How to store results from loop in Julia

October 13, 2022

Suppose that I have a function that makes some calculation and return a dataframe in Julia

using DataFrames, Random
function dummy_df(;theta)
    A = randn(100); B = randn(100); 
    A = A .* theta; B = B .* theta
    df_ = DataFrame(A = A, B = B)

    return(df_)
end

a = dummy_df(theta = 1)

Now suppose I have this values for theta.

tau = range(0.85, 1, length = 10)

So I want to create some object that contains the dataframes produced for each element in tau vector. (In R it would be a list)

I think this is like a list from R

a = []

So I want to fill that list with all the dataframes.

So I am trying to do a for loop that runs dummy_df and stores that dataframe like this:

for (i,t) in pairs(tau)
    a[i] = dummy_df(theta = t)
end

But I have this error:

ERROR: BoundsError: attempt to access 0-element Vector{Any} at index [1]

>Solution :

The syntax to add items to an empty list is push!, using a[i] will just try to access an empty list that doesn’t have any items.

I also replaced your tau with t because I’m guessing you want the theta to change every loop not feed tau into the dummy_df.

I managed to create a list of dataframe with no errors.

using DataFrames, Random
function dummy_df(;theta)
    A = randn(100); B = randn(100); 
    A = A .* theta; B = B .* theta
    df_ = DataFrame(A = A, B = B)

    return(df_)
end

tau = range(0.85, 1, length = 10)
a = []
for (i,t) in pairs(tau)
    push!(a, dummy_df(theta = t)) #guessing you made a mistake here and meant to put t instead of tau
end

A printout of a looks like this,

10-element Vector{Any}:
 100×2 DataFrame
 Row │ A            B           
     │ Float64      Float64     
─────┼──────────────────────────
   1 │  0.958768    -0.302111
   2 │  0.370959     1.12717
   3 │  1.69035      1.45085
   4 │ -0.586892     0.258312
   5 │ -1.43566      1.01867
   6 │  0.0389977    0.471484
   7 │ -0.314276    -0.347831
   8 │ -0.00176065   0.494805
  ⋮  │      ⋮            ⋮
  94 │  0.700048    -1.50753
  95 │ -0.154113     1.04487
  96 │ -0.716135    -0.883592
  97 │  0.487718    -0.0188041
  98 │  1.01621      0.863817
  99 │ -0.578018     0.270933
 100 │  1.89336     -0.837134
                 85 rows omitted
 100×2 DataFrame
 Row │ A           B           
     │ Float64     Float64     
─────┼─────────────────────────
   1 │  0.388522   -1.18676