Andrew minkymorgan

Useful Pandas Snippets

A personal diary of DataFrame munging over the years.

Convert Series datatype to numeric (will error if column has non-numeric values)
(h/t @makmanalp)

	# Derek M. Tishler
	# nx plot example deap individual
	# For post: https://groups.google.com/g/deap-users/c/nZFZpm5OPZA

	import matplotlib.pyplot as plt
	import networkx as nx
	from networkx.drawing.nx_agraph import graphviz_layout

	## May need is resuming session
	#from deap import creator

	###
	### This is a batched LSTM forward and backward pass. Written by Andrej Karpathy (@karpathy)
	### BSD License
	### Re-written in R by @georgeblck
	###

	rm(list=ls(all=TRUE))

	LSTM.init <- function(input_size, hidden_size, fancy_forget_bias_init = 3){
	# Initialize parameters of the LSTM (both weights and biases in one matrix)

	from __future__ import print_function

	import numpy as np
	from keras.callbacks import Callback
	from keras.layers import Dense
	from keras.layers import LSTM
	from keras.models import Sequential
	from numpy.random import choice

	from utils import prepare_sequences

	--[[
	-- Element-Research Torch RNN Tutorial for recurrent neural nets : let's predict time series with a laptop GPU
	-- https://christopher5106.github.io/deep/learning/2016/07/14/element-research-torch-rnn-tutorial.html
	--]]

	--[[
	-- Part 1
	--]]

	require 'rnn'

	:) CREATE TABLE test.nested (EventDate Date, UserID UInt64, Attrs Nested(Key String, Value String)) ENGINE = MergeTree(EventDate, UserID, 8192)

	CREATE TABLE test.nested
	(
	EventDate Date,
	UserID UInt64,
	Attrs Nested(
	Key String,
	Value String)
	) ENGINE = MergeTree(EventDate, UserID, 8192)

	<!-- place this in an %angular paragraph -->

	<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.5/leaflet.css" />
	<div id="map" style="height: 800px; width: 100%"></div>

	<script type="text/javascript">
	function initMap() {
	var map = L.map('map').setView([30.00, -30.00], 3);

	L.tileLayer('http://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', {

	Installing GeoMesa on GeoServer
	- Downloaded geoserver 2.5.2 war wget http://downloads.sourceforge.net/project/geoserver/GeoServer/2.5.2/geoserver-2.5.2-war.zip
	- and the corresponding WPS plugin wget http://downloads.sourceforge.net/project/geoserver/GeoServer/2.5.2/extensions/geoserver-2.5.2-wps-plugin.zip
	- Deployed war file to Tomcat_HOME/webapps folder and then copied WPS plugin jars over to WEB-INF/lib and checked to ensure permissions were good
	- Download Accumulo 1.5.3 :: wget http://apache.go-parts.com/accumulo/1.5.3/accumulo-1.5.3-bin.tar.gz
	- Download Scala 2.10 :: wget http://downloads.typesafe.com/scala/2.10.5/scala-2.10.5.tgz
	- Download Kafka 2.10-0.8.2.1 (Kafka 0.8 install that corresponds to scala 2.10) http://ftp.wayne.edu/apache/kafka/0.8.2.1/kafka_2.10-0.8.2.1.tgz
	- Download Hadoop wget http://apache.mirrors.tds.net/hadoop/common/hadoop-2.6.0/hadoop-2.6.0.tar.gz

	Unzip each of the downloads

	--[[
	Efficient LSTM in Torch using nngraph library. This code was optimized
	by Justin Johnson (@jcjohnson) based on the trick of batching up the
	LSTM GEMMs, as also seen in my efficient Python LSTM gist.
	--]]

	function LSTM.fast_lstm(input_size, rnn_size)
	local x = nn.Identity()()
	local prev_c = nn.Identity()()
	local prev_h = nn.Identity()()

	-- sudo luarocks install csvigo
	-- でインストールしておく
	require 'csvigo'

	--[[ test.csv
	a,b,c,d
	1,2,3,4
	5,5,3,3
	--]]