Python for data analysis: data wrangling with Pandas, NumPy, and IPython
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo
O'Reilly
2018-09-21
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| Ausgabe: | Second edition, fourth release |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xvi, 528 Seiten Illustrationen, Diagramme |
| ISBN: | 9781491957660 |
Internformat
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Datensatz im Suchindex
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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 (Hue 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 PythonPackages 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 Jargon 14 14 2. Python Language Basics, (Python, and Jupyter Notebooks.................................... 15 2.1 The Python Interpreter 2.2 IPython Basics Running the IPython Shell Running the Jupyter Notebook Tab Completion Introspection The %run Command Executing Code from the Clipboard Terminal Keyboard Shortcuts About Magic Commands Matplotlib Integration 2.3 Python Language Basics Language Semantics Scalar Types Control Flow 16 17 17 18 21 23 25 26 27 28 29 30 30 38 46 3. Built-in Data Structures, Fundions, and Files.................................................. 51 3.1 Data Structures and Sequences Tuple List Built-in Sequence Functions diet set List, Set, and Diet Comprehensions 3.2 Functions Namespaces, Scope, and Local Functions Returning Multiple Values Functions Are Objects Anonymous (Lambda) Functions Currying: Partial Argument Application Generators Errors and Exception Handling 3.3 Files and the Operating System Bytes and Unicode with Files 3.4 Conclusion 51 51 54 59 61 65 67 69 70 71 72 73 74 75 77 80 83 85 4. NumPy Basics: Arrays and Vedorized Computation........................................... 87 4.1 The NumPy ndarray: A Multidimensional Array Object iv I Table of Contents 89
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 90 92 95 96 101 104 105 107 110 111 113 115 115 116 117 118 120 121 123 124 5. Getting Started with pandas................................................................ 125 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 126 126 130 136 138 138 140 142 147 148 154 156 159 160 163 165 167 6. Data Loading, Storage, and File Formats................................................... 169 6.1 Reading and Writing Data in Text Format 169 Table of Contents | v
Reading Text Files in Pieces Writing Data to Text Format Working with Delimited Formats JSON Data XML and HTML: Web Scraping 6.2 Binary Data Formats Using HDF 5 Format Reading Microsoft Excel Files 6.3 Interacting with Web APIs 6.4 Interacting with Databases 6.5 Conclusion 175 176 178 180 181 185 186 188 189 191 193 7. Data Cleaning and Preparation................................................................... 195 7.1 Handling Missing Data Filtering Out Missing Data Filling In Missing Data 7.2 Data Transformation Removing Duplicates Transforming Data Using a Function or Mapping Replacing Values Renaming Axis Indexes Discretization and Binning Detecting and Filtering Outliers Permutation and Random Sampling Computing Indicator/Dummy Variables 7.3 String Manipulation String Object Methods Regular Expressions Vectorized String Functions in pandas 7.4 Conclusion 8. Data Wrangling: Join, Combine, and Reshape............................................... 8.1 Hierarchical Indexing Reordering and Sorting Levels Summary Statistics by Level Indexing with a DataFrame’s columns 8.2 Combining and Merging Datasets Database-Style DataFrame Joins Merging on Index Concatenating Along an Axis Combining Data with Overlap 8.3 Reshaping and Pivoting vi I Table of Contents 195 197 199 201 201 202 204 205 207 209 210 212 215 215 217 220 223 225 225 228 229 230 231 231 236 240 245 246
Reshaping with Hierarchical Indexing Pivoting “Long” to “Wide” Format Pivoting “Wide” to “Long” Format 8.4 Conclusion 247 250 253 255 9. Plotting and Visualization........................................................................ 257 9.1 A Brief matplotlib API Primer Figures and Subplots Colors, Markers, and Line Styles Ticks, Labels, and Legends Annotations and Drawing on a Subplot Saving Plots to File matplotlib Configuration 9.2 Plotting with pandas and seaborn Line Plots Bar Plots Histograms and Density Plots Scatter or Point Plots Facet Grids and Categorical Data 9.3 Other Python Visualization Tools 9.4 Conclusion 258 259 264 266 270 272 273 273 274 277 283 286 289 291 292 10. Data Aggregation and Group Operations........................................................ 293 10.1 GroupBy Mechanics Iterating Over Groups Selecting a Column or Subset of Columns Grouping with Diets and Series Grouping with Functions Grouping by Index Levels 10.2 Data Aggregation Column-Wise and Multiple Function Application Returning Aggregated Data Without Row Indexes 10.3 Apply: General split-apply-combine Suppressing the Group Keys Quantile and Bucket Analysis Example: Filling Missing Values with Group-Specific Values Example: Random Sampling and Permutation Example: Group Weighted Average and Correlation Example: Group-Wise Linear Regression 10.4 Pivot Tables and Cross-Tabulation Cross-Tabulations: Crosstab 10.5 Conclusion 294 297 299 300 301 301 302 304 307 308 310 311 312 314 316 318 319 321 322 Table of Contents | vii
11. Timeseries................................................................................................. 11.1 Date and Time Data Types and Tools Converting Between String and Datetime 11.2 Time Series Basics Indexing, Selection, Subsetting Time Series with Duplicate Indices 11.3 Date Ranges, Frequencies, and Shifting Generating Date Ranges Frequencies and Date Offsets Shifting (Leading and Lagging) Data 11.4 Time Zone Handling Time Zone Localization and Conversion Operations with Time Zone-Aware Timestamp Objects Operations Between Different Time Zones 11.5 Periods and Period Arithmetic Period Frequency Conversion Quarterly Period Frequencies Converting Timestamps to Periods (and Back) Creating a Periodindex from Arrays 11.6 Resampling and Frequency Conversion Downsampling Upsampling and Interpolation Resampling with Periods 11.7 Moving Window Functions Exponentially Weighted Functions Binary Moving Window Functions User-Defined Moving Window Functions 11.8 Conclusion 324 325 328 329 332 333 334 336 338 341 341 344 345 345 346 348 350 351 354 355 358 359 360 364 365 367 368 12. Advanced pandas................................................................................... 369 12.1 Categorical Data Background and Motivation Categorical Type in pandas Computations with Categoricals Categorical Methods 12.2 Advanced GroupBy Use Group Transforms and “Unwrapped” GroupBys Grouped Time Resampling 12.3 Techniques for Method Chaining The pipe Method 12.4 Conclusion viii ļ Table of Contents 369 369 371 373 376 379 379 383 384 386 387
13. Introduction to Modeling Libraries in Python................................................ 389 13.1 Interfacing Between pandas and Model Code 13.2 Creating Model Descriptions with Patsy Data Transformations in Patsy Formulas Categorical Data and Patsy 13.3 Introduction to statsmodels Estimating Linear Models Estimating Time Series Processes 13.4 Introduction to scikit-learn 13.5 Continuing Your Education 389 392 395 396 399 399 402 403 407 14. Data Analysis Examples.......................................................................... 409 14.1 l.USA.gov Data from Bitly Counting Time Zones in Pure Python Counting Time Zones with pandas 14.2 MovieLens IM Dataset Measuring Rating Disagreement 14.3 US Baby Names 1880-2010 Analyzing Naming Trends 14.4 USDA Food Database 14.5 2012 Federal Election Commission Database Donation Statistics by Occupation and Employer Bucketing Donation Amounts Donation Statistics by State 14.6 Conclusion 409 410 412 419 424 425 431 440 446 448 451 453 454 A. Advanced NumPy..................................................................................... 455 455 456 457 458 460 460 463 465 466 468 471 472 472 474 475 A. 1 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
Table of Contents | ix
Nested dtypes and Multidimensional Fields Why Use Structured Arrays? A.6 More About Sorting Indirect Sorts: argsort and lexsort Alternative Sort Algorithms Partially Sorting Arrays numpy.searchsorted: Finding Elements in a Sorted Array A.7 Writing Fast NumPy Functions with Numba Creating Custom numpy.ufunc Objects with Numba A.8 Advanced Array Input and Output Memory-Mapped Files HDF5 and Other Array Storage Options A.9 Performance Tips The Importance of Contiguous Memory B. More on the IPython System................................................................. B.l Using the Command History Searching and Reusing the Command History Input and Output Variables B.2 Interacting with the Operating System Shell Commands and Aliases Directory Bookmark System B.3 Software Development Tools Interactive Debugger Timing Code: %time and %timeit Basic Profiling: %prun and %run -p Profiling a Function Line by Line B.4 Tips for Productive Code Development Using IPython Reloading Module Dependencies Code Design Tips B.5 Advanced IPython Features Making Your Own Classes IPython-Friendly Profiles and Configuration B.6 Conclusion 475 476 477 478 48o 48θ 481 482 484 484 484 486 486 486 489 489 489 490 491 492 493 493 494 498 500 502 504 504 505 506 507 507 509 Index............................................................................................... 511 x I Table of Contents
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| adam_txt |
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 (Hue 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 PythonPackages 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 Jargon 14 14 2. Python Language Basics, (Python, and Jupyter Notebooks. 15 2.1 The Python Interpreter 2.2 IPython Basics Running the IPython Shell Running the Jupyter Notebook Tab Completion Introspection The %run Command Executing Code from the Clipboard Terminal Keyboard Shortcuts About Magic Commands Matplotlib Integration 2.3 Python Language Basics Language Semantics Scalar Types Control Flow 16 17 17 18 21 23 25 26 27 28 29 30 30 38 46 3. Built-in Data Structures, Fundions, and Files. 51 3.1 Data Structures and Sequences Tuple List Built-in Sequence Functions diet set List, Set, and Diet Comprehensions 3.2 Functions Namespaces, Scope, and Local Functions Returning Multiple Values Functions Are Objects Anonymous (Lambda) Functions Currying: Partial Argument Application Generators Errors and Exception Handling 3.3 Files and the Operating System Bytes and Unicode with Files 3.4 Conclusion 51 51 54 59 61 65 67 69 70 71 72 73 74 75 77 80 83 85 4. NumPy Basics: Arrays and Vedorized Computation. 87 4.1 The NumPy ndarray: A Multidimensional Array Object iv I Table of Contents 89
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 90 92 95 96 101 104 105 107 110 111 113 115 115 116 117 118 120 121 123 124 5. Getting Started with pandas. 125 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 126 126 130 136 138 138 140 142 147 148 154 156 159 160 163 165 167 6. Data Loading, Storage, and File Formats. 169 6.1 Reading and Writing Data in Text Format 169 Table of Contents | v
Reading Text Files in Pieces Writing Data to Text Format Working with Delimited Formats JSON Data XML and HTML: Web Scraping 6.2 Binary Data Formats Using HDF 5 Format Reading Microsoft Excel Files 6.3 Interacting with Web APIs 6.4 Interacting with Databases 6.5 Conclusion 175 176 178 180 181 185 186 188 189 191 193 7. Data Cleaning and Preparation. 195 7.1 Handling Missing Data Filtering Out Missing Data Filling In Missing Data 7.2 Data Transformation Removing Duplicates Transforming Data Using a Function or Mapping Replacing Values Renaming Axis Indexes Discretization and Binning Detecting and Filtering Outliers Permutation and Random Sampling Computing Indicator/Dummy Variables 7.3 String Manipulation String Object Methods Regular Expressions Vectorized String Functions in pandas 7.4 Conclusion 8. Data Wrangling: Join, Combine, and Reshape. 8.1 Hierarchical Indexing Reordering and Sorting Levels Summary Statistics by Level Indexing with a DataFrame’s columns 8.2 Combining and Merging Datasets Database-Style DataFrame Joins Merging on Index Concatenating Along an Axis Combining Data with Overlap 8.3 Reshaping and Pivoting vi I Table of Contents 195 197 199 201 201 202 204 205 207 209 210 212 215 215 217 220 223 225 225 228 229 230 231 231 236 240 245 246
Reshaping with Hierarchical Indexing Pivoting “Long” to “Wide” Format Pivoting “Wide” to “Long” Format 8.4 Conclusion 247 250 253 255 9. Plotting and Visualization. 257 9.1 A Brief matplotlib API Primer Figures and Subplots Colors, Markers, and Line Styles Ticks, Labels, and Legends Annotations and Drawing on a Subplot Saving Plots to File matplotlib Configuration 9.2 Plotting with pandas and seaborn Line Plots Bar Plots Histograms and Density Plots Scatter or Point Plots Facet Grids and Categorical Data 9.3 Other Python Visualization Tools 9.4 Conclusion 258 259 264 266 270 272 273 273 274 277 283 286 289 291 292 10. Data Aggregation and Group Operations. 293 10.1 GroupBy Mechanics Iterating Over Groups Selecting a Column or Subset of Columns Grouping with Diets and Series Grouping with Functions Grouping by Index Levels 10.2 Data Aggregation Column-Wise and Multiple Function Application Returning Aggregated Data Without Row Indexes 10.3 Apply: General split-apply-combine Suppressing the Group Keys Quantile and Bucket Analysis Example: Filling Missing Values with Group-Specific Values Example: Random Sampling and Permutation Example: Group Weighted Average and Correlation Example: Group-Wise Linear Regression 10.4 Pivot Tables and Cross-Tabulation Cross-Tabulations: Crosstab 10.5 Conclusion 294 297 299 300 301 301 302 304 307 308 310 311 312 314 316 318 319 321 322 Table of Contents | vii
11. Timeseries. 11.1 Date and Time Data Types and Tools Converting Between String and Datetime 11.2 Time Series Basics Indexing, Selection, Subsetting Time Series with Duplicate Indices 11.3 Date Ranges, Frequencies, and Shifting Generating Date Ranges Frequencies and Date Offsets Shifting (Leading and Lagging) Data 11.4 Time Zone Handling Time Zone Localization and Conversion Operations with Time Zone-Aware Timestamp Objects Operations Between Different Time Zones 11.5 Periods and Period Arithmetic Period Frequency Conversion Quarterly Period Frequencies Converting Timestamps to Periods (and Back) Creating a Periodindex from Arrays 11.6 Resampling and Frequency Conversion Downsampling Upsampling and Interpolation Resampling with Periods 11.7 Moving Window Functions Exponentially Weighted Functions Binary Moving Window Functions User-Defined Moving Window Functions 11.8 Conclusion 324 325 328 329 332 333 334 336 338 341 341 344 345 345 346 348 350 351 354 355 358 359 360 364 365 367 368 12. Advanced pandas. 369 12.1 Categorical Data Background and Motivation Categorical Type in pandas Computations with Categoricals Categorical Methods 12.2 Advanced GroupBy Use Group Transforms and “Unwrapped” GroupBys Grouped Time Resampling 12.3 Techniques for Method Chaining The pipe Method 12.4 Conclusion viii ļ Table of Contents 369 369 371 373 376 379 379 383 384 386 387
13. Introduction to Modeling Libraries in Python. 389 13.1 Interfacing Between pandas and Model Code 13.2 Creating Model Descriptions with Patsy Data Transformations in Patsy Formulas Categorical Data and Patsy 13.3 Introduction to statsmodels Estimating Linear Models Estimating Time Series Processes 13.4 Introduction to scikit-learn 13.5 Continuing Your Education 389 392 395 396 399 399 402 403 407 14. Data Analysis Examples. 409 14.1 l.USA.gov Data from Bitly Counting Time Zones in Pure Python Counting Time Zones with pandas 14.2 MovieLens IM Dataset Measuring Rating Disagreement 14.3 US Baby Names 1880-2010 Analyzing Naming Trends 14.4 USDA Food Database 14.5 2012 Federal Election Commission Database Donation Statistics by Occupation and Employer Bucketing Donation Amounts Donation Statistics by State 14.6 Conclusion 409 410 412 419 424 425 431 440 446 448 451 453 454 A. Advanced NumPy. 455 455 456 457 458 460 460 463 465 466 468 471 472 472 474 475 A. 1 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
Table of Contents | ix
Nested dtypes and Multidimensional Fields Why Use Structured Arrays? A.6 More About Sorting Indirect Sorts: argsort and lexsort Alternative Sort Algorithms Partially Sorting Arrays numpy.searchsorted: Finding Elements in a Sorted Array A.7 Writing Fast NumPy Functions with Numba Creating Custom numpy.ufunc Objects with Numba A.8 Advanced Array Input and Output Memory-Mapped Files HDF5 and Other Array Storage Options A.9 Performance Tips The Importance of Contiguous Memory B. More on the IPython System. B.l Using the Command History Searching and Reusing the Command History Input and Output Variables B.2 Interacting with the Operating System Shell Commands and Aliases Directory Bookmark System B.3 Software Development Tools Interactive Debugger Timing Code: %time and %timeit Basic Profiling: %prun and %run -p Profiling a Function Line by Line B.4 Tips for Productive Code Development Using IPython Reloading Module Dependencies Code Design Tips B.5 Advanced IPython Features Making Your Own Classes IPython-Friendly Profiles and Configuration B.6 Conclusion 475 476 477 478 48o 48θ 481 482 484 484 484 486 486 486 489 489 489 490 491 492 493 493 494 498 500 502 504 504 505 506 507 507 509 Index. 511 x I Table of Contents |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | McKinney, Wes 1985- |
| author_GND | (DE-588)1028982925 |
| author_facet | McKinney, Wes 1985- |
| author_role | aut |
| author_sort | McKinney, Wes 1985- |
| author_variant | w m wm |
| building | Verbundindex |
| bvnumber | BV047403045 |
| classification_rvk | ST 250 |
| ctrlnum | (OCoLC)1159228671 (DE-599)BSZ520332245 |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| edition | Second edition, fourth release |
| format | Book |
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| id | DE-604.BV047403045 |
| illustrated | Illustrated |
| index_date | 2024-07-03T17:53:12Z |
| indexdate | 2024-07-10T09:11:09Z |
| institution | BVB |
| isbn | 9781491957660 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032804077 |
| oclc_num | 1159228671 |
| open_access_boolean | |
| owner | DE-703 DE-355 DE-BY-UBR |
| owner_facet | DE-703 DE-355 DE-BY-UBR |
| physical | xvi, 528 Seiten Illustrationen, Diagramme |
| publishDate | 2018 |
| publishDateSearch | 2018 |
| publishDateSort | 2018 |
| publisher | O'Reilly |
| record_format | marc |
| spelling | McKinney, Wes 1985- Verfasser (DE-588)1028982925 aut Python for data analysis data wrangling with Pandas, NumPy, and IPython Wes McKinney Second edition, fourth release Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo O'Reilly 2018-09-21 xvi, 528 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier 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 Data Mining (DE-588)4428654-5 gnd rswk-swf Python (Computer program language) Programming languages (Electronic computers) Data mining 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 Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032804077&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 Python Programmiersprache (DE-588)4434275-5 gnd Programmbibliothek (DE-588)4121521-7 gnd Datenanalyse (DE-588)4123037-1 gnd Data Mining (DE-588)4428654-5 gnd |
| subject_GND | (DE-588)4434275-5 (DE-588)4121521-7 (DE-588)4123037-1 (DE-588)4428654-5 |
| 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_exact_search_txtP | 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 | Python Programmiersprache (DE-588)4434275-5 gnd Programmbibliothek (DE-588)4121521-7 gnd Datenanalyse (DE-588)4123037-1 gnd Data Mining (DE-588)4428654-5 gnd |
| topic_facet | Python Programmiersprache Programmbibliothek Datenanalyse Data Mining |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032804077&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT mckinneywes pythonfordataanalysisdatawranglingwithpandasnumpyandipython |