# UC San Diego School of Global Policy and Strategy Skills Course
# 3 weeks - Introduction to Python 1/28/2019 - 2/11/2019
GPS Auditorium: 6:00 - 9:00pm
TritonEd
Schedule: Posted in TritonEd

---
## Instructors:
Bia Villas Boas
Reid Otsuji
TA: Caio Mansini
Helper: Arden Tran, Gui Castelao

# TAKE SURVEY
https://goo.gl/forms/Fqkgnz0Q6RxtD7Bo2

# Collaborative note taking!

### Need Help?
- #### If you need help- put a pink sticky on your laptop so an instructor can come assist
- Also, your instructors/helpers are on HackMD here, so feel free to throw a question or ask for help via the notes here as we go along

## Python Day 1
### Getting Started

- Get data here: https://swcarpentry.github.io/python-novice-inflammation/setup/

- if you have a mac, double-click the downloaded .zip files to unpack them - they'll create the 'data' and 'code' folders automatically...along with the necessary data.
- You should end up with 2 folders - 'data' and 'code'.
- make sure they're located in an easy to find place like your Desktop

- Go to your terminal
- for mac: shortcut to Spotlight by pressing `CMD` + `SpaceBar` and then type in `terminal`

- Run the command to open Jupyter Notebooks in your terminal by entering `jupyter notebook` and hit enter

Some info about the Jupyter Notebook environment and Python programming language in general:

` # this is a comment`
Use the `#` symbol to denote a comment within your Jupyter Notebook - this is also frequently used a means to help document your analysis and code for your collaborators

### Running some code in Jupyter

- You can run code within the Jupyter Notebook kernel and it will allow you to program and see the direct output all within a single document

# this is a comment

# multiplication in Python with the * operator
5 * 3 
output = 15

# assigning variables with the assignment operator =

weight_kg_text = 'weight in kilograms'

print(weight_kg_text)

output: `weight in kilograms`

Overview of conceptO: we made an arbitrary variable name with `weight_kg_text` and then used the assignment operator `=` to assign that variable to the value `'weight in kilograms'` and then we used the `print()` function to output the value of the variable by using the `print()` function with the argument `weight_kg_text` like this: `print(weight_kg_text)`

What is an argument? In Python and many programming languages, the functions or commands you'll run will have arguments that help define the desired output. In basic terms, arguments are extra instructions given to a function so that it knows what to do!

For example, the `print()` function has many arguments you can pass along, but for this start we will just focus on the first argument that looks like this:

`print(argument1)`

### Exercise 1:

a = 1
b = a
a = 2

print("a =", a)

print("b =", b)

What's the output?

### Exercise 2:
What does `print(third, fourth)` shows after you run:

first, second = 'global', 'policy'
third, fourth = second, first

What's the putput?

### Python in the Kitchen: Libraries

- a library is to python as a kitchen appliance is to a kitchen
- Libraries have Functions/Modules
- To access a library, use the `import` statement like so:

import numpy

numpy.loadtxt(fname = 'inflammation-01.csv', delimiter = ',')

Library and function/module syntax (i.e. how do I do that function call?)

Syntax for calling on a library's function/modules is this:

`v`

What happens when you don't add an argument to a function? Python resorts to default values, which is set by the library/module/function

Need help with a library/function/module?
- Use the help statement `?` following the lib/function/module you need help with like this: `numpy.loadtxt?`
- Python will pull up the documentation on that specified item for you - if that fails - google time!

### Arguments continued...

numpy.loadtxt(fname = 'inflammation-01.csv', delimiter = ',')

print(type(data))

print(data.dtype)

print(data.shape) #returns a tuple with x = rows, y = columns

### Numpy Arrays

- first of all, Numpy is a portmanteu of numbers and python, hence NumPy - you'll find a lot of Python libraries have some sort of name origin that ties in with Python.
- Numpy arrays have the attribute shape
- shape gives you the dimensions of the array in the format [rows x columns]
- we can check the value of an array at a specific position by specifying indices: data[1,2]

print('first value is:', data[0,0])

output: `first value is: 0.0`

print('middle value is:', data[30,20])

output: `middle value is: 13.0`

#### Slicing with `:`

print(data[0:4, 0:10])

- Remember that number sequencing in Python starts with `0` and when sliced, the last number argument will be omitted. For example:
- `0:4` will give you `0,1,2,3` - which is 4 values

print(data[5:10, 3:5])

print(data[0,0:10])

#example of syntax shortcut with slicing

small = data[:3, 36:] # data[0:3, 36:40]

print(small.shape)

print(data.shape)

double_data = data * 2

print(double_data)

print('original')
print(data[:3,36:])
print('double')
print(double_data[:3,36:])

trip_data = data + double_data

print(trip_data[:3,36:])

print(small.shape)

print(numpy.mean(data))

Ok, more complexity:

maxval, minval, stdval = numpy.max(data), numpy.min(data), numpy.std(data)

print('maximum is:', maxval)
print('minimum is:', minval)
print('standard deviation is:', stdval)

The above code is the same as:

maxval = numpy.max(data)
minval = numpy.min(data)
stdval = numpy.std(data)

Regarding our data and approach:

patient_0 = data[0, :]

#how do I select all the data for patient_0?

print(patient_0.shape)

print(numpy.max(patient_0))

numpy.max(data, axis = 1)
#What does the axis option do? is 1=column? finding the max in each column? Nevermind, got crossed up - check out: https://i.stack.imgur.com/p2PGi.png

Hold on, getting reference cheat sheets for you all

max_infla = numpy.max(data, axis = 1)

print(max_infla.shape)

ELI5 this voodoo pls:

print(data[0, :])
print(data[1, :])

#what the heck happens?

print(data[0, :]) # this one takes the first row, hence the 0 in the rows argument

print(data[1, :]) # this one takes the second row, hence the 1 in the rows argument because everything in Python starts with 0, not 1. 

Conceptually, let's break down what happens above:
- `print(data[0, :])` is just a `print()` function with the argument for the object `data` and within `data` we also specified with `[0, :]` what portion of the object `data` we wanted the `print()` function to evaluate.
- The `data[0, :]` is just us adding two arguments for starting and ending of the data - think back to algebra/geometry with planar X, Y coordinates. So, we are just specifying that the start of what we want from `data` is `0` and that's easy. The next part - the end of what we want from `data` is ` :` which is using the `:` slicing operator to denote the default slice values - so that translates to everything! So, we are asking `data` start at row 1 (because Python starts everything as 0) and then take everything after that. Hence, we are asking it to print all of row 1!

## Python and related cheat sheets
- Cheat Sheets (link expires in 7 days): https://ucsdcloud-my.sharepoint.com/:b:/g/personal/artran_ucsd_edu/ERm-GDiD9pxAqtrlBu_7vbcBKIUqHW5JeKnLFrwol7h2Uw
- Good to have in your back pocket - feel free to download a copy and save to your local drive
- Courtesy of Data Science & Engineering master's program and HDSI (HALICIOĞLU DATA SCIENCE INSTITUTE)

### matplotlib

ave_infla = numpy.mean(data, axis = 0)

print(ave_infla.shape)

#now let's import pyplot from matplotlib library

import matplotlib.pyplot

# why the 'matplotlib.pyplot' and not just import all matplotlib?
# Because we only need pyplot so this helps us save memory and create faster and more responsive code

%matplotlib inline

image = matplotlib.pyplot.imshow(datra)
matplotlib.pyplot

ave_infla = numpy.mean(data, axis = 0)
ave_plot = matplotlib.pyplot.plot(ave_infla)
matplotlib.pyplot.show()

max_plot = matplotlib.pyplot.plot(numpy.max(data, axis = 0))
matplotlib.pyplot.show()

min_plot = matplotlib.pyplot.plot(numpy.min(data, axis = 0))
matplotlib.pyplot.show()

- Do we need to run show? On Jupyter Notebook upon running the `%matplotlib inline` statement, you won't actually need to use `matplotlib.pyplot.show()` to show the image. However, we add that in for cases where we may be exporting this notebook elsewhere that would require the show method to bring up the image.

import numpy
import matplotlib.pyplot

data = numpy.loadtxt('inflammation-01.csv', delimiter = ',')

#make a plot that has one row and three columns - max, min, mean

fig = matplotlib.pyplot.figure(figsize = (10, 3))

# figsize argument (width, height) ratio

axes1 = fig.add_subplot(1, 3, 1) #(rows, columns, which subplot)
axes2 = fig.add_subplot(1, 3, 2)
axes3 = fig.add_subplot(1, 3, 3)

axes1.plot(numpy.max(data, axis = 0))
axes1.set_ylabel('Max Inflammation')

axes2.plot(numpy.min(data, axis = 0))
axes2.set_ylabel('Min Inflammation')

axes3.plot(numpy.mean(data, axis = 0))
axes3.set_ylabel('Average Inflammation')

matplotlib.pyplot.show()

fig.tight_layout()
matplotlib.pyplot.show()

### Exercise 3:
Write a code that plots the average, maximum, minimum, and standard deviation aacross the patients as four subplots.

| Average | Standard Deviation |
| ----------- | -------------------|
| Maximum | Minimum |

Solution (you can rearrange into order by renumbering axes1-4):

#let's resize the fig

fig = matplotlib.pyplot.figure(figsize = (16, 4))

axes1 = fig.add_subplot(1, 4, 1)
axes2 = fig.add_subplot(1, 4, 2)
axes3 = fig.add_subplot(1, 4, 3)
axes4 = fig.add_subplot(1, 4, 4)

axes1.plot(numpy.max(data, axis = 0))
axes1.set_ylabel('Max Inflammation')

axes2.plot(numpy.min(data, axis = 0))
axes2.set_ylabel('Min Inflammation')

axes3.plot(numpy.mean(data, axis = 0))
axes3.set_ylabel('Average Inflammation')

axes4.plot(numpy.std(data, axis = 0))
axes4.set_ylabel('Standard Deviation')

matplotlib.pyplot.show()

fig.tight_layout()
matplotlib.pyplot.show()

## Week 1 - Please sign in below:
##### Name (A ### )
Reid Otsuji (A12345678)
Yifan Dong(A53256391)
Camila Gomez Wills (A53265648)
Navdhrishty Singh (A12972272)
Isabelle Chen (A12692259)
Greg Householter (A53288838)
Yi Liu (A53257075)
James Lee (A53272394)
Hui Zhang(A53248115)
Kelis Wong (A53233278)
Noah Gerber (A11844436)
Savas Tarhan (A53222479)
Qinghui Zhang (A14166055)
Zixuan Dai (A53270250)
Jiahang Zhang (A53278367)
Diego Jimenez (A92016548)
Gala Ledezma (A12804158)
Cesar Perez (A53265549)
XINYU ZHANG(A53287451)
Inderpal Pamma (A53282970)
Wenlin Zhao(A53271207)
Renee Johnson (A12699790)
Yucheng Shen (A53290227)
Ha Pham (A53257685)
Grace Yuan(A53274857)
Yunzhou Luo(A53233781)
Sibo Su (A53255631)
Hannah Ashby (A13026919)
Wenhan Zhu (A53277425)
Malena Hernandez (A53289951)
Jicuo Dai(A53251206)
Anh Nguyen (A53245210)

Julian Del Castillo (A12825937)
Katherine Tian (A12828524)
Paul Koenig (A12629448)
Nick Rhodes (A11893860)
JingTing Xu (A53281950)
Yifan Huang (A53268424)
imge su cetin
Mingpu Xiao (A53281212)
Wendy Romero (A53217156)
Xuan Gu(A53291635)
Qiurong Ren (A53281118)
Grace Yuan(A53274857)
Qiaochu Cong(A53290453)
Wendy Romero (A53217156)
Courtney Geigle (a00310086)

---
## Python Day 2
### Repeating Actions with loops
- range() function

for counter in range(N): #(do things with counter and other variables, N times)

- Let's open Jupyter Notebook by going to our terminal and typing `jupyter notebook`
- open a new Python 3 notebook and for ease, try to open in the same folder you have your datasets we downloaded last week

for counter in range(3):
    print(counter) 
    
#remember to get that walk-in indent before print(counter)

Output should be:

0
1
2

### Exercise 1:
- Python has a built-in function called **range** that creates a sequence of numbers. Range can accept 1, 2, or 3 parameters.
- If one parameter is given, range creates a sequence of that length, starting at zero and incrementing by 1. For example, range(3) produces the numbers 0,1,2
- If two parameters are given, range starts at the first and ends just before the second, incrememting by one. For example, range(2, 5) produces 2, 3, 4.
- If range is given 3 paramters, it starts at the first one, ends just before the second one, and increments by the third one. For example, range(3, 10, 2) produces 3, 5, 7, 9.
- Using python's built-in functions **len** and **range**, complete the loops below so that it prints **every other** letter of 'sociology'

`len()` = short for Length and is a built-in function to return the number of characters in the argument you input.

word = 'sociology'
for i in range(0,len(word),2):
    print(word[i])

Alternatively,

for i in range(0,len('sociology'),2):
    print(word[i])

### Repeating Actions with Loops - Tracking Variables

### Challenge:
- Python allows you to create an empty string by assigning empty quotes to variables
- `my_variable = ''`
- Knowing that two strings can be concatenated using the `+` operator, write a loop that takes a string and produces a new string with the characters in reverse order, so that 'pizza' becomes 'azzip'

word = 'pizza'

for i in range(len(word)-1, -1, -1):
    print('i = ', i, word[i])

Solution:

word = 'pizza'

for i in reversed(word):
    print(i)

The above takes into use the built-in python function `reversed()`

Another solution:

word = 'pizza'

for i in word[::-1]:
    print(i)

The above takes into account what we learned about slicing lists and using the slicers `:`

**Write that answer here:**
________________________

string1 = 'p'
string2 = 'i'
string3 = 'z'
string4 = 'z'
string5 = 'a'

print (string5 + string4 + string3 + string2 + string1)

pizza="pizza"
azzip=""
for i in range(len(pizza)):
    azzip=pizza[i]+azzip
    print(azzip)

word='pizza'
b=''
for i in range(len(word)-1,-1,-1):
    b= b+ word[i]
print(b)


word = 'pizza'
my_variable = ''
for i in word: 
    my_variable = i + my_variable 
    print(my_variable)
    

________________

### Important note on a mistake you want to avoid:

print = 'foo'

for i in range(10):
    print(i)

You get this error:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-82-32dd6fa70972> in <module>
      1 for i in range(10):
----> 2     print(i)

TypeError: 'str' object is not callable

Why? Because `print` was used as a variable name and assigned a value, so when we try to run the `print()` function, we get the above error. Stay woke. Variable names creepin'.

### About Markdown

# This is a comment

- We can turn a cell in our Jupyter Notebook into a Markdown cell rather than a Python3 cell. This let's us avoid having to use the `#` everytime we want to make a comment and just format with Markdown!
- Go to `cell` and then choose `cell type` and then select `Markdown`
- This let's us format in the cell with headings like below:

`# Level 1`
# Level 1 heading
`Level 2`
## Level 2 heading
`**bold**`
**bold**
`*italics*`
*italics*

### Python, Jupyter, Markdown, etc. Cheat Sheets:
- download here: [Link expires in 14 days, so get and download now](https://ucsdcloud-my.sharepoint.com/:f:/g/personal/artran_ucsd_edu/ElIRBJImwLJCuTj8qp2wKxQB0EJVaUNz5Ra-7hrW5kN-6g)

### Storing Multiple Values in Lists

odds = [1, 3, 5, 7]
print(odds)

print('first and last are', odds[0], odds[-1])

s = 'string'
print('I think', s[-1], 'is equal to', s[5])

for number in odds:
    print(number)
    
for char in 'string':
    print(char)

### Important note: can use the term `banana` or whatever in lieu of `char`

### Strings and immutability

my_word = 'Brasil'
my_word[3] = 'z'

We get the error:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-94-5d1222434421> in <module>
----> 1 my_word[3] = 'z'

TypeError: 'str' object does not support item assignment

We cannot change an immutable object such as a string, and need to either reassign it completely with `my_word = 'Brazil'`

We can change lists via index like so:

countries = ['France', 'Australia', 'Brasil']
print(countries[2])

print('countries[2] was', countries[2])
countries[2] = 'Brazil'
print('Now, countries[2] is', countries[2])

### Nesting Lists (lists inside of lists inside of lists inside of lists)

- remember the salt and pepper shaker example

list_of_lists = ['1', ['avocadoes', 'jalapenos'], 3, ['other things', 'and some more']]

print(list_of_lists)

- alright, now how do I get jalapenos?

print(list_of_lists[1][1])

- Should get jalapenos

### Append function

empty = []
empty.append(9)
print(empty)
empty = empty + ['surf']
print(empty)

### reverse() method

odds.reverse()
print('odds after reversing', odds)

The above method does change the list permanently, so when you call the list `odds` it will now be in the reversed order.

### Exercise 2:
- Use a for-loop to convert the string 'surf' into a list of letters: ['s', 'u', 'r', 'f']

Solution:

list_wanna = []
word='surf'
for i in range(len(word)):
    list_wanna=list_wanna+[word[i]]
print(list_wanna)

list = []
word = 'surf'

for i in word:
    list.append(i)
    
print(list)

the above is an example of using for-loops to iterate and this is a microscopic example but the power of for-loops is hugely demonstrated with large datasets that will require its iterative functionality.

print(list('surf'))

The above is a great example of Python's concise code and built in functions

jedi_name = 'Luke Skywalker'

first_name = jedi_name[0:4]

print(first_name)

## Using ranges to extract values from a list:

planets = ['Tatooine', 'Naboo', 'Jupiter', 'Arrakis']

star_wars_planets = planets[:2]

print(star_wars_planets)

The difference between the extremas (first and second values) in a range with a slice `:` is the total number of elements. For example, `[0:2]` would mean trhe two extremas difference calculated as 2-0 = 2. So if we extracrted values from a list with the range `0:2` we would get two elements. In this case, that would be 'Tatooine' and 'Naboo'

#this counts 0 - 10

for i in range(10):
    print(i)
    
#this counts 0 - 10 by every 3rd value (think Lil Jon's song - 3, 6, 9)

for i in range(10)[::3]:
    print(i)
    

# Use stepsize index to print every other charactere
“In an octopus’s garden in the shade”

use the \ to enter a break to help compile strings with quotations or apostrophes in the string value like so:

string = 'In an octopus\'s garden in the shade'
print(string[::2])

### Let's get this data
- in Jupyter, whenever you start the notebook, you'll typically be taken to a file directory system
- use that directory system to navigate to where we stored the data from last class (try your desktop)
- Start a new notebook

### glob

import glob
print(glob.glob('inflammation*.csv'))

The list is more disorganized than Congress, let's sort this, fam:

print(sorted(glob.glob('inflammation*.csv')))

And let's import numpy and matplotlib.pyplot and grab those files

import numpy
import matplotlib.pyplot

filenames = sorted(glob.glob('inflammation*.csv'))

sub_set = filenames[0:3]

for f in sub_set:
    print(f)+
    
    data = numpy.loadtxt(f, delimiter = ',')
    
    fig = matplotlib.pyplot.figure(figsize = (10,3))
    
    ax1 = fig.add_subplot(1, 3, 1)
    ax2 = fig.add_subplot(1, 3, 2)
    ax3 = fig.add_subplot(1, 3, 3)
    
    ax1.plot(numpy.mean(data, axis = 0))
    ax1.set_ylabel('average')
    
    ax2.plot(numpy.max(data, axis = 0))
    ax2.set_ylabel('maximum')
    
    ax3.plot(numpy.min(data, axis = 0))
    ax3.set_ylabel('minimum')
    
    fig.tight_layout()

### Arguments and kwargs (Key Word Arguments)
[ELI5 on Args and kwargs](https://pythontips.com/2013/08/04/args-and-kwargs-in-python-explained/)

### Great work, fam - see you next week fellow kids

## Write your notes here:
## Week 2 - Please sign in below:
##### Name (A ### )
Diego Jimenez A92016548
Inderpal Pamma A53282970
Kelis Wong A53233278
Wenlin Zhao A53271207
Zixuan Dai A53270250
Jicuo Dai A53251206
Isabelle Chen (A12692259)
Yucheng Shen (A53290227)
Noah Gerber (A11844436)
Yifan Dong (A53256391)
XINYU ZHANG(A53287451)
Qiurong Ren (A53281118)
Qiaochu Cong(A53290453)
Qinghui Zhang(A14166055)
Wenhan Zhu (A53277425)
Savas Tarhan (A53222479)
Greg Householter (A53288838)
Anh Nguyen (A53245210)
Navdhrishty Singh (A12972272)
Yang Xuan (A14466001)
Jiahang Zhang(A53278367)
Yifan Huang(A53268424)
Ha Pham (A53257685)
Cesar Perez (A53265549)
Katherine Tian (A12828524)
Camila Gomez Wills (A53265648)
Renee Johnson (A12699790)
Valeria Buelna (A53247990)
Yunzhou Luo (A53233781)
Hui Zhang(A53248115)
James Lee (A53272394)
Yi Liu(A53257075)
Wendy Romero (A53217156)
Gala Ledezma (A12804158)
Xuan Gu (A53291635)
Sibo Su (A53255631)
Paul Koenig (A12629448)

# Python Day 3
### Recap
- Variables
- Libraries
- Numpy Arrays, indexing, slicing
- reading data with `numpy.loadtxt`
- for-loops
- lists

### Cheat Sheets for Python, numpy, etc.
- download here: [Link expires in 7 days, so get and download now](https://ucsdcloud-my.sharepoint.com/:f:/g/personal/artran_ucsd_edu/ElIRBJImwLJCuTj8qp2wKxQB0EJVaUNz5Ra-7hrW5kN-6g)
- High-res and pre-formatted for printing into your own binder notebook

### Warm-up
- Knowing that '' creates an empty string and that two strings can be concatenated using the + operator, write a loop that takes a string and produces a new string with the characters in reverse order, so 'stressed' becomes 'desserts'!

### Notes

oldstring = 'stressed'
new_string = ''
for char in oldstring:
    new_string = char + new_string
print(new_string)

## Python Conditionals (loops)

average = numpy.zeros([60,40])
count = 0
filenames = glob.glob('infla_data/inflammation*.csv')
for f in filenames:
    print('processing file', f)
    data = numpy.loadtxt(f, delimiter = ',')
    average + average + data
    count = count+1
average = average/len(filenames)
print(average.shape)

- Conditional statements

num = 37
if num > 100:
    print('greater')
else:
    print('not greater')
print('done')

if num > 100:
    print('greater')
else:
    print('not greater')

### boolean values
- "Boolean values are the two constant objects False and True. They are used to represent truth values (other values can also be considered false or true)."

### Exercise:

if 4 > 5:
    print('A')
elif 4 == 5:
    print('B')
elif 4 < 5:
    print('C')

### Assignment vesus Equivalency `=` or `==`
- the `=` operator is assigns a value like this: ` x = 'gon give it to you`
- the `==` operator compares equivalency like so: ` x == rapper` which would yield a boolean of either true of false. In this case, false.

num = 10
if num > 3:
    print('greater')
elif num == 10:
    print('equal')
elif num < 3:
    print('not greater')

Output: `greater`

- Why? Because the above conditionals are run in order and return the first value that resolves, and in this case that is 10 is greater than 3, ergo 'greater'

Logical Operators
`(num > 3) and (num = 10)`
Output: False
- Why? We're testing for both values with the `and` operator - so if any one of them is false, the return is false!

`(num > 3) and (num <= 10)`
Output: True

num = 20
(num > 3) or (num <= 10)

Output: True
- Why? The logical operator `or` seeks only one value to be true to return true!

num = 10
cond1 = num >3
cond2 = num == 10
print('cond1 is ', cond1, ' and cond2 is ', cond2,'.', sep='')

Can we multiply them? Consider that booleans are read as either numeric values of 0 and 1 or True and False. So, yeah, we can multiple them!

`print(cond1 * cond2)`
output: `1`

### Scenario:
- You are in charge of organizing the website for a symposium. In order to submit and abstract, participants have to create an account on the website. Write a Python program to check the validity of password input by users.
- Validation:
- At least 1 character from `[$#@]`
- Minimum length 6 cahracters.
- Maximum length 16 characters.
- Ask your partner to make a password and test with your script.
- tip:

string = 'THi$isastring'
'z' in string

password = 'jdsddd@'
if len(password)<=6:
print('too shot')
elif len(password)>=11:
print('too long')
elif ('$' in password)+('@' in password)+('#' in password)<1:
print('char')
else:
print('pass')

### doggo password script

print('gib password plz')
password = input()

chars = '@#$'
for i in range(len(password)):
    if password[i] in chars:
        has_chars = True
    else:
        has_chars = False
        
if len(password) < 6:
    print('TOO SMOL')
elif len(password) > 16:
    print('TOO CHONK')
elif has_chars is False:
    print('TOO BASIC')
elif len(password) >= 6 and len(password) <= 16 and has_chars is True:
    print('GOOD BOYE')

password=""

if ("$" not in password) and ("#" not in password) and ("@" not in password):
print("password must contain $ # or @")

elif len(password) < 6:
print("password must contain at least 6 characters")

elif len(password) > 16:
print("password cannot contain more than 16 characters")

password = 'Scrspp@'
if '#@$' in password:
print('right')
elif len(password)>=6:
print('right')
elif len(password)<=16:
print('right')
else :
print('wrong')

password = 'giuihijhu$'
if (len(password)>16) or (len(password)<6):
print("Password must be between 6 and 16 characters")
elif ('@' in password ) or ('$' in password) or ('#' in password):
print("Good to go")
else:
print("Missing special character (@$#)")

password = "tes@ting"
for i in password:
if i in ["@","$","#"]:
specialcharacter = specialcharacter + 1
else:
specialcharacter = specialcharacter + 0
if len(password) < 6:
print("You need at least 6 characters")
elif len(password) > 16:
print("You need less than 16 characters")
elif specialcharacter < 1:
print("Your password must include $ # or @")
else:
print("You have a good password")

http://swcarpentry.github.io/python-novice-gapminder/files/python-novice-gapminder-data.zip

## Week 3 - Please sign in below:
##### Name (A ### )
Greg Householter (A53288838)
Paul Koenig (A12629448)
Katherine Tian (A12828524)
Kelis Wong (A53233278)
Navdhrishty Singh (A12972272)
Noah Gerber (A11844436)
Savas Tarhan (A53222479)
Jicuo Dai(A53251206)
Hui Zhang(A53248115)
Yi Liu(A53257075)
Yang Xuan (A14466001)
Jiahang Zhang (A53278367)
Isabelle Chen (A12692259)
Gala Ledezma (A12804158)
Nick Rhodes (A11893860)
Yucheng Shen (A53290227)
Qiaochu Cong (A53290453)
Wendy Romero (A53217156)
James Lee (A53272394)
Courtney Geigle (A00310086)
Ha Pham (A53257685)
Inderpal Pamma (A53282970)
Diego Jimenez (A92016548)
Camila Gomez Wills (A53265648)
Yunzhou Luo (A53233781)
Haotian Chen (A53257482)

### Gapminder Dataset

http://swcarpentry.github.io/python-novice-gapminder/files/python-novice-gapminder-data.zip

### loading data into pandas

import pandas
data = pandas.read_csv('data/gapminder_gdp_oceania.csv')

data = pandas.read_csv('gapminder_gdp_oceania.csv',index_col='country')

pirnt(data)

## Pandas functions:

data.info()  #() are used because this is a function

data.columns  #no () are used because it is a built in command

data.T

Data set information:
gapminder.org
find the HDI Human Development Index - copy the link
https://docs.google.com/spreadsheet/pub?key=tyadrylIpQ1K_iHP407374Q&output=xlsx

url = 'https://docs.google.com/spreadsheet/pub?key=tyadrylIpQ1K_iHP407374Q&output=xlsx'

data = read_excel(url)

data

### Plotting data in Pandas

##### import matplotlib

import matplotlib.pyplot as plt #plt is an alias
%matplotlib inline #prints plots to notebook

df = americas.loc['Argentina']
df

years = americas.columns.str.strip('gdpPercap_')
years

americas.drop(labels='continent',axis=1, inplace=True)  #needed to use americas.drop to remove the continent column

years = americas.columns.str.strip('gdpPercap_') # remove gdpPercap from headings to leave only years
years

americas.columns = years.astype(int) # convert years to integers

americas.loc['Argentina'].plot() # plot data

##### plot more data

#plot data with labels
americas.loc['Argentina'].plot(label ='Argentina', marker ='o')
americas.loc['Chile'].plot(label='Chile', marker ='o')

#add another "--" line with markers
americas.loc['Ecuador'].plot(label='Ecuador', linestyle ='--', marker='o')


#add labels to plot
plt.legend()
plt.xlabel('year')
plt.ylabel('GDP per capita')
plt.savefig('americas.png', dpi=150) # save plot to file - there are different file types matplotlib can be save by changing file extentsion

### check matplotlib.org gallery to see and make many more different plots

programming in python
plotting and programming in python