Pandas for Everyone: Python Data Analysis (Paperback)

Manage and Automate Data Analysis with Pandas 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 data sets.

Pandas for Everyone, 2nd Edition, 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 data science problems such as using regularization to prevent data overfitting, or when to use unsupervised machine learning methods to find the underlying structure in a data set.

New features to the second edition include:

• Extended coverage of plotting and the seaborn data visualization library
• Expanded examples and resources
• Updated Python 3.9 code and packages coverage, including statsmodels and scikit-learn libraries
• Online bonus material on geopandas, Dask, and creating interactive graphics with Altair

Chen gives you a jumpstart on using Pandas with a realistic data set and covers combining data sets, handling missing data, and structuring data sets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes.

Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability and introduces you to the wider Python data analysis ecosystem.

• Work with DataFrames and Series, and import or export data
• Create plots with matplotlib, seaborn, and pandas
• Combine data sets and handle missing data
• Reshape, tidy, and clean data sets so they're easier to work with
• Convert data types and manipulate text strings
• Apply functions to scale data manipulations
• Aggregate, transform, and filter large data sets with groupby
• Leverage Pandas' advanced date and time capabilities
• Fit linear models using statsmodels and scikit-learn libraries
• Use generalized linear modeling to fit models with different response variables
• Compare multiple models to select the "best" one
• Regularize to overcome overfitting and improve performance
• Use clustering in unsupervised machine learning

使用Python中的Pandas管理和自動化數據分析

如今，分析師必須處理具有非凡多樣性、速度和容量的數據。使用開源的Pandas庫，您可以使用Python快速自動化並執行幾乎任何大小或複雜度的數據分析任務。Pandas可以幫助您確保數據的真實性，將其可視化以進行有效的決策，並可在多個數據集中可靠地重現分析結果。

《Pandas for Everyone, 2nd Edition》將實用知識和見解結合起來，解決使用Pandas解決真實問題的問題，即使您對Python數據分析還不熟悉。Daniel Y. Chen通過簡單但實用的示例介紹了關鍵概念，並逐步構建這些概念以解決更困難的現實世界數據科學問題，例如使用正則化來防止數據過度擬合，或者何時使用無監督機器學習方法來找到數據集中的潛在結構。

第二版的新功能包括：

- 擴展了繪圖和seaborn數據可視化庫的涵蓋範圍
- 擴展了示例和資源
- 更新了Python 3.9代碼和套件的涵蓋範圍，包括statsmodels和scikit-learn庫
- 在線額外資料，包括geopandas、Dask和使用Altair創建交互式圖形

Chen通過一個真實的數據集為您提供了使用Pandas的起點，並涵蓋了結合數據集、處理缺失數據以及為更輕鬆的分析和可視化結構化數據集的方法。他演示了強大的數據清理技術，從基本的字符串操作到同時應用函數於數據框。

一旦數據準備好，Chen將引導您進行預測、聚類、推斷和探索模型的擬合。他提供了性能和可擴展性的技巧，並向您介紹了更廣泛的Python數據分析生態系統。

- 使用DataFrame和Series，導入或導出數據
- 使用matplotlib、seaborn和pandas創建圖表
- 結合數據集並處理缺失數據
- 重新塑造、整理和清理數據集，使其更易於處理
- 轉換數據類型並操作文本字符串
- 應用函數以擴展數據操作
- 使用groupby對大型數據集進行聚合、轉換和過濾
- 利用Pandas的高級日期和時間功能
- 使用statsmodels和scikit-learn庫擬合線性模型
- 使用廣義線性建模擬合具有不同響應變量的模型
- 比較多個模型以選擇“最佳”模型
- 正則化以克服過度擬合並提高性能
- 在無監督機器學習中使用聚類

Daniel Chen is a graduate student in the Interdisciplinary PhD program in Genetics, Bioinformatics & Computational Biology (GBCB) at Virginia Polytechnic Institute and State University (Virginia Tech). He is involved with Software Carpentry as an instructor, Mentoring Committee Member, and currently serves as the Assessment Committee Chair. He completed his Masters in Public Health at Columbia University Mailman School of Public Health in Epidemiology with a certificate in Advanced Epidemiology and currently extending his Master's thesis work in the Social and Decision Analytics Laboratory under the Virginia Bioinformatics Institute on attitude diffusion in social networks.

Daniel Chen是維吉尼亞理工學院（Virginia Tech）遺傳學、生物信息學和計算生物學（GBCB）跨學科博士學位計劃的研究生。他是軟件工程師培訓計劃（Software Carpentry）的講師，並擔任指導委員會成員，目前擔任評估委員會主席。他在哥倫比亞大學梅爾曼公共衛生學院（Columbia University Mailman School of Public Health）完成了流行病學碩士學位，並獲得高級流行病學證書，目前在維吉尼亞生物信息學研究所（Virginia Bioinformatics Institute）的社會和決策分析實驗室延續他的碩士論文工作，研究社交網絡中的態度傳播。