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Deprecate head and tail in favor of first and last #1607

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Dec 1, 2018
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Deprecate head and tail in favor of first and last #1607

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6967260
Deprecate head and tail in favor of first and last
nalimilan Nov 24, 2018
4436646
Remove exports
nalimilan Nov 25, 2018
cf55ae5
Define first and last with single argument
nalimilan Nov 25, 2018
605f9ef
Use end
nalimilan Nov 25, 2018
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2 changes: 0 additions & 2 deletions docs/src/lib/functions.md
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Expand Up @@ -40,7 +40,6 @@ eachcol
eltypes
filter
filter!
head
insertcols!
mapcols
names!
Expand All @@ -51,7 +50,6 @@ repeat
show
sort
sort!
tail
unique!
permutecols!
```
6 changes: 3 additions & 3 deletions docs/src/man/getting_started.md
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Expand Up @@ -208,10 +208,10 @@ julia> df = DataFrame(A = 1:2:1000, B = repeat(1:10, inner=50), C = 1:500)

Printing options can be adjusted by calling the `show` function manually: `show(df, allrows=true)` prints all rows even if they do not fit on screen and `show(df, allcols=true)` does the same for columns.

The `head` and `tail` functions can be used to look at the first and last rows of a data frame (respectively):
The `first` and `last` functions can be used to look at the first and last rows of a data frame (respectively):

```jldoctest dataframe
julia> head(df)
julia> first(df, 6)
6×3 DataFrame
│ Row │ A │ B │ C │
│ │ Int64 │ Int64 │ Int64 │
Expand All @@ -223,7 +223,7 @@ julia> head(df)
│ 5 │ 9 │ 1 │ 5 │
│ 6 │ 11 │ 1 │ 6 │

julia> tail(df)
julia> last(df, 6)
6×3 DataFrame
│ Row │ A │ B │ C │
│ │ Int64 │ Int64 │ Int64 │
Expand Down
43 changes: 21 additions & 22 deletions docs/src/man/reshaping_and_pivoting.md
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Expand Up @@ -7,7 +7,7 @@ julia> using DataFrames, CSV

julia> iris = CSV.read(joinpath(dirname(pathof(DataFrames)), "../test/data/iris.csv"));

julia> head(iris)
julia> first(iris, 6)
6×5 DataFrame
│ Row │ SepalLength │ SepalWidth │ PetalLength │ PetalWidth │ Species │
│ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Categorical…⍰ │
Expand All @@ -19,7 +19,7 @@ julia> head(iris)
│ 5 │ 5.0 │ 3.6 │ 1.4 │ 0.2 │ setosa │
│ 6 │ 5.4 │ 3.9 │ 1.7 │ 0.4 │ setosa │

julia> tail(iris)
julia> last(iris, 6)
6×5 DataFrame
│ Row │ SepalLength │ SepalWidth │ PetalLength │ PetalWidth │ Species │
│ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Categorical…⍰ │
Expand All @@ -33,7 +33,7 @@ julia> tail(iris)

julia> d = stack(iris, 1:4);

julia> head(d)
julia> first(d, 6)
6×3 DataFrame
│ Row │ variable │ value │ Species │
│ │ Symbol │ Float64⍰ │ Categorical…⍰ │
Expand All @@ -45,7 +45,7 @@ julia> head(d)
│ 5 │ SepalLength │ 5.0 │ setosa │
│ 6 │ SepalLength │ 5.4 │ setosa │

julia> tail(d)
julia> last(d, 6)
6×3 DataFrame
│ Row │ variable │ value │ Species │
│ │ Symbol │ Float64⍰ │ Categorical…⍰ │
Expand All @@ -63,7 +63,7 @@ The second optional argument to `stack` indicates the columns to be stacked. The
```jldoctest reshape
julia> d = stack(iris, [:SepalLength, :SepalWidth, :PetalLength, :PetalWidth]);

julia> head(d)
julia> first(d, 6)
6×3 DataFrame
│ Row │ variable │ value │ Species │
│ │ Symbol │ Float64⍰ │ Categorical…⍰ │
Expand All @@ -75,8 +75,7 @@ julia> head(d)
│ 5 │ SepalLength │ 5.0 │ setosa │
│ 6 │ SepalLength │ 5.4 │ setosa │


julia> tail(d)
julia> last(d, 6)
6×3 DataFrame
│ Row │ variable │ value │ Species │
│ │ Symbol │ Float64⍰ │ Categorical…⍰ │
Expand All @@ -99,7 +98,7 @@ A third optional argument to `stack` represents the id columns that are repeated
```jldoctest reshape
julia> d = stack(iris, [:SepalLength, :SepalWidth], :Species);

julia> head(d)
julia> first(d, 6)
6×3 DataFrame
│ Row │ variable │ value │ Species │
│ │ Symbol │ Float64⍰ │ Categorical…⍰ │
Expand All @@ -111,7 +110,7 @@ julia> head(d)
│ 5 │ SepalLength │ 5.0 │ setosa │
│ 6 │ SepalLength │ 5.4 │ setosa │

julia> tail(d)
julia> last(d, 6)
6×3 DataFrame
│ Row │ variable │ value │ Species │
│ │ Symbol │ Float64⍰ │ Categorical…⍰ │
Expand All @@ -129,7 +128,7 @@ julia> tail(d)
```jldoctest reshape
julia> d = melt(iris, :Species);

julia> head(d)
julia> first(d, 6)
6×3 DataFrame
│ Row │ variable │ value │ Species │
│ │ Symbol │ Float64⍰ │ Categorical…⍰ │
Expand All @@ -141,7 +140,7 @@ julia> head(d)
│ 5 │ SepalLength │ 5.0 │ setosa │
│ 6 │ SepalLength │ 5.4 │ setosa │

julia> tail(d)
julia> last(d, 6)
6×3 DataFrame
│ Row │ variable │ value │ Species │
│ │ Symbol │ Float64⍰ │ Categorical…⍰ │
Expand All @@ -162,7 +161,7 @@ julia> iris[:id] = 1:size(iris, 1)

julia> longdf = melt(iris, [:Species, :id]);

julia> head(longdf)
julia> first(longdf, 6)
6×4 DataFrame
│ Row │ variable │ value │ Species │ id │
│ │ Symbol │ Float64⍰ │ Categorical…⍰ │ Int64 │
Expand All @@ -174,7 +173,7 @@ julia> head(longdf)
│ 5 │ SepalLength │ 5.0 │ setosa │ 5 │
│ 6 │ SepalLength │ 5.4 │ setosa │ 6 │

julia> tail(longdf)
julia> last(longdf, 6)
6×4 DataFrame
│ Row │ variable │ value │ Species │ id │
│ │ Symbol │ Float64⍰ │ Categorical…⍰ │ Int64 │
Expand All @@ -188,7 +187,7 @@ julia> tail(longdf)

julia> widedf = unstack(longdf, :id, :variable, :value);

julia> head(widedf)
julia> first(widedf, 6)
6×5 DataFrame
│ Row │ id │ PetalLength │ PetalWidth │ SepalLength │ SepalWidth │
│ │ Int64 │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │
Expand All @@ -200,7 +199,7 @@ julia> head(widedf)
│ 5 │ 5 │ 1.4 │ 0.2 │ 5.0 │ 3.6 │
│ 6 │ 6 │ 1.7 │ 0.4 │ 5.4 │ 3.9 │

julia> tail(widedf)
julia> last(widedf, 6)
6×5 DataFrame
│ Row │ id │ PetalLength │ PetalWidth │ SepalLength │ SepalWidth │
│ │ Int64 │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │
Expand All @@ -218,7 +217,7 @@ If the remaining columns are unique, you can skip the id variable and use:
```jldoctest reshape
julia> longdf = melt(iris, [:Species, :id]);

julia> head(longdf)
julia> first(longdf, 6)
6×4 DataFrame
│ Row │ variable │ value │ Species │ id │
│ │ Symbol │ Float64⍰ │ Categorical…⍰ │ Int64 │
Expand All @@ -232,7 +231,7 @@ julia> head(longdf)

julia> widedf = unstack(longdf, :variable, :value);

julia> head(widedf)
julia> first(widedf, 6)
6×6 DataFrame
│ Row │ Species │ id │ PetalLength │ PetalWidth │ SepalLength │ SepalWidth │
│ │ Categorical…⍰ │ Int64 │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │
Expand All @@ -250,7 +249,7 @@ julia> head(widedf)
```jldoctest reshape
julia> d = stackdf(iris);

julia> head(d)
julia> first(d, 6)
6×4 DataFrame
│ Row │ variable │ value │ Species │ id │
│ │ Symbol │ Float64⍰ │ Categorical…⍰ │ Int64 │
Expand All @@ -262,7 +261,7 @@ julia> head(d)
│ 5 │ SepalLength │ 5.0 │ setosa │ 5 │
│ 6 │ SepalLength │ 5.4 │ setosa │ 6 │

julia> tail(d)
julia> last(d, 6)
6×4 DataFrame
│ Row │ variable │ value │ Species │ id │
│ │ Symbol │ Float64⍰ │ Categorical…⍰ │ Int64 │
Expand Down Expand Up @@ -291,7 +290,7 @@ None of these reshaping functions perform any aggregation. To do aggregation, us
```jldoctest reshape
julia> d = melt(iris, :Species);

julia> head(d)
julia> first(d, 6)
6×3 DataFrame
│ Row │ variable │ value │ Species │
│ │ Symbol │ Float64⍰ │ Categorical…⍰ │
Expand All @@ -305,7 +304,7 @@ julia> head(d)

julia> x = by(d, [:variable, :Species], df -> DataFrame(vsum = mean(df[:value])));

julia> head(x)
julia> first(x, 6)

6×3 DataFrame
│ Row │ variable │ Species │ vsum │
Expand All @@ -318,7 +317,7 @@ julia> head(x)
│ 5 │ SepalWidth │ versicolor │ 2.77 │
│ 6 │ SepalWidth │ virginica │ 2.974 │

julia> head(unstack(x, :Species, :vsum))
julia> first(unstack(x, :Species, :vsum), 6)
5×4 DataFrame
│ Row │ variable │ setosa │ versicolor │ virginica │
│ │ Symbol │ Float64⍰ │ Float64⍰ │ Float64⍰ │
Expand Down
24 changes: 12 additions & 12 deletions docs/src/man/sorting.md
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Expand Up @@ -9,7 +9,7 @@ julia> iris = CSV.read(joinpath(dirname(pathof(DataFrames)), "../test/data/iris.

julia> sort!(iris);

julia> head(iris)
julia> first(iris, 6)
6×5 DataFrame
│ Row │ SepalLength │ SepalWidth │ PetalLength │ PetalWidth │ Species │
│ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Categorical…⍰ │
Expand All @@ -21,7 +21,7 @@ julia> head(iris)
│ 5 │ 4.5 │ 2.3 │ 1.3 │ 0.3 │ setosa │
│ 6 │ 4.6 │ 3.1 │ 1.5 │ 0.2 │ setosa │

julia> tail(iris)
julia> last(iris, 6)
6×5 DataFrame
│ Row │ SepalLength │ SepalWidth │ PetalLength │ PetalWidth │ Species │
│ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Categorical…⍰ │
Expand All @@ -39,7 +39,7 @@ In Sorting `DataFrame`s, you may want to sort different columns with different o
```jldoctest sort
julia> sort!(iris, rev = true);

julia> head(iris)
julia> first(iris, 6)
6×5 DataFrame
│ Row │ SepalLength │ SepalWidth │ PetalLength │ PetalWidth │ Species │
│ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Categorical…⍰ │
Expand All @@ -51,7 +51,7 @@ julia> head(iris)
│ 5 │ 7.7 │ 2.6 │ 6.9 │ 2.3 │ virginica │
│ 6 │ 7.6 │ 3.0 │ 6.6 │ 2.1 │ virginica │

julia> tail(iris)
julia> last(iris, 6)
6×5 DataFrame
│ Row │ SepalLength │ SepalWidth │ PetalLength │ PetalWidth │ Species │
│ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Categorical…⍰ │
Expand All @@ -65,7 +65,7 @@ julia> tail(iris)

julia> sort!(iris, (:SepalWidth, :SepalLength));

julia> head(iris)
julia> first(iris, 6)
6×5 DataFrame
│ Row │ SepalLength │ SepalWidth │ PetalLength │ PetalWidth │ Species │
│ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Categorical…⍰ │
Expand All @@ -77,7 +77,7 @@ julia> head(iris)
│ 5 │ 4.5 │ 2.3 │ 1.3 │ 0.3 │ setosa │
│ 6 │ 5.0 │ 2.3 │ 3.3 │ 1.0 │ versicolor │

julia> tail(iris)
julia> last(iris, 6)
6×5 DataFrame
│ Row │ SepalLength │ SepalWidth │ PetalLength │ PetalWidth │ Species │
│ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Categorical…⍰ │
Expand All @@ -92,7 +92,7 @@ julia> tail(iris)
julia> sort!(iris, (order(:Species, by = uppercase),
order(:SepalLength, rev = true)));

julia> head(iris)
julia> first(iris, 6)
6×5 DataFrame
│ Row │ SepalLength │ SepalWidth │ PetalLength │ PetalWidth │ Species │
│ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Categorical…⍰ │
Expand All @@ -104,7 +104,7 @@ julia> head(iris)
│ 5 │ 5.5 │ 4.2 │ 1.4 │ 0.2 │ setosa │
│ 6 │ 5.4 │ 3.4 │ 1.7 │ 0.2 │ setosa │

julia> tail(iris)
julia> last(iris, 6)
6×5 DataFrame
│ Row │ SepalLength │ SepalWidth │ PetalLength │ PetalWidth │ Species │
│ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Categorical…⍰ │
Expand All @@ -127,7 +127,7 @@ The following two examples show two ways to sort the `iris` dataset with the sam
julia> sort!(iris, (:Species, :SepalLength, :SepalWidth),
rev = (true, false, false));

julia> head(iris)
julia> first(iris, 6)
6×5 DataFrame
│ Row │ SepalLength │ SepalWidth │ PetalLength │ PetalWidth │ Species │
│ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Categorical…⍰ │
Expand All @@ -139,7 +139,7 @@ julia> head(iris)
│ 5 │ 5.8 │ 2.7 │ 5.1 │ 1.9 │ virginica │
│ 6 │ 5.8 │ 2.8 │ 5.1 │ 2.4 │ virginica │

julia> tail(iris)
julia> last(iris, 6)
6×5 DataFrame
│ Row │ SepalLength │ SepalWidth │ PetalLength │ PetalWidth │ Species │
│ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Categorical…⍰ │
Expand All @@ -153,7 +153,7 @@ julia> tail(iris)

julia> sort!(iris, (order(:Species, rev = true), :SepalLength, :SepalWidth));

julia> head(iris)
julia> first(iris, 6)
6×5 DataFrame
│ Row │ SepalLength │ SepalWidth │ PetalLength │ PetalWidth │ Species │
│ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Categorical…⍰ │
Expand All @@ -165,7 +165,7 @@ julia> head(iris)
│ 5 │ 5.8 │ 2.7 │ 5.1 │ 1.9 │ virginica │
│ 6 │ 5.8 │ 2.8 │ 5.1 │ 2.4 │ virginica │

julia> tail(iris)
julia> last(iris, 6)
6×5 DataFrame
│ Row │ SepalLength │ SepalWidth │ PetalLength │ PetalWidth │ Species │
│ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Categorical…⍰ │
Expand Down
4 changes: 2 additions & 2 deletions docs/src/man/split_apply_combine.md
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Expand Up @@ -11,7 +11,7 @@ julia> using DataFrames, CSV, Statistics

julia> iris = CSV.read(joinpath(dirname(pathof(DataFrames)), "../test/data/iris.csv"));

julia> head(iris)
julia> first(iris, 6)
6×5 DataFrame
│ Row │ SepalLength │ SepalWidth │ PetalLength │ PetalWidth │ Species │
│ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Categorical…⍰ │
Expand All @@ -23,7 +23,7 @@ julia> head(iris)
│ 5 │ 5.0 │ 3.6 │ 1.4 │ 0.2 │ setosa │
│ 6 │ 5.4 │ 3.9 │ 1.7 │ 0.4 │ setosa │

julia> tail(iris)
julia> last(iris, 6)
6×5 DataFrame
│ Row │ SepalLength │ SepalWidth │ PetalLength │ PetalWidth │ Species │
│ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Float64⍰ │ Categorical…⍰ │
Expand Down
3 changes: 0 additions & 3 deletions src/DataFrames.jl
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Expand Up @@ -41,7 +41,6 @@ export AbstractDataFrame,
eachrow,
eltypes,
groupby,
head,
insertcols!,
mapcols,
melt,
Expand All @@ -58,8 +57,6 @@ export AbstractDataFrame,
stackdf,
unique!,
unstack,
head,
tail,
permutecols!


Expand Down
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