| import ruamel.yaml | |
| yaml = ruamel.yaml.YAML() | |
| data = yaml.load(open('environment.yml')) | |
| requirements = [] | |
| for dep in data['dependencies']: | |
| if isinstance(dep, str): | |
| package, package_version, python_version = dep.split('=') | |
| if python_version == '0': |
| # List unique values in a DataFrame column | |
| df['Column Name'].unique() | |
| # To extract a specific column (subset the dataframe), you can use [ ] (brackets) or attribute notation. | |
| df.height | |
| df['height'] | |
| # are same thing!!! (from http://www.stephaniehicks.com/learnPython/pages/pandas.html | |
| # -or- | |
| # http://www.datacarpentry.org/python-ecology-lesson/02-index-slice-subset/) |
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.
| #!/usr/bin/env bash | |
| local_json=$1 | |
| remote_ip=$2 | |
| remote_json=$3 | |
| remote_json_on_local=$(jupyter --runtime-dir)/remote.json | |
| scp $remote_ip:$remote_json $remote_json_on_local | |
| for socket_name in hb_port control_port stdin_port iopub_port shell_port; do | |
| local_port=$(sed 's/,/\n/g' $local_json | grep $socket_name | grep -o "[0-9]\+") | |
| remote_port=$(grep $socket_name $remote_json_on_local | grep -o "[0-9]\+") |
This Gist is created in 2014, and it's highliy outdated now, according to one of mitmproxy's manjor contributor (check his comment below). Thanks for letting us know, @mhils!
Modern applications usually make use of back-end API servers to provide their services. With a non-transparent HTTPs proxy, which intercepts the communication between clients and servers (aka the man-in-the-middle scheme), you can easily manipulate both API requests and responses.