Day 11 In-class Assignment: Get the Lead Out: Understanding The Water Crisis in Flint, MI

Contents

Day 11 In-class Assignment: Get the Lead Out: Understanding The Water Crisis in Flint, MI#

✅ Put your name here.

#

✅ Put your group member names here.

#

Learning Goals:#

By the end of this assignment you should be able to:

  • Use Pandas to filter data to select particular subsets of interest

  • Articulate, based on your own perception, what you thinks makes a data visualization “good” versus “bad”

  • Use data to support a claim or make an argument

Assignment instructions#

Work with your group to complete this assignment. Instructions for submitting this assignment are at the end of the notebook. The assignment is due at the end of class.

Background and motivation#

Today we want you to think about what the data is telling you, but still use coding to help you; and, use some of the visualization ideas you explored above. We’ll be looking at the publicly released Flint Water Quality dataset. The water testing method involves collecting three different bottles worth of water.

Review the properties of the data, read the descriptions of the data fields, and review what the Evironmental Protection Agency (EPA) limits are on lead in water supplies.

import matplotlib.pyplot as plt

import numpy as np
import pandas as pd

# Loading the data
flint_data = pd.read_csv('flint_water_data.csv')

Double-check that your data loaded correctly by displaying the first first lines.

# Put your code here

Description of data fields in this data set:#

  • SampleID: Unique study code for each sample

  • Zip Code: location where samples were collected

  • Ward: location where samples were collected

  • PbBottle1_ppb: Concentration of lead in parts per billion (ppb) in sample acquired at initial turn on of water

  • PbBottle2_ppb: Concentration of lead in parts per billion (ppb) in sample acquired after 45 seconds of flushing water

  • PbBottle3_ppb: Concentration of lead in parts per billion (ppb) in sample acquired after 120 seconds of flushing water

EPA Limits (i.e. how does the EPA define unsafe lead levels in a water supply?)#

The U.S. Environmental Protection Agency (EPA) guidelines about lead contaminants 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 (1300 ppb), and for lead is 0.015 mg/L (15 ppb).

Source: (http://www.epa.gov/your-drinking-water/table-regulated-drinking-water-contaminants#seven).

1. Analysis Using Descriptive Statistics#

1.1#

✅  Use describe() function to determine the mean, standard deviation, min, median, and max of the first set of samples (PbBottle1_ppb).#

# Write your code here

1.2#

✅  From this information alone, can you determine if the water supply in Flint met the EPA standard?#

✎ Write your answer here

1.3#

✅  What is the median of this sample? How does it compare to the mean? Do you expect this sample to be close to a normal (Gaussian) distribution? (think back on your pre-class assignment about the properties of normal distributions)#

✎ Write your answer here

2. Analysis Using Visualizations#

2.1#

✅  Make a histogram visualization of the distribution of the first set of samples (PbBottle1_ppb). Use the axvline() function to add a line showing where the EPA limit is. (You will may need to us google to remind yourself what this function does.)#

Also, make sure that your plot has x and y-axes labels and a title.

# Write your code here

2.2#

✅  Does the distribution of samples look like a normal (Gaussian) distribution? Is that what you predicted based on your analysis of the descriptive statistics (Part 1.3)?#

✎ Write your answer here

2.3#

✅  Looking at this, there’s a big pile up of samples that are near or below the EPA limit, and it makes it hard to get a sense of what the full distribution looks like. Use plt.yscale() to make the y-axis logarithmic.#

# Write your code here

2.4#

✅  As discussed in the pre-class, logarithmic scales allow us to get a better handle on distributions with a large dynamic range. Use your log plot estimate the number of samples that lie above the EPA limit by counting the number of samples that fall into the bins that are above the limit.#

What fraction of the total samples appear to fall above the EPA limit?

✎ Write your answer here

2.5#

✅  Based on this analysis, does it appear that the Flint samples met the EPA standards?#

✎ Write your answer here

STOP#

✅  Based on your analysis so far, answer the following question: Would you feel comfortable drinking a glass of water in Flint at the height of the water crisis?#

✎ Write your answer here

3. Analysis Using Masks#

In the previous part, you used descriptive statistics and a visualization to try to estimate the number of samples that fell above the EPA limit. Now we’re going to actually count the number that actually don’t meet the EPA regulation.

3.1#

✅  Task#

First, let’s step back from the Flint Water data and try a masking example. The cell below contains a small DataFrame with randomly generted data that is similar to the real data you are working with. Write some code that creates a mask on the 'test_bottle1' column for values greater than 15 and print out that mask (you should see True where the condition is met and False where it isn’t).

# here is some test data!
data = {
    'Zipcode': [25687, 27734, 45253, 23558, 11378, 94490, 38385, 11324, 29878, 17790],
    'test_bottle1': [12.16, 11.73, 18.20, 7.15, 8.91, 9.63, 15.12, 0.72, 22.36, 13.12],
    'test_bottle2': [7.15, 11.17, 11.93, 13.49, 7.51, 12.47, 22.13, 24.57, 7.00, 13.42],
    'test_bottle3': [24.66, 9.53, 23.46, 14.63, 2.02, 1.74, 0.07, 4.29, 14.61, 5.24]
}

df_for_practice = pd.DataFrame(data)
df_for_practice
Zipcode test_bottle1 test_bottle2 test_bottle3
0 25687 12.16 7.15 24.66
1 27734 11.73 11.17 9.53
2 45253 18.20 11.93 23.46
3 23558 7.15 13.49 14.63
4 11378 8.91 7.51 2.02
5 94490 9.63 12.47 1.74
6 38385 15.12 22.13 0.07
7 11324 0.72 24.57 4.29
8 29878 22.36 7.00 14.61
9 17790 13.12 13.42 5.24 
# put your code here

✅  Task#

Now, create a new DataFrame (using your mask) that removes any rows below 15 and save it as a new variable. Then, display your DataFrame. How do you know your mask worked?

# put your code here

3.2#

Now, back to our data!

✅  Create a mask and a new dataframe that only contain samples where PbBottle1_ppb was greater than the EPA limit.#

# Write your code here

3.3#

✅  Answer the following questions:#

  1. How many samples were above the EPA limit?

  2. What fraction of the total samples does this comprise?

✎ Write your answer here

3.4#

✅  Based on this analysis, do these samples meet the EPA standards?#

✎ Write your answer here

4. Extending Analysis to the Other Samples#

✅  Repeat the analysis you did parts 1, 2, and 3 for both the PbBottle2_ppb and PbBottle3_ppb samples. Recall that these are samples drawn 45 seconds after the taps are turned on (PbBottle2_ppb) and two minutes after the taps are turned on (PbBottle3_ppb).#

To complete this section, you should:

  1. Fill in the table below with descriptive statistics and fraction of samples above the EPA limit.

  2. Have a three panel plot showing the distribution of samples. The y-axes for each of them should be logarithmic and each plot should have x and y-axes labels and a title.

Sample

Mean

Std. Dev

Min

Median

Max

Fraction > EPA

PbBottle1_ppb

??

??

??

??

??

??

PbBottle2_ppb

??

??

??

??

??

??

PbBottle3_ppb

??

??

??

??

??

??

 
# Write your code here

5. Conclusions#

✅  Based on your new analysis, answer the following question: Would you feel comfortable drinking a glass of water in Flint at the height of the water crisis?#

Use statistical values and/or data visualizations to back up your choice.

✎ Write your answer here

6. (Time Permitting) Looking for Other Sources of Analysis#

For your project, it will be good to draw upon other resources to compare your results/conclusions to.

Go through the following article from the New York Times and compare it’s conclusions to your own from Part 5. Given the information in this article, Would you feel comfortable drinking a glass of water in Flint at the height of the water criss?

NOTE: If you are an MSU student, you have access to a free New York Times account. Click this link and follow the steps to get access through MSU.

✎ Write your answer here

Assignment wrapup#

Please fill out the form that appears when you run the code below. You must completely fill this out in order to receive credit for the assignment!

from IPython.display import HTML
HTML(
"""
<iframe 
	src="https://cmse.msu.edu/cmse201-ic-survey" 
	width="800px" 
	height="600px" 
	frameborder="0" 
	marginheight="0" 
	marginwidth="0">
	Loading...
</iframe>
"""
)

Congratulations, you’re done!#

Submit this assignment by uploading your notebook to the course Desire2Learn web page. Go to the “In-Class Assignments” folder, find the appropriate submission link, and upload everything there. Make sure your name is on it!

© Copyright 2024, The Department of Computational Mathematics, Science and Engineering at Michigan State University

Contents