Top 10 Data Cleaning Methods in Power BI

Data cleaning is an essential step in the data analysis process, and Power BI provides several tools and features to help you clean and prepare your data.

The top 10 data cleaning methods in Power BI:

Remove Duplicates: Use the "Remove Duplicates" option to eliminate duplicate rows from your dataset.

Replace Values: Replace incorrect or inconsistent values in your dataset using the "Replace Values" feature.

Split Columns: Use the "Split Column" feature to divide a column into multiple columns based on a delimiter, such as a space or comma.

Trim: Remove leading and trailing spaces from text data using the "Trim" function.

Clean: Use the "Clean" function to remove non-printable characters from text data.

Fill Down/Up: Use the "Fill Down" or "Fill Up" feature to propagate values down or up a column to fill in missing values.

Remove Rows: Remove unnecessary rows from your dataset, such as blank rows or rows with specific criteria, using the "Remove Rows" feature.

Transform Data Types: Convert columns to the appropriate data type (e.g., text, number, date) using the "Data Type" transformation.

Pivot/Unpivot Columns: Use the "Pivot" and "Unpivot" features to reshape your data for better analysis.

Conditional Column: Create new columns based on conditions using the "Conditional Column" feature, allowing you to categorize or flag data based on specific criteria.

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