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 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 datasets and handle missing data
• Reshape, tidy, and clean datasets 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 datasets 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”
• Regularize to overcome overfitting and improve performance
• Use clustering in unsupervised machine learning

Part I. Introduction 1. Panda's Dataframe Basics 2. Panda's Data Strucutrea 3. Introduction to Plotting Part II. Data Manipulation 4. Data Assembly 5. Missing Data 6. Tldy Data Part III. Data Munging 7. Data Types 8. Strings and Text Data 9. Apply 10.Groupby Operations: Split-Apply Combine 11. The datetime DataType Part IV. Date Modeling 12. Linear Models 13. Generallzed Lineer Models 14. Modelimg Diagnostrics 15. Regularization 16. Clustering Part V. Conclusion 17. Life Outside of Pandas 18. Toward a Self-Directed Learner Part Ⅵ. Appendixes Appendix A Installation Appendix B Command Line Appendix C Project Templates Appendix D Using Python Appendix E Working Directories Appendix F Environments Appendix G Install Packages Appendix H Importing Libraries Appendix I Lists Appendix J Tuples Appendix K Dictionaries Appendix L Slicing Values Appendix M Loops Appendix N Comprehensions Appendix O Functions Appendix P Ranges and Gernerators Appendix Q Multiple Assignment Appendix R numpy ndarray Appendix S Classes Appendix T Odo: The Shapeshifter Index