Python for data analysis: data wrangling with Pandas, NumPy, and IPython
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo
O'Reilly
October 2017
|
| Edition: | Second edition |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Hier auch später erschienene, unveränderte Nachdrucke |
| Physical Description: | xvi, 524 Seiten Illustrationen |
| ISBN: | 9781491957660 |
Staff View
MARC
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| adam_text | Table of Contents
Preface.......................................................... xi
1. Preliminaries....................................
1.1 What Is This Book About?
What Kinds of Data?
1.2 Why Python for Data Analysis?
Python as Glue
Solving the “Two-Language” Problem
Why Not Python?
1.3 Essential Python Libraries
NumPy
pandas
matplotlib
IPython and Jupyter
SciPy
scikit-learn
statsmodels
1.4 Installation and Setup
Windows
Apple (OS X, macOS)
GNU/Linux
Installing or Updating Python Packages
Python 2 and Python 3
Integrated Development Environments (IDEs) and Text Editors
1.5 Community and Conferences
1.6 Navigating This Book
Code Examples
Data for Examples
1
1
1
2
2
3
3
4
4
4
5
6
6
7
8
8
9
9
9
10
11
11
12
12
13
13
iii
Import Conventions 14
Jargon 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 18
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 38
Control Flow 46
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
diet 61
set 65
List, Set, and Diet Comprehensions 67
3.2 Functions 69
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 84
4. NumPy Basics: Arrays and Vectorized Computation...................... 85
4.1 The NumPy ndarray: A Multidimensional Array Object 87
iv | Table of Contents
Creating ndarrays
Data Types for ndarrays
Arithmetic with NumPy Arrays
Basic Indexing and Slicing
Boolean Indexing
Fancy Indexing
Transposing Arrays and Swapping Axes
4.2 Universal Functions: Fast Element-Wise Array Functions
4.3 Array-Oriented Programming with Arrays
Expressing Conditional Logic as Array Operations
Mathematical and Statistical Methods
Methods for Boolean Arrays
Sorting
Unique and Other Set Logic
4.4 File Input and Output with Arrays
4.5 Linear Algebra
4.6 Pseudorandom Number Generation
4.7 Example: Random Walks
Simulating Many Random Walks at Once
4.8 Conclusion
5. Getting Started with pandas...........................
5.1 Introduction to pandas Data Structures
Series
DataFrame
Index Objects
5.2 Essential Functionality
Reindexing
Dropping Entries from an Axis
Indexing, Selection, and Filtering
Integer Indexes
Arithmetic and Data Alignment
Function Application and Mapping
Sorting and Ranking
Axis Indexes with Duplicate Labels
5.3 Summarizing and Computing Descriptive Statistics
Correlation and Covariance
Unique Values, Value Counts, and Membership
5.4 Conclusion
6. Data Loading, Storage, and File Formats................
6.1 Reading and Writing Data in Text Format
Table of Contents
88
90
93
94
99
102
103
105
108
109
111
113
113
114
115
116
118
119
121
122
123
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136
136
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145
146
151
153
157
158
160
162
165
167
167
I 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 190
7. Data Cleaning and Preparation........................................... 191
7.1 Handling Missing Data 191
Filtering Out Missing Data 193
Filling In Missing Data 195
7.2 Data Transformation 197
Removing Duplicates 197
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 | 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 GroupBy 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....................................................... 319
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 350
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 364
12. Advanced pandas.............................................. 365
12.1 Categorical Data 365
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 383
viii | 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. AdvancedNumPy........................
A.l ndarray Object Internals
NumPy dtype Hierarchy
A.2 Advanced Array Manipulation
Reshaping Arrays
C Versus Fortran Order
Concatenating and Splitting Arrays
Repeating Elements: tile and repeat
Fancy Indexing Equivalents: take and put
A.3 Broadcasting
Broadcasting Over Other Axes
Setting Array Values by Broadcasting
A.4 Advanced ufunc Usage
ufunc Instance Methods
Writing New ufuncs in Python
A. 5 Structured and Record Arrays
451
451
452
453
454
456
456
459
461
462
464
467
468
468
470
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 482
The Importance of Contiguous Memory 482
B. More on the IPython System.................. ........................ 485
B. l Using the Command History 485
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 490
Timing Code: %time 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 | Table of Contents
|
| any_adam_object | 1 |
| author | McKinney, Wes 1985- |
| author_GND | (DE-588)1028982925 |
| author_facet | McKinney, Wes 1985- |
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| ctrlnum | (OCoLC)1009931510 (DE-599)HBZHT019442991 |
| discipline | Informatik |
| edition | Second edition |
| format | Book |
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| institution | BVB |
| isbn | 9781491957660 |
| language | English |
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| physical | xvi, 524 Seiten Illustrationen |
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| spelling | McKinney, Wes 1985- Verfasser (DE-588)1028982925 aut Python for data analysis data wrangling with Pandas, NumPy, and IPython Wes McKinney Second edition Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo O'Reilly October 2017 xvi, 524 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier Hier auch später erschienene, unveränderte Nachdrucke Data Mining (DE-588)4428654-5 gnd rswk-swf Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Programmbibliothek (DE-588)4121521-7 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Python Programmiersprache (DE-588)4434275-5 s Programmbibliothek (DE-588)4121521-7 s Datenanalyse (DE-588)4123037-1 s Data Mining (DE-588)4428654-5 s DE-604 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029935746&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | McKinney, Wes 1985- Python for data analysis data wrangling with Pandas, NumPy, and IPython Data Mining (DE-588)4428654-5 gnd Python Programmiersprache (DE-588)4434275-5 gnd Programmbibliothek (DE-588)4121521-7 gnd Datenanalyse (DE-588)4123037-1 gnd |
| subject_GND | (DE-588)4428654-5 (DE-588)4434275-5 (DE-588)4121521-7 (DE-588)4123037-1 |
| title | Python for data analysis data wrangling with Pandas, NumPy, and IPython |
| title_auth | Python for data analysis data wrangling with Pandas, NumPy, and IPython |
| title_exact_search | Python for data analysis data wrangling with Pandas, NumPy, and IPython |
| title_full | Python for data analysis data wrangling with Pandas, NumPy, and IPython Wes McKinney |
| title_fullStr | Python for data analysis data wrangling with Pandas, NumPy, and IPython Wes McKinney |
| title_full_unstemmed | Python for data analysis data wrangling with Pandas, NumPy, and IPython Wes McKinney |
| title_short | Python for data analysis |
| title_sort | python for data analysis data wrangling with pandas numpy and ipython |
| title_sub | data wrangling with Pandas, NumPy, and IPython |
| topic | Data Mining (DE-588)4428654-5 gnd Python Programmiersprache (DE-588)4434275-5 gnd Programmbibliothek (DE-588)4121521-7 gnd Datenanalyse (DE-588)4123037-1 gnd |
| topic_facet | Data Mining Python Programmiersprache Programmbibliothek Datenanalyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029935746&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT mckinneywes pythonfordataanalysisdatawranglingwithpandasnumpyandipython |