Essential Data Cleaning Techniques in Power Query (Beginners Edition)

Data cleaning is the cornerstone of any successful data analysis or visualization project. It involves the process of detecting and correcting errors and inconsistencies in data to improve its quality and reliability.

However, data cleaning can be a time-consuming and challenging task, especially for a beginner. Fortunately, with the advent of Power Query in Microsoft Excel and Power BI, data cleaning has become more efficient and user-friendly than ever before.

Power Query is a robust toolset that empowers users to transform, clean, and prepare data for analysis with ease. From simple data formatting to complex transformations, Power Query offers several tools and functions to streamline the data-cleaning process. In this blog post, I will be sharing some essential Power Query tools to help you get started with data cleaning.

DATA CLEANING TECHNIQUES/STEPS YOU SHOULD KNOW

1. Data Type Handling: One of the first steps in data cleaning is ensuring that each column has the correct data type. A column containing text should have the data type “text”, and so on. Power Query provides intuitive options to change data types, such as converting text to numbers or dates, ensuring consistency and accuracy in your dataset.

2. Removing Duplicates: Duplicate records can skew analysis results and lead to inaccuracies. Power Query simplifies the process of identifying and removing duplicates with just a few clicks, ensuring that your dataset remains in perfect condition.

To remove duplicates, right click on the column of interest and choose “Remove Duplicates”

3. Filtering and Sorting: Filtering and sorting data are fundamental operations in data cleaning. Power Query offers powerful filtering and sorting capabilities, allowing users to isolate specific rows or columns based on custom criteria and arrange data in a logical order for analysis.

To sort click on the arrow (Column drop-down), 1 = sorting options, 2= Data being filtered (You can do a number filter as well), 3= Filtering Options.

4. Text Manipulation: Cleaning textual data often involves tasks like removing leading or trailing spaces, converting text to lowercase or uppercase, or extracting substrings. Power Query provides a range of text manipulation functions that enable users to perform these tasks efficiently.

Find the transform tab and click Format; Click the drop-down on Extract for further text manipulation. 

5. Handling Missing Values: Missing data can introduce bias and errors into your analysis. Power Query equips users with tools to handle missing values, including options to replace null values with custom values or interpolate missing data based on neighboring values. 

Click on the Replace Values drop-down to choose the action of choice

6. Splitting and Merging Columns: Sometimes, data may be stored in a single column in a format that is not conducive to analysis. Power Query allows users to split columns based on delimiters or merge multiple columns into a single column, enabling better organization and analysis of data.

Find the Transform tab, to Split or Merge columns

7. Data Transformation: Beyond basic cleaning operations, Power Query enables users to be able to perform pivoting and unpivoting cleaning activities.

In simple terms, to Pivot is to turns rows into columns and to Unpivot is to turns columns into rows.

Both Pivot and Unpivot options are under the Transform Tab.

8. Data Profiling: Understanding the quality and distribution of your data is essential for effective cleaning. Power Query includes data profiling features that provide insights into the structure and characteristics of your dataset, helping you identify potential issues and anomalies. 

To activate data profiling options, locate the View Tab, and tick tools in 1, 2 Shows Column quality, 3 Shows Column Distribution while 4 shows Column Profile.

Important Tip: Power Query provides several more tools not discussed above; However Right-clicking on a Column Header is a shortcut to have access to some of the essential tools in Power Query for quick cleaning exercise.

Conclusion

Data cleaning is a crucial aspect of data analysis, and Power Query equips users with essential tools to carry out this activity. By mastering these fundamental Power Query techniques, beginners can efficiently clean and prepare their data for analysis and visualization.

With continued practice, beginners can harness the full potential of Power Query to enhance their data-cleaning skills and derive actionable insights from their data.