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).
The data we'll consider will be stored in a Pandas dataframe, which is a convenient way to access data that we wish to analyze. The code below creates a dataframe called flint_data.
In [ ]:
# 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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In [ ]:
# We can use pandas to help us see what the data even looks like
## If we have a dataframe (like `flint_data`, which we loaded in),
## then we can use the `.head()` method to look at the first few rows
flint_data.head()
pandas has some really nice features. One of them is that it can give us nice, formatted tabular output (like when we call .head() on a dataframe or a series).
But, the tables pandas generates aren't interactive.
If you're used to working with spreadsheets (like Microsoft Excel or Google Sheets), you're probably used to double-clicking on a cell to edit it. And, if you try, you'll find that won't work here.
pandas isn't exactly Microsoft Excel, but that's OK
As we'll see, we'll enact certain operations (like changing a piece of data) differently in pandas than we would in a spreadsheet program. But, using pandas (instead of just a spreadhseet) will let us do things that would be hard or even impossible in Excel-land.
^ Look at that data above. And ask yourself some questions.
These kinds of questions are in the toolbox of every good data scientist.
With pandas, you can try to get information about a piece of data using its .head() and .tail() to see the first few and last few entries in the data. Most pandas data structures also have a describe() method. describe() will try to tell you some useful things about your data, but it's up to you to interpret them.
flint_data.head() # first few entries
flint_data.tail() # last few entries
flint_data.describe()# ???
Dora the Data Scientist asks
describe() giving me?describe() seems...less than helpful?
In [ ]:
# Try out the `.describe()` method by calling it on the flint_data dataframe
# What happens if you call `.describe()` on just a part of the data?
flint_data['SampleID'] # make this line of code describe the SampleID column
flint_data['SampleID'].describe()
The lead 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?
(1 part-per-million, or 1ppm, equals 1 milligram per liter (1mg/L))
$$ x \text{ ppb} \times \frac{1 \text{ ppm}}{1000 \text{ ppb}} \times \frac{1 \text{ mg/L}}{1 \text{ ppm}} $$
In [ ]:
# Write any python you need to here
flint_data.columns
# create a new column and assign its value to be a draw column divided by 1000
flint_data['converted_first_lead_readings'] = flint_data['Pb Bottle 1 (ppb) - First Draw'] / 1000
flint_data['converted_first_lead_readings']
// write any markdown here - explain what you did in the cells above!
In [ ]:
# Write any python you need to here
flint_data['Pb Bottle 1 (ppb) - First Draw'].mean() / 1000 # for mg/L units
// 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 [ ]:
# just some examples
## Visualizing it using cumulative distributions
flint_data['Pb Bottle 1 (ppb) - First Draw'].plot.hist(cumulative = True)
# calculating the percentage that exceed it
first_lead_levels = flint_data['Pb Bottle 1 (ppb) - First Draw'] / 1000
number_of_readings = first_lead_levels.count()
readings_that_exceed_the_limit = first_lead_levels[first_lead_levels > 0.015].count()
percentage_of_readings_over_the_limit = readings_that_exceed_the_limit / number_of_readings * 100
print('%f percent of readings exceeded the EPA limit' % percentage_of_readings_over_the_limit)
## Using quantiles
print('The top 10%% of readings exceed %f mg/L' % first_lead_levels.quantile(q = 0.90))
// 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
The mean levels are below the action limit, but our analysis clearly shows that 16% of the samples exceed the EPA action limit. Put another way, the 90th percentile of readings is more than 1.5 times the EPA leagl limit.
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
In [ ]:
from IPython.display import HTML
HTML(
"""
<iframe
src="https://goo.gl/forms/204OddKcRRhcZW7t1?embedded=true"
width="80%"
height="1200px"
frameborder="0"
marginheight="0"
marginwidth="0">
Loading...
</iframe>
"""
)