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0 (hodnocen0 x )

BK

Příručka

Second edition

Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo : O’Reilly, 2017

xvi, 528 stran : ilustrace ; 24 cm

ISBN 978-1-491-95766-0 (brožováno) ISBN !978-1-491-95766-0 (chyb.)

Obsahuje rejstřík

001637087

Table of Contents // Preface... xj // 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 2 // Solving the “Two-Language” Problem 3 // Why Not Python? 3 // 1.3 Essential Python Libraries 4 // NumPy 4 // pandas 4 // matplotlib 5 // IPython and Jupyter 6 // SciPy 6 // scikit-learn 7 // statsmodels g // 1.4 Installation and Setup g // Windows 9 // Apple (OS X, macOS) 9 // GNU/Linux 9 // Installing or Updating Python Packages 10 // Python 2 and Python 3 11 // Integrated Development Environments (IDEs) and Text Editors 11 // 1.5 Community and Conferences 12 // 1.6 Navigating This Book 12 // Code Examples I3 // Data for Examples I3 // Import Conventions Jargon // 14 // 14 // 2. Python Language Basics, IPython, and Jupyter Notebooks...15 // 2.1 The Python Interpreter 16 // 2.2 IPython Basics 17 // Running the IPython Shell 17 // Running the Jupyter Notebook 1 g // Tab Completion 21 // Introspection 23 // The %run Command 25 // Executing Code from the Clipboard 26 // Terminal Keyboard Shortcuts 27 // About Magic Commands 28 // Matplotlib Integration 29 // 2.3 Python Language Basics 30 // Language Semantics 30 // Scalar Types 3g // Control Flow 45 // 3. Built-in Data Structures, Functions, and Files... 51 // 3.1 Data Structures and Sequences 51 // Tuple 51 // List 54 // Built-in Sequence Functions 59 // dict 6i // set . 65 // List, Set, and Dict Comprehensions 57 // 3.2 Functions 59 // Namespaces,

Scope, and Local Functions 70 // Returning Multiple Values 71 // Functions Are Objects 72 // Anonymous (Lambda) Functions 73 // Currying: Partial Argument Application 74 // Generators 75 // Errors and Exception Handling 77 // 3.3 Files and the Operating System 80 // Bytes and Unicode with Files 83 // 3.4 Conclusion 34 // 4. NumPy Basics: Arrays and Vectorized Computation...85 // 4.1 The NumPy ndarray: A Multidimensional Array Object 87 // iv I Table of Contents // Creating ndarrays 88 // Data Types for ndarrays 90 // Arithmetic with NumPy Arrays 93 // Basic Indexing and Slicing 94 // Boolean Indexing 99 // Fancy Indexing 102 // Transposing Arrays and Swapping Axes 103 // 4.2 Universal Functions: Fast Element-Wise Array Functions 105 // 4.3 Array-Oriented Programming with Arrays 108 // Expressing Conditional Logic as Array Operations 109 // Mathematical and Statistical Methods 111 // Methods for Boolean Arrays 113 // Sorting 113 // Unique and Other Set Logic 114 // 4.4 File Input and Output with Arrays 115 // 4.5 Linear Algebra 116 // 4.6 Pseudorandom Number Generation 118 // 4.7 Example: Random Walks 119 // Simulating Many Random Walks at Once 121 // 4.8 Conclusion 122 // 5. Getting Started with pandas... 123 // 5.1 Introduction to pandas Data Structures 124 // Series 124 // DataFrame 128 // Index Objects 134 // 5.2 Essential Functionality 136 // Reindexing 136 // Dropping Entries from an Axis 138 // Indexing, Selection, and Filtering 140 // Integer Indexes 145 // Arithmetic and

Data Alignment 146 // Function Application and Mapping 151 // Sorting and Ranking 153 // Axis Indexes with Duplicate Labels 157 // 5.3 Summarizing and Computing Descriptive Statistics 158 // Correlation and Covariance 160 // Unique Values, Value Counts, and Membership 162 // 5.4 Conclusion 165 // 6. Data Loading, Storage, and File Formats...167 // 6.1 Reading and Writing Data in Text Format 167 // Table of Contents | v // Reading Text Files in Pieces 173 // Writing Data to Text Format 175 // Working with Delimited Formats 176 // JSON Data 178 // XML and HTML: Web Scraping 180 // 6.2 Binary Data Formats 183 // Using HDF5 Format 184 // Reading Microsoft Excel Files 186 // 6.3 Interacting with Web APIs 187 // 6.4 Interacting with Databases 189 // 6.5 Conclusion 19Q // 7. Data Cleaning and Preparation... 191 // 7.1 Handling Missing Data 191 // Filtering Out Missing Data I93 // Filling In Missing Data I95 // 7.2 Data Transformation I97 // Removing Duplicates I97 // Transforming Data Using a Function or Mapping 198 // Replacing Values 200 // Renaming Axis Indexes 201 // Discretization and Binning 203 // Detecting and Filtering Outliers 205 // Permutation and Random Sampling 206 // Computing Indicator/Dummy Variables 208 // 7.3 String Manipulation 211 // String Object Methods 211 // Regular Expressions 213 // Vectorized String Functions in pandas 216 // 7.4 Conclusion 219 // 8. Data Wrangling: Join, Combine, and Reshape... 221 // 8.1 Hierarchical Indexing 221 // Reordering and Sorting

Levels 224 // Summary Statistics by Level 225 // Indexing with a DataFrame’s columns 225 // 8.2 Combining and Merging Datasets 227 // Database-Style DataFrame Joins 227 // Merging on Index 232 // Concatenating Along an Axis 236 // Combining Data with Overlap 241 // 8.3 Reshaping and Pivoting 242 // vi I Table of Contents // Reshaping with Hierarchical Indexing 243 // Pivoting “Long” to “Wide” Format 246 // Pivoting “Wide” to “Long” Format 249 // 8.4 Conclusion 251 // 9. Plotting and Visualization... 253 // 9.1 A Brief matplotlib API Primer 254 // Figures and Subplots 255 // Colors, Markers, and Line Styles 260 // Ticks, Labels, and Legends 262 // Annotations and Drawing on a Subplot 266 // Saving Plots to File 268 // matplotlib Configuration 269 // 9.2 Plotting with pandas and seaborn 269 // Line Plots 270 // Bar Plots 273 // Histograms and Density Plots 278 // Scatter or Point Plots 281 // Facet Grids and Categorical Data 284 // 9.3 Other Python Visualization Tools 286 // 9.4 Conclusion 287 // 10. Data Aggregation and Group Operations...289 // 10.1 Group By Mechanics 290 // Iterating Over Groups 293 // Selecting a Column or Subset of Columns 295 // Grouping with Diets and Series 296 // Grouping with Functions 297 // Grouping by Index Levels 297 // 10.2 Data Aggregation 298 // Column-Wise and Multiple Function Application 300 // Returning Aggregated Data Without Row Indexes 303 // 10.3 Apply: General split-apply-combine 304 // Suppressing the Group Keys 306

Quantile and Bucket Analysis 307 // Example: Filling Missing Values with Group-Specific Values 308 // Example: Random Sampling and Permutation 310 // Example: Group Weighted Average and Correlation 312 // Example: Group-Wise Linear Regression 314 // 10.4 Pivot Tables and Cross-Tabulation 315 // Cross-Tabulations: Crosstab 317 // 10.5 Conclusion 318 // Table of Contents | vii // 11. Time Series... // 11.1 Date and Time Data Types and Tools 320 // Converting Between String and Datetime 321 // 11.2 Time Series Basics 324 // Indexing, Selection, Subsetting 325 // Time Series with Duplicate Indices 328 // 11.3 Date Ranges, Frequencies, and Shifting 329 // Generating Date Ranges 330 // Frequencies and Date Offsets 332 // Shifting (Leading and Lagging) Data 334 // 11.4 Time Zone Handling 337 // Time Zone Localization and Conversion 337 // Operations with Time Zone-Aware Timestamp Objects 340 // Operations Between Different Time Zones 341 // 11.5 Periods and Period Arithmetic 341 // Period Frequency Conversion 342 // Quarterly Period Frequencies 344 // Converting Timestamps to Periods (and Back) 346 // Creating a Periodlndex from Arrays 347 // 11.6 Resampling and Frequency Conversion 35O // Downsampling 351 // Upsampling and Interpolation 354 // Resampling with Periods 355 // 11.7 Moving Window Functions 356 // Exponentially Weighted Functions 360 // Binary Moving Window Functions 361 // User-Defined Moving Window Functions 363 // 11.8 Conclusion 354 // 12. Advanced pandas... 365 // 12.1

Categorical Data 355 // Background and Motivation 365 // Categorical Type in pandas 367 // Computations with Categoricals 369 // Categorical Methods 372 // 12.2 Advanced GroupBy Use 375 // Group Transforms and “Unwrapped” GroupBys 375 // Grouped Time Resampling 379 // 12.3 Techniques for Method Chaining 380 // The pipe Method 382 // 12.4 Conclusion ?83 // viii j Table of Contents // 13. Introduction to Modeling Libraries in Python...385 // 13.1 Interfacing Between pandas and Model Code 385 // 13.2 Creating Model Descriptions with Patsy 388 // Data Transformations in Patsy Formulas 391 // Categorical Data and Patsy 392 // 13.3 Introduction to statsmodels 395 // Estimating Linear Models 395 // Estimating Time Series Processes 398 // 13.4 Introduction to scikit-learn 399 // 13.5 Continuing Your Education 403 // 14. Data Analysis Examples... 405 // 14.1 1.USA.gov Data from Bitly 405 // Counting Time Zones in Pure Python 406 // Counting Time Zones with pandas 408 // 14.2 MovieLens 1M Dataset 415 // Measuring Rating Disagreement 420 // 14.3 US Baby Names 1880-2010 421 // Analyzing Naming Trends 427 // 14.4 USDA Food Database 436 // 14.5 2012 Federal Election Commission Database 442 // Donation Statistics by Occupation and Employer 444 // Bucketing Donation Amounts 447 // Donation Statistics by State 449 // 14.6 Conclusion 450 // A. Advanced NumPy...451 // A. 1 ndarray Object Internals 451 // NumPy dtype Hierarchy 452 // A.2 Advanced Array Manipulation 453 // Reshaping Arrays 454

// C Versus Fortran Order 456 // Concatenating and Splitting Arrays 456 // Repeating Elements: tile and repeat 459 // Fancy Indexing Equivalents: take and put 461 // A.3 Broadcasting 462 // Broadcasting Over Other Axes 464 // Setting Array Values by Broadcasting 467 // A.4 Advanced ufunc Usage 468 // ufunc Instance Methods 468 // Writing New ufuncs in Python 470 // A.5 Structured and Record Arrays 471 // Table of Contents | ix // Nested dtypes and Multidimensional Fields 471 // Why Use Structured Arrays? 472 // A.6 More About Sorting 473 // Indirect Sorts: argsort and lexsort 474 // Alternative Sort Algorithms 476 // Partially Sorting Arrays 476 // numpy.searchsorted: Finding Elements in a Sorted Array 477 // A.7 Writing Fast NumPy Functions with Numba 478 // Creating Custom numpy.ufunc Objects with Numba 480 // A.8 Advanced Array Input and Output 480 // Memory-Mapped Files 480 // HDF5 and Other Array Storage Options 482 // A. 9 Performance Tips 4g2 // The Importance of Contiguous Memory 482 // B. More on the IPython System... 485 // B. l Using the Command History 435 // Searching and Reusing the Command History 485 // Input and Output Variables 486 // B.2 Interacting with the Operating System 487 // Shell Commands and Aliases 488 // Directory Bookmark System 489 // B.3 Software Development Tools 489 // Interactive Debugger 49O // Timing Code: %dme and %timeit 494 // Basic Profiling: %prun and %run -p 496 // Profiling a Function Line by Line 498 // B.4 Tips for Productive Code

Development Using IPython 500 // Reloading Module Dependencies 500 // Code Design Tips 501 // B.5 Advanced IPython Features 502 // Making Your Own Classes IPython-Friendly 503 // Profiles and Configuration 503 // B.6 Conclusion 505 // Index... 507 // x j Table of Contents