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2/8/24, 8 : 33 PM hw3 Page 1 of 14 about:srcdoc DS 3000 HW 3 Due: Thursday Feb 8th @ 11 : 59 PM EST Submission Instructions Submit this ipynb file and the a PDF file included with the coding results to Gradescope (this can also be done via the assignment on Canvas). To ensure that your submitted files represent your latest code, make sure to give a fresh Kernel > Restart & Run All just before uploading the files to gradescope. Tips for success Start early (even though you have two weeks on this homework) Make use of Piazza Make use of Office hour Remember to use cells and headings to make the notebook easy to read (if a grader cannot find the answer to a problem, you will receive no points for it) Under no circumstances may one student view or share their ungraded homework or quiz with another student (see also) , though you are welcome to talk about (not show each other) the problems. Part 1: Plotting Warm Up (18 points) Plot each of the functions below over 100 evenly spaced points in the domain $ [0, 10] $ on the same graph. Be sure to use the line specifications given below: Name Value Color Line Width Style sinusoid 3 * sin (2/3 x) Red 4 dotted polynomial (x-3) (x - 2) (x-8) / 10 Blue 2 solid abs value min(abs(x - 3), abs(x - 8)) Green 3 dashed add a legend which specifies the name of each function use seaborn's sns.set() before plotting to make the graph look nice

2/8/24, 8 : 33 PM hw3 Page 2 of 14 about:srcdoc Make sure that the axes are labeled x and f(x) You may find the arithmetic functions needed in numpy (sin, abs, minimum) import numpy as np import matplotlib.pyplot as plt import seaborn as sns sns . set () x = np . linspace ( 0 , 10 , 100 ) plt . figure ( figsize = ( 10 , 6 )) plt . plot ( x , 3 * np . sin ( 2 / 3 * x ), 'r:' , label = 'sinusoid' , linewidth = 4 ) # Red plt . plot ( x , ( x - 3 ) * ( x - 2 ) * ( x - 8 ) / 10 , 'b-' , label = 'polynomial' , line plt . plot ( x , np . minimum ( np . abs ( x - 3 ), np . abs ( x - 8 )), 'g--' , label = 'abs valu plt . legend () plt . xlabel ( 'x' ) plt . ylabel ( 'f(x)' ) plt . show () Part 2: FIFA Players (22 points) In [1]:

2/8/24, 8 : 33 PM hw3 Page 3 of 14 about:srcdoc Create a plotly scatter plot which shows the mean Overall rating for all soccer players (rows) of a particular Age . Color your scatter plot per Nationality of the player, focusing on three countries ( England , Germany , Spain ). Download the players_fifa23.csv from Canvas and make sure it is in the same directory as this notebook file. Export your graph as an html file age_ratings_nationality.html and submit it with your completed homework ipynb to gradescope. Hints: There may be multiple ways/approaches to accomplish this task. One approach: you may use groupby() and boolean indexing to build these values in a loop which runs per each Nationality . px.scatter() will only graph data from columns (not the index). Some approaches may need to graph data from the index. You can use df.reset_index() to make your index a new column as shown in this example In some approaches you may need to pass multiple rows to df.append() if need be as shown in this example In some approaches you may need to go from "wide" data to "long" data by using df.melt() as discussed here The first few code cells below get you started with looking at the data set. import warnings warnings . simplefilter ( action = 'ignore' , category = FutureWarning ) # use pandas to read in the data import pandas as pd df_fifa = pd . read_csv ( 'players_fifa23.csv' , index_col = 'ID' ) df_fifa . head () In [2]:

2/8/24, 8 : 33 PM hw3 Page 4 of 14 about:srcdoc import plotly.express as px filtered_df = df_fifa [ df_fifa [ 'Nationality' ] . isin ([ 'England' , 'Germany' , 'Sp grouped_df = filtered_df . groupby ([ 'Age' , 'Nationality' ])[ 'Overall' ] . mean () . r fig = px . scatter ( grouped_df , x = 'Age' , y = 'Overall' , color = 'Nationality' , labels = { 'Overall' : 'Mean Overall Rating' }, title = 'Mean Overall Rating by Age and Nationality' ) fig . write_html ( 'age_ratings_nationality.html' ) Part 3: Daylight through the year The remainder of the homework asks you to complete the pipeline which, given the lattitude / longitude and timezone of some cities: loc_dict = { 'Boston' : ( 42.3601 , - 71.0589 , 'US/Eastern' ), 'Lusaka' : ( - 15.3875 , 28.3228 , 'Africa/Lusaka' ), 'Sydney' : ( - 33.8688 , 151.2093 , 'Australia/Sydney' )} the keys are the name of the city and the values are tuples of `lat, lon, timezone_name is able to: query a sunrise / sunset API clean and process data (timezone management & building datetime objects) Name FullName Age Height Weight ID 165153 K. Benzema Karim Benzema 34 185 81 https://cdn.sofifa.net/players/16 158023 L. Messi Lionel Messi 35 169 67 https://cdn.sofifa.net/players/15 231747 K. Mbappé Kylian Mbappé 23 182 73 https://cdn.sofifa.net/players/2 192985 K. De Bruyne Kevin De Bruyne 31 181 70 https://cdn.sofifa.net/players/19 188545 R. Lewandowski Robert Lewandowski 33 185 81 https://cdn.sofifa.net/players/18 5 rows × 89 columns Out[2]: In [3]: