Learn Power Query with Microsoft Power BI

Load data from Excel into Power Query in Power BI, transform data by removing top or bottom rows, and split a column into two by a delimiter, then apply changes.

Learn to replace column values in Power Query to automate data cleaning and transformation in Power BI, using replace values, transform data, and practical examples with large spreadsheets.

Merge two columns in Power Query for Power BI and name the new field. Use a separator, such as space or comma, and learn to replace or split as needed.

Learn to add prefix and suffix in power query editor, applying currency symbols or titles to data, while avoiding numeric disruption and enabling separate column creation when needed.

Convert column data to uppercase or lowercase using Power Query's transform options, preserving numbers and special characters while standardizing text like country names.

Learn the extract function in Power Query to transform or add columns in Power BI. Extract characters, ranges, months or years from delimited data and derive domains from email IDs.

Learn to extract text based on a delimiter in Power Query for Microsoft Power BI, using before, after, and between options with advanced delimiter controls.

Learn to add an index column and create conditional columns in Power Query, using custom ranges and if-else logic to classify data by price, text, and dates.

Learn how to reopen the Power Query editor and edit queries. Enable the formula bar and use the view options to assess column quality, distribution, and profile.

Sort your data in Power Query with Microsoft Power BI by selecting a column and choosing ascending or descending order, including alphabetic, numeric, and date/time formats.

Explore date functions in Power Query to create multiple date columns, extract year, quarter, week of year, day of week, and leap-year days for richer visualizations.

Explore Power Query date and time functions in Power BI to extract date and time into columns, calculate age from birth dates, and convert durations to years, seconds, or timestamps.

Learn how to use time functions in Power Query with Microsoft Power BI to extract hour, minute, second, perform time differences, and calculate working hours from login and logout data.

Create and customize a bar chart in Power BI using Excel data, assign data axis and values, and format colors, titles, and axes to visualize price changes over time.

Learn to create and customize a line chart in Power BI using Power Query, selecting axis and values, configuring data labels, and exploring solid, dotted, and stepped line options.

Learn to create a ring chart in Power BI, pair it with a pie chart, and use categories, subcategories, data labels, and filters to craft a focused dashboard.

Master treemap visuals in Power BI and connect them with other charts on a dashboard to enable cross-chart filtering by category and country data.

Learn to create and format tables and matrices in Power BI, including adding rows, columns, and values with measures and dimensions, and applying formatting and drill-down.

Explore drill down in table and matrix visuals with Power Query and Power BI, enabling deeper hierarchies and real-time updates as you click categories and regions.

Use a slicer as a lightweight filter with checkboxes to select options like country or state, filtering related data in visuals. It supports multiple selections and can drive several charts.

Learn to create and use date slicers in Power BI with sliders and checkboxes, filtering by range, year, quarter, and month to drive real-time visuals.

Identify a chart, show data as a table, and export a chart's subset as comma separated value or spreadsheet for targeted teams in Power BI.

Create a simple map in Power BI using geographic names without latitude. Adjust bubble size by order quantity, color, and tooltips, and explore map styles.

Use a Python script in Power Query to replace null values with the mean for numerical columns or remove rows when appropriate, then build charts in Power BI.

Master Power Query by appending queries to join two tables, recover lost date data, and replace nulls with the mean value while preserving date information for analysis in Power BI.