How can i sampling Data Frame?

**Like python data.sample() method**

**NB: replace must need**

Just can just use random row indices like:

```
julia> using DataFrames, Random
julia> df = DataFrame(a = 1:10, b = rand(10))
10×2 DataFrame
│ Row │ a │ b │
│ │ Int64 │ Float64 │
├─────┼───────┼──────────┤
│ 1 │ 1 │ 0.180922 │
│ 2 │ 2 │ 0.726072 │
│ 3 │ 3 │ 0.802304 │
│ 4 │ 4 │ 0.769662 │
│ 5 │ 5 │ 0.705299 │
│ 6 │ 6 │ 0.266686 │
│ 7 │ 7 │ 0.332831 │
│ 8 │ 8 │ 0.393075 │
│ 9 │ 9 │ 0.1936 │
│ 10 │ 10 │ 0.830922 │
julia> df[shuffle(1:nrow(df))[1:5], :]
5×2 DataFrame
│ Row │ a │ b │
│ │ Int64 │ Float64 │
├─────┼───────┼──────────┤
│ 1 │ 7 │ 0.332831 │
│ 2 │ 8 │ 0.393075 │
│ 3 │ 1 │ 0.180922 │
│ 4 │ 5 │ 0.705299 │
│ 5 │ 9 │ 0.1936 │
```

The `shuffle`

function returns a random ordering of the range from 1 to the number of rows of your dataframe, which you can then index with `[1:x]`

where x is the number of samples you want.

Alternatively, there are ML/stats packages that implement their own way of splitting data into train and test data, like MLJ or Turing - check their docs if that’s of interest.

need 100 rows data to 1000 sample

I’m not sure I understand - do you want to sample 100 rows from a 1,000 row `DataFrame`

? Or do you want to draw 1,000 samples of length 100 from a larger data set? My suggestion above can work in both cases, can you clarify what you’re looking for (and what isn’t working for you) ideally by way of a minimal working example?

yes i want 1,000 samples from length 100 data set

Okay to adapt my example from above, you have a length 100 data set:

```
df = DataFrame(a = 1:100, b = rand(100))
```

now we can get 1,000 random samples from this - I’m assuming each sample has length 10 here:

```
samples = [df[shuffle(1:nrow(df))[1:10], :] for _ in 1:1_000]
```

`samples`

is now a vector of lenght 1,000 which holds a 10-row random sample of your original data set in each location.

Or if you’d like to sample 1,000 rows with replacement:

```
df[rand(1:nrow(df),1000),:]
```

I imagine you are trying to bootstrap data. In addition to the solutions given here, see if `bootstrap.jl`

is a package that works for you.

DependentBootstrap will also work here. One of the options is an iid bootstrap which will do what the OP wants, ie:

```
using DependentBootstrap
dbootdata(mydataframe, numresample=1000, bootmethod=:iid)
```

will return a vector of length `1000`

where each element is a resampled `DataFrame`

.

This is how I split my DataFrame into “training” and “testing”

```
function createTrainTest(df::DataFrame,prop=0.5,randomseed=1234)
df_training = similar(df,0)
df_testing = similar(df,0)
# Now split the df into df_training and df_testing
df_size = size(df,1)
training_proportion = prop
trainingsize = round(df_size*training_proportion)
# Create a random permutation vector
randvec = randperm!(MersenneTwister(randomseed),
Vector{Int64}(undef,df_size))
for k in axes(df)[1]
push!( k ≤ trainingsize ?
df_training : df_testing ,
df[randvec[k],:]
)
end
return (df_training,df_testing)
end
```

If you want 1000 samples with each sample having 100 rows then just change the trainingsize to a fixed value of 100 and call the above function 1000 times.

PS: do not forget to use a different randomseed each time!

```
using StatsBase:sample
using DataFrames
df = DataFrame(a = 1:1000)
sample_rows = sample(1:nrow(df), 100, replace=false)
df_sample = df[sample_rows, :]
test_rows = setdiff(1:nrow(df), sample_rows)
df_test = df[test_rows, :]
```