GitHub Repository: robertopucp/1eco35_2022_2
Path: blob/main/Lab1/Julia_lab_1.ipynb
²⁷¹⁴ views

Kernel: Julia 1.7.3

Laboratorio 1 - Julia

@author : Roberto Mendoza

1.0 Types of variables

In [4]:

# cargamos las librerias, similar a install.packages("nombre de liberia")

# import Pkg 

# Pkg.add("LinearAlgebra")
# Pkg.add("Random")
# Pkg.add("Distributions")
# Pkg.add("Statistics")

# Una vez instalado no se necesita cargar nuevamente 

using LinearAlgebra, Random, Distributions, Statistics # similar a library("nombre de libreria")

In [1]:

a2 = 3.1614
print(a2)
typeof(a2)

Out[1]:

3.1614

Float64

In [2]:

typeof(floor(a2)) # Entero menor más cercano

Out[2]:

Float64

In [3]:

Int(floor(a2))

Out[3]:

3

In [4]:

b1 = 10000
typeof(b1)

Out[4]:

Int64

In [5]:

# Int to float

convert(Float64,b1)

Out[5]:

10000.0

In [6]:

# Symbol type 

print(:a)
typeof(:a)

Out[6]:

a

Symbol

In [7]:

c1 = "My first python code"
print(c1)
typeof(c1)

Out[7]:

My first python code

String

In [8]:

c1 = "First python code"
print(c1, '\n','\n')

Out[8]:

First python code

In [9]:

c2 = "Lab1 Pucp"
print(c1," : ",c2)

Out[9]:

First python code : Lab1 Pucp

In [10]:

print("$c1 : semester 2022-1")

Out[10]:

First python code : semester 2022-1

In [11]:

# f-string

d = 2022
print("$c1 : semester $d-1")

Out[11]:

First python code : semester 2022-1

In [12]:

#first character
print("First letter is :", c1[1]  )

Out[12]:

First letter is :F

In [13]:

#first word
print("First word is :", c1[1:5]  )

Out[13]:

First word is :First

2.0 Boolean

It’s used to represent the truth value of an expression.

In [15]:

"a" == "a"

Out[15]:

true

In [16]:

z1 = (1==1)
typeof(z1)

Out[16]:

Bool

In [17]:

z1 = Int(z1)
print(z1)

Out[17]:

1

In [18]:

z2 = (10 > 20)
Int(z2)

Out[18]:

0

In [19]:

z3 = (100 != 100)
z3 = Int(z3)
typeof(z3)

Out[19]:

Int64

1.3 List

In [21]:

L1 = []

Out[21]:

Any[]

In [22]:

#double quote is mandatory

dis1 = ["ATE", "BARRANCO","BREÑA", "CALLAO", "CARABAYLLO"]
dis1

Out[22]:

5-element Vector{String}:
 "ATE"
 "BARRANCO"
 "BREÑA"
 "CALLAO"
 "CARABAYLLO"

In [23]:

typeof(dis1)

Out[23]:

Vector{String} (alias for Array{String, 1})

In [24]:

dis2 = ["ATE", "BARRANCO","BREÑA", "CALLAO", "CARABAYLLO","CHACLACAYO","CHORRILLOS","CIENEGUILLA"
            ,"COMAS","EL_AGUSTINO","INDEPENDENCIA"]
dis2

Out[24]:

11-element Vector{String}:
 "ATE"
 "BARRANCO"
 "BREÑA"
 "CALLAO"
 "CARABAYLLO"
 "CHACLACAYO"
 "CHORRILLOS"
 "CIENEGUILLA"
 "COMAS"
 "EL_AGUSTINO"
 "INDEPENDENCIA"

In [25]:

dis1[1]

Out[25]:

"ATE"

In [26]:

dis1[2:5]

Out[26]:

4-element Vector{String}:
 "BARRANCO"
 "BREÑA"
 "CALLAO"
 "CARABAYLLO"

In [27]:

num = [13,5,5,8,9,10,5,8,13,1,20]

Out[27]:

11-element Vector{Int64}:
 13
  5
  5
  8
  9
 10
  5
  8
 13
  1
 20

In [28]:

a = sort(num)
print(a)
a[11]

Out[28]:

[1, 5, 5, 5, 8, 8, 9, 10, 13, 13, 20]

20

In [29]:

maximum(num)

Out[29]:

20

In [30]:

length(dis2)

Out[30]:

11

In [31]:

# ! permite alterar el objeto sin asignar nuevamente

push!(num, 102)

Out[31]:

12-element Vector{Int64}:
  13
   5
   5
   8
   9
  10
   5
   8
  13
   1
  20
 102

In [33]:

num2 = [10,20,30]
append!(num, num2)

Out[33]:

15-element Vector{Int64}:
  13
   5
   5
   8
   9
  10
   5
   8
  13
   1
  20
 102
  10
  20
  30

In [34]:

print("Suma:", sum(num),'\n', "Minimo:", minimum(num), '\n', "Maximo:", maximum(num))

Out[34]:

Suma:259
Minimo:1
Maximo:102

3.0 Dictionary

In [1]:

Postal_code = Dict("Majes" => "string", "Mollendo" => 40701, "Islay" => 40704, "Cotahuasi" => 40801, "Alca" => 40802 )
print(Postal_code)

Out[1]:

Dict{String, Any}("Islay" => 40704, "Majes" => "string", "Cotahuasi" => 40801, "Mollendo" => 40701, "Alca" => 40802)

In [67]:

keys(Postal_code)

Out[67]:

KeySet for a Dict{String, Int64} with 5 entries. Keys:
  "Islay"
  "Majes"
  "Cotahuasi"
  "Mollendo"
  "Alca"

In [2]:

Postal_code = Dict{String,String}("Majes" => 40520, "Mollendo" => 40701, 
    "Islay" => 40704, "Cotahuasi" => 40801, "Alca" => 40802 )

print(Postal_code)

Out[2]:

MethodError: Cannot `convert` an object of type Int64 to an object of type String
Closest candidates are:
  convert(::Type{String}, ::String) at C:\Users\Roberto\AppData\Local\Programs\Julia-1.7.3\share\julia\base\essentials.jl:223
  convert(::Type{T}, ::T) where T<:AbstractString at C:\Users\Roberto\AppData\Local\Programs\Julia-1.7.3\share\julia\base\strings\basic.jl:231
  convert(::Type{T}, ::AbstractString) where T<:AbstractString at C:\Users\Roberto\AppData\Local\Programs\Julia-1.7.3\share\julia\base\strings\basic.jl:232
  ...

Stacktrace:
 [1] setindex!(h::Dict{String, String}, v0::Int64, key::String)
   @ Base .\dict.jl:381
 [2] Dict{String, String}(::Pair{String, Int64}, ::Vararg{Pair{String, Int64}})
   @ Base .\dict.jl:113
 [3] top-level scope
   @ In[2]:1
 [4] eval
   @ .\boot.jl:373 [inlined]
 [5] include_string(mapexpr::typeof(REPL.softscope), mod::Module, code::String, filename::String)
   @ Base .\loading.jl:1196

In [75]:

# Declarando el tipo de variable en cada llave del diccionario
Postal_code = Dict{String, Any}("Majes" => 40520, "Mollendo" => 40701, 
    "Islay" => 40704, "Cotahuasi" => 40801, "Alca" => 40802 )
print(Postal_code)

Out[75]:

Dict{String, Any}("Islay" => 40704, "Majes" => 40520, "Cotahuasi" => 40801, "Mollendo" => 40701, "Alca" => 40802)

In [76]:

Postal_code["Islay"]

Out[76]:

40704

In [77]:

# add new element

merge!(Postal_code, Dict("CHARCANA"=> 40803))

# add dictionary 
dt1 = Dict{String,Any}("LOMAS" => Dict("UBIGEO" => 40311, "Poverty Rate" => "18.2%", "Population" => "20 mil"))
union(Postal_code, dt1)

Out[77]:

7-element Vector{Pair{String, Any}}:
     "Islay" => 40704
  "CHARCANA" => 40803
     "Majes" => 40520
 "Cotahuasi" => 40801
  "Mollendo" => 40701
      "Alca" => 40802
     "LOMAS" => Dict{String, Any}("Poverty Rate" => "18.2%", "UBIGEO" => 40311, "Population" => "20 mil")

In [61]:

# keys
cities = ["Fray Martin","Santa Rosa de Puquio","Cuchicorral","Santiago de Punchauca","La Cruz (11 Amigos)","Cerro Cañon",
            "Cabaña Suche","San Lorenzo","Jose Carlos Mariategui","Pascal"]

# values
postal_code1 = [15001,15003,15004,15006,15018,15019,15046,15072,15079,15081]

ct_pc = Dict( zip( cities , postal_code1) )

Out[61]:

Dict{String, Int64} with 10 entries:
  "Santa Rosa de Puquio"   => 15003
  "Fray Martin"            => 15001
  "La Cruz (11 Amigos)"    => 15018
  "San Lorenzo"            => 15072
  "Cuchicorral"            => 15004
  "Santiago de Punchauca"  => 15006
  "Cabaña Suche"           => 15046
  "Pascal"                 => 15081
  "Cerro Cañon"            => 15019
  "Jose Carlos Mariategui" => 15079

In [62]:

ct_pc["Santa Rosa de Puquio"]

Out[62]:

15003

6.0 Array and Matrix

In [79]:

# 1D array
a = [1, 2, 3, 4, 5.2]
typeof(a)
a

Out[79]:

5-element Vector{Float64}:
0
0
0
0
2

In [80]:

using Statistics

a = [1 2 3 4 5]

print(mean(a), "\n")
print(std(a))

Out[80]:

3.0
1.5811388300841898

In [81]:

# 2D array
M = [1 2 3; 4 5 6]

typeof(M)
size(M)
M

Out[81]:

2×3 Matrix{Int64}:
 1  2  3
 4  5  6

In [12]:

print(mean(M, dims =1), "\n")  # by coloumn
print(mean(M, dims =2))  # by row

Out[12]:

[2.5 3.5 4.5]
[2.0; 5.0;;]

In [216]:

#rows
print("Rows:",size(M,1),"\n", "Columns: ", size(M,2))

Out[216]:

Rows:2
Columns: 3

In [82]:

# Create a 1D NumPy array with values from 0 to 20 (exclusively) incremented by 2.5:
y = collect(0:2:19)
print(y)
typeof(y)

Out[82]:

[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]

Vector{Int64} (alias for Array{Int64, 1})

In [83]:

y = collect(0:10)
print(y)

Out[83]:

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

In [84]:

M1 = zeros(8, 2)
print(M1)

Out[84]:

[0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0; 0.0 0.0]

8×2 Matrix{Float64}:
0  0.0
0  0.0
0  0.0
0  0.0
0  0.0
0  0.0
0  0.0
0  0.0

In [85]:

M2 = ones(8, 4)
M2

Out[85]:

8×4 Matrix{Float64}:
0  1.0  1.0  1.0
0  1.0  1.0  1.0
0  1.0  1.0  1.0
0  1.0  1.0  1.0
0  1.0  1.0  1.0
0  1.0  1.0  1.0
0  1.0  1.0  1.0
0  1.0  1.0  1.0

In [86]:

M3 = hcat(M1,M2)
size(M3)
M3

Out[86]:

8×6 Matrix{Float64}:
0  0.0  1.0  1.0  1.0  1.0
0  0.0  1.0  1.0  1.0  1.0
0  0.0  1.0  1.0  1.0  1.0
0  0.0  1.0  1.0  1.0  1.0
0  0.0  1.0  1.0  1.0  1.0
0  0.0  1.0  1.0  1.0  1.0
0  0.0  1.0  1.0  1.0  1.0
0  0.0  1.0  1.0  1.0  1.0

In [87]:

M4 = [[2 2 3 4 5 1];[1 5 5 9 8 2]]
M4

Out[87]:

2×6 Matrix{Int64}:
 2  2  3  4  5  1
 1  5  5  9  8  2

In [88]:

M5 = vcat(M3,M4)

M5
transpose(M5)

Out[88]:

6×10 transpose(::Matrix{Float64}) with eltype Float64:
0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  2.0  1.0
0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  2.0  5.0
0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  3.0  5.0
0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  4.0  9.0
0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  5.0  8.0
0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  2.0

In [89]:

w = ones(10)

Out[89]:

10-element Vector{Float64}:
0
0
0
0
0
0
0
0
0
0

In [90]:

using LinearAlgebra

Iden = Matrix(1I,8,8)

Out[90]:

8×8 Matrix{Int64}:
0  0  0  0  0  0  0
1  0  0  0  0  0  0
0  1  0  0  0  0  0
0  0  1  0  0  0  0
0  0  0  1  0  0  0
0  0  0  0  1  0  0
0  0  0  0  0  1  0
0  0  0  0  0  0  1

In [91]:

I3 = reshape(Iden, 32, 2)
I3

Out[91]:

32×2 Matrix{Int64}:
0
0
0
0
1
0
0
0
0
0
0
0
0
 ⋮  
0
0
1
0
0
0
0
0
0
0
0
1

In [92]:

print(I3') # transpose
size(I3')

Out[92]:

[1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0; 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1]

(2, 32)

In [8]:

using Random
using Distributions
Random.seed!(756)

x1 = rand(500)  # uniform distribution
x2 = rand(500) # uniform distribution
x3 = rand(500) # uniform distribution
x4 = rand(500) # uniform distribution

e = randn(500) # normal dsitribution mean = 0 y sd = 1

# DGP

Y = ones(500) + 0.8.*x1 + 1.2.*x2 + 0.5.*x3 + 1.5.*x4 + e

Out[8]:

500-element Vector{Float64}:
896566070281705
621974431654904
526833836299257
10837079046204412
6527452719436257
412358539595111
06859432331957
4565096841391636
9559135914316514
4897010290426653
322449429266815
9847006960490069
3249652391864455
 ⋮
488724947070592
1546739990899377
505306608738553
111049918149344
607534071526726
2385856370553174
20976100605967
8433212962236212
998832343279894
203629452752528
895110270410055
9342847527281901

In [94]:

# joint vectors to matrix 

X = hcat(ones(500),x1,x2,x3,x4)

Out[94]:

500×5 Matrix{Float64}:
0  0.69395    0.230708   0.755381    0.114891
0  0.342808   0.391855   0.509551    0.864573
0  0.878973   0.688652   0.400967    0.569701
0  0.437436   0.0768201  0.885261    0.123005
0  0.189015   0.399809   0.407279    0.394585
0  0.218179   0.109448   0.00840352  0.108008
0  0.544024   0.205616   0.267832    0.0436557
0  0.962239   0.925667   0.216082    0.352726
0  0.502289   0.480901   0.288146    0.917227
0  0.0176643  0.874951   0.452493    0.725432
0  0.702488   0.203521   0.835733    0.277678
0  0.387177   0.185274   0.630065    0.00152385
0  0.75172    0.170344   0.887998    0.0214684
 ⋮                                      
0  0.517387   0.0729328  0.315226    0.189123
0  0.32472    0.409452   0.256903    0.110878
0  0.848717   0.0184663  0.557234    0.792108
0  0.839263   0.819206   0.587164    0.0276174
0  0.296967   0.375418   0.44435     0.955923
0  0.461611   0.392524   0.155386    0.329141
0  0.652895   0.973743   0.164328    0.56274
0  0.422581   0.780213   0.350788    0.626888
0  0.471142   0.635874   0.0478847   0.641411
0  0.243467   0.652978   0.630408    0.26652
0  0.21262    0.738478   0.286239    0.69063
0  0.19871    0.16188    0.645123    0.29906

In [95]:

# OLS method

beta = inv(transpose(X) * X) * (transpose(X) * Y)

Out[95]:

5-element Vector{Float64}:
975872412578692
862370979221387
385111654309122
5676058489648621
3399985203750866

References

https://docs.julialang.org/en/v1/manual/arrays/

https://fncbook.github.io/fnc/linsys/demos/matrices-julia.html

Laboratorio 1 - Julia

@author : Roberto Mendoza

1.0 Types of variables

2.0 Boolean

1.3 List

3.0 Dictionary

6.0 Array and Matrix

References

Product

Resources

Company