Pandas for Everyone: Python Data Analysis, 1st edition

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

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

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

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

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

A.1 Installing Anaconda 315

A.2 Uninstall Anaconda 316

Appendix B: Command Line 317

B.1 Installation 317

B.2 Basics 318

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

G.1 Updating Packages 330

O.1 Default Parameters 347

O.2 Arbitrary Parameters 347

Index 359