Python data science handbook: essential tools for working with data
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| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Beijing; Boston; Farnham ; Sebastopol ; Tokyo
O'Reilly
December 2016
|
| Edition: | First edition |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis Klappentext |
| Item Description: | Hier auch später erschienene, unveränderte Nachdrucke |
| Physical Description: | XVI, 529 Seiten Illustrationen, Diagramme, Karten |
| ISBN: | 9781491912058 1491912057 |
Staff View
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| adam_text | Table of Contents
Preface................................................................ xi
1. IPython: Beyond Normal Python..........................................1
Shell or Notebook? 2
Launching the IPython Shell 2
Launching the Jupyter Notebook 2
Help and Documentation in IPython 3
Accessing Documentation with ? 3
Accessing Source Code with ?? 5
Exploring Modules with Tab Completion 6
Keyboard Shortcuts in the IPython Shell 8
Navigation Shortcuts 8
Text Entry Shortcuts 9
Command History Shortcuts 9
Miscellaneous Shortcuts 10
IPython Magic Commands 10
Pasting Code Blocks: %paste and %cpaste 11
Running External Code: %run 12
Timing Code Execution: %timeit 12
Help on Magic Functions: ?, %magic, and %lsmagic 13
Input and Output History 13
IPythons In and Out Objects 13
Underscore Shortcuts and Previous Outputs 15
Suppressing Output 15
Related Magic Commands 16
IPython and Shell Commands 16
Quick Introduction to the Shell 16
Shell Commands in IPython 18
iii
Passing Values to and from the Shell 18
Shell-Related Magic Commands 19
Errors and Debugging 20
Controlling Exceptions: %xmode 20
Debugging: When Reading Tracebacks Is Not Enough 22
Profiling and Timing Code 25
Timing Code Snippets: %timeit and %time 25
Profiling Full Scripts: %prun 27
Line-by-Line Profiling with %lprun 28
Profiling Memory Use: %memit and %mprun 29
More IPython Resources 30
Web Resources 30
Books 31
2. Introduction to NumPy................................................... 33
Understanding Data Types in Python 34
A Python Integer Is More Than Just an Integer 35
A Python List Is More Than Just a List 37
Fixed-Type Arrays in Python 38
Creating Arrays from Python Lists 39
Creating Arrays from Scratch 39
NumPy Standard Data Types 41
The Basics of NumPy Arrays 42
NumPy Array Attributes 42
Array Indexing: Accessing Single Elements 43
Array Slicing: Accessing Subarrays 44
Reshaping of Arrays 47
Array Concatenation and Splitting 48
Computation on NumPy Arrays: Universal Functions 50
The Slowness of Loops 50
Introducing UFuncs 51
Exploring NumPy’s UFuncs 52
Advanced Ufunc Features 56
Ufuncs: Learning More 58
Aggregations: Min, Max, and Everything in Between 58
Summing the Values in an Array 59
Minimum and Maximum 59
Example: What Is the Average Height of US Presidents? 61
Computation on Arrays: Broadcasting 63
Introducing Broadcasting 63
Rules of Broadcasting • 65
Broadcasting in Practice 68
iv | Table of Contents
Comparisons, Masks, and Boolean Logic 70
Example: Counting Rainy Days 70
Comparison Operators as ufuncs 71
Working with Boolean Arrays 73
Boolean Arrays as Masks 75
Fancy Indexing 78
Exploring Fancy Indexing 79
Combined Indexing 80
Example: Selecting Random Points 81
Modifying Values with Fancy Indexing 82
Example; Binning Data 83
Sorting Arrays 85
Fast Sorting in NumPy: np.sort and np.argsort 86
Partial Sorts: Partitioning 88
Example: k-Nearest Neighbors 88
Structured Data: NumPy’s Structured Arrays 92
Creating Structured Arrays 94
More Advanced Compound Types 95
RecordArrays: Structured Arrays with a Twist 96
On to Pandas 96
3. Data Manipulation with Pandas..................................... 97
Installing and Using Pandas 97
Introducing Pandas Objects 98
The Pandas Series Object 99
The Pandas DataFrame Object 102
The Pandas Index Object 105
Data Indexing and Selection 107
Data Selection in Series 107
Data Selection in DataFrame 110
Operating on Data in Pandas 115
Ufuncs: Index Preservation 115
UFuncs: Index Alignment 116
Ufuncs: Operations Between DataFrame and Series 118
Handling Missing Data 119
Trade-Offs in Missing Data Conventions 120
Missing Data in Pandas 120
Operating on Null Values 124
Hierarchical Indexing 128
A Multiply Indexed Series 128
Methods of Multiindex Creation 131
Indexing and Slicing a Multiindex 134
Table of Contents | v
Rearranging Multi-Indices
Data Aggregations on Multi-Indices
Combining Datasets: Concat and Append
Recall: Concatenation of NumPy Arrays
Simple Concatenation with pdxoncat
Combining Datasets: Merge and Join
Relational Algebra
Categories of Joins
Specification of the Merge Key
Specifying Set Arithmetic for Joins
Overlapping Column Names: The suffixes Keyword
Example: US States Data
Aggregation and Grouping
Planets Data
Simple Aggregation in Pandas
GroupBy: Split, Apply, Combine
Pivot Tables
Motivating Pivot Tables
Pivot Tables by Hand
Pivot Table Syntax
Example: Birthrate Data
Vectorized String Operations
Introducing Pandas String Operations
Tables of Pandas String Methods
Example: Recipe Database
Working with Time Series
Dates and Times in Python
Pandas Time Series: Indexing by Time
Pandas Time Series Data Structures
Frequencies and Offsets
Resampling, Shifting, and Windowing
Where to Learn More
Example: Visualizing Seattle Bicycle Counts
High-Performance Pandas: eval() and query()
Motivating queryO and eval(): Compound Expressions
pandas.evalO for Efficient Operations
DataFrame.eval() for Column-Wise Operations
DataFrame.queryO Method
Performance: When to Use These Functions
Further Resources
137
140
141
142
142
146
146
147
149
152
153
154
158
159
159
161
170
170
171
171
174
178
178
180
184
188
188
192
193
195
196
202
202
208
209
210
211
213
214
215
vi | Table of Contents
4. Visualization with Matplotlib......................................... 217
General Matplotlib Tips 218
Importing matplotlib 218
Setting Styles 218
show() or No show()? How to Display Your Plots 218
Saving Figures to File 221
Two Interfaces for the Price of One 222
Simple Line Plots 224
Adjusting the Plot: Line Colors and Styles 226
Adjusting the Plot: Axes Limits 228
Labeling Plots 230
Simple Scatter Plots 233
Scatter Plots with pltplot 233
Scatter Plots with plt.scatter 235
plot Versus scatter: A Note on Efficiency 237
Visualizing Errors 237
Basic Errorbars 238
Continuous Errors 239
Density and Contour Plots 241
Visualizing a Three-Dimensional Function 241
Histograms, Binnings, and Density 245
Two-Dimensional Histograms and Binnings 247
Customizing Plot Legends 249
Choosing Elements for the Legend 251
Legend for Size of Points 252
Multiple Legends 254
Customizing Colorbars 255
Customizing Colorbars 256
Example: Handwritten Digits 261
Multiple Subplots 262
pltaxes: Subplots by Hand 263
pltsubplot: Simple Grids of Subplots 264
pltsubplots: The Whole Grid in One Go 265
pltGridSpec: More Complicated Arrangements 266
Text and Annotation 268
Example: Effect of Holidays on US Births 269
Transforms and Text Position 270
Arrows and Annotation 272
Customizing Ticks 275
Major and Minor Ticks 276
Hiding Ticks or Labels 277
Reducing or Increasing the Number of Ticks 278
Table of Contents | vii
Fancy Tick Formats 279
Summary of Formatters and Locators 281
Customizing Matplotlib: Configurations and Stylesheets 282
Plot Customization by Hand 282
Changing the Defaults: rcParams 284
Stylesheets 285
Three-Dimensional Plotting in Matplotlib 290
Three-Dimensional Points and Lines 291
Three-Dimensional Contour Plots 292
Wireframes and Surface Plots 293
Surface Triangulations 295
Geographic Data with Basemap 298
Map Projections 300
Drawing a Map Background 304
Plotting Data on Maps 307
Example: California Cities 308
Example: Surface Temperature Data 309
Visualization with Seaborn 311
Seaborn Versus Matplotlib 312
Exploring Seaborn Plots 313
Example: Exploring Marathon Finishing Times 322
Further Resources 329
Matplotlib Resources 329
Other Python Graphics Libraries 330
5. Machine Learning....................................................... 331
What Is Machine Learning? 332
Categories of Machine Learning 332
Qualitative Examples of Machine Learning Applications 333
Summary 342
Introducing Scikit-Learn 343
Data Representation in Scikit-Learn 343
Scikit-Learn’s Estimator API 346
Application: Exploring Handwritten Digits 354
Summary 359
Hyperparameters and Model Validation 359
Thinking About Model Validation 359
Selecting the Best Model 363
Learning Curves 370
Validation in Practice: Grid Search 373
Summary ’ 375
Feature Engineering 375
viii I Table of Contents
Categorical Features 376
Text Features 377
Image Features 378
Derived Features 378
Imputation of Missing Data 381
Feature Pipelines 381
In Depth: Naive Bayes Classification 382
Bayesian Classification 383
Gaussian Naive Bayes 383
Multinomial Naive Bayes 386
When to Use Naive Bayes 389
In Depth: Linear Regression 390
Simple Linear Regression 390
Basis Function Regression 392
Regularization - 396
Example: Predicting Bicycle Traffic 400
In-Depth: Support Vector Machines 405
Motivating Support Vector Machines 405
Support Vector Machines: Maximizing the Margin 407
Example: Face Recognition 416
Support Vector Machine Summary 420
In-Depth: Decision Trees and Random Forests 421
Motivating Random Forests: Decision Trees 421
Ensembles of Estimators: Random Forests 426
Random Forest Regression 428
Example: Random Forest for Classifying Digits 430
Summary of Random Forests 432
In Depth: Principal Component Analysis 433
Introducing Principal Component Analysis 433
PCA as Noise Filtering 440
Example: Eigenfaces 442
Principal Component Analysis Summary 445
In-Depth: Manifold Learning 445
Manifold Learning: “HELLO” 446
Multidimensional Scaling (MDS) 447
MDS as Manifold Learning 450
Nonlinear Embeddings: Where MDS Fails 452
Nonlinear Manifolds: Locally Linear Embedding 453
Some Thoughts on Manifold Methods 455
Example: Isomap on Faces 456
Example: Visualizing Structure in Digits 460
In Depth: k-Means Clustering 462
Table of Contents | ix
Introducing k-Means 463
k-Means Algorithm: Expectation-Maximization 465
Examples 470
In Depth: Gaussian Mixture Models 476
Motivating GMM: Weaknesses of k-Means 477
Generalizing E-M: Gaussian Mixture Models 480
GMM as Density Estimation 484
Example: GMM for Generating New Data 488
In-Depth: Kernel Density Estimation 491
Motivating KDE: Histograms 491
Kernel Density Estimation in Practice 496
Example: KDE on a Sphere 498
Example: Not-So-Naive Bayes 501
Application: A Face Detection Pipeline 506
HOG Features 506
HOG in Action: A Simple Face Detector 507
Caveats and Improvements 512
Further Machine Learning Resources 514
Machine Learning in Python 514
General Machine Learning 515
Index.................................................................. 517
x | Table of Contents
O REILLY
Python Data Science Handbook
For many researchers, Python is a first-class tool mainly 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 Python Data Science Handbook do you get them alHPython,
NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.
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 to use:
■ IPython and Jupyter: provide computational environments for
data scientists using Python
■ NumPy: includes the ndarray for efficient storage and
manipulation of dense data arrays in Python
i Pandas: features the DataFrame for efficient storage and
manipulation of labeled/columnar data in Python
■ Matplotlib: includes capabilities for a flexible range of data
visualizations in Python
Scikit-Learn: for efficient and clean Python implementations of
the most important and established machine learning algorithms
u If you want to learn data
science with Python,
this book is a fantastic
starting point. I ve used
it with great success to
teach computer science
and statistics majors.
Jake goes far beyond
the basics of open
source tools; he also
explains the underly-
ing concepts, patterns,
and abstractions of
data science using clear
language and approach-
able explanations.”
-Brian Granger
Associate Professor of Physics,
Cal Poly; cofounder of Project Jupyter
Jake VanderPlas, a long-time user and developer of the Python scientific stack,
currently works as an interdisciplinary research director at the University of
Washington, He conducts his own astronomy research, and spends time advising
and consulting with local scientists from a wide range of fields.
|
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| author | VanderPlas, Jake |
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| discipline | Informatik Wirtschaftswissenschaften |
| edition | First edition |
| format | Book |
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| id | DE-604.BV043634402 |
| illustrated | Illustrated |
| indexdate | 2025-02-20T06:44:49Z |
| institution | BVB |
| isbn | 9781491912058 1491912057 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029048362 |
| oclc_num | 967942274 |
| open_access_boolean | |
| owner | DE-634 DE-20 DE-473 DE-BY-UBG DE-523 DE-739 DE-11 DE-355 DE-BY-UBR DE-Aug4 DE-19 DE-BY-UBM DE-29T DE-83 DE-898 DE-BY-UBR DE-703 DE-B768 DE-706 DE-862 DE-BY-FWS DE-573 |
| owner_facet | DE-634 DE-20 DE-473 DE-BY-UBG DE-523 DE-739 DE-11 DE-355 DE-BY-UBR DE-Aug4 DE-19 DE-BY-UBM DE-29T DE-83 DE-898 DE-BY-UBR DE-703 DE-B768 DE-706 DE-862 DE-BY-FWS DE-573 |
| physical | XVI, 529 Seiten Illustrationen, Diagramme, Karten |
| publishDate | 2016 |
| publishDateSearch | 2016 |
| publishDateSort | 2016 |
| publisher | O'Reilly |
| record_format | marc |
| spellingShingle | VanderPlas, Jake Python data science handbook essential tools for working with data Python Programmiersprache (DE-588)4434275-5 gnd Datenanalyse (DE-588)4123037-1 gnd Data Science (DE-588)1140936166 gnd Datenmanagement (DE-588)4213132-7 gnd Data Mining (DE-588)4428654-5 gnd |
| subject_GND | (DE-588)4434275-5 (DE-588)4123037-1 (DE-588)1140936166 (DE-588)4213132-7 (DE-588)4428654-5 |
| title | Python data science handbook essential tools for working with data |
| title_auth | Python data science handbook essential tools for working with data |
| title_exact_search | Python data science handbook essential tools for working with data |
| title_full | Python data science handbook essential tools for working with data Jake VanderPlas |
| title_fullStr | Python data science handbook essential tools for working with data Jake VanderPlas |
| title_full_unstemmed | Python data science handbook essential tools for working with data Jake VanderPlas |
| title_short | Python data science handbook |
| title_sort | python data science handbook essential tools for working with data |
| title_sub | essential tools for working with data |
| topic | Python Programmiersprache (DE-588)4434275-5 gnd Datenanalyse (DE-588)4123037-1 gnd Data Science (DE-588)1140936166 gnd Datenmanagement (DE-588)4213132-7 gnd Data Mining (DE-588)4428654-5 gnd |
| topic_facet | Python Programmiersprache Datenanalyse Data Science Datenmanagement Data Mining |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029048362&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029048362&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT vanderplasjake pythondatasciencehandbookessentialtoolsforworkingwithdata |
Indexes
THWS Schweinfurt Zentralbibliothek Lesesaal
| Call Number: |
2000 ST 250 P99 V239 |
|---|---|
| Copy 1 | ausleihbar Available Place a Hold |