hute37

#!/bin/bash

# This installs alacritty terminal on ubuntu (https://github.com/jwilm/alacritty)
# You have to have rust/cargo installed for this to work

# Install required tools
sudo apt-get install -y cmake libfreetype6-dev libfontconfig1-dev xclip

# Download, compile and install Alacritty
git clone https://github.com/jwilm/alacritty

hute37

VIM KEYBOARD SHORTCUTS

MOVEMENT

h        -   Move left
j        -   Move down
k        -   Move up
l        -   Move right
$        -   Move to end of line
0        -   Move to beginning of line (including whitespace)

hute37

#!/bin/bash
#If no argument is specified, ask for it and exit
if [[ -z "$@" ]];
then
    echo "An argument is needed to run this script";
    exit
else
    arg="$@"
    #Basic check to make sure argument number is valid. If not, display error and exit
    if [[ $(($(echo $arg | grep -o "\s" | wc --chars) / 2 )) -ne 2 ]];

hute37

#!/bin/bash
##
# Microsoft Azure: show active VM via curl 
#

# Azure OAUTH HOWTO from: 
# - https://medium.com/@mauridb/calling-azure-rest-api-via-curl-eb10a06127
#

AR=0; \

hute37

<?php # -*- coding: utf-8 -*-
declare( strict_types = 1 ); 
/**
 * Plugin Name: Port Switch
 * Plugin URI:  https://gist.github.com/bueltge/51013ab809f5f0f5e305c70c2d393fff
 * Description: Port switch to get an workaround to use WordPress Multisite also without the default port 80.
 * Version:     dev
 * Author:      Frank Bültge
 * Author URI:  https://bueltge.de
 * License:     MIT

hute37

#!/bin/sh


##
# from: https://unix.stackexchange.com/questions/17045/how-to-preserve-bookmarks-when-rearranging-pages-of-a-pdf-file-with-tools-like-p
#

pdftk A=in.pdf cat A2-end output temp.pdf

pdftk in.pdf dump_data > in.info

hute37

#different aggregate functions
table = pd.pivot_table(df,index=['Sex','Pclass'],aggfunc={'Age':np.mean,'Survived':np.sum})
table

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df.groupby(['Outlet_Type','Item_Type']).agg(mean_MRP=('Item_MRP',np.mean),mean_Sales=('Item_Outlet_Sales',np.mean))

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df['Item_Weight'] = df.groupby(['Item_Fat_Content','Item_Type'])['Item_Weight'].transform(lambda x: x.fillna(x.mean()))

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def filter_func(x):
    return x['Item_Weight'].std() < 3

df_filter = df.groupby(['Item_Weight']).filter(filter_func)
df_filter.shape