Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter

 Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter

"Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter" is a book that teaches the reader how to use the Python programming language and its associated libraries, such as pandas, NumPy, and Jupyter, for data analysis and manipulation. The book covers topics such as how to work with pandas data structures, how to use pandas for data wrangling, how to use NumPy for numerical computation, and how to use Jupyter for interactive data visualization. The book also provides practical examples and exercises to help the reader gain hands-on experience with these tools. Overall, the book aims to provide a comprehensive introduction to using Python for data analysis and manipulation.

"Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter" is a book that covers the use of the Python programming language for data analysis, specifically using the pandas, NumPy, and Jupyter libraries. The book covers topics such as cleaning and manipulating data, working with missing data, and handling time series data. It also covers advanced topics such as merging and joining data, reshaping data, and working with data in different formats. Additionally, the book includes practical examples and exercises to help the reader apply the concepts learned.

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Table of Contents

1. Preliminaries. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1 What Is This Book About? 1

What Kinds of Data? 1

1.2 Why Python for Data Analysis? 2

Python as Glue 3

Solving the “Two-Language” Problem 3

Why Not Python? 3

1.3 Essential Python Libraries 4

NumPy 4

pandas 5

matplotlib 6

IPython and Jupyter 6

SciPy 7

scikit-learn 8

statsmodels 8

Other Packages 9

1.4 Installation and Setup 9

Miniconda on Windows 9

GNU/Linux 10

Miniconda on macOS 11

Installing Necessary Packages 11

Integrated Development Environments and Text Editors 12

1.5 Community and Conferences 13

1.6 Navigating This Book 14

Code Examples 15

Data for Examples 15

Import Conventions 16

2. Python Language Basics, IPython, and Jupyter Notebooks. . . . . . . . . . . . . . . . . . . . . . . . 17

2.1 The Python Interpreter 18

2.2 IPython Basics 19

Running the IPython Shell 19

Running the Jupyter Notebook 20

Tab Completion 23

Introspection 25

2.3 Python Language Basics 26

Language Semantics 26

Scalar Types 34

Control Flow 42

2.4 Conclusion 45

3. Built-In Data Structures, Functions, and Files. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

3.1 Data Structures and Sequences 47

Tuple 47

List 51

Dictionary 55

Set 59

Built-In Sequence Functions 62

List, Set, and Dictionary Comprehensions 63

3.2 Functions 65

Namespaces, Scope, and Local Functions 67

Returning Multiple Values 68

Functions Are Objects 69

Anonymous (Lambda) Functions 70

Generators 71

Errors and Exception Handling 74

3.3 Files and the Operating System 76

Bytes and Unicode with Files 80

3.4 Conclusion 82

4. NumPy Basics: Arrays and Vectorized Computation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

4.1 The NumPy ndarray: A Multidimensional Array Object 85

Creating ndarrays 86

Data Types for ndarrays 88

Arithmetic with NumPy Arrays 91

Basic Indexing and Slicing 92

Boolean Indexing 97

Fancy Indexing 100

Transposing Arrays and Swapping Axes 102

4.2 Pseudorandom Number Generation 103

4.3 Universal Functions: Fast Element-Wise Array Functions 105

4.4 Array-Oriented Programming with Arrays 108

Expressing Conditional Logic as Array Operations 110

Mathematical and Statistical Methods 111

Methods for Boolean Arrays 113

Sorting 114

Unique and Other Set Logic 115

4.5 File Input and Output with Arrays 116

4.6 Linear Algebra 116

4.7 Example: Random Walks 118

Simulating Many Random Walks at Once 120

4.8 Conclusion 121

5. Getting Started with pandas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

5.1 Introduction to pandas Data Structures 124

Series 124

DataFrame 129

Index Objects 136

5.2 Essential Functionality 138

Reindexing 138

Dropping Entries from an Axis 141

Indexing, Selection, and Filtering 142

Arithmetic and Data Alignment 152

Function Application and Mapping 158

Sorting and Ranking 160

Axis Indexes with Duplicate Labels 164

5.3 Summarizing and Computing Descriptive Statistics 165

Correlation and Covariance 168

Unique Values, Value Counts, and Membership 170

5.4 Conclusion 173

6. Data Loading, Storage, and File Formats. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175

6.1 Reading and Writing Data in Text Format 175

Reading Text Files in Pieces 182

Writing Data to Text Format 184

Working with Other Delimited Formats 185

JSON Data 187

XML and HTML: Web Scraping 189

6.2 Binary Data Formats 193

Reading Microsoft Excel Files 194

Using HDF5 Format 195

6.3 Interacting with Web APIs 197

6.4 Interacting with Databases 199

6.5 Conclusion 201

7. Data Cleaning and Preparation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203

7.1 Handling Missing Data 203

Filtering Out Missing Data 205

Filling In Missing Data 207

7.2 Data Transformation 209

Removing Duplicates 209

Transforming Data Using a Function or Mapping 211

Replacing Values 212

Renaming Axis Indexes 214

Discretization and Binning 215

Detecting and Filtering Outliers 217

Permutation and Random Sampling 219

Computing Indicator/Dummy Variables 221

7.3 Extension Data Types 224

7.4 String Manipulation 227

Python Built-In String Object Methods 227

Regular Expressions 229

String Functions in pandas 232

7.5 Categorical Data 235

Background and Motivation 236

Categorical Extension Type in pandas 237

Computations with Categoricals 240

Categorical Methods 242

7.6 Conclusion 245

8. Data Wrangling: Join, Combine, and Reshape. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247

8.1 Hierarchical Indexing 247

Reordering and Sorting Levels 250

Summary Statistics by Level 251

Indexing with a DataFrame’s columns 252

8.2 Combining and Merging Datasets 253

Database-Style DataFrame Joins 254

Merging on Index 259

Concatenating Along an Axis 263

Combining Data with Overlap 268

8.3 Reshaping and Pivoting 270

Reshaping with Hierarchical Indexing 270

Pivoting “Long” to “Wide” Format 273

Pivoting “Wide” to “Long” Format 277

8.4 Conclusion 279

9. Plotting and Visualization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281

9.1 A Brief matplotlib API Primer 282

Figures and Subplots 283

Colors, Markers, and Line Styles 288

Ticks, Labels, and Legends 290

Annotations and Drawing on a Subplot 294

Saving Plots to File 296

matplotlib Configuration 297

9.2 Plotting with pandas and seaborn 298

Line Plots 298

Bar Plots 301

Histograms and Density Plots 309

Scatter or Point Plots 311

Facet Grids and Categorical Data 314

9.3 Other Python Visualization Tools 317

9.4 Conclusion 317

10. Data Aggregation and Group Operations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319

10.1 How to Think About Group Operations 320

Iterating over Groups 324

Selecting a Column or Subset of Columns 326

Grouping with Dictionaries and Series 327

Grouping with Functions 328

Grouping by Index Levels 328

10.2 Data Aggregation 329

Column-Wise and Multiple Function Application 331

Returning Aggregated Data Without Row Indexes 335

10.3 Apply: General split-apply-combine 335

Suppressing the Group Keys 338

Quantile and Bucket Analysis 338

Example: Filling Missing Values with Group-Specific Values 340

Example: Random Sampling and Permutation 343

Example: Group Weighted Average and Correlation 344

Example: Group-Wise Linear Regression 347

10.4 Group Transforms and “Unwrapped” GroupBys 347

10.5 Pivot Tables and Cross-Tabulation 351

Cross-Tabulations: Crosstab 354

10.6 Conclusion 355

11. Time Series. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 357

11.1 Date and Time Data Types and Tools 358

Converting Between String and Datetime 359

11.2 Time Series Basics 361

Indexing, Selection, Subsetting 363

Time Series with Duplicate Indices 365

11.3 Date Ranges, Frequencies, and Shifting 366

Generating Date Ranges 367

Frequencies and Date Offsets 370

Shifting (Leading and Lagging) Data 371

11.4 Time Zone Handling 374

Time Zone Localization and Conversion 375

Operations with Time Zone-Aware Timestamp Objects 377

Operations Between Different Time Zones 378

11.5 Periods and Period Arithmetic 379

Period Frequency Conversion 380

Quarterly Period Frequencies 382

Converting Timestamps to Periods (and Back) 384

Creating a PeriodIndex from Arrays 385

11.6 Resampling and Frequency Conversion 387

Downsampling 388

Upsampling and Interpolation 391

Resampling with Periods 392

Grouped Time Resampling 394

11.7 Moving Window Functions 396

Exponentially Weighted Functions 399

Binary Moving Window Functions 401

User-Defined Moving Window Functions 402

11.8 Conclusion 403

12. Introduction to Modeling Libraries in Python. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405

12.1 Interfacing Between pandas and Model Code 405

12.2 Creating Model Descriptions with Patsy 408

Data Transformations in Patsy Formulas 410

Categorical Data and Patsy 412

12.3 Introduction to statsmodels 415

Estimating Linear Models 415

Estimating Time Series Processes 419

12.4 Introduction to scikit-learn 420

12.5 Conclusion 423

13. Data Analysis Examples. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425

13.1 Bitly Data from 1.USA.gov 425

Counting Time Zones in Pure Python 426

Counting Time Zones with pandas 428

13.2 MovieLens 1M Dataset 435

Measuring Rating Disagreement 439

13.3 US Baby Names 1880–2010 443

Analyzing Naming Trends 448

13.4 USDA Food Database 457

13.5 2012 Federal Election Commission Database 463

Donation Statistics by Occupation and Employer 466

Bucketing Donation Amounts 469

Donation Statistics by State 471

13.6 Conclusion 472

A. Advanced NumPy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 473

A.1 ndarray Object Internals 473

NumPy Data Type Hierarchy 474

A.2 Advanced Array Manipulation 476

Reshaping Arrays 476

C Versus FORTRAN Order 478

Concatenating and Splitting Arrays 479

Repeating Elements: tile and repeat 481

Fancy Indexing Equivalents: take and put 483

A.3 Broadcasting 484

Broadcasting over Other Axes 487

Setting Array Values by Broadcasting 489

A.4 Advanced ufunc Usage 490

ufunc Instance Methods 490

Writing New ufuncs in Python 493

A.5 Structured and Record Arrays 493

Nested Data Types and Multidimensional Fields 494

Why Use Structured Arrays? 495

A.6 More About Sorting 495

Indirect Sorts: argsort and lexsort 497

Alternative Sort Algorithms 498

Partially Sorting Arrays 499

numpy.searchsorted: Finding Elements in a Sorted Array 500

A.7 Writing Fast NumPy Functions with Numba 501

Creating Custom numpy.ufunc Objects with Numba 502

A.8 Advanced Array Input and Output 503

Memory-Mapped Files 503

HDF5 and Other Array Storage Options 504

A.9 Performance Tips 505

The Importance of Contiguous Memory 505

B. More on the IPython System. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 509

B.1 Terminal Keyboard Shortcuts 509

B.2 About Magic Commands 510

The %run Command 512

Executing Code from the Clipboard 513

B.3 Using the Command History 514

Searching and Reusing the Command History 514

Input and Output Variables 515

B.4 Interacting with the Operating System 516

Shell Commands and Aliases 517

Directory Bookmark System 518

B.5 Software Development Tools 519

Interactive Debugger 519

Timing Code: %time and %timeit 523

Basic Profiling: %prun and %run -p 525

Profiling a Function Line by Line 527

B.6 Tips for Productive Code Development Using IPython 529

Reloading Module Dependencies 529

Code Design Tips 530

B.7 Advanced IPython Features 532

Profiles and Configuration 532

B.8 Conclusion 533