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start new with session name:
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| #!/usr/bin/env python | |
| import argparse | |
| import sys | |
| import jinja2 | |
| import markdown | |
| TEMPLATE = """<!DOCTYPE html> | |
| <html> |
| # The MIT License (MIT) | |
| # Copyright (c) 2016 Vladimir Ignatev | |
| # | |
| # Permission is hereby granted, free of charge, to any person obtaining | |
| # a copy of this software and associated documentation files (the "Software"), | |
| # to deal in the Software without restriction, including without limitation | |
| # the rights to use, copy, modify, merge, publish, distribute, sublicense, | |
| # and/or sell copies of the Software, and to permit persons to whom the Software | |
| # is furnished to do so, subject to the following conditions: | |
| # |
Picking the right architecture = Picking the right battles + Managing trade-offs
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:
| #/usr/bin/python3 | |
| """ Demonstration of logging feature for a Flask App. """ | |
| from logging.handlers import RotatingFileHandler | |
| from flask import Flask, request, jsonify | |
| from time import strftime | |
| __author__ = "@ivanleoncz" | |
| import logging |
| """ | |
| Upsert gist | |
| Requires at least postgres 9.5 and sqlalchemy 1.1 | |
| Initial state: | |
| [] | |
| Initial upsert: |