Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.
$ python -m SimpleHTTPServer 8000
Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.
$ python -m SimpleHTTPServer 8000
FWIW: I (@rondy) am not the creator of the content shared here, which is an excerpt from Edmond Lau's book. I simply copied and pasted it from another location and saved it as a personal note, before it gained popularity on news.ycombinator.com. Unfortunately, I cannot recall the exact origin of the original source, nor was I able to find the author's name, so I am can't provide the appropriate credits.
I have moved this over to the Tech Interview Cheat Sheet Repo and has been expanded and even has code challenges you can run and practice against!
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Attention: the list was moved to
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The dplyr
package in R makes data wrangling significantly easier.
The beauty of dplyr
is that, by design, the options available are limited.
Specifically, a set of key verbs form the core of the package.
Using these verbs you can solve a wide range of data problems effectively in a shorter timeframe.
Whilse transitioning to Python I have greatly missed the ease with which I can think through and solve problems using dplyr in R.
The purpose of this document is to demonstrate how to execute the key dplyr verbs when manipulating data using Python (with the pandas
package).
dplyr is organised around six key verbs:
# You don't need Fog in Ruby or some other library to upload to S3 -- shell works perfectly fine | |
# This is how I upload my new Sol Trader builds (http://soltrader.net) | |
# Based on a modified script from here: http://tmont.com/blargh/2014/1/uploading-to-s3-in-bash | |
S3KEY="my aws key" | |
S3SECRET="my aws secret" # pass these in | |
function putS3 | |
{ | |
path=$1 |
Once in a while, you may need to cleanup resources (containers, volumes, images, networks) ...
// see: https://github.com/chadoe/docker-cleanup-volumes
$ docker volume rm $(docker volume ls -qf dangling=true)
$ docker volume ls -qf dangling=true | xargs -r docker volume rm
This simple script will take a picture of a whiteboard and use parts of the ImageMagick library with sane defaults to clean it up tremendously.
The script is here:
#!/bin/bash
convert "$1" -morphology Convolve DoG:15,100,0 -negate -normalize -blur 0x1 -channel RBG -level 60%,91%,0.1 "$2"
This is free and unencumbered software released into the public domain. | |
Anyone is free to copy, modify, publish, use, compile, sell, or | |
distribute this software, either in source code form or as a compiled | |
binary, for any purpose, commercial or non-commercial, and by any | |
means. | |
In jurisdictions that recognize copyright laws, the author or authors | |
of this software dedicate any and all copyright interest in the | |
software to the public domain. We make this dedication for the benefit |