Reid Williams ReidWilliams

## sign.html
<html>
<body>
    <script type="module">
        // Use a simple local http server like 'python3 -m http.server'
        // Needs to liston on port 8000
        // Janky, but I use a local copy of the Ethers libray to avoid CORS headaches
        // https://cdn.ethers.io/lib/ethers-5.2.esm.min.js

        // Ethers docs:
        // https://docs.ethers.io/v5/getting-started/

## regexp-point.txt
POINT \((-?\d+.\d+) (-?\d+.\d+)\)
$1,$2

## git.md

      
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                ReidWilliams
                / git.md
            
            
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                Git cheatsheet
              
          
    git checkout --track -b  origin/

  
## container.js
import React from 'react'

const createContainer = (ComposedComponent) => {
  class Container extends React.Component {
    render() {
      const componentProps = {
        ...this.props
      }

      return (

## component.story.js
// Dependencies
import React from 'react'
import { storiesOf } from '@storybook/react'
import centered from '@storybook/addon-centered'
import { action } from '@storybook/addon-actions'

// Local Dependencies
import { ComponentDecorator } from 'stories/decorators'
import MyComponent from './index'

## component.js
import React from 'react'
import PropTypes from 'prop-types'
import styled from 'styled-components'

// locals
// import { theme } from 'styles'

const Root = styled.div``

class MyComponent extends React.Component {

## rsync-cheatsheet.md

      
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                ReidWilliams
                / rsync-cheatsheet.md
            
            
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              October 18, 2019 05:51
            
          
    rsync -avh source/ destination
-a, archive (recursive and preserve everything about the file)

-v, verbose

-h, human readable sizes
Also useful:

-n, dry run

-z, compress

--delete, delete files that don't exist on the source side

  
## gist:20cfdeb1dd4d90201b24d40a3e5a181d
def estimate(self, X):
    """ Use saved model to predict/estimate retention probability.

    Arguments:
        X (dict): dictionary of features, values.
            Automatically uses model trained on same set of features as
            X argument.

    Returns:
        List of one element, retention probability.

## pandas.txt
# rename a dataframe column
dataframe = dataframe.rename(index=str, columns={'old_col_name': 'new_col_name'})

# get all possible values for a string / categorical column
dataframe[column].value_counts(dropna=False)

# group data and count number of rows in a group
grouped = dataframe.groupby(color)
nreds = len(grouped.get_group('red'))

## freeze-install.txt
$ pip freeze > requirements.txt
$ pip install -r requirements.txt
	<html>
	<body>
	<script type="module">
	// Use a simple local http server like 'python3 -m http.server'
	// Needs to liston on port 8000
	// Janky, but I use a local copy of the Ethers libray to avoid CORS headaches
	// https://cdn.ethers.io/lib/ethers-5.2.esm.min.js

	// Ethers docs:
	// https://docs.ethers.io/v5/getting-started/
	import React from 'react'

	const createContainer = (ComposedComponent) => {
	class Container extends React.Component {
	render() {
	const componentProps = {
	...this.props
	}

	return (
	// Dependencies
	import React from 'react'
	import { storiesOf } from '@storybook/react'
	import centered from '@storybook/addon-centered'
	import { action } from '@storybook/addon-actions'

	// Local Dependencies
	import { ComponentDecorator } from 'stories/decorators'
	import MyComponent from './index'
	import React from 'react'
	import PropTypes from 'prop-types'
	import styled from 'styled-components'

	// locals
	// import { theme } from 'styles'

	const Root = styled.div``

	class MyComponent extends React.Component {
	def estimate(self, X):
	""" Use saved model to predict/estimate retention probability.

	Arguments:
	X (dict): dictionary of features, values.
	Automatically uses model trained on same set of features as
	X argument.

	Returns:
	List of one element, retention probability.
	# rename a dataframe column
	dataframe = dataframe.rename(index=str, columns={'old_col_name': 'new_col_name'})

	# get all possible values for a string / categorical column
	dataframe[column].value_counts(dropna=False)

	# group data and count number of rows in a group
	grouped = dataframe.groupby(color)
	nreds = len(grouped.get_group('red'))
	$ pip freeze > requirements.txt
	$ pip install -r requirements.txt