Data Visualization with Python Proficiency (Advanced Level)

Topics covered

• calculate deltas and percentage returns in stock prices
• change column values by applying functions
• compare the use cases for swarm plots, bar plots strip plots, and categorical plots
• configure a FacetGrid to convey more information and to draw one's focus to specific plots
• create a Bokeh chart and save it in PNG and HTML formats
• create a FacetGrid to visualize distributions within a range of categories
• create interactive graphs and image files
• define the aesthetic parameters for a plot and make use of Seaborn's built-in templates for creating shareable graphs
• define time deltas and date ranges in Pandas
• describe the basic aesthetic themes and styles available in Seaborn
• describe what a color palette is and select from the built-in color palettes available
• display your visualization inline in a Jupyter notebook
• illustrate how autocorrelation and cross-correlation can be used to identify recurring patterns in data through Matplotlib
• plot graphs using line and markers
• plot multiple lines in a single graph using different line styles and markers
• transform data with user-defined functions
• use boxplots and violin plots to visualize the distributions of data within specific categories of your dataset
• use Matplotlib to visualize compositions over a period of time using area charts and changes over time using stem plots
• visualize proportions in data using donut charts
• visualize proportions in data using pie charts