TD_GetRowsWithMissingValues | GetRowsWithMissingValues - TD_GetRowsWithMissingValues - Analytics Database

Database Analytic Functions

Analytics Database
Release Number
June 2022
English (United States)
Last Update
Product Category
Teradata Vantageā„¢

TD_GetRowsWithMissingValues is a function or method that retrieves all rows or records from a dataset that contain one or more missing or null values.

This function is often used in data cleaning or preprocessing tasks to identify and handle missing values in datasets. Finding rows with missing values is an essential step in data cleaning and preprocessing for analytics. By identifying and handling missing values, data analysts and scientists can ensure that their analyses and models are based on complete and accurate data, which can improve the quality and reliability of their results.

Missing values can lead to biased or inaccurate analysis results if not appropriately addressed. Here are some common use cases for finding rows with missing values in analytics:
  • Imputation: One approach for handling missing values is to impute them, which means to update them with an estimated value. Before imputing missing values, you must identify which rows contain them to ensure that imputed values are accurate and appropriate.
  • Outlier detection: Missing values can sometimes be an indication of outliers or extreme values. By identifying and investigating the rows with missing values, you can determine whether there are outliers or other anomalies in the data that need to be addressed.
  • Data quality assessment: Missing values can also be a sign of poor data quality. By identifying the rows with missing values, you can assess the overall quality of the data and determine whether it is suitable for analysis.
  • Feature engineering: In machine learning, missing values can be problematic because many algorithms cannot handle them. Therefore, you must identify the rows with missing values and decide whether to impute them or drop them. Additionally, missing values can be indicative of a particular pattern in the data that can be used to engineer new features that improve model performance.

Identifying rows with missing values enables data analysts to make informed decisions about how to handle missing values and ensures that their analysis results are based on complete and accurate data.