elect Elect & Comp Engn Manila Philippines Mapua Univ Sch Elect Elect & Comp Engn Caloocan Philippines Mapua Univ Sch Elect Elect & Comp Engn Marikina Philippines

ISBN: (纸本)9781665400268

The importance of load forecasting has provided valuable information for power grid analysis since the early 2000's. It has been established that no specific load forecasting model can be generalized for all demand types. This study aims to fill the gaps among the plethora of existing mathematical forecasting methods, specifically using the Naive Bayes Theorem. Naive Bayes, by itself, has an issue when dealing with large amounts of input which is the reason it has not been used in load forecasting. The integration of Naive Bayes along with the Ensemble method and Empirical Mode Decomposition provided our Hybridized Naive Bayes Algorithm with adequate improvement in its accuracy given the large amount of input data. The results were justified using key performance indicators MAE, MAPE and MSE. We obtained an average of 34.35 for MSE, 60.72MW for MAE and 4.41% for its MAPE. Although the hybridized Naive Bayes presented in this study is not ready for industrial use, it is very promising due to its mathematical prediction model and even more improvement is highly feasible.

关键词： load forecasting Naive Bayes Empirical Mode Decomposition (EMD) Ensemble Method python programming

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Dorper BSI Monitoring with Load Cells and Raspberry PI 9

Dorper BSI Monitoring with Load Cells and Raspberry PI

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9th IEEE Symposium on Computer Applications and Industrial Electronics (ISCAIE)

作者： Yahaya, Faieza Hanum Shauri, Ruhizan Liza Ahmad Abu Bakar, Shahriman Univ Teknol Mara Fac Elect Engn Shah Alam 40450 Selangor Malaysia Univ Malaysia Perlis Motorsport Engn Res Unit Kg Ulu Pauh 02600 Perlis Malaysia

ISBN: (纸本)9781538685464

The paper describes an alternative method to weigh and monitor the health of livestock via BSI. One method is by recording the measured weight for each livestock for daily weight comparison. The farmer needs to manually lift each of the livestock onto the weighing scale, using rope, producing risk of injury to both farmer and livestock. The main purpose of this project is to enhance the existing weight process by developing a weighing scale which is durable, easy to maintain as well as equipped with online database to store the weight of the livestock for monitoring purposes. Hardware consisting of load cell, HX711 and Raspberry Pi while the software will be written using python programming language and LAMP to create a web server for storing acquired data. The system's prototype takes accurate and stable reading in experiments simulating a livestock walking onto the weighing platform with an error rate of 0.1kg. Finally, the implementation of this system shall increase farmers productivity as well as redusing risk for injury.

关键词： Dorper body score index load cell python programming LAMP

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Automated Sound Intensity Measurement Using Doosan Robot: Off-Line programming for Enhanced Accuracy 28

Automated Sound Intensity Measurement Using Doosan Robot: Of...

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28th IEEE International Conference on Intelligent Engineering Systems (INES)

作者： Triki, Oussema Cherif, Raef UQAR Univ Quebec Rimouski Dept Math Informat & Genie Rimouski PQ Canada

ISBN: (纸本)9798350367607;9798350367591

Sound intensity measurement is a critical task in numerous industries, particularly in assessing the noise levels generated by vibrating machinery. Existing methods for such measurements often prove cumbersome, time-consuming, and reliant on multiple measurement tools, requiring continuous operator intervention throughout the process. To address this challenge, several studies have explored the integration of collaborative robots to automate these measurements. However, the diversity of programming languages among different robots complicates the implementation process and prolongs setup time. To resolve this issue, our work introduces a unified python program, leveraging the RoboDK software, to seamlessly adapt our python code for various robot types. Additionally, we employ offline simulation to ensure smooth operation and validate the effectiveness of our approach through experimental validation. This holistic approach not only streamlines the measurement process but also enhances accuracy and minimizes the need for continuous human intervention.

关键词： Sound intensity measurement robotics Offline programming acoustic data python programming noise reduction

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Fuzzy Inventory Implementation of Minimum Value Unused Storing Profitable by python Execution

Fuzzy Inventory Implementation of Minimum Value Unused Stori...

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International Conference on Intelligent and Fuzzy Systems (INFUS)

作者： Iyappan, K. Anand, M. Clement Joe Kalaiarasi, K. Sindhuja, N. Sumathi, G. Tiwari, Mohit SRM TRP Engn Coll Dept Math Tiruchirappalli 621105 Tamil Nadu India Mt Carmel Coll Autonomous Dept Math Bengaluru 560052 Karnataka India Bharathidasan Univ Cauvery Coll Women Autonomous PG & Res Dept Math Tiruchirappalli 620018 Tamil Nadu India K Ramakrishnan Coll Engn Dept Math Tiruchirappalli 621112 Tamil Nadu India Bharati Vidyapeeths Coll Engn Dept Comp Sci & Engn Delhi 110063 India

ISBN: (纸本)9783031671944;9783031671951

Stock management holdings would be required in this scenario to reduce losses and increase profits. In light of this, we proposed a model by combining time series data with machine learning machines using algorithms and employing the Lagrangian method. Combinatorial optimization is used to discover opportunities for acquiring stock at a more affordable cost and sell a portion of the unused stock to make more money for the corporation. To guarantee production, we use time series and machine learning models to forecast stock prices and demand forecasts. The Inventory Management system can be employed to oversee shop merchandise and keep appraised of every product in stock, as well as to verify the store's previous purchases. We then incorporate these projections into a combinatorial optimization algorithm to aid in the decision-making process (buy, sell, or hold), and we additionally determine the exact amounts to buy or sell in the appropriate sequence. By minimizing the sharp model in fuzzy optimization via python programming.

关键词： Fuzzy inventory Total cost python programming

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Hydroelectric Power-Plant Simulator Implemented in python 23

Hydroelectric Power-Plant Simulator Implemented in Python

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23rd International Conference on Process Control (PC)

作者： Kuchar, Michal Peichl, Adam Kucera, Milan Fiser, Jaromir Kulik, Pavel Vyhlidal, Tomas Czech Tech Univ Fac Mech Engn Dept Instrumentat & Control Engn Tech 4 Prague 16607 6 Czech Republic Czech Tech Univ Czech Inst Informat Robot & Cybernet Tech 4 Prague 16607 6 Czech Republic ZAT As K Podlesi 541 Pribram 26101 Czech Republic

ISBN: (纸本)9781665403306

Design of a hydroelectric power-plant simulator and its implementation in python are presented. The simulator is aimed as a supportive tool for PID parameter pre-tuning for the vital power-plant regimes. Typically, the pre-tuned parameters are to be used as starting values at the physical control system within its installation phase. The simulator can also be used for testing the control system performance at the safety-critical regimes. The simulator core is formed by a low-parameter dynamical model, which is parameterized based on power-plant dimension and available experimental data. For the simulator implementation, python programming language is used due to its enhanced readability and suitability for rapid prototyping. Runge-Kutta numerical implementation of model parts is formed as compact python functions. These are embedded and called within the simulation part of the whole program, which also handles smooth transients at switching of the regimes. The simulator performance is validated on available process data.

关键词： Simulator design power-plant model switched system python programming server application

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Smart Object Detection using ESP32-CAM based on YOLO Algorithm 2

Smart Object Detection using ESP32-CAM based on YOLO Algorit...

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2nd International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)

作者： Narwaria, Ravindra Pratap Ahirwar, Anand Prajapati, Abhinay K. Kumar, Abhishek Tiwari, Arin K. MITS Dept Elect Engn Gwalior MP India

ISBN: (纸本)9798331540661;9798331540678

In the present research work, a system is developed that can detect objects in real-time using a combination of the ESP32 CAM module and python programming. The goal was to show how affordable hardware and free software can be used to make a system that recognizes objects quickly and accurately. By using computer vision and machine learning tricks, the proposed system can figure out the different objects with great precision. The setup process involves putting some code onto the ESP32 CAM module, finding its IP address, and then making it work smoothly with python. The proposed system was tested in different situations, like watching for things in surveillance, making tasks easier with automation, and helping out in assistive technologies.

关键词： ESP32-CAM Module Computer Vision Real-time Object Detection Machine Learning (ML) Surveillance Systems Object Recognition python programming Image Processing YOLO Open CV

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