lec04.ipynb

Kernel: Python 3 (ipykernel)

Lecture 4 – Strings and Arrays

DSC 10, Fall 2022

Announcements

• Lab 1 is released and is due Saturday at 11:59PM.

• Homework 1 is released and is due Tuesday at 11:59PM.

  • Finish the lab before you work on the homework!

• Issues with DataHub? See here. Issues with Gradescope? See here. Other issues? Post on EdStem.

Agenda

• Strings.

• Lists.

• Arrays.

• Ranges.

Resources

• We're covering a lot of content very quickly. If you're overwhelmed, just know that we're here to support you!

  • Office hours and EdStem are your friends 🤝.

• Remember to check the Resources tab of the course website for programming resources.

Strings

Strings

• A string is a snippet of text of any length.

• In Python, strings are enclosed by either single quotes or double quotes.

'woof'

type('woof')

"woof"

# A string, not an int!
"1998"

String arithmetic

When using the + symbol between two strings, the operation is called "concatenation".

s1 = 'baby'
s2 = '🐼'

s1 + s2

s1 + ' ' + s2

s2 * 3

String methods

• Strings are associated with certain functions called string methods.

• Access string methods with a . after the string (dot notation).

  • For instance, to use the upper method on string s, we write s.upper().

• Examples include upper, title, and replace.

my_cool_string = 'data science is super cool!'

my_cool_string.title()

my_cool_string.upper()

my_cool_string.replace('super cool', '💯' * 3)

# len is not a method, since it doesn't use dot notation
len(my_cool_string)

Special characters in strings

Single quotes and double quotes are usually interchangeable, except when the string itself contains a single or double quote.

'my string's full of apostrophes!'

"my string's full of apostrophes!"

# escape the apostrophe with a backslash!
'my string\'s "full" of apostrophes!'

print('my string\'s "full" of apostrophes!')

Aside: print

• By default Jupyter notebooks display the "raw" value of the expression of the last line in a cell.

• The print function displays the value in human readable text when it's evaluated.

12 # 12 won't be displayed, since Python only shows the value of the last expression
23

# Note, there is no Out[number] to the left! That only appears when displaying a non-printed value.
# But both 12 and 23 are displayed.
print(12)
print(23)

# '\n' inserts a new line
my_newline_str = 'here is a string with two lines.\nhere is the second line'  
my_newline_str

# The quotes disappeared and the newline is rendered!
print(my_newline_str)  

Type conversion to and from strings

• Any value can be converted to a string using str.

• Some strings can be converted to int and float.

str(3)

float('3')

int('4')

int('baby panda')

Concept Check ✅ – Answer at cc.dsc10.com

Assume you have run the following statements:

x = 3
y = '4'
z = '5.6'

Choose the expression that will be evaluated without an error.

A. x + y

B. x + int(y + z)

C. str(x) + int(y)

D. str(x) + z

E. All of them have errors

Lists

Motivation

How would we store the temperatures for each of the first 6 days in the month of September?

Our best solution right now is to create a separate variable for each day.

temperature_on_sept_01 = 84
temperature_on_sept_02 = 78
temperature_on_sept_03 = 81
temperature_on_sept_04 = 75
temperature_on_sept_05 = 79
temperature_on_sept_06 = 75

This technically allows us to do things like compute the average temperature through the first 6 days:

avg_temperature = 1/6 * (
    temperature_on_sept_01
    + temperature_on_sept_02
    + temperature_on_sept_03
    + ...)

Imagine a whole month's data, or a whole year's data. It seems like we need a better solution.

Lists in Python

In Python, a list is used to store multiple values in a single value/variable. To create a new list from scratch, we use [square brackets].

temperature_list = [84, 78, 81, 75, 79, 75]

len(temperature_list)

Notice that the elements in a list don't need to be unique!

Lists make working with sequences easy!

To find the average temperature, we just need to divide the sum of the temperatures by the number of temperatures recorded:

temperature_list

sum(temperature_list) / len(temperature_list)

Types

The type of a list is... list.

temperature_list

type(temperature_list)

Within a list, you can store elements of different types.

mixed_list = [-2, 2.5, 'ucsd', [1, 3]]
mixed_list

There's a problem...

• Lists are very slow.

• This is not a big deal when there aren't many entries, but it's a big problem when there are millions or billions of entries.

Arrays

NumPy

• NumPy (pronounced "num pie") is a Python library (module) that provides support for arrays and operations on them.

• The babypandas library, which you will learn about next week, goes hand-in-hand with NumPy.

  • NumPy is used heavily in the real world.

• To use numpy, we need to import it. It's usually imported as np (but doesn't have to be!)

import numpy as np

Arrays

Think of NumPy arrays (just "arrays" from now on) as fancy, faster lists.

To create an array, we pass a list as input to the np.array function.

np.array([4, 9, 1, 2])

temperature_array = np.array([84, 78, 81, 75, 79, 75])
temperature_array

temperature_list

# No square brackets, because temperature_list is already a list!
np.array(temperature_list)

Positions

When people stand in a line, each person has a position.

Similarly, each element of an array (and list) has a position.

Accessing elements by position

• Python, like most programming languages, is "0-indexed."

  • This means that the position of the first element in an array is 0, not 1.

  • One reason: an element's position represents the number of elements in front of it.

• To access the element in array arr_name at position pos, we use the syntax arr_name[pos].

temperature_array

temperature_array[0]

temperature_array[1]

temperature_array[3]

# Access last element
temperature_array[5]

temperature_array[6]

# If a position is negative, count from the end!
temperature_array[-1]

Types

Earlier in the lecture, we saw that lists can store elements of multiple types.

nums_and_strings_lst = ['uc', 'sd', 1961, 3.14]
nums_and_strings_lst

This is not true of arrays – all elements in an array must be of the same type.

# All elements are converted to strings!
np.array(nums_and_strings_lst)

Array-number arithmetic

Arrays make it easy to perform the same operation to every element. This behavior is formally known as "broadcasting".

temperature_array

# Increase all temperatures by 3 degrees
temperature_array + 3

# Halve all temperatures
temperature_array / 2

# Convert all temperatures to Celsius
(5 / 9) * (temperature_array - 32)

Note: In none of the above cells did we actually modify temperature_array! Each of those expressions created a new array.

temperature_array

To actually change temperature_array, we need to reassign it to a new array.

temperature_array = (5 / 9) * (temperature_array - 32)

# Now in Celsius!
temperature_array

Element-wise arithmetic

• We can apply arithmetic operations to multiple arrays, provided they have the same length.

• The result is computed element-wise, which means that the arithmetic operation is applied to one pair of elements from each array at a time.

• For example, a + b is an array whose first element is the sum of the first element of a and first element of b.

a = np.array([1, 2, 3])
b = np.array([-4, 5, 9])

a + b

a / b

a ** 2 + b ** 2

Example: TikTok views 🎬

Baby Panda made a series five TikTok videos called "A Day In the Life of a Data Science Mascot". The number of views they've received on these videos are stored in the array views below.

views = np.array([158, 352, 195, 1423916, 46])

Some questions:

What was their average view count?

views

sum(views) / len(views)

# The mean method exists for arrays (but not for lists)
views.mean()

How many views did their most and least popular videos receive?

views

views.max()

views.min()

How many views above average did each of their videos receive? How many views above average did their most viewed video receive?

views

views - views.mean()

(views - views.mean()).max()

It has been estimated that TikTok pays their creators $0.03 per 1000 views. If this is true, how many dollars did Baby Panda earn on their most viewed video?

views

views.max() * 0.03 / 1000

Ranges

Motivation

We often find ourselves needing to make arrays like this:

days_in_september = np.array([
    1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 
    13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 
    23, 24, 25, 26, 27, 28, 29, 30
])

There needs to be an easier way to do this!

Ranges

• A range is an array of evenly spaced numbers. We create ranges using np.arange.

• The most general way to create a range is np.arange(start, end, step). This returns an array such that:

  • The first number is start. By default, start is 0.

  • All subsequent numbers are spaced out by step, until (but excluding) end. By default, step is 1.

# Start at 0, end before 8, step by 1
# This will be our most common use-case!
np.arange(8)

# Start at 5, end before 10, step by 1
np.arange(5, 10)

# Start at 3, end before 32, step by 5
np.arange(3, 32, 5)

# Steps can be fractional!
np.arange(-3, 2, 0.5)

# If step is negative, we count backwards.
np.arange(1, -10, -3)

Activity

🎉 Congrats! 🎉 You won the lottery 💰. Here's how your payout works: on the first day of September, you are paid $0.01. Every day thereafter, your pay doubles, so on the second day you're paid $0.02, on the third day you're paid $0.04, on the fourth day you're paid $0.08, and so on.

September has 30 days.

Write a one-line expression that uses the numbers 2 and 30, along with the function np.arange and the method .sum(), that computes the total amount in dollars you will be paid in September.

...

Summary, next time

Summary

• Strings are used to store text. Enclose them in single or double quotes.

• Lists and arrays are used to store sequences.

  • Arrays are faster and more convenient for numerical operations.

  • You can easily perform numerical operations on all elements of an array and perform operations on multiple arrays.

• Ranges are arrays of equally-spaced numbers.

• Remember to refer to the resources from the start of lecture!

Next time

We'll learn about how to use Python to work with real-world tabular data.