In [1]:

```
# Don't worry about what this code does, but make sure to run it if you're following along.
from IPython.display import IFrame
def show_nested_eval():
src = 'https://docs.google.com/presentation/d/e/2PACX-1vQpW0NzwT3LjZsIIDAgtSMRM1cl41Gp_Lf8k9GT-gm5sGAIynw4rsgiEFbIybClD6QtxarKaVKLbR9U/embed?start=false&loop=false&delayms=60000&rm=minimal" frameborder="0" width="960" height="569" allowfullscreen="true" mozallowfullscreen="true" webkitallowfullscreen="true"'
width = 960
height = 569
return IFrame(src, width, height)
```

- Everything you need for this class is at dsc10.com. This class
**does not use Canvas**. - The solutions to the pretest are now posted. See how you did and watch the 🎥 video at the end to learn more about important test-taking skills.
- Lab 0 is out and is now due on
**Saturday, October 7 at 11:59PM**.- It's worthwhile to watch the 🎥 video towards the end on how to navigate DataHub and Jupyter Notebooks.

- The office hours schedule is now posted, and office hours start today in HDSI 155. Please visit!
- Post on Ed with any questions.
- It was great seeing so many of you on Friday at our Meet the Professors event. We made it on the HDSI Instagram!

- There are two events coming up this week that may interest you:
**Python Bootcamp**for DSC 10 students new to programming. Tomorrow and Thursday 1-3pm on Zoom. Sign up here!**HDSI Undergraduate Social**. Thursday, October 5th from 4-6pm on the HDSI Patio.

- We'll post events like this on our "Opportunities" thread on Ed. Check it out periodically!

- There is
**no discussion today**, or any Monday. We are only holding discussions on Wednesday afternoons:- Section A: Wednesday 3-3:50PM in Pepper Canyon Hall 109.
- Section B: Wednesday 4-4:50PM in Pepper Canyon Hall 109.
- Section C: Wednesday 5-5:50PM in Mandeville B-210.

- Please fill out the Welcome Survey as soon as possible; here you can request to change discussions or get assigned to a discussion if you're enrolled in Section D.

- What is code? What are Jupyter Notebooks?
- Expressions.
- Variables.
- Calling functions.
- Data types.

There will be lots of programming – follow along in the notebook by clicking the "Expressions and Data Types" link on the course website.

- Instructions for computers are written in
**programming languages**, and are referred to as**code**. - “Computer programs” are nothing more than
**recipes**: we write programs that tell the computer exactly what to do, and it does exactly that – nothing more, and nothing less.

- It's popular!

- It has a variety of use cases. Some examples:
- Web development.
- Data science and machine learning.
- Scripting and automation.

- It's (relatively) easy to dive right in! 🏊

- Often, but not in this class, code is written in a text editor and then run in a command-line interface (or both steps are done in an IDE).

**Jupyter Notebooks**allow us to write and run code within a single document. They also allow us to embed text and code.**We will be using Jupyter Notebooks throughout the quarter**.

- DataHub is a server that allows you to run Jupyter Notebooks from your web browser without having to install any software locally.

- The lecture slides you're viewing right now are also in the form of a Jupyter Notebook – we're just using an extension (called
*RISE*) to make them look like slides. - When you click a lecture DataHub link on the course website, you'll see the lecture notebook in regular notebook form.
- To view it in slides form, click the bar chart button in the toolbar.

- An
**expression**is a combination of values, operators, and functions that**evaluates**to some**value**.

- For now, let's think of Python like a calculator – it takes expressions and evaluates them.

- We will enter our expressions in
**code cells**. To run a code cell, either:**Hit**, or`shift`

+`enter`

(or`shift`

+`return`

) on your keyboard (strongly preferred)- Press the "▶ Run" button in the toolbar.

In [2]:

```
23
```

Out[2]:

23

In [3]:

```
-15 + 2.718
```

Out[3]:

-12.282

In [4]:

```
4 ** 3
```

Out[4]:

64

In [5]:

```
(2 + 3 + 4) / 3
```

Out[5]:

3.0

In [6]:

```
# Only one value is displayed. Why?
9 + 10
13 / 4
21
```

Out[6]:

21

Operation | Operator | Example | Value |
---|---|---|---|

Addition | `+` |
`2 + 3` |
`5` |

Subtraction | `-` |
`2 - 3` |
`-1` |

Multiplication | `*` |
`2 * 3` |
`6` |

Division | `/` |
`7 / 3` |
`2.66667` |

Remainder | `%` |
`7 % 3` |
`1` |

Exponentiation | `**` |
`2 ** 0.5` |
`1.41421` |

In [7]:

```
5 * 2 ** 3
```

Out[7]:

40

In [8]:

```
(5 * 2) ** 3
```

Out[8]:

1000

In the cell below, replace the ellipses with an expression that's equivalent to

$$(19 + 6 \cdot 3) - 15 \cdot \left(\sqrt{100} \cdot \frac{1}{30}\right) \cdot \frac{3}{5} + \frac{4^2}{2^3} + \left( 6 - \frac{2}{3} \right) \cdot 12 $$Try to use parentheses only when necessary.

In [ ]:

```
```

Below, we compute the number of seconds in a year.

In [10]:

```
60 * 60 * 24 * 365
```

Out[10]:

31536000

If we want to use the above value later in our notebook to find, say, the number of seconds in 12 years, we'd have to copy-and-paste the expression. **This is inconvenient, and prone to introducing errors.**

In [11]:

```
60 * 60 * 24 * 365 * 12
```

Out[11]:

378432000

It would be great if we could **store** the initial value and refer to it later on!

- A
**variable**is a place to store a value so that it can be referred to later in our code. To define a variable, we use an**assignment statement**.

- An assignment statement changes the meaning of the
**name**to the left of the`=`

symbol.

- The expression on the right-hand side of the
`=`

symbol is evaluated before being assigned to the name on the left-hand side.- e.g.
`zebra`

is bound to`9`

(value) not`23 - 14`

(expression).

- e.g.

In [12]:

```
# Note: This is an assignment statement, not an expression.
# Assignment statements don't output anything!
a = 1
```

In [13]:

```
a = 2
```

In [14]:

```
b = 2
```

Note that before we use it in an assignment statement, `triton`

has no meaning.

In [15]:

```
triton
```

After using it in an assignment statement, we can ask Python for its value.

In [16]:

```
triton = 15 - 5
```

In [17]:

```
triton
```

Out[17]:

10

Any time we use `triton`

in an expression, `10`

is substituted for it.

In [18]:

```
triton * -4
```

Out[18]:

-40

Note that the above expression **did not change** the value of `triton`

, because **we did not re-assign triton**!

In [19]:

```
triton
```

Out[19]:

10

- Give your variables helpful names so that you know what they refer to.
- Variable names can contain uppercase and lowercase characters, the digits 0-9, and underscores.
- They cannot start with a number.
- They are case sensitive!

The following assignment statements are **valid**, but use **poor** variable names 😕.

In [20]:

```
six = 15
```

In [21]:

```
i_45love_chocolate_9999 = 60 * 60 * 24 * 365
```

The following assignment statements are **valid**, and use **good** variable names ✅.

In [22]:

```
seconds_per_hour = 60 * 60
hours_per_year = 24 * 365
seconds_per_year = seconds_per_hour * hours_per_year
```

The following "assignment statements" are **invalid ❌**.

In [23]:

```
7_days = 24 * 7
```

In [24]:

```
3 = 2 + 1
```

- Unlike in math, where $x = 3$ means the same thing as $3 = x$, assignment statements are
**not**"symmetric". - An assignment statement assigns (or "binds") the name on the left of
`=`

to the value to the right of`=`

, nothing more.

In [25]:

```
x = 3
```

In [26]:

```
3 = x
```

In [27]:

```
uc = 2
sd = 3 + uc
```

Assignment statements are **not promises** – the value of a variable can change!

In [28]:

```
uc = 7
```

Note that even after changing `uc`

, we did not change `sd`

, so it is still the same as before.

In [29]:

```
sd
```

Out[29]:

5

Assume you have run the following three lines of code:

```
side_length = 5
area = side_length ** 2
side_length = side_length + 2
```

What are the values of `side_length`

and `area`

after execution?

A. `side_length = 5`

, `area = 25`

B. `side_length = 5`

, `area = 49`

C. `side_length = 7`

, `area = 25`

D. `side_length = 7`

, `area = 49`

E. None of the above

`tab`

to autocomplete a set name¶In [ ]:

```
```

- In math, functions take in some input and return some output.

- We can determine the output of a function even if we pass in complicated-looking inputs.

- Functions in Python work the same way functions in math do.
- The inputs to functions are called
**arguments**. - Python comes with a number of built-in functions that we are free to use.
**Calling**a function, or using a function, means asking the function to "run its recipe" on the given input.

In [31]:

```
abs(-23)
```

Out[31]:

23

In [32]:

```
max(4, -8)
```

Out[32]:

4

In [33]:

```
max(2, -3, -6, 10, -4)
```

Out[33]:

10

In [34]:

```
max(9)
```

In [35]:

```
# Only two arguments!
max(9 + 10, 9 - 10)
```

Out[35]:

19

`?`

after a function's name to see its documentation 📄¶Or use the `help`

function, e.g. `help(round)`

.

In [36]:

```
round(1.45678)
```

Out[36]:

1

In [37]:

```
round?
```

In [38]:

```
round(1.45678, 3)
```

Out[38]:

1.457

We can **nest** many function calls to evaluate sophisticated expressions.

In [39]:

```
min(abs(max(-1, -2, -3, min(4, -2))), max(5, 100))
```

Out[39]:

1

...how did that work?

In [40]:

```
show_nested_eval()
```

Out[40]:

- Python doesn't have everything we need built in.
- In order to gain additional functionality, we import
**modules**through**import statements**. **Modules**are collections of Python functions and values.- Call these functions using the syntax
`module.function()`

, called "dot notation".

`import math`

¶Some of the many functions built into the `math`

module are `sqrt`

, `pow`

, and `log`

.

In [41]:

```
import math
```

In [42]:

```
math.sqrt(16)
```

Out[42]:

4.0

In [43]:

```
math.pow(2, 5)
```

Out[43]:

32.0

In [44]:

```
# What base is log?
math.log?
```

In [ ]:

```
# Tab completion for browsing.
math.
```

`math`

also has constants built in!

In [46]:

```
math.pi
```

Out[46]:

3.141592653589793

Assume you have run the following statements:

```
x = 3
y = -2
```

Which of these examples results in an error?

A. `abs(x, y)`

B. `math.pow(x, abs(y))`

C. `round(x, max(abs(y ** 2)))`

D. `math.pow(x, math.pow(y, x))`

E. More than one of the above

In [47]:

```
4 / 2
```

Out[47]:

2.0

In [48]:

```
5 - 3
```

Out[48]:

2

To us, `2.0`

and `2`

are the same number, $2$. But to Python, these appear to be different!

- Every value in Python has a
**type**.- Use the
`type`

function to check a value's type.

- Use the
- It's important to understand how different types work with different operations, as the results may not always be what we expect.

`int`

and `float`

¶`int`

: An integer of any size.`float`

: A number with a decimal point.

`int`

¶- If you add (
`+`

), subtract (`-`

), multiply (`*`

), or exponentiate (`**`

)`int`

s, the result will be another`int`

. `int`

s have arbitrary precision in Python, meaning that your calculations will always be exact.

In [49]:

```
7 - 15
```

Out[49]:

-8

In [50]:

```
type(7 - 15)
```

Out[50]:

int

In [51]:

```
2 ** 300
```

Out[51]:

2037035976334486086268445688409378161051468393665936250636140449354381299763336706183397376

In [52]:

```
2 ** 3000
```

Out[52]:

1230231922161117176931558813276752514640713895736833715766118029160058800614672948775360067838593459582429649254051804908512884180898236823585082482065348331234959350355845017413023320111360666922624728239756880416434478315693675013413090757208690376793296658810662941824493488451726505303712916005346747908623702673480919353936813105736620402352744776903840477883651100322409301983488363802930540482487909763484098253940728685132044408863734754271212592471778643949486688511721051561970432780747454823776808464180697103083861812184348565522740195796682622205511845512080552010310050255801589349645928001133745474220715013683413907542779063759833876101354235184245096670042160720629411581502371248008430447184842098610320580417992206662247328722122088513643683907670360209162653670641130936997002170500675501374723998766005827579300723253474890612250135171889174899079911291512399773872178519018229989376

`float`

¶- A
`float`

is specified using a**decimal**point. - A
`float`

might be printed using scientific notation.

In [53]:

```
3.2 + 2.5
```

Out[53]:

5.7

In [54]:

```
type(3.2 + 2.5)
```

Out[54]:

float

In [55]:

```
# The result is in scientific notation: e+90 means "times 10^90".
2.0 ** 300
```

Out[55]:

2.037035976334486e+90

`float`

¶`floats`

have limited precision; after arithmetic, the final few decimal places can be wrong in unexpected ways.`float`

s have limited size, though the limit is huge.

In [56]:

```
1 + 0.2
```

Out[56]:

1.2

In [57]:

```
1 + 0.1 + 0.1
```

Out[57]:

1.2000000000000002

In [58]:

```
2.0 ** 3000
```

`int`

and `float`

¶- If you mix
`int`

s and`float`

s in an expression, the result will always be a`float`

.- Note that when you divide two
`int`

s, you get a`float`

back.

- Note that when you divide two
- A value can be explicity
**coerced**(i.e. converted) using the`int`

and`float`

functions.

In [59]:

```
2.0 + 3
```

Out[59]:

5.0

In [60]:

```
12 / 2
```

Out[60]:

6.0

In [61]:

```
# Want an integer back.
int(12 / 2)
```

Out[61]:

6

In [62]:

```
# int chops off the decimal point!
int(-2.9)
```

Out[62]:

-2

Our notebook **still** remembers all of the variables we defined earlier in the lecture.

In [63]:

```
triton
```

Out[63]:

10

- However, if you come back to your notebook after a few hours, it will usually "forget" all of the variables it once knew about.
- When this happens, you will need to run the cells in your notebook again.
- See Navigating DataHub and Jupyter Notebooks for more.

- Expressions evaluate to values. Python will display the value of the last expression in a cell by default.
- Python knows about all of the standard mathematical operators and follows PEMDAS.
- Assignment statements allow us to bind values to variables.
- We can call functions in Python similar to how we call functions in math.
- Python knows some functions by default, and import statements allow us to bring additional functionality from modules.

- All values in Python have a data type.
`int`

s and`float`

s are numbers.`int`

s are integers, while`float`

s contain decimal points.

- We'll learn about strings, a data type in Python designed to store text.
- We'll also learn how to store sequences, or many pieces of information, in a single variable.

**Note**: We will introduce some code in labs and homeworks as well. Not everything will be in lecture. **You will learn by doing!**