# Introduction

Before I start let me comment that exactly one year ago this blog has been started. I hope to keep posting weekly updates on the Julia language, and especially its ecosystem for data science, so:

Now let us go back to business.

The 1.1 release of the DataFrames.jl package introduced a small fix of how the subset function works. Today I will discuss its design and compare it to the filter function.

In this post I am using Julia 1.6.1 and DataFrames.jl 1.1.0.

# The design of filter

The filter function is defined in Julia Base. Therefore in DataFrames.jl we add methods to it. Let us start with the contract for filter(f, a) then:

Return a copy of collection a, removing elements for which f is false. The function f is passed one argument.

How do we translate this into DataFrames.jl realm? We have to cases.

If a is an AbstractDataFrame then we treat it as a collection of rows. Therefore f will get one row of data and we expect it to return a Bool value. As a result of the operation we produce a DataFrame (unless view keyword argument is true in which case we return a SubDataFrame).

Here is a basic example:

julia> using DataFrames

julia> df = DataFrame(a=1:3)
3×1 DataFrame
Row │ a
│ Int64
─────┼───────
1 │     1
2 │     2
3 │     3

julia> filter(row -> row.a != 2, df)
2×1 DataFrame
Row │ a
│ Int64
─────┼───────
1 │     1
2 │     3


A more efficient (faster to execute) way to express the same is:

julia> filter(:a => !=(2), df)
2×1 DataFrame
Row │ a
│ Int64
─────┼───────
1 │     1
2 │     3


As you can see the style is that you pass a Pair or column name and a predicate function (i.e. a function that produces Bool). This has two benefits. Firstly, the operation is type stable (thus faster). Secondly, in the row -> row.a != 2 we define a new anonymous function with each call of filter, which causes compilation (unless the operation is wrapped in a function or we predefine the predicate function).

The second case is when a is a GroupedDataFrame. In this case f will get one group and should return a Bool value again. The result will be a GroupedDataFrame with groups appropriately removed:

julia> gdf = groupby(df, :a)
GroupedDataFrame with 3 groups based on key: a
First Group (1 row): a = 1
Row │ a
│ Int64
─────┼───────
1 │     1
⋮
Last Group (1 row): a = 3
Row │ a
│ Int64
─────┼───────
1 │     3

julia> filter(sdf -> sdf.a != [2], gdf)
GroupedDataFrame with 2 groups based on key: a
First Group (1 row): a = 1
Row │ a
│ Int64
─────┼───────
1 │     1
⋮
Last Group (1 row): a = 3
Row │ a
│ Int64
─────┼───────
1 │     3


A Pair version is also supported:

julia> filter(:a => !=([2]), gdf)
GroupedDataFrame with 2 groups based on key: a
First Group (1 row): a = 1
Row │ a
│ Int64
─────┼───────
1 │     1
⋮
Last Group (1 row): a = 3
Row │ a
│ Int64
─────┼───────
1 │     3


A crucial thing to note is that this time the predicate gets a data frame (or its column/columns).

In summary — the filter function (apart from the view keyword argument and a special Pair syntax that improves the performance) works exactly like the Julia Base contract requires.

Before we move forward you might notice that the Pair syntax for the AbstractDataFrame case is different than the same syntax for select, transform, and combine functions, where always a whole column is passed. Indeed there is a small inconsistency. It was left for user convenience and consistency with Julia Base.

On the other hand subset is fully consistent with the rest of DataFrames.jl ecosystem, so let us move to it now.

# The design of subset

The subset function is designed for filtering of rows in a way consistent with the select, transform, and combine functions. The contract for the subset(df, args...) function is:

Return a copy of data frame df containing only rows for which all values produced by transformation(s) args for a given row are true.

If instead of a df data frame you pass a GroupedDataFrame the rules are the same, but the difference is that they apply to the parent of the GroupedDataFrame. So this leads us to a list of differences from filter, as in subset:

• the AbstactDataFrame/GroupedDataFrame argument goes first;
• you are allowed do pass multiple conditions on which you want to perform row selection;
• always works on whole columns;
• always filters rows;
• the transformation is expected to return a vector (not a scalar Bool — remember we are filtering rows so the length of the vector must match the number of rows);
• by default always produces a data frame.

• all transformations must produce vectors containing true or false; however, optionally missing is allowed if skipmissing=true (this option is not available in filter);
• for GroupedDataFrame case if ungroup=false the resulting data frame is re-grouped based on the same grouping columns as the source GroupedDataFrame (but by default a data frame is returned).

The view keyword argument works like in filter and allows you to produce a SubDataFrame instead of a DataFrame.

Enough theory, let us get to the examples:

julia> df2 = DataFrame(a=repeat(1:3, 2), b=1:6)
6×2 DataFrame
Row │ a      b
│ Int64  Int64
─────┼──────────────
1 │     1      1
2 │     2      2
3 │     3      3
4 │     1      4
5 │     2      5
6 │     3      6

julia> subset(df2, :a => ByRow(==(1)), :b => ByRow(isodd))
1×2 DataFrame
Row │ a      b
│ Int64  Int64
─────┼──────────────
1 │     1      1


Here you can see that we had to wrap predicates in ByRow to make sure that a vector of Bool is produce by the filtering conditions. Otherwise you would get an error:

julia> subset(df2, :a => ==(1))
ERROR: ArgumentError: functions passed to subset must return an AbstractVector.


(By the way: this is a thing that was changed in DataFrames.jl 1.1 release; previously unintentionally returning scalar Bool was allowed which was error prone, as the comparison was made against a whole vector — not its elements.)

The second key thing to remember is that subset filters rows always, also in GroupedDataFrame case:

julia> gdf2 = groupby(df2, :a)
GroupedDataFrame with 3 groups based on key: a
First Group (2 rows): a = 1
Row │ a      b
│ Int64  Int64
─────┼──────────────
1 │     1      1
2 │     1      4
⋮
Last Group (2 rows): a = 3
Row │ a      b
│ Int64  Int64
─────┼──────────────
1 │     3      3
2 │     3      6

julia> subset(gdf2, :b => (x -> x .== maximum(x)))
3×2 DataFrame
Row │ a      b
│ Int64  Int64
─────┼──────────────
1 │     1      4
2 │     2      5
3 │     3      6


This is often very useful if we want to filter rows by some within-group condition, like in the example above.

Finally, let me show the skipmissing keyword argument at work:

julia> df3 = DataFrame(a=[1, missing, 3, 4])
4×1 DataFrame
Row │ a
│ Int64?
─────┼─────────
1 │       1
2 │ missing
3 │       3
4 │       4

julia> subset(df3, :a => ByRow(isodd))
ERROR: ArgumentError: missing was returned in condition number 1 but only true or false are allowed; pass skipmissing=true to skip missing values

julia> subset(df3, :a => ByRow(isodd), skipmissing=true)
2×1 DataFrame
Row │ a
│ Int64?
─────┼────────
1 │      1
2 │      3


# Conclusions

In summary both filter and subset are useful, but in different contexts. The basic rules are:

• if you have multiple conditions to apply use subset;
• if you want to easily handle missing values use subset;
• if you have a single predicate that takes a single row (or a scalar) and returns Bool and want to filter a data frame use filter (this saves you typing ByRow in subset);
• if you have a single predicate that returns Bool and want to filter whole groups of a GroupedDataFrame (as opposed to rows) use filter.

The things are unfortunately a bit complex, but we provide them for user convenience as both filter and subset are useful in different contexts.

Before I finish let me highlight that there are also in-place filter! and subset! variants of these functions.