Abstract
Chapter 9 sees us return to Jupyter to use its features for data visualization and easy user interaction. We will look at how to use our asynchronous functions with widgets in Jupyter notebooks as well as advanced use of iterators and ways of implementing complex data types.
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Notes
- 1.
Size is calculated using sys.getsizeof(...). This does not include the size of attributes on an object, which can be found with sys.getsizeof(obj.__dict__) for simple objects.
- 2.
Estimated using timeit.timeit(...), as follows:
setup = """ import datetime import uuid from sqlalchemy.util._collections import lightweight_named_tuple result = lightweight_named_tuple("result", ["id", "collected_at", "sensor_name", "deployment_id", "data",]) data = (1, datetime.datetime.now(), "Example", uuid.uuid4(), None) """ timeit.timeit("result(data)", setup)
- 3.
The “o” style specifies a circular marker and no line. The string can contain a marker type, a line style, and a color. *r would plot red stars, - is a line in the default color with no markers, s--m is magenta squares connected by a dashed line, etc. The additional resource list in this chapter contains a link to the full specification.
- 4.
zip(*iterables) flips the way that an iterable of iterables is split up. I find it easiest to imagine this as equivalent to rotating a spreadsheet. If your input iterables are ["Matt", "Leeds"], ["Jesse", "Seattle"], and ["Nejc", "Ljubljana"], you can imagine that as being equivalent to a spreadsheet where the names are in column A and the cities in column B. In that case, Matt is row 1, Jesse is row 2, and Nejc is row 3. tuple(zip(*names_and_cities)) reads out the columns in order, so it would be (('Matt', 'Jesse', 'Nejc'), ('Leeds', 'Seattle', 'Ljubljana')).
- 5.
I’d recommend not adding a commit() call, as this will allow changes to the database to be rolled back between tests.
- 6.
The temperature sensor is intended as a measure of ambient temperature. If we write a new sensor type to collect a different type of temperature data, we may need to reconsider this filter.
- 7.
Specifically, IPython tries to compile the cell to bytecode and checks for a SyntaxError. If there is a SyntaxError, it will wrap the code in a coroutine and try again.
- 8.
asyncio.run(...) is not re-entrant: calls cannot be nested.
- 9.
Mypy would word the error as error: Argument 2 to "add_number_from_callback" has incompatible type "Callable[[], Coroutine[Any, Any, int]]"; expected "Callable[[], int]"
- 10.
- 11.
The code used to extract these is as follows. The data is from the www.naturalearthdata.com data set.
import fiona path = "ne_10m_admin_0_countries.shp" shape = fiona.open(path) countries = tuple(shape) UK = [country for country in countries if country['properties']['ADMIN'] == "United Kingdom"][0] coastlines = UK['geometry']['coordinates'] by_complexity = sorted(coastlines, key=lambda coords: len(coords[0])) gb_boundary = by_complexity[-1][0]
This was omitted from the Jupyter notebook to reduce the dependencies needed to use it. In practice, this function would be used rather than literal tuples.
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© 2020 Matthew Wilkes
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Wilkes, M. (2020). Viewing the data. In: Advanced Python Development. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-5793-7_9
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DOI: https://doi.org/10.1007/978-1-4842-5793-7_9
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