In [1]:
# Set up packages for lecture. Don't worry about understanding this code, but
# make sure to run it if you're following along.
import numpy as np
import babypandas as bpd
import pandas as pd
from matplotlib_inline.backend_inline import set_matplotlib_formats
import matplotlib.pyplot as plt
set_matplotlib_formats("svg")
plt.style.use('ggplot')

np.set_printoptions(threshold=20, precision=2, suppress=True)
pd.set_option("display.max_rows", 7)
pd.set_option("display.max_columns", 8)
pd.set_option("display.precision", 2)

Lecture 10 – Conditional Statements and Iteration¶

DSC 10, Winter 2023¶

Announcements¶

  • Lab 3 is due Saturday 2/4 at 11:59PM.
  • Homework 3 is due Tuesday 2/7 at 11:59PM.
  • The Midterm Project (Restaurants 🍔🍟) is released and due Tuesday 2/14 at 11:59PM.
    • Working with a partner is recommended but not required.
    • Partners must follow these partner guidelines. In particular, you must both contribute to all parts of the project and not split up the problems.
    • Your partner can be from any lecture section.
    • Course tutors will host a mixer today from 1:30 to 2:15PM in the outdoor courtyard behind Center Hall. Come before or after discussion section to connect with other students looking for a project partner. Here's a menu of questions to start the conversation.

Agenda¶

  • Booleans.
  • Conditional statements (i.e. if-statements).
  • Iteration (i.e. for-loops).

Note:

  • We've finished introducing new DataFrame manipulation techniques.
  • Today we'll cover some foundational programming tools, which will be very relevant as we start to cover more ideas in statistics (next week).

Booleans¶

Recap: Booleans¶

  • bool is a data type in Python, just like int, float, and str.
    • It stands for "Boolean", named after George Boole, an early mathematician.
  • There are only two possible Boolean values: True or False.
    • Yes or no.
    • On or off.
    • 1 or 0.
  • Comparisons result in Boolean values.
In [2]:
capstone = 'finished'
units = 123
In [3]:
units >= 180
Out[3]:
False
In [4]:
type(units >= 180)
Out[4]:
bool

Boolean operators; not¶

There are three operators that allow us to perform arithmetic with Booleans – not, and, and or.

not flips True ↔️ False.

In [5]:
capstone
Out[5]:
'finished'
In [6]:
capstone == 'finished'
Out[6]:
True
In [7]:
not capstone == 'finished'
Out[7]:
False

The and operator¶

The and operator is placed between two bools. It is True if both are True; otherwise, it's False.

In [8]:
capstone
Out[8]:
'finished'
In [9]:
units
Out[9]:
123
In [10]:
capstone == 'finished' and units >= 180
Out[10]:
False
In [11]:
capstone == 'finished' and units >= 120
Out[11]:
True

The or operator¶

The or operator is placed between two bools. It is True if at least one is True; otherwise, it's False.

In [12]:
capstone
Out[12]:
'finished'
In [13]:
units
Out[13]:
123
In [14]:
capstone == 'finished' or units >= 180
Out[14]:
True
In [15]:
# Both are True!
capstone == 'finished' or units >= 0
Out[15]:
True
In [16]:
# Both are False!
capstone == 'not started' or units >= 180
Out[16]:
False

Order of operations¶

  • By default, the order of operations is not, and, or. See the precedence of all operators in Python here.
  • As usual, use (parentheses) to make expressions more clear.
In [17]:
capstone
Out[17]:
'finished'
In [18]:
units
Out[18]:
123
In [19]:
capstone == 'finished' or (capstone == 'in progress' and units >= 180)
Out[19]:
True
In [20]:
# Different meaning!
(capstone == 'finished' or capstone == 'in progress') and units >= 180
Out[20]:
False
In [21]:
# "and" has precedence.
capstone == 'finished' or capstone == 'in progress' and units >= 180
Out[21]:
True

Booleans can be tricky!¶

For instance, not (a and b) is different than not a and not b! If you're curious, read more about De Morgan's Laws.

In [22]:
capstone
Out[22]:
'finished'
In [23]:
units
Out[23]:
123
In [24]:
not (capstone == 'finished' and units >= 180)
Out[24]:
True
In [25]:
(not capstone == 'finished') and (not units >= 180)
Out[25]:
False

Note: & and | vs. and and or¶

  • Use the & and | operators between two Series. Arithmetic will be done element-wise (separately for each row).
    • This is relevant when writing DataFrame queries, e.g. df[(df.get('capstone') == 'finished') & (df.get('units') >= 180)].
  • Use the and and or operators between two individual Booleans.
    • e.g. capstone == 'finished' and units >= 180.

Concept Check ✅ – Answer at cc.dsc10.com¶

Suppose we define a = True and b = True. What does the following expression evaluate to?

not (((not a) and b) or ((not b) or a))

A. True

B. False

C. Could be either one

Aside: the in operator¶

Sometimes, we'll want to check if a particular element is in a list/array, or a particular substring is in a string. The in operator can do this for us:

In [26]:
3 in [1, 2, 3]
Out[26]:
True
In [27]:
'hey' in 'hey my name is'
Out[27]:
True
In [28]:
'dog' in 'hey my name is'
Out[28]:
False

Conditionals¶

if-statements¶

  • Often, we'll want to run a block of code only if a particular conditional expression is True.
  • The syntax for this is as follows (don't forget the colon!):
if <condition>:
    <body>
  • Indentation matters!
In [29]:
capstone = 'finished'
capstone
Out[29]:
'finished'
In [30]:
if capstone == 'finished':
    print('Looks like you are ready to graduate!')

Looks like you are ready to graduate!

else¶

else: Do something else if the specified condition is False.

In [31]:
capstone = 'finished'
capstone
Out[31]:
'finished'
In [32]:
if capstone == 'finished':
    print('Looks like you are ready to graduate!')
else:
    print('Before you graduate, you need to finish your capstone project.')

Looks like you are ready to graduate!

elif¶

  • What if we want to check more than one condition? Use elif.
  • elif: if the specified condition is False, check the next condition.
  • If that condition is False, check the next condition, and so on, until we see a True condition.
    • After seeing a True condition, it evaluates the indented code and stops.
  • If none of the conditions are True, the else body is run.
In [33]:
capstone = 'in progress'
units = 123
In [34]:
if capstone == 'finished' and units >= 180:
    print('Looks like you are ready to graduate!')
elif capstone != 'finished' and units < 180:
    print('Before you graduate, you need to finish your capstone project and take', 180 - units, 'more units.')
elif units >= 180:
    print('Before you graduate, you need to finish your capstone project.')
else:
    print('Before you graduate, you need to take', 180 - units, 'more units.')

Before you graduate, you need to finish your capstone project and take 57 more units.

What if we use if instead of elif?

In [35]:
if capstone == 'finished' and units >= 180:
    print('Looks like you are ready to graduate!')
if capstone != 'finished' and units < 180:
    print('Before you graduate, you need to finish your capstone project and take', 180 - units, 'more units.')
if units >= 180:
    print('Before you graduate, you need to finish your capstone project.')
else:
    print('Before you graduate, you need to take', 180 - units, 'more units.')

Before you graduate, you need to finish your capstone project and take 57 more units.
Before you graduate, you need to take 57 more units.

Example: Percentage to letter grade¶

Below, complete the implementation of the function, grade_converter, which takes in a percentage grade (grade) and returns the corresponding letter grade, according to this table:

Letter Range
A [90, 100]
B [80, 90)
C [70, 80)
D [60, 70)
F [0, 60)

Your function should work on these examples:

>>> grade_converter(84)
'B'

>>> grade_converter(60)
'D'
In [36]:
def grade_converter(grade):
    ...
In [37]:
grade_converter(84)
In [38]:
grade_converter(60)

Activity¶

def mystery(a, b):
    if (a + b > 4) and (b > 0):
        return 'bear'
    elif (a * b >= 4) or (b < 0):
        return 'triton'
    else:
        return 'bruin'

Without running code:

  1. What does mystery(2, 2) return?
  2. Find inputs so that calling mystery will produce 'bruin'.
In [39]:
def mystery(a, b):
    if (a + b > 4) and (b > 0):
        return 'bear'
    elif (a * b >= 4) or (b < 0):
        return 'triton'
    else:
        return 'bruin'

Iteration¶

for-loops¶

In [40]:
import time

print('Launching in...')

for x in [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]:
    print('t-minus', x)
    time.sleep(0.5) # Pauses for half a second
    
print('Blast off! 🚀')

Launching in...
t-minus 10
t-minus 9
t-minus 8
t-minus 7
t-minus 6
t-minus 5
t-minus 4
t-minus 3
t-minus 2
t-minus 1
Blast off! 🚀

for-loops¶

  • Loops allow us to repeat the execution of code. There are two types of loops in Python; the for-loop is one of them.
  • The syntax of a for-loop is as follows:
    for <element> in <sequence>:
  <for body>
  • Read this as: "for each element of this sequence, repeat this code."
    • Note: lists, arrays, and strings are all examples of sequences.
  • Like with if-statements, indentation matters!

Example: Squares¶

In [41]:
num = 4
print(num, 'squared is', num ** 2)

num = 2
print(num, 'squared is', num ** 2)

num = 1
print(num, 'squared is', num ** 2)

num = 3
print(num, 'squared is', num ** 2)

4 squared is 16
2 squared is 4
1 squared is 1
3 squared is 9
In [42]:
# The loop variable can be anything!

list_of_numbers = [4, 2, 1, 3]

for num in list_of_numbers:
    print(num, 'squared is', num ** 2)

4 squared is 16
2 squared is 4
1 squared is 1
3 squared is 9

The line print(num, 'squared is', num ** 2) is run four times:

  • On the first iteration, num is 4.
  • On the second iteration, num is 2.
  • On the third iteration, num is 1.
  • On the fourth iteration, num is 3.

This happens, even though there is no num = anywhere.

Activity¶

Using the array colleges, write a for-loop that prints:

Revelle College
John Muir College
Thurgood Marshall College
Earl Warren College
Eleanor Roosevelt College
Sixth College
Seventh College


Click here to see the solution after you've tried it yourself. 
for college in colleges:
    print(college + ' College')
In [43]:
colleges = np.array(['Revelle', 'John Muir', 'Thurgood Marshall', 
            'Earl Warren', 'Eleanor Roosevelt', 'Sixth', 'Seventh'])
In [44]:
...
Out[44]:
Ellipsis

Ranges¶

  • Recall, each element of a list/array has a numerical position.
    • The position of the first element is 0, the position of the second element is 1, etc.
  • We can write a for-loop that accesses each element in an array by using its position.
  • np.arange will come in handy.
In [45]:
actions = np.array(['ate', 'slept', 'exercised'])
feelings = np.array(['content 🙂', 'energized 😃', 'exhausted 😩'])
In [46]:
len(actions)
Out[46]:
3
In [47]:
for i in np.arange(len(actions)):
    print(i)

0
1
2
In [48]:
for i in np.arange(len(actions)):
    print('I', actions[i], 'and I felt', feelings[i])

I ate and I felt content 🙂
I slept and I felt energized 😃
I exercised and I felt exhausted 😩

Example: Goldilocks and the Three Bears¶

We don't have to use the loop variable!

In [49]:
for i in np.arange(3):
    print('🐻')
print('👧🏼')

🐻
🐻
🐻
👧🏼

Randomization and iteration¶

  • In the next few lectures, we'll learn how to simulate random events, like flipping a coin.
  • Often, we will:
    1. Run an experiment, e.g. "flip 10 coins."
    2. Keep track of some result, e.g. "number of heads."
    3. Repeat steps 1 and 2 many, many times using a for-loop.

Storing the results¶

  • To store our results, we'll typically use an int or an array.
  • If using an int, we define an int variable (usually to 0) before the loop, then use + to add to it inside the loop.
  • If using an array, we create an array (usually empty) before the loop, then use np.append to add to it inside the loop.

np.append¶

  • This function takes two inputs:
    • an array
    • an element to add on to the end of the array
  • It returns a new array. It does not modify the input array.
  • We typically use it like this to extend an array by one element: name_of_array = np.append(name_of_array, element_to_add)
  • ⚠️ Remember to store the result!
In [50]:
some_array = np.array([])
In [51]:
np.append(some_array, 'hello')
Out[51]:
array(['hello'], dtype='<U32')
In [52]:
some_array
Out[52]:
array([], dtype=float64)
In [53]:
# Need to save the new array!
some_array = np.append(some_array, 'hello')
some_array
Out[53]:
array(['hello'], dtype='<U32')
In [54]:
some_array = np.append(some_array, 'there')
some_array
Out[54]:
array(['hello', 'there'], dtype='<U32')

Example: Coin flipping¶

The function flip(n) flips n fair coins and returns the number of heads it saw. (Don't worry about how it works for now.)

In [55]:
def flip(n):
    '''Returns the number of heads in n simulated coin flips, using randomness.'''
    return np.random.multinomial(n, [0.5, 0.5])[0]
In [56]:
# Run this cell a few times – you'll see different results!
flip(10)
Out[56]:
3

Let's repeat the act of flipping 10 coins, 10000 times.

  • Each time, we'll use the flip function to flip 10 coins and compute the number of heads we saw.
  • We'll store these numbers in an array, heads_array.
  • Every time we use our flip function to flip 10 coins, we'll add an element to the end of heads_array.
In [57]:
# heads_array starts empty – before the simulation, we haven't flipped any coins!
heads_array = np.array([])

for i in np.arange(10000):
    
    # Flip 10 coins and count the number of heads.
    num_heads = flip(10)
    
    # Add the number of heads seen to heads_array.
    heads_array = np.append(heads_array, num_heads)

Now, heads_array contains 10000 numbers, each corresponding to the number of heads in 10 simulated coin flips.

In [58]:
heads_array
Out[58]:
array([3., 4., 3., ..., 7., 5., 4.])
In [59]:
len(heads_array)
Out[59]:
10000
In [60]:
(bpd.DataFrame().assign(num_heads=heads_array)
 .plot(kind='hist', density=True, bins=np.arange(0, 12), ec='w', legend=False, 
       title = 'Distribution of the number of heads in 10 coin flips')
);
2023-01-31T22:23:05.888248 image/svg+xml Matplotlib v3.5.2, https://matplotlib.org/

The accumulator pattern¶

for-loops in DSC 10¶

  • Almost every for-loop in DSC 10 will use the accumulator pattern.

    • This means we initialize a variable, and repeatedly add on to it within a loop.

  • Do not use for-loops to perform mathematical operations on every element of an array or Series.

    • Instead use DataFrame manipulations and built-in array or Series methods.

  • Helpful video 🎥: For Loops (and when not to use them) in DSC 10.

Working with strings¶

String are sequences, so we can iterate over them, too!

In [61]:
for letter in 'uc san diego':
    print(letter.upper())

U
C
 
S
A
N
 
D
I
E
G
O
In [62]:
'california'.count('a')
Out[62]:
2

Example: Vowel count¶

Below, complete the implementation of the function vowel_count, which returns the number of vowels in the input string s (including repeats). Example behavior is shown below.

>>> vowel_count('king triton')
3

>>> vowel_count('i go to uc san diego')
8
In [63]:
def vowel_count(s):
    
    # We need to keep track of the number of vowels seen so far. Before we start, we've seen zero vowels.
    number = 0
    
    # For each of the 5 vowels:
       
        # Count the number of occurrences of this vowel in s.
        
        # Add this count to the variable number.
    
    # Once we've gotten through all 5 vowels, return the answer.
In [64]:
vowel_count('king triton')
In [65]:
vowel_count('i go to uc san diego')

Summary, next time¶

Summary¶

  • if-statements allow us to run pieces of code depending on whether certain conditions are True.
  • for-loops are used to repeat the execution of code for every element of a sequence.
    • Lists, arrays, and strings are examples of sequences.

Next time¶

  • Probability.
  • A math lesson – no code!