Daniel Chen

Pandas for Everyone

Python Data Analysis

• Broschiertes Buch

Produktbeschreibung

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

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Produktdetails

• Verlag: Pearson Education
• 2nd edition
• Seitenzahl: 512
• Erscheinungstermin: 30. Dezember 2022
• Englisch
• Abmessung: 229mm x 177mm x 27mm
• Gewicht: 812g
• ISBN-13: 9780137891153
• ISBN-10: 0137891156
• Artikelnr.: 64223691

Autorenporträt

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.

Inhaltsangabe

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