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How can two data sets with varying row numbers be combined?

asked 2023-03-25 11:00:00 +0000

devzero gravatar image

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answered 2022-07-13 06:00:00 +0000

lalupa gravatar image

There are several ways to combine two data sets with varying row numbers, depending on the goal and the software used. Here are three common methods:

  1. Concatenation: In this method, the rows of one dataset are simply appended to the rows of the other dataset. This can be done vertically (adding rows below each other) or horizontally (adding columns next to each other). If the datasets have different column names or types, they may need to be reformatted or aligned before concatenating. This method works well when the datasets have no overlapping rows or when the order of the rows is not important.

  2. Joining: In this method, the rows of one dataset are matched to the rows of the other dataset based on one or more common features (e.g. a unique identifier). There are several types of joins, such as inner join (keeping only the matching rows), left join (keeping all rows from the left dataset and matching rows from the right dataset), right join (keeping all rows from the right dataset and matching rows from the left dataset), and outer join (keeping all rows from both datasets, filling missing values with null or default values). Joining requires careful attention to the common features and the order of the datasets.

  3. Merging: In this method, two datasets with multiple variables are combined based on a common variable (or variables) such that the variables of both datasets are kept when they are unique, and variables are matched in new columns when they are common. It is, therefore different from concatenation and joining. It allows us to combine variables from two datasets that are related in some way. It is used when one dataset has extra variables that we want to include in another dataset. When merging datasets, it is essential to identify the shared variables early on and have the same variable values to gain good results.

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Asked: 2023-03-25 11:00:00 +0000

Seen: 6 times

Last updated: Jul 13 '22