Patterns
M Data Cleaning
Cleaning Data with M
M data cleaning removes nulls and duplicates with Table functions.
Introduction to M Data Cleaning
Data cleaning is a crucial step in data processing that ensures the quality and accuracy of your data. In M, the data transformation language used in Power Query, data cleaning often involves removing nulls and duplicates. This tutorial will guide you through using Table functions to clean data efficiently.
Removing Null Values
Null values can disrupt data analysis by introducing inaccuracies. In M, you can remove rows with null values using the Table.SelectRows function. Below is an example of how to filter out rows that contain nulls in a specific column.
Removing Duplicate Rows
Duplicate rows can skew your data analysis results. To remove duplicates in M, you can use the Table.Distinct function. Here’s how to eliminate duplicate rows from a table.
Combining Data Cleaning Steps
Often, data cleaning involves multiple steps. You can combine the null removal and duplicate removal processes into a single operation. Below is an example demonstrating how to perform these steps consecutively.
Conclusion
M data cleaning with Table functions is both powerful and flexible. By leveraging functions like Table.SelectRows and Table.Distinct, you can efficiently clean data, ensuring its quality for further analysis. Practice these techniques to enhance your data handling skills.
Patterns
- Data Cleaning
- Data Aggregation
- Data Shaping
- Dynamic Filtering
- Bulk Import
- Data Validation
- Previous
- Query Steps
- Next
- Data Aggregation
