How to Analyze Missing Values in a Dataset
When beginning an analysis of a dataset, a good first step is to get an understanding of which data points have blank or missing values.
Identifying where there are missing values in a dataset can help you make more informed decisions about your strategy and approach for analyzing the data.
Flow provides a specific function for analyzing the missing values in a dataset called Profile Blank Data. The Profile Blank Data function takes in a dataset as input and generates a new profile dataset describing each data point and how many missing or blank values it contains.
In this blog post, I will demonstrate how to configure and implement the Profile Blank Data function to describe the missing values in a dataset.
The example we will explore will focus on data loaded from a delimited file. We will use the delimited file integration interface to import a sample data file.
Once we have imported this dataset, we will use the Profile Blank Data function to compute a profile collection containing quantitative scores for the number of missing values across each data point.
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The video below provides a step-by-step walkthrough of how to perform the analysis of blanks. Check out the video here: