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Using Cross Join on DataFrames in Julia

To perform a cross-join (also known as a cartesian join) on data frames in Julia, you can use the crossjoin function. A cross-join is a type of join operation that combines every row from one data frame with every row from another data frame, resulting in a new data frame that has all possible combinations of rows from the two input data frames. For example, if you have two data frames df1 and df2 with three rows each, a cross-join on these data frames will result in a new data frame with nine rows, with each row in df1 being paired with each row in df2.

Cross Join on DataFrames in Julia Examples

Here is an example of how to use crossjoin to perform a cross-join on two data frames df1 and df2:

using DataFrames

df1 = DataFrame(x = [1, 2, 3], y = ["a", "b", "c"])
df2 = DataFrame(z = [4, 5, 6])

df_crossjoin = crossjoin(df1, df2)

The resulting data frame df_crossjoin will have nine rows, with each row in df1 being paired with each row in df2. The resulting data frame will have three columns: x, y, and z.

9Γ—3 DataFrame
 Row β”‚ x      y       z     
     β”‚ Int64  String  Int64 
─────┼──────────────────────
   1 β”‚     1  a           4
   2 β”‚     1  a           5
   3 β”‚     1  a           6
   4 β”‚     2  b           4
   5 β”‚     2  b           5
   6 β”‚     2  b           6
   7 β”‚     3  c           4
   8 β”‚     3  c           5
   9 β”‚     3  c           6

Here is another example of how to use the crossjoin function from the Query.jl package to perform a cross-join on two data frames in Julia:

using DataFrames

df1 = DataFrame(a = [1, 2, 3], b = ["x", "y", "z"])
df2 = DataFrame(c = [4, 5], d = ["p", "q"])

df_crossjoin = crossjoin(df1, df2)

The resulting data frame df_crossjoin will have six rows, with each row in df1 being paired with each row in df2. The resulting data frame will have four columns: a, b, c, and d.

6Γ—4 DataFrame
 Row β”‚ a      b       c      d      
     β”‚ Int64  String  Int64  String 
─────┼──────────────────────────────
   1 β”‚     1  x           4  p
   2 β”‚     1  x           5  q
   3 β”‚     2  y           4  p
   4 β”‚     2  y           5  q
   5 β”‚     3  z           4  p
   6 β”‚     3  z           5  q

Related:

  1. Using Anti Join on DataFrames in Julia
  2. Using Left Join on DataFrames in Julia
  3. Using Right Join on DataFrames in Julia
  4. Using Inner Join on DataFrames in Julia
  5. Using Outer Join on DataFrames in Julia