Julia By Example

Julia By Example

源自於  https://juliabyexample.helpmanual.io/#Hello-World

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Set of unofficial examples of Julia the high-level, high-performance dynamic programming language for technical computing.

Below are a series of examples of common operations in Julia. They assume you already have Julia installed and working (the examples are currently tested with Julia v0.6.2).

Hello World

The simplest possible script.

println("hello world")

With Julia installed and added to your path this script can be run by julia hello_world.jl, it can also be run from REPL by typinginclude("hello_world.jl"), that will evaluate all valid expressions in that file and return the last output.

Simple Functions

The example below shows two simple functions, how to call them and print the results. Further examples of number formatting are shown below.

# function to calculate the volume of a sphere
function sphere_vol(r)
    # julia allows Unicode names (in UTF-8 encoding)
    # so either "pi" or the symbol π can be used
    return 4/3*pi*r^3
end

# functions can also be defined more succinctly
quadratic(a, sqr_term, b) = (-b + sqr_term) / 2a

# calculates x for 0 = a*x^2+b*x+c, arguments types can be defined in function definitions
function quadratic2(a::Float64, b::Float64, c::Float64)
    # unlike other languages 2a is equivalent to 2*a
    # a^2 is used instead of a**2 or pow(a,2)
    sqr_term = sqrt(b^2-4a*c)
    r1 = quadratic(a, sqr_term, b)
    r2 = quadratic(a, -sqr_term, b)
    # multiple values can be returned from a function using tuples
    # if the return keyword is omitted, the last term is returned
    r1, r2
end

vol = sphere_vol(3)
# @printf allows number formatting but does not automatically append the \n to statements, see below
@printf "volume = %0.3f\n" vol 
#> volume = 113.097

quad1, quad2 = quadratic2(2.0, -2.0, -12.0)
println("result 1: ", quad1)
#> result 1: 3.0
println("result 2: ", quad2)
#> result 2: -2.0

Strings Basics

Collection of different string examples (string indexing is the same as array indexing: see below).

# strings are defined with double quotes
# like variables, strings can contain any unicode character
s1 = "The quick brown fox jumps over the lazy dog α,β,γ"
println(s1)
#> The quick brown fox jumps over the lazy dog α,β,γ

# println adds a new line to the end of output
# print can be used if you dont want that:
print("this")
#> this
print(" and")
#> and
print(" that.\n")
#> that.

# chars are defined with single quotes
c1 = 'a'
println(c1)
#> a
# the ascii value of a char can be found with Int():
println(c1, " ascii value = ", Int(c1))
#> a ascii value = 97
println("Int('α') == ", Int('α'))
#> Int('α') == 945

# so be aware that
println(Int('1') == 1)
#> false

# strings can be converted to upper case or lower case:
s1_caps = uppercase(s1)
s1_lower = lowercase(s1)
println(s1_caps, "\n", s1_lower)
#> THE QUICK BROWN FOX JUMPS OVER THE LAZY DOG Α,Β,Γ
#> the quick brown fox jumps over the lazy dog α,β,γ

# sub strings can be indexed like arrays:
# (show prints the raw value)
show(s1[11]); println()
#> 'b'

# or sub strings can be created:
show(s1[1:10]); println()
#> "The quick "

# end is used for the end of the array or string
show(s1[end-10:end]); println()
#> "dog α,β,γ"

# julia allows string Interpolation:
a = "wolcome"
b = "julia"
println("$a to $b.")
#> wolcome to julia.

# this can extend to evaluate statements:
println("1 + 2 = $(1 + 2)")
#> 1 + 2 = 3

# strings can also be concatenated using the * operator
# using * instead of + isn't intuitive when you start with Julia,
# however people think it makes more sense
s2 = "this" * " and" * " that"
println(s2)
#> this and that

# as well as the string function
s3 = string("this", " and", " that")
println(s3)
#> this and that

String: Converting and formatting

# strings can be converted using float and int:
e_str1 = "2.718"
e = float(e_str1)
println(5e)
#> 13.5914
num_15 = parse(Int, "15")
println(3num_15)
#> 45

# numbers can be converted to strings and formatted using printf
@printf "e = %0.2f\n" e
#> 2.718
# or to create another string sprintf
e_str2 = @sprintf("%0.3f", e)

# to show that the 2 strings are the same
println("e_str1 == e_str2: $(e_str1 == e_str2)")
#> e_str1 == e_str2: true

# available number format characters are f, e, g, c, s, p, d:
# (pi is a predefined constant; however, since its type is 
# "MathConst" it has to be converted to a float to be formatted)
@printf "fix trailing precision: %0.3f\n" float(pi)
#> fix trailing precision: 3.142
@printf "scientific form: %0.6e\n" 1000pi
#> scientific form: 3.141593e+03
# g is not implemented yet
@printf "a character: %c\n" 'α'
#> a character: α
@printf "a string: %s\n" "look I'm a string!"
#> a string: look I'm a string!
@printf "right justify a string: %50s\n" "width 50, text right justified!"
#> right justify a string:                    width 50, text right justified!
@printf "a pointer: %p\n" 100000000
#> a pointer: 0x0000000005f5e100
@printf "print a integer: %d\n" 1e10
#> print an integer: 10000000000

String Manipulations

s1 = "The quick brown fox jumps over the lazy dog α,β,γ"

# search returns the first index of a char
i = search(s1, 'b')
println(i)
#> 11
# the second argument is equivalent to the second argument of split, see below

# or a range if called with another string
r = search(s1, "brown")
println(r)
#> 11:15


# string replace is done thus:
r = replace(s1, "brown", "red")
show(r); println()
#> "The quick red fox jumps over the lazy dog"

# search and replace can also take a regular expressions by preceding the string with 'r'
r = search(s1, r"b[\w]*n")
println(r)
#> 11:15

# again with a regular expression
r = replace(s1, r"b[\w]*n", "red")
show(r); println()
#> "The quick red fox jumps over the lazy dog"

# there are also functions for regular expressions that return RegexMatch types
# match scans left to right for the first match (specified starting index optional)
r = match(r"b[\w]*n", s1)
println(r)
#> RegexMatch("brown")

# RegexMatch types have a property match that holds the matched string
show(r.match); println()
#> "brown"

# matchall returns a vector with RegexMatches for each match
r = matchall(r"[\w]{4,}", s1)
println(r)
#> SubString{UTF8String}["quick","brown","jumps","over","lazy"]

# eachmatch returns an iterator over all the matches
r = eachmatch(r"[\w]{4,}", s1)
for i in r print("\"$(i.match)\" ") end
println()
#> "quick" "brown" "jumps" "over" "lazy" 

# a string can be repeated using the repeat function, 
# or more succinctly with the ^ syntax:
r = "hello "^3
show(r); println() #> "hello hello hello "

# the strip function works the same as python:
# e.g., with one argument it strips the outer whitespace
r = strip("hello ")
show(r); println() #> "hello"
# or with a second argument of an array of chars it strips any of them;
r = strip("hello ", ['h', ' '])
show(r); println() #> "ello"
# (note the array is of chars and not strings)

# similarly split works in basically the same way as python:
r = split("hello, there,bob", ',')
show(r); println() #> ["hello"," there","bob"]
r = split("hello, there,bob", ", ")
show(r); println() #> ["hello","there,bob"]
r = split("hello, there,bob", [',', ' '], limit=0, keep=false)
show(r); println() #> ["hello","there","bob"]
# (the last two arguements are limit and include_empty, see docs)

# the opposite of split: join is simply
r = join(collect(1:10), ", ")
println(r) #> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10

Arrays

function printsum(a)
    # summary generates a summary of an object
    println(summary(a), ": ", repr(a))
end

# arrays can be initialised directly:
a1 = [1,2,3]
printsum(a1)
#> 3-element Array{Int64,1}: [1,2,3]

# or initialised empty:
a2 = []
printsum(a2)
#> 0-element Array{None,1}: None[]

# since this array has no type, functions like push! (see below) don't work
# instead arrays can be initialised with a type:
a3 = Int64[]
printsum(a3)
#> 0-element Array{Int64,1}: []

# ranges are different from arrays:
a4 = 1:20
printsum(a4)
#> 20-element UnitRange{Int64}: 1:20

# however they can be used to create arrays thus:
a4 = collect(1:20)
printsum(a4)
#> 20-element Array{Int64,1}: [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]

# arrays can also be generated from comprehensions:
a5 = [2^i for i = 1:10]
printsum(a5)
#> 10-element Array{Int64,1}: [2,4,8,16,32,64,128,256,512,1024]

# arrays can be any type, so arrays of arrays can be created:
a6 = (Array{Int64, 1})[]
printsum(a6)
#> 0-element Array{Array{Int64,1},1}: []
# (note this is a "jagged array" (i.e., an array of arrays), not a multidimensional array,
# these are not covered here)

# Julia provided a number of "Dequeue" functions, the most common for appending to the end of arrays
# is push!
# ! at the end of a function name indicates that the first argument is updated.

push!(a1, 4)
printsum(a1)
#> 4-element Array{Int64,1}: [1,2,3,4]

# push!(a2, 1) would cause error:

push!(a3, 1)
printsum(a3) #> 1-element Array{Int64,1}: [1]
#> 1-element Array{Int64,1}: [1]

push!(a6, [1,2,3])
printsum(a6)
#> 1-element Array{Array{Int64,1},1}: [[1,2,3]]

# using repeat() to create arrays
# you must use the keywords "inner" and "outer"
# all arguments must be arrays (not ranges)
a7 = repeat(a1,inner=[2],outer=[1])
printsum(a7)
#> 8-element Array{Int64,1}: [1,1,2,2,3,3,4,4]
a8 = repeat(collect(4:-1:1),inner=[1],outer=[2])
printsum(a8)
#> 8-element Array{Int64,1}: [4,3,2,1,4,3,2,1]

Error Handling

a=[]
# try, catch can be used to deal with errors as with many other languages
try
    push!(a,1)
catch err
    showerror(STDOUT, err, backtrace());println()
end
println("Continuing after error")

Multidimensional Arrays

Julia has very good multidimensional array capabilities. Check out the manual.

# repeat can be useful to expand a grid
# as in R's expand.grid() function:

m1 = hcat(repeat([1,2],inner=[1],outer=[3*2]),
          repeat([1,2,3],inner=[2],outer=[2]),
          repeat([1,2,3,4],inner=[3],outer=[1]))
printsum(m1)
#> 12×3 Array{Int64,2}: [1 1 1; 2 1 1; 1 2 1; 2 2 2; 1 3 2; 2 3 2; 1 1 3; 2 1 3; 1 2 3; 2 2 4; 1 3 4; 2 3 4]

# for simple repetitions of arrays,
# use repmat
m2 = repmat(m1,1,2)     # replicate a9 once into dim1 and twice into dim2
println("size: ", size(m2))
#> size: (12,6)

m3 = repmat(m1,2,1)     # replicate a9 twice into dim1 and once into dim2
println("size: ", size(m3))
#> size: (24,3)

# Julia comprehensions are another way to easily create
# multidimensional arrays

m4 = [i+j+k for i=1:2, j=1:3, k=1:2] # creates a 2x3x2 array of Int64
m5 = ["Hi Im # $(i+2*(j-1 + 3*(k-1)))" for i=1:2, j=1:3, k=1:2]
# expressions are very flexible
# you can specify the type of the array by just
# placing it in front of the expression
Pkg.add("LegacyStrings")
import LegacyStrings
m5 = LegacyStrings.ASCIIString["Hi Im element # $(i+2*(j-1 + 3*(k-1)))" for i=1:2, j=1:3, k=1:2]
printsum(m5)
#> 2x3x2 Array{LegacyStrings.ASCIIString,3}: LegacyStrings.ASCIIString["Hi Im element # 7"
#> "Hi Im element # 9" "Hi Im element # 11"
#>             "Hi Im element # 8" "Hi Im element # 10" "Hi Im element # 12"]
#>
#> LegacyStrings.ASCIIString["Hi Im element # 7" "Hi Im element # 9" "Hi Im element # 11"
#>             "Hi Im element # 8" "Hi Im element # 10" "Hi Im element # 12"]

# Array reductions
# many functions in Julia have an array method
# to be applied to specific dimensions of an array:

sum(m4,3)        # takes the sum over the third dimension
sum(m4,(1,3)) # sum over first and third dim

maximum(m4,2)    # find the max elt along dim 2
findmax(m4,3)    # find the max elt and its index along dim 3 (available only in very recent Julia versions)

# Broadcasting
# when you combine arrays of different sizes in an operation,
# an attempt is made to "spread" or "broadcast" the smaller array
# so that the sizes match up. broadcast operators are preceded by a dot:

m4 .+ 3       # add 3 to all elements
m4 .+ [1,2]      # adds vector [1,2] to all elements along first dim

# slices and views
m4=m4[:,:,1] # holds dim 3 fixed
m4[:,2,:]  # that's a 2x1x2 array. not very intuititive to look at

# get rid of dimensions with size 1:
squeeze(m4[:,2,:],2)  # that's better

# assign new values to a certain view
m4[:,:,1] = rand(1:6,2,3)
printsum(m4)
#> 2x3x2 Array{Int64,3}: [3 5 2
#>  2 2 2]
#>
#> [4 5 6
#>  5 6 7]

# (for more examples of try, catch see Error Handling above)
try
    # this will cause an error, you have to assign the correct type
    m4[:,:,1] = rand(2,3)
catch err
    println(err)
end
#> InexactError()

try
    # this will cause an error, you have to assign the right shape
    m4[:,:,1] = rand(1:6,3,2)
catch err
    println(err)
end
#> DimensionMismatch("tried to assign 3x2 array to 2x3x1 destination")

Dictionaries

Julia uses Dicts as associative collections. Usage is very like python except for the rather odd => definition syntax.

# dicts can be initialised directly:
a1 = Dict(1=>"one", 2=>"two")
printsum(a1) #> Dict{Int64,String}: {2=>"two",1=>"one"}

# then added to:
a1[3]="three"
printsum(a1) #> Dict{Int64,String}: {2=>"two",3=>"three",1=>"one"}
# (note dicts cannot be assumed to keep their original order)

# dicts may also be created with the type explicitly set
a2 = Dict{Int64, AbstractString}()
a2[0]="zero"
printsum(a2)
#> Dict{Int64,AbstractString} with 1 entry: Dict{Int64,AbstractString}(Pair{Int64,AbstractString}(0,"zero"))

# dicts, like arrays, may also be created from comprehensions
a3 = Dict([i => @sprintf("%d", i) for i = 1:10])
printsum(a3)
#> Dict{Any,Any}: {5=>"5",4=>"4",6=>"6",7=>"7",2=>"2",10=>"10",9=>"9",8=>"8",3=>"3",1=>"1"}

# as you would expect, Julia comes with all the normal helper functions
# for dicts, e.g., haskey
println(haskey(a1,1)) #> true

# which is equivalent to
println(1 in keys(a1)) #> true
# where keys creates an iterator over the keys of the dictionary

# similar to keys, values get iterators over the dict's values:
printsum(values(a1)) 
#> Base.ValueIterator for a Dict{Int64,String} with 3 entries: String["two","three","one"]

# use collect to get an array:
printsum(collect(values(a1)))
#> 3-element Array{String,1}: String["two","three","one"]

Loops and Map

For loops can be defined in a number of ways.

for i in 1:5
    print(i, ", ")
end
#> 1, 2, 3, 4, 5,
# In loop definitions "in" is equivilent to "=" (AFAIK, the two are interchangable in this context)
for i = 1:5
    print(i, ", ")
end
println() #> 1, 2, 3, 4, 5,

# arrays can also be looped over directly:
a1 = [1,2,3,4]
for i in a1
    print(i, ", ")
end
println() #> 1, 2, 3, 4,

# continue and break work in the same way as python
a2 = collect(1:20)
for i in a2
    if i % 2 != 0
        continue
    end
    print(i, ", ")
    if i >= 8
        break
    end
end
println() #> 2, 4, 6, 8,

# if the array is being manipulated during evaluation a while loop shoud be used
# pop removes the last element from an array
while !isempty(a1)
    print(pop!(a1), ", ")
end
println() #> 4, 3, 2, 1,

d1 = Dict(1=>"one", 2=>"two", 3=>"three")
# dicts may be looped through using the keys function:
for k in sort(collect(keys(d1)))
    print(k, ": ", d1[k], ", ")
end
println() #> 1: one, 2: two, 3: three,

# like python enumerate can be used to get both the index and value in a loop
a3 = ["one", "two", "three"]
for (i, v) in enumerate(a3)
    print(i, ": ", v, ", ")
end
println() #> 1: one, 2: two, 3: three,

# (note enumerate starts from 1 since Julia arrays are 1 indexed unlike python)

# map works as you might expect performing the given function on each member of an array or iter
# much like comprehensions
a4 = map((x) -> x^2, [1, 2, 3, 7])
print(a4) #> [1, 4, 9, 49]

Types

Types are a key way of structuring data within Julia.

# Type Definitions are probably most similar to tyepdefs in c?
# a simple type with no special constructor functions might look like this
type Person
    name::AbstractString
    male::Bool
    age::Float64
    children::Int
end

p = Person("Julia", false, 4, 0)
printsum(p)
#> Person: Person("Julia",false,4.0,0)

people = Person[]
push!(people, Person("Steve", true, 42, 0))
push!(people, Person("Jade", false, 17, 3))
printsum(people)
#> 2-element Array{Person,1}: [Person("Steve",true,42.0,0),Person("Jade",false,17.0,3)]

# types may also contains arrays and dicts
# constructor functions can be defined to easily create objects
type Family
    name::AbstractString
    members::Array{AbstractString, 1}
    extended::Bool
    # constructor that takes one argument and generates a default
    # for the other two values
    Family(name::AbstractString) = new(name, AbstractString[], false)
    # constructor that takes two arguements and infers the third
    Family(name::AbstractString, members) = new(name, members, length(members) > 3)
end

fam1 = Family("blogs")
println(fam1)
#> Family("blogs",AbstractString[],false)
fam2 = Family("jones", ["anna", "bob", "charlie", "dick"])
println(fam2)
#> Family("jones",AbstractString["anna","bob","charlie","dick"],true)

Input & Output

The basic syntax for reading and writing files in Julia is quite similar to python.

The simple.dat file used in this example is available from github.

fname = "simple.dat"
# using do means the file is closed automatically
# in the same way "with" does in python
open(fname,"r") do f
    for line in eachline(f)
        print(line)
    end
end
#> this is a simple file containing
#> text and numbers:
#> 43.3
#> 17

f = open(fname,"r")
showall(readlines(f))
#> String["this is a simple file containing","text and numbers:","43.3","17"]
close(f)

f = open(fname,"r")
fstring = readstring(f)
close(f)
println(summary(fstring))
#> String
print(fstring)
#> this is a simple file containing
#> text and numbers:
#> 43.3
#> 17

outfile = "outfile.dat"
# writing to files is very similar:
f = open(outfile, "w")
# both print and println can be used as usual but with f as their first arugment
println(f, "some content")
print(f, "more content")
print(f, " more on the same line")
close(f)

# we can then check the content of the file written
# "do" above just creates an anonymous function and passes it to open
# we can use the same logic to pass readall and thereby succinctly
# open, read and close a file in one line
outfile_content = open(readstring, outfile, "r")
println(repr(outfile_content))
#> "some content\nmore content more on the same line"

Packages and Including of Files

Packages extend the functionality of Julia's standard library.

# You might not want to run this file completely, as the Pkg-commands can take a
# long time to complete.

# list all available packages:
Pkg.available()

# install one package (e.g. Calculus) and all its dependencies:
Pkg.add("Calculus")

# to list all installed packages
Pkg.installed()

# to update all packages to their newest version
Pkg.update()

# to use a package:
using Calculus
# will import all functions of that package into the current namespace, so that
# it is possible to call
derivative(x -> sin(x), 1.0)
# without specifing the package it is included in.

import Calculus
# will enable you to specify which package the function is called from
Calculus.derivative(x -> cos(x), 1.0)

# Using `import` is especially useful if there are conflicts in function/type-names
# between packages.

Plotting

Plotting in Julia is only possible with additional Packages. Examples of some of the main packages are given below.

Winston

Winston Package Page

Matlab-like plotting. Installed via Pkg.add("Winston")

using Winston

# plot some data
pl = plot(cumsum(rand(500) .- 0.5), "r", cumsum(rand(500) .- 0.5), "b")
# display the plot (not done automatically!)
display(pl)

# save the current figure
savefig("winston.svg")
# .eps, .pdf, & .png are also supported
# we used svg here because it respects the width and height specified above

DataFrames

The DataFrames.jl package provides tool for working with tabular data.

The iris.csv file used in this example is available from github.

using DataFrames
showln(x) = (show(x); println())
# TODO: needs more links to docs.

# A DataFrame is an in-memory database
df = DataFrame(A = [1, 2], B = [e, pi], C = ["xx", "xy"])
showln(df)
#> 2x3 DataFrame
#> |-------|---|---------|------|
#> | Row # | A | B       | C    |
#> | 1     | 1 | 2.71828 | "xx" |
#> | 2     | 2 | 3.14159 | "xy" |

# The columns of a DataFrame can be indexed using numbers or names
showln(df[1])
#> [1,2]
showln(df[:A])
#> [1,2]

showln(df[2])
#> [2.718281828459045,3.141592653589793]
showln(df[:B])
#> [2.718281828459045,3.141592653589793]

showln(df[3])
#> String["xx","xy"]
showln(df[:C])
#> String["xx","xy"]

# The rows of a DataFrame can be indexed only by using numbers
showln(df[1, :])
#> 1x3 DataFrame
#> |-------|---|---------|------|
#> | Row # | A | B       | C    |
#> | 1     | 1 | 2.71828 | "xx" |
showln(df[1:2, :])
#> 2x3 DataFrame
#> |-------|---|---------|------|
#> | Row # | A | B       | C    |
#> | 1     | 1 | 2.71828 | "xx" |
#> | 2     | 2 | 3.14159 | "xy" |

# importing data into DataFrames
# ------------------------------

# DataFrames can be loaded from CSV files using readtable()
iris = readtable("iris.csv")

# the iris dataset (and plenty of others) is also available from
using RData, RDatasets
iris = dataset("datasets","iris")

# you can directly import your own R .rda dataframe with
# mydf = DataFrame(read_rda("path/to/your/df.rda")["name_of_df"]), e.g.
diamonds = DataFrame(load(joinpath(Pkg.dir("RDatasets"),"data","ggplot2","diamonds.rda"))["diamonds"])

# showing DataFrames
# ------------------

# Check the names and element types of the columns of our new DataFrame
showln(names(iris))
#> [:SepalLength,:SepalWidth,:PetalLength,:PetalWidth,:Species]
showln(eltypes(iris))
#> Type[Float64,Float64,Float64,Float64,UTF8String]

# Subset the DataFrame to only include rows for one species
showln(iris[iris[:Species] .== "setosa", :])
#> 50x5 DataFrame
#> |-------|-------------|------------|-------------|------------|----------|
#> | Row # | SepalLength | SepalWidth | PetalLength | PetalWidth | Species  |
#> | 1     | 5.1         | 3.5        | 1.4         | 0.2        | "setosa" |
#> | 2     | 4.9         | 3.0        | 1.4         | 0.2        | "setosa" |
#> | 3     | 4.7         | 3.2        | 1.3         | 0.2        | "setosa" |
#> | 4     | 4.6         | 3.1        | 1.5         | 0.2        | "setosa" |
#> | 5     | 5.0         | 3.6        | 1.4         | 0.2        | "setosa" |
#> | 6     | 5.4         | 3.9        | 1.7         | 0.4        | "setosa" |
#> | 7     | 4.6         | 3.4        | 1.4         | 0.3        | "setosa" |
#> | 8     | 5.0         | 3.4        | 1.5         | 0.2        | "setosa" |
#> | 9     | 4.4         | 2.9        | 1.4         | 0.2        | "setosa" |
#> ⋮
#> | 41    | 5.0         | 3.5        | 1.3         | 0.3        | "setosa" |
#> | 42    | 4.5         | 2.3        | 1.3         | 0.3        | "setosa" |
#> | 43    | 4.4         | 3.2        | 1.3         | 0.2        | "setosa" |
#> | 44    | 5.0         | 3.5        | 1.6         | 0.6        | "setosa" |
#> | 45    | 5.1         | 3.8        | 1.9         | 0.4        | "setosa" |
#> | 46    | 4.8         | 3.0        | 1.4         | 0.3        | "setosa" |
#> | 47    | 5.1         | 3.8        | 1.6         | 0.2        | "setosa" |
#> | 48    | 4.6         | 3.2        | 1.4         | 0.2        | "setosa" |
#> | 49    | 5.3         | 3.7        | 1.5         | 0.2        | "setosa" |
#> | 50    | 5.0         | 3.3        | 1.4         | 0.2        | "setosa" |

# Count the number of rows for each species
showln(by(iris, :Species, df -> size(df, 1)))
#> 3x2 DataFrame
#> |-------|--------------|----|
#> | Row # | Species      | x1 |
#> | 1     | "setosa"     | 50 |
#> | 2     | "versicolor" | 50 |
#> | 3     | "virginica"  | 50 |

# Discretize entire columns at a time
iris[:SepalLength] = round.(Integer, iris[:SepalLength])
iris[:SepalWidth] = round.(Integer, iris[:SepalWidth])


# Tabulate data according to discretized columns to see "clusters"
tabulated = by(
    iris,
    [:Species, :SepalLength, :SepalWidth],
    df -> size(df, 1)
)
showln(tabulated)
#> 17x4 DataFrame
#> |-------|--------------|-------------|------------|----|
#> | Row # | Species      | SepalLength | SepalWidth | x1 |
#> | 1     | "setosa"     | 4           | 3          | 4  |
#> | 2     | "setosa"     | 5           | 2          | 1  |
#> | 3     | "setosa"     | 5           | 3          | 23 |
#> | 4     | "setosa"     | 5           | 4          | 17 |
#> | 5     | "setosa"     | 6           | 4          | 5  |
#> | 6     | "versicolor" | 5           | 2          | 3  |
#> | 7     | "versicolor" | 5           | 3          | 3  |
#> | 8     | "versicolor" | 6           | 2          | 6  |
#> | 9     | "versicolor" | 6           | 3          | 29 |
#> | 10    | "versicolor" | 7           | 3          | 9  |
#> | 11    | "virginica"  | 5           | 3          | 1  |
#> | 12    | "virginica"  | 6           | 2          | 1  |
#> | 13    | "virginica"  | 6           | 3          | 22 |
#> | 14    | "virginica"  | 7           | 3          | 19 |
#> | 15    | "virginica"  | 7           | 4          | 1  |
#> | 16    | "virginica"  | 8           | 3          | 4  |
#> | 17    | "virginica"  | 8           | 4          | 2  |

# you can setup a grouped dataframe like this
gdf = groupby(iris,[:Species, :SepalLength, :SepalWidth])

# and then iterate over it
for idf in gdf
    println(size(idf,1))
end

# Adding/Removing columns
# -----------------------

# insert!(df::DataFrame,index::Int64,item::AbstractArray{T,1},name::Symbol)
# insert random numbers at col 5:
insert!(iris, 5, rand(nrow(iris)), :randCol)

# remove it
delete!(iris, :randCol)