In this Data Visualisation with Python Training course, you’ll learn how to use Python’s data visualisation libraries, including NumPy, Pandas, Matplotlib, and Seaborn to better understand data analytics.

Utbildningsmål

• Learn how to use various plot types with Python
• Explore and work with different libraries for data visualisation
• Understand and create effective visualisations
• Improve your Python data wrangling skills
• Work with industry-standard tools, including Matplotlib, Seaborn, and Bokeh
• Learn different data formats and representations
• Learn how to use Geoplotlib and Bokeh
• Continue learning and face new challenges with after-course one-on-one instructor coaching

Förkunskaper

A working knowledge of Python to the level of Introduction to Python Training

Innehåll

Module 1: Fundamentals of Python

• Importance of Data Visualisation
• Visualisation Using Python
• Data Cleaning
• Data Wrangling
• Types of Data
• Statistics
• Probability
• Exploratory Data Analysis
• Python
• Jupyter Notebook
• Google Colab and Kaggle Notebooks
• JupyterLab
• Basic Python Data Types
• Flow Control
• Slicing
• Defining Functions
• Lambdas
• Classes

Module 2: NumPy and Pandas

• NumPy
• The NumPy ndarray Object
• Slicing ndarrays
• Boolean Indexing
• Element-wise Arithmetic
• Transpose of a ndarray
• Dot Products
• Stacking
• SciPy
• pandas
• Series and DataFrames
• Loading and Saving Data With pandas
• Creating DataFrames
• Inspecting Data
• Selecting Columns and Rows
• The head() and tail() methods
• Basic Plots
• Descriptive Statistics From a DataFrame
• Filtering, Sorting, and Grouping
• Replacing Values and Renaming Columns
• Joining and Combining Dataframes
• Reading Data From Files
• Reading From a Relational Database
• Loading External Data From NoSQL Stores (MongoDB)
• SciPy
• Sci-Kit Learn

Module 3: Visualisation with Matplotlib

• Matplotlib
• Architecture
• The Figure Object
• Axes, Labels, Titles, Legends and Grids
• Reading Data from Files and Other DataSources
• The pyplot API
• The plot() Method
• The Format String
• Markers and Line Styles
• Plotting Labelled Data
• Plotting Multiple Graphs on the Same Axes
• Saving Figures
• Labels and Titles
• Annotations
• Legends
• Line Chart
• Area Chart
• Stacked Area Chart
• Scatter Plot
• Bubble Chart
• Heat Map
• Contour Plot
• Histogramme
• Kernel Density Estimate Plot
• Box Plots
• Violin Plots
• Bar Plot
• Grouped bar or column chart
• Stacked Bar Plots
• Error bars
• Radar Plots
• Pie Plots and Donuts
• Tree Maps

Module 4: Simplifying Visualisation with Seaborn

• Seaborn
• Styling
• Scaling and the Plotting Context
• Overriding Context Settings with the rc Parameter
• Themes
• Colors in Seaborn
• Varying Hue to Distinguish Categories
• Vary Luminance to Represent Numbers
• Choosing a Palette with the color_palette() Function
• Qualitative Color Palettes
• Sequential Palettes
• Diverging Palettes
• Histogrammes
• Multiple Histogrammes on the Same Axes
• Kernel Density Plots
• Box Plots
• Violin Plots
• Contour Plots
• The FacetGrid
• Some Functions that Return a FacetGrid
• Pair Plots
• The relplot() Function
• The regplot() and implot() Functions
• Creating a Regression Plot
• Variables That Take Discrete Values
• Using a Representative value
• Squarify

Module 5: Plotting geospatial data with Geoplotlib

• Geoplotlib
• Input and Output
• Interaction
• The dot Visualisation
• Zooming
• 2D Histogramme
• Heat Map
• Voronoi Tessellation
• Seed Points
• Delaunay Triangulation
• GeoJSON
• Adding Color and Tooltips
• Tile Providers
• The DarkMatter Tiles

Module 6: Adding interaction with Bokeh

• How Bokeh Works
• Bokeh Server
• Programming Interfaces
• The Bokeh Models
• Glyphs, Plots, and Layouts
• The bokeh.plotting Interface
• Some Glyph Methods on the Figure Object
• Widgets in Bokeh
• Using Bokeh Server
• Setting Up the Widgets
• The TextField Widget
• The Other Widgets
• Running Bokeh Server
• Widgets Using CustomJS
• Widgets with ipwidgets

Kursen levereras genom utbildningspartner: Learning Tree