CIS 024|Lab 4: Functions and Visualizations

Lab 4: Functions and Visualizations ¶ Welcome to lab 4! This week, we'll learn about functions and the table method apply from Section 8.1 (https://www.inferentialthinking.com/chapters/08/1/applying-a-function-to-a-col ...

Lab 4: Functions and Visualizations ¶ Welcome to lab 4! This week, we'll learn about functions and the table method apply from Section 8.1 (https://www.inferentialthinking.com/chapters/08/1/applying-a-function-to-a-column.html). We'll also learn about visualization from Chapter 7 (https://www.inferentialthinking.com/chapters/07/visualization.html). First, set up the tests and imports by running the cell below. In [86]: import numpy as np from datascience import * np.seterr(divide='ignore', invalid='ignore') # These lines set up graphing capabilities. import matplotlib %matplotlib inline import matplotlib.pyplot as plt plt.style.use('fivethirtyeight') import warnings warnings.simplefilter('ignore', FutureWarning) from ipywidgets import interact, interactive, fixed, interact_manual import ipywidgets as widgets from gofer.ok import check 1. Functions and CEO Incomes Let's start with a real data analysis task. We'll look at the 2015 compensation of CEOs at the 100 largest companies in California. The data were compiled for a Los Angeles Times analysis here (http://spreadsheets.latimes.com/california-ceo-compensation/), and ultimately came from filings (https://www.sec.gov/answers/proxyhtf.htm) mandated by the SEC from all publicly-traded companies. Two companies have two CEOs, so there are 102 CEOs in the dataset. We've copied the data in raw form from the LA Times page into a file called raw_compensation.csv . (The page notes that all dollar amounts are in millions of dollars.) 6/21/2019 lab04 localhost:8889/nbconvert/html/Downloads/lab04.ipynb?download=false 2/26 In [2]: raw_compensation = Table.read_table('raw_compensation.csv') raw_compensation Question 1.1. We want to compute the average of the CEOs' pay. Try running the cell below. Out[2]: Rank Name Company (Headquarters) Total Pay % Change Cash Pay Equity Pay Other Pay Ratio of CEO pay to average industry worker pay 1 Mark V. Hurd* Oracle (Redwood City) $53.25 (No previous year) $0.95 $52.27 $0.02 362 2 Safra A. Catz* Oracle (Redwood City) $53.24 (No previous year) $0.95 $52.27 $0.02 362 3 Robert A. Iger Walt Disney (Burbank) $44.91 -3% $24.89 $17.28 $2.74 477 4 Marissa A. Mayer Yahoo! (Sunnyvale) $35.98 -15% $1.00 $34.43 $0.55 342 5 Marc Benioff salesforce.com (San Francisco) $33.36 -16% $4.65 $27.26 $1.45 338 6 John H. Hammergren McKesson (San Francisco) $24.84 -4% $12.10 $12.37 $0.37 222 7 John S. Watson Chevron (San Ramon) $22.04 -15% $4.31 $14.68 $3.05 183 8 Jeffrey Weiner LinkedIn (Mountain View) $19.86 27% $2.47 $17.26 $0.13 182 9 John T. Chambers** Cisco Systems (San Jose) $19.62 19% $5.10 $14.51 $0.01 170 10 John G. Stumpf Wells Fargo (San Francisco) $19.32 -10% $6.80 $12.50 $0.02 256 ... (92 rows omitted) 6/21/2019 lab04 localhost:8889/nbconvert/html/Downloads/lab04.ipynb?download=false 3/26 In [5]: np.average(raw_compensation.column("Total Pay")) You should see an error. Let's examine why this error occured by looking at the values in the "Total Pay" column. Use the type function and set total_pay_type to the type of the first value in the "Total Pay" column. In [6]: total_pay_type = type(raw_compensation.column("Total Pay").item(0)) total_pay_type In [7]: check('tests/q1_1.py') Question 1.2. You should have found that the values in "Total Pay" column are strings (text). It doesn't make sense to take the average of the text values, so we need to convert them to numbers if we want to do this. Extract the first value in the "Total Pay" column. It's Mark Hurd's pay in 2015, in millions of dollars. Call it mark_hurd_pay_string . In [8]: mark_hurd_pay_string = (raw_compensation.column("Total Pay").item(0)) mark_hurd_pay_string In [9]: check('tests/q1_2.py') --------------------------------------------------------------------------- TypeError Traceback (most recent call last) in ----> 1 np.average(raw_compensation.column("Total Pay")) /srv/conda/envs/notebook/lib/python3.7/site-packages/numpy/lib/function_base. py in average(a, axis, weights, returned) 390 391 if weights is None: --> 392 avg = a.mean(axis) 393 scl = avg.dtype.type(a.size/avg.size) 394 else: /srv/conda/envs/notebook/lib