Python Interview Question and Answers For the last few weeks I have been interviewing several people for Python/Django developers so I thought that it might be helpful to show the questions I am asking together with the answers. The reason is … OK, let me tell you a story first. I remember when one of my university professors introduced to us his professor – the one who thought him. It was a really short visit but I still remember one if the things he said. “Ignorance is not bad, the bad thing is when you do no want to learn.” So back to the reason – if you have at least taken care to prepare for the interview, look for a standard questions and their answers and learn them this is a good start. Answering these question may not get you the job you are applying for but learning them will give you some valuable knowledge about Python. This post will include the questions that are Python specific and I’ll post the Django question separately. How are arguments passed – by reference of by value? The short answer is “neither”, actually it is called “call by object” or “call by sharing”(you can check here for more info). The longer one starts with the fact that this terminology is probably not the best one to describe how Python works. In Python everything is an object and all variables hold references to objects. The values of these references are to the functions. As result you can not change the value of the reference but you can modify the object if it is mutable. Remember numbers, strings and tuples are immutable, list and dicts are mutable. Do you know what list and dict comprehensions are? Can you give an example? List/Dict comprehensions are syntax constructions to ease the creation of a list/dict based on existing iterable. According to the 3rd edition of “Learning Python” list comprehensions are generally faster than normal loops but this is something that may change between releases. Examples:# simple iterationa = # list comprehensiona = # dict comprehensiona = {x: x*2 for x in range(10)}# a == {0: 0, 1: 2, 2: 4, 3: 6, 4: 8, 5: 10, 6: 12, 7: 14, 8: 16, 9: 18} What is PEP 8? PEP 8 is a coding convention(a set of recommendations) how to write your Python code in order to make it more readable and useful for those after you. For more information check PEP 8 . Do you use virtual environments? I personally and most(by my observation) of the Python developers find the virtual environment tool extremely useful. Yeah, probably you can live without it but this will make the work and support of multiple projects that requires different package versions a living hell. Can you sum all of the elements in the list, how about to multuply them and get the result? # the basic ways = 0for x in range(10): s += x# the right ways = sum(range(10))# the basic ways = 1for x in range(1, 10): s = s * x# the other wayfrom operator import mulreduce(mul, range(1, 10)) As for the last example, I know Guido van Rossum is not a fan of reduce, more info here , but still for some simple tasks reduce can come quite handy. Do you know what is the difference between lists and tuples? Can you give me an example for their usage? First list are mutable while tuples are not, and second tuples can be hashed e.g. to be used as keys for dictionaries. As an example of their usage, tuples are used when the order of the elements in the sequence matters e.g. a geographic coordinates, “list” of points in a path or route, or set of actions that should be executed in specific order. Don’t forget that you can use them a dictionary keys. For everything else use lists. Do you know the difference between range and xrange? Range returns a list while xrange returns a generator xrange object which takes the same memory no matter of the range size. In the first case you have all items already generated(this can take a lot of time and memory) while in the second you get the elements one by one e.g. only one element is generated and available per iteration. Simple example of generator usage can be find in the problem 2 of the “homework” for my presentation Functions in Python Tell me a few differences between Python 2.x and 3.x There are many answers here but for me some of the major changes in Python 3.x are: all strings are now Unicode, print is now function not a statement. There is no range, it has been replaced by xrange which is removed. All classes are new style and the division of integers now returns float. What are decorators and what is their usage? According to Bruce Eckel’s Introduction to Python Decorators “Decorators allow you to inject or modify code in functions or classes”. In other words decorators allow you to wrap a function or class method call and execute some code before or after the execution of the original code. And also you can nest them e.g. to use more than one decorator for a specific function. Usage examples include – logging the calls to specific method, checking for permission(s), checking and/or modifying the arguments passed to the method etc. The with statement and its usage. In a few words the with statement allows you to executed code before and/or after a specific set of operations. For example if you open a file for reading and parsing no matter what happens during the parsing you want to be sure that at the end the file is closed. This is normally achieved using the try… finally construction but the with statement simplifies it usin the so called “context management protocol”. To use it with your own objects you just have to define __enter__ and __exit__ methods. Some standard objects like the file object automatically support this protocol. For more information you may check Understanding Python’s “with” statement . Well I hope this will be helpful, if you have any question or suggestion feel free to comment. Update : Due to the lots of comments on Reddit and LinkedIn, I understood that there is some misunderstanding about the post. First, the questions I have published are not the only ones I ask, the interview also includes such related to general programming knowledge and logical thinking. Second the questions above help me get a basic understanding of your Python knowledge but they are not the only think that makes my decision. Not answering some of them does not mean that you won’t get the job, but it may show me on which parts we should to work. 总结了10道题的考试侧重点，供参考： 1.How are arguments passed – by reference of by value? 考的是语法，基本功，虽说python程序员可以不用关心堆栈指针那些头疼的东东，但传引用和传值的区别还是必需清楚的。个人感觉从python中一切都是对象的角度看，第一题问传值还是传引用其实是考官有意看面试者是不是概念清楚，真正希望考生回答的是哪些对象传递到函数中是只读的或者说不可变的。 2.Do you know what list and dict comprehensions are? Can you give an example? 典型的pythonic用法 3.What is PEP 8? 是不是有良好的编码习惯 4.Do you use virtual environments? 是不是有独立配开发环境的能力 5.Can you sum all of the elements in the list, how about to multuply them and get the result? 期待你用pythonic方式解决 6.Do you know what is the difference between lists and tuples? Can you give me an example for their usage? python中两种最基本常用的数据结构 7.Do you know the difference between range and xrange? 期待你清楚generator及其是如何使用内存的，generator用法还是典型的pythonic 8.Tell me a few differences between Python 2.x and 3.x 希望你对新老产品都有所关注 9.What are decorators and what is their usage? 典型的pythonic用法 10.The with statement and its usage. 典型的pythonic语法 参考链接 http://ilian.i-n-i.org/python-interview-question-and-answers/ http://www.**.com/group/topic/38792398/
http://markdow.tumblr.com/post/39933598670/the-effects-of-qe-on-ust-yields-now-the-answers-start The Effects of QE on UST Yields—Now the Answers Start to Matter The debate about the impact on US treasury yields from the Federal Reserve’s LSAP programs—often referred to as Quantitative Easing—is raging into its fourth year. In fact, now that the time series are getting long enough for more robust number crunching, I suspect academics are going to really start diving in and begin the writing of history. Practitioners, however—both policy makers and those of us who have money on the line—don’t have the luxury of time. We are entering a critical phase right now. Why? Household leverage has been the prime impediment to a normal functioning of monetary policy. And it is now starting to get down to a point where monetary policy will start gaining traction. Not a lot of traction, because these processes are slow, but any traction at all means the days of the dreaded liquidity trap are numbered. So we are now going to have think harder, and in more practical terms, about the counterfactual: where would UST rates be without the exceptional monetary stimuli the Fed hath wrought. There are two basic camps in this debate, and from where I sit, neither side has it quite right. One camp says the Fed’s massive purchases of USTs and agency mortgages have artificially lowered rates a lot. Looking at UST yields and spreads throughout the fixed income complex gives them sticker shock. Some fixed income managers are even mad. They fear that this is inducing a misallocation of resources, incenting higher government spending than would otherwise be the case, is hurting savers, and might constitute a new bubble. Many in this camp fear high inflation will follow. Their prediction for when the Fed stops buying? Pain—pain in markets, pain in the economy, and pain in the budget, stemming from the higher UST rates they assume will follow. This camp comprises much of the professional fixed income asset management crowd, the majority of sell-side strategists, a fair number of economists (e.g. the recent op-ed by Marty Feldstein in the WSJ ), and virtually all of the Policy Bears (think, for example ZeroHedge or CNBC’s Ric Santelli). The second camp claims it is all about expectations, not physical purchases, and that QEs have actually raised UST yields relative to where they would otherwise be. Joe Wiesenthal over at Business Insider was perhaps the first to propagate this view. Others, like Matt O’Brien at the Atlantic and Matt Yglesias at Slate , have more recently laid out the same basic case: looser monetary policy from central bank bond buying raises, rather than lowers, rates because the indirect effect through expectations on future nominal GDP growth is greater than the countervailing pressures from bond purchases. This camp comprises an increasing number of sharp-eyed financial/economic journalists, some of the more nuanced fixed income veterans, most salt water economists, and a lot of equity managers (who always seem to be on the lookout for a bullish story). This view is always buttressed by some version of the very convincing chart shown below, in this instance lifted from Matt O’Brien: In it, one can see very clearly that when the physical purchases of USTs and mortgages were taking place bond prices were indeed going down and yields higher. Conversely, the moves higher in price and lower in yield happened when the Fed “wasn’t in the market”. The rationale is simple: the Fed tended to hint at or announce QE programs when the economy and markets appeared to be weakening sharply. The chart shows that even though we were in the throes of a deep in a liquidity trap, the psychological effect of Fed support was strong enough to snap us out of slide into self-reinforcing pessimism and move us away from nastier equilibria. Okay, that was easy. So, case closed? QE means higher rates, right? Not so fast. Look more closely at the chart. The idea behind large scale asset purchases, of course, is that they are supposed to drive down interest rates and facilitate the healing of bloated private sector balance sheets, in our case, in the household and financial sectors. This, in turn, would lead ultimately to a resumption of lending, once the lenders and borrowers have worked themselves back into stronger financial positions. The theoretical debate has taken for granted that LSAPs lowers rates, and instead focused on the channel through which the purchases would achieve this. Thinking about these channels is important. First, there is the “Flow” channel. Some academics and most markets participants have been inclined to believe that LSAPs lower rates through a flow effect, that is, through the physical purchases. The intuition here is powerful: sharply increased demand means higher prices, lower yields. On the other hand, many academics and policy makers—including the bulk of the Fed—believe rates are lowered through a stock effect. That is, asset purchases reduce the available stock of assets, and so, for a given view, the clearing price will be higher (and the yield lower) than otherwise would have been the case. The implication is that this affects, over time, the level of yields, even if the oscillations in yields are driven by other factors, such as economic expectations. Now, let’s go back and look at the chart again. You will see what technicians call “lower highs and lower lows”. And it’s important to note that this pattern was taking place against the backdrop of an improving economy which would normally push UST yields higher. This to me means two important things: one, LSAPs have almost certainly over time lowered the clearing rate for UST yields—even though the impulse correlation, driven by economic expectations, has worked in the short-term in the opposite direction. The second observation is that the “expectations effect” was of lesser amplitude with each Fed announcement, again, against the backdrop of an improving economy. In fact, after QE3, there was virtually no bump at all. This is because sentiment surrounding monetary policy has done a 180 over the past two/three years. Because the shifts in expectations were not subsequently validated by fundamentals, market participants progressively came to view effects from Fed policy as psychological and ephemeral. It went from ‘very hard’ two years ago to make the case that QE wouldn’t be inflationary to ‘fairly easy’ today. Most everyone by now has wrapped their head around the notion of “liquidity trap”. Two conclusions can be drawn from this. One, the end of LSAPs will matter for yield levels—even if the Fed decides not to sell any of its holdings and let their book run off. So, if you think it is entirely about economic expectations you are likely to underestimate the magnitude of yield “normalization”. Two, many investors and analysts have settled into the notion that we are in a liquidity trap, and that monetary policy here is largely “pushing on a string”. While this is still for the most part the current environment, it is finally, slowly, starting to change. Monetary policy can be very, very powerful when the soil is fertile. This is not the time to become complacent about the impotence of monetary policy. That time has passed. It may not be tomorrow, but the efficacy of monetary policy has now become, as the economists might say, a positive function of time.
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