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" frameborder="0" width="960" height="569" allowfullscreen="true" mozallowfullscreen="true" webkitallowfullscreen="true"'
width = 960
height = 569
return IFrame(src, width, height)
```

- Lab 0 is released and is due
**Saturday at 11:59PM**.- It contains a video 🎥 towards the end, Navigating DataHub and Jupyter Notebooks. Watching this video should be a worthwhile investment of your time! ⌚
- Post on EdStem or come to office hours for help!

- Discussion meets today at 1pm and 2pm.
- You must attend the discussion you've been assigned to. See this table.
- Today's discussion is about getting started with Jupyter Notebooks.

- Fill out the Beginning of Quarter Survey.

- What is code? What are Jupyter Notebooks?
- Expressions.
- Variables.
- Call expressions.
- Data types.

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.

- Popular.
- Variety of uses.
- Web development.
- Data science and machine learning.
- Not really used for developing applications.

- 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.

**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" 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]:

```
17
```

Out[2]:

17

In [3]:

```
-1 + 3.14
```

Out[3]:

2.14

In [4]:

```
2 ** 3
```

Out[4]:

8

In [5]:

```
(17 - 14) / 2
```

Out[5]:

1.5

In [6]:

```
# Only one value is displayed. Why?
3 * 4
5
```

Out[6]:

5

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]:

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

Out[7]:

12

In [8]:

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

Out[8]:

36

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 [9]:

```
...
```

Out[9]:

Ellipsis

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

In [10]:

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

Out[10]:

31536000

**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.
`myvariable`

is bound to`5`

(value) not`2 + 3`

(expression).

- e.g.

Note that before we use it in an assignment statement, `more_than_1`

has no meaning.

In [12]:

```
more_than_1
```

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

In [13]:

```
# Note that an assignment statement doesn't output anything!
more_than_1 = 15 - 5
```

In [14]:

```
more_than_1
```

Out[14]:

10

Any time we use `more_than_1`

in an expression, `10`

is substituted for it.

In [15]:

```
more_than_1 * 2
```

Out[15]:

20

**did not change** the value of `more_than_1`

, because **we did not re-assign more_than_1**!

In [16]:

```
more_than_1
```

Out[16]:

10

- Generally, 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 [17]:

```
six = 15
```

In [18]:

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

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

In [19]:

```
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 [20]:

```
6 = 15
```

In [21]:

```
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 [ ]:

```
x = 3
x
```

In [ ]:

```
4 = x
```

In [22]:

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

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

In [23]:

```
uc = 7
```

Note that even after changing `uc`

, we did not change `sd`

, so it is still the same as before.

In [24]:

```
sd
```

Out[24]:

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

In [ ]:

```
```

`tab`

to autocomplete a set name¶In [25]:

```
...
```

Out[25]:

Ellipsis

- 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 things.

**Call expressions**in Python invoke functions – they tell a function to "run its recipe".- Functions in Python work the same way functions in math do.
- The inputs to functions are called
**arguments**.

In [26]:

```
abs(-12)
```

Out[26]:

12

In [27]:

```
max(3, -4)
```

Out[27]:

3

In [28]:

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

Out[28]:

10

In [29]:

```
# Only two arguments!
max(4 + 5, 5 - 4)
```

Out[29]:

9

`?`

after a function to see the documentation for a function¶Or, use the `help`

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

.

In [30]:

```
max?
```

`round`

¶In [31]:

```
my_number = 1.22
round(my_number)
```

Out[31]:

1

In [32]:

```
round?
```

In [33]:

```
round(1.22222, 3)
```

Out[33]:

1.222

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

In [34]:

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

Out[34]:

1

...how did that work?

In [35]:

```
show_nested_eval()
```

Out[35]:

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

, called "dot notation".

`import math`

¶`sqrt`

, `log`

, `pow`

, etc.

In [36]:

```
import math
```

In [37]:

```
math.sqrt(9)
```

Out[37]:

3.0

In [38]:

```
math.pow(3, 2)
```

Out[38]:

9.0

In [39]:

```
# What base is log?
math.log?http://localhost:8888/notebooks/Documents/GitHub/dsc10-2023-wi-private/lectures/lec02/lec02.ipynb#
```

In [40]:

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

It also has constants built-in!

In [41]:

```
math.pi
```

Out[41]:

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 [ ]:

```
```

In [42]:

```
4 / 2
```

Out[42]:

2.0

In [43]:

```
5 - 3
```

Out[43]:

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 use these operations between
`int`

s (`+`

,`-`

,`*`

,`**`

), the result will be another`int`

. `int`

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

In [44]:

```
3 + 5
```

Out[44]:

8

In [45]:

```
type(3 + 5)
```

Out[45]:

int

In [46]:

```
2 ** 300
```

Out[46]:

2037035976334486086268445688409378161051468393665936250636140449354381299763336706183397376

In [47]:

```
2 ** 3000
```

Out[47]:

1230231922161117176931558813276752514640713895736833715766118029160058800614672948775360067838593459582429649254051804908512884180898236823585082482065348331234959350355845017413023320111360666922624728239756880416434478315693675013413090757208690376793296658810662941824493488451726505303712916005346747908623702673480919353936813105736620402352744776903840477883651100322409301983488363802930540482487909763484098253940728685132044408863734754271212592471778643949486688511721051561970432780747454823776808464180697103083861812184348565522740195796682622205511845512080552010310050255801589349645928001133745474220715013683413907542779063759833876101354235184245096670042160720629411581502371248008430447184842098610320580417992206662247328722122088513643683907670360209162653670641130936997002170500675501374723998766005827579300723253474890612250135171889174899079911291512399773872178519018229989376

`float`

¶- A
`float`

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

might be printed using scientific notation.

In [48]:

```
2.0 + 3.2
```

Out[48]:

5.2

In [49]:

```
type(2.0 + 3.2)
```

Out[49]:

float

In [50]:

```
2.0 ** 300
```

Out[50]:

2.037035976334486e+90

`float`

¶`float`

s have limited size (but the limit is huge).`float`

s have limited precision of 15-16 decimal places.- After arithmetic, the final few decimal places can be wrong in unexpected ways (limited precision!).

In [51]:

```
1 + 0.2
```

Out[51]:

1.2

In [52]:

```
1 + 0.1 + 0.1
```

Out[52]:

1.2000000000000002

In [53]:

```
2.0 ** 3000
```

`int`

and `float`

¶- By default, Python changes an
`int`

to a`float`

in a mixed expression involving both types.- Note that the division of two
`int`

s automatically returns a`float`

value.

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

and`float`

functions.

In [54]:

```
2.0 + 3
```

Out[54]:

5.0

In [55]:

```
2 / 1
```

Out[55]:

2.0

In [56]:

```
# Want an integer back
int(2 / 1)
```

Out[56]:

2

In [57]:

```
# int chops off the decimal point, effectively rounding DOWN
int(3.9)
```

Out[57]:

3

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

In [58]:

```
more_than_1
```

Out[58]:

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!**