Data analysis and visualization using Python [electronic resource] : analyze data to create visualizations for BI systems / Ossama Embarak.

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.

Look at Python from a data science point of view and learn proven techniques for data visualization as used in making critical business decisions. Starting with an introduction to data science with Python, you will take a closer look at the Python environment and get acquainted with editors such as Jupyter Notebook and Spyder. After going through a primer on Python programming, you will grasp fundamental Python programming techniques used in data science. Moving on to data visualization, you will see how it caters to modern business needs and forms a key factor in decision-making. You will also take a look at some popular data visualization libraries in Python. Shifting focus to data structures, you will learn the various aspects of data structures from a data science perspective. You will then work with file I/O and regular expressions in Python, followed by gathering and cleaning data. Moving on to exploring and analyzing data, you will look at advanced data structures in Python. Then, you will take a deep dive into data visualization techniques, going through a number of plotting systems in Python. In conclusion, you will complete a detailed case study, where you'll get a chance to revisit the concepts you've covered so far.