Data Visualization with Python and Power BI

Define the Python home path and install pandas and seaborn via pip to enable Python visuals in Power BI, then refresh Power BI to run Python scripts.

Create line charts with Python in Power BI by importing Excel data, selecting a sheet, defining x and y values, and customizing colors, titles, and layout.

Add x and y axis labels and customize charts in Power BI and Python to clarify data. Create line charts, scatterplots, and dashed lines, and combine them to reveal patterns.

Create a violin chart with seaborn in python to visualize continent-based distribution of average time spent on a website, using x axis as continent and y axis as average time.

Learn to create violin charts in Python to visualize distribution and concentration across continents using site visitor data, customize color palettes, and troubleshoot simple syntax issues.

Create a python stripplot with seaborn to visualize average time spent (x) versus site visitors (y) from a dataset. Explore customizing markers and colors to enhance the chart.

Learn to customize strip plots by applying palettes, marker shapes, sizes, edge colors, and transparency, then manage errors, refresh data, and create multiple charts to reveal distribution patterns.

Learn to create a box plot in python using clip and seabourne, by passing two parameters for the column and value, and fix a name error to run the code.

Create advanced box plots with Seaborn to visualize distribution, quartiles, and mean, and customize axes, colors, and titles for data science and business intelligence applications.

Learn to build an lmplot-style alignment chart in Python using seaborn to plot average time spent online against site visitors with a linear fit line.

Explore building a ribbon chart in Power BI to visualize rank flow over time, using time-based axes and category measures to show shifts in category ranks.

Explore creating tables and matrices in Power BI, adding columns, measures, and dimensions, and using drill down to explore multi-dimensional data with formatting options.

Explore drill down in tables and matrices to reveal deeper hierarchies, updating per country, state, and category while highlighting relevant data across connected charts.

Learn to create a donut chart from a pie chart, assign category data to labels and measures to values, and synchronize colors and legends for a dashboard.

Drop country or state into the location tab and size by order quantity to visualize a simple map in Power BI, with options for map styles and colors.

Learn to import web data into Power BI, extract and clean tables from a web page, and build multiple visualizations—bar, ring, and line charts—exploring EU population and parliamentary representation.

Explore the basics of slicers in Power BI, using checkboxes to filter by country or state, create multiple slicers for isolation, and link them to charts for focused data views.

Create and configure a date slicer in Power BI to filter visuals by date ranges from year to day, using hierarchy-based filters for quarters, months, and dates.

Remove null values in datasets using Power Query and Python, filling with mean for numerical columns, handling missing dates and categories, and preparing charts in Power BI.