TY - BOOK AU - McKinny Wes TI - Python for Data Analysis: Data Wrangling with Pandas,Numpy and Ipython SN - 978-93-5213-641-4 U1 - 005.133 23 PY - 2021/// CY - New Delhi PB - Shroff Publishers & Distributors Pvt.Ltd N1 - Preface New for the Second Edition Conventions Used in This Book Using Code Examples O’Reilly Safari How to Contact Us Acknowledgments In Memoriam: John D. Hunter (1968–2012) Acknowledgments for the Second Edition (2017) Acknowledgments for the First Edition (2012) 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 Import Conventions Jargon Python Language Basics, IPython, and Jupyter Notebooks 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 Built-in Data Structures, Functions, and Files 3.1 Data Structures and Sequences Tuple List Built-in Sequence Functions dict set List, Set, and Dict 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 NumPy Basics: Arrays and Vectorized Computation 4.1 The NumPy ndarray: A Multidimensional Array Object 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 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 Data Loading, Storage, and File Formats 6.1 Reading and Writing Data in Text Format 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 HDF5 Format Reading Microsoft Excel Files 6.3 Interacting with Web APIs 6.4 Interacting with Databases 6.5 Conclusion 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 String Manipulation String Object Methods Regular Expressions Vectorized String Functions in pandas 7.4 Conclusion 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 Reshaping with Hierarchical Indexing Pivoting “Long” to “Wide” Format Pivoting “Wide” to “Long” Format 8.4 Conclusion Plotting and Visualization 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 Data Aggregation and Group Operations 10.1 GroupBy Mechanics Iterating Over Groups Selecting a Column or Subset of Columns Grouping with Dicts 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 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 11.7 Moving Window Functions Exponentially Weighted Functions Binary Moving Window Functions User-Defined Moving Window Functions 11.8 Conclusion Advanced pandas 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 Introduction to Modeling Libraries in Python 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 Data Analysis Examples 14.1 1.USA.gov Data from Bitly Counting Time Zones in Pure Python Counting Time Zones with pandas 14.2 MovieLens 1M 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 Advanced NumPy 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 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 More on the IPython System B.1 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 Index ER -