Python data science handbook: essential tools for working with data
Python is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the new edition of Python Data Science Handbook do you get them all-...
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| Zusammenfassung: | Python is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the new edition of Python Data Science Handbook do you get them all--IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. In this second edition, working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you'll learn how: IPython and Jupyter provide computational environments for scientists using Python NumPy includes the ndarray for efficient storage and manipulation of dense data arrays Pandas contains the DataFrame for efficient storage and manipulation of labeled/columnar data Matplotlib includes capabilities for a flexible range of data visualizations Scikit-Learn helps you build efficient and clean Python implementations of the most important and established machine learning algorithms |
| Beschreibung: | xxiv, 563 Seiten Illustrationen, Diagramme |
| ISBN: | 9781098121228 |
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| adam_text | Table of Contents Preface..................................................................................................... xix Part I. Jupyter: Beyond Normal Python 1. Getting Started in IPython and Jupyter......................................................... 3 Launching the IPython Shell Launching the Jupyter Notebook Help and Documentation in IPython Accessing Documentation with ? Accessing Source Code with ?? Exploring Modules with Tab Completion Keyboard Shortcuts in the IPython Shell Navigation Shortcuts Text Entry Shortcuts Command History Shortcuts Miscellaneous Shortcuts 3 4 4 5 6 7 9 10 10 10 12 2. Enhanced Interactive Features.................................................................. 13 IPython Magic Commands Running External Code: %run Timing Code Execution: %timeit Help on Magic Functions: ?, %magic, and %lsmagic Input and Output History IPythons In and Out Objects Underscore Shortcuts and Previous Outputs Suppressing Output Related Magic Commands 13 13 14 15 15 15 16 17 17 v
IPython and Shell Commands Quick Introduction to the Shell Shell Commands in IPython Passing Values to and from the Shell Shell-Related Magic Commands 3. Debugging and Profiling.......................................................................... Errors and Debugging Controlling Exceptions: %xmode Debugging: When Reading Tracebacks Is Not Enough Profiling and Timing Code Timing Code Snippets: %timeit and %time Profiling Full Scripts: %prun Line-by-Line Profiling with %lprun Profiling Memory Use: %memit and %mprun More IPython Resources Web Resources Books Partii. 18 18 19 20 20 22 22 22 24 26 27 28 29 30 31 31 32 Introduction to NumPy 4. Understanding Data Types in Python............................................................. 35 A Python Integer Is More Than Just an Integer A Python List Is More Than Just a List Fixed-Type Arrays in Python Creating Arrays from Python Lists Creating Arrays from Scratch NumPy Standard Data Types 36 37 39 39 40 41 5. The Basics of NumPy Arrays......................................................................... 43 NumPy Array Attributes Array Indexing: Accessing Single Elements Array Slicing: Accessing Subarrays One-Dimensional Subarrays Multidimensional Subarrays Subarrays as No-Copy Views Creating Copies of Arrays Reshaping of Arrays Array Concatenation and Splitting Concatenation of Arrays Splitting of Arrays vi I Table of Contents 44 44 45 45 46 47 47 48 49 49 50
6. Computation on NumPy Arrays: Universal Functions......................................... 51 The Slowness of Loops Introducing Ufuncs Exploring NumPy’s Ufuncs Array Arithmetic Absolute Value Trigonometric Functions Exponents and Logarithms Specialized Ufuncs Advanced Ufunc Features Specifying Output Aggregations Outer Products Ufuncs: Learning More 51 52 53 53 55 55 56 56 57 57 58 59 59 7. Aggregations: min, max, and Everything in Between........................................ 60 Summing the Values in an Array Minimum and Maximum Multidimensional Aggregates Other Aggregation Functions Example: What Is the Average Height of US Presidents? 60 61 61 62 63 8. Computation on Arrays: Broadcasting......................................................... 65 Introducing Broadcasting Rules of Broadcasting Broadcasting Example 1 Broadcasting Example 2 Broadcasting Example 3 Broadcasting in Practice Centering an Array Plotting a Two-Dimensional Function 65 67 68 68 69 70 70 71 9. Comparisons, Masks, and Boolean Logic...................................................... 72 Example: Counting Rainy Days Comparison Operators as Ufuncs Working with Boolean Arrays Counting Entries Boolean Operators Boolean Arrays as Masks Using the Keywords and/or Versus the Operators /| 72 73 75 75 76 77 78 Table of Contents | vii
10. Fancy indexing................................................................................. 80 Exploring Fancy Indexing Combined Indexing Example: Selecting Random Points Modifying Values with Fancy Indexing Example: Binning Data 80 81 82 84 85 11. Sorting Arrays................................................................................ 88 Fast Sorting in NumPy: np.sort and np.argsort Sorting Along Rows or Columns Partial Sorts: Partitioning Example: k-Nearest Neighbors 89 89 90 90 12. Structured Data: NumP/s Structured Arrays................................................ 94 Exploring Structured Array Creation More Advanced Compound Types Record Arrays: Structured Arrays with a Twist On to Pandas Part III. 96 97 97 98 Data Manipulation with Pandas 13. Introducing Pandas Objects.................................................................. 101 The Pandas Series Object Series as Generalized NumPy Array Series as Specialized Dictionary Constructing Series Objects The Pandas DataFrame Object DataFrame as Generalized NumPy Array DataFrame as Specialized Dictionary Constructing DataFrame Objects The Pandas Index Object Index as Immutable Array Index as Ordered Set 101 102 103 104 104 105 106 106 108 108 108 14. Data Indexing and Selection............................................................... 110 Data Selection in Series Series as Dictionary Series as One-Dimensional Array Indexers: loc and iloc Data Selection in DataFrames viii I Table of Contents 110 110 111 112 113
DataFrame as Dictionary DataFrame as Two-Dimensional Array Additional Indexing Conventions 113 115 116 15. Operating on Data in Pandas..................................................................... 118 Ufuncs: Index Preservation Ufanes: Index Alignment Index Alignment in Series Index Alignment in DataFrames Ufuncs: Operations Between DataFrames and Series 118 119 119 120 121 16. Handling Missing Data.......................................................................... 123 Trade-offs in Missing Data Conventions Missing Data in Pandas None as a Sentinel Value NaN: Missing Numerical Data NaN and None in Pandas Pandas Nullable Dtypes Operating on Null Values Detecting Null Values Dropping Null Values Filling Null Values 123 124 125 125 126 127 128 128 129 130 17. Hierarchical Indexing............................................................................ 132 A Multiply Indexed Series The Bad Way The Better Way: The Pandas Multilndex Multilndex as Extra Dimension Methods of Multilndex Creation Explicit Multilndex Constructors Multilndex Level Names Multilndex for Columns Indexing and Slicing a Multilndex Multiply Indexed Series Multiply Indexed DataFrames Rearranging Multi-Indexes Sorted and Unsorted Indices Stacking and Unstacking Indices Index Setting and Resetting 132 133 133 134 136 136 137 138 138 139 140 141 141 143 143 18. Combining Datasets , concat and append................................................... Recall: Concatenation of NumPy Arrays 145 146 Table of Contents | ix
Simple Concatenation with pd.concat Duplicate Indices Concatenation with Joins The append Method 147 148 149 150 19. Combining Datasets: merge and join........................................................ 151 Relational Algebra Categories of Joins One-to-One Joins Many-to-One Joins Many-to-Many Joins Specification of the Merge Key The on Keyword The left_on and right_on Keywords The left_index and right-index Keywords Specifying Set Arithmetic for Joins Overlapping Column Names: The suffixes Keyword Example: US States Data 20. Aggregation and Grouping................................................................ Planets Data Simple Aggregation in Pandas groupby: Split, Apply, Combine Split, Apply, Combine The GroupBy Object Aggregate, Filter, Transform, Apply Specifying the Split Key Grouping Example 151 152 152 153 153 154 154 155 155 157 158 159 164 165 165 167 167 169 171 174 175 21. PivotTables.................................................................................. 176 Motivating Pivot Tables Pivot Tables by Hand Pivot Table Syntax Multilevel Pivot Tables Additional Pivot Table Options Example: Birthrate Data 176 177 178 178 179 180 22. Vectorized String Operations................................................................ 185 Introducing Pandas String Operations Tables of Pandas String Methods Methods Similar to Python String Methods Methods Using Regular Expressions x ļ Table of Contents 185 186 186 187
Miscellaneous Methods Example: Recipe Database A Simple Recipe Recommender Going Further with Recipes 188 190 192 193 23. Working with Time Series................................................................. 194 Dates and Times in Python Native Python Dates and Times: datetime and dateutil Typed Arrays of Times: NumPy s datetime64 Dates and Times in Pandas: The Best of Both Worlds Pandas Time Series: Indexing by Time Pandas Time Series Data Structures Regular Sequences: pd.date_range Frequencies and Offsets Resampling, Shifting, and Windowing Resampling and Converting Frequencies Time Shifts Rolling Windows Example: Visualizing Seattle Bicycle Counts Visualizing the Data Digging into the Data 195 195 196 197 198 199 200 201 202 203 205 206 208 209 211 24. High-Performance Pandas: eval and query................................................ 215 Motivating query and eval: Compound Expressions pandas.eval for Efficient Operations DataFrame.eval for Column-Wise Operations Assignment in DataFrame.eval Local Variables in DataFrame.eval The DataFrame.query Method Performance: When to Use These Functions Further Resources Part IV. 215 216 218 219 219 220 220 221 Visualization with Matplotlib 25. General Matplotlib Tips.................................................................... 225 225 225 226 226 227 227 Importing Matplotlib Setting Styles show or No show? How to Display Your Plots Plotting from a Script Plotting from an IPython Shell Plotting from a Jupyter Notebook Table of Contents | xi
Saving Figures to File Two Interfaces for the Price of One 228 230 26. Simple Line Plots.............................................................................. 232 Adjusting the Plot: Line Colors and Styles Adjusting the Plot: Axes Limits Labeling Plots Matplotlib Gotchas 235 238 240 242 27. Simple Scatter Plots......................................................................... 244 Scatter Plots with plt.plot Scatter Plots with plt.scatter plot Versus scatter: A Note on Efficiency Visualizing Uncertainties Basic Errorbars Continuous Errors 244 247 250 251 251 253 28. Density and Contour Plots.................................................................. 255 Visualizing a Three-Dimensional Function Histograms, Binnings, and Density Two-Dimensional Histograms and Binnings plt.hist2d: Two-Dimensional Histogram pithexbin: Hexagonal Binnings Kernel Density Estimation 255 260 263 263 264 264 29. Customizing Plot Legends................................................................. 267 Choosing Elements for the Legend Legend for Size of Points Multiple Legends 270 272 274 30. Customizing Colorbars..................................................................... 276 Customizing Colorbars Choosing the Colormap Color Limits and Extensions Discrete Colorbars Example: Handwritten Digits 31. Multiple Subplots.......................................................................... plt.axes: Subplots by Hand plt.subplot: Simple Grids of Subplots plt.subplots: The Whole Grid in One Go plt.GridSpec: More Complicated Arrangements xii Į Table of Contents 277 278 280
281 282 285 285 287 289 291
32. Text and Annotation......................................................................... 294 Example: Effect of Holidays on US Births Transforms and Text Position Arrows and Annotation 294 296 298 33. Customizing Ticks.......................................................................... 302 Major and Minor Ticks Hiding Ticks or Labels Reducing or Increasing the Number of Ticks Fancy Tick Formats Summary of Formatters and Locators 302 304 306 307 310 34. Customizing Matplotlib: Configurations and Stylesheets............................... 312 Plot Customization by Hand Changing the Defaults: reParams Stylesheets Default Style FiveThiryEight Style ggplot Style Bayesian Methods for Hackers Style Dark Background Style Grayscale Style Seaborn Style 312 314 316 317 317 318 318 319 319 320 35. Three-Dimensional Plotting in Matplotlib................................................ 321 322 323 325 328 330 Three-Dimensional Points and Lines Three-Dimensional Contour Plots Wireframes and Surface Plots Surface Triangulations Example: Visualizing a Möbius Strip 36. Visualization with Seaborn............................................................... 332 Exploring Seaborn Plots Histograms, KDE, and Densities Pair Plots Faceted Histograms Categorical Plots Joint Distributions Bar Plots Example: Exploring Marathon Finishing Times Further Resources Other Python Visualization Libraries 333 333 335 336 338 339 340 342 350 351 Table of Contents | xiii
Part V. Machine Learning 37. What Is Machine Learning?...................................................................... 355 Categories of Machine Learning Qualitative Examples of Machine Learning Applications Classification: Predicting Discrete Labels Regression: Predicting Continuous Labels Clustering: Inferring Labels on Unlabeled Data Dimensionality Reduction: Inferring Structure of Unlabeled Data Summary 355 356 356 359 363 364 366 38. Introducing Scikit-Learn........................................................................... 367 Data Representation in Scikit-Learn The Features Matrix The Target Array The Estimator API Basics of the API Supervised Learning Example: Simple Linear Regression Supervised Learning Example: Iris Classification Unsupervised Learning Example: Iris Dimensionality Unsupervised Learning Example: Iris Clustering Application: Exploring Handwritten Digits Loading and Visualizing the Digits Data Unsupervised Learning Example: Dimensionality Reduction Classification on Digits Summary 367 368 368 370 371 372 375 376 377 378 378 380 381 383 39. Hyperparameters and Model Validation..................................................... 384 Thinking About Model Validation Model Validation the Wrong Way Model Validation the Right Way: Holdout Sets Model Validation via Cross-Validation Selecting the Best Model The Bias-Variance Trade-off Validation Curves in Scikit-Learn Learning Curves Validation in Practice: Grid Search Summary 384 385 385 386 388 389 391 395 400 401 40. Feature
Engineering............................................................................... 402 Categorical Features xiv I Table of Contents 402
Text Features Image Features Derived Features Imputation of Missing Data Feature Pipelines 404 405 405 408 409 41. In Depth: Naive Bayes Classification............................................................ 410 Bayesian Classification Gaussian Naive Bayes Multinomial Naive Bayes Example: Classifying Text When to Use Naive Bayes 410 411 414 414 417 42. In Depth: Linear Regression...................................................................... 419 Simple Linear Regression Basis Function Regression Polynomial Basis Functions Gaussian Basis Functions Regularization Ridge Regression (L2 Regularization) Lasso Regression (L1 Regularization) Example: Predicting Bicycle Traffic 419 422 422 424 425 427 428 429 43. In Depth: Support Vector Machines........................................................... 435 Motivating Support Vector Machines Support Vector Machines: Maximizing the Margin Fitting a Support Vector Machine Beyond Linear Boundaries: Kernel SVM Tuning the SVM: Softening Margins Example: Face Recognition Summary 435 437 438 441 444 445 450 44. In Depth: Decision Trees and Random Forests............................................. 451 Motivating Random Forests: Decision Trees Creating a Decision Tree Decision Trees and Overfitting Ensembles of Estimators: Random Forests Random Forest Regression Example: Random Forest for Classifying Digits Summary 451 452 455 456 458 459 462 Table of Contents | xv
45. In Depth: Principal Component Analysis.................................................. 463 Introducing Principal Component Analysis PCA as Dimensionality Reduction PCA for Visualization: Handwritten Digits What Do the Components Mean? Choosing the Number of Components PCA as Noise Filtering Example: Eigenfaces Summary 463 466 467 469 470 471 473 476 46. In Depth: Manifold Learning............................................................... 477 Manifold Learning: “HELLO” Multidimensional Scaling MDS as Manifold Learning Nonlinear Embeddings: Where MDS Fails Nonlinear Manifolds: Locally Linear Embedding Some Thoughts on Manifold Methods Example: Isomap on Faces Example: Visualizing Structure in Digits 47. In Depth: k-Means Clustering............................................................. Introducing k-Means Expectation-Maximization Examples Example 1: k-Means on Digits Example 2: k-Means for Color Compression 478 479 482 484 486 488 489 493 496 496 498 504 504 507 48. In Depth: Gaussian Mixture Models....................................................... 512 Motivating Gaussian Mixtures: Weaknesses of k-Means Generalizing E-M: Gaussian Mixture Models Choosing the Covariance Type Gaussian Mixture Models as Density Estimation Example: GMMs for Generating New Data 512 516 520 520 524 49. In Depth: Kernel Density Estimation..................................................... 528 Motivating Kernel Density Estimation: Histograms Kernel Density Estimation in Practice Selecting the Bandwidth via Cross-Validation Example: Not-so-Naive Bayes Anatomy of a Custom
Estimator Using Our Custom Estimator xvi I Table of Contents 528 533 535 535 537 539
50. Application: A Face Detection Pipeline...................................................... HOG Features HOG in Action: A Simple Face Detector 1. Obtain a Set of Positive Training Samples 2. Obtain a Set of Negative Training Samples 3. Combine Sets and Extract HOG Features 4. Train a Support Vector Machine 5. Find Faces in a New Image Caveats and Improvements Further Machine Learning Resources 541 542 543 543 543 545 546 546 548 550 Index...................................................................................................... 551 Table of Contents | xvii
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Table of Contents Preface. xix Part I. Jupyter: Beyond Normal Python 1. Getting Started in IPython and Jupyter. 3 Launching the IPython Shell Launching the Jupyter Notebook Help and Documentation in IPython Accessing Documentation with ? Accessing Source Code with ?? Exploring Modules with Tab Completion Keyboard Shortcuts in the IPython Shell Navigation Shortcuts Text Entry Shortcuts Command History Shortcuts Miscellaneous Shortcuts 3 4 4 5 6 7 9 10 10 10 12 2. Enhanced Interactive Features. 13 IPython Magic Commands Running External Code: %run Timing Code Execution: %timeit Help on Magic Functions: ?, %magic, and %lsmagic Input and Output History IPythons In and Out Objects Underscore Shortcuts and Previous Outputs Suppressing Output Related Magic Commands 13 13 14 15 15 15 16 17 17 v
IPython and Shell Commands Quick Introduction to the Shell Shell Commands in IPython Passing Values to and from the Shell Shell-Related Magic Commands 3. Debugging and Profiling. Errors and Debugging Controlling Exceptions: %xmode Debugging: When Reading Tracebacks Is Not Enough Profiling and Timing Code Timing Code Snippets: %timeit and %time Profiling Full Scripts: %prun Line-by-Line Profiling with %lprun Profiling Memory Use: %memit and %mprun More IPython Resources Web Resources Books Partii. 18 18 19 20 20 22 22 22 24 26 27 28 29 30 31 31 32 Introduction to NumPy 4. Understanding Data Types in Python. 35 A Python Integer Is More Than Just an Integer A Python List Is More Than Just a List Fixed-Type Arrays in Python Creating Arrays from Python Lists Creating Arrays from Scratch NumPy Standard Data Types 36 37 39 39 40 41 5. The Basics of NumPy Arrays. 43 NumPy Array Attributes Array Indexing: Accessing Single Elements Array Slicing: Accessing Subarrays One-Dimensional Subarrays Multidimensional Subarrays Subarrays as No-Copy Views Creating Copies of Arrays Reshaping of Arrays Array Concatenation and Splitting Concatenation of Arrays Splitting of Arrays vi I Table of Contents 44 44 45 45 46 47 47 48 49 49 50
6. Computation on NumPy Arrays: Universal Functions. 51 The Slowness of Loops Introducing Ufuncs Exploring NumPy’s Ufuncs Array Arithmetic Absolute Value Trigonometric Functions Exponents and Logarithms Specialized Ufuncs Advanced Ufunc Features Specifying Output Aggregations Outer Products Ufuncs: Learning More 51 52 53 53 55 55 56 56 57 57 58 59 59 7. Aggregations: min, max, and Everything in Between. 60 Summing the Values in an Array Minimum and Maximum Multidimensional Aggregates Other Aggregation Functions Example: What Is the Average Height of US Presidents? 60 61 61 62 63 8. Computation on Arrays: Broadcasting. 65 Introducing Broadcasting Rules of Broadcasting Broadcasting Example 1 Broadcasting Example 2 Broadcasting Example 3 Broadcasting in Practice Centering an Array Plotting a Two-Dimensional Function 65 67 68 68 69 70 70 71 9. Comparisons, Masks, and Boolean Logic. 72 Example: Counting Rainy Days Comparison Operators as Ufuncs Working with Boolean Arrays Counting Entries Boolean Operators Boolean Arrays as Masks Using the Keywords and/or Versus the Operators /| 72 73 75 75 76 77 78 Table of Contents | vii
10. Fancy indexing. 80 Exploring Fancy Indexing Combined Indexing Example: Selecting Random Points Modifying Values with Fancy Indexing Example: Binning Data 80 81 82 84 85 11. Sorting Arrays. 88 Fast Sorting in NumPy: np.sort and np.argsort Sorting Along Rows or Columns Partial Sorts: Partitioning Example: k-Nearest Neighbors 89 89 90 90 12. Structured Data: NumP/s Structured Arrays. 94 Exploring Structured Array Creation More Advanced Compound Types Record Arrays: Structured Arrays with a Twist On to Pandas Part III. 96 97 97 98 Data Manipulation with Pandas 13. Introducing Pandas Objects. 101 The Pandas Series Object Series as Generalized NumPy Array Series as Specialized Dictionary Constructing Series Objects The Pandas DataFrame Object DataFrame as Generalized NumPy Array DataFrame as Specialized Dictionary Constructing DataFrame Objects The Pandas Index Object Index as Immutable Array Index as Ordered Set 101 102 103 104 104 105 106 106 108 108 108 14. Data Indexing and Selection. 110 Data Selection in Series Series as Dictionary Series as One-Dimensional Array Indexers: loc and iloc Data Selection in DataFrames viii I Table of Contents 110 110 111 112 113
DataFrame as Dictionary DataFrame as Two-Dimensional Array Additional Indexing Conventions 113 115 116 15. Operating on Data in Pandas. 118 Ufuncs: Index Preservation Ufanes: Index Alignment Index Alignment in Series Index Alignment in DataFrames Ufuncs: Operations Between DataFrames and Series 118 119 119 120 121 16. Handling Missing Data. 123 Trade-offs in Missing Data Conventions Missing Data in Pandas None as a Sentinel Value NaN: Missing Numerical Data NaN and None in Pandas Pandas Nullable Dtypes Operating on Null Values Detecting Null Values Dropping Null Values Filling Null Values 123 124 125 125 126 127 128 128 129 130 17. Hierarchical Indexing. 132 A Multiply Indexed Series The Bad Way The Better Way: The Pandas Multilndex Multilndex as Extra Dimension Methods of Multilndex Creation Explicit Multilndex Constructors Multilndex Level Names Multilndex for Columns Indexing and Slicing a Multilndex Multiply Indexed Series Multiply Indexed DataFrames Rearranging Multi-Indexes Sorted and Unsorted Indices Stacking and Unstacking Indices Index Setting and Resetting 132 133 133 134 136 136 137 138 138 139 140 141 141 143 143 18. Combining Datasets', concat and append. Recall: Concatenation of NumPy Arrays 145 146 Table of Contents | ix
Simple Concatenation with pd.concat Duplicate Indices Concatenation with Joins The append Method 147 148 149 150 19. Combining Datasets: merge and join. 151 Relational Algebra Categories of Joins One-to-One Joins Many-to-One Joins Many-to-Many Joins Specification of the Merge Key The on Keyword The left_on and right_on Keywords The left_index and right-index Keywords Specifying Set Arithmetic for Joins Overlapping Column Names: The suffixes Keyword Example: US States Data 20. Aggregation and Grouping. Planets Data Simple Aggregation in Pandas groupby: Split, Apply, Combine Split, Apply, Combine The GroupBy Object Aggregate, Filter, Transform, Apply Specifying the Split Key Grouping Example 151 152 152 153 153 154 154 155 155 157 158 159 164 165 165 167 167 169 171 174 175 21. PivotTables. 176 Motivating Pivot Tables Pivot Tables by Hand Pivot Table Syntax Multilevel Pivot Tables Additional Pivot Table Options Example: Birthrate Data 176 177 178 178 179 180 22. Vectorized String Operations. 185 Introducing Pandas String Operations Tables of Pandas String Methods Methods Similar to Python String Methods Methods Using Regular Expressions x ļ Table of Contents 185 186 186 187
Miscellaneous Methods Example: Recipe Database A Simple Recipe Recommender Going Further with Recipes 188 190 192 193 23. Working with Time Series. 194 Dates and Times in Python Native Python Dates and Times: datetime and dateutil Typed Arrays of Times: NumPy s datetime64 Dates and Times in Pandas: The Best of Both Worlds Pandas Time Series: Indexing by Time Pandas Time Series Data Structures Regular Sequences: pd.date_range Frequencies and Offsets Resampling, Shifting, and Windowing Resampling and Converting Frequencies Time Shifts Rolling Windows Example: Visualizing Seattle Bicycle Counts Visualizing the Data Digging into the Data 195 195 196 197 198 199 200 201 202 203 205 206 208 209 211 24. High-Performance Pandas: eval and query. 215 Motivating query and eval: Compound Expressions pandas.eval for Efficient Operations DataFrame.eval for Column-Wise Operations Assignment in DataFrame.eval Local Variables in DataFrame.eval The DataFrame.query Method Performance: When to Use These Functions Further Resources Part IV. 215 216 218 219 219 220 220 221 Visualization with Matplotlib 25. General Matplotlib Tips. 225 225 225 226 226 227 227 Importing Matplotlib Setting Styles show or No show? How to Display Your Plots Plotting from a Script Plotting from an IPython Shell Plotting from a Jupyter Notebook Table of Contents | xi
Saving Figures to File Two Interfaces for the Price of One 228 230 26. Simple Line Plots. 232 Adjusting the Plot: Line Colors and Styles Adjusting the Plot: Axes Limits Labeling Plots Matplotlib Gotchas 235 238 240 242 27. Simple Scatter Plots. 244 Scatter Plots with plt.plot Scatter Plots with plt.scatter plot Versus scatter: A Note on Efficiency Visualizing Uncertainties Basic Errorbars Continuous Errors 244 247 250 251 251 253 28. Density and Contour Plots. 255 Visualizing a Three-Dimensional Function Histograms, Binnings, and Density Two-Dimensional Histograms and Binnings plt.hist2d: Two-Dimensional Histogram pithexbin: Hexagonal Binnings Kernel Density Estimation 255 260 263 263 264 264 29. Customizing Plot Legends. 267 Choosing Elements for the Legend Legend for Size of Points Multiple Legends 270 272 274 30. Customizing Colorbars. 276 Customizing Colorbars Choosing the Colormap Color Limits and Extensions Discrete Colorbars Example: Handwritten Digits 31. Multiple Subplots. plt.axes: Subplots by Hand plt.subplot: Simple Grids of Subplots plt.subplots: The Whole Grid in One Go plt.GridSpec: More Complicated Arrangements xii Į Table of Contents 277 278 280
281 282 285 285 287 289 291
32. Text and Annotation. 294 Example: Effect of Holidays on US Births Transforms and Text Position Arrows and Annotation 294 296 298 33. Customizing Ticks. 302 Major and Minor Ticks Hiding Ticks or Labels Reducing or Increasing the Number of Ticks Fancy Tick Formats Summary of Formatters and Locators 302 304 306 307 310 34. Customizing Matplotlib: Configurations and Stylesheets. 312 Plot Customization by Hand Changing the Defaults: reParams Stylesheets Default Style FiveThiryEight Style ggplot Style Bayesian Methods for Hackers Style Dark Background Style Grayscale Style Seaborn Style 312 314 316 317 317 318 318 319 319 320 35. Three-Dimensional Plotting in Matplotlib. 321 322 323 325 328 330 Three-Dimensional Points and Lines Three-Dimensional Contour Plots Wireframes and Surface Plots Surface Triangulations Example: Visualizing a Möbius Strip 36. Visualization with Seaborn. 332 Exploring Seaborn Plots Histograms, KDE, and Densities Pair Plots Faceted Histograms Categorical Plots Joint Distributions Bar Plots Example: Exploring Marathon Finishing Times Further Resources Other Python Visualization Libraries 333 333 335 336 338 339 340 342 350 351 Table of Contents | xiii
Part V. Machine Learning 37. What Is Machine Learning?. 355 Categories of Machine Learning Qualitative Examples of Machine Learning Applications Classification: Predicting Discrete Labels Regression: Predicting Continuous Labels Clustering: Inferring Labels on Unlabeled Data Dimensionality Reduction: Inferring Structure of Unlabeled Data Summary 355 356 356 359 363 364 366 38. Introducing Scikit-Learn. 367 Data Representation in Scikit-Learn The Features Matrix The Target Array The Estimator API Basics of the API Supervised Learning Example: Simple Linear Regression Supervised Learning Example: Iris Classification Unsupervised Learning Example: Iris Dimensionality Unsupervised Learning Example: Iris Clustering Application: Exploring Handwritten Digits Loading and Visualizing the Digits Data Unsupervised Learning Example: Dimensionality Reduction Classification on Digits Summary 367 368 368 370 371 372 375 376 377 378 378 380 381 383 39. Hyperparameters and Model Validation. 384 Thinking About Model Validation Model Validation the Wrong Way Model Validation the Right Way: Holdout Sets Model Validation via Cross-Validation Selecting the Best Model The Bias-Variance Trade-off Validation Curves in Scikit-Learn Learning Curves Validation in Practice: Grid Search Summary 384 385 385 386 388 389 391 395 400 401 40. Feature
Engineering. 402 Categorical Features xiv I Table of Contents 402
Text Features Image Features Derived Features Imputation of Missing Data Feature Pipelines 404 405 405 408 409 41. In Depth: Naive Bayes Classification. 410 Bayesian Classification Gaussian Naive Bayes Multinomial Naive Bayes Example: Classifying Text When to Use Naive Bayes 410 411 414 414 417 42. In Depth: Linear Regression. 419 Simple Linear Regression Basis Function Regression Polynomial Basis Functions Gaussian Basis Functions Regularization Ridge Regression (L2 Regularization) Lasso Regression (L1 Regularization) Example: Predicting Bicycle Traffic 419 422 422 424 425 427 428 429 43. In Depth: Support Vector Machines. 435 Motivating Support Vector Machines Support Vector Machines: Maximizing the Margin Fitting a Support Vector Machine Beyond Linear Boundaries: Kernel SVM Tuning the SVM: Softening Margins Example: Face Recognition Summary 435 437 438 441 444 445 450 44. In Depth: Decision Trees and Random Forests. 451 Motivating Random Forests: Decision Trees Creating a Decision Tree Decision Trees and Overfitting Ensembles of Estimators: Random Forests Random Forest Regression Example: Random Forest for Classifying Digits Summary 451 452 455 456 458 459 462 Table of Contents | xv
45. In Depth: Principal Component Analysis. 463 Introducing Principal Component Analysis PCA as Dimensionality Reduction PCA for Visualization: Handwritten Digits What Do the Components Mean? Choosing the Number of Components PCA as Noise Filtering Example: Eigenfaces Summary 463 466 467 469 470 471 473 476 46. In Depth: Manifold Learning. 477 Manifold Learning: “HELLO” Multidimensional Scaling MDS as Manifold Learning Nonlinear Embeddings: Where MDS Fails Nonlinear Manifolds: Locally Linear Embedding Some Thoughts on Manifold Methods Example: Isomap on Faces Example: Visualizing Structure in Digits 47. In Depth: k-Means Clustering. Introducing k-Means Expectation-Maximization Examples Example 1: k-Means on Digits Example 2: k-Means for Color Compression 478 479 482 484 486 488 489 493 496 496 498 504 504 507 48. In Depth: Gaussian Mixture Models. 512 Motivating Gaussian Mixtures: Weaknesses of k-Means Generalizing E-M: Gaussian Mixture Models Choosing the Covariance Type Gaussian Mixture Models as Density Estimation Example: GMMs for Generating New Data 512 516 520 520 524 49. In Depth: Kernel Density Estimation. 528 Motivating Kernel Density Estimation: Histograms Kernel Density Estimation in Practice Selecting the Bandwidth via Cross-Validation Example: Not-so-Naive Bayes Anatomy of a Custom
Estimator Using Our Custom Estimator xvi I Table of Contents 528 533 535 535 537 539
50. Application: A Face Detection Pipeline. HOG Features HOG in Action: A Simple Face Detector 1. Obtain a Set of Positive Training Samples 2. Obtain a Set of Negative Training Samples 3. Combine Sets and Extract HOG Features 4. Train a Support Vector Machine 5. Find Faces in a New Image Caveats and Improvements Further Machine Learning Resources 541 542 543 543 543 545 546 546 548 550 Index. 551 Table of Contents | xvii |
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| author | VanderPlas, Jake |
| author_GND | (DE-588)1122834322 |
| author_facet | VanderPlas, Jake |
| author_role | aut |
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| discipline | Informatik Wirtschaftswissenschaften |
| discipline_str_mv | Informatik Wirtschaftswissenschaften |
| edition | Second edition |
| format | Book |
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| illustrated | Illustrated |
| index_date | 2024-07-03T20:18:25Z |
| indexdate | 2024-07-10T09:36:31Z |
| institution | BVB |
| isbn | 9781098121228 |
| language | English |
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| spelling | VanderPlas, Jake Verfasser (DE-588)1122834322 aut Python data science handbook essential tools for working with data Jake VanderPlas Second edition Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo O'Reilly [2023] xxiv, 563 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Python is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the new edition of Python Data Science Handbook do you get them all--IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. In this second edition, working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you'll learn how: IPython and Jupyter provide computational environments for scientists using Python NumPy includes the ndarray for efficient storage and manipulation of dense data arrays Pandas contains the DataFrame for efficient storage and manipulation of labeled/columnar data Matplotlib includes capabilities for a flexible range of data visualizations Scikit-Learn helps you build efficient and clean Python implementations of the most important and established machine learning algorithms Datenmanagement (DE-588)4213132-7 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Data Science (DE-588)1140936166 gnd rswk-swf Python Programmiersprache (DE-588)4434275-5 s Data Science (DE-588)1140936166 s Datenanalyse (DE-588)4123037-1 s Data Mining (DE-588)4428654-5 s Datenmanagement (DE-588)4213132-7 s DE-604 Erscheint auch als Online-Ausgabe 978-1-4919-1214-0 (DE-604)BV043948641 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033758938&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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