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03-matplotlib.md

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@@ -48,7 +48,7 @@ image = matplotlib.pyplot.imshow(data)
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matplotlib.pyplot.show()
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```
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![](fig/inflammation-01-imshow.svg){alt='Heat map representing the data variable. Each cell is colored by value along a color gradientfrom blue to yellow.'}
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![](fig/inflammation-01-imshow.svg){alt='Heat map representing the data variable. Each cell is colored by value along a color gradient from blue to yellow.'}
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Each row in the heat map corresponds to a patient in the clinical trial dataset, and each column
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corresponds to a day in the dataset. Blue pixels in this heat map represent low values, while
@@ -242,7 +242,7 @@ axes3.set_ylim(numpy.amin(min_data), numpy.amax(min_data) * 1.1)
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## Drawing Straight Lines
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In the center and right subplots above, we expect all lines to look like step functions because
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non-integer value are not realistic for the minimum and maximum values. However, you can see
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non-integer values are not realistic for the minimum and maximum values. However, you can see
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that the lines are not always vertical or horizontal, and in particular the step function
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in the subplot on the right looks slanted. Why is this?
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04-lists.md

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@@ -169,7 +169,7 @@ Since a list can contain any Python variables, it can even contain other lists.
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For example, you could represent the products on the shelves of a small grocery shop
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as a nested list called `veg`:
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![](fig/04_groceries_veg.png){alt='veg is represented as a shelf full of produce. There are three rows of vegetableson the shelf, and each row contains three baskets of vegetables. We can labeleach basket according to the type of vegetable it contains, so the top rowcontains (from left to right) lettuce, lettuce, and peppers.'}
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![](fig/04_groceries_veg.png){alt='veg is represented as a shelf full of produce. There are three rows of vegetables on the shelf, and each row contains three baskets of vegetables. We can label each basket according to the type of vegetable it contains, so the top row contains (from left to right) lettuce, lettuce, and peppers.'}
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To store the contents of the shelf in a nested list, you write it this way:
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you can reference each row on the shelf as a separate list. For example, `veg[2]`
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represents the bottom row, which is a list of the baskets in that row.
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![](fig/04_groceries_veg0.png){alt='veg is now shown as a list of three rows, with veg\[0\] representing the top row ofthree baskets, veg\[1\] representing the second row, and veg\[2\] representing the bottom row.'}
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![](fig/04_groceries_veg0.png){alt='veg is now shown as a list of three rows, with veg\[0\] representing the top row of three baskets, veg\[1\] representing the second row, and veg\[2\] representing the bottom row.'}
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Index operations using the image would work like this:
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To reference a specific basket on a specific shelf, you use two indexes. The first
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index represents the row (from top to bottom) and the second index represents
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the specific basket (from left to right).
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![](fig/04_groceries_veg00.png){alt='veg is now shown as a two-dimensional grid, with each basket labeled according toits index in the nested list. The first index is the row number and the secondindex is the basket number, so veg\[1\]\[3\] represents the basket on the far rightside of the second row (basket 4 on row 2): zucchini'}
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![](fig/04_groceries_veg00.png){alt='veg is now shown as a two-dimensional grid, with each basket labeled according to its index in the nested list. The first index is the row number and the second index is the basket number, so veg\[1\]\[3\] represents the basket on the far right side of the second row (basket 4 on row 2): zucchini'}
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```python
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print(veg[0][0])

05-loop.md

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we wrote Python code that plots values of interest from our first
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inflammation dataset (`inflammation-01.csv`), which revealed some suspicious features in it.
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![](fig/03-loop_2_0.png){alt="Line graphs showing average, maximum and minimum inflammation across all patients over a 40-dayperiod."}
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![](fig/03-loop_2_0.png){alt="Line graphs showing average, maximum and minimum inflammation across all patients over a 40-day period."}
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We have a dozen data sets right now and potentially more on the way if Dr. Maverick
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can keep up their surprisingly fast clinical trial rate. We want to create plots for all of
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Using the odds example above, the loop might look like this:
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![](fig/05-loops_image_num.png){alt="Loop variable 'num' being assigned the value of each element in the list odds in turn andthen being printed"}
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![](fig/05-loops_image_num.png){alt="Loop variable 'num' being assigned the value of each element in the list odds in turn and then being printed"}
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where each number (`num`) in the variable `odds` is looped through and printed one number after
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another. The other numbers in the diagram denote which loop cycle the number was printed in (1

md5sum.txt

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"episodes/01-intro.md" "0d994b7ac56219852fcaa82bb54e2ee2" "site/built/01-intro.md" "2025-05-07"
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"episodes/02-numpy.md" "1b9c4000cea628f84024abab49012a06" "site/built/02-numpy.md" "2023-11-07"
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"episodes/03-matplotlib.md" "3bb8e6088285f01152e2e24da6f36fbc" "site/built/03-matplotlib.md" "2023-11-07"
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"episodes/04-lists.md" "d1768916dafc023eeb2f3838932b6289" "site/built/04-lists.md" "2023-11-07"
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"episodes/05-loop.md" "80a7584340db6ec23b0c592d8e9553d2" "site/built/05-loop.md" "2024-08-25"
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"episodes/04-lists.md" "d319b00a34dd6e1fb95247496e192de7" "site/built/04-lists.md" "2025-05-21"
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"episodes/06-files.md" "eda3d2e5ff2a23f37048631427cb4f48" "site/built/06-files.md" "2023-11-07"
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"episodes/08-func.md" "c52d2937ad2fc21d3594d0a0c07012cf" "site/built/08-func.md" "2025-05-07"

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