Python for data analysis: data wrangling with pandas, NumPy, and Jupyter
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo
O'Reilly
August 2022
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| Ausgabe: | Third edition |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xvi, 561 Seiten Illustrationen, Diagramme |
| ISBN: | 9781098104030 109810403X |
Internformat
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Datensatz im Suchindex
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Table of Contents Preface. xi 1. Preliminaries. 1 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 Other Packages 1.4 Installation and Setup Miniconda on Windows GNU/Linux Miniconda on macOS Installing Necessary Packages Integrated Development Environments and Text Editors 1.5 Community and Conferences 1.6 Navigating This Book Code Examples 1 1 2 3 3 3 4 4 5 6 6 7 8 8 9 9 9 Ю 11 11 12 13 14 15
Data for Examples Import Conventions Python Language Basics, I Python, ana jupyier nioieouuw. 2.1 The Python Interpreter 2.2 IPython Basics Running the IPython Shell Running the Jupyter Notebook Tab Completion Introspection 2.3 Python Language Basics Language Semantics Scalar Types Control Flow 2.4 Conclusion 15 16 .17 iß IQ 1Q 20 ¿3 26 26 34 42 45 Built-In Data Structures, Fundions, and Files. . 47 47 3.1 Data Structures and Sequences 47 Tuple 51 List 55 Dictionary 59 Set 62 Built-In Sequence Functions 63 List, Set, and Dictionary Comprehensions 3.2 Functions 65 Namespaces, Scope, and Local Functions 67 Returning Multiple Values 68 Functions Are Objects 69 Anonymous (Lambda) Functions 70 Generators 71 Errors and Exception Handling 74 3.3 Files and the Operating System 76 Bytes and Unicode with Files 80 3.4 Conclusion 82 NumPy Basics: Arrays and Vectorized Computation. . 83 4.1 The NumPy ndarray: A Multidimensional Array Object 85 Creating ndarrays 86 Data Types for ndarrays 88 Arithmetic with NumPy Arrays 91 Basic Indexing and Slicing 92 iv I Table of Contents
Boolean Indexing Fancy Indexing Transposing Arrays and Swapping Axes 4.2 Pseudorandom Number Generation 4.3 Universal Functions: Fast Element-Wise Array Functions 4.4 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 4.5 File Input and Output with Arrays 4.6 Linear Algebra 4.7 Example: Random Walks Simulating Many Random Walks at Once 4.8 Conclusion 97 юо 102 103 105 108 110 111 113 114 115 116 116 118 120 121 5. Getting Started with pandas. 123 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 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 124 124 129 136 138 138 141 142 152 158 160 164 165 168 170 173 6. Data Loading, Storage, and File Formats. 175 6.1 Reading and Writing Data in Text Format Reading Text Files in Pieces Writing Data to Text Format Working with Other Delimited Formats JSONData 175 182 184 185 187 Table of Contents | v
XML and HTML: Web Scraping 6.2 Binary Data Formats Reading Microsoft Excel Files Using HDF5 Format 6.3 Interacting with Web APIs 6.4 Interacting with Databases 6.5 Conclusion ļ $ 7. Data Cleaning and Preparation. 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 Extension Data Types 7.4 String Manipulation Python Built-In String Object Methods Regular Expressions String Functions in pandas 7.5 Categorical Data Background and Motivation Categorical Extension Type in pandas Computations with Categoricals Categorical Methods 7.6 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 vi I Table of Contents 205 2θ2 2θ$ 2θ$ 211 212 214 215 217 219 221 224 227 227 229 232 235 236 237 240 242 245 247 247 250 շ5 ļ շ5շ 253 254 э Q
Concatenating Along an Axis Combining Data with Overlap 8.3 Reshaping and Pivoting Reshaping with Hierarchical Indexing Pivoting “Long” to “Wide” Format Pivoting “Wide” to “Long” Format 8.4 Conclusion 263 268 270 270 273 277 279 9. Plotting and Visualization. 281 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 282 283 288 290 294 296 297 298 298 301 309 311 314 317 317 10. Data Aggregation and Group Operations. 319 10.1 How to Think About Group Operations Iterating over Groups Selecting a Column or Subset of Columns Grouping with Dictionaries 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 320 324 326 327 328 328 329 331 335 335 338 338 340 343 344 Table of Contents | vii
Example: Group-Wise Linear Regression 10.4 Group Transforms and “Unwrapped” GroupBys 10.5 Pivot Tables and Cross-Tabulation Cross-Tabulations: Crosstab 10.6 Conclusion 11. Time Series. 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 Grouped Time Resampling 11.7 Moving Window Functions Exponentially Weighted Functions Binary Moving Window Functions User-Defined Moving Window Functions 11.8 Conclusion 12. Introduction to Modeling Libraries in Python. 12.1 Interfacing Between pandas and Model Code 12.2 Creating Model Descriptions with Patsy Data Transformations in Patsy Formulas Categorical Data and Patsy viii I Table of Contents 347 347 351 354 355 . 357 358 359 361 363 365 366 367 370 371 374 375 377 378 379 380 382 384 385 387 388 391 392 394 396 399 401 402 403 . 405
105 108 110 112
12-3 btr^ Estimig į "6peries Processes Estimating T cikit-learn 12 4 Introduction to scik 415 415 419 420 423 12.5 Conclusion Data Analysis txample-1з 1 Bitly Data from 1 -USA.g ¿X Time Zones in Pure Python Counting Time Zones with pandas 13 2 MovieLens 1M Dataset Measuring Rating Disagreement 13.3 US Baby Names 1880-2010 Analyzing Naming Trends 13.4 USDA Food Database 13.5 2012 Federal Election Commission Database Donation Statistics by Occupation and Employer Bucketing Donation Amounts Donation Statistics by State 13.6 Conclusion A. Advanced NumPy. A. Indarray Object Internals NumPy Data Typo Hierarchy A^ncedÄtcayMani lation Reshaping Arrays C Versus FORTRAN Order Fancy Indexing and repeat A·3 Broadcasting 4U Va ents: take and put Setting Triâț Ļ^Other A«s Rested Data T d Record Аггял А.6 M^T^^dA MuItidimensional Fields ^'-die^ . 425 425 426 428 435 439 443 448 457 463 466 469 471 472 473 473 474 476 47£ 478 479 481 483 484 487 489 490 490 493 493 494 495 495 497 Table of Contents | ix
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 498 499 500 501 502 503 503 504 505 505 B. More on the IPython System. . 509 B. 1 Terminal Keyboard Shortcuts B.2 About Magic Commands The %run Command Executing Code from the Clipboard B.3 Using the Command History Searching and Reusing the Command History Input and Output Variables B.4 Interacting with the Operating System Shell Commands and Aliases Directory Bookmark System B.5 Software Development Tools Interactive Debugger Timing Code: %time and %timeit Basic Profiling: %prun and %run -p Profiling a Function Line by Line B.6 Tips for Productive Code Development Using IPython Reloading Module Dependencies Code Design Tips B.7 Advanced IPython Features Profiles and Configuration B.8 Conclusion Index. x I Table of Contents 509 510 512 513 514 514 515 516 517 518 519 519 523 525 527 529 529 530 532 532 533 |
| adam_txt |
Table of Contents Preface. xi 1. Preliminaries. 1 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 Other Packages 1.4 Installation and Setup Miniconda on Windows GNU/Linux Miniconda on macOS Installing Necessary Packages Integrated Development Environments and Text Editors 1.5 Community and Conferences 1.6 Navigating This Book Code Examples 1 1 2 3 3 3 4 4 5 6 6 7 8 8 9 9 9 Ю 11 11 12 13 14 15
Data for Examples Import Conventions Python Language Basics, I Python, ana jupyier nioieouuw. 2.1 The Python Interpreter 2.2 IPython Basics Running the IPython Shell Running the Jupyter Notebook Tab Completion Introspection 2.3 Python Language Basics Language Semantics Scalar Types Control Flow 2.4 Conclusion 15 16 .17 iß IQ 1Q 20 ¿3 26 26 34 42 45 Built-In Data Structures, Fundions, and Files. . 47 47 3.1 Data Structures and Sequences 47 Tuple 51 List 55 Dictionary 59 Set 62 Built-In Sequence Functions 63 List, Set, and Dictionary Comprehensions 3.2 Functions 65 Namespaces, Scope, and Local Functions 67 Returning Multiple Values 68 Functions Are Objects 69 Anonymous (Lambda) Functions 70 Generators 71 Errors and Exception Handling 74 3.3 Files and the Operating System 76 Bytes and Unicode with Files 80 3.4 Conclusion 82 NumPy Basics: Arrays and Vectorized Computation. . 83 4.1 The NumPy ndarray: A Multidimensional Array Object 85 Creating ndarrays 86 Data Types for ndarrays 88 Arithmetic with NumPy Arrays 91 Basic Indexing and Slicing 92 iv I Table of Contents
Boolean Indexing Fancy Indexing Transposing Arrays and Swapping Axes 4.2 Pseudorandom Number Generation 4.3 Universal Functions: Fast Element-Wise Array Functions 4.4 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 4.5 File Input and Output with Arrays 4.6 Linear Algebra 4.7 Example: Random Walks Simulating Many Random Walks at Once 4.8 Conclusion 97 юо 102 103 105 108 110 111 113 114 115 116 116 118 120 121 5. Getting Started with pandas. 123 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 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 124 124 129 136 138 138 141 142 152 158 160 164 165 168 170 173 6. Data Loading, Storage, and File Formats. 175 6.1 Reading and Writing Data in Text Format Reading Text Files in Pieces Writing Data to Text Format Working with Other Delimited Formats JSONData 175 182 184 185 187 Table of Contents | v
XML and HTML: Web Scraping 6.2 Binary Data Formats Reading Microsoft Excel Files Using HDF5 Format 6.3 Interacting with Web APIs 6.4 Interacting with Databases 6.5 Conclusion ļ $ 7. Data Cleaning and Preparation. 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 Extension Data Types 7.4 String Manipulation Python Built-In String Object Methods Regular Expressions String Functions in pandas 7.5 Categorical Data Background and Motivation Categorical Extension Type in pandas Computations with Categoricals Categorical Methods 7.6 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 vi I Table of Contents 205 2θ2 2θ$ 2θ$ 211 212 214 215 217 219 221 224 227 227 229 232 235 236 237 240 242 245 247 247 250 շ5 ļ շ5շ 253 254 э Q
Concatenating Along an Axis Combining Data with Overlap 8.3 Reshaping and Pivoting Reshaping with Hierarchical Indexing Pivoting “Long” to “Wide” Format Pivoting “Wide” to “Long” Format 8.4 Conclusion 263 268 270 270 273 277 279 9. Plotting and Visualization. 281 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 282 283 288 290 294 296 297 298 298 301 309 311 314 317 317 10. Data Aggregation and Group Operations. 319 10.1 How to Think About Group Operations Iterating over Groups Selecting a Column or Subset of Columns Grouping with Dictionaries 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 320 324 326 327 328 328 329 331 335 335 338 338 340 343 344 Table of Contents | vii
Example: Group-Wise Linear Regression 10.4 Group Transforms and “Unwrapped” GroupBys 10.5 Pivot Tables and Cross-Tabulation Cross-Tabulations: Crosstab 10.6 Conclusion 11. Time Series. 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 Grouped Time Resampling 11.7 Moving Window Functions Exponentially Weighted Functions Binary Moving Window Functions User-Defined Moving Window Functions 11.8 Conclusion 12. Introduction to Modeling Libraries in Python. 12.1 Interfacing Between pandas and Model Code 12.2 Creating Model Descriptions with Patsy Data Transformations in Patsy Formulas Categorical Data and Patsy viii I Table of Contents 347 347 351 354 355 . 357 358 359 361 363 365 366 367 370 371 374 375 377 378 379 380 382 384 385 387 388 391 392 394 396 399 401 402 403 . 405
105 108 110 112
12-3 btr^ Estimig į "6peries Processes Estimating T cikit-learn 12 4 Introduction to scik 415 415 419 420 423 12.5 Conclusion Data Analysis txample-1з 1 Bitly Data from 1 -USA.g ¿X Time Zones in Pure Python Counting Time Zones with pandas 13 2 MovieLens 1M Dataset Measuring Rating Disagreement 13.3 US Baby Names 1880-2010 Analyzing Naming Trends 13.4 USDA Food Database 13.5 2012 Federal Election Commission Database Donation Statistics by Occupation and Employer Bucketing Donation Amounts Donation Statistics by State 13.6 Conclusion A. Advanced NumPy. A. Indarray Object Internals NumPy Data Typo Hierarchy A^ncedÄtcayMani lation Reshaping Arrays C Versus FORTRAN Order Fancy Indexing and repeat A·3 Broadcasting 4U Va ents: take and put Setting Triâț Ļ^Other A«s Rested Data T d Record Аггял А.6 M^T^^dA MuItidimensional Fields ^'-die^ . 425 425 426 428 435 439 443 448 457 463 466 469 471 472 473 473 474 476 47£ 478 479 481 483 484 487 489 490 490 493 493 494 495 495 497 Table of Contents | ix
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 498 499 500 501 502 503 503 504 505 505 B. More on the IPython System. . 509 B. 1 Terminal Keyboard Shortcuts B.2 About Magic Commands The %run Command Executing Code from the Clipboard B.3 Using the Command History Searching and Reusing the Command History Input and Output Variables B.4 Interacting with the Operating System Shell Commands and Aliases Directory Bookmark System B.5 Software Development Tools Interactive Debugger Timing Code: %time and %timeit Basic Profiling: %prun and %run -p Profiling a Function Line by Line B.6 Tips for Productive Code Development Using IPython Reloading Module Dependencies Code Design Tips B.7 Advanced IPython Features Profiles and Configuration B.8 Conclusion Index. x I Table of Contents 509 510 512 513 514 514 515 516 517 518 519 519 523 525 527 529 529 530 532 532 533 |
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| discipline | Informatik Mathematik Wirtschaftswissenschaften |
| discipline_str_mv | Informatik Mathematik Wirtschaftswissenschaften |
| edition | Third edition |
| format | Book |
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| id | DE-604.BV047947725 |
| illustrated | Illustrated |
| index_date | 2024-07-03T19:36:56Z |
| indexdate | 2025-01-27T13:02:11Z |
| institution | BVB |
| isbn | 9781098104030 109810403X |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033329126 |
| oclc_num | 1346087687 |
| open_access_boolean | |
| owner | DE-473 DE-BY-UBG DE-384 DE-1102 DE-739 DE-11 DE-188 DE-860 DE-573 DE-1043 DE-525 DE-355 DE-BY-UBR DE-945 DE-898 DE-BY-UBR DE-703 |
| owner_facet | DE-473 DE-BY-UBG DE-384 DE-1102 DE-739 DE-11 DE-188 DE-860 DE-573 DE-1043 DE-525 DE-355 DE-BY-UBR DE-945 DE-898 DE-BY-UBR DE-703 |
| physical | xvi, 561 Seiten Illustrationen, Diagramme |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| 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 Jupyter Wes McKinney Third edition Beijing ; Boston ; Farnham ; Sebastopol ; Tokyo O'Reilly August 2022 xvi, 561 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Programming languages (Electronic computers) Data mining Python (Computer program language) Python 3.6 Datenanalyse Datenmanagement Data Mining Programmbibliothek (DE-588)4121521-7 gnd rswk-swf Python 3.0 (DE-588)7624871-9 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Python Programmiersprache (DE-588)4434275-5 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 Python 3.0 (DE-588)7624871-9 s Erscheint auch als Online-Ausgabe 978-1-098-10400-9 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033329126&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 Jupyter Programming languages (Electronic computers) Data mining Python (Computer program language) Python 3.6 Datenanalyse Datenmanagement Data Mining Programmbibliothek (DE-588)4121521-7 gnd Python 3.0 (DE-588)7624871-9 gnd Data Mining (DE-588)4428654-5 gnd Datenanalyse (DE-588)4123037-1 gnd Python Programmiersprache (DE-588)4434275-5 gnd |
| subject_GND | (DE-588)4121521-7 (DE-588)7624871-9 (DE-588)4428654-5 (DE-588)4123037-1 (DE-588)4434275-5 |
| title | Python for data analysis data wrangling with pandas, NumPy, and Jupyter |
| title_auth | Python for data analysis data wrangling with pandas, NumPy, and Jupyter |
| title_exact_search | Python for data analysis data wrangling with pandas, NumPy, and Jupyter |
| title_exact_search_txtP | Python for data analysis data wrangling with pandas, NumPy, and Jupyter |
| title_full | Python for data analysis data wrangling with pandas, NumPy, and Jupyter Wes McKinney |
| title_fullStr | Python for data analysis data wrangling with pandas, NumPy, and Jupyter Wes McKinney |
| title_full_unstemmed | Python for data analysis data wrangling with pandas, NumPy, and Jupyter Wes McKinney |
| title_short | Python for data analysis |
| title_sort | python for data analysis data wrangling with pandas numpy and jupyter |
| title_sub | data wrangling with pandas, NumPy, and Jupyter |
| topic | Programming languages (Electronic computers) Data mining Python (Computer program language) Python 3.6 Datenanalyse Datenmanagement Data Mining Programmbibliothek (DE-588)4121521-7 gnd Python 3.0 (DE-588)7624871-9 gnd Data Mining (DE-588)4428654-5 gnd Datenanalyse (DE-588)4123037-1 gnd Python Programmiersprache (DE-588)4434275-5 gnd |
| topic_facet | Programming languages (Electronic computers) Data mining Python (Computer program language) Python 3.6 Datenanalyse Datenmanagement Data Mining Programmbibliothek Python 3.0 Python Programmiersprache |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033329126&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT mckinneywes pythonfordataanalysisdatawranglingwithpandasnumpyandjupyter |