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Kyle Kelley rgbkrk

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Think globally, act locally
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blahah / 1_basic_browser.js
Last active February 8, 2023 09:04
loading geoJSON examples
// we have a geoJSON file (in this case for Guinea) on the web:
var guinea = 'https://raw.githubusercontent.com/johan/world.geo.json/master/countries/GIN.geo.json'
// and we have a function that does something with the geoJSON file:
var handleGeoJSON = function (data) {
// data is the JSON parsed into a JS object
console.log(data)
}
// using jquery (you must have loaded jQuery first) - http://api.jquery.com/jquery.getjson
@ceejbot
ceejbot / esm_in_node_proposal.md
Last active June 20, 2024 10:45
npm's proposal for supporting ES modules in node

ESM modules in node: npm edition

The proposal you’re about to read is not just a proposal. We have a working implementation of almost everything we discussed here. We encourage you to checkout and build our branch: our fork, with the relevant branch selected. Building and using the implementation will give you a better understanding of what using it as a developer is like.

Our implementation ended up differing from the proposal on some minor points. As our last action item before making a PR, we’re writing documentation on what we did. While I loathe pointing to tests in lieu of documentation, they will be helpful until we complete writing docs: the unit tests.

This repo also contains a bundled version of npm that has a new command, asset. You can read the documentation for and goals of that comma

@glebmikha
glebmikha / eda_prompt.md
Last active July 10, 2025 20:09
EDA Prompt for ChatGPT (and Humans)
  1. Calculate the percentage of missing values in each column and sort them in descending order.
    1. Missing values and outliers are not problems to be fixed! They are facts.
    2. During EDA you must not “fix” them because you have to deal with your data and problem as it is.
    3. If you see missing values, just report them.
  2. Identify and understand your target variable.
    1. Understand the type of the target variable: binary, categorical, or numeric.
    2. Examine the distribution of the target variable.
      1. For a binary variable (which needs to be converted into 0s and 1s if it is in string format), the mean (a proportion of 1s) is simply used.
      2. For a categorical variable, value counts are used.
  3. For a numeric variable, a histogram or a pandas' describe table is used.