Pandas for Everyone

Name: Pandas for Everyone | Python Data Analysis
Brand: Addison Wesley
Price: 45.99 EUR
Availability: OnlineOnly

Python Data Analysis

Daniel Chen(Autor*in)

Addison Wesley (Verlag)

2. Auflage

Erschienen am 20. Februar 2023

512 Seiten

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Beschreibung

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

Weitere Details

Weitere Ausgaben

Person

Inhalt

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

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