def _get_contents(self):
return lp.Frame([
lp.Block(self.block_contents, title=self.block_title),
pl.VFill(),
pl.UnorderedList([
lp.DimAndRevealListItems(
self.main_contents,
vertical_fill=True,
)
])
],
title=self.title,
**self.kwargs)
def _get_contents(self):
contents = [
*self.content,
pl.VFill(),
]
if self.bottom_content:
bottom_contents = list(
more_itertools.chunked(self.bottom_content, 3))
if len(bottom_contents) > 1:
# Multiple rows
new_bottom_contents = []
for content_row in bottom_contents:
# Deal with incomplete rows
if len(content_row) == 1:
# Single item, center it
value = content_row[0]
new_bottom_contents.append(['', value, ''])
elif len(content_row) == 2:
# Two items, put on edges
value1, value2 = content_row
new_bottom_contents.append([value1, '', value2])
else:
new_bottom_contents.append(content_row)
bottom_contents = new_bottom_contents
# Add padding
new_bottom_contents = []
for row in bottom_contents:
new_bottom_contents.append([pl.HFill(), *row, pl.HFill()])
bottom_contents = new_bottom_contents
align = 'c' * len(bottom_contents[0])
tab = lt.TabularStar([
lt.TopRule(),
lt.ValuesTable.from_list_of_lists(bottom_contents)
],
align=align)
tab = self.format_contents(tab)
contents.append(tab)
lb = _LabBlock(contents, **self.kwargs)
return lb
def get_content():
random.seed(1000)
next_until_end_ov = lp.Overlay([lp.UntilEnd(lp.NextWithIncrement())])
next_slide = lp.Overlay([lp.NextWithIncrement()])
numpy_mono = pl.Monospace('numpy')
lecture = get_dynamic_salary_python_lecture()
return [
pl.Section(
[
lp.DimRevealListFrame(
[
'We have seen how structure and organization can help the readability and maintainability of '
'an Excel model. The same concept exists for our Python models.',
['We already learned that we should use functions to organize logic and a',
pl.Monospace('dataclass'),
'for the model inputs'],
"Typically you'll have functions for each step, those may be wrapped up into other functions which "
"perform larger steps, and ultimately you'll have one function which does everything by calling the"
"other functions.",
"Those are good ideas with any Python model, but working in Jupyter allows us some additional "
"organization and presentation of the model"
],
title='How to Structure a Python Financial Model'
),
lp.DimRevealListFrame(
[
'In Jupyter, we can have code, nicely formatted text, equations, sections, hyperlinks, '
'and graphics, all in one document',
['For all you can do with these nicely formatted "Markdown" cells, see',
Hyperlink('https://www.markdownguide.org/basic-syntax/', 'here'), 'and',
Hyperlink('https://www.markdownguide.org/extended-syntax/', 'here.')],
'We can think of sections in Jupyter as analagous to Excel sheets/tabs. One section for each '
'logical part of your model. Then you can have smaller headings for subsections of the model.',
'Break your code up into small sections dealing with each step, with nicely formatted text '
'explaining it. Add comments where anything is unclear in the code.',
],
title='Using Jupyter for Structure of a Model'
),
lp.GraphicFrame(
[
get_model_structure_graphic()
],
title='Structuring a Python Model'
),
lp.DimRevealListFrame(
[
'When I develop in Jupyter, I have lots of cells going everywhere testing things out',
['When I finish a project in Jupyter, I remove these testing cells and make sure it runs and '
'logically flows from end to end', pl.Monospace('(restart kernel and run all cells)')],
"Run your model with different inputs, and make sure the outputs change in the expected fashion. This "
"is a good way to check your work.",
'There may be outputs in each section, but the final output should be at the end of the notebook',
],
title='Workflow and Final Output'
),
],
title='Structuring a Model in Python and Jupyter',
short_title='Model Structure'
),
pl.Section(
[
lp.DimRevealListFrame(
[
'The first project is aimed at approaching a new time value of money and cash flow model',
'It covers the same concepts as the retirement model, but in a capital budgeting setting',
'We need to introduce some economic equations to handle this model. You should have covered these in microeconomics.'
],
title='Introducing Project 1'
),
lp.GraphicFrame(
images_path('supply-demand-graph.png'),
title='A Quick Review of Supply and Demand'
),
lp.Frame(
[
lp.Block(
[
"There are a couple of basic economic equations we haven't talked about that "
"we'll need for this:",
pl.Equation(str_eq='R = PQ', inline=False),
pl.Equation(str_eq='Q = min(D, S)', inline=False),
pl.UnorderedList([
f'{pl.Equation(str_eq="R")}: Revenue',
f'{pl.Equation(str_eq="Q")}: Quantity Purchased',
f'{pl.Equation(str_eq="D")}: Quantity Demanded',
f'{pl.Equation(str_eq="S")}: Quantity Supplied',
])
],
title='New Required Equations'
)
],
title='Equations for Project 1'
),
lp.Frame(
[
pl.UnorderedList([
lp.DimAndRevealListItems([
'We need to cover one more Python concept and one gotcha before you can complete the first project.',
"On the next slide I'll introduce error handling, and show an example of how it's useful"
],
vertical_fill=True)
]),
lp.AlertBlock(
[
pl.UnorderedList([
f'The NPV function in {numpy_mono} works slightly differently than the NPV function in Excel.',
f'Excel treats the first cash flow as period 1, while {numpy_mono} treats the first cash flow as period 0.',
'If taking NPV where the first cash flow is period 1, pass directly to Excel, and for Python, pass 0 as the first cash flow, then the rest.',
'If taking NPV where the first cash flow is period 0, pass from period 1 to end to Excel and add period 0 separately, pass directly to Python.'
])
],
title='NPV Gotcha',
overlay=next_slide
)
],
title='A Couple More Things on the Python Side'
),
],
title='Project 1 Additional Material',
short_title='Project 1'
),
pl.Section(
[
get_retirement_model_overview_frame(),
lp.Frame(
[
lp.Block(
salary_block_content,
title='Salary with Promotions and Cost of Living Raises'
)
],
title='Revisiting the Model Salary Equation'
),
lp.Frame(
[
pl.UnorderedList([
'For wealths, we need to add the investment return and then the savings in each year',
], overlay=next_until_end_ov),
pl.VFill(),
lp.Block(
[
lp.adjust_to_full_size_and_center(
pl.Equation(str_eq=r'W_t = W_{t-1} (1 + r_i) + S_t v')),
pl.UnorderedList([
f'{pl.Equation(str_eq="S_t")}: Salary at year {pl.Equation(str_eq="t")}',
f'{pl.Equation(str_eq="W_t")}: Wealth at year {pl.Equation(str_eq="t")}',
f'{pl.Equation(str_eq="r_i")}: Investment return',
f'{pl.Equation(str_eq="t")}: Number of years',
f'{pl.Equation(str_eq="v")}: Savings rate',
])
],
title='Calculating Wealth',
overlay=next_until_end_ov,
)
],
title='Building the Wealth Model'
),
InClassExampleFrame(
[
'I will now show the process I use to create a full model.',
'I will be recreating the model "Dynamic Salary Retirement Model.ipynb"',
'Go ahead and download that to follow along as you will also extend it in a lab exercise',
],
title='Creating a Full Model in Python',
block_title='Dynamic Salary Retirement Model in Python',
),
get_extend_dynamic_retirement_python_lab_lecture().to_pyexlatex().presentation_frames(),
LabExercise(
[
[
"Usually I would try to have smaller labs but it didn't fit the format of this lecture. "
"Most will not be able to complete this during class.",
"For this lab, attempt the practice problem "
'"P1 Python Retirement Savings Rate Problem.pdf"',
'This is similar to how the projects will be assigned, so it is good preparation',
"I would encourage you to try it from scratch. If you are totally stuck, try working off "
"of the retirement model I completed today to have a lot of the structure already. If you "
"still are having trouble with that, check the solution and see me in office hours.",
'Note: this is not an official lab exercise you need to submit, it is practice only, but '
'I would highly encourage you to complete it.'
]
],
block_title='Practice Building A Model',
frame_title='Extending the Simple Retirement Model in a Different Way',
label='lab:retire-model'
),
],
title='Building the Dynamic Salary Retirement Model',
short_title='Build the Model'
),
pl.PresentationAppendix(
[
lecture.pyexlatex_resources_frame,
get_extend_dynamic_retirement_python_lab_lecture().to_pyexlatex().appendix_frames(),
]
)
]
def get_content():
lecture = get_python_basics_lecture()
conditionals_lab = get_python_basics_conditionals_lab_lecture().to_pyexlatex()
lists_lab = get_python_basics_lists_lab_lecture().to_pyexlatex()
functions_lab = get_python_basics_functions_lab_lecture().to_pyexlatex()
data_types_lab = get_python_basics_data_types_lab_lecture().to_pyexlatex()
classes_lab = get_python_basics_classes_lab_lecture().to_pyexlatex()
appendix_frames = [
*conditionals_lab.appendix_frames(),
*lists_lab.appendix_frames(),
*functions_lab.appendix_frames(),
*data_types_lab.appendix_frames(),
*classes_lab.appendix_frames()
]
next_slide = lp.Overlay([lp.NextWithIncrement()])
function_example = pl.Python(
"""
def my_func(a, b, c=10):
return a + b + c
>>> my_func(5, 6)
21
""")
use_class_example = pl.Python(
"""
from car_example import Car
>>> my_car = Car('Honda', 'Civic')
>>> print(my_car)
Car(make='Honda', model='Civic')
>>> type(my_car)
car_example.Car
>>> my_car.make
'Honda'
>>> my_car.drive()
'The Honda Civic is driving away!'
""")
dataclass_example = pl.Python(
"""
from dataclasses import dataclass
@dataclass
class ModelInputs:
interest_rates: tuple = (0.05, 0.06, 0.07)
pmt: float = 1000
>>> inputs = ModelInputs(pmt=2000)
>>> print(inputs)
ModelInputs(interest_rates=(0.05, 0.06, 0.07), pmt=2000)
>>> type(inputs)
__main__.ModelInputs
>>> inputs.interest_rates
(0.05, 0.06, 0.07)
>>> inputs.pmt
2000
""")
if_example = [pl.Python(
"""
>>> if 5 == 6:
>>> print('not true')
>>> else:
>>> print('else clause')
>>>
>>> this = 'woo'
>>> that = 'woo'
>>>
>>> if this == that:
>>> print('yes, print me')
>>> if this == 5:
>>> print('should not print')
"""
), pl.Monospace('else clause'), OutputLineBreak(), pl.Monospace('yes, print me')]
build_list_example = pl.Python(
"""
>>> inputs = [1, 2, 3]
>>> outputs = []
>>> for inp in inputs:
>>> outputs.append(
>>> inp + 10
>>> )
>>> outputs.insert(0, 'a')
>>> print(outputs)
['a', 11, 12, 13]
"""
)
enumerate_example = [pl.Python(
"""
>>> inputs = ['a', 'b', 'c']
>>> for i, inp in enumerate(inputs):
>>> print(f'input number {i}: {inp}')
"""
),
pl.Monospace('input number 0: a'),
OutputLineBreak(),
pl.Monospace('input number 1: b'),
OutputLineBreak(),
pl.Monospace('input number 2: c')
]
list_indexing_example = pl.Python(
"""
>>> my_list = ['a', 'b', 'c', 'd']
>>> my_list[0] # first item
'a'
>>> my_list[1] # second item
'b'
>>> my_list[-1] # last item
'd'
>>> my_list[:-1] # up until last item
['a', 'b', 'c']
>>> my_list[1:] # after the first item
['b', 'c', 'd']
>>> my_list[1:3] # from the second to the third item
['b', 'c']
"""
)
f_string_example = pl.Python(
"""
>>> my_num = 5 / 6
>>> print(my_num)
0.8333333333333334
>>> print(f'My number is {my_num:.2f}')
'My number is 0.83'
"""
)
f_string = pl.Monospace("f''")
f_mono = pl.Monospace('f')
try_except_example = pl.Python(
"""
>>> my_list = ['a', 'b']
>>> try:
>>> my_value = my_list[10]
>>> except IndexError:
>>> print('caught the error')
caught the error
"""
)
list_100_5 = pl.Monospace('[100] * 5')
next_slide = lp.Overlay([lp.NextWithIncrement()])
annuity_example = pl.Python(
"""
>>> annuity = [100] * 5
>>> annuities = [
>>> annuity,
>>> [0, 0, 0] + annuity
>>> ]
>>> n_years = 10
>>> output = [0] * n_years
>>> for i in range(n_years):
>>> for ann in annuities:
>>> try:
>>> output[i] += ann[i]
>>> except IndexError:
>>> pass
>>> print(output)
[100, 100, 100, 200, 200, 100, 100, 100, 0, 0]
"""
)
site_link = Hyperlink(SITE_URL, 'the course site')
return [
pl.Section(
[
lp.TwoColumnGraphicDimRevealFrame(
[
"Now we are going to build our first complex Python model",
"We will also learn a bit more Python before we can get there",
"Just as we did in Excel, we need to add structure to make the model navigatable",
"Logic should be organized in functions and be documented",
],
graphics=[
images_path('python-logo.png')
],
title='An Organized Structure of an Advanced Python Model'
),
lp.GraphicFrame(
[
get_model_structure_graphic()
],
title='The Structure of a Complex Model'
),
],
title='Introduction',
short_title='Intro'
),
pl.Section(
[
lp.Frame(
[
lp.Block(
[
pl.TextSize(-1),
if_example,
],
title='If Statements in Python'
),
],
title='Python Conditionals - If Statement'
),
lp.DimRevealListFrame(
[
'Use two equals signs to compare things (single to assign things)',
'Else is equivalent to value if false behavior in Excel',
'We can do a lot more than just set a single value, anything can be done in an if or else statement',
[pl.Monospace('elif'),
' is a shorthand for else if, e.g. not the last condition, but this condition']
],
title='Explaining the If-Else Statements'
),
InClassExampleFrame(
[
f"On {site_link}, there is a Jupyter notebook called Python Basics containing all "
f"of the examples for today's lecture",
'Now I will go through the example material under "Conditionals"'
],
title='Conditionals Example',
block_title='Trying out Conditionals'
),
conditionals_lab.presentation_frames(),
],
title='Conditionals'
),
pl.Section(
[
lp.Frame(
[
lp.Block(
build_list_example,
title='List Building'
),
pl.UnorderedList([
lp.DimAndRevealListItems([
['Use ', pl.Monospace('.append'), ' to add an item to the end of a list'],
['Use ', pl.Monospace('.insert'), ' to add an item at a certain position'],
])
])
],
title='Python Patterns - Building a List'
),
lp.Frame(
[
pl.UnorderedList([
'Index is base zero (0 means first item, 1 means second item)',
]),
pl.VFill(),
list_indexing_example,
],
title='List Indexing and Slicing'
),
InClassExampleFrame(
[
"We will keep working off of Python Basics.ipynb",
'Now I will go through the example material under "Working more with Lists"'
],
title='Lists Example',
block_title='Doing More with Lists'
),
lists_lab.presentation_frames(),
],
title='More with Lists',
short_title='Lists'
),
pl.Section(
[
lp.DimRevealListFrame(
[
"In Python, we can group logic into functions",
"Functions have a name, inputs, and outputs",
"Functions are objects like everything else in Python",
function_example,
],
title='Functions - Grouping Reusable Logic'
),
InClassExampleFrame(
[
"We will keep working off of Python Basics.ipynb",
'Now I will go through the example material under "Functions"'
],
title='Functions Example',
block_title='Structuring Code using Functions'
),
functions_lab.presentation_frames(),
],
title='Functions'
),
pl.Section(
[
lp.DimRevealListFrame(
[
'In Python, everything is an object except for variable names, which are references to objects',
'Every object has a type. We have learned about strings, numbers, lists, and booleans (True, False)',
'In the next section on classes, we will learn more about the relationship between the type '
'and the object'
],
title='What are Types?'
),
# TODO [#12]: add f-strings to Jupyter example
lp.Frame(
[
pl.UnorderedList([
lp.DimAndRevealListItems([
'You may have noticed that we can end up with a lot of decimals in Python output',
'Further, you may want to include your results as part of a larger output, such as a sentence.',
f'For these operations, we have {f_mono} strings: {f_string}'
],
vertical_fill=True)
]),
lp.Block(
f_string_example,
title='Example',
overlay=next_slide
)
],
title='Formatting Python Strings'
),
lp.DimRevealListFrame(
[
'So far I have just said that numbers are a type in Python, but this is a simplification',
['There are two main types of numbers in python:', pl.Monospace('float'), 'and',
pl.Monospace('int'), 'corresponding to a floating point number and an integer, respectively'],
['An', pl.Monospace('int'), 'is a number without decimals, while a', pl.Monospace('float'),
'has decimals, regardless of whether they are zero'],
['For example,', pl.Monospace('3.5'), 'and', pl.Monospace('3.0'), 'are floats, while',
pl.Monospace('3'), 'is an int, even though', pl.Monospace('3.0 == 3 is True')],
["Usually, this doesn't matter. But to loop a number of times, you must pass an",
pl.Monospace('int')]
],
title='Numeric Types'
),
lp.DimRevealListFrame(
[
['A', pl.Monospace('tuple'), 'is like a', pl.Monospace('list'), "but you can't change it after "
"it has been created (it is immutable)"],
['Tuples are in parentheses instead of brackets, e.g.', pl.Monospace('("a", "b")')],
['A', pl.Monospace('dict'), 'short for dictionary, stores a mapping. Use them if you want '
'to store values associated to other values'],
['We will come back to', pl.Monospace('dicts'), 'later in the course, but I wanted to',
'introduce them now as they are a very fundamental data type']
],
title='Additional Built-In Types'
),
InClassExampleFrame(
[
"We will keep working off of Python Basics.ipynb",
'Now I will go through the example material under "Exploring Data Types"'
],
title='Data Types Example',
block_title='Understanding the Different Data Types'
),
data_types_lab.presentation_frames(),
],
title='More about Data Types',
short_title='Data Types'
),
pl.Section(
[
lp.GraphicFrame(
images_path('class-object.pdf'),
title='Overview of Classes and Objects'
),
lp.DimRevealListFrame(
[
'In Python, everything is an object except for variable names, which are references to objects',
'Strings, floats, ints, lists, and tuples are types of objects. There are many more types of '
'objects and users can define their own types of objects',
'A class is a definition for a type of object. It defines how it is created, the data '
'stored in it, and the functions attached to it',
'We can write our own classes to create new types of objects to work with'
],
title='Everything is an Object. Every Object has a Class'
),
lp.DimRevealListFrame(
[
'From a single class definition, an unlimited number of objects can be created',
'Typically the class definition says it should accept some data to create the object',
'Then when you have multiple objects of the same type (created from the same class), '
'they will have the same functions (methods) attached to them, but different data stored within',
['For example, we can create two different lists. They will have different contents, but we can',
'do', pl.Monospace('.append'), 'on either of the lists']
],
title='Many Objects to One Class'
),
lp.MultiGraphicFrame(
[
images_path('class-object.pdf'),
images_path('list-object.pdf')
],
title='Lists are Objects',
vertical=False
),
lp.MultiGraphicFrame(
[
images_path('class-object.pdf'),
images_path('car-object.pdf')
],
title='We can Make Custom Objects Too',
vertical=False
),
lp.Frame(
[
pl.UnorderedList(['Constructing an object from a class looks like calling a function:']),
lp.Block(
[
pl.TextSize(-1),
use_class_example,
],
title='Using Custom Classes in Python'
),
],
title='Creating and Using Objects'
),
lp.DimRevealListFrame(
[
'I will not be teaching you about creating general classes in this course. It is very useful '
'but is generally more advanced. I encourage you to learn them outside the course.',
"We covered this material for two reasons:",
['To give a better understanding of how Python works in general, and why sometimes we '
'call functions as', pl.Monospace('something.my_func()'), 'rather than',
pl.Monospace('my_func()')],
['We are going to use', pl.Monospace('dataclasses'), 'to store our model data. They are',
'a simplified version of classes used mainly for storing data.']
],
title='Where we Will Focus in This Course'
),
lp.Frame(
[
pl.UnorderedList(['An organized way to store our model input data:']),
lp.Block(
[
pl.TextSize(-3),
dataclass_example,
],
title='Using Dataclasses in Python'
),
],
title='Dataclass Intro'
),
lp.DimRevealListFrame(
[
['A', pl.Monospace('dataclass'), 'is just a class which is more convenient to create, and',
'is typically used to group data together'],
['If you need to pass around multiple variables together, they make sense. For our models, we '
'will want to pass around all the inputs, so one', pl.Monospace('dataclass'), 'for all the',
'inputs to the model makes sense'],
'This way instead of having to pass around every input individually to every function, just '
'pass all the input data as one argument',
['Also enables easy tab-completion. What were the names of my inputs? Just hit tab after',
pl.Monospace('data.')]
],
title='What, When and Why Dataclasses?'
),
InClassExampleFrame(
[
"We will keep working off of Python Basics.ipynb",
'For this example, also go and download car_example.py and put it in the same folder',
'Now I will go through the example material under "Working with Classes"'
],
title='Classes Example',
block_title='Working with Classes and Creating Dataclasses'
),
classes_lab.presentation_frames(),
],
title='Classes and Dataclasses',
short_title='Classes'
),
pl.Section(
[
# TODO [#13]: add error handling to Jupyter example
lp.Frame(
[
pl.UnorderedList([
lp.DimAndRevealListItems([
"You have certainly already seen errors coming from your Python code. When they have come up, the code doesn't run.",
'Sometimes you actually expect to get an error, and want to handle it in some way, rather than having your program fail.'
],
vertical_fill=True)
]),
lp.Block(
try_except_example,
title='Example',
overlay=next_slide
)
],
title='Python Error Handling'
),
lp.DimRevealListFrame(
[
"Let's say you're receiving annuities. There is a single annuity which produces \$100 for 5 years. You receive this annuity in year 0 and in year 3.",
f"You might define the annuity cash flows as a list of 100, 5 times ({list_100_5})",
'Then you want to come up with your overall cash flows, going out to 15 years'
],
title='An Example where Error Handling is Useful'
),
lp.Frame(
[
lp.Block(
[
pl.TextSize(-1),
annuity_example,
],
title='Calculating the Sum of Unaligned Annuity Cash-Flows'
)
],
title='Applying Error Handling'
),
],
title='Error Handling',
short_title='Errors'
),
pl.PresentationAppendix(
[
lecture.pyexlatex_resources_frame,
*appendix_frames
]
)
]
def get_content():
pd_mono = pl.Monospace('pandas')
dfs_mono = pl.Monospace('DataFrames')
df_mono = pl.Monospace('DataFrame')
next_slide = lp.Overlay([lp.UntilEnd(lp.NextWithIncrement())])
df_to_excel_example = pl.Python(
"df.to_excel('data.xlsx', sheet_name='My Data', index=False)")
df_from_excel_example = pl.Python(
"df = pd.read_excel('data.xlsx', sheet_name='My Data')")
index_false_mono = pl.Monospace('index=False')
addin_install_mono = pl.Monospace('xlwings addin install')
addin_install_success = pl.Monospace(
'Successfuly installed the xlwings add-in! Please restart Excel.')
random_seed_py = pl.Monospace('random_seed.py')
random_seed_excel = pl.Monospace('random_seed.xlsm')
quickstart_mono = pl.Monospace('xlwings quickstart')
quickstart_project_mono = pl.Monospace(
'xlwings quickstart my_project_name')
cd_mono = pl.Monospace('cd')
xw_func_decorator = pl.Monospace('@xw.func')
xw_arg_decorator = pl.Monospace('@xw.arg')
xw_ret_decorator = pl.Monospace('@xw.ret')
x_mono = pl.Monospace('x')
expand_table_mono = pl.Monospace("expand='table'")
random_choice_mono = pl.Monospace('random_choice')
random_choice_py = pl.Monospace('random.choices')
lecture = get_combining_excel_python_lecture()
pd_read_write_exercise = get_read_write_excel_pandas_lab_lecture(
).to_pyexlatex()
xlwings_exercise = get_read_write_xlwings_lab_lecture().to_pyexlatex()
read_from_excel_example = pl.Python("""
my_value = sht.range("G11").value # single value
# all values in cell range
my_value = sht.range("G11:F13").value
# expands cell range down and right getting all values
my_values = sht.range("G11").expand().value
""")
write_to_excel_example = pl.Python("""
sht.range("G11").value = 10
sht.range("G11").value = [10, 11] # horizontal
sht.range("G11:G12").value = [10, 11] # vertical
# table, DataFrame from elsewhere
sht.range("G11").value = df
""")
return [
pl.Section([
lp.DimRevealListFrame([
"We have learned how to use both Excel and Python to solve problems. Throughout this process, there "
"were advantages and disadvantages of each tool for each problem.",
"I wanted you to know both tools so you could pick whichever is best to tackle your problem",
"For larger problems, you'll likely find some parts are better with Excel and some with Python",
"After this lecture, you won't need to choose one anymore, you can use both at once."
],
title='Leveraging the Power of Both Tools'),
],
title='Introduction'),
pl.Section([
lp.DimRevealListFrame([
[pd_mono, 'has built-in tools for working with Excel'],
[
pd_mono, 'can read Excel workbooks into', dfs_mono,
'and it can write', dfs_mono, 'back to Excel workbooks'
],
'For simple uses, this may be enough. If you just need to get data from somewhere once and put it in your '
'workbook, or you have your data in Excel and want to analyze it in Python, this is sufficient',
[
"If you want to manipulate your workbook from Python, or you want to run Python code from your "
"workbook, look to", xlwings_mono
]
],
title=f'How Far does {pd_mono} Get Us?'),
lp.Frame([
lp.Block([
df_from_excel_example,
pl.VSpace(-0.3),
pl.UnorderedList([
"If you don't pass a sheet name, it will take the first sheet."
])
],
title='Reading Excel Files',
overlay=next_slide),
pl.VFill(),
lp.Block(
[
df_to_excel_example,
pl.VSpace(-0.3),
pl.UnorderedList(
[[
'We are passing', index_false_mono,
'because usually the 0, 1, 2 ... index is not useful'
],
[
"If you had set your index to something useful, then don't include",
index_false_mono
]])
],
title='Writing to Excel Files',
overlay=next_slide),
pl.VFill(),
lp.AlertBlock([[
'When', pd_mono,
'writes to a workbook, it replaces the file. Do not write over an existing '
'workbook that you want to keep!'
]],
title='Careful When Writing!',
overlay=next_slide),
],
title=f'Reading and Writing to Excel Files with {pd_mono}'
),
lp.Frame([
InClassExampleBlock([
pl.UnorderedList([
'Download the contents of the "Read Write Excel Pandas" folder in Examples',
'Ensure that you put the Excel file and notebook in the same folder for it to work',
'Follow along with the notebook'
])
],
title=
f'Read and Write to Excel using {pd_mono}')
],
title='Showcasing Reading and Writing to Excel Files'),
pd_read_write_exercise.presentation_frames(),
],
title=f'To and From Excel with {pd_mono}',
short_title=pd_mono),
pl.Section([
lp.TwoColumnGraphicDimRevealFrame([[
'The easiest way to use Python from in Excel, or Excel from in Python, is',
xlwings_mono
], "In Windows, it's based off the Microsoft COM API, which is some common tools they give for creating "
"plugins.",
"It's still in active development, but overall it works pretty well and is far beyond where we were "
"a few years ago"],
graphics=[
images_path(
'xlwings-logo.png')
],
title=
f'Introducing {xlwings_mono}'),
lp.DimRevealListFrame(
[['There are two main ways to use', xlwings_mono],
[
'You can',
pl.
Bold('manipulate Excel from Python,'),
'which gives you the full power of Excel from',
"within Python. In this class we'll focus on reading and writing values, but you can do anything",
"that you would normally be able to in Excel, but by executing Python code."
],
[
'Or you can',
pl.Bold('run Python from Excel'),
'using one of two approaches:',
pl.Underline('Python as a VBA replacement'), 'and',
pl.Underline('user-defined functions (UDFs)')
],
'We will focus on manipulating Excel from Python in this class. I encourage you to explore '
'the other two approaches on your own.'],
title=f'What are the Main Ways to use {xlwings_mono}?'),
lp.TwoColumnGraphicDimRevealFrame([
pl.TextSize(-1),
[
xlwings_mono,
'allows us to write Python values into Excel and fetch Excel values into Python'
],
'There is also a complete VBA API, meaning you can do everything that you could do with VBA '
'from within Python, which means you have the full capabilities of Excel within Python',
'There are also convenient features to work with entire tables at once rather than '
'a single value'
],
graphics=[
images_path(
'python-excel-logo.png')
],
title=
'Using Python to Drive Excel Models'
),
lp.Frame([
lp.Block([read_from_excel_example],
title='Read Values from Excel'),
lp.Block([write_to_excel_example],
title='Write Values to Excel')
],
title='Write and Read Values to and from Excel'),
InClassExampleFrame([
'Download the contents of the "xlwings" folder in Examples',
'Ensure that you put the Excel file and notebook in the same folder for it to work',
'Follow along with the notebook'
],
title=f'How to Use {xlwings_mono}',
block_title=f'Trying out {xlwings_mono}'),
xlwings_exercise.presentation_frames(),
],
title=
f'Introducing Full Python-Excel Connection with {xlwings_mono}',
short_title=f'{xlwings_mono}'),
pl.PresentationAppendix([
lecture.pyexlatex_resources_frame,
pd_read_write_exercise.appendix_frames(),
xlwings_exercise.appendix_frames(),
])
]
def get_content():
random.seed(1000)
lecture = get_visualization_lecture()
intro_pandas_lab = get_intro_to_pandas_lab_lecture().to_pyexlatex()
styling_pandas_lab = get_pandas_styling_lab_lecture().to_pyexlatex()
graphing_lab = get_intro_python_visualization_lab_lecture().to_pyexlatex()
appendix_frames = [
lecture.pyexlatex_resources_frame,
intro_pandas_lab.appendix_frames(),
styling_pandas_lab.appendix_frames(),
graphing_lab.appendix_frames()
]
ret_model = RetirementModel()
ret_df = ret_model.get_formatted_df(num_years=12)
ret_table = lt.Tabular.from_df(ret_df, extra_header=pl.Bold('Retirement Info'))
plt_mono = pl.Monospace('matplotlib')
df_mono = pl.Monospace('DataFrame')
df_basic_example = pl.Python(
"""
>>> import pandas as pd
>>> df = pd.DataFrame()
>>> df['Sales'] = [1052, 212, 346]
>>> df['Category'] = ['Aprons', 'Apples', 'Bowties']
df
"""
)
plot_example_code = pl.Python(
"""
>>> %matplotlib inline
>>> ret_df.plot.line(x='Time', y='Salaries')
"""
)
return [
pl.Section(
[
lp.DimRevealListFrame(
[
"So far we've had one main output from our model, number of years",
"Salaries and wealth over time have also been outputs, but we haven't had a good way of understanding "
"that output. It's a bunch of numbers.",
"This is where visualization comes in. We have some complex result, and want to make it easily "
"interpretable."
],
title='Why Visualize?'
),
lp.Frame(
[
pl.Center(ret_table)
],
title='What we Have so Far'
),
lp.GraphicFrame(
images_path('excel-insert-chart.png'),
title='Visualization in Excel'
),
lp.GraphicFrame(
lg.ModifiedPicture(
images_path('python-visualization-landscape.jpg'),
[
lg.Path('draw', [(0.52, 0.52), (0.85, 0.67)], options=['red'], draw_type='rectangle',
overlay=lp.Overlay([2]))
]
),
title='An Overwhelming Number of Options in Python'
),
lp.DimRevealListFrame(
[
["Ultimately, we will be creating graphs using", plt_mono, "but we won't use it directly."],
["Instead, we will use", pd_mono],
[pd_mono, "is actually creating its graphs using", plt_mono,
"for us, but it is simpler to use."]
],
title='Explaining Python Visualization in This Class'
),
InClassExampleFrame(
[
'I will now go back to the "Dynamic Salary Retirement Model.xlsx" Excel model to '
'add visualization',
'I have also uploaded the completed workbook from this exercise '
'as "Dynamic Salary Retirement Model Visualized.xlsx"',
'Follow along as I go through the example.',
],
title='Visualization in Excel',
block_title='Adding Graphs to the Dynamic Salary Retirement Excel Model'
),
],
title='Visualization Introduction',
short_title='Intro'
),
pl.Section(
[
lp.DimRevealListFrame(
[
[pd_mono, "does", pl.Bold('a lot'), 'more than just graphing. We will use it throughout the '
'rest of the class.'],
"Previously we've worked with lists, numbers, strings, and even our custom types (our model dataclasses)",
[pd_mono, "provides the", df_mono, "as a new type that we can use."],
f'Before we can get to graphing, we must learn how to use the {df_mono}.'
],
title='Some Setup Before we can Visualize in Python'
),
lp.Frame(
[
['A', df_mono, 'is essentially a table. It has rows and columns, just like in Excel.'],
pl.VFill(),
lp.Block(
[
pl.UnorderedList([
'Add or remove columns or rows',
'Group by and aggregate',
'Load in and output data from/to Excel and many other formats',
'Merge and join data sets',
'Reshape and pivot data',
'Time-series functionality',
'Slice and query your data',
'Handle duplicates and missing data'
])
],
title=f'Some Features of the {df_mono}'
)
],
title=f'What is a {df_mono}?'
),
lp.Frame(
[
df_basic_example,
pl.Graphic(images_path('df-basic-example.png'), width=0.3)
],
title=f'A Basic {df_mono} Example'
),
InClassExampleFrame(
[
'I will now go through the notebook in '
'"Intro to Pandas and Table Visualization.ipynb"',
'Follow along as I go through the example.',
'We will complete everything up until DataFrame Styling'
],
title='Introduction to Pandas',
block_title='Creating and Using Pandas DataFrames'
),
intro_pandas_lab.presentation_frames(),
lp.DimRevealListFrame(
[
['It is possible to add styling to our displayed tabular data by styling the', df_mono],
'The styling is very flexible and essentially allows you to do anything',
'Out of the box, it is easy to change colors, size, and positioning of text, add a caption, do '
'conditional formatting, and draw a bar graph over the cells.'
],
title='Styling Pandas DataFrames'
),
InClassExampleFrame(
[
'I will now go through the next section in '
'"Intro to Pandas and Table Visualization.ipynb"',
'Follow along as I go through the example.',
'This time we are covering the remainder of the notebook starting from "DataFrame Styling"'
],
title='Introduction to Pandas',
block_title='Creating and Using Pandas DataFrames'
),
styling_pandas_lab.presentation_frames(),
],
title='Tables with Pandas DataFrames',
short_title='Pandas'
),
pl.Section(
[
lp.Frame(
[
lp.Block(
[
plot_example_code,
pl.Graphic(images_path('python-salaries-line-graph.pdf'), width=0.5)
],
title=f'Line Graphs using {pd_mono}'
)
],
title='A Minimal Plotting Example'
),
lp.MultiGraphicFrame(
[
images_path('excel-salaries-line-graph.png'),
images_path('python-salaries-line-graph.pdf'),
],
vertical=False,
title='Basic Graph Types: Line Graphs'
),
lp.MultiGraphicFrame(
[
images_path('excel-salaries-bar-graph.png'),
images_path('python-salaries-bar-graph.pdf'),
],
vertical=False,
title='Basic Graph Types: Bar Graphs'
),
lp.MultiGraphicFrame(
[
images_path('excel-salaries-box-whisker-plot.png'),
images_path('python-salaries-box-graph.pdf'),
],
vertical=False,
title='Basic Graph Types: Box and Whisker Plots'
),
InClassExampleFrame(
[
'I will now go through '
'"Intro to Graphics.ipynb"',
'Follow along as I go through the entire example notebook.',
],
title='Introduction to Graphing',
block_title='Graphing Using Pandas'
),
graphing_lab.presentation_frames(),
],
title='Graphing using Pandas',
short_title='Graphs'
),
pl.PresentationAppendix(appendix_frames)
]
def get_content():
random.seed(1000)
ev_bet = (999999 / 1000000) * 1 + (1 / 1000000) * (-750001)
xlwings_mono = pl.Monospace('xlwings')
pd_mono = pl.Monospace('pandas')
quickstart_mono = pl.Monospace('quickstart')
read_from_excel_example = pl.Python("""
my_value = xw.Range("G11").value # single value
# all values in cell range
my_value = xw.Range("G11:F13").value
# expands cell range down and left getting all values
my_values = xw.Range("G11").expand().value
""")
write_to_excel_example = pl.Python("""
xw.Range("G11").value = 10
xw.Range("G11").value = [10, 11] # horizontal
xw.Range("G11").value = [[10], [11]] # vertical
xw.Range("G11").value = [[10, 11], [12, 13]] # table
""")
ball_options = ['fill', 'circle', 'inner sep=8pt']
blue_ball_options = ball_options + ['blue']
red_ball_options = ball_options + ['red']
def rand_pos():
return random.randrange(-150, 150) / 100
blue_nodes = [
lg.Node(None, (rand_pos(), rand_pos()), options=blue_ball_options)
for _ in range(10)
]
red_nodes = [
lg.Node(None, (rand_pos(), rand_pos()), options=red_ball_options)
for _ in range(10)
]
red_blue_ball_graphic = lg.TikZPicture([
lg.Rectangle(5, 5, shape_options=['blue', 'thick']), *blue_nodes,
*red_nodes
])
lecture = get_monte_carlo_lecture()
intro_mc_python_lab = get_intro_monte_carlo_lab_lecture().to_pyexlatex()
mc_python_lab = get_python_retirement_monte_carlo_lab_lecture(
).to_pyexlatex()
mc_excel_lab = get_excel_retirement_monte_carlo_lab_lecture().to_pyexlatex(
)
return [
pl.Section([
lp.
TwoColumnGraphicDimRevealFrame([
[
pl.Bold('Monte Carlo Simulation'),
'is a technique which allows understanding the probability '
'of acheiving certain outputs from a model.'
],
'This gives the modeler a greater understanding of the likelihood of different outputs, rather '
'than relying on a single number',
],
graphics=[
images_path(
'random-numbers.jpg')
],
title=
'What is Monte Carlo Simulation?'),
lp.DimRevealListFrame([
r'Imagine you have a one-time opportunity to place a bet for \$1. ',
r'If you win the bet, you will receive \$2. If you lose the bet, you will lose \$750,000. '
r'You cannot avoid the payment by declaring bankruptcy.',
r'The odds of winning the bet are 999,999/1,000,000. In 1/1,000,000 you lose the \$750,000.',
fr'The expected profit from the bet is \${ev_bet:.2f}. Should you take it? Depends on your '
fr'risk tolerance.',
'Therefore not only the expected outcome matters, but also what other outcomes may occur and '
'their probabilities.'
],
title='Why Use Monte Carlo Simulation?'),
lp.GraphicFrame(explore_parameters_graphic(),
title='Monte Carlo Simulation in One Picture'),
lp.DimRevealListFrame([
'Monte Carlo simulation is carried out similarly to external scenario analysis.',
'The main difference is that we manually picked specific cases for the inputs with scenario '
'analysis.',
'In Monte Carlo simulation, we assign distributions to the inputs, and input values are drawn '
'from the distributions for each run of the model',
'Finally, we can fit a probability distribution to the outputs to be able to talk about the '
'chance of a certain outcome occurring'
],
title=
'Basic Process for Monte Carlo Simulation')
],
title='Introduction'),
pl.Section(
[
lp.DimRevealListFrame([
'Monte Carlo simulation can be applied to any model',
'It is generally easier to run them in Python than in Excel.',
"With pure Excel, you're either going to VBA or hacking something with data tables",
'In Python, just loop for N iterations, each time drawing inputs, running the model, and collecting '
'outputs.',
[
'We will start with a pure Python model, then move to using',
xlwings_mono, 'to add Monte Carlo '
'simulations to our Excel models.'
],
],
title=
'Running Monte Carlo Simulations - Python or Excel?'
),
lp.Frame([
lp.Block([
r'You have \$1,000 now and need to pay \$1,050 in one year. You have available to you '
r'two assets: a risk free asset that returns 3%, and a stock that returns 10% with a '
r'20% standard deviation. How much should you invest in the two assets to maximize '
r'your probability of having at least \$1,050 in one year?'
],
title='An Investment Problem'),
pl.VFill(),
pl.UnorderedList([
lp.DimAndRevealListItems([
'We must first construct the basic model which gets the portfolio value for given '
'returns',
'Then draw values of the stock return from a normal distribution, and run the model '
'many times and visualize the outputs. ',
'Then repeat this process with each weight to determine the best weight.'
])
])
],
title='An Example Application'),
InClassExampleFrame([
'Go to the course site and download the Jupyter notebook "MC Investment Returns.ipynb" from '
'Monte Carlo Examples',
'I will go through this example notebook to solve the problem from the prior slide.'
],
title='Simluating Portfolio Values',
block_title=
'Example for Simulating Portfolio Values'),
pl.TextSize(-2),
intro_mc_python_lab.presentation_frames(),
pl.TextSize(0),
],
title='Running a First Monte Carlo Simulation',
short_title='Run MC',
),
pl.Section(
[
lp.Frame([
pl.TextSize(-2), 'For the model given by:',
pl.Equation(str_eq='y = f(X)', inline=False),
pl.Equation(str_eq='X = [x_1, x_2, ..., x_n]',
inline=False),
pl.UnorderedList([[
pl.Equation(str_eq='y:'), 'Model output'
], [pl.Equation(str_eq='X:'), 'Model input matrix'],
[
pl.Equation(str_eq='x_i:'),
'Value of $i$th $x$ variable'
]]),
'To run $N$ Monte Carlo simulations, follow the following steps:',
pl.OrderedList(
[[
'Assign a probability distribution for each',
pl.Equation(str_eq='x_i')
],
[
'For each',
pl.Equation(str_eq='x_i'),
'randomly pick a value from its probability distribution. Store them as',
pl.Equation(str_eq='X_j')
],
[
'Repeat the previous step $N$ times, yielding',
pl.Equation(str_eq='[X_1, X_2, ..., X_N]')
],
[
'For each',
pl.Equation(str_eq='X_j'), 'calculate',
pl.Equation(str_eq='y_j = f(X_j)')
],
[
'Store the values of',
pl.Equation(str_eq='X_j'), 'mapped to',
pl.Equation(str_eq='y_j')
],
[
'Visualize and analyze',
pl.Equation(str_eq='y_j'), 'versus',
pl.Equation(str_eq='X_j')
]])
],
title='Monte Carlo Simulation Process'),
lp.DimRevealListFrame([
'There are a multitude of outputs we can get from a Monte Carlo simulation. We saw a few '
'already in the example.',
[
pl.Bold('Outcome probability distributions'),
'are the main output. We saw this with two '
'approaches in the example, a',
pl.Underline('histogram'), 'and a',
pl.Underline('probability table.')
],
[
'We also examined the',
pl.Bold('probability of a certain outcome'),
'in whether we reached '
'the desired cash.'
],
[
'The last main output is examining the',
pl.Bold('relationship between inputs and outputs.'),
'for which common approaches include',
pl.Underline('scatter plots'), 'and',
pl.Underline('regressions.')
]
],
title=
'Outputs from Monte Carlo Simulation'),
lp.TwoColumnGraphicDimRevealFrame(
[
pl.TextSize(-3),
'The outcome probability distribution represents the chance of receiving different '
'outcomes from your model.',
'There are two main ways to visualize a probability distribution: a plot and a table.',
[
'The plot, usually a',
pl.Underline('histogram'), 'or',
pl.Underline('KDE'),
'gives a high-level overview of the probabilities and can uncover any non-normal '
'features of the distribution.'
],
[
'The probability table represents the chance of receiving the given value or '
'lower.'
],
'The Value at Risk (VaR) is a common measure calculated in the industry, and it represents '
'the probability of losing at least a certain amount. This would be a subset of this analysis '
'and so this analysis can be used to calculate VaR',
],
graphics=[
images_path('outcome-probability-distribution.png'),
lt.Tabular([
pl.MultiColumnLabel('Probability Table', span=2),
lt.TopRule(),
lt.ValuesTable.from_list_of_lists(
[['Probability', 'Value']]),
lt.TableLineSegment(0, 1),
lt.ValuesTable.from_list_of_lists(
[['25%', '1020'], ['50%', '1039'],
['75%', '1053']]),
lt.BottomRule()
],
align='c|c')
],
title='Outcome Probability Distributions',
graphics_on_right=False,
),
lp.TwoColumnGraphicDimRevealFrame(
[
'Imagine a box which contains red and blue balls. You do not know in advance how many there '
'are of each color.',
'You want to estimate the probability of getting a blue ball when pulling a ball from the box.',
'To evaluate this, you grab a ball, write down its color, and put it back, 1,000 times.',
'You pull a blue ball in 350 out of the 1,000 trials. What is the probability of getting blue?'
],
graphics=[red_blue_ball_graphic
],
title='Probability of a Certain Outcome - A Simple Example'
),
lp.DimRevealListFrame([
'We followed the same logic when estimating the probability of receiving our desired cash '
'in the investment example.',
pl.Equation(
str_eq=
fr'p = \frac{{{pl.Text("Count of positive outcomes")}}}{{{pl.Text("Count of trials")}}}'
),
[
'For the balls example, this is simply',
pl.Equation(str_eq=r'p = \frac{350}{1000} = 0.35'),
],
[
'In the investment example, we used', pd_mono,
'to check for each trial, whether it was a '
'positive outcome (made it a 1) or not (made it a 0). Then the sum is the count of '
'positive outcomes and so the mean is the probability.'
],
],
title=
'Probability of a Certain Outcome, Formally'
),
lp.DimRevealListFrame([
'Monte Carlo simulation can also provide a more comprehensive look at the relationship between '
'inputs and outputs.',
'While sensitivity analysis can be used to estimate the relationship between an input and '
'output, it is usually done with other inputs at their base case',
'The values of inputs may affect how other inputs affect the output. E.g. for the retirement '
'model, an increase in interest rate increases wealth more if the initial salary was higher.',
'As all the inputs change each time, you can get a more realistic view of the relationship, e.g. '
'some trials with a higher interest rate will have high salary and some will have low salary.'
],
title=
'Why Monte Carlo Simulations Help Understand Inputs vs. Outputs'
),
lp.TwoColumnGraphicDimRevealFrame(
[
pl.TextSize(-1),
'A scatter plot is a simple way to visualize the relationship between two variables',
'If there is a relationship, you will see some defined pattern in the points. This may be '
'somewhat of an upward or downward line (linear relationship) or some other shape such '
'as a U (non-linear relationship).',
'If there is no relationship, then there will just be a random cloud of points (lower '
'plot) or a horizontal line.'
],
graphics=[
images_path('scatter-plot-line.png'),
images_path('scatter-plot-no-relationship.png')
],
graphics_on_right=False,
title=
'Visualizing the Relationship between Inputs and Outputs'),
lp.TwoColumnGraphicDimRevealFrame([
pl.TextSize(-2),
'The scatter plots help give a broad understanding of the relationship but do not answer the '
'question, how much will my output change if my input changes? E.g. if I earn 10,000 more '
'for a starting salary, how much sooner can I retire?',
'Simply increasing the input in your model and checking the output is not enough, because it '
'does not take into account how all the other variables may be changing.',
'Multivariate regression is a general tool which is good at answering these kinds of questions, '
'while taking into account all the changing inputs.'
],
graphics=[
images_path(
'excel-multivariate-reg.png'
)
],
title=
'Numerically Analyzing the Relationships'
),
lp.DimRevealListFrame([
pl.TextSize(-1),
'The coefficient in a multivariate regression represents how much the outcome variable '
'changes with a one unit change in the input variable.',
'E.g. a coefficient of -.0002 on starting salary in explaining years to retirement would mean '
r'that a \$1 increase in starting salary is associated with a decrease in years to retirement by .0002 years, or '
r'a \$10,000 increase in starting salary is associated with a decrease in years to retirement by 2 years.',
'All interpretations are "all else constant", meaning that it does not consider relationships '
'between the inputs. E.g. if starting salary is higher because of a good economy, and interest '
'rates are also higher due to the good economy, the starting salary coefficient is not taking '
'into account the increase in interest rates.',
'Be careful about units. If you use decimals for percentages, you will need to multiply or '
'divide by 100 to get the effect in percentages.'
],
title='How to use Multivariate Regression'
),
InClassExampleFrame(
[
'I will now go through adding a Monte Carlo simulation to the Dynamic Salary Retirement '
'Model in Python',
'The completed example is on the course site in '
'Monte Carlo Examples',
],
title='Adding Monte Carlo Simulation to a Formal Model',
block_title='Dynamic Salary Retirement with Monte Carlo'),
mc_python_lab.presentation_frames(),
],
title='A More Formal Treatment of Monte Carlo Simulation',
short_title='Formal MC',
),
pl.Section([
lp.DimRevealListFrame([
'In pure Excel, it is much more difficult to run a Monte Carlo Simulation',
'Without going to VBA, typically the only way is to use a data table',
'A data table can be used in situations where you only want to have one or two inputs '
'varying at once. Just generate the random inputs and use them as the axes of the data table',
'If you want to vary more than two inputs, VBA or Python would be required',
'There are also add-ons that accomplish this but they are usually not free'
],
title=
"How is it Different Running MC in Excel?"),
lp.DimRevealListFrame([
'The process for Monte Carlo Simulation which works for any number of variables is '
'very similar to what we were doing in Python.',
'We are still just changing the inputs, running the model, and storing the outputs from each run',
[
'Using', xlwings_mono,
'from Python code we can change and retrieve the values of cells'
],
'This allows us to change inputs, run the model, and store outputs, just as in Python, but running our Excel model.',
'We can either analyze the outputs in Python or output them back to Excel for analysis'
],
title=
'Monte Carlo in Excel with More than Two Variables'
),
InClassExampleFrame([
'Go to the course site and download the "Dynamic Salary Retirement Model.xlsx" and '
'"Excel Monte Carlo.ipynb" from the Monte Carlo Examples',
'Open up the Jupyter notebook and follow along with me',
'The completed Excel model is also there in case you lose track. Visualizations '
'were added after running the Jupyter notebook on the original Excel model.',
],
title='Monte Carlo Excel Retirement Model',
block_title=
f'Using {xlwings_mono} to Run Monte Carlo Simulations'
),
mc_excel_lab.presentation_frames(),
],
title='Monte Carlo Simulation in Excel',
short_title='Excel MC'),
pl.PresentationAppendix([
lecture.pyexlatex_resources_frame,
intro_mc_python_lab.appendix_frames(),
mc_python_lab.appendix_frames(),
mc_excel_lab.appendix_frames(),
])
]