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Data analysis and visualization using Python : analyze data to create visualizations for BI systems /
Look at Python from a data science point of view and learn proven techniques for data visualization as used in making critical business decisions.
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[Berkeley, CA] :
Apress,
[2018]
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Introduction to data science with Python
- The importance of data visualization in business intelligence
- Data collection structures
- File I/O processing and regular expressions
- Data gathering and cleaning
- Data exploring and analysis
- Data visualization
- Case studies.
- Intro; Table of Contents; About the Author; About the Technical Reviewers; Introduction; Chapter 1: Introduction to Data Science with Python; The Stages of Data Science; Why Python?; Basic Features of Python; Python Learning Resources; Python Environment and Editors; Portable Python Editors (No Installation Required); Azure Notebooks; Offline and Desktop Python Editors; The Basics of Python Programming; Basic Syntax; Lines and Indentation; Multiline Statements; Quotation Marks in Python; Multiple Statements on a Single Line; Read Data from Users; Declaring Variables and Assigning Values
- Multiple AssignsVariable Names and Keywords; Statements and Expressions; Basic Operators in Python; Arithmetic Operators; Relational Operators; Assign Operators; Logical Operators; Python Comments; Formatting Strings; Conversion Types; The Replacement Field, {}; The Date and Time Module; Time Module Methods; Python Calendar Module; Fundamental Python Programming Techniques; Selection Statements; Iteration Statements; The Use of Break, Continues, and Pass Statements; try and except; String Processing; String Special Operators; String Slicing and Concatenation
- String Conversions and Formatting SymbolsLoop Through String; Python String Functions and Methods; The in Operator; Parsing and Extracting Strings; Tabular Data and Data Formats; Python Pandas Data Science Library; A Pandas Series; A Pandas Data Frame; A Pandas Panels; Python Lambdas and the Numpy Library; The map() Function; The filter() Function; The reduce () Function; Python Numpy Package; Data Cleaning and Manipulation Techniques; Abstraction of the Series and Data Frame; Running Basic Inferential Analyses; Summary; Exercises and Answers
- Chapter 2: The Importance of Data Visualization in Business IntelligenceShifting from Input to Output; Why Is Data Visualization Important?; Why Do Modern Businesses Need Data Visualization?; The Future of Data Visualization; How Data Visualization Is Used for Business Decision-Making; Faster Responses; Simplicity; Easier Pattern Visualization; Team Involvement; Unify Interpretation; Introducing Data Visualization Techniques; Loading Libraries; Popular Libraries for Data Visualization in Python; Matplotlib; Seaborn; Plotly; Geoplotlib; Pandas; Introducing Plots in Python; Summary
- Exercises and AnswersChapter 3: Data Collection Structures; Lists; Creating Lists; Accessing Values in Lists; Adding and Updating Lists; Deleting List Elements; Basic List Operations; Indexing, Slicing, and Matrices; Built-in List Functions and Methods; List Functions; List Methods; List Sorting and Traversing; Lists and Strings; Parsing Lines; Aliasing; Dictionaries; Creating Dictionaries; Updating and Accessing Values in Dictionaries; Deleting Dictionary Elements; Built-in Dictionary Functions; Built-in Dictionary Methods; Tuples; Creating Tuples; Concatenating Tuples