To perform an inner join of two dataframes in Julia, you can use the innerjoin function from the DataFrames package.
An inner join returns only the rows that have matching values in both dataframes. The resulting dataframe will contain only the columns that exist in both dataframes.
Inner Join on DataFrames in Julia Example
Here is the syntax of how to perform an inner join of two dataframes df1 and df2 on the column "id":
using DataFrames # Inner join df1 and df2 on the "id" column df_inner = innerjoin(df1, df2, on = :id)
Here is a complete example of how to use innerjoin with sample data:
using DataFrames # Create two dataframes df1 = DataFrame(id = [1, 2, 3, 4], name = ["Alice", "Bob", "Charlie", "Dave"]) df2 = DataFrame(id = [2, 3, 4, 5], age = [30, 40, 50, 60]) # Inner join df1 and df2 on the "id" column df_inner = innerjoin(df1, df2, on = :id) # Print the resulting dataframe println(df_inner)
The output of this code will be:
3×3 DataFrame
Row │ id name age
│ Int64 String Int64
─────┼───────────────────────
1 │ 2 Bob 30
2 │ 3 Charlie 40
3 │ 4 Dave 50Here is another example of the code that demonstrates how to perform an inner join on three dataframes in Julia using the innerjoin function from the DataFrames package:
Here is an example of how you can do this:
using DataFrames # Create three dataframes df1 = DataFrame(id = [1, 2, 3, 4], name = ["Alice", "Bob", "Charlie", "Dave"]) df2 = DataFrame(id = [2, 3, 4, 5], age = [30, 40, 50, 60]) df3 = DataFrame(id = [3, 4, 5, 6], country = ["USA", "Canada", "Mexico", "Brazil"]) # Inner join df1, df2, and df3 on the "id" column df_inner = innerjoin(df1, df2, df3, on = :id) # Print the resulting dataframe println(df_inner)
The output of this code will be:
2×4 DataFrame
Row │ id name age country
│ Int64 String Int64 String
─────┼────────────────────────────────
1 │ 3 Charlie 40 USA
2 │ 4 Dave 50 CanadaIn this example, the inner join is performed directly on all three dataframes, and the resulting dataframe is stored in the variable df_inner.
Performing the inner join in this way can be more efficient than performing multiple inner joins sequentially, because it avoids the need to create intermediate dataframes. However, it may be more difficult to understand the code if you are not familiar with the data contained in the three dataframes.
