Lecture "Administration and visualization: Chapter 6 - Tools for data visualization" provides students with content about: Three kinds of visualization; Mathematical visualization; Scientific visualization; Information visualization; Introduction to pandas, numpy; Introduction to matplotlib; . Please refer to the detailed content of the lecture! | 1 Chapter 6 Tools for Data Visualization Lecture 0 Introduction to Course 2 Outline 1. Overview 2. Introduction to pandas numpy 3. Introduction to matplotlib 3 1. Overview Three kinds of visualization Mathematical Visualization Scientific Visualization Information Visualization 4 Mathematical Visualization Data results from a mathematical equation Missing data can be readily generated by a computer program 5 Scientific Visualization Visualization of scientific data Data measured from real world scientific devices or come from expensive simulations Coordinate data Spatial coordinates Temperature pressure time 6 Information Visualization Visualization of more abstract non- coordinate data Process abstract data into a more concrete form that can be more effectively perceived by an observer 7 Modes of Visualization Interactive Presentation Visualization Visualization Used for Used for discovery communication Intended for a Intended for large single investigator group or mass Re-renders based audience on user input Does not support user input 8 Goal of visualization Comparison Distribution Relationship 9 Data Visualization Framework 10 Data Types Discrete Continuous Ordered values are comparable Unordered values are not comparable 11 2. Introduction to numpy pandas Python provides some library to manipulating with data numpy a basic library to working with arrays pandas another library with more functionalities 12 Numpy Stands for Numerical Python or Numeric Python Introduces objects for multidimensional arrays and matrices as well as functions that allow to easily perform advanced mathematical and statistical operations on those objects Provides vectorization of mathematical operations on arrays and matrices which significantly improves the performance Many other python libraries are built on NumPy Link http 13 Pandas Adds data structures and tools designed to work with table-like data similar to Series and Data Frames in R Provides tools for data .
