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1.Preliminaries1

1.1 What Is This Book About？1

What Kinds of Data？1

1.2 Why Python for Data Analysis？2

Python as Glue2

Solving the “Two-Language” Problem3

Why Not Python？3

1.3 Essential Python Libraries4

NumPy4

pandas4

matplotlib5

IPython and Jupyter6

Scipy6

scikit-learn7

statsmodels8

1.4 Installation and Setup8

Windows9

Apple （OS X，macOS）9

GNU/Linux9

Installing or Updating Python Packages10

Python 2 and Python 311

Integrated Development Environments （IDEs） and Text Editors11

1.5 Community and Conferences12

1.6 Navigating This Book12

Code Examples13

Data for Examples13

Import Conventions14

Jargon14

2.Python Language Basics，IPython，and Jupyter Notebooks15

2.1 The Python Interpreter16

2.2 IPython Basics17

Running the IPython Shell17

Running the Jupyter Notebook18

Tab Completion21

Introspection23

The %run Command25

Executing Code from the Clipboard26

Terminal Keyboard Shortcuts27

About Magic Commands28

Matplotlib Integration29

2.3 Python Language Basics30

Language Semantics30

Scalar Types38

Control Flow46

3.Built-in Data Structures，Functions，and Files51

3.1 Data Structures and Sequences51

Tuple51

List54

Built-in Sequence Functions59

dict61

set65

List，Set，and Dict Comprehensions67

3.2 Functions69

Namespaces，Scope，and Local Functions70

Returning Multiple Values71

Functions Are Objects72

Anonymous （Lambda） Functions73

Currying：Partial Argument Application74

Generators75

Errors and Exception Handling77

3.3 Files and the Operating System80

Bytes and Unicode with Files83

3.4 Conclusion84

4.NumPy Basics：Arrays and Vectorized Computation85

4.1 The NumPy ndarray：A Multidimensional Array Object87

Creating ndarrays88

Data Types for ndarrays90

Arithmetic with NumPy Arrays93

Basic Indexing and Slicing94

Boolean Indexing99

Fancy Indexing102

Transposing Arrays and Swapping Axes103

4.2 Universal Functions：Fast Element-Wise Array Functions105

4.3 Array-Oriented Programming with Arrays108

Expressing Conditional Logic as Array Operations109

Mathematical and Statistical Methods111

Methods for Boolean Arrays113

Sorting113

Unique and Other Set Logic114

4.4 File Input and Output with Arrays115

4.5 Linear Algebra116

4.6 Pseudorandom Number Generation118

4.7 Example：Random Walks119

Simulating Many Random Walks at Once121

4.8 Conclusion122

5.Getting Started with pandas123

5.1 Introduction to pandas Data Structures124

Series124

DataFrame128

Index Objects134

5.2 Essential Functionality136

Reindexing136

Dropping Entries from an Axis138

Indexing，Selection，and Filtering140

Integer Indexes145

Arithmetic and Data Alignment146

Function Application and Mapping151

Sorting and Ranking153

Axis Indexes with Duplicate Labels157

5.3 Summarizing and Computing Descriptive Statistics158

Correlation and Covariance160

Unique Values，Value Counts，and Membership162

5.4 Conclusion165

6.Data Loading，Storage，and File Formats167

6.1 Reading and Writing Data in Text Format167

Reading Text Files in Pieces173

Writing Data to Text Format175

Working with Delimited Formats176

JSON Data178

XML and HTML：Web Scraping180

6.2 Binary Data Formats183

Using HDF5 Format184

Reading Microsoft Excel Files186

6.3 Interacting with Web APIs187

6.4 Interacting with Databases188

6.5 Conclusion190

7.Data Cleaning and Preparation191

7.1 Handling Missing Data191

Filtering Out Missing Data193

Filling In Missing Data195

7.2 Data Transformation197

Removing Duplicates197

Transforming Data Using a Function or Mapping198

Replacing Values200

Renaming Axis Indexes201

Discretization and Binning203

Detecting and Filtering Outliers205

Permutation and Random Sampling206

Computing Indicator/Dummy Variables208

7.3 String Manipulation211

String Object Methods211

Regular Expressions213

Vectorized String Functions in pandas216

7.4 Conclusion219

8.Data Wrangling：Join，Combine，and Reshape221

8.1 Hierarchical Indexing221

Reordering and Sorting Levels224

Summary Statistics by Level225

Indexing with a DataFrame’s columns225

8.2 Combining and Merging Datasets227

Database-Style DataFrame Joins227

Merging on Index232

Concatenating Along an Axis236

Combining Data with Overlap241

8.3 Reshaping and Pivoting242

Reshaping with Hierarchical Indexing243

Pivoting “Long” to “Wide” Format246

Pivoting “Wide” to “Long” Format249

8.4 Conclusion251

9.Plotting and Visualization253

9.1 A Brief matplotlib API Primer253

Figures and Subplots255

Colors，Markers，and Line Styles259

Ticks，Labels，and Legends261

Annotations and Drawing on a Subplot265

Saving Plots to File267

matplotlib Configuration268

9.2 Plotting with pandas and seaborn268

Line Plots269

Bar Plots272

Histograms and Density Plots277

Scatter or Point Plots280

Facet Grids and Categorical Data283

9.3 Other Python Visualization Tools285

9.4 Conclusion286

10.Data Aggregation and Group Operations287

10.1 GroupBy Mechanics288

Iterating Over Groups291

Selecting a Column or Subset of Columns293

Grouping with Dicts and Series294

Grouping with Functions295

Grouping by Index Levels295

10.2 Data Aggregation296

Column-Wise and Multiple Function Application298

Returning Aggregated Data Without Row Indexes301

10.3 Apply：General split-apply-combine302

Suppressing the Group Keys304

Quantile and Bucket Analysis305

Example：Filling Missing Values with Group-Specific Values306

Example：Random Sampling and Permutation308

Example：Group Weighted Average and Correlation310

Example：Group-Wise Linear Regression312

10.4 Pivot Tables and Cross-Tabulation313

Cross-Tabulations：Crosstab315

10.5 Conclusion316

11.Time Series317

11.1 Date and Time Data Types and Tools318

Converting Between String and Datetime319

11.2 Time Series Basics322

Indexing，Selection，Subsetting323

Time Series with Duplicate Indices326

11.3 Date Ranges，Frequencies，and Shifting327

Generating Date Ranges328

Frequencies and Date Offsets330

Shifting （Leading and Lagging） Data332

11.4 Time Zone Handling335

Time Zone Localization and Conversion335

Operations with Time Zone-Aware Timestamp Objects338

Operations Between Different Time Zones339

11.5 Periods and Period Arithmetic339

Period Frequency Conversion340

Quarterly Period Frequencies342

Converting Timestamps to Periods （and Back）344

Creating a PeriodIndex from Arrays345

11.6 Resampling and Frequency Conversion348

Downsampling349

Upsampling and Interpolation352

Resampling with Periods353

11.7 Moving Window Functions354

Exponentially Weighted Functions358

Binary Moving Window Functions359

User-Defined Moving Window Functions361

11.8 Conclusion362

12.Advanced pandas363

12.1 Categorical Data363

Background and Motivation363

Categorical Type in pandas365

Computations with Categoricals367

Categorical Methods370

12.2 Advanced GroupBy Use373

Group Transforms and “Unwrapped” GroupBys373

Grouped Time Resampling377

12.3 Techniques for Method Chaining378

The pipe Method380

12.4 Conclusion381

13.Introduction to Modeling Libraries in Python383

13.1 Interfacing Between pandas and Model Code383

13.2 Creating Model Descriptions with Patsy386

Data Transformations in Patsy Formulas389

Categorical Data and Patsy390

13.3 Introduction to statsmodels393

Estimating Linear Models393

Estimating Time Series Processes396

13.4 Introduction to scikit-learn397

13.5 Continuing Your Education401

14.Data Analysis Examples403

14.1 1.USA.gov Data from Bitly403

Counting Time Zones in Pure Python404

Counting Time Zones with pandas406

14.2 MovieLens 1M Dataset413

Measuring Rating Disagreement418

14.3 US Baby Names 1880-2010419

Analyzing Naming Trends425

14.4 USDA Food Database434

14.5 2012 Federal Election Commission Database440

Donation Statistics by Occupation and Employer442

Bucketing Donation Amounts445

Donation Statistics by State447

14.6 Conclusion448

A.Advanced NumPy449

A.1 ndarray Object Internals449

NumPy dtype Hierarchy450

A.2 Advanced Array Manipulation451

Reshaping Arrays452

C Versus Fortran Order454

Concatenating and Splitting Arrays454

Repeating Elements：tile and repeat457

Fancy Indexing Equivalents：take and put459

A.3 Broadcasting460

Broadcasting Over Other Axes462

Setting Array Values by Broadcasting465

A.4 Advanced ufunc Usage466

ufunc Instance Methods466

Writing New ufuncs in Python468

A.5 Structured and Record Arrays469

Nested dtypes and Multidimensional Fields469

Why Use Structured Arrays？470

A.6 More About Sorting471

Indirect Sorts：argsort and lexsort472

Alternative Sort Algorithms474

Partially Sorting Arrays474

numpy.searchsorted：Finding Elements in a Sorted Array475

A.7 Writing Fast NumPy Functions with Numba476

Creating Custom numpy.ufunc Objects with Numba478

A.8 Advanced Array Input and Output478

Memory-Mapped Files478

HDF5 and Other Array Storage Options480

A.9 Performance Tips480

The Importance of Contiguous Memory480

B.More on the IPython System483

B.1 Using the Command History483

Searching and Reusing the Command History483

Input and Output Variables484

B.2 Interacting with the Operating System485

Shell Commands and Aliases486

Directory Bookmark System487

B.3 Software Development Tools487

Interactive Debugger488

Timing Code：%time and %timeit492

Basic Profiling：0016D2C0run and %run-P494

Profiling a Function Line by Line496

B.4 Tips for Productive Code Development Using IPython498

Reloading Module Dependencies498

Code Design Tips499

B.5 Advanced IPython Features500

Making Your Own Classes IPython-Friendly500

Profiles and Configuration501

B.6 Conclusion503

Index505