MARC details
| 000 -LEADER |
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| fixed length control field |
09557nam a22001577a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
220310b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
978-93-5213-641-4 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Edition number |
23 |
| Classification number |
005.133 |
| Item number |
MCK |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
McKinny Wes |
| 245 ## - TITLE STATEMENT |
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| Title |
Python for Data Analysis |
| Remainder of title |
Data Wrangling with Pandas,Numpy and Ipython |
| Statement of responsibility, etc. |
Wes Mckinny |
| Medium |
English |
| 250 ## - EDITION STATEMENT |
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| Edition statement |
2nd ed |
| Remainder of edition statement |
2021 |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc. |
New Delhi |
| Name of publisher, distributor, etc. |
Shroff Publishers & Distributors Pvt.Ltd |
| Date of publication, distribution, etc. |
2021 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
iii-522 p. |
| Other physical details |
soft bound |
| Dimensions |
17.6*22.7 cm |
| 505 ## - FORMATTED CONTENTS NOTE |
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| FORMATTED CONTENTS NOTE |
Preface<br/>New for the Second Edition<br/>Conventions Used in This Book<br/>Using Code Examples<br/>O’Reilly Safari<br/>How to Contact Us<br/>Acknowledgments<br/>In Memoriam: John D. Hunter (1968–2012)<br/>Acknowledgments for the Second Edition (2017)<br/>Acknowledgments for the First Edition (2012)<br/>Preliminaries<br/>1.1 What Is This Book About?<br/>What Kinds of Data?<br/>1.2 Why Python for Data Analysis?<br/>Python as Glue<br/>Solving the “Two-Language” Problem<br/>Why Not Python?<br/>1.3 Essential Python Libraries<br/>NumPy<br/>pandas<br/>matplotlib<br/>IPython and Jupyter<br/>SciPy<br/>scikit-learn<br/>statsmodels<br/>1.4 Installation and Setup<br/>Windows<br/>Apple (OS X, macOS)<br/>GNU/Linux<br/>Installing or Updating Python Packages<br/>Python 2 and Python 3<br/>Integrated Development Environments (IDEs) and Text Editors<br/>1.5 Community and Conferences<br/>1.6 Navigating This Book<br/>Code Examples<br/>Data for Examples<br/>Import Conventions<br/>Jargon<br/>Python Language Basics, IPython, and Jupyter Notebooks<br/>2.1 The Python Interpreter<br/>2.2 IPython Basics<br/>Running the IPython Shell<br/>Running the Jupyter Notebook<br/>Tab Completion<br/>Introspection<br/>The %run Command<br/>Executing Code from the Clipboard<br/>Terminal Keyboard Shortcuts<br/>About Magic Commands<br/>Matplotlib Integration<br/>2.3 Python Language Basics<br/>Language Semantics<br/>Scalar Types<br/>Control Flow<br/>Built-in Data Structures, Functions, and Files<br/>3.1 Data Structures and Sequences<br/>Tuple<br/>List<br/>Built-in Sequence Functions<br/>dict<br/>set<br/>List, Set, and Dict Comprehensions<br/>3.2 Functions<br/>Namespaces, Scope, and Local Functions<br/>Returning Multiple Values<br/>Functions Are Objects<br/>Anonymous (Lambda) Functions<br/>Currying: Partial Argument Application<br/>Generators<br/>Errors and Exception Handling<br/>3.3 Files and the Operating System<br/>Bytes and Unicode with Files<br/>3.4 Conclusion<br/>NumPy Basics: Arrays and Vectorized Computation<br/>4.1 The NumPy ndarray: A Multidimensional Array Object<br/>Creating ndarrays<br/>Data Types for ndarrays<br/>Arithmetic with NumPy Arrays<br/>Basic Indexing and Slicing<br/>Boolean Indexing<br/>Fancy Indexing<br/>Transposing Arrays and Swapping Axes<br/>4.2 Universal Functions: Fast Element-Wise Array Functions<br/>4.3 Array-Oriented Programming with Arrays<br/>Expressing Conditional Logic as Array Operations<br/>Mathematical and Statistical Methods<br/>Methods for Boolean Arrays<br/>Sorting<br/>Unique and Other Set Logic<br/>4.4 File Input and Output with Arrays<br/>4.5 Linear Algebra<br/>4.6 Pseudorandom Number Generation<br/>4.7 Example: Random Walks<br/>Simulating Many Random Walks at Once<br/>4.8 Conclusion<br/>Getting Started with pandas<br/>5.1 Introduction to pandas Data Structures<br/>Series<br/>DataFrame<br/>Index Objects<br/>5.2 Essential Functionality<br/>Reindexing<br/>Dropping Entries from an Axis<br/>Indexing, Selection, and Filtering<br/>Integer Indexes<br/>Arithmetic and Data Alignment<br/>Function Application and Mapping<br/>Sorting and Ranking<br/>Axis Indexes with Duplicate Labels<br/>5.3 Summarizing and Computing Descriptive Statistics<br/>Correlation and Covariance<br/>Unique Values, Value Counts, and Membership<br/>5.4 Conclusion<br/>Data Loading, Storage, and File Formats<br/>6.1 Reading and Writing Data in Text Format<br/>Reading Text Files in Pieces<br/>Writing Data to Text Format<br/>Working with Delimited Formats<br/>JSON Data<br/>XML and HTML: Web Scraping<br/>6.2 Binary Data Formats<br/>Using HDF5 Format<br/>Reading Microsoft Excel Files<br/>6.3 Interacting with Web APIs<br/>6.4 Interacting with Databases<br/>6.5 Conclusion<br/>Data Cleaning and Preparation<br/>7.1 Handling Missing Data<br/>Filtering Out Missing Data<br/>Filling In Missing Data<br/>7.2 Data Transformation<br/>Removing Duplicates<br/>Transforming Data Using a Function or Mapping<br/>Replacing Values<br/>Renaming Axis Indexes<br/>Discretization and Binning<br/>Detecting and Filtering Outliers<br/>Permutation and Random Sampling<br/>Computing Indicator/Dummy Variables<br/>7.3 String Manipulation<br/>String Object Methods<br/>Regular Expressions<br/>Vectorized String Functions in pandas<br/>7.4 Conclusion<br/>Data Wrangling: Join, Combine, and Reshape<br/>8.1 Hierarchical Indexing<br/>Reordering and Sorting Levels<br/>Summary Statistics by Level<br/>Indexing with a DataFrame’s columns<br/>8.2 Combining and Merging Datasets<br/>Database-Style DataFrame Joins<br/>Merging on Index<br/>Concatenating Along an Axis<br/>Combining Data with Overlap<br/>8.3 Reshaping and Pivoting<br/>Reshaping with Hierarchical Indexing<br/>Pivoting “Long” to “Wide” Format<br/>Pivoting “Wide” to “Long” Format<br/>8.4 Conclusion<br/>Plotting and Visualization<br/>9.1 A Brief matplotlib API Primer<br/>Figures and Subplots<br/>Colors, Markers, and Line Styles<br/>Ticks, Labels, and Legends<br/>Annotations and Drawing on a Subplot<br/>Saving Plots to File<br/>matplotlib Configuration<br/>9.2 Plotting with pandas and seaborn<br/>Line Plots<br/>Bar Plots<br/>Histograms and Density Plots<br/>Scatter or Point Plots<br/>Facet Grids and Categorical Data<br/>9.3 Other Python Visualization Tools<br/>9.4 Conclusion<br/>Data Aggregation and Group Operations<br/>10.1 GroupBy Mechanics<br/>Iterating Over Groups<br/>Selecting a Column or Subset of Columns<br/>Grouping with Dicts and Series<br/>Grouping with Functions<br/>Grouping by Index Levels<br/>10.2 Data Aggregation<br/>Column-Wise and Multiple Function Application<br/>Returning Aggregated Data Without Row Indexes<br/>10.3 Apply: General split-apply-combine<br/>Suppressing the Group Keys<br/>Quantile and Bucket Analysis<br/>Example: Filling Missing Values with Group-Specific Values<br/>Example: Random Sampling and Permutation<br/>Example: Group Weighted Average and Correlation<br/>Example: Group-Wise Linear Regression<br/>10.4 Pivot Tables and Cross-Tabulation<br/>Cross-Tabulations: Crosstab<br/>10.5 Conclusion<br/>Time Series<br/>11.1 Date and Time Data Types and Tools<br/>Converting Between String and Datetime<br/>11.2 Time Series Basics<br/>Indexing, Selection, Subsetting<br/>Time Series with Duplicate Indices<br/>11.3 Date Ranges, Frequencies, and Shifting<br/>Generating Date Ranges<br/>Frequencies and Date Offsets<br/>Shifting (Leading and Lagging) Data<br/>11.4 Time Zone Handling<br/>Time Zone Localization and Conversion<br/>Operations with Time Zone−Aware Timestamp Objects<br/>Operations Between Different Time Zones<br/>11.5 Periods and Period Arithmetic<br/>Period Frequency Conversion<br/>Quarterly Period Frequencies<br/>Converting Timestamps to Periods (and Back)<br/>Creating a PeriodIndex from Arrays<br/>11.6 Resampling and Frequency Conversion<br/>Downsampling<br/>Upsampling and Interpolation<br/>Resampling with Periods<br/>11.7 Moving Window Functions<br/>Exponentially Weighted Functions<br/>Binary Moving Window Functions<br/>User-Defined Moving Window Functions<br/>11.8 Conclusion<br/>Advanced pandas<br/>12.1 Categorical Data<br/>Background and Motivation<br/>Categorical Type in pandas<br/>Computations with Categoricals<br/>Categorical Methods<br/>12.2 Advanced GroupBy Use<br/>Group Transforms and “Unwrapped” GroupBys<br/>Grouped Time Resampling<br/>12.3 Techniques for Method Chaining<br/>The pipe Method<br/>12.4 Conclusion<br/>Introduction to Modeling Libraries in Python<br/>13.1 Interfacing Between pandas and Model Code<br/>13.2 Creating Model Descriptions with Patsy<br/>Data Transformations in Patsy Formulas<br/>Categorical Data and Patsy<br/>13.3 Introduction to statsmodels<br/>Estimating Linear Models<br/>Estimating Time Series Processes<br/>13.4 Introduction to scikit-learn<br/>13.5 Continuing Your Education<br/>Data Analysis Examples<br/>14.1 1.USA.gov Data from Bitly<br/>Counting Time Zones in Pure Python<br/>Counting Time Zones with pandas<br/>14.2 MovieLens 1M Dataset<br/>Measuring Rating Disagreement<br/>14.3 US Baby Names 1880–2010<br/>Analyzing Naming Trends<br/>14.4 USDA Food Database<br/>14.5 2012 Federal Election Commission Database<br/>Donation Statistics by Occupation and Employer<br/>Bucketing Donation Amounts<br/>Donation Statistics by State<br/>14.6 Conclusion<br/>Advanced NumPy<br/>A.1 ndarray Object Internals<br/>NumPy dtype Hierarchy<br/>A.2 Advanced Array Manipulation<br/>Reshaping Arrays<br/>C Versus Fortran Order<br/>Concatenating and Splitting Arrays<br/>Repeating Elements: tile and repeat<br/>Fancy Indexing Equivalents: take and put<br/>A.3 Broadcasting<br/>Broadcasting Over Other Axes<br/>Setting Array Values by Broadcasting<br/>A.4 Advanced ufunc Usage<br/>ufunc Instance Methods<br/>Writing New ufuncs in Python<br/>A.5 Structured and Record Arrays<br/>Nested dtypes and Multidimensional Fields<br/>Why Use Structured Arrays?<br/>A.6 More About Sorting<br/>Indirect Sorts: argsort and lexsort<br/>Alternative Sort Algorithms<br/>Partially Sorting Arrays<br/>numpy.searchsorted: Finding Elements in a Sorted Array<br/>A.7 Writing Fast NumPy Functions with Numba<br/>Creating Custom numpy.ufunc Objects with Numba<br/>A.8 Advanced Array Input and Output<br/>Memory-Mapped Files<br/>HDF5 and Other Array Storage Options<br/>A.9 Performance Tips<br/>The Importance of Contiguous Memory<br/>More on the IPython System<br/>B.1 Using the Command History<br/>Searching and Reusing the Command History<br/>Input and Output Variables<br/>B.2 Interacting with the Operating System<br/>Shell Commands and Aliases<br/>Directory Bookmark System<br/>B.3 Software Development Tools<br/>Interactive Debugger<br/>Timing Code: %time and %timeit<br/>Basic Profiling: %prun and %run -p<br/>Profiling a Function Line by Line<br/>B.4 Tips for Productive Code Development Using IPython<br/>Reloading Module Dependencies<br/>Code Design Tips<br/>B.5 Advanced IPython Features<br/>Making Your Own Classes IPython-Friendly<br/>Profiles and Configuration<br/>B.6 Conclusion<br/>Index |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Books |