# Tables.jl Documentation

This guide provides documentation around the powerful tables interfaces in the Tables.jl package. Note that the package, and hence, documentation, are geared towards package and library developers who intend to implement and consume the interfaces. Users, on the other hand, benefit from these other packages that provide useful access to table data in various formats or workflows. While everyone is encouraged to understand the interfaces and the functionality they allow, just note that most users don't need to use Tables.jl directly.

With that said, don't hesitate to open a new issue, even just for a question, or come chat with us on the #data slack channel with questions, concerns, or clarifications. Also one can find list of packages that supports Tables.jl interface in INTEGRATIONS.md.

Please refer to TableOperations.jl for common table operations such as select, transform, filter and map.

## Using the Interface (i.e. consuming Tables.jl-compatible sources)

We start by discussing usage of the Tables.jl interface functions, since that can help contextualize implementing them for custom table types.

At a high level, Tables.jl provides two powerful APIs for predictably accessing data from any table-like source:

# 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 individul, 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

So we see two high-level functions here, Tables.rows, and Tables.columns.

Tables.rowsFunction
Tables.rows(x) => Row iterator

Accesses data of input table source x row-by-row by returning an AbstractRow-compatible iterator. Note that even if the input table source is column-oriented by nature, an efficient generic definition of Tables.rows is defined in Tables.jl to return an iterator of row views into the columns of the input.

The Tables.Schema of an AbstractRow iterator can be queried via Tables.schema(rows), which may return nothing if the schema is unknown. Column names can always be queried by calling Tables.columnnames(row) on an individual row, and row values can be accessed by calling Tables.getcolumn(rows, i::Int ) or Tables.getcolumn(rows, nm::Symbol) with a column index or name, respectively.

See also rowtable and namedtupleiterator.

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Tables.columnsFunction
Tables.columns(x) => AbstractColumns-compatible object

Accesses data of input table source x by returning an AbstractColumns-compatible object, which allows retrieving entire columns by name or index. A retrieved column is a 1-based indexable object that has a known length, i.e. supports length(col) and col[i] for any i = 1:length(col). Note that even if the input table source is row-oriented by nature, an efficient generic definition of Tables.columns is defined in Tables.jl to build a AbstractColumns- compatible object object from the input rows.

The Tables.Schema of a AbstractColumns object can be queried via Tables.schema(columns), which may return nothing if the schema is unknown. Column names can always be queried by calling Tables.columnnames(columns), and individual columns can be accessed by calling Tables.getcolumn(columns, i::Int ) or Tables.getcolumn(columns, nm::Symbol) with a column index or name, respectively.

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Given these two powerful data access methods, let's walk through real, albeit somewhat simplified versions of how packages actually use these methods.

### Tables.rows usage

First up, let's take a look at the SQLite.jl package and how it uses the Tables.jl interface to allow loading of generic table-like data into a sqlite relational table. Here's the code:

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

This is pretty straightforward usage: it calls Tables.rows on the input table source, and since we need the schema to setup the database table, we query it via Tables.schema. We then iterate the rows in our table via for row in rows, and use the convenient Tables.eachcolumn to efficiently apply a function to each value in the row. Note that we didn't call Tables.columnnames or Tables.getcolumn at all, since they're utilized by Tables.eachcolumn itself. Tables.eachcolumn is optimized to provide type-stable, and even constant-propagation of column index, name, and type in some cases to allow for efficient consumption of row values.

One wrinkle to consider is the "unknown schema" case; i.e. what if our Tables.schema call had returned nothing (this can be the case for exotic table sources like lazily mapped transformations over rows in a table):

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

The strategy taken here is to start iterating the input source, and using the first row as a guide, we make a Tables.Schema object with just the column names, which we can then still pass to Tables.eachcolumn to apply our bind! function to each row value.

### Tables.columns usage

Ok, now let's take a look at a case utlizing Tables.columns. The following code is taken from the DataFrames.jl Tables.jl implementation:

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

# 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; 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

So here we have a generic DataFrame constructor that takes a single, untyped argument, calls Tables.columns on it, then Tables.columnnames to get the column names. It then passes the Tables.AbstractColumns-compatible object to an internal function fromcolumns, which dispatches on a special kind of Tables.AbstractColumns object called a Tables.CopiedColumns, which wraps any Tables.AbstractColumns-compatible object that has already had copies of its columns made, and are thus safe for the columns-consumer to assume ownership of (this is because DataFrames.jl, by default makes copies of all columns upon construction). In both cases, individual columns are collected in Vector{AbstractVector}s by calling Tables.getcolumn(x, nm) for each column name. A final note is the call to getvector on each column, which ensures each column is materialized as an AbstractVector, as is required by the DataFrame constructor.

Note in both the rows and columns usages, we didn't need to worry about the natural orientation of the input data; we just called Tables.rows or Tables.columns as was most natural for the table-specific use-case, knowing that it will Just Work™️.

### Tables.jl Utilities

Before moving on to implementing the Tables.jl interfaces, we take a quick break to highlight some useful utility functions provided by Tables.jl:

Tables.SchemaType
Tables.Schema(names, types)

Create a Tables.Schema object that holds the column names and types for an AbstractRow iterator returned from Tables.rows or an AbstractColumns object returned from Tables.columns. Tables.Schema is dual-purposed: provide an easy interface for users to query these properties, as well as provide a convenient "structural" type for code generation.

To get a table's schema, one can call Tables.schema on the result of Tables.rows or Tables.columns, but also note that a table may return nothing, indicating that its column names and/or column element types are unknown (usually not inferrable). This is similar to the Base.EltypeUnknown() trait for iterators when Base.IteratorEltype is called. Users should account for the Tables.schema(tbl) => nothing case by using the properties of the results of Tables.rows(x) and Tables.columns(x) directly.

To access the names, one can simply call sch.names to return a collection of Symbols (Tuple or Vector). To access column element types, one can similarly call sch.types, which will return a collection of types (like (Int64, Float64, String)).

The actual type definition is

struct Schema{names, types}
storednames::Union{Nothing, Vector{Symbol}}
storedtypes::Union{Nothing, Vector{Type}}
end

Where names is a tuple of Symbols or nothing, and types is a tuple type of types (like Tuple{Int64, Float64, String}) or nothing. Encoding the names & types as type parameters allows convenient use of the type in generated functions and other optimization use-cases, but users should note that when names and/or types are the nothing value, the names and/or types are stored in the storednames and storedtypes fields. This is to account for extremely wide tables with columns in the 10s of thousands where encoding the names/types as type parameters becomes prohibitive to the compiler. So while optimizations can be written on the typed names/types type parameters, users should also consider handling the extremely wide tables by specializing on Tables.Schema{nothing, nothing}.

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Tables.schemaFunction
Tables.schema(x) => Union{Nothing, Tables.Schema}

Attempt to retrieve the schema of the object returned by Tables.rows or Tables.columns. If the AbstractRow iterator or AbstractColumns object can't determine its schema, nothing will be returned. Otherwise, a Tables.Schema object is returned, with the column names and types available for use.

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Tables.partitionsFunction
Tables.partitions(x)

Request a "table" iterator from x. Each iterated element must be a "table" in the sense that one may call Tables.rows or Tables.columns to get a row-iterator or collection of columns. All iterated elements must have identical schema, so that users may call Tables.schema(first_element) on the first iterated element and know that each subsequent iteration will match the same schema. The default definition is:

Tables.partitions(x) = (x,)

So that any input is assumed to be a single "table". This means users should feel free to call Tables.partitions anywhere they're currently calling Tables.columns or Tables.rows, and get back an iterator of those instead. In other words, "sink" functions can use Tables.partitions whether or not the user passes a partionable table, since the default is to treat a single input as a single, non-partitioned table.

Tables.partitioner(itr)(@ref) is a convenience wrapper to provide table partitions from any table iterator; this allows for easy wrapping of a Vector or iterator of tables as valid partitions, since by default, they'd be treated as a single table.

A 2nd convenience method is provided with the defintion:

Tables.partitions(x...) = x

That allows passing vararg tables and they'll be treated as separate partitions. Sink functions may allow vararg table inputs and can "splat them through" to partitions.

For convenience, Tables.partitions(Iterators.partition(...)) is defined for cases where user-controlled partitioning is desired over an applicable input (an input iterator).

source
Tables.partitionerFunction
Tables.partitioner(f, itr)
Tables.partitioner(x)

Convenience methods to generate table iterators. The first method takes a "materializer" function f and an iterator itr, and will call Tables.LazyTable(f, x) for x in itr for each iteration. This allows delaying table materialization until Tables.columns or Tables.rows are called on the LazyTable object (which will call f(x)). This allows a common desired pattern of materializing and processing a table on a remote process or thread, like:

for tbl in Tables.partitions(Tables.partitioner(CSV.File, list_of_csv_files))
cols = Tables.columns(tbl)
# do stuff with cols
end
end

The second method is provided because the default behavior of Tables.partition(x) is to treat x as a single, non-partitioned table. This method allows users to easily wrap a Vector or generator of tables as table partitions to pass to sink functions able to utilize Tables.partitions.

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Tables.rowtableFunction
Tables.rowtable(x) => Vector{NamedTuple}

Take any input table source, and produce a Vector of NamedTuples, also known as a "row table". A "row table" is a kind of default table type of sorts, since it satisfies the Tables.jl row interface naturally, i.e. a Vector naturally iterates its elements, and NamedTuple satisifes the AbstractRow interface by default (allows indexing value by index, name, and getting all names).

For a lazy iterator over rows see rows and namedtupleiterator.

Not for use with extremely wide tables with # of columns > 67K; current fundamental compiler limits prevent constructing NamedTuples that large.

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Tables.columntableFunction
Tables.columntable(x) => NamedTuple of Vectors

Takes any input table source x and returns a NamedTuple of Vectors, also known as a "column table". A "column table" is a kind of default table type of sorts, since it satisfies the Tables.jl column interface naturally.

Not for use with extremely wide tables with # of columns > 67K; current fundamental compiler limits prevent constructing NamedTuples that large.

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Tables.dictrowtableFunction
Tables.dictrowtable(x) => Tables.DictRowTable

Take any Tables.jl-compatible source x and return a DictRowTable, which can be thought of as a Vector of Dict rows mapping column names as Symbols to values. The order of the input table columns is preserved via the Tables.schema(::DictRowTable).

For "schema-less" input tables, dictrowtable employs a "column unioning" behavior, as opposed to inferring the schema from the first row like Tables.columns. This means that as rows are iterated, each value from the row is joined into an aggregate final set of columns. This is especially useful when input table rows may not include columns if the value is missing, instead of including an actual value missing, which is common in json, for example. This results in a performance cost tracking all seen values and inferring the final unioned schemas, so it's recommended to use only when the union behavior is needed.

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Tables.dictcolumntableFunction
Tables.dictcolumntable(x) => Tables.DictColumnTable

Take any Tables.jl-compatible source x and return a DictColumnTable, which can be thought of as a Dict mapping column names as Symbols to AbstractVectors. The order of the input table columns is preserved via the Tables.schema(::DictColumnTable).

For "schema-less" input tables, dictcolumntable employs a "column unioning" behavior, as opposed to inferring the schema from the first row like Tables.columns. This means that as rows are iterated, each value from the row is joined into an aggregate final set of columns. This is especially useful when input table rows may not include columns if the value is missing, instead of including an actual value missing, which is common in json, for example. This results in a performance cost tracking all seen values and inferring the final unioned schemas, so it's recommended to use only when needed.

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Tables.datavaluerowsFunction
Tables.datavaluerows(x) => NamedTuple iterator

Takes any table input x and returns a NamedTuple iterator that will replace missing values with DataValue-wrapped values; this allows any table type to satisfy the TableTraits.jl Queryverse integration interface by defining:

IteratorInterfaceExtensions.getiterator(x::MyTable) = Tables.datavaluerows(x)
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Tables.nondatavaluerowsFunction
Tables.nondatavaluerows(x)

Takes any Queryverse-compatible NamedTuple iterator source and converts to a Tables.jl-compatible AbstractRow iterator. Will automatically unwrap any DataValues, replacing NA with missing. Useful for translating Query.jl results back to non-DataValue-based tables.

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Tables.tableFunction
Tables.table(m::AbstractVecOrMat; [header])

Wrap an AbstractVecOrMat (Matrix, Vector, Adjoint, etc.) in a MatrixTable, which satisfies the Tables.jl interface. (An AbstractVector is treated as a 1-column matrix.) This allows accessing the matrix via Tables.rows and Tables.columns. An optional keyword argument iterator header can be passed which will be converted to a Vector{Symbol} to be used as the column names. Note that no copy of the AbstractVecOrMat is made.

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Tables.matrixFunction
Tables.matrix(table; transpose::Bool=false)

Materialize any table source input as a new Matrix or in the case of a MatrixTable return the originally wrapped matrix. If the table column element types are not homogenous, they will be promoted to a common type in the materialized Matrix. Note that column names are ignored in the conversion. By default, input table columns will be materialized as corresponding matrix columns; passing transpose=true will transpose the input with input columns as matrix rows or in the case of a MatrixTable apply permutedims to the originally wrapped matrix.

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Tables.eachcolumnFunction
Tables.eachcolumn(f, sch::Tables.Schema{names, types}, x::Union{Tables.AbstractRow, Tables.AbstractColumns})
Tables.eachcolumn(f, sch::Tables.Schema{names, nothing}, x::Union{Tables.AbstractRow, Tables.AbstractColumns})

Takes a function f, table schema sch, x, which is an object that satisfies the AbstractRow or AbstractColumns interfaces; it generates calls to get the value for each column (Tables.getcolumn(x, nm)) and then calls f(val, index, name), where f is the user-provided function, val is the column value (AbstractRow) or entire column (AbstractColumns), index is the column index as an Int, and name is the column name as a Symbol.

An example using Tables.eachcolumn is:

rows = Tables.rows(tbl)
sch = Tables.schema(rows)
if sch === nothing
state = iterate(rows)
state === nothing && return
row, st = state
sch = Tables.schema(Tables.columnnames(row), nothing)
while state !== nothing
Tables.eachcolumn(sch, row) do val, i, nm
bind!(stmt, i, val)
end
state = iterate(rows, st)
state === nothing && return
row, st = state
end
else
for row in rows
Tables.eachcolumn(sch, row) do val, i, nm
bind!(stmt, i, val)
end
end
end

Note in this example we account for the input table potentially returning nothing from Tables.schema(rows); in that case, we start iterating the rows, and build a partial schema using the column names from the first row sch = Tables.schema(Tables.columnnames(row), nothing), which is valid to pass to Tables.eachcolumn.

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Tables.materializerFunction
Tables.materializer(x) => Callable

For a table input, return the "sink" function or "materializing" function that can take a Tables.jl-compatible table input and make an instance of the table type. This enables "transform" workflows that take table inputs, apply transformations, potentially converting the table to a different form, and end with producing a table of the same type as the original input. The default materializer is Tables.columntable, which converts any table input into a NamedTuple of Vectors.

It is recommended that for users implementing MyType, they define only materializer(::Type{MyType}). materializer(::MyType) will then automatically delegate to this method.

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Tables.columnindexFunction
Tables.columnindex(table, name::Symbol)

Return the column index (1-based) of a column by name in a table with a known schema; returns 0 if name doesn't exist in table

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given names and a Symbol name, compute the index (1-based) of the name in names

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Tables.columntypeFunction
Tables.columntype(table, name::Symbol)

Return the column element type of a column by name in a table with a known schema; returns Union{} if name doesn't exist in table

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given tuple type and a Symbol name, compute the type of the name in the tuples types

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Tables.rowmergeFunction
rowmerge(row, other_rows...)
rowmerge(row; fields_to_merge...)

Return a NamedTuple by merging row (an AbstractRow-compliant value) with other_rows (one or more AbstractRow-compliant values) via Base.merge. This function is similar to Base.merge(::NamedTuple, ::NamedTuple...), but accepts AbstractRow-compliant values instead of NamedTuples.

A convenience method rowmerge(row; fields_to_merge...) = rowmerge(row, fields_to_merge) is defined that enables the fields_to_merge to be specified as keyword arguments.

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Tables.RowType
Tables.Row(row)

Convenience type to wrap any AbstractRow interface object in a dedicated struct to provide useful default behaviors (allows any AbstractRow to be used like a NamedTuple):

• Indexing interface defined; i.e. row[i] will return the column value at index i, row[nm] will return column value for column name nm
• Property access interface defined; i.e. row.col1 will retrieve the value for the column named col1
• Iteration interface defined; i.e. for x in row will iterate each column value in the row
• AbstractDict methods defined (get, haskey, etc.) for checking and retrieving column values
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Tables.ColumnsType
Tables.Columns(columns)

Convenience type to wrap any AbstractColumns interface object in a dedicated struct to provide useful default behaviors (allows any AbstractColumns to be used like a NamedTuple of Vectors):

• Indexing interface defined; i.e. row[i] will return the column at index i, row[nm] will return column for column name nm
• Property access interface defined; i.e. row.col1 will retrieve the value for the column named col1
• Iteration interface defined; i.e. for x in row will iterate each column in the row
• AbstractDict methods defined (get, haskey, etc.) for checking and retrieving columns
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## Implementing the Interface (i.e. becoming a Tables.jl source)

Now that we've seen how one uses the Tables.jl interface, let's walk-through how to implement it; i.e. how can I make my custom type valid for Tables.jl consumers?

For a type MyTable, the interface to becoming a proper table is straightforward:

Required MethodsDefault DefinitionBrief Description
Tables.istable(::Type{MyTable})Declare that your table type implements the interface
One of:
Tables.rowaccess(::Type{MyTable})Declare that your table type defines a Tables.rows(::MyTable) method
Tables.rows(x::MyTable)Return an Tables.AbstractRow-compatible iterator from your table
Or:
Tables.columnaccess(::Type{MyTable})Declare that your table type defines a Tables.columns(::MyTable) method
Tables.columns(x::MyTable)Return an Tables.AbstractColumns-compatible object from your table
Optional methods
Tables.schema(x::MyTable)Tables.schema(x) = nothingReturn a Tables.Schema object from your Tables.AbstractRow iterator or Tables.AbstractColumns object; or nothing for unknown schema
Tables.materializer(::Type{MyTable})Tables.columntableDeclare a "materializer" sink function for your table type that can construct an instance of your type from any Tables.jl input

Based on whether your table type has defined Tables.rows or Tables.columns, you then ensure that the Tables.AbstractRow iterator or Tables.AbstractColumns object satisfies the respective interface.

As an additional source of documentation, see this discourse post outlining in detail a walk-through of making a row-oriented table.

### Tables.AbstractRow

Tables.AbstractRowType
Tables.AbstractRow

Abstract interface type representing the expected eltype of the iterator returned from Tables.rows(table). Tables.rows must return an iterator of elements that satisfy the Tables.AbstractRow interface. While Tables.AbstractRow is an abstract type that custom "row" types may subtype for useful default behavior (indexing, iteration, property-access, etc.), users should not use it for dispatch, as Tables.jl interface objects are not required to subtype, but only implement the required interface methods.

Interface definition:

Required MethodsDefault DefinitionBrief Description
Tables.getcolumn(row, i::Int)getfield(row, i)Retrieve a column value by index
Tables.getcolumn(row, nm::Symbol)getproperty(row, nm)Retrieve a column value by name
Tables.columnnames(row)propertynames(row)Return column names for a row as an indexable collection
Optional methods
Tables.getcolumn(row, ::Type{T}, i::Int, nm::Symbol)Tables.getcolumn(row, nm)Given a column element type T, index i, and column name nm, retrieve the column value. Provides a type-stable or even constant-prop-able mechanism for efficiency.

Note that subtypes of Tables.AbstractRow must overload all required methods listed above instead of relying on these methods' default definitions.

While custom row types aren't required to subtype Tables.AbstractRow, benefits of doing so include:

• Indexing interface defined (using getcolumn); i.e. row[i] will return the column value at index i
• Property access interface defined (using columnnames and getcolumn); i.e. row.col1 will retrieve the value for the column named col1
• Iteration interface defined; i.e. for x in row will iterate each column value in the row
• AbstractDict methods defined (get, haskey, etc.) for checking and retrieving column values
• A default show method

This allows the custom row type to behave as close as possible to a builtin NamedTuple object.

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### Tables.AbstractColumns

Tables.AbstractColumnsType
Tables.AbstractColumns

An interface type defined as an ordered set of columns that support retrieval of individual columns by name or index. A retrieved column must be a 1-based indexable collection with known length, i.e. an object that supports length(col) and col[i] for any i = 1:length(col). Tables.columns must return an object that satisfies the Tables.AbstractColumns interface. While Tables.AbstractColumns is an abstract type that custom "columns" types may subtype for useful default behavior (indexing, iteration, property-access, etc.), users should not use it for dispatch, as Tables.jl interface objects are not required to subtype, but only implement the required interface methods.

Interface definition:

Required MethodsDefault DefinitionBrief Description
Tables.getcolumn(table, i::Int)getfield(table, i)Retrieve a column by index
Tables.getcolumn(table, nm::Symbol)getproperty(table, nm)Retrieve a column by name
Tables.columnnames(table)propertynames(table)Return column names for a table as an indexable collection
Optional methods
Tables.getcolumn(table, ::Type{T}, i::Int, nm::Symbol)Tables.getcolumn(table, nm)Given a column eltype T, index i, and column name nm, retrieve the column. Provides a type-stable or even constant-prop-able mechanism for efficiency.

Note that subtypes of Tables.AbstractColumns must overload all required methods listed above instead of relying on these methods' default definitions.

While types aren't required to subtype Tables.AbstractColumns, benefits of doing so include:

• Indexing interface defined (using getcolumn); i.e. tbl[i] will retrieve the column at index i
• Property access interface defined (using columnnames and getcolumn); i.e. tbl.col1 will retrieve column named col1
• Iteration interface defined; i.e. for col in table will iterate each column in the table
• AbstractDict methods defined (get, haskey, etc.) for checking and retrieving columns
• A default show method

This allows a custom table type to behave as close as possible to a builtin NamedTuple of vectors object.

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### Implementation Example

As an extended example, let's take a look at some code defined in Tables.jl for treating AbstractVecOrMats as tables.

First, we define a special MatrixTable type that will wrap an AbstractVecOrMat, and allow easy overloading for the Tables.jl interface.

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
# getter methods to avoid getproperty clash
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(mat(m), 2)))

Here we defined Tables.istable for all MatrixTable types, signaling that they implement the Tables.jl interfaces. We also defined Tables.schema by pulling the column names out that we stored, and since AbstractVecOrMat have a single eltype, we repeat it for each column (the call to fill). Note that defining Tables.schema is optional on tables; by default, nothing is returned and Tables.jl consumers should account for both known and unknown schema cases. Returning a schema when possible allows consumers to have certain optimizations when they can know the types of all columns upfront (and if the # of columns isn't too large) to generate more efficient code.

Now, in this example, we're actually going to have MatrixTable implement both Tables.rows and Tables.columns methods itself, i.e. it's going to return itself from those functions, so here's first how we make our MatrixTable a valid Tables.AbstractColumns object:

# 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)

We define columnaccess for our type, then columns just returns the MatrixTable itself, and then we define the three getcolumn methods and columnnames. Note the use of a lookup Dict that maps column name to column index so we can figure out which column to return from the matrix. We're also storing the column names in our names field so the columnnames implementation is trivial. And that's it! Literally! It can now be written out to a csv file, stored in a sqlite or other database, converted to DataFrame or JuliaDB table, etc. Pretty fun.

And now for the Tables.rows implementation:

# 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))

Here we start by defining Tables.rowaccess and Tables.rows, and then the iteration interface methods, since we declared that a MatrixTable itself is an iterator of Tables.AbstractRow-compatible objects. For eltype, we say that a MatrixTable iterates our own custom row type, MatrixRow. MatrixRow subtypes Tables.AbstractRow, which provides interface implementations for several useful behaviors (indexing, iteration, property-access, etc.); essentially it makes our custom MatrixRow type more convenient to work with.

Implementing the Tables.AbstractRow interface is straightfoward, and very similar to our implementation of Tables.AbstractColumns previously (i.e. the same methods for getcolumn and columnnames).

And that's it. Our MatrixTable type is now a fully fledged, valid Tables.jl source and can be used throughout the ecosystem. Now, this is obviously not a lot of code; but then again, the actual Tables.jl interface implementations tend to be fairly simple, given the other behaviors that are already defined for table types (i.e. table types tend to already have a getcolumn like function defined).

### Tables.isrowtable

One option for certain table types is to define Tables.isrowtable to automatically satisfy the Tables.jl interface. This can be convenient for "natural" table types that already iterate rows.

Tables.isrowtableFunction
Tables.isrowtable(x) => Bool

For convenience, some table objects that are naturally "row oriented" can define Tables.isrowtable(::Type{TableType}) = true to simplify satisfying the Tables.jl interface. Requirements for defining isrowtable include:

• Tables.rows(x) === x, i.e. the table object itself is a Row iterator
• If the table object is mutable, it should support:
• push!(x, row): allow pushing a single row onto table
• append!(x, rows): allow appending set of rows onto table
• If table object is mutable and indexable, it should support:
• x[i] = row: allow replacing of a row with another row by index

A table object that defines Tables.isrowtable will have definitions for Tables.istable, Tables.rowaccess, and Tables.rows automatically defined.

source

### Testing Tables.jl Implementations

One question that comes up is what the best strategies are for testing a Tables.jl implementation. Continuing with our MatrixTable example, let's see some useful ways to test that things are working as expected.

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

First, we define a matrix literal with three columns of various differently typed values.

# 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]

So, it looks like our MatrixTable type is looking good. It's doing everything we'd expect with regards to accessing its rows or columns via the Tables.jl API methods. Testing a table source like this is fairly straightforward since we're really just testing that our interface methods are doing what we expect them to do.

Now, while we didn't go over a "sink" function for matrices in our walkthrough, there does indeed exist a Tables.matrix function that allows converting any table input source into a plain Julia Matrix object.

Having both Tables.jl "source" and "sink" implementations (i.e. a type that is a Tables.jl-compatible source, as well as a way to consume other tables), allows us to do some additional "round trip" testing:

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])

In addition to our mat object earlier, we can define a couple simple "tables"; in this case rt is a kind of default "row table" as a Vector of NamedTuples, while ct is a default "column table" as a NamedTuple of Vectors. Notice that they contain mostly the same data as our matrix literal earlier, yet in slightly different storage formats. These default "row" and "column" tables are supported by default in Tables.jl due do their natural table representations, and hence can be excellent tools in testing table integrations.

# 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]