Data analysis and visualization using Python : analyze data to create visualizations for BI systems /: analyze data to create visualizations for BI systems. (2018)
- Record Type:
- Book
- Title:
- Data analysis and visualization using Python : analyze data to create visualizations for BI systems /: analyze data to create visualizations for BI systems. (2018)
- Main Title:
- Data analysis and visualization using Python : analyze data to create visualizations for BI systems
- Further Information:
- Note: Ossama Embarak.
- Authors:
- Embarak, Ossama
- Contents:
- 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()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 … (more)
- Publisher Details:
- Berkeley, CA : Apress
- Publication Date:
- 2018
- Extent:
- 1 online resource
- Subjects:
- 005.133
Python (Computer program language)
Programming languages (Electronic computers)
Data mining
Qualitative research -- Methodology
COMPUTERS / Programming Languages / Python
Data mining
Programming languages (Electronic computers)
Python (Computer program language)
Qualitative research -- Methodology
Electronic books - Languages:
- English
- ISBNs:
- 9781484241097
1484241096 - Related ISBNs:
- 9781484241080
1484241088 - Notes:
- Note: Online resource; title from PDF title page (EBSCO, viewed November 27, 2018)
- Access Rights:
- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
- Access Usage:
- Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD.DS.377745
- Ingest File:
- 02_358.xml