chrishwiggins

frequently asked question:

Q: I would like to ask your advice about preparing for a role in data science

A:

my advice would be to put together a portfolio of projects, on GitHub, evidencing that you know how to

staltz

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

---

This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

---

vasanthk

System Design Cheatsheet

Picking the right architecture = Picking the right battles + Managing trade-offs

Basic Steps

1. Clarify and agree on the scope of the system

• User cases (description of sequences of events that, taken together, lead to a system doing something useful)
  • Who is going to use it?
  • How are they going to use it?

redlotus

# -*- coding: utf-8 -*-
"""Example Google style docstrings.

This module demonstrates documentation as specified by the `Google Python
Style Guide`_. Docstrings may extend over multiple lines. Sections are created
with a section header and a colon followed by a block of indented text.

Example:
    Examples can be given using either the ``Example`` or ``Examples``
    sections. Sections support any reStructuredText formatting, including

bvaughn

// This is an example of how to fetch external data in response to updated props,
// If you are using an async mechanism that does not support cancellation (e.g. a Promise).

class ExampleComponent extends React.Component {
  _currentId = null;

  state = {
    externalData: null
  };

qpwo

"""
A minimal implementation of Monte Carlo tree search (MCTS) in Python 3
Luke Harold Miles, July 2019, Public Domain Dedication
See also https://en.wikipedia.org/wiki/Monte_Carlo_tree_search
https://gist.github.com/qpwo/c538c6f73727e254fdc7fab81024f6e1
"""
from abc import ABC, abstractmethod
from collections import defaultdict
import math

AlephNotation

# coding=utf-8

import pandas as pd
import itertools
import time
import multiprocessing
from typing import Callable, Tuple, Union

def groupby_parallel(groupby_df: pd.core.groupby.DataFrameGroupBy,
                     func: Callable[[Tuple[str, pd.DataFrame]], Union[pd.DataFrame, pd.Series]],