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[custom] fix lesson contents
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episodes/02-numpy.md

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but one way to remember the rule is that
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the index is how many steps we have to take from the start to get the item we want.
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![](fig/python-zero-index.svg){alt="\\"data\\" is a 3 by 3 numpy array containing row 0: \['A', 'B', 'C'\], row 1: \['D', 'E', 'F'\], androw 2: \['G', 'H', 'I'\]. Starting in the upper left hand corner, data\[0, 0\] = 'A', data\[0, 1\] = 'B',data\[0, 2\] = 'C', data\[1, 0\] = 'D', data\[1, 1\] = 'E', data\[1, 2\] = 'F', data\[2, 0\] = 'G',data\[2, 1\] = 'H', and data\[2, 2\] = 'I',in the bottom right hand corner."}
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![](fig/python-zero-index.svg){alt="'data' is a 3 by 3 numpy array containing row 0: \['A', 'B', 'C'\], row 1: \['D', 'E', 'F'\], androw 2: \['G', 'H', 'I'\]. Starting in the upper left hand corner, data\[0, 0\] = 'A', data\[0, 1\] = 'B',data\[0, 2\] = 'C', data\[1, 0\] = 'D', data\[1, 1\] = 'E', data\[1, 2\] = 'F', data\[2, 0\] = 'G',data\[2, 1\] = 'H', and data\[2, 2\] = 'I',in the bottom right hand corner."}
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::::::::::::::::::::::::::::::::::::::::: callout
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episodes/11-debugging.md

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Data analysts frequently use simple visualizations to check both
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the science they're doing
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and the correctness of their code
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(just as we did in the [opening lesson](01-numpy) of this tutorial).
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(just as we did in the [opening lesson](02-numpy.html) of this tutorial).
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This should only be used for debugging as a last resort,
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though,
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since it's very hard to compare two visualizations automatically.

episodes/12-cmdline.md

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## Finding Particular Files
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Using the `glob` module introduced [earlier](04-files),
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Using the `glob` module introduced [earlier](06-files.html),
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write a simple version of `ls` that shows files in the current directory
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with a particular suffix.
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A call to this script should look like this:
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index.md

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1. Calculate the average inflammation per day across all patients.
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2. Plot the result to discuss and share with colleagues.
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![](fig/lesson-overview.svg "Lesson Overview"){alt='3-step flowchart shows inflammation data records for patients moving to the Analysis stepwhere a heat map of provided data is generated moving to the Conclusion step that asks thequestion, How does the medication affect patients?'}
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![](episodes/fig/lesson-overview.svg "Lesson Overview"){alt='3-step flowchart shows inflammation data records for patients moving to the Analysis stepwhere a heat map of provided data is generated moving to the Conclusion step that asks thequestion, How does the medication affect patients?'}
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### Data Format
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instructors/instructor-notes.md

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We make reference in the lesson that this data is suspicious and has been
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synthetically generated in Python by the imaginary "Dr. Maverick"! The script used to generate
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the inflammation data is included as [`code/gen_inflammation.py`](code/gen_inflammation.py).
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the inflammation data is included as [`code/gen_inflammation.py`](../episodes/files/code/gen_inflammation.py).
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## Overall
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- NumPy methods.
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We used to use NumPy array methods in the first [NumPy topic](01-numpy).
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We used to use NumPy array methods in the first [NumPy topic](02-numpy.html).
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We switched these methods to the equivalent functions because a majority
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of instructors supported the change; see
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[PR #244](https://github.com/swcarpentry/python-novice-inflammation/pull/244)

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