Type the names of everybody in your group here!
First, because data analysis is something that can and should be used for (among other things):
Second, because data visualization is one of the most important parts of modeling and understanding a system. How we represent data affects everything from to understanding poverty in the developing world to grappling with the global spread of lethal diseases. We want you to be able to find things out about the models you create and use visual information to make convincing arguments. So, in this tutorial we'll learn some of the nuts and bolts of using Python to create data visualizations. When you're done, you'll be able to use the industry-standard matplotlib plotting package to:
We'll be looking at the recently released Flint Water Quality dataset. This is a dataset of nearly 300 tests run by volunteers at Virginia Tech on water samples obtained from Flint residents. The water testing method involves collecting three different bottles worth of water at timed intervals; our analysis will focus on just the first collection at each testing site.
You'll be considering the following questions in the context of U.S. Environmental Protection Agency (EPA) guidelines about lead contaminants, which state:
Lead and copper are regulated by a treatment technique that requires systems to control the corrosiveness of their water. If more than 10% of tap water samples exceed the action level, water systems must take additional steps. For copper, the action level is 1.3 mg/L, and for lead is 0.015 mg/L.
Source: (http://www.epa.gov/your-drinking-water/table-regulated-drinking-water-contaminants#seven).
For simplicity, the data we'll consider will be stored in two 1-dimensional numpy arrays. The code below loads the data, then creates those two numpy arrays for you to reference in your code.
In [ ]:
# Loading the data
from IPython.display import set_matplotlib_formats
set_matplotlib_formats('pdf', 'svg')
import matplotlib.pyplot as plt
import numpy as np
import pandas
flint_data = pandas.read_json("""[{"SampleID":1,"Zip Code":48504,"Ward":6,"Pb Bottle 1 (ppb) - First Draw":0.344,"Pb Bottle 2 (ppb) - 45 secs flushing":0.226,"Pb Bottle 3 (ppb) - 2 mins flushing":0.145},{"SampleID":2,"Zip Code":48507,"Ward":9,"Pb Bottle 1 (ppb) - First Draw":8.133,"Pb Bottle 2 (ppb) - 45 secs flushing":10.77,"Pb Bottle 3 (ppb) - 2 mins flushing":2.761},{"SampleID":4,"Zip Code":48504,"Ward":1,"Pb Bottle 1 (ppb) - First Draw":1.111,"Pb Bottle 2 (ppb) - 45 secs flushing":0.11,"Pb Bottle 3 (ppb) - 2 mins flushing":0.123},{"SampleID":5,"Zip Code":48507,"Ward":8,"Pb Bottle 1 (ppb) - First Draw":8.007,"Pb Bottle 2 (ppb) - 45 secs flushing":7.446,"Pb Bottle 3 (ppb) - 2 mins flushing":3.384},{"SampleID":6,"Zip Code":48505,"Ward":3,"Pb Bottle 1 (ppb) - First Draw":1.951,"Pb Bottle 2 (ppb) - 45 secs flushing":0.048,"Pb Bottle 3 (ppb) - 2 mins flushing":0.035},{"SampleID":7,"Zip Code":48507,"Ward":9,"Pb Bottle 1 (ppb) - First Draw":7.2,"Pb Bottle 2 (ppb) - 45 secs flushing":1.4,"Pb Bottle 3 (ppb) - 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In [ ]:
# Creating Numpy Arrays from the Data
## lead_levels are in
lead_levels_first_draw = np.array(flint_data["Pb Bottle 1 (ppb) - First Draw"])
## zip_code data are in
zip_code = np.array(flint_data["Zip Code"])
## For reference, the full dataset looks like this
flint_data.head()
In [ ]:
# Write any python you need to here
// write any markdown here - explain what you did in the cells above!
In [ ]:
# Write any python you need to here
// write any markdown here - explain what you did in the cells above!
Do you think the mean value is representative of how good (or bad) the overall lead levels in Flint water? If so, why; if not, why not? Really take some time to think this one through. Try to justify your group's opinions by using plots, calculations, or anything else you feel appropriately supports your point.
In [ ]:
# Write any python you need to here
// write any markdown here
How does the mean value for all the readings compare to the EPA's "action level"? What about the range of values from the readings? As a reminder, here's what the EPA guidelines say:
Lead and copper are regulated by a treatment technique that requires systems to control the corrosiveness of their water. If more than 10% of tap water samples exceed the action level, water systems must take additional steps. For copper, the action level is 1.3 mg/L, and for lead is 0.015 mg/L.
Source: (http://www.epa.gov/your-drinking-water/table-regulated-drinking-water-contaminants#seven).
In [ ]:
# Write any python you need to here
// write any markdown here
Is comparing the mean to the action level enough to tell us whether Flint had a definite problem with its drinking water? If so, why? If not, why not? Take some time to think this through.
In [ ]:
# Write any python you need to here
// write any markdown here
As part of this week's homework, You're going to create a letter to send to the Governor's office based on your data anlysis here. You will consider this core question: Did water lead levels exceed the EPA's action limits? And if they did, how can we understand how badly it exceeded the limits?
So that you're prepared to work on the homework, make sure to have the person who creates this notebook email it to everybdoy in the group!
// Write your answer here
// Write your answer here
Log into the course Desire2Learn website (d2l.msu.edu) and go to the "In-class assignments" folder. There will be a dropbox labeled "Day 4". Upload this notebook there (but not pictures of drawings, etc.). You only have to upload one notebook per group - just make sure that everybody's name is at the top of the notebook!