### Installing Tables.jl Package in Julia

Source: https://github.com/juliadata/tables.jl/blob/main/README.md

This snippet demonstrates how to install the Tables.jl package in a Julia environment. It uses Julia's built-in package manager, Pkg, to add the 'Tables' package to the current environment, making its functionalities available for use.

```Julia
import Pkg; Pkg.add("Tables")
```

--------------------------------

### Defining Row and Column Tables for Round Trip Testing

Source: https://github.com/juliadata/tables.jl/blob/main/docs/src/implementing-the-interface.md

Creates example standard "row" and "column" tables using a Vector of NamedTuples and a NamedTuple of Vectors, respectively. These serve as common Tables.jl sources for performing round trip tests with sink functions.

```julia
rt = [(a=1, b=4.0, c="7"), (a=2, b=5.0, c="8"), (a=3, b=6.0, c="9")]
ct = (a=[1,2,3], b=[4.0, 5.0, 6.0])
```

--------------------------------

### Helper Function to Get Vector - Julia

Source: https://github.com/juliadata/tables.jl/blob/main/docs/src/using-the-interface.md

This is a helper function used in the DataFrames.jl Tables.jl implementation. It returns the input directly if it's already an `AbstractVector`, otherwise it collects the input into a new vector.

```julia
getvector(x::AbstractVector) = x
getvector(x) = collect(x)
```

--------------------------------

### Accessing Tables.jl Data Row-by-Row and Column-by-Column (Julia)

Source: https://github.com/juliadata/tables.jl/blob/main/docs/src/using-the-interface.md

This snippet demonstrates the two primary ways to access data from a Tables.jl-compatible source: row-by-row using `Tables.rows` and column-by-column using `Tables.columns`. It shows basic iteration over rows and columns, and how to retrieve individual values or entire columns.

```julia
# access data of input table `x` row-by-row
# Tables.rows must return a row iterator
rows = Tables.rows(x)

# we can iterate through each row
for row in rows
    # example of getting all values in the row
    # don't worry, there are other ways to more efficiently process rows
    rowvalues = [Tables.getcolumn(row, col) for col in Tables.columnnames(row)]
end

# access data of input table `x` column-by-column
# Tables.columns returns an object where individual, entire columns can be accessed
columns = Tables.columns(x)

# iterate through each column name in table
for col in Tables.columnnames(columns)
    # retrieve entire column by column name
    # a column is an indexable collection
    # with known length (i.e. supports
    # `length(column)` and `column[i]`)
    column = Tables.getcolumn(columns, col)
end
```

--------------------------------

### Performing Round Trip Tests with Tables.jl Sink Functions

Source: https://github.com/juliadata/tables.jl/blob/main/docs/src/implementing-the-interface.md

Demonstrates converting different table sources (row table, column table, matrix) into other table types or standard Julia structures using sink functions like `Tables.matrix`, `columntable`, and `rowtable`, and verifies the results using `@test` assertions.

```julia
# let's turn our row table into a plain Julia Matrix object
mat = Tables.matrix(rt)
# test that our matrix came out like we expected
@test mat[:, 1] == [1, 2, 3]
@test size(mat) == (3, 3)
@test eltype(mat) == Any
# so we successfully consumed a row-oriented table,
# now let's try with a column-oriented table
mat2 = Tables.matrix(ct)
@test eltype(mat2) == Float64
@test mat2[:, 1] == ct.a

# now let's take our matrix input, and make a column table out of it
tbl = Tables.table(mat) |> columntable
@test keys(tbl) == (:Column1, :Column2, :Column3)
@test tbl.Column1 == [1, 2, 3]
# and same for a row table
tbl2 = Tables.table(mat2) |> rowtable
@test length(tbl2) == 3
@test map(x->x.Column1, tbl2) == [1.0, 2.0, 3.0]
```

--------------------------------

### Testing Tables.jl Interface for a MatrixTable Source

Source: https://github.com/juliadata/tables.jl/blob/main/docs/src/implementing-the-interface.md

Demonstrates how to test a custom table source type (`MatrixTable`) using the Tables.jl API methods like `istable`, `rowaccess`, `columnaccess`, `getcolumn`, `columnnames`, and property access (`mattbl.Column1`) to ensure correct interface implementation.

```julia
# first, create a MatrixTable from our matrix input
mattbl = Tables.table(mat)
# test that the MatrixTable `istable`
@test Tables.istable(typeof(mattbl))
# test that it defines row access
@test Tables.rowaccess(typeof(mattbl))
@test Tables.rows(mattbl) === mattbl
# test that it defines column access
@test Tables.columnaccess(typeof(mattbl))
@test Tables.columns(mattbl) === mattbl
# test that we can access the first "column" of our matrix table by column name
@test mattbl.Column1 == [1,2,3]
# test our `Tables.AbstractColumns` interface methods
@test Tables.getcolumn(mattbl, :Column1) == [1,2,3]
@test Tables.getcolumn(mattbl, 1) == [1,2,3]
@test Tables.columnnames(mattbl) == [:Column1, :Column2, :Column3]
# now let's iterate our MatrixTable to get our first MatrixRow
matrow = first(mattbl)
@test eltype(mattbl) == typeof(matrow)
# now we can test our `Tables.AbstractRow` interface methods on our MatrixRow
@test matrow.Column1 == 1
@test Tables.getcolumn(matrow, :Column1) == 1
@test Tables.getcolumn(matrow, 1) == 1
@test propertynames(mattbl) == propertynames(matrow) == [:Column1, :Column2, :Column3]
```

--------------------------------

### Loading Table Data into SQLite (Unknown Schema) - Julia

Source: https://github.com/juliadata/tables.jl/blob/main/docs/src/using-the-interface.md

This function handles loading data from a Tables.jl compatible source into SQLite when the schema is initially unknown (`Tables.schema` returns `nothing`). It iterates the first row to determine column names, constructs a partial `Tables.Schema`, and then proceeds to iterate all rows using `Tables.eachcolumn` to bind values to an INSERT statement within a transaction.

```julia
function load!(sch::Nothing, rows, db::SQLite.DB, tablename)
    # sch is nothing === unknown schema
    # start iteration on input table rows
    state = iterate(rows)
    state === nothing && return
    row, st = state
    # query column names of first row
    names = Tables.columnnames(row)
    # partially construct Tables.Schema by at least passing
    # the column names to it
    sch = Tables.Schema(names, nothing)
    # create table if needed
    createtable!(db, tablename, sch)
    # build insert statement
    params = chop(repeat("?,", length(names)))
    stmt = Stmt(db, "INSERT INTO $nm VALUES ($params)")
    # start a transaction for inserting rows
    transaction(db) do
        while true
            # just like before, we can still use `Tables.eachcolumn`
            # even with our partially constructed Tables.Schema
            # to apply a function to each value in the row
            Tables.eachcolumn(sch, row) do val, i, nm
                bind!(stmt, i, val)
            end
            sqlite3_step(stmt.handle)
            sqlite3_reset(stmt.handle)
            # keep iterating rows until we finish
            state = iterate(rows, st)
            state === nothing && break
            row, st = state
        end
    end
    return name
end
```

--------------------------------

### Loading Table Data into SQLite (Known Schema) - Julia

Source: https://github.com/juliadata/tables.jl/blob/main/docs/src/using-the-interface.md

This function loads data from a Tables.jl compatible source into a SQLite database table. It assumes the schema is known and provided by `Tables.schema`. It iterates through rows and uses `Tables.eachcolumn` to bind values to an INSERT statement within a transaction.

```julia
function load!(table, db::SQLite.DB, tablename)
    # get input table rows
    rows = Tables.rows(table)
    # query for schema of data
    sch = Tables.schema(rows)
    # create table using tablename and schema from input table
    createtable!(db, tablename, sch)
    # build insert statement
    params = chop(repeat("?,", length(sch.names)))
    stmt = Stmt(db, "INSERT INTO $tablename VALUES ($params)")
    # start a transaction for inserting rows
    transaction(db) do
        # iterate over rows in the input table
        for row in rows
            # Tables.jl provides a utility function
            # Tables.eachcolumn, which allows efficiently
            # applying a function to each column value in a row
            # it's called with a schema and row, and applies
            # a user-provided function to the column value `val`, index `i`
            # and column name `nm`. Here, we bind the row values
            # to our parameterized SQL INSERT statement and then
            # call `sqlite3_step` to execute the INSERT statement.
            Tables.eachcolumn(sch, row) do val, i, nm
                bind!(stmt, i, val)
            end
            sqlite3_step(stmt.handle)
            sqlite3_reset(stmt.handle)
        end
    end
    return
end
```

--------------------------------

### DataFrame Construction from Tables.jl Source (Julia)

Source: https://github.com/juliadata/tables.jl/blob/main/docs/src/using-the-interface.md

Defines functions to construct a DataFrame from a Tables.jl compatible source. It extracts columns and names using Tables.jl functions and dispatches to an internal `fromcolumns` function, handling potential column copying based on the source type.

```Julia
# note that copycols is ignored in this definition (Tables.CopiedColumns implies copies have already been made)
fromcolumns(x::Tables.CopiedColumns, names; copycols::Bool=true) =
    DataFrame(AbstractVector[getvector(Tables.getcolumn(x, nm) for nm in names],
              Index(names),
              copycols=false)
fromcolumns(x, names; copycols::Bool=true) =
    DataFrame(AbstractVector[getvector(Tables.getcolumn(x, nm) for nm in names],
              Index(names),
              copycols=copycols)

function DataFrame(x; copycols::Bool=true)
    # get columns from input table source
    cols = Tables.columns(x)
    # get column names as Vector{Symbol}, which is required
    # by core DataFrame constructor
    names = collect(Symbol, Tables.columnnames(cols))
    return fromcolumns(cols, names; copycols=copycols)
end
```

--------------------------------

### Defining a Julia Matrix for Tables.jl Testing

Source: https://github.com/juliadata/tables.jl/blob/main/docs/src/implementing-the-interface.md

Defines a 3x3 Julia matrix literal with mixed data types (Integer, Float, String) to be used as input for testing custom Tables.jl source implementations.

```julia
mat = [1 4.0 "7"; 2 5.0 "8"; 3 6.0 "9"]
```

--------------------------------

### Implement Tables.schema for MatrixTable (Julia)

Source: https://github.com/juliadata/tables.jl/blob/main/docs/src/implementing-the-interface.md

Defines basic getter methods (`names`, `matrix`, `lookup`) to access internal fields of a `MatrixTable` instance without conflicting with `getproperty`. It then implements the `Tables.schema` method from the Tables.jl interface, returning a `Tables.Schema` object containing column names and their common element type.

```Julia
names(m::MatrixTable) = getfield(m, :names)
matrix(m::MatrixTable) = getfield(m, :matrix)
lookup(m::MatrixTable) = getfield(m, :lookup)
# schema is column names and types
Tables.schema(m::MatrixTable{T}) where {T} = Tables.Schema(names(m), fill(eltype(T), size(matrix(m), 2)))
```

--------------------------------

### Implement Tables.AbstractColumns Interface for MatrixTable (Julia)

Source: https://github.com/juliadata/tables.jl/blob/main/docs/src/implementing-the-interface.md

Implements the `Tables.columnaccess` and `Tables.columns` methods, indicating that `MatrixTable` provides column access and returns itself as the columns object. It then defines the required `Tables.getcolumn` methods for accessing columns by type, name, or index, and the `Tables.columnnames` method to return the column names.

```Julia
# column interface
Tables.columnaccess(::Type{<:MatrixTable}) = true
Tables.columns(m::MatrixTable) = m
# required Tables.AbstractColumns object methods
Tables.getcolumn(m::MatrixTable, ::Type{T}, col::Int, nm::Symbol) where {T} = matrix(m)[:, col]
Tables.getcolumn(m::MatrixTable, nm::Symbol) = matrix(m)[:, lookup(m)[nm]]
Tables.getcolumn(m::MatrixTable, i::Int) = matrix(m)[:, i]
Tables.columnnames(m::MatrixTable) = names(m)
```

--------------------------------

### Defining MatrixTable Type and Declaring it as a Table (Julia)

Source: https://github.com/juliadata/tables.jl/blob/main/docs/src/implementing-the-interface.md

This snippet defines a Julia struct `MatrixTable` that wraps an `AbstractVecOrMat` to serve as a custom table type. It also implements the `Tables.istable` method for `MatrixTable`, declaring that this type conforms to the Tables.jl interface.

```Julia
struct MatrixTable{T <: AbstractVecOrMat} <: Tables.AbstractColumns
    names::Vector{Symbol}
    lookup::Dict{Symbol, Int}
    matrix::T
end
# declare that MatrixTable is a table
Tables.istable(::Type{<:MatrixTable}) = true
```

--------------------------------

### Implement Tables.rows Interface and MatrixRow Type for MatrixTable (Julia)

Source: https://github.com/juliadata/tables.jl/blob/main/docs/src/implementing-the-interface.md

Implements the `Tables.rowaccess` and `Tables.rows` methods, indicating that `MatrixTable` provides row access and returns itself as the rows iterator. It defines the iteration interface (`eltype`, `length`, `iterate`) for `MatrixTable`, using a custom `MatrixRow` type as the element type. The `MatrixRow` struct is defined to represent a single row view, and the required `Tables.AbstractRow` methods (`getcolumn`, `columnnames`) are implemented for this type.

```Julia
# declare that any MatrixTable defines its own \`Tables.rows\` method
rowaccess(::Type{<:MatrixTable}) = true
# just return itself, which means MatrixTable must iterate \`Tables.AbstractRow\`-compatible objects
rows(m::MatrixTable) = m
# the iteration interface, at a minimum, requires \`eltype\`, \`length\`, and \`iterate\`
# for \`MatrixTable\` \`eltype\`, we're going to provide a custom row type
Base.eltype(m::MatrixTable{T}) where {T} = MatrixRow{T}
Base.length(m::MatrixTable) = size(matrix(m), 1)

Base.iterate(m::MatrixTable, st=1) = st > length(m) ? nothing : (MatrixRow(st, m), st + 1)

# a custom row type; acts as a "view" into a row of an AbstractVecOrMat
struct MatrixRow{T} <: Tables.AbstractRow
    row::Int
    source::MatrixTable{T}
end
# required \`Tables.AbstractRow\` interface methods (same as for \`Tables.AbstractColumns\` object before)
# but this time, on our custom row type
getcolumn(m::MatrixRow, ::Type, col::Int, nm::Symbol) = getfield(getfield(m, :source), :matrix)[getfield(m, :row), col]
getcolumn(m::MatrixRow, i::Int) = getfield(getfield(m, :source), :matrix)[getfield(m, :row), i]
getcolumn(m::MatrixRow, nm::Symbol) = getfield(getfield(m, :source), :matrix)[getfield(m, :row), getfield(getfield(m, :source), :lookup)[nm]]
columnnames(m::MatrixRow) = names(getfield(m, :source))
```

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