Functions

Functions

Grouping, Joining, and Split-Apply-Combine

DataFrames.aggregate — Function.

Split-apply-combine that applies a set of functions over columns of an AbstractDataFrame or GroupedDataFrame

aggregate(d::AbstractDataFrame, cols, fs)
aggregate(gd::GroupedDataFrame, fs)

Arguments

  • d : an AbstractDataFrame
  • gd : a GroupedDataFrame
  • cols : a column indicator (Symbol, Int, Vector{Symbol}, etc.)
  • fs : a function or vector of functions to be applied to vectors within groups; expects each argument to be a column vector

Each fs should return a value or vector. All returns must be the same length.

Returns

  • ::DataFrame

Examples

julia> using Statistics

julia> df = DataFrame(a = repeat([1, 2, 3, 4], outer=[2]),
                      b = repeat([2, 1], outer=[4]),
                      c = 1:8);

julia> aggregate(df, :a, sum)
4×3 DataFrame
│ Row │ a     │ b_sum │ c_sum │
│     │ Int64 │ Int64 │ Int64 │
├─────┼───────┼───────┼───────┤
│ 1   │ 1     │ 4     │ 6     │
│ 2   │ 2     │ 2     │ 8     │
│ 3   │ 3     │ 4     │ 10    │
│ 4   │ 4     │ 2     │ 12    │

julia> aggregate(df, :a, [sum, x->mean(skipmissing(x))])
4×5 DataFrame
│ Row │ a     │ b_sum │ c_sum │ b_function │ c_function │
│     │ Int64 │ Int64 │ Int64 │ Float64    │ Float64    │
├─────┼───────┼───────┼───────┼────────────┼────────────┤
│ 1   │ 1     │ 4     │ 6     │ 2.0        │ 3.0        │
│ 2   │ 2     │ 2     │ 8     │ 1.0        │ 4.0        │
│ 3   │ 3     │ 4     │ 10    │ 2.0        │ 5.0        │
│ 4   │ 4     │ 2     │ 12    │ 1.0        │ 6.0        │

julia> aggregate(groupby(df, :a), [sum, x->mean(skipmissing(x))])
4×5 DataFrame
│ Row │ a     │ b_sum │ c_sum │ b_function │ c_function │
│     │ Int64 │ Int64 │ Int64 │ Float64    │ Float64    │
├─────┼───────┼───────┼───────┼────────────┼────────────┤
│ 1   │ 1     │ 4     │ 6     │ 2.0        │ 3.0        │
│ 2   │ 2     │ 2     │ 8     │ 1.0        │ 4.0        │
│ 3   │ 3     │ 4     │ 10    │ 2.0        │ 5.0        │
│ 4   │ 4     │ 2     │ 12    │ 1.0        │ 6.0        │
source
DataFrames.by — Function.
by(d::AbstractDataFrame, keys, cols => f...; sort::Bool = false)
by(d::AbstractDataFrame, keys; (colname = cols => f)..., sort::Bool = false)
by(d::AbstractDataFrame, keys, f; sort::Bool = false)
by(f, d::AbstractDataFrame, keys; sort::Bool = false)

Split-apply-combine in one step: apply f to each grouping in d based on grouping columns keys, and return a DataFrame.

keys can be either a single column index, or a vector thereof.

If the last argument(s) consist(s) in one or more cols => f pair(s), or if colname = cols => f keyword arguments are provided, cols must be a column name or index, or a vector or tuple thereof, and f must be a callable. A pair or a (named) tuple of pairs can also be provided as the first or last argument. If cols is a single column index, f is called with a SubArray view into that column for each group; else, f is called with a named tuple holding SubArray views into these columns.

If the last argument is a callable f, it is passed a SubDataFrame view for each group, and the returned DataFrame then consists of the returned rows plus the grouping columns. Note that this second form is much slower than the first one due to type instability. A method is defined with f as the first argument, so do-block notation can be used.

f can return a single value, a row or multiple rows. The type of the returned value determines the shape of the resulting data frame:

  • A single value gives a data frame with a single column and one row per group.
  • A named tuple of single values or a DataFrameRow gives a data frame with one column for each field and one row per group.
  • A vector gives a data frame with a single column and as many rows for each group as the length of the returned vector for that group.
  • A data frame, a named tuple of vectors or a matrix gives a data frame with the same columns and as many rows for each group as the rows returned for that group.

f must always return the same kind of object (as defined in the above list) for all groups, and if a named tuple or data frame, with the same fields or columns. Named tuples cannot mix single values and vectors. Due to type instability, returning a single value or a named tuple is dramatically faster than returning a data frame.

As a special case, if multiple pairs are passed as last arguments, each function is required to return a single value or vector, which will produce each a separate column.

In all cases, the resulting data frame contains all the grouping columns in addition to those generated by the application of f. Column names are automatically generated when necessary: for functions operating on a single column and returning a single value or vector, the function name is appended to the input colummn name; for other functions, columns are called x1, x2 and so on. The resulting data frame will be sorted on keys if sort=true. Otherwise, ordering of rows is undefined.

Optimized methods are used when standard summary functions (sum, prod, minimum, maximum, mean, var, std, first, last and length) are specified using the pair syntax (e.g.col => sum). When computing thesumormeanover floating point columns, results will be less accurate than the standard [sum](@ref) function (which uses pairwise summation). Usecol => x -> sum(x)` to avoid the optimized method and use the slower, more accurate one.

by(d, cols, f) is equivalent to combine(f, groupby(d, cols)) and to the less efficient combine(map(f, groupby(d, cols))).

Examples

julia> df = DataFrame(a = repeat([1, 2, 3, 4], outer=[2]),
                      b = repeat([2, 1], outer=[4]),
                      c = 1:8);

julia> by(df, :a, :c => sum)
4×2 DataFrame
│ Row │ a     │ c_sum │
│     │ Int64 │ Int64 │
├─────┼───────┼───────┤
│ 1   │ 1     │ 6     │
│ 2   │ 2     │ 8     │
│ 3   │ 3     │ 10    │
│ 4   │ 4     │ 12    │

julia> by(df, :a, d -> sum(d.c)) # Slower variant
4×2 DataFrame
│ Row │ a     │ x1    │
│     │ Int64 │ Int64 │
├─────┼───────┼───────┤
│ 1   │ 1     │ 6     │
│ 2   │ 2     │ 8     │
│ 3   │ 3     │ 10    │
│ 4   │ 4     │ 12    │

julia> by(df, :a) do d # do syntax for the slower variant
           sum(d.c)
       end
4×2 DataFrame
│ Row │ a     │ x1    │
│     │ Int64 │ Int64 │
├─────┼───────┼───────┤
│ 1   │ 1     │ 6     │
│ 2   │ 2     │ 8     │
│ 3   │ 3     │ 10    │
│ 4   │ 4     │ 12    │

julia> by(df, :a, :c => x -> 2 .* x)
8×2 DataFrame
│ Row │ a     │ c_function │
│     │ Int64 │ Int64      │
├─────┼───────┼────────────┤
│ 1   │ 1     │ 2          │
│ 2   │ 1     │ 10         │
│ 3   │ 2     │ 4          │
│ 4   │ 2     │ 12         │
│ 5   │ 3     │ 6          │
│ 6   │ 3     │ 14         │
│ 7   │ 4     │ 8          │
│ 8   │ 4     │ 16         │

julia> by(df, :a, c_sum = :c => sum, c_sum2 = :c => x -> sum(x.^2))
4×3 DataFrame
│ Row │ a     │ c_sum │ c_sum2 │
│     │ Int64 │ Int64 │ Int64  │
├─────┼───────┼───────┼────────┤
│ 1   │ 1     │ 6     │ 26     │
│ 2   │ 2     │ 8     │ 40     │
│ 3   │ 3     │ 10    │ 58     │
│ 4   │ 4     │ 12    │ 80     │

julia> by(df, :a, (:b, :c) => x -> (minb = minimum(x.b), sumc = sum(x.c)))
4×3 DataFrame
│ Row │ a     │ minb  │ sumc  │
│     │ Int64 │ Int64 │ Int64 │
├─────┼───────┼───────┼───────┤
│ 1   │ 1     │ 2     │ 6     │
│ 2   │ 2     │ 1     │ 8     │
│ 3   │ 3     │ 2     │ 10    │
│ 4   │ 4     │ 1     │ 12    │
source
DataFrames.combine — Function.
combine(gd::GroupedDataFrame, cols => f...)
combine(gd::GroupedDataFrame; (colname = cols => f)...)
combine(gd::GroupedDataFrame, f)
combine(f, gd::GroupedDataFrame)

Transform a GroupedDataFrame into a DataFrame.

If the last argument(s) consist(s) in one or more cols => f pair(s), or if colname = cols => f keyword arguments are provided, cols must be a column name or index, or a vector or tuple thereof, and f must be a callable. A pair or a (named) tuple of pairs can also be provided as the first or last argument. If cols is a single column index, f is called with a SubArray view into that column for each group; else, f is called with a named tuple holding SubArray views into these columns.

If the last argument is a callable f, it is passed a SubDataFrame view for each group, and the returned DataFrame then consists of the returned rows plus the grouping columns. Note that this second form is much slower than the first one due to type instability. A method is defined with f as the first argument, so do-block notation can be used.

f can return a single value, a row or multiple rows. The type of the returned value determines the shape of the resulting data frame:

  • A single value gives a data frame with a single column and one row per group.
  • A named tuple of single values or a DataFrameRow gives a data frame with one column for each field and one row per group.
  • A vector gives a data frame with a single column and as many rows for each group as the length of the returned vector for that group.
  • A data frame, a named tuple of vectors or a matrix gives a data frame with the same columns and as many rows for each group as the rows returned for that group.

f must always return the same kind of object (as defined in the above list) for all groups, and if a named tuple or data frame, with the same fields or columns. Named tuples cannot mix single values and vectors. Due to type instability, returning a single value or a named tuple is dramatically faster than returning a data frame.

As a special case, if a tuple or vector of pairs is passed as the first argument, each function is required to return a single value or vector, which will produce each a separate column.

In all cases, the resulting data frame contains all the grouping columns in addition to those generated by the application of f. Column names are automatically generated when necessary: for functions operating on a single column and returning a single value or vector, the function name is appended to the input column name; for other functions, columns are called x1, x2 and so on. The resulting data frame will be sorted if sort=true was passed to the groupby call from which gd was constructed. Otherwise, ordering of rows is undefined.

Optimized methods are used when standard summary functions (sum, prod, minimum, maximum, mean, var, std, first, last and length) are specified using the pair syntax (e.g.col => sum). When computing thesumormeanover floating point columns, results will be less accurate than the standard [sum](@ref) function (which uses pairwise summation). Usecol => x -> sum(x)` to avoid the optimized method and use the slower, more accurate one.

Examples

julia> df = DataFrame(a = repeat([1, 2, 3, 4], outer=[2]),
                      b = repeat([2, 1], outer=[4]),
                      c = 1:8);

julia> gd = groupby(df, :a);

julia> combine(gd, :c => sum)
4×2 DataFrame
│ Row │ a     │ c_sum │
│     │ Int64 │ Int64 │
├─────┼───────┼───────┤
│ 1   │ 1     │ 6     │
│ 2   │ 2     │ 8     │
│ 3   │ 3     │ 10    │
│ 4   │ 4     │ 12    │

julia> combine(:c => sum, gd)
4×2 DataFrame
│ Row │ a     │ c_sum │
│     │ Int64 │ Int64 │
├─────┼───────┼───────┤
│ 1   │ 1     │ 6     │
│ 2   │ 2     │ 8     │
│ 3   │ 3     │ 10    │
│ 4   │ 4     │ 12    │

julia> combine(df -> sum(df.c), gd) # Slower variant
4×2 DataFrame
│ Row │ a     │ x1    │
│     │ Int64 │ Int64 │
├─────┼───────┼───────┤
│ 1   │ 1     │ 6     │
│ 2   │ 2     │ 8     │
│ 3   │ 3     │ 10    │
│ 4   │ 4     │ 12    │

See by for more examples.

See also

by(f, df, cols) is a shorthand for combine(f, groupby(df, cols)).

map: combine(f, groupby(df, cols)) is a more efficient equivalent of combine(map(f, groupby(df, cols))).

source
DataFrames.groupby — Function.

A view of an AbstractDataFrame split into row groups

groupby(d::AbstractDataFrame, cols; sort = false, skipmissing = false)
groupby(cols; sort = false, skipmissing = false)

Arguments

  • d : an AbstractDataFrame to split (optional, see Returns)
  • cols : data table columns to group by
  • sort : whether to sort rows according to the values of the grouping columns cols
  • skipmissing : whether to skip rows with missing values in one of the grouping columns cols

Returns

A GroupedDataFrame : a grouped view into d

Details

An iterator over a GroupedDataFrame returns a SubDataFrame view for each grouping into d. A GroupedDataFrame also supports indexing by groups, map (which applies a function to each group) and combine (which applies a function to each group and combines the result into a data frame).

See the following for additional split-apply-combine operations:

  • by : split-apply-combine using functions
  • aggregate : split-apply-combine; applies functions in the form of a cross product
  • map : apply a function to each group of a GroupedDataFrame (without combining)
  • combine : combine a GroupedDataFrame, optionally applying a function to each group

Examples

julia> df = DataFrame(a = repeat([1, 2, 3, 4], outer=[2]),
                      b = repeat([2, 1], outer=[4]),
                      c = 1:8);

julia> gd = groupby(df, :a)
GroupedDataFrame with 4 groups based on key: a
First Group (2 rows): a = 1
│ Row │ a     │ b     │ c     │
│     │ Int64 │ Int64 │ Int64 │
├─────┼───────┼───────┼───────┤
│ 1   │ 1     │ 2     │ 1     │
│ 2   │ 1     │ 2     │ 5     │
⋮
Last Group (2 rows): a = 4
│ Row │ a     │ b     │ c     │
│     │ Int64 │ Int64 │ Int64 │
├─────┼───────┼───────┼───────┤
│ 1   │ 4     │ 1     │ 4     │
│ 2   │ 4     │ 1     │ 8     │

julia> gd[1]
2×3 SubDataFrame
│ Row │ a     │ b     │ c     │
│     │ Int64 │ Int64 │ Int64 │
├─────┼───────┼───────┼───────┤
│ 1   │ 1     │ 2     │ 1     │
│ 2   │ 1     │ 2     │ 5     │

julia> last(gd)
2×3 SubDataFrame
│ Row │ a     │ b     │ c     │
│     │ Int64 │ Int64 │ Int64 │
├─────┼───────┼───────┼───────┤
│ 1   │ 4     │ 1     │ 4     │
│ 2   │ 4     │ 1     │ 8     │

julia> for g in gd
           println(g)
       end
2×3 SubDataFrame
│ Row │ a     │ b     │ c     │
│     │ Int64 │ Int64 │ Int64 │
├─────┼───────┼───────┼───────┤
│ 1   │ 1     │ 2     │ 1     │
│ 2   │ 1     │ 2     │ 5     │
2×3 SubDataFrame
│ Row │ a     │ b     │ c     │
│     │ Int64 │ Int64 │ Int64 │
├─────┼───────┼───────┼───────┤
│ 1   │ 2     │ 1     │ 2     │
│ 2   │ 2     │ 1     │ 6     │
2×3 SubDataFrame
│ Row │ a     │ b     │ c     │
│     │ Int64 │ Int64 │ Int64 │
├─────┼───────┼───────┼───────┤
│ 1   │ 3     │ 2     │ 3     │
│ 2   │ 3     │ 2     │ 7     │
2×3 SubDataFrame
│ Row │ a     │ b     │ c     │
│     │ Int64 │ Int64 │ Int64 │
├─────┼───────┼───────┼───────┤
│ 1   │ 4     │ 1     │ 4     │
│ 2   │ 4     │ 1     │ 8     │
source
DataFrames.groupindices — Function.
groupindices(gd::GroupedDataFrame)

Return a vector of group indices for each row of parent(gd).

Rows appearing in group gd[i] are attributed index i. Rows not present in any group are attributed missing (this can happen if skipmissing=true was passed when creating gd, or if gd is a subset from a larger GroupedDataFrame).

source
DataFrames.groupvars — Function.
groupvars(gd::GroupedDataFrame)

Return a vector of column names in parent(gd) used for grouping.

source
Base.join — Function.
join(df1, df2; on = Symbol[], kind = :inner, makeunique = false,
     indicator = nothing, validate = (false, false))

Join two DataFrame objects

Arguments

  • df1, df2 : the two AbstractDataFrames to be joined

Keyword Arguments

  • on : A column, or vector of columns to join df1 and df2 on. If the column(s) that df1 and df2 will be joined on have different names, then the columns should be (left, right) tuples or left => right pairs, or a vector of such tuples or pairs. on is a required argument for all joins except for kind = :cross

  • kind : the type of join, options include:

    • :inner : only include rows with keys that match in both df1 and df2, the default
    • :outer : include all rows from df1 and df2
    • :left : include all rows from df1
    • :right : include all rows from df2
    • :semi : return rows of df1 that match with the keys in df2
    • :anti : return rows of df1 that do not match with the keys in df2
    • :cross : a full Cartesian product of the key combinations; every row of df1 is matched with every row of df2

  • makeunique : if false (the default), an error will be raised if duplicate names are found in columns not joined on; if true, duplicate names will be suffixed with _i (i starting at 1 for the first duplicate).

  • indicator : Default: nothing. If a Symbol, adds categorical indicator column named Symbol for whether a row appeared in only df1 ("left_only"), only df2 ("right_only") or in both ("both"). If Symbol is already in use, the column name will be modified if makeunique=true.

  • validate : whether to check that columns passed as the on argument define unique keys in each input data frame (according to isequal). Can be a tuple or a pair, with the first element indicating whether to run check for df1 and the second element for df2. By default no check is performed.

For the three join operations that may introduce missing values (:outer, :left, and :right), all columns of the returned data table will support missing values.

When merging on categorical columns that differ in the ordering of their levels, the ordering of the left DataFrame takes precedence over the ordering of the right DataFrame

Result

  • ::DataFrame : the joined DataFrame

Examples

name = DataFrame(ID = [1, 2, 3], Name = ["John Doe", "Jane Doe", "Joe Blogs"])
job = DataFrame(ID = [1, 2, 4], Job = ["Lawyer", "Doctor", "Farmer"])

join(name, job, on = :ID)
join(name, job, on = :ID, kind = :outer)
join(name, job, on = :ID, kind = :left)
join(name, job, on = :ID, kind = :right)
join(name, job, on = :ID, kind = :semi)
join(name, job, on = :ID, kind = :anti)
join(name, job, kind = :cross)

job2 = DataFrame(identifier = [1, 2, 4], Job = ["Lawyer", "Doctor", "Farmer"])
join(name, job2, on = (:ID, :identifier))
join(name, job2, on = :ID => :identifier)
source
Base.map — Function.
map(cols => f, gd::GroupedDataFrame)
map(f, gd::GroupedDataFrame)

Apply a function to each group of rows and return a GroupedDataFrame.

If the first argument is a cols => f pair, cols must be a column name or index, or a vector or tuple thereof, and f must be a callable. If cols is a single column index, f is called with a SubArray view into that column for each group; else, f is called with a named tuple holding SubArray views into these columns.

If the first argument is a vector, tuple or named tuple of such pairs, each pair is handled as described above. If a named tuple, field names are used to name each generated column.

If the first argument is a callable, it is passed a SubDataFrame view for each group, and the returned DataFrame then consists of the returned rows plus the grouping columns. Note that this second form is much slower than the first one due to type instability.

f can return a single value, a row or multiple rows. The type of the returned value determines the shape of the resulting data frame:

  • A single value gives a data frame with a single column and one row per group.
  • A named tuple of single values or a DataFrameRow gives a data frame with one column for each field and one row per group.
  • A vector gives a data frame with a single column and as many rows for each group as the length of the returned vector for that group.
  • A data frame, a named tuple of vectors or a matrix gives a data frame with the same columns and as many rows for each group as the rows returned for that group.

f must always return the same kind of object (as defined in the above list) for all groups, and if a named tuple or data frame, with the same fields or columns. Named tuples cannot mix single values and vectors. Due to type instability, returning a single value or a named tuple is dramatically faster than returning a data frame.

As a special case, if a tuple or vector of pairs is passed as the first argument, each function is required to return a single value or vector, which will produce each a separate column.

In all cases, the resulting GroupedDataFrame contains all the grouping columns in addition to those generated by the application of f. Column names are automatically generated when necessary: for functions operating on a single column and returning a single value or vector, the function name is appended to the input column name; for other functions, columns are called x1, x2 and so on.

Optimized methods are used when standard summary functions (sum, prod, minimum, maximum, mean, var, std, first, last and length) are specified using the pair syntax (e.g.col => sum). When computing thesumormeanover floating point columns, results will be less accurate than the standard [sum](@ref) function (which uses pairwise summation). Usecol => x -> sum(x)` to avoid the optimized method and use the slower, more accurate one.

Examples

julia> df = DataFrame(a = repeat([1, 2, 3, 4], outer=[2]),
                      b = repeat([2, 1], outer=[4]),
                      c = 1:8);

julia> gd = groupby(df, :a);

julia> map(:c => sum, gd)
GroupedDataFrame{DataFrame} with 4 groups based on key: :a
First Group: 1 row
│ Row │ a     │ c_sum │
│     │ Int64 │ Int64 │
├─────┼───────┼───────┤
│ 1   │ 1     │ 6     │
⋮
Last Group: 1 row
│ Row │ a     │ c_sum │
│     │ Int64 │ Int64 │
├─────┼───────┼───────┤
│ 1   │ 4     │ 12    │

julia> map(df -> sum(df.c), gd) # Slower variant
GroupedDataFrame{DataFrame} with 4 groups based on key: :a
First Group: 1 row
│ Row │ a     │ x1    │
│     │ Int64 │ Int64 │
├─────┼───────┼───────┤
│ 1   │ 1     │ 6     │
⋮
Last Group: 1 row
│ Row │ a     │ x1    │
│     │ Int64 │ Int64 │
├─────┼───────┼───────┤
│ 1   │ 4     │ 12    │

See by for more examples.

See also

combine(f, gd) returns a DataFrame rather than a GroupedDataFrame

source
DataFrames.melt — Function.

Stacks a DataFrame; convert from a wide to long format; see stack.

source
DataFrames.stack — Function.

Stacks a DataFrame; convert from a wide to long format

stack(df::AbstractDataFrame, [measure_vars], [id_vars];
      variable_name::Symbol=:variable, value_name::Symbol=:value)
melt(df::AbstractDataFrame, [id_vars], [measure_vars];
     variable_name::Symbol=:variable, value_name::Symbol=:value)

Arguments

  • df : the AbstractDataFrame to be stacked

  • measure_vars : the columns to be stacked (the measurement variables), a normal column indexing type, like a Symbol, Vector{Symbol}, Int, etc.; for melt, defaults to all variables that are not id_vars. If neither measure_vars or id_vars are given, measure_vars defaults to all floating point columns.

  • id_vars : the identifier columns that are repeated during stacking, a normal column indexing type; for stack defaults to all variables that are not measure_vars

  • variable_name : the name of the new stacked column that shall hold the names of each of measure_vars

  • value_name : the name of the new stacked column containing the values from each of measure_vars

Result

  • ::DataFrame : the long-format DataFrame with column :value holding the values of the stacked columns (measure_vars), with column :variable a Vector of Symbols with the measure_vars name, and with columns for each of the id_vars.

See also stackdf and meltdf for stacking methods that return a view into the original DataFrame. See unstack for converting from long to wide format.

Examples

d1 = DataFrame(a = repeat([1:3;], inner = [4]),
               b = repeat([1:4;], inner = [3]),
               c = randn(12),
               d = randn(12),
               e = map(string, 'a':'l'))

d1s = stack(d1, [:c, :d])
d1s2 = stack(d1, [:c, :d], [:a])
d1m = melt(d1, [:a, :b, :e])
d1s_name = melt(d1, [:a, :b, :e], variable_name=:somemeasure)
source
DataFrames.unstack — Function.

Unstacks a DataFrame; convert from a long to wide format

unstack(df::AbstractDataFrame, rowkeys::Union{Symbol, Integer},
        colkey::Union{Symbol, Integer}, value::Union{Symbol, Integer})
unstack(df::AbstractDataFrame, rowkeys::AbstractVector{<:Union{Symbol, Integer}},
        colkey::Union{Symbol, Integer}, value::Union{Symbol, Integer})
unstack(df::AbstractDataFrame, colkey::Union{Symbol, Integer},
        value::Union{Symbol, Integer})
unstack(df::AbstractDataFrame)

Arguments

  • df : the AbstractDataFrame to be unstacked

  • rowkeys : the column(s) with a unique key for each row, if not given, find a key by grouping on anything not a colkey or value

  • colkey : the column holding the column names in wide format, defaults to :variable

  • value : the value column, defaults to :value

Result

  • ::DataFrame : the wide-format DataFrame

If colkey contains missing values then they will be skipped and a warning will be printed.

If combination of rowkeys and colkey contains duplicate entries then last value will be retained and a warning will be printed.

Examples

wide = DataFrame(id = 1:12,
                 a  = repeat([1:3;], inner = [4]),
                 b  = repeat([1:4;], inner = [3]),
                 c  = randn(12),
                 d  = randn(12))

long = stack(wide)
wide0 = unstack(long)
wide1 = unstack(long, :variable, :value)
wide2 = unstack(long, :id, :variable, :value)
wide3 = unstack(long, [:id, :a], :variable, :value)

Note that there are some differences between the widened results above.

source
DataFrames.stackdf — Function.

A stacked view of a DataFrame (long format)

Like stack and melt, but a view is returned rather than data copies.

stackdf(df::AbstractDataFrame, [measure_vars], [id_vars];
        variable_name::Symbol=:variable, value_name::Symbol=:value)
meltdf(df::AbstractDataFrame, [id_vars], [measure_vars];
       variable_name::Symbol=:variable, value_name::Symbol=:value)

Arguments

  • df : the wide AbstractDataFrame

  • measure_vars : the columns to be stacked (the measurement variables), a normal column indexing type, like a Symbol, Vector{Symbol}, Int, etc.; for melt, defaults to all variables that are not id_vars

  • id_vars : the identifier columns that are repeated during stacking, a normal column indexing type; for stack defaults to all variables that are not measure_vars

Result

  • ::DataFrame : the long-format DataFrame with column :value holding the values of the stacked columns (measure_vars), with column :variable a Vector of Symbols with the measure_vars name, and with columns for each of the id_vars.

The result is a view because the columns are special AbstractVectors that return indexed views into the original DataFrame.

Examples

d1 = DataFrame(a = repeat([1:3;], inner = [4]),
               b = repeat([1:4;], inner = [3]),
               c = randn(12),
               d = randn(12),
               e = map(string, 'a':'l'))

d1s = stackdf(d1, [:c, :d])
d1s2 = stackdf(d1, [:c, :d], [:a])
d1m = meltdf(d1, [:a, :b, :e])
source
DataFrames.meltdf — Function.

A stacked view of a DataFrame (long format); see stackdf

source

Basics

allowmissing!
categorical!
completecases
copy
DataFrame
DataFrame!
deletecols!
deletecols
deleterows!
describe
disallowmissing!
dropmissing
dropmissing!
eachrow
eachcol
eltypes
filter
filter!
hcat
insertcols!
mapcols
names!
nonunique
nrow
ncol
rename!
rename
repeat
select
select!
show
sort
sort!
unique!
permutecols!
vcat
append!
push!