# C14008 Lesson 5: Free Day¶

Today, we're going to be teaching a bunch of random Julia concepts we think are interesting. Hopefully, you guys like it and find some of these concepts enjoyable. Because we're going to be dealing with more packages this week, use this block of code to instantiate packages if you're having issues loading them:

In [1]:
using Pkg; Pkg.instantiate()


## Presentations¶

First, let's present the problems for homework. These were:

• Problem 22
• Problem 42
• Problem 10
• Minesweeper

## Writing Python in Julia¶

"The kids will go nuts for this one" - Christian

Julia is great in that it provides the PyCall package. You can import the PyCall package and import Python packages and run Python code.

In [3]:
using PyCall

base64 = pyimport("base64") # importing modules with pyimport

# b64 encode a string
base64.b64encode(b"Hi class!")

Out[3]:
"SGkgY2xhc3Mh"
In [4]:
# b64 decode a string
base64.b64decode(b"SGkgY2xhc3Mh")

Out[4]:
"Hi class!"
In [13]:
# Import whatever modules you want (ex: math, hashlib, requests etc)
hl = pyimport("hashlib")
hl.sha256(b"hi class!").hexdigest()

# current XKCD comic: https://xkcd.com/info.0.json
requests = pyimport("requests")

requests.get("https://xkcd.com/info.0.json").json()["alt"]

Out[13]:
"Oh no, a musician just burst in through the door confidently twirling a treble clef."

We can also run Python code in Julia with py strings. Triple quoted py"""""" strings are equivalent to an exec call and return nothing! Single quoted py"" strings are equivalent to using eval and allow you to call Python functions and access python variables.

In [16]:
# writing a method using a triple quoted string
py"""

def hello_world():
return "Hello world!"
"""

# calling the method
py"hello_world"()

# evaluating stuff
py"5+5"

Out[16]:
10

## Writing Julia in Python¶

We can also write Julia in Python using the julia package in Python. You can sorta just import Julia modules into Python.

In [ ]:
# going to the terminal to run python-jl


## Writing Julia in Julia¶

Now we're going to be covering some more advanced Julia programming features. First up, Julia macros! A Julia macro returns a piece of code, wrapped in a :( code ) that is executed when you call the macro. So, it's kinda like a function that returns a function, which then, when you call it, calls the code it returns.

In [18]:
# Write sayhello macro
macro sayhello()
return :( println("hi class!") )
end

# call sayhello
@sayhello

hi class!


We can also pass functions to our macros, so our macros can control how the functions execute, like Python decorators.

In [19]:
isAllowed = false

# Write security macro
macro security(e::Expr)
if isAllowed
return e
end
return "You aren't allowed to do that!"
end

Out[19]:
@security (macro with 1 method)
In [21]:
# test security macro
@security println("Hello!")

Out[21]:
"You aren't allowed to do that!"

### Let's talk about Symbols¶

A Symbol in Julia is sort of in between a string, but they're treated similar to the names of functions or variables. Symbols start with a colon : and can have any name you want.

In [22]:
# Defining some symbols
sinsymb = :sin

Out[22]:
:sin

We can use symbols to talk about functions or variables. Notice that if we eval a symbol, we get the thing it represents.

In [24]:
# eval-ing some symbols
eval(sinsymb)(π)

Out[24]:
1.2246467991473532e-16

So this gets really neat, because now we can use symbols as standins for function names, especially in common with the @eval macro, which does eval(quote( code )). Say, we want to make a unit circle:

In [31]:
for funcname = [:fun1, :fun2, :fun3]
@eval $funcname(x) = "I am$($funcname) with param$x"
end

In [34]:
fun3(5)

Out[34]:
"I am fun3 with param 5"

In short, Symboleval call → thing the symbol represents (function, variable). It's beyond the scope of this class, but Symbols and Exprs allow you to generate Julia code really quickly, which is documented in the Metaprogramming section of the Julia docs.

### Let's talk about Exprs¶

An Expr in Julia is a piece of code that can be evaluated, kind of like an "expanded" Symbol. They're what a macro returns. If we want to get extra fancy, we can talk about quote blocks. They can contain code and we can call them with the eval(code) method.

In [38]:
# Defining and calling a quote block
code = quote
num = 5+5
string = "hello"
println(num, string)
end

# Writing a macro to return a quote
macro doExpression()
return code
end

Out[38]:
@doExpression (macro with 1 method)
In [39]:
@doExpression

10hello


## Data Science bits¶

### DataFrames.jl¶

The Julia DataFrames package is intended to be a little like the Python pandas DataFrames class, giving us easy access to our data. For this, I've pulled the class roster.

In [40]:
using CSV, DataFrames

# pulling in roster and setup
students = DataFrame(CSV.File("C14008 Roster and Setup - Attendance Wk 2.csv"))

Out[40]:

48 rows × 8 columns (omitted printing of 5 columns)

StringInt64String
1Aaron Huang10Boston College High in Dorchester, MA
2Advika Agrawal11Hopkinton High School in Contoocook, NH
3Akshika Chawade10Braintree High School in Braintree, MA
4Alexander Sholl9California Academy of Mathematics and Science
6Andrew Sparks10Roxbury Latin in West Roxbury, MA
7Annanta Budhathoki11Bedford High in Bedford, MA
8Anthony Mundy9BAXTER ACADEMY FOR SCIENCE AND TECHNOLOGY
9Arthur Lee10Winchester High School
10Chloe Chan10None
11Christopher Hunt10Medford High in Medford, MA
12Daniel Cho9Belmont Hill
13Danya Rose10Garden Spot High School
14Emily Campos10Everett High in Everett, MA
15Evan Johnston9NuVu
16Fazil Uskuplu10CLASSICAL HIGH SCHOOL in PROVIDENCE, RI
17Grace Ou9Lexington High in Lexington, MA
18Hubert Huang10Boston Latin in Boston, MA
19Janie Wu9Andover High in Andover, MA
20Jeremiah Hugo10None
21Jiayi Liu10Forest Hills High School
22Joseph Stocke9The Grayson School
23Julia Sun9Horace Greeley
24Junwoo Kim9Winston Churchill High School
25Kashish Sethi10Sharon Middle in Sharon, MA
26Krishnan Shankar9None
27Lara Scaria12Framingham H S in Framingham, MA
28Lauren Hall9Hanover High School in Hanover, NH
29Leah Maciel11Rising Tide Charter School in Plymouth, MA
30Manavi Vajhallya9Andover High in Andover, MA

We access DataFrames similarly to Julia matrices, df[rows, cols].

In [43]:
# pull attendance for 1-Aug and Student Name
students[:, ["25-Jul","1-Aug"]]

Out[43]:

48 rows × 2 columns

25-Jul1-Aug
BoolBool
111
211
311
411
511
610
711
811
910
1011
1110
1211
1311
1411
1501
1611
1710
1811
1911
2011
2100
2211
2311
2411
2511
2611
2710
2811
2911
3011

We can use the describe method to learn overall information about our DataFrame, like the percentage of students that showed up to each class!

In [44]:
# describe students
describe(students)[:, Not(["min", "max", "nmissing"])]

Out[44]:

8 rows × 5 columns

variablemeanmediannuniqueeltype
SymbolUnion…Union…Union…DataType
1Student Name48String
3School40String
418-Jul0.8541671.0Bool
525-Jul0.8541671.0Bool
61-Aug0.7291671.0Bool
78-Aug0.00.0Bool
815-Aug0.00.0Bool

We can also add conditions while we're picking which rows and columns we want, by specifying the "1-Aug" column in the rows parameter.

In [46]:
# only pick students that showed up for class on August 1st
students[students[:, "1-Aug"], :]

Out[46]:

35 rows × 8 columns (omitted printing of 5 columns)

StringInt64String
1Aaron Huang10Boston College High in Dorchester, MA
2Advika Agrawal11Hopkinton High School in Contoocook, NH
3Akshika Chawade10Braintree High School in Braintree, MA
4Alexander Sholl9California Academy of Mathematics and Science
6Annanta Budhathoki11Bedford High in Bedford, MA
7Anthony Mundy9BAXTER ACADEMY FOR SCIENCE AND TECHNOLOGY
8Chloe Chan10None
9Daniel Cho9Belmont Hill
10Danya Rose10Garden Spot High School
11Emily Campos10Everett High in Everett, MA
12Evan Johnston9NuVu
13Fazil Uskuplu10CLASSICAL HIGH SCHOOL in PROVIDENCE, RI
14Hubert Huang10Boston Latin in Boston, MA
15Janie Wu9Andover High in Andover, MA
16Jeremiah Hugo10None
17Joseph Stocke9The Grayson School
18Julia Sun9Horace Greeley
19Junwoo Kim9Winston Churchill High School
20Kashish Sethi10Sharon Middle in Sharon, MA
21Krishnan Shankar9None
22Lauren Hall9Hanover High School in Hanover, NH
23Leah Maciel11Rising Tide Charter School in Plymouth, MA
24Manavi Vajhallya9Andover High in Andover, MA
25Maxime Malaussena10Montclair High School
26Megan Santamore11Notre Dame Preparatory High School in Fitchburg, MA
27Michael Mo10Staten Island Tech high school
28Neel Joshi11Westford Academy in Westford, MA
29Quinn Armentrout11The Nueva School
30Ramakrishnan Gopinath9None
In [47]:
# A closer look at how this actually works
students[:, "1-Aug"]

Out[47]:
48-element Array{Bool,1}:
1
1
1
1
1
0
1
1
0
1
0
1
1
⋮
0
0
1
1
0
1
1
1
1
1
0
0

### Plotting stuff with Plots¶

Now, let's try and use the Plots package to visualize some of this data. First, we need to import the Plots package and tell it we're going to use the GR backend.

In [48]:
using Plots
gr()

Out[48]:
Plots.GRBackend()

Now, let's just plot some mathematical functions, by using a function in place of y.

In [52]:
# plotting functions
plot(x=0:5, x -> ℯ^x)

Out[52]:

Now, to plot some data out of our Data Frame, we can plot the number of students that showed up to class on each date:

In [53]:
dates = ["18-Jul", "25-Jul", "1-Aug"]
bar([sum(col) for col in eachcol(students[:, dates])], xaxis=("date"))

Out[53]:

Plotting daily weather data for the memes

In [54]:
# pull temp data
using PyCall
py"""
import requests
weather = requests.get("https://forecast.weather.gov/MapClick.php?lat=30.33&lon=-97.74&unit=0&lg=english&FcstType=json").json()
"""

data = py"weather"

Out[54]:
Dict{Any,Any} with 11 entries:
"time"               => Dict{Any,Any}("layoutKey"=>"k-p12h-n13-1","startPerio…
"data"               => Dict{Any,Any}("hazardUrl"=>Any[],"hazard"=>Any[],"pop…
"creationDateLocal"  => "8 Aug 12:51 pm CDT"
"location"           => Dict{Any,Any}("zone"=>"TXZ192","county"=>"TXC453","me…
"productionCenter"   => "Austin/San Antonio, TX"
"srsName"            => "WGS 1984"
"moreInformation"    => "http://weather.gov"
"operationalMode"    => "Production"
"credit"             => "https://www.weather.gov/ewx"
"currentobservation" => Dict{Any,Any}("Date"=>"8 Aug 12:51 pm CDT","timezone"…
"creationDate"       => "2020-08-08T11:51:46-05:00"
In [56]:
# plot temperature data
plot(parse.(Int, data["data"]["temperature"]), yaxis=("temperature", (75,105)))

Out[56]:

## Homework¶

There's actually no homework! Today's lecture was just mostly for fun, and next lecture is challenge day. If you're itching for something to do, then go have some fun solving Euler problems. Otherwise, sit back, relax, ask questions, and get ready for next week!