Python data analytics : data analysis and science using Pandas, matplotlib, and the Python programming language /: data analysis and science using Pandas, matplotlib, and the Python programming language. (2015)

• Record Type: Book
• Further Information: Note: Fabio Nelli.
• Authors: Nelli, Fabio
• Contents: At a Glance -- Contents -- About the Author -- About the Technical Reviewer -- Acknowledgments -- Chapter 1: An Introduction to Data Analysis -- Data Analysis -- Knowledge Domains of the Data Analyst -- Computer Science -- Mathematics and Statistics -- Machine Learning and Artificial Intelligence -- Professional Fields of Application -- Understanding the Nature of the Data -- When the Data Become Information -- When the Information Becomes Knowledge -- Types of Data -- The Data Analysis Process -- Problem Definition -- Data Extraction -- Data Preparation -- Data Exploration/Visualization -- Predictive Modeling -- Model Validation -- Deployment -- Quantitative and Qualitative Data Analysis -- Open Data -- Python and Data Analysis -- Conclusions -- Chapter 2: Introduction to the Python's World -- Python-The Programming Language -- Python-The Interpreter -- Cython -- Jython -- PyPy -- Python 2 and Python 3 -- Installing Python -- Python Distributions -- Anaconda -- Enthought Canopy -- Python(x, y) -- Using Python -- Python Shell -- Run an Entire Program Code -- Implement the Code Using an IDE -- Interact with Python -- Writing Python Code -- Make Calculations -- Import New Libraries and Functions -- Data Structure -- Functional Programming (Only for Python 3.4) -- Indentation -- IPython -- IPython Shell -- IPython Qt-Console -- IPython Notebook -- The Jupyter Project -- PyPI-The Python Package Index -- The IDEs for Python -- IDLE (Integrated DeveLopment Environment) -- Spyder -- Eclipse (pyDev) -- Sublime -- Liclipse -- NinjaIDE -- Komodo IDE -- SciPy -- NumPy -- Pandas -- matplotlib -- Conclusions -- Chapter 3: The NumPy Library -- NumPy: A Little History -- The NumPy Installation -- Ndarray: The Heart of the Library -- Create an Array -- Types of Data -- The dtype Option -- Intrinsic Crea tion of an Array -- Basic Operations. Arit hmetic Operators -- The M atrix Product -- Increm ent and Decrement Operators -- Universal Functions (ufunc) -- Aggregat e Functions -- Indexing, Slicing, and Iterating -- Indexing -- Slicing -- Iterating an Array -- Conditions an d Boolean Arrays -- Shape Manipulation -- Array Manipulation -- Joining Arrays -- Splitting Arrays -- General Concepts -- Copies or Views of Objects -- Vectorization -- Broadcasting -- Structured Arrays -- Reading and Writing Array Data on Files -- Loading and Saving Data in Binary Files -- Reading File with T abular Data -- Conclusions -- Chapter 4: The pandas Library-An Introduction -- pandas: The Python Data Analysis Library -- Installation -- Installation from Anaconda -- Installation from PyPI -- Installation on Linux -- Installation from Source -- A Module Repository for Windows -- Test Your pandas Installation -- Getting Started with pandas -- Introduction to pandas Data Structures -- The Series -- Declaring a Series -- Selecting the Internal Elements -- Assigning Values to the Elements -- Defining Series from NumPy Arrays and Other Series -- Filtering Values -- Operations and Mathematical Functions -- Evaluating Values -- NaN Values -- Series as Dictionaries -- Operations between Series -- The DataFrame -- Defining a DataFrame -- Selecting Elements -- Assigning Values -- Membership of a Value -- Deleting a Column -- Filtering -- DataFrame from Nested dict -- Transposition of a DataFrame -- The Index Objects -- Methods on Index -- Index with Duplicate Labels -- Other Functionalities on Indexes -- Reindexing -- Dropping -- Arithmetic and Data Alignment -- Operations between Data Structures -- Flexible Arithmetic Methods -- Operations between DataFrame and Series -- Function Application and Mapping -- Functions by Element -- Functions by Row or Column -- Statistics Functions -- Sorting and Ranking. Correlation and Covariance -- "Not a Number" Data -- Assigning a NaN Value -- Filtering Out NaN Values -- Filling in NaN Occurrences -- Hierarchical Indexing and Leveling -- Reordering and Sorting Levels -- Summary Statistic by Level -- Conclusions -- Chapter 5: pandas: Reading and Writing Data -- I/O API Tools -- CSV and Textual Files -- Reading Data in CSV or Text Files -- Using RegExp for Parsing TXT Files -- Reading TXT Files into Parts or Partially -- Writing Data in CSV -- Reading and Writing HTML Files -- Writing Data in HTML -- Reading Data from an HTML File -- Reading Data from XML -- Reading and Writing Data on Microsoft Excel Files -- JSON Data -- The Format HDF5 -- Pickle-Python Object Serialization -- Serialize a Python Object with cPickle -- Pickling with pandas -- Interacting with Databases -- Loading and Writing Data with SQLite3 -- Loading and Writing Data with PostgreSQL -- Reading and Writing Data with a NoSQL Database: MongoDB -- Conclusions -- Chapter 6: pandas in Depth: Data Manipulation -- Data Preparation -- Merging -- Merging on Index -- Concatenating -- Combining -- Pivoting -- Pivoting with Hierarchical Indexing -- Pivoting from "Long" to "Wide" Format -- Removing -- Data Transformation -- Removing Duplicates -- Mapping -- Replacing Values via Mapping -- Adding Values via Mapping -- Rename the Indexes of the Axes -- Discretization and Binning -- Detecting and Filtering Outliers -- Permutation -- Random Sampling -- String Manipulation -- Built-in Methods for Manipulation of Strings -- Regular Expressions -- Data Aggregation -- GroupBy -- A Practical Example -- Hierarchical Grouping -- Group Iteration -- Chain of Transformations -- Functions on Groups -- Advanced Data Aggregation -- Conclusions -- Chapter 7: Data Visualization with matplotlib -- The matplotlib Library -- Installation -- IPython and IPython QtConsole. Matplotlib Architecture -- Backend Layer -- Artist Layer -- Scripting Layer (pyplot) -- pylab and pyplot -- pyplot -- A Simple Interactive Chart -- Set the Properties of the Plot -- matplotlib and NumPy -- Using the kwargs -- Working with Multiple Figures and Axes -- Adding Further Elements to the Chart -- Adding Text -- Adding a Grid -- Adding a Legend -- Saving Your Charts -- Saving the Code -- Converting Your Session as an HTML File -- Saving Your Chart Directly as an Image -- Handling Date Values -- Chart Typology -- Line Chart -- Line Charts with pandas -- Histogram -- Bar Chart -- Horizontal Bar Chart -- Multiserial Bar Chart -- Multiseries Bar Chart with pandas DataFrame -- Multiseries Stacked Bar Charts -- Stacked Bar Charts with pandas DataFrame -- Other Bar Chart Representations -- Pie Charts -- Pie Charts with pandas DataFrame -- Advanced Charts -- Contour Plot -- Polar Chart -- mplot3d -- 3D Surfaces -- Scatter Plot in 3D -- Bar Chart 3D -- Multi-Panel Plots -- Display Subplots within Other Subplots -- Grids of Subplots -- Conclusions -- Chapter 8: Machine Learning with scikit-learn -- The scikit-learn Library -- Machine Learning -- Supervised and Unsupervised Learning -- Training Set and Testing Set -- Supervised Learning with scikit-learn -- The Iris Flower Dataset -- The PCA Decomposition -- K-Nearest Neighbors Classifier -- Diabetes Dataset -- Linear Regression: The Least Square Regression -- Support Vector Machines (SVMs) -- Support Vector Classification (SVC) -- Nonlinear SVC -- Plotting Different SVM Classifiers Using the Iris Dataset -- Support Vector Regression (SVR) -- Conclusions -- Chapter 9: An Example-Meteorological Data -- A Hypothesis to Be Tested: The Influence of the Proximity of the Sea -- The System in the Study: The Adriatic Sea and the Po Valley -- Data Source -- Data Analysis on IPython Notebook -- The RoseWind. Calculating the Distribution of the Wind Speed Means -- Conclusions -- Chapter 10: Embedding the JavaScript D3 Library in IPython Notebook -- The Open Data Source for Demographics -- The JavaScript D3 Library -- Drawing a Clustered Bar Chart -- The Choropleth Maps -- The Choropleth Map of the US Population in 2014 -- Conclusions -- Chapter 11: Recognizing Handwritten Digits -- Handwriting Recognition -- Recognizing Handwritten Digits with scikit-learn -- The Digits Dataset -- Learning and Predicting -- Conclusions -- Appendix A: Writing Mathematical Expressions with LaTeX -- With matplotlib -- With IPython Notebook in a Markdown Cell -- With IPython Notebook in a Python 2 Cell -- Subscripts and Superscripts -- Fractions, Binomials, and Stacked Numbers -- Radicals -- Fonts -- Accents -- Appendix B: Open Data Sources -- Political and Government Data -- Health Data -- Social Data -- Miscellaneous and Public Data Sets -- Financial Data -- Climatic Data -- Sports Data -- Publications, Newspapers, and Books -- Musical Data -- Index.
• Publisher Details: Berkeley, CA : Apress
• Publication Date: 2015
• Copyright Date: 2015
• Extent: 1 online resource (xxi, 337 pages)
• Subjects: 005.13/3; Computer science; Python (Computer program language); Data mining; Data mining; Python (Computer program language); Computers -- Programming Languages -- General; Computers -- Data Processing; Programming & scripting languages: general; Public administration; Python (Computer program language); Information systems; Computers -- Programming Languages -- Python; Electronic books
• Languages: English
• ISBNs: 9781484209585; 1484209583; 1484209591; 9781484209592
• Related ISBNs: 9781484209592
• Notes: Note: Online resource; title from PDF title page (SpringerLink, viewed September 1, 2015).
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