Now that we understand how missing data occurs and why it’s important, let’s go over some high-level strategies on how to identify and check for missing data.
Verify that data was uploaded correctly in the first place. The easiest way to avoid missing data is to prevent it from happening in the first place! Since most missing data use cases happen from a systematic error, try to find the culprit and correct the faulty data feed.
Try looking at small chunks of the data. Oftentimes, missing data can be easy to spot when looking at the data directly. Most commonly, data scientists, data analysts, and data professionals will look at either the beginning or end of a dataset, or retrieve a random sampling of data to look at. If there is a significant amount of missing data, it will be apparent by doing so.
Look at statistics for the entire dataset. Sometimes, however, missing data might be hard to find and could be a small subset of the data. A quick method to find out if there are any missing values at all is to collect some basic summary statistics about our data. In particular, we can count how many values there are in each column of your dataset, and note any discrepancies. If a column has a count lower than our total number of rows, it has missing data!
Identifying and Checking for Missing Data
Now that we understand how missing data occurs and why it’s important, let’s go over some high-level strategies on how to identify and check for missing data.