window frame的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列懶人包和總整理

Data Analysis with Python and Pyspark

為了解決window frame的問題，作者Rioux, Jonathan 這樣論述：

Think big about your data! PySpark brings the powerful Spark big data processing engine to the Python ecosystem, letting you seamlessly scale up your data tasks and create lightning-fast pipelines.In Data Analysis with Python and PySpark you will learn how to: Manage your data as it scales acros

s multiple machines Scale up your data programs with full confidence Read and write data to and from a variety of sources and formats Deal with messy data with PySpark’s data manipulation functionality Discover new data sets and perform exploratory data analysis Build automated data pipelines that t

ransform, summarize, and get insights from data Troubleshoot common PySpark errors Creating reliable long-running jobs Data Analysis with Python and PySpark is your guide to delivering successful Python-driven data projects. Packed with relevant examples and essential techniques, this practical book

teaches you to build pipelines for reporting, machine learning, and other data-centric tasks. Quick exercises in every chapter help you practice what you’ve learned, and rapidly start implementing PySpark into your data systems. No previous knowledge of Spark is required. Purchase of the print boo

k includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology The Spark data processing engine is an amazing analytics factory: raw data comes in, insight comes out. PySpark wraps Spark’s core engine with a Python-based API. It helps simplify Spark’s steep

learning curve and makes this powerful tool available to anyone working in the Python data ecosystem. About the bookData Analysis with Python and PySpark helps you solve the daily challenges of data science with PySpark. You’ll learn how to scale your processing capabilities across multiple machin

es while ingesting data from any source--whether that’s Hadoop clusters, cloud data storage, or local data files. Once you’ve covered the fundamentals, you’ll explore the full versatility of PySpark by building machine learning pipelines, and blending Python, pandas, and PySpark code. What’s inside

Organizing your PySpark code Managing your data, no matter the size Scale up your data programs with full confidence Troubleshooting common data pipeline problems Creating reliable long-running jobs About the reader Written for data scientists and data engineers comfortable with Python. About th

e author As a ML director for a data-driven software company, Jonathan Rioux uses PySpark daily. He teaches the software to data scientists, engineers, and data-savvy business analysts. Table of Contents 1 Introduction PART 1 GET ACQUAINTED: FIRST STEPS IN PYSPARK 2 Your first data program in PySp

ark 3 Submitting and scaling your first PySpark program 4 Analyzing tabular data with pyspark.sql 5 Data frame gymnastics: Joining and grouping PART 2 GET PROFICIENT: TRANSLATE YOUR IDEAS INTO CODE 6 Multidimensional data frames: Using PySpark with JSON data 7 Bilingual PySpark: Blending Python and

SQL code 8 Extending PySpark with Python: RDD and UDFs 9 Big data is just a lot of small data: Using pandas UDFs 10 Your data under a different lens: Window functions 11 Faster PySpark: Understanding Spark’s query planning PART 3 GET CONFIDENT: USING MACHINE LEARNING WITH PYSPARK 12 Setting the stag

e: Preparing features for machine learning 13 Robust machine learning with ML Pipelines 14 Building custom ML transformers and estimators

window frame進入發燒排行的影片

範例引導與問題導向混合學習策略對國小學生機器人程式學習成效的影響

為了解決window frame的問題，作者楊秀全 這樣論述：

問題導向的學習策略已被廣泛地運用在機器人程式設計等跨領域課程，但在此策略中加入範例的引導是否能更具學習成效，是常被關切的課題。本研究以準實驗法，探究「範例引導混合問題導向學習」以及「一般問題導向學習」兩種不同但常用之教學策略的成效，針對95名國民小學五年級學生，進行各14節的機器人程式設計課程。課程前後以自編關鍵能力量表評量學生的自主學習、合作學習、問題解決、批判思考及創造創新等能力。在評量實施後以國際運算思維測驗（Bebras test）評量學生運算思維，和以認知負荷量表評量學生在兩種學習策略下，學習機器人程式設計的認知負荷。研究結果顯示混合範例引導與問題導向學習策略，因為有適切的鷹架範例

導入，可以：提升國小學生的自主學習、合作學習、問題解決以及批判思考等能力，並在學習機器人程式設計上有較好的學習成就，同時提升學生在機器人程式設計的運算思維、降低學生的知負荷。

Excel VBA最強入門邁向辦公室自動化之路王者歸來下冊(全彩印刷)

為了解決window frame的問題，作者洪錦魁 這樣論述：

本書特色 　　★★★★★Excel VBA帶領辦公室自動化的最佳著作★★★★★ 　　☆☆☆☆☆【38個主題】、【865個程式實例】☆☆☆☆☆ 　　常聽人說Excel VBA不好學，原因是目前沒有一本Excel VBA的中文書是從零開始，逐步與完整解說程式語法、物件與工作表應用的書籍，這也是筆者撰寫這本書的動力來源。 　　這是國內中文書第一本從零開始帶領讀者完整學習Excel VBA的書籍，【上、下冊】共有38個章節，其中【上冊有18章】著重在認識Excel VBA完整語法與工作表基本操作，【下冊有20章】重點是完整辦公室自動化的應用。除了完整講解VBA語法，每個語法皆有多個程式實例輔

助解說，可以增進讀者學習效率，讀者可以使用VBA學會下列的應用。 　　☆【建立與美化工作表】 　　☆【徹底了解儲存格】、【工作表】與【活頁簿】 　　☆【資料驗證】 　　☆【數據統計】、【排序】與【篩選】 　　☆【樞紐分析表】 　　☆【走勢圖分析】 　　☆【視覺化圖表】 　　☆【決策分析】 　　☆【靜態】與【動態表單設計】 　　☆【印表機控制】 　　☆【財務管理】 　　有了上述知識，讀者可以輕鬆將上述觀念應用在【人力】、【財會】、【業務】、【管理】、【分析】等自動化工作。

嵌入式磁性隨機存取記憶體設計空間分析及其於極低溫金氧半電路之最佳化設計

為了解決window frame的問題，作者江宏禮 這樣論述：

Chinese Abstract iEnglish Abstract iiAcknowledgement iiiContents ivTable Captions vFigure Captions viList of Symbols viiChapter 1 Introduction 11.1 Background 11.2 Description of the Problem 21.3 Organization of this Dissertation

3Chapter 2 Design Space Analysis for Cross-Point 1S1MTJ MRAM: Selector-MTJ Co-Optimization 92.1 Preface 92.2 STT MRAM Used in 1T1MTJ Architecture 102.2.1 Characteristic of MTJ in STT MRAM 102.2.2 Criterion for Read and Write 102.3 Difficulties of 1S1MTJ by Using ST

T MRAM in Current 1T1MTJ MRAM Array 122.3.1 1S1MTJ with Exponential-Type Selector 122.3.2 1S1MTJ with Threshold-Type Selector 142.4 MTJ Optimization for Existing Selectors 152.4.1 Optimization for Exponential-Type Selector 152.4.2 Optimization for Threshold-Type Selector

172.4.3 Endurance Limitation in 1S1MTJ Array 172.5 Design Space Analysis for 1S1MTJ Array 182.5.1 Methodology for Array Level Analysis 182.5.2 Exponential-Type Selector 192.5.3 Threshold-Type Selector 202.6 Summary 21Chapter 3Cryogenic CMOS as a Power-Performance

-Reliability Booster for Advanced FinFETs 423.1 Preface 423.2 FinFET Characterization at Cryogenic Conditions 433.2.1 Long-Channel Mobility Enhancement 433.2.2 Short-Channel Device Characteristics 433.2.3 Reduction of Line Resistance BEOL 453.2.4 Reliability Improveme

nt 453.3 System-Level Analysis 463.3.1 Ring Oscillator Measurement 463.3.2 Power-Performance Analysis 473.3.3 Benefits and Optimization in SRAM 483.4 VTH Design and Adjustment for Cryogenic CMOS 483.4.1 Threshold Voltage Design 483.4.2 Threshold Voltage Adjustme

nt 493.5 Summary 49Chapter 4Cryogenic MRAM as a Density Booster for Embedded NVM in Advanced Technology 714.1 Preface 714.2 MRAM Characterization at Cryogenic Conditions 724.2.1 Steady-State DC Characteristics 724.2.2 Transient AC Characteristics 724.2.3 Thermal S

tability and MTJ Optimization 734.3 Write Analysis and Cell Design for Cryogenic MRAM 744.3.1 Design for Write Voltage/ Current/ Time 744.3.2 Access Transistor Characteristics 754.3.3 Cell Design for Cryogenic MRAM 754.4 Read Margin Analysis 764.3.1 Read Current Distr

ibution and Tailing Bits 764.3.2 Read Margin Enhancement 764.5 Summary 77Chapter 5 Conclusions 94References 96Vita 115Publication List 116