Using Right Join on DataFrames in Julia

In Julia, you can use rightjoin function to perform a right outer join on two data structures. Below are more details and examples:

A right outer join combines the rows from two data structures and returns all the rows from the second data structure, along with the matching rows from the first data structure. If there is no match in the first data structure for a row in the second data structure, the function will return NULL or missing for the columns in the first data structure.

Right Outer Join on DataFrames in Julia Examples

Here is an example of how to use the rightjoin function in Julia:

using DataFrames

# Create two data frames
df1 = DataFrame(id = [1, 2, 3, 4], name = ["Alice", "Bob", "Charlie", "Dave"])
df2 = DataFrame(id = [3, 4, 5, 6], age = [20, 25, 30, 35])

# Perform a right outer join on the two data frames
df3 = rightjoin(df1, df2, on = :id)

# Print the resulting data frame
println(df3)

The output of this code will be:

4×3 DataFrame
 Row │ id     name     age   
     │ Int64  String?  Int64 
─────┼───────────────────────
   1 │     3  Charlie     20
   2 │     4  Dave        25
   3 │     5  missing     30
   4 │     6  missing     35

As you can see, the resulting data frame contains all the rows from the second data frame, along with the matching rows from the first data frame. If there is no match in the first data frame for a row in the second data frame, the function returns missing for the columns in the first data frame.

You can also perform a right outer join on multiple columns by specifying a vector of symbols for the on keyword argument. For example:

df1 = DataFrame(id = [1, 2, 3, 4], name = ["Alice", "Bob", "Charlie", "Dave"], city = ["New York", "Chicago", "Los Angeles", "San Francisco"])
df2 = DataFrame(id = [3, 4, 5, 6], age = [20, 25, 30, 35], city = ["Los Angeles", "San Francisco", "Miami", "Dallas"])

df3 = rightjoin(df1, df2, on = [:id, :city])

This will perform a right outer join on both the id and city columns, resulting in the following data frame:

The output of this code will be:

4×4 DataFrame
 Row │ id     name     city           age   
     │ Int64  String?  String         Int64 
─────┼──────────────────────────────────────
   1 │     3  Charlie  Los Angeles       20
   2 │     4  Dave     San Francisco     25
   3 │     5  missing  Miami             30
   4 │     6  missing  Dallas            35

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