Effective Python

"數據科學項目日益增多，每個項目在提出原型到生產的過程中都需要可靠的基礎設施。使用《Effective數據科學基礎設施》介紹的一些新技術和新工具，你將能建立一個適用於各類組織(無論是初創企業還是大型企業)的基礎設施堆棧。《Effective數據科學基礎設施》可幫助你建立數據流程和項目工作流，為你開發項目帶來強大動力。《Effective數據科學基礎設施》呈現Netflix數據操作的**進工具和概念，並在此基礎上介紹一種可定制的基於雲的模型開發和MLOps方法，可輕鬆適應公司的特定需求。當團隊把數據科學和機器學習應用於廣泛的業務問題時，這些實用的數據流程將更高效地生成更完美的結果。主要內容● 在雲中處理計算和編排● 將基於雲的工具耦合到一個內聚的數據科學環境中● 使用Metaflow、AWS和Python數據生態系統開發可複製的數據科學項目● 構建需要大型數據集和模型，需要數據科學家團隊參與的複雜應用程序"

The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Pandas for Everyone brings together practical knowledge and insight for solving real problems with Pandas, even if you're new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world problems. Chen gives you a jumpstart on using Pandas with a realistic dataset and covers combining datasets, handling missing data, and structuring datasets for easier analysis and visualization. He demonstrates powerful dat

All social and policy researchers need to synthesize data into a visual representation. Producing good visualizations combines creativity and technique. This book teaches the techniques and basics to produce a variety of visualizations, allowing readers to communicate data and analyses in a creative and effective way. Visuals for tables, time series, maps, text, and networks are carefully explained and organized, showing how to choose the right plot for the type of data being analysed and displayed. Examples are drawn from public policy, public safety, education, political tweets, and public health. The presentation proceeds step by step, starting from the basics, in the programming languages R and Python so that readers learn the coding skills while simultaneously becoming familiar with the advantages and disadvantages of each visualization. No prior knowledge of either Python or R is required. Code for all the visualizations are available from the book's website.

All social and policy researchers need to synthesize data into a visual representation. Producing good visualizations combines creativity and technique. This book teaches the techniques and basics to produce a variety of visualizations, allowing readers to communicate data and analyses in a creative and effective way. Visuals for tables, time series, maps, text, and networks are carefully explained and organized, showing how to choose the right plot for the type of data being analysed and displayed. Examples are drawn from public policy, public safety, education, political tweets, and public health. The presentation proceeds step by step, starting from the basics, in the programming languages R and Python so that readers learn the coding skills while simultaneously becoming familiar with the advantages and disadvantages of each visualization. No prior knowledge of either Python or R is required. Code for all the visualizations are available from the book's website.