stefanv

#!/usr/bin/python
# coding=utf-8

# Python version of Zach Holman's "spark"
# https://github.com/holman/spark
# by Stefan van der Walt <[email protected]>

"""
USAGE:

hellerbarde

Latency numbers every programmer should know

L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns             
Compress 1K bytes with Zippy ............. 3,000 ns  =   3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns  =  20 µs
SSD random read ........................ 150,000 ns  = 150 µs

Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs

why-not

"""making a dataframe"""
df = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB'))

"""quick way to create an interesting data frame to try things out""" 
df = pd.DataFrame(np.random.randn(5, 4), columns=['a', 'b', 'c', 'd'])

"""convert a dictionary into a DataFrame"""
"""make the keys into columns"""
df = pd.DataFrame(dic, index=[0])

dan-blanchard

For ETS's SKLL project, we found out the hard way that Travis-CI's support for numpy and scipy is pretty abysmal. There are pre-installed versions of numpy for some versions of Python, but those are seriously out of date, and scipy is not there are at all. The two most popular approaches for working around this are to (1) build everything from scratch, or (2) use apt-get to install more recent (but still out of date) versions of numpy and scipy. Both of these approaches lead to longer build times, and with the second approach, you still don't have the most recent versions of anything. To circumvent these issues, we've switched to using Miniconda (Anaconda's lightweight cousin) to install everything.

A template for installing a simple Python package that relies on numpy and scipy using Miniconda is provided below. Since it's a common s