Learn Data Visualization with Python, Plotly and Power BI

Install and configure python packages for data visualization in Power BI, set Python home, install pandas and seaborn via pip, and enable Python visuals to build charts.

Create a simple line chart with matplotlib in Python by importing library, loading data from Excel source, and plotting site visitors versus average time, then customize visuals in Power BI.

Modify line charts into dashed lines and scatterplots, add x and y axis labels, customize colors and markers, and blend chart types using Python and Power BI.

Learn to create a violin chart with seaborn in Python to visualize continent-wise distributions of average time spent on a website. Interpret how continent distributions reveal concentration and dispersion.

Explore seaborn to create rolling charts and distribution visuals in Python, using site visitors by continent, customize color palettes, and interpret concentration patterns across categories.

Explore how to build a Python-driven data visualization workflow in Power BI, creating a report, tables, and a stripplot with seaborn to compare average time spent and site visitors.

Explore how to customize a strip plot in Python with palette, diamond or hexagonal markers, dot size, and alpha transparency to reveal patterns while handling errors and data updates.

Learn to create a box plot in Python using libraries like Clip and Seabourne, pass data and column parameters, and troubleshoot simple name errors due to case.

Create advanced box plots in Seaborn with mean lines and axis customization to visualize distribution, supporting data like average time spent in courses and site visitors.

Explore creating an lmplot style linear plot using seaborn to visualize the relationship between average time spent online and site visitors, with a linear trend line.

Open a Jupyter notebook or Google Colab, install Plotly and related libraries for data visualization, and build a simple interactive line chart with zoom and auto scale.

Learn to create line charts and time series visualizations using stock price data with dummy datasets in Python and Plotly, highlighting ups and downs and comparing two companies.

Create a scatter chart from a dummy, random dataset to explore the relationship between stock price and price change, using Python data visualization tools and a simple dataframe.

Enhance scatter plots with titles, colors, symbols, and data labels to create interactive, visually clear visualizations. Learn axis scaling, zoom, and presentation tricks to avoid misleading charts.

Learn to extend a scatter chart into a bubble chart by using volume as bubble size, adding a third dimension to visualize data with Python, Plotly, and Power BI.

Visualize sector composition and stock weightage by generating data, counting sector occurrences, and rendering a pie chart with the express library. Limit slices to seven for clarity and avoid clutter.

Learn how donut charts, a hole-in-center variant of pie charts, visualize categories as segments and support interactive options in Plotly to compare sectors like health care and finances.

Create sunburst charts to visualize hierarchical data with an inner circle and concentric rings, distributing angles by values. Drill down by tapping slices and compare to pie charts.

Create a vertical bar chart from an excel dataset using read_excel, import the necessary libraries, and compare ownership categories—promoters, FICA, and the public—over time with a title and year-based colors.

Explore creating vertical grouped and stacked bar charts in Python visualization tools, visualizing ownership across categories—promoters, public, and F ideas—using color and bar modes.

Change the orientation to horizontal to convert vertical bar charts, placing categories on the y-axis and values on the x-axis, and explore bar modes including stacking.