Stratified and weighted sampling in dataframes

When working with dataframes in Julia, it is often necessary to perform stratified and weighted sampling. This article will explore three different ways to achieve this in Julia, each with its own advantages and disadvantages.

Option 1: Using the DataFramesMeta.jl package

The DataFramesMeta.jl package provides a convenient way to perform stratified and weighted sampling in Julia. To use this package, you first need to install it by running the following command:


using Pkg
Pkg.add("DataFramesMeta")

Once the package is installed, you can use the `@by` macro provided by DataFramesMeta.jl to perform stratified sampling. Here is an example:


using DataFrames, DataFramesMeta

df = DataFrame(group = repeat(["A", "B", "C"], inner = 10), value = 1:30)

sampled_df = @by(df, :group, sample = rand(:value, 2))

This code will create a new dataframe `sampled_df` that contains two random samples from each group in the original dataframe `df`. The `rand` function is used to perform the sampling, and the `:value` column is specified as the column to sample from.

Option 2: Using the StatsBase.jl package

If you prefer a more low-level approach, you can use the StatsBase.jl package to perform stratified and weighted sampling in Julia. To use this package, you first need to install it by running the following command:


using Pkg
Pkg.add("StatsBase")

Once the package is installed, you can use the `sample` function provided by StatsBase.jl to perform stratified sampling. Here is an example:


using DataFrames, StatsBase

df = DataFrame(group = repeat(["A", "B", "C"], inner = 10), value = 1:30)

group_indices = groupindices(df, :group)
sampled_indices = sample(group_indices, 2)

sampled_df = df[sampled_indices, :]

This code will create a new dataframe `sampled_df` that contains two random samples from each group in the original dataframe `df`. The `groupindices` function is used to get the indices of each group, and the `sample` function is used to randomly select two indices from each group.

Option 3: Manual implementation

If you prefer complete control over the sampling process, you can manually implement stratified and weighted sampling in Julia. Here is an example:


using DataFrames

df = DataFrame(group = repeat(["A", "B", "C"], inner = 10), value = 1:30)

sampled_df = DataFrame(group = String[], value = Int[])

for group in unique(df.group)
    group_df = filter(row -> row.group == group, df)
    weights = group_df.value ./ sum(group_df.value)
    sampled_indices = sample(1:length(group_df.value), 2, replace = false, weights = weights)
    push!(sampled_df, group_df[sampled_indices, :])
end

This code will create a new dataframe `sampled_df` that contains two random samples from each group in the original dataframe `df`. The `filter` function is used to filter the dataframe by group, and the `sample` function is used to randomly select two indices from each group, taking into account the weights of each value.

After comparing the three options, it is clear that using the DataFramesMeta.jl package provides the most concise and readable solution. It abstracts away the low-level details and provides a simple syntax for performing stratified and weighted sampling. Therefore, Option 1 is the recommended approach for performing stratified and weighted sampling in Julia.

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