lab04.pdf - lab04 1 Lab 4 Functions and Visualizations Welcome to Lab 4 This week well learn about functions table methods such as apply and how to

lab04.pdf - lab04 1 Lab 4 Functions and Visualizations...

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lab04 September 16, 2018 1 Lab 4: Functions and Visualizations Welcome to Lab 4! This week, we’ll learn about functions, table methods such as apply , and how to generate visualizations! Recommended Reading: Applying a Function to a Column Visualizations First, set up the notebook by running the cell below. In [1]: import numpy as np from datascience import * # 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 client.api.notebook import Notebook ok = Notebook( ' lab04.ok ' ) _ = ok . auth(inline = True ) ===================================================================== Assignment: Functions and Visualizations OK, version v1.12.5 ===================================================================== Successfully logged in as [email protected] 1
1.1 1. Functions and CEO Incomes In this question, we’ll look at the 2015 compensation of CEOs at the 100 largest companies in California. The data was compiled from a Los Angeles Times analysis , and ultimately came from filings 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 raw data from the LA Times page into a file called raw_compensation.csv . (The page notes that all dollar amounts are in millions of dollars.) In [2]: raw_compensation = Table . read_table( ' raw_compensation.csv ' ) raw_compensation Out[2]: Rank | Name | Company (Headquarters) | Total Pay | % Change 1 | Mark V. Hurd* | Oracle (Redwood City) | \$53.25 | (No previous y 2 | Safra A. Catz* | Oracle (Redwood City) | \$53.24 | (No previous y 3 | Robert A. Iger | Walt Disney (Burbank) | \$44.91 | -3% 4 | Marissa A. Mayer | Yahoo! (Sunnyvale) | \$35.98 | -15% 5 | Marc Benioff | salesforce.com (San Francisco) | \$33.36 | -16% 6 | John H. Hammergren | McKesson (San Francisco) | \$24.84 | -4% 7 | John S. Watson | Chevron (San Ramon) | \$22.04 | -15% 8 | Jeffrey Weiner | LinkedIn (Mountain View) | \$19.86 | 27% 9 | John T. Chambers** | Cisco Systems (San Jose) | \$19.62 | 19% 10 | John G. Stumpf | Wells Fargo (San Francisco) | \$19.32 | -10% ... (92 rows omitted) Question 1. We want to compute the average of the CEOs’ pay. Try running the cell below. In [4]: np . average(raw_compensation . column( "Total Pay" )) --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-4-f97fab5a8083> in <module>() ----> 1 np.average(raw_compensation.column("Total Pay")) /srv/app/venv/lib/python3.6/site-packages/numpy/lib/function_base.py in average(a, axis 1126 1127 if weights is None: -> 1128 avg = a.mean(axis) 1129 scl = avg.dtype.type(a.size/avg.size) 1130 else: /srv/app/venv/lib/python3.6/site-packages/numpy/core/_methods.py in _mean(a, axis, dtyp 68 is_float16_result = True 69 2
---> 70 ret = umr_sum(arr, axis, dtype, out, keepdims) 71 if isinstance(ret, mu.ndarray): 72 ret = um.true_divide( TypeError: cannot perform reduce with flexible type You should see an error. Let’s examine why this error occurred 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.

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