我愛你，你是自由的。
Hi there folks. It’s been a long time since I last published a post. I have been busy. However in this post I am going to share some really informative tips and tricks which you might not have known about. So without wasting any time lets get straight to them:
Instead of doing:
i = 0
for item in iterable:
print i, item
i += 1
We can do:
for i, item in enumerate(iterable):
print i, item
Enumerate can also take a second argument. Here is an example:
Python is one of the best programming languages out there, with an extensive coverage in scientific computing: computer vision, artificial intelligence, mathematics, astronomy to name a few. Unsurprisingly, this holds true for machine learning as well.
Of course, it has some disadvantages too; one of which is that the tools and libraries for Python are scattered. If you are a unix-minded person, this works quite conveniently as every tool does one thing and does it well. However, this also requires you to know different libraries and tools, including their advantages and disadvantages, to be able to make a sound decision for the systems that you are building. Tools by themselves do not make a system or product better, but with the right tools we can work much more efficiently and be more productive. Therefore, knowing the right tools for your work domain is crucially important.
最近，Analysis with Programming加入了Planet Python。作為該網站的首批特約博客，我這裏來分享一下如何通過Python來開始數據分析。具體內容如下：
Python is a very expressive language. It provides us with a large standard library and many builtins to get the job done quickly. However, many can get lost in the power that it provides, fail to make full use of the standard library, value one liners over clarity and misunderstand its basic constructs. This is a non-exhaustive list of a few of the pitfalls programmers new to Python fall into.
Simply Statistics: Written by the Biostatistics professors at Johns Hopkins University who also run Coursera’s Data Science Specialization
yhat’s blog: Beginner-friendly content, usually in Python
No Free Hunch (Kaggle’s blog): Mostly interviews with competition winners, or updates on their competitions
FastML: Various machine learning content, often with code
Edwin Chen: Infrequently updated, but long and thoughtful pieces
FiveThirtyEight: Tons of timely data-related content
Machine Learning Mastery: Frequent posts on machine learning, very accessible
Data School: Kevin Markham’s blog! Beginner-focused, with reference guides and videos
MLWave: Detailed posts on Kaggle competitions, by a Kaggle Master
Data Science 101: Short, frequent content about all aspects of data science
ML in the Valley: Thoughtful pieces by the Director of Analytics at Codecademy
一個問題之所以很少有人回答，要麽是因為很少有人知道問題的答案，要麽是因為它涉及到一個晦澀而隱蔽的知識點(但可能是你關心的)。我過去認為是我在Java IAQ中發明了這個詞組，但是它也出現在了以資料豐富而著稱的About.com Urban Legends網站上. 關於Python的FAQ有很多,但是Python的IAQ只有這一個。(“少見問題列表”倒是有一些，其中一個是有諷刺意味的C。)
I Come, I Live, I Experience.