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

Table of Contents:
Foreword by Anne M. Brown     xxiii Foreword by Jared Lander     xxv Preface     xxvii Changes in the Second Edition     xxxix   Part I: Introduction    1 Chapter 1. Pandas DataFrame Basics     3        Learning Objectives      3        1.1 Introduction      3        1.2 Load Your First Data Set      4        1.3 Look at Columns, Rows, and Cells      6        1.4 Grouped and Aggregated Calculations      23        1.5 Basic Plot      27        Conclusion      28   Chapter 2. Pandas Data Structures Basics      31        Learning Objectives      31        2.1 Create Your Own Data      31        2.2 The Series      33        2.3 The DataFrame      42        2.4 Making Changes to Series and DataFrames      45        2.5 Exporting and Importing Data      52        Conclusion      63   Chapter 3. Plotting Basics      65        Learning Objectives      65        3.1 Why Visualize Data?       65        3.2 Matplotlib Basics      66        3.3 Statistical Graphics Using matplotlib      72        3.4 Seaborn      78        3.5 Pandas Plotting Method      111        Conclusion      115   Chapter 4. Tidy Data      117        Learning Objectives      117        Note About This Chapter       117        4.1 Columns Contain Values, Not Variables      118        4.2 Columns Contain Multiple Variables      122        4.3 Variables in Both Rows and Columns      126        Conclusion      129   Chapter 5. Apply Functions      131        Learning Objectives      131        Note About This Chapter      131        5.1 Primer on Functions      131        5.2 Apply (Basics)       133        5.3 Vectorized Functions      138        5.4 Lambda Functions (Anonymous Functions)       141        Conclusion      142   Part II: Data Processing     143 Chapter 6. Data Assembly      145        Learning Objectives      145        6.1 Combine Data Sets      145        6.2 Concatenation      146        6.3 Observational Units Across Multiple Tables      154        6.4 Merge Multiple Data Sets      160        Conclusion      167   Chapter 7. Data Normalization      169        Learning Objectives      169        7.1 Multiple Observational Units in a Table (Normalization)     169        Conclusion      173   Chapter 8. Groupby Operations: Split-Apply-Combine      175        Learning Objectives      175        8.1 Aggregate      176        8.2 Transform      184        8.3 Filter      188        8.4 The pandas.core.groupby.DataFrameGroupBy object      190        8.5 Working with a MultiIndex      195        Conclusion      199   Part III: Data Types    203 Chapter 9. Missing Data      203        Learning Objectives      203        9.1 What Is a NaN Value?       203        9.2 Where Do Missing Values Come From?       205        9.3 Working with Missing Data      210        9.4 Pandas Built-In NA Missing      216        Conclusion      218   Chapter 10. Data Types      219        Learning Objectives      219        10.1 Data Types      219        10.2 Converting Types      220        10.3 Categorical Data      225        Conclusion      227   Chapter 11. Strings and Text Data      229        Introduction      229        Learning Objectives      229        11.1 Strings      229        11.2 String Methods      233        11.3 More String Methods      234        11.4 String Formatting (F-Strings)       236        11.5 Regular Expressions (RegEx)      239        11.6 The regex Library      247        Conclusion      247   Chapter 12. Dates and Times      249        Learning Objectives      249        12.1 Python's datetime Object      249        12.2 Converting to datetime      250        12.3 Loading Data That Include Dates      253        12.4 Extracting Date Components      254        12.5 Date Calculations and Timedeltas      257        12.6 Datetime Methods      259        12.7 Getting Stock Data      261        12.8 Subsetting Data Based on Dates      263        12.9 Date Ranges      266        12.10 Shifting Values      270        12.11 Resampling      276        12.12 Time Zones      278        12.13 Arrow for Better Dates and Times      280        Conclusion      280   Part IV: Data Modeling    281 Chapter 13. Linear Regression (Continuous Outcome Variable)      283        13.1 Simple Linear Regression      283        13.2 Multiple Regression      287        13.3 Models with Categorical Variables      289        13.4 One-Hot Encoding in scikit-learn with Transformer Pipelines      294        Conclusion      296   Chapter 14. Generalized Linear Models      297        About This Chapter      297        14.1 Logistic Regression (Binary Outcome Variable)       297        14.2 Poisson Regression (Count Outcome Variable)       304        14.3 More Generalized Linear Models      308        Conclusion      309   Chapter 15. Survival Analysis      311        15.1 Survival Data      311        15.2 Kaplan Meier Curves      312        15.3 Cox Proportional Hazard Model      314        Conclusion      317   Chapter 16. Model Diagnostics      319        16.1 Residuals      319        16.2 Comparing Multiple Models      324        16.3 k-Fold Cross-Validation      329        Conclusion      334   Chapter 17. Regularization      335        17.1 Why Regularize?       335        17.2 LASSO Regression      337        17.3 Ridge Regression      338        17.4 Elastic Net      340        17.5 Cross-Validation      341        Conclusion      343   Chapter 18. Clustering      345        18.1 k-Means      345        18.2 Hierarchical Clustering      351        Conclusion     356   Part V. Conclusion    357 Chapter 19. Life Outside of Pandas      359        19.1 The (Scientific) Computing Stack      359        19.2 Performance      360        19.3 Dask      360        19.4 Siuba      360        19.5 Ibis      361        19.6 Polars      361        19.7 PyJanitor      361        19.8 Pandera      361        19.9 Machine Learning      361        19.10 Publishing      362        19.11 Dashboards      362        Conclusion      362   Chapter 20. It's Dangerous To Go Alone!      363        20.1 Local Meetups      363        20.2 Conferences      363        20.3 The Carpentries      364        20.4 Podcasts      364        20.5 Other Resources      365        Conclusion      365   Appendices      367 A.      Concept Maps      369 B.      Installation and Setup     373 C.      Command Line     377 D.      Project Templates     379 E.      Using Python       381 F.       Working Directories       383 G.      Environments       385 H.      Install Packages       389 I.       Importing Libraries       391 J.       Code Style       393 K.      Containers: Lists, Tuples, and Dictionaries       395 L.      Slice Values       399 M.     Loops       401 N.     Comprehensions       403 O.     Functions       405 P.      Ranges and Generators       409 Q.     Multiple Assignment       413 R.     Numpy ndarray       415 S.     Classes       417 T.      SettingWithCopyWarning       419 U.     Method Chaining       423 V.      Timing Code       427 W.     String Formatting       429 X.      Conditionals (if-elif-else)        433 Y.      New York ACS Logistic Regression Example       435 Z.      Replicating Results in R       443 Index      451