Multiple conditionals in Julia DataFrame-CodePudding

I have a DataFrame with 3 columns, named :x :y and :z which are Float64 type. :x and "y are iid uniform on (0,1) and z is the sum of x and y.
I want to a simple task. If x and y are both greater than 0.5 I want to print z and replace its value to 1.0. For some reason the following code is running but not working

if df.x .> 0.5 && df.y .> 0.5
  println(df.z)
  replace!(df, :z) .= 1.0
end

Would appreciate any help on this

CodePudding user response：

Your code is working on whole columns, and you want the code to work on rows. The simplest way to do it is (there are faster ways to do it, but the one I show you is simplest):

julia> using DataFrames

julia> df = DataFrame(rand(10, 2), [:x, :y]);

julia> df.z = df.x   df.y;
julia> df = DataFrame(rand(10, 2), [:x, :y]);

julia> df.z = df.x   df.y;

julia> df
10×3 DataFrame
 Row │ x           y         z
     │ Float64     Float64   Float64
─────┼────────────────────────────────
   1 │ 0.00461518  0.767149  0.771764
   2 │ 0.670752    0.891172  1.56192
   3 │ 0.531777    0.78527   1.31705
   4 │ 0.0666402   0.265558  0.332198
   5 │ 0.700547    0.25959   0.960137
   6 │ 0.764978    0.84093   1.60591
   7 │ 0.720063    0.795599  1.51566
   8 │ 0.524065    0.260897  0.784962
   9 │ 0.577509    0.62598   1.20349
  10 │ 0.363896    0.266637  0.630533

julia> for row in eachrow(df)
           if row.x > 0.5 && row.y > 0.5
               println(row.z)
               row.z = 1.0
           end
       end
1.5619237447442418
1.3170464579861205
1.6059082278386194
1.515661749106264
1.2034891678047939

julia> df
10×3 DataFrame
 Row │ x           y         z
     │ Float64     Float64   Float64
─────┼────────────────────────────────
   1 │ 0.00461518  0.767149  0.771764
   2 │ 0.670752    0.891172  1.0
   3 │ 0.531777    0.78527   1.0
   4 │ 0.0666402   0.265558  0.332198
   5 │ 0.700547    0.25959   0.960137
   6 │ 0.764978    0.84093   1.0
   7 │ 0.720063    0.795599  1.0
   8 │ 0.524065    0.260897  0.784962
   9 │ 0.577509    0.62598   1.0
  10 │ 0.363896    0.266637  0.630533

Edit

Assuming you do not need to print here is a benchmark of several options:

julia> df = DataFrame(rand(10^7, 2), [:x, :y]);

julia> df.z = df.x   df.y;

julia> @time for row in eachrow(df) # slowest
           if row.x > 0.5 && row.y > 0.5
               row.z = 1.0
           end
       end
  3.469350 seconds (90.00 M allocations: 2.533 GiB, 10.07% gc time)

julia> @time df.z[df.x .> 0.5 .&& df.y .> 0.5] .= 1.0; # fast and simple
  0.026041 seconds (15 allocations: 20.270 MiB)

julia> function update_condition!(x, y, z)
           @inbounds for i in eachindex(x, y, z)
               if x[i] > 0.5 && y[i] > 0.5
                   z[i] = 1.0
               end
           end
           return nothing
       end
update_condition! (generic function with 1 method)

julia> update_condition!(df.x, df.y, df.z); # compilation

julia> @time update_condition!(df.x, df.y, df.z); # faster but more complex
  0.011243 seconds (3 allocations: 96 bytes)

CodePudding user response：

The following ifelse is 60X faster than a loop for 500k rows dataframe.

using DataFrames
x = rand(500_000)
y = rand(500_000)
z = x   y
df = DataFrame(x = x, y = y, z = z)

df.z .= ifelse.((df.x .> 0.5) .&& (df.y .> 0.5), 1.0, df.z)