Daniel Chen

Pandas for Everyone (eBook, ePUB)

Python Data Analysis

• Format: ePub

Produktbeschreibung

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

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Produktdetails

• Verlag: Pearson ITP
• Seitenzahl: 416
• Erscheinungstermin: 15. Dezember 2017
• Englisch
• ISBN-13: 9780134547053
• Artikelnr.: 50516079

Autorenporträt

Daniel Y. Chen is a data engineer and research associate at the Social and Decision Analytics Laboratory, a leading laboratory of the Biocomplexity Institute of Virginia Tech. He is also pursuing a Ph.D. in the interdisciplinary program in Genetics, Bioinformatics & Computational Biology (GBCB), and is involved with The Carpentries (formerly Software and Data Carpentry) as an instructor and lesson maintainer. He is an instructor for DataCamp and is a data scientist at Lander Analytics. Chen holds a master's degree in public health from Columbia University's Mailman School of Public Health in epidemiology, where he looked at attitude diffusion in social networks. He is currently working on repurposing administrative data to inform policy decision-making.

Inhaltsangabe

Foreword xix
Preface xxi
Acknowledgments xxvii
About the Author xxxi
 
Part I: Introduction 1
 
Chapter 1: Pandas DataFrame Basics 3
1.1 Introduction 3
1.2 Loading Your First Data Set 4
1.3 Looking at Columns, Rows, and Cells 7
1.4 Grouped and Aggregated Calculations 18
1.5 Basic Plot 23
1.6 Conclusion 24
 
Chapter 2: Pandas Data Structures 25
2.1 Introduction 25
2.2 Creating Your Own Data 26
2.3 The Series 28
2.4 The DataFrame 36
2.5 Making Changes to Series and DataFrames 38
2.6 Exporting and Importing Data 43
2.7 Conclusion 47
 
Chapter 3: Introduction to Plotting 49
3.1 Introduction 49
3.2 Matplotlib 51
3.3 Statistical Graphics Using matplotlib 56
3.4 Seaborn 61
3.5 Pandas Objects 83
3.6 Seaborn Themes and Styles 86
3.7 Conclusion 90
 
Part II: Data Manipulation 91
 
Chapter 4: Data Assembly 93
4.1 Introduction 93
4.2 Tidy Data 93
4.3 Concatenation 94
4.4 Merging Multiple Data Sets 102
4.5 Conclusion 107
 
Chapter 5: Missing Data 109
5.1 Introduction 109
5.2 What Is a NaN Value? 109
5.3 Where Do Missing Values Come From? 111
5.4 Working with Missing Data 116
5.5 Conclusion 121
 
Chapter 6: Tidy Data 123
6.1 Introduction 123
6.2 Columns Contain Values, Not Variables 124
6.3 Columns Contain Multiple Variables 128
6.4 Variables in Both Rows and Columns 133
6.5 Multiple Observational Units in a Table (Normalization) 134
6.6 Observational Units Across Multiple Tables 137
6.7 Conclusion 141
 
Part III: Data Munging 143
 
Chapter 7: Data Types 145
7.1 Introduction 145
7.2 Data Types 145
7.3 Converting Types 146
7.4 Categorical Data 152
7.5 Conclusion 153
 
Chapter 8: Strings and Text Data 155
8.1 Introduction 155
8.2 Strings 155
8.3 String Methods 158
8.4 More String Methods 160
8.5 String Formatting 161
8.6 Regular Expressions (RegEx) 164
8.7 The regex Library 170
8.8 Conclusion 170
 
Chapter 9: Apply 171
9.1 Introduction 171
9.2 Functions 171
9.3 Apply (Basics) 172
9.4 Apply (More Advanced) 177
9.5 Vectorized Functions 182
9.6 Lambda Functions 185
9.7 Conclusion 187
 
Chapter 10: Groupby Operations: Split–Apply–Combine 189
10.1 Introduction 189
10.2 Aggregate 190
10.3 Transform 197
10.4 Filter 201
10.5 The pandas.core.groupby.DataFrameGroupBy Object 202
10.6 Working with a MultiIndex 207
10.7 Conclusion 211
 
Chapter 11: The datetime Data Type 213
11.1 Introduction 213
11.2 Python’s datetime Object 213
11.3 Converting to datetime 214
11.4 Loading Data That Include Dates 217
11.5 Extracting Date Components 217
11.6 Date Calculations and Timedeltas 220
11.7 Datetime Methods 221
11.8 Getting Stock Data 224
11.9 Subsetting Data Based on Dates 225
11.10 Date Ranges 227
11.11 Shifting Values 230
11.12 Resampling 237
11.13 Time Zones 238
11.14 Conclusion 240
 
Part IV: Data Modeling 241
 
Chapter 12: Linear Models 243
12.1 Introduction 243
12.2 Simple Linear Regression 243
12.3 Multiple Regression 247
12.4 Keeping Index Labels From sklearn 251
12.5 Conclusion 252
 
Chapter 13: Generalized Linear Models 253
13.1 Introduction 253
13.2 Logistic Regression 253
13.3 Poisson Regression 257
13.4 More Generalized Linear Models 260
13.5 Survival Analysis 260
13.6 Conclusion 264
 
Chapter 14: Model Diagnostics 265
14.1 Introduction 265
14.2 Residuals 265
14.3 Comparing Multiple Models 270
14.4 k-Fold Cross-Validation 275
14.5 Conclusion 278
 
Chapter 15: Regularization 279
15.1 Introduction 279
15.2 Why Regularize? 279
15.3 LASSO Regression 281
15.4 Ridge Regression 283
15.5 Elastic Net 285
15.6 Cross-Validation 287
15.7 Conclusion 289
 
Chapter 16: Clustering 291
16.1 Introduction 291
16.2 k-Means 291
16.3 Hierarchical Clustering 297
16.4 Conclusion 301
 
Part V: Conclusion 303
 
Chapter 17: Life Outside of Pandas 305
17.1 The (Scientific) Computing Stack 305
17.2 Performance 306
17.3 Going Bigger and Faster 307
 
Chapter 18: Toward a Self-Directed Learner 309
18.1 It’s Dangerous to Go Alone! 309
18.2 Local Meetups 309
18.3 Conferences 309
18.4 The Internet 310
18.5 Podcasts 310
18.6 Conclusion 311
 
Part VI: Appendixes 313
 
Appendix A: Installation 315
A.1 Installing Anaconda 315
A.2 Uninstall Anaconda 316
 
Appendix B: Command Line 317
B.1 Installation 317
B.2 Basics 318
 
Appendix C: Project Templates 319
 
Appendix D: Using Python 321
D.1 Command Line and Text Editor 321
D.2 Python and IPython 322
D.3 Jupyter 322
D.4 Integrated Development Environments (IDEs) 322
 
Appendix E: Working Directories 325
 
Appendix F: Environments 327
 
Appendix G: Install Packages 329
G.1 Updating Packages 330
 
Appendix H: Importing Libraries 331
 
Appendix I: Lists 333
 
Appendix J: Tuples 335
 
Appendix K: Dictionaries 337
 
Appendix L: Slicing Values 339
 
Appendix M: Loops 341
 
Appendix N: Comprehensions 343
 
Appendix O: Functions 345
O.1 Default Parameters 347
O.2 Arbitrary Parameters 347
 
Appendix P: Ranges and Generators 349
 
Appendix Q: Multiple Assignment 351
 
Appendix R: numpy ndarray 353
 
Appendix S: Classes 355
 
Appendix T: Odo: The Shapeshifter 357
 
Index 359