Approx median fixes #20
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Added a quick fix for #24 |
README.rst
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| .. code-block:: python | ||
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| >>> moe_example = [ | ||
| dict(min=math.nan, max=9999, n=6, moe=1), |
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If we're going to advise leaving first min and the last max null, we need to be totally consistent and do that in every single example. I also think that we should the default built in None type variable rather than nan so that we're hewing as closely as possible to the standard Python library.
README.rst
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| @@ -131,25 +131,27 @@ Approximating medians | |||
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| Estimate a median and approximate the margin of error. Follows the U.S. Census Bureau's official guidelines for estimation. Useful for generating medians for measures like household income and age when aggregating census geographies. | |||
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| Expects a list of dictionaries that divide the full range of data values into continuous categories. Each dictionary should have three keys: | |||
| Expects a list of dictionaries that divide the full range of data values into continuous categories. Each dictionary should have three keys with an optional fourth key for margin of error inputs: | |||
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When we first describe this input. We need to be emphatically clear that the first min and the last max must be None type objects and briefly explain why.
README.rst
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| dict(min=200000, max=math.nan, n=18, moe=10) | ||
| ] | ||
| >>> import numpy | ||
| >>> numpy.random.seed(711355) |
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I don't think we need the seed in example unless we are the point about reproducibility.
| Find the value for the dataset you are estimating by referring to `the bureau's reference material <https://www.census.gov/programs-surveys/acs/technical-documentation/pums/documentation.html>`_. | ||
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| If you have an associated "jam values" for your dataset provided in the `American Community Survey's technical documentation <https://www.documentcloud.org/documents/6165752-2017-SummaryFile-Tech-Doc.html#document/p20/a508561>`_, input the pair as a list to the `jam_values` keyword argument. | ||
| Then if the median falls in the first or last bin, the jam value will be returned instead of `None`. |
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Add a jam values example here that does not use the simulation method.
README.rst
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| ] | ||
| >>> import numpy | ||
| >>> numpy.random.seed(711355) | ||
| >>> census_data_aggregator.approximate_median(moe_example, design_factor=1, sampling_percentage=5*2.5, simulations=50, jam_values=[2499, 200001]) |
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This example probably doesn't need the jam value inputs
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| .. code-block:: python |
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If we believe that the moe-based method is superior, we should make it the first and default example.
Deals with #17 and #18