Top 10 steps to clean data on PowerBI

By incorporating these top 10 data cleaning commands into your Power BI workflow, you can ensure that your data is accurate, consistent, and ready for in-depth analysis and visualization.

Cleaning and preparing your data is a crucial step in the data analysis process, and Power BI provides a set of powerful commands to help you achieve this. Here are the top 10 commands to clean data in Power BI:

Remove Duplicates:
Duplicate values can skew your analysis. Use the "Remove Duplicates" command to identify and eliminate duplicate rows from your dataset.

Select the column(s) containing potential duplicates.

Navigate to the "Home" tab and click on "Remove Duplicates."

Filter Rows:
Filtering rows based on specific conditions helps focus your analysis on relevant data. Use the "Filter Rows" command to include or exclude rows based on certain criteria.

Select the column you want to filter.

Go to the "Home" tab and click on "Keep Rows" or "Remove Rows."

Replace Values:
Incomplete or inconsistent data can be problematic. The "Replace Values" command allows you to replace specific values with desired ones.

Select the column to be modified.

Go to the "Transform" tab and click on "Replace Values."

Fill Down/Up:
When dealing with missing data, the "Fill Down" or "Fill Up" commands come in handy. These commands propagate the values from the above or below cells to fill in the gaps.

Right-click on the column with missing values.

Choose "Fill" and then "Down" or "Up."

Split Columns:
Sometimes, you might need to split a column into multiple columns or extract specific information. The "Split Columns" command provides flexibility in managing your data structure.

Select the column to be split.

Navigate to the "Transform" tab and click on "Split Column."

Trim:
Leading and trailing spaces in text data can lead to inconsistencies. The "Trim" command removes extra spaces, ensuring uniformity in your text data.

Select the column with text data.

Go to the "Transform" tab and click on "Trim."

Change Data Types:
Ensuring that each column has the correct data type is crucial for accurate analysis. The "Change Type" command allows you to convert data types easily.

Select the column to be modified.

Go to the "Transform" tab and click on "Data Type."

Merge Queries:
Combining data from multiple sources is common, and the "Merge Queries" command lets you merge tables based on a common column.

Select the queries to be merged.

Go to the "Home" tab and click on "Merge Queries."

Remove Columns:
Unnecessary columns can clutter your dataset. The "Remove Columns" command lets you selectively remove columns that are not needed for your analysis.

Select the columns to be removed.

Right-click and choose "Remove."

Conditional Columns:
Create new columns based on specific conditions using the "Conditional Columns" command. This is useful for deriving additional insights from your data.

Go to the "Transform" tab and click on "Conditional Column."

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