Get the Lead Out: Understanding The Water Crisis in Flint, MI

Student Names

Type the names of everybody in your group here!

Learning Goals (Why are we asking you to do this?)

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:

Create plots from data (Use code to make pictures!)
Modify plots using color, size, and shape to help pull out patterns in data
Combine different sets of data into the same plot
Communicate plots by supplying axis titles, legends, and annotations

In-Class Activity

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 [1]:

    
# Loading the data

%matplotlib inline
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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First Draw":2.37,"Pb Bottle 2 (ppb) - 45 secs flushing":7.333,"Pb Bottle 3 (ppb) - 2 mins flushing":3.797},{"SampleID":234,"Zip Code":48504,"Ward":2,"Pb Bottle 1 (ppb) - First Draw":0.828,"Pb Bottle 2 (ppb) - 45 secs flushing":1.318,"Pb Bottle 3 (ppb) - 2 mins flushing":0.233},{"SampleID":235,"Zip Code":48503,"Ward":7,"Pb Bottle 1 (ppb) - First Draw":0.719,"Pb Bottle 2 (ppb) - 45 secs flushing":0.254,"Pb Bottle 3 (ppb) - 2 mins flushing":0.058},{"SampleID":236,"Zip Code":48504,"Ward":1,"Pb Bottle 1 (ppb) - First Draw":2.822,"Pb Bottle 2 (ppb) - 45 secs flushing":1.221,"Pb Bottle 3 (ppb) - 2 mins flushing":0.258},{"SampleID":237,"Zip Code":48504,"Ward":8,"Pb Bottle 1 (ppb) - First Draw":2.867,"Pb Bottle 2 (ppb) - 45 secs flushing":0.723,"Pb Bottle 3 (ppb) - 2 mins flushing":0.744},{"SampleID":238,"Zip Code":48503,"Ward":7,"Pb Bottle 1 (ppb) - First Draw":2.332,"Pb Bottle 2 (ppb) - 45 secs flushing":3.588,"Pb Bottle 3 (ppb) - 2 mins flushing":1.221},{"SampleID":240,"Zip Code":48503,"Ward":8,"Pb Bottle 1 (ppb) - First Draw":4.401,"Pb Bottle 2 (ppb) - 45 secs flushing":2.111,"Pb Bottle 3 (ppb) - 2 mins flushing":1.572},{"SampleID":241,"Zip Code":48504,"Ward":6,"Pb Bottle 1 (ppb) - First Draw":2.708,"Pb Bottle 2 (ppb) - 45 secs flushing":2.238,"Pb Bottle 3 (ppb) - 2 mins flushing":0.809},{"SampleID":242,"Zip Code":48504,"Ward":6,"Pb Bottle 1 (ppb) - First Draw":34.13,"Pb Bottle 2 (ppb) - 45 secs flushing":6.002,"Pb Bottle 3 (ppb) - 2 mins flushing":1.71},{"SampleID":243,"Zip Code":48503,"Ward":7,"Pb Bottle 1 (ppb) - First Draw":5.218,"Pb Bottle 2 (ppb) - 45 secs flushing":2.614,"Pb Bottle 3 (ppb) - 2 mins flushing":0.831},{"SampleID":244,"Zip Code":48503,"Ward":7,"Pb Bottle 1 (ppb) - First Draw":15.73,"Pb Bottle 2 (ppb) - 45 secs flushing":13.95,"Pb Bottle 3 (ppb) - 2 mins flushing":3.584},{"SampleID":245,"Zip Code":48504,"Ward":6,"Pb Bottle 1 (ppb) - First Draw":3.045,"Pb Bottle 2 (ppb) - 45 secs flushing":2.744,"Pb Bottle 3 (ppb) - 2 mins flushing":0.299},{"SampleID":246,"Zip Code":48503,"Ward":7,"Pb Bottle 1 (ppb) - First Draw":1.1,"Pb Bottle 2 (ppb) - 45 secs flushing":0.5,"Pb Bottle 3 (ppb) - 2 mins flushing":0.3},{"SampleID":247,"Zip Code":48503,"Ward":7,"Pb Bottle 1 (ppb) - First Draw":1.386,"Pb Bottle 2 (ppb) - 45 secs flushing":0.288,"Pb Bottle 3 (ppb) - 2 mins flushing":0.432},{"SampleID":248,"Zip Code":48504,"Ward":6,"Pb Bottle 1 (ppb) - First Draw":0.915,"Pb Bottle 2 (ppb) - 45 secs flushing":0.354,"Pb Bottle 3 (ppb) - 2 mins flushing":0.306},{"SampleID":249,"Zip Code":48504,"Ward":6,"Pb Bottle 1 (ppb) - First Draw":2.145,"Pb Bottle 2 (ppb) - 45 secs flushing":0.345,"Pb Bottle 3 (ppb) - 2 mins flushing":3.738},{"SampleID":250,"Zip Code":48507,"Ward":8,"Pb Bottle 1 (ppb) - First Draw":4.056,"Pb Bottle 2 (ppb) - 45 secs flushing":0.547,"Pb Bottle 3 (ppb) - 2 mins flushing":0.378},{"SampleID":251,"Zip Code":48503,"Ward":7,"Pb Bottle 1 (ppb) - First Draw":1.668,"Pb Bottle 2 (ppb) - 45 secs flushing":1.508,"Pb Bottle 3 (ppb) - 2 mins flushing":2.72},{"SampleID":252,"Zip Code":48503,"Ward":7,"Pb Bottle 1 (ppb) - First Draw":7.575,"Pb Bottle 2 (ppb) - 45 secs flushing":1.362,"Pb Bottle 3 (ppb) - 2 mins flushing":1.094},{"SampleID":253,"Zip Code":48507,"Ward":8,"Pb Bottle 1 (ppb) - First Draw":5.59,"Pb Bottle 2 (ppb) - 45 secs flushing":4.306,"Pb Bottle 3 (ppb) - 2 mins flushing":2.019},{"SampleID":254,"Zip Code":48503,"Ward":5,"Pb Bottle 1 (ppb) - First Draw":0.708,"Pb Bottle 2 (ppb) - 45 secs flushing":0.326,"Pb Bottle 3 (ppb) - 2 mins flushing":0.303},{"SampleID":255,"Zip Code":48504,"Ward":6,"Pb Bottle 1 (ppb) - First Draw":1.701,"Pb Bottle 2 (ppb) - 45 secs flushing":4.397,"Pb Bottle 3 (ppb) - 2 mins flushing":1.287},{"SampleID":256,"Zip Code":48504,"Ward":6,"Pb Bottle 1 (ppb) - First Draw":1.467,"Pb Bottle 2 (ppb) - 45 secs flushing":0.149,"Pb Bottle 3 (ppb) - 2 mins flushing":0.137},{"SampleID":258,"Zip Code":48504,"Ward":2,"Pb Bottle 1 (ppb) - First Draw":2.582,"Pb Bottle 2 (ppb) - 45 secs flushing":259.8,"Pb Bottle 3 (ppb) - 2 mins flushing":61.96},{"SampleID":259,"Zip Code":48505,"Ward":2,"Pb Bottle 1 (ppb) - First Draw":22.08,"Pb Bottle 2 (ppb) - 45 secs flushing":15.86,"Pb Bottle 3 (ppb) - 2 mins flushing":9.262},{"SampleID":260,"Zip Code":48507,"Ward":8,"Pb Bottle 1 (ppb) - First Draw":16.51,"Pb Bottle 2 (ppb) - 45 secs flushing":2.024,"Pb Bottle 3 (ppb) - 2 mins flushing":7.068},{"SampleID":262,"Zip Code":48507,"Ward":8,"Pb Bottle 1 (ppb) - First Draw":56.26,"Pb Bottle 2 (ppb) - 45 secs flushing":4.692,"Pb Bottle 3 (ppb) - 2 mins flushing":1.243},{"SampleID":263,"Zip Code":48504,"Ward":6,"Pb Bottle 1 (ppb) - First Draw":2.433,"Pb Bottle 2 (ppb) - 45 secs flushing":1.334,"Pb Bottle 3 (ppb) - 2 mins flushing":1.376},{"SampleID":264,"Zip Code":48503,"Ward":7,"Pb Bottle 1 (ppb) - First Draw":0.5,"Pb Bottle 2 (ppb) - 45 secs flushing":0.2,"Pb Bottle 3 (ppb) - 2 mins flushing":0.5},{"SampleID":265,"Zip Code":48504,"Ward":6,"Pb Bottle 1 (ppb) - First Draw":29.13,"Pb Bottle 2 (ppb) - 45 secs flushing":11.57,"Pb Bottle 3 (ppb) - 2 mins flushing":6.388},{"SampleID":266,"Zip Code":48505,"Ward":1,"Pb Bottle 1 (ppb) - First Draw":12.3,"Pb Bottle 2 (ppb) - 45 secs flushing":0.5,"Pb Bottle 3 (ppb) - 2 mins flushing":0.4},{"SampleID":267,"Zip Code":48504,"Ward":6,"Pb Bottle 1 (ppb) - First Draw":3.445,"Pb Bottle 2 (ppb) - 45 secs flushing":0.29,"Pb Bottle 3 (ppb) - 2 mins flushing":0.167},{"SampleID":268,"Zip Code":48507,"Ward":9,"Pb Bottle 1 (ppb) - First Draw":16.49,"Pb Bottle 2 (ppb) - 45 secs flushing":12.83,"Pb Bottle 3 (ppb) - 2 mins flushing":9.018},{"SampleID":269,"Zip Code":48503,"Ward":7,"Pb Bottle 1 (ppb) - First Draw":3.365,"Pb Bottle 2 (ppb) - 45 secs flushing":2.45,"Pb Bottle 3 (ppb) - 2 mins flushing":1.675},{"SampleID":270,"Zip Code":48503,"Ward":7,"Pb Bottle 1 (ppb) - First Draw":1.154,"Pb Bottle 2 (ppb) - 45 secs flushing":0.176,"Pb Bottle 3 (ppb) - 2 mins flushing":0.12},{"SampleID":271,"Zip Code":48507,"Ward":9,"Pb Bottle 1 (ppb) - First Draw":13.53,"Pb Bottle 2 (ppb) - 45 secs flushing":21.91,"Pb Bottle 3 (ppb) - 2 mins flushing":4.675},{"SampleID":272,"Zip Code":48504,"Ward":6,"Pb Bottle 1 (ppb) - 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First Draw":16.99,"Pb Bottle 2 (ppb) - 45 secs flushing":6.32,"Pb Bottle 3 (ppb) - 2 mins flushing":3.585},{"SampleID":293,"Zip Code":48503,"Ward":7,"Pb Bottle 1 (ppb) - First Draw":3.322,"Pb Bottle 2 (ppb) - 45 secs flushing":2.559,"Pb Bottle 3 (ppb) - 2 mins flushing":1.512},{"SampleID":294,"Zip Code":48506,"Ward":4,"Pb Bottle 1 (ppb) - First Draw":14.33,"Pb Bottle 2 (ppb) - 45 secs flushing":1.284,"Pb Bottle 3 (ppb) - 2 mins flushing":0.323},{"SampleID":295,"Zip Code":48503,"Ward":7,"Pb Bottle 1 (ppb) - First Draw":18.11,"Pb Bottle 2 (ppb) - 45 secs flushing":20.21,"Pb Bottle 3 (ppb) - 2 mins flushing":4.263},{"SampleID":296,"Zip Code":48503,"Ward":7,"Pb Bottle 1 (ppb) - First Draw":12.81,"Pb Bottle 2 (ppb) - 45 secs flushing":7.874,"Pb Bottle 3 (ppb) - 2 mins flushing":1.78},{"SampleID":298,"Zip Code":48503,"Ward":7,"Pb Bottle 1 (ppb) - First Draw":1.083,"Pb Bottle 2 (ppb) - 45 secs flushing":0.322,"Pb Bottle 3 (ppb) - 2 mins flushing":0.26},{"SampleID":299,"Zip Code":48503,"Ward":7,"Pb Bottle 1 (ppb) - First Draw":29.59,"Pb Bottle 2 (ppb) - 45 secs flushing":3.258,"Pb Bottle 3 (ppb) - 2 mins flushing":1.843},{"SampleID":300,"Zip Code":48505,"Ward":1,"Pb Bottle 1 (ppb) - First Draw":4.287,"Pb Bottle 2 (ppb) - 45 secs flushing":4.345,"Pb Bottle 3 (ppb) - 2 mins flushing":4.905}]""")



In [13]:

    
# 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.tail()









    Out[13]:






  
    
      
      Notes
      Pb Bottle 1 (ppb) - First Draw
      Pb Bottle 2 (ppb) - 45 secs flushing
      Pb Bottle 3 (ppb) - 2 mins flushing
      SampleID
      Ward
      Zip Code
    
  
  
    
      266
      NaN
      18.110
      20.210
      4.263
      295
      7
      48503
    
    
      267
      NaN
      12.810
      7.874
      1.780
      296
      7
      48503
    
    
      268
      NaN
      1.083
      0.322
      0.260
      298
      7
      48503
    
    
      269
      NaN
      29.590
      3.258
      1.843
      299
      7
      48503
    
    
      270
      NaN
      4.287
      4.345
      4.905
      300
      1
      48505

Data Cleaning

The sample readings are in ppb (parts per billion). But, the U.S. Environmental Protection Agency's guidelines are expressed in mg/L, or milligrams per liter. What will we need to do in order to compare the data collected to the EPA's guideline threshold?



In [3]:

    
# Write any python you need to here
conversion_factor = 1 / 1000
# print(conversion_factor)

converted_lead_levels = conversion_factor * lead_levels_first_draw
converted_lead_levels[0:4] # checking the data









    Out[3]:





array([ 0.000344,  0.008133,  0.001111,  0.008007])

$$ x \text{ ppb} \times \frac{1 \text{ ppm}}{1000 \text{ ppb}} \times \frac{1 \text{ mg/L}}{1 \text{ ppm}} $$

Getting a Sense of the Data

What's the mean value of all the lead readings?



In [4]:

    
# Write any python you need to here

converted_lead_levels.mean()









    Out[4]:





0.010645992619926199

// 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 [14]:

    
# Write any python you need to here

import pandas as pd
import seaborn as sns



converted_lead_levels = pd.Series(converted_lead_levels)
epa_action_threshold = 0.015
critical_lead_levels = pd.Series(
    converted_lead_levels[converted_lead_levels > epa_action_threshold])

critical_lead_levels.hist()
critical_lead_levels.count()









    Out[14]:





45

// 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

Analysis

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

Homework - Informing Government

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!

Feedback (required!)

How far did you get on this in class?

// Write your answer here

What questions do you (or does your group) have after this assignment?

// Write your answer here

How to submit this assignment

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!

Want to learn more?

Two talks by Hans Rosling on visualizing public health data
DataKind: data analysis for humanity
Code For America
The U.S. Digital Service

	Notes	Pb Bottle 1 (ppb) - First Draw	Pb Bottle 2 (ppb) - 45 secs flushing	Pb Bottle 3 (ppb) - 2 mins flushing	SampleID	Ward	Zip Code
266	NaN	18.110	20.210	4.263	295	7	48503
267	NaN	12.810	7.874	1.780	296	7	48503
268	NaN	1.083	0.322	0.260	298	7	48503
269	NaN	29.590	3.258	1.843	299	7	48503
270	NaN	4.287	4.345	4.905	300	1	48505