hw02

hw02 November 30, 2023 [2]: # Initialize Otter import otter grader = otter . Notebook( "hw02.ipynb" ) 1 Homework 2: Arrays and Tables Please complete this notebook by filling in the cells provided. Before you begin, execute the previous cell to load the provided tests. [3]: # Run this cell and ignore the output. It is checking to see if you ran the ␣ ↪ first cell! # If you get a NameError, please run the cell at the very TOP of this notebook! grader [3]: <otter.check.notebook.Notebook at 0x7f1a7c6cdd90> Helpful Resource: - Python Reference : Cheat sheet of helpful array & table methods used in Data 8! Recommended Readings: - Arrays - Data 8 Arrays Guide - What is Data Science? - Causality and Experiments - Programming in Python For all problems that you must write explanations and sentences for, you must provide your answer in the designated space. Moreover, throughout this homework and all future ones, please be sure to not re-assign variables throughout the notebook! For example, if you use max_temperature in your answer to one question, do not reassign it later on. Otherwise, you will fail tests that you thought you were passing previously! Deadline: This assignment is due Wednesday, 9/6 at 11:00pm PT . Turn it in by Tuesday, 9/5 at 11:00pm PT for 5 extra credit points. Late work will not be accepted as per the policies page. Note: This homework has hidden tests on it. That means even though tests may say 100% passed, it doesn’t mean your final grade will be 100%. We will be running more tests for correctness once everyone turns in the homework. Directly sharing answers is not okay, but discussing problems with the course staff or with other students is encouraged. Refer to the policies page to learn more about how to learn cooperatively. 1

You should start early so that you have time to get help if you’re stuck. Offce hours are held Monday through Friday in Warren Hall 101B. The offce hours schedule appears on http://data8.org/ fa23/officehours/ . 1.1 1. Creating Arrays [4]: # Run this cell to set up the notebook, but please don't change it. import numpy as np from datascience import * import warnings warnings . simplefilter( 'ignore' , FutureWarning ) Question 1. Make an array called weird_numbers containing the following numbers (in the given order) (4 Points) : 1. -2 2. the floor of 17.6 3. 3 4. 5 to the power of the ceil of 5.3 Hint: floor and ceil are functions in the math module. Importing modules is covered in Lab 2! Note: Python lists are different/behave differently than NumPy arrays. In Data 8, we use NumPy arrays, so please make an array , not a Python list. [5]: # Our solution involved one extra line of code before creating weird_numbers. import math weird_numbers = np . array([ -2 , 17.6 , 3 , 3** math . ceil( 5.3 )]) weird_numbers [5]: array([ -2. , 17.6, 3. , 729. ]) [6]: grader . check( "q1_1" ) [6]: q1_1 results: All test cases passed! Question 2. Make an array called book_title_words containing the following three strings: “Eats”, “Shoots”, and “and Leaves”. (4 Points) [7]: book_title_words = np . array([ "Eats" , "Shoots" , "and Leaves" ]) book_title_words [7]: array(['Eats', 'Shoots', 'and Leaves'], dtype='<U10') [8]: grader . check( "q1_2" ) [8]: q1_2 results: All test cases passed! 2

[12]: grader . check( "q2_1" ) [12]: q2_1 results: All test cases passed! Question 2. The next cell creates a table that displays some information about the elements of some_numbers and their order. Run the cell to see the partially-completed table, then fill in the missing information (the cells that say “Ellipsis”) by assigning blank_a , blank_b , blank_c , and blank_d to the correct elements in the table. (4 Points) Hint: Replace the ... with strings or numbers. As a reminder, indices should be integers . [13]: blank_a = "third" blank_b = "fourth" blank_c = 0 blank_d = 3 elements_of_some_numbers = Table() . with_columns( "English name for position" , make_array( "first" , "second" , blank_a, ␣ ↪ blank_b, "fifth" ), "Index" , make_array(blank_c, 1 , 2 , blank_d, 4 ), "Element" , some_numbers) elements_of_some_numbers [13]: English name for position | Index | Element first | 0 | -1 second | 1 | -3 third | 2 | -6 fourth | 3 | -10 fifth | 4 | -15 [14]: grader . check( "q2_2" ) [14]: q2_2 results: All test cases passed! Question 3. You’ll sometimes want to find the last element of an array. Suppose an array has 142 elements. What is the index of its last element? (4 Points) Note: Your answer must be a positive number. [15]: index_of_last_element = 141 [16]: grader . check( "q2_3" ) [16]: q2_3 results: All test cases passed! More often, you don’t know the number of elements in an array, its length . (For example, it might be a large dataset you found on the Internet.) The function len takes a single argument, an array, and returns an integer that represents the len gth of that array. Question 4. The cell below loads an array called president_birth_years . Calling tbl.column(...) on a table returns an array of the column specified, in this case the Birth Year 4

column of the president_births table. The last element in that array is the most recent among the birth years of all the deceased Presidents. Assign that year to most_recent_birth_year . (4 Points) Note: Avoid Googling the answer. You should be able to answer this question only using table methods. [23]: president_birth_years = Table . read_table( "president_births.csv" ) . column( 'Birth ␣ ↪ Year' ) most_recent_birth_year = president_birth_years . item( len (president_birth_years) ␣ ↪ - 1 ) most_recent_birth_year [23]: 1917 [24]: grader . check( "q2_4" ) [24]: q2_4 results: All test cases passed! Question 5. Finally, assign min_of_birth_years to the minimum of the first, sixteenth, and last birth years listed in president_birth_years . (4 Points) Note: Use the Python min function and table methods to find the answer. Avoid manually calculating the result yourself! [25]: min_of_birth_years = min (president_birth_years . item( 1 ), president_birth_years . ↪ item( 16 ), president_birth_years . item( -1 ),) min_of_birth_years [25]: 1735 [26]: grader . check( "q2_5" ) [26]: q2_5 results: All test cases passed! 1.3 3. Basic Array Arithmetic Question 1. Multiply the numbers 42, -4224, 424224242, and 250 by 157. Assign each variable below such that first_product is assigned to the result of 42 ∗ 157 , second_product is assigned to the result of −4224 ∗ 157 , and so on. (4 Points) Note : For this question, don’t use arrays. [27]: nx = 157 first_product = 42* nx second_product = -4224* nx third_product = 424224242* nx fourth_product = 250* nx print ( "First Product:" , first_product) 5

[38]: max_temperatures = Table . read_table( "temperatures.csv" ) . column( "Daily Max ␣ ↪ Temperature" ) max_temperatures_celsius = ( 5/9 ) * (max_temperatures - 32 ) celsius_temps_rounded = np . round(max_temperatures_celsius) celsius_temps_rounded [38]: array([-4., 31., 32., …, 17., 23., 16.]) [39]: grader . check( "q3_4" ) [39]: q3_4 results: All test cases passed! Question 5. The cell below loads all the lowest temperatures from each day (in Fahrenheit). Compute the daily temperature range for each day. That is, compute the difference between each daily maximum temperature and the corresponding daily minimum temperature. Pay attention to the units and give your answer in Celsius! Make sure NOT to round your answer for this question! (4 Points) Hint: Use min_temperatures and/or max_temperatures , and be careful with when you perform your unit conversions. Write out the mathematical computation by hand if you’re stuck! [42]: min_temperatures = Table . read_table( "temperatures.csv" ) . column( "Daily Min ␣ ↪ Temperature" ) celsius_temperature_ranges = (( 5/9 ) * (max_temperatures - 32 )) - (( 5/9 ) * ␣ ↪ (min_temperatures - 32 )) celsius_temperature_ranges [42]: array([ 6.66666667, 10. , 12.22222222, …, 17.22222222, 11.66666667, 11.11111111]) [43]: grader . check( "q3_5" ) [43]: q3_5 results: All test cases passed! 1.4 4. Old Faithful Old Faithful is a geyser in Yellowstone that erupts every 44 to 125 minutes (according to Wikipedia ). People are often told that the geyser erupts every hour , but in fact the waiting time between eruptions is more variable. Let’s take a look. Question 1. The first line below assigns waiting_times to an array of 272 consecutive waiting times between eruptions, taken from a classic 1938 dataset. Assign the names shortest , longest , and average so that the print statement is correct. (4 Points) [56]: waiting_times = Table . read_table( 'old_faithful.csv' ) . column( 'waiting' ) 7

shortest = min (waiting_times) longest = max (waiting_times) average = np . mean(waiting_times) print ( "Old Faithful erupts every" , shortest, "to" , longest, "minutes and ␣ ↪ every" , average, "minutes on average." ) Old Faithful erupts every 43 to 96 minutes and every 70.8970588235294 minutes on average. [57]: grader . check( "q4_1" ) [57]: q4_1 results: All test cases passed! Question 2. Assign biggest_decrease to the biggest decrease in waiting time between two consecutive eruptions. For example, the third eruption occurred after 74 minutes and the fourth after 62 minutes, so the decrease in waiting time was 74 - 62 = 12 minutes. (4 Points) Hint : We want to return the absolute value of the biggest decrease. Note : np.diff() calculates the difference between subsequent values in an array. For example, calling np.diff() on the array make_array(1, 8, 3, 5) evaluates to array([8 - 1, 3 - 8, 5 - 3]) , or array([7, -5, 2]) . [62]: # np.diff() calculates the difference between subsequent values # in a NumPy array. differences = np . diff(waiting_times) biggest_decrease = abs (np . min(differences)) biggest_decrease [62]: 45 [63]: grader . check( "q4_2" ) [63]: q4_2 results: All test cases passed! Question 3. The faithful_with_eruption_nums table contains two columns: eruption_number , which represents the number of that eruption, and waiting , which represents the time spent waiting after that eruption. For example, take the first two rows of the table: eruption number waiting 1 79 2 54 We can read this as follows: after the first eruption, we waited 79 minutes for the second eruption. Then, after the second eruption, we waited 54 minutes for the third eruption. Suppose Oscar and Wendy started watching Old Faithful at the start of the first eruption. Assume 8

[89]: # Our solution uses 1 statement split over 3 lines. fruits = Table() . with_columns( "fruit name" ,[ "apple" , "orange" , "pineapple" ], ␣ ↪ "amount" , [ 4 , 3 , 3 ]) fruits [89]: fruit name | amount apple | 4 orange | 3 pineapple | 3 [90]: grader . check( "q5_1" ) [90]: q5_1 results: All test cases passed! Question 2. The file inventory.csv contains information about the inventory at a fruit stand. Each row represents the contents of one box of fruit. Load it as a table named inventory using the Table.read_table() function. Table.read_table(...) takes one argument (data file name in string format) and returns a table. (4 Points) [92]: inventory = Table . read_table( "inventory.csv" ) inventory [92]: box ID | fruit name | count 53686 | kiwi | 45 57181 | strawberry | 123 25274 | apple | 20 48800 | orange | 35 26187 | strawberry | 255 57930 | grape | 517 52357 | strawberry | 102 43566 | peach | 40 [93]: grader . check( "q5_2" ) [93]: q5_2 results: All test cases passed! Question 3. Does each box at the fruit stand contain a different fruit? Set all_different to True if each box contains a different fruit or to False if multiple boxes contain the same fruit. (4 Points) Hint: You don’t have to write code to calculate the True/False value for all_different . Just look at the inventory table and assign all_different to either True or False according to what you can see from the table in answering the question. [94]: all_different = False all_different [94]: False 10

[95]: grader . check( "q5_3" ) [95]: q5_3 results: All test cases passed! Question 4. The file sales.csv contains the number of fruit sold from each box last Saturday. It has an extra column called price per fruit ($) that’s the price per item of fruit for fruit in that box. The rows are in the same order as the inventory table. Load these data into a table called sales . (5 Points) [96]: sales = Table . read_table( "sales.csv" ) sales [96]: box ID | fruit name | count sold | price per fruit ($) 53686 | kiwi | 3 | 0.5 57181 | strawberry | 101 | 0.2 25274 | apple | 0 | 0.8 48800 | orange | 35 | 0.6 26187 | strawberry | 25 | 0.15 57930 | grape | 355 | 0.06 52357 | strawberry | 102 | 0.25 43566 | peach | 17 | 0.8 [97]: grader . check( "q5_4" ) [97]: q5_4 results: All test cases passed! Question 5. How many fruits did the store sell in total on that day? (5 Points) [98]: total_fruits_sold = np . sum(sales . column( "count sold" )) total_fruits_sold [98]: 638 [99]: grader . check( "q5_5" ) [99]: q5_5 results: All test cases passed! Question 6. What was the store’s total revenue (the total price of all fruits sold) on that day? (5 Points) Hint: If you’re stuck, think first about how you would compute the total revenue from just the grape sales. [105]: total_revenue = np . sum(sales . column( "count sold" ) * sales . column( "price per ␣ ↪ fruit ($)" )) total_revenue [105]: 106.85000000000001 11

Please save before exporting! [112]: # Save your notebook first, then run this cell to export your submission. grader . export(pdf = False , run_tests = True ) Running your submission against local test cases… Your submission received the following results when run against available test cases: q1_1 results: All test cases passed! q1_2 results: All test cases passed! q1_3 results: All test cases passed! q2_1 results: All test cases passed! q2_2 results: All test cases passed! q2_3 results: All test cases passed! q2_4 results: All test cases passed! q2_5 results: All test cases passed! q3_1 results: All test cases passed! q3_2 results: All test cases passed! q3_3 results: All test cases passed! q3_4 results: All test cases passed! q3_5 results: All test cases passed! q4_1 results: All test cases passed! q4_2 results: All test cases passed! q4_3 results: All test cases passed! q4_4 results: All test cases passed! q5_1 results: All test cases passed! 13

q5_2 results: All test cases passed! q5_3 results: All test cases passed! q5_4 results: All test cases passed! q5_5 results: All test cases passed! q5_6 results: All test cases passed! q5_7 results: All test cases passed! <IPython.core.display.HTML object> 14