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Jodie Burchell t-redactyl

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View diamonds_sample_weighted.csv
carat cut color clarity depth table price x y z nclarity
0.42 Ideal G VS1 61.4 56.0 921 4.82 4.85 2.97 5
0.3 Ideal D VS1 62.2 56.0 835 4.31 4.27 2.67 5
0.31 Ideal G IF 61.5 54.0 871 4.4 4.41 2.71 1
1.79 Premium H VS1 62.6 56.0 14904 7.81 7.76 4.87 5
0.28 Very Good F VS1 62.1 59.0 487 4.16 4.21 2.6 5
1.2 Premium E VS1 60.7 57.0 10053 6.89 6.81 4.16 5
1.52 Fair J VS1 65.4 58.0 7186 7.22 7.17 4.7 5
0.31 Ideal E VS1 61.8 55.0 692 4.36 4.38 2.7 5
0.52 Ideal G VS1 60.9 55.0 1815 5.22 5.16 3.16 5
View copper_data.csv
product year export percentage sum
copper 2006 4176 79 5255
copper 2007 8560 81 10505
copper 2008 6473 76 8519
copper 2009 10465 80 13027
copper 2010 14977 86 17325
copper 2011 15421 83 18629
copper 2012 14805 82 18079
copper 2013 15183 80 19088
copper 2014 14012 76 18437
View diamonds_sample_weighted.csv
0.42 Ideal G VS1 61.4 56.0 921 4.82 4.85 2.97 5
0.3 Ideal D VS1 62.2 56.0 835 4.31 4.27 2.67 5
0.31 Ideal G IF 61.5 54.0 871 4.4 4.41 2.71 1
1.79 Premium H VS1 62.6 56.0 14904 7.81 7.76 4.87 5
0.28 Very Good F VS1 62.1 59.0 487 4.16 4.21 2.6 5
1.2 Premium E VS1 60.7 57.0 10053 6.89 6.81 4.16 5
1.52 Fair J VS1 65.4 58.0 7186 7.22 7.17 4.7 5
0.31 Ideal E VS1 61.8 55.0 692 4.36 4.38 2.7 5
0.52 Ideal G VS1 60.9 55.0 1815 5.22 5.16 3.16 5
View diamonds_sample.csv
carat cut color clarity depth table price x y z
0.32 Ideal G VVS1 61.2 55.0 814 4.41 4.44 2.71
0.7 Fair I SI1 62.0 67.0 1848 5.54 5.5 3.42
1.46 Premium J SI2 60.1 58.0 6387 7.43 7.34 4.44
0.38 Premium G VS2 60.4 57.0 983 4.7 4.67 2.83
0.7 Very Good F VS2 62.9 56.0 2400 5.66 5.73 3.58
0.32 Ideal E SI2 62.7 55.0 576 4.42 4.39 2.76
0.71 Ideal F VS1 62.1 57.0 3066 5.73 5.76 3.57
0.3 Ideal E VS2 61.5 55.0 844 4.31 4.28 2.64
0.36 Ideal E VVS2 61.8 54.0 928 4.6 4.62 2.85
@t-redactyl
t-redactyl / remove_numbers.py
Last active Jun 22, 2017
Function designed to strip out all numbers (alphabetic - English only - and numeric) from a string as part of a text normalisation process.
View remove_numbers.py
# Function designed to strip out all numbers (alphabetic - English only - and numeric) from a string as part of a
# text normalisation process.
# Based on the text2num package (https://github.com/ghewgill/text2num) and using code from
# here (http://stackoverflow.com/questions/25346058/removing-list-of-words-from-a-string)
from string import digits
# List of number terms
nums = ['zero', 'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine', 'ten', 'eleven',
View contraction cleaner.py
# This code is not mine! Copied from https://gist.github.com/nealrs/96342d8231b75cf4bb82, but with suggested alteration to include text.lower() in the function.
import re
cList = {
"ain't": "am not",
"aren't": "are not",
"can't": "cannot",
"can't've": "cannot have",
"'cause": "because",
"could've": "could have",
View Analyses of resolutions.R
library(ggplot2)
positions = c("Travel and Holidays", "Finances", "Learning and Career", "Mental Wellbeing",
"Relationships", "Physical Health")
# What are the most popular resolutions?
p1 <- ggplot(twitter_df[twitter_df$Resolution.type != "",], aes(x = Resolution.type, fill = Resolution.type)) +
geom_bar() +
coord_flip() +
ggtitle("Number of tweets by resolution type") +
View Resolutions matching.py
# Import the relevant packages
import numpy as np
import re
# Create 6 new dummy variables which indicate whether one of the words associated with a resolution is present in the tweet.
twitter_df['Physical Health'] = np.where(twitter_df['Tweet'].str.contains('(?:^|\W)(weight|fit|exercise|gym|muscle|health|water|smoking|alcohol|drinking|walk|run|swim)(?:$|\W)',
flags = re.IGNORECASE), 1, 0)
twitter_df['Learning and Career'] = np.where(twitter_df['Tweet'].str.contains('(?:^|\W)(business|job|career|professional|study|learn|develop|advance|grades|school|university| read|study|skill|education)(?:$|\W)',
flags = re.IGNORECASE), 1, 0)
View gohan.go
package main
import (
"fmt"
)
type Saiyan struct {
Name string
Power int
Father *Saiyan
View christmas_tree.R
# Load in the packages
library(ggplot2)
library(extrafont)
font_import()
loadfonts()
# Read in the base Christmas tree data
ChristmasTree <- read.csv("https://raw.githubusercontent.com/t-redactyl/Blog-posts/master/Christmas%20tree%20base%20data.csv")
# Generate the "lights"
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