我可以想到的最简单的趋势算法可能是n天移动平均线。我不知道你的数据是如何结构化的，但是你可以这样说：

  books = {'Twilight' [500,555,580,577,523,533,556,593]，
'Harry Potter'：[650,647,663,642,633,621,625,613]，
'计算机程序的结构和解释：[1，4，15，12，7，3，8，19] 
} 
 

一个简单的移动平均值只需要最后一个 n 值，并对它们进行平均：

  def moving_av（l，n）：
取一个列表l，返回其最后n个元素的平均值

观察= len（l [-n：]）
返回总和（l [-n：]）/ float（观察）
 

切片符号简单地从第n个到最后一个变量抓取列表的尾部。移动平均线是平滑任何可能引入的穗状或倾角的噪声的相当标准的方法。该功能可以这样使用：

  book_scores = {} 
为book，reader_list在books.iteritems（） ：
 book_scores [book] = moving_av（reader_list，5）
 

玩你平均的天数。如果您想强调最近的趋势，您还可以使用加权移动平均线。

如果你想集中精力放在绝对读者群中，而不是增加读者群，那么只需找出30天移动平均线的百分比变化和5天移动均线：

  d5_moving_av = moving_av（reader_list，5）
 d30_moving_av = moving_av（reader_list，30 ）
 book_score =（d5_moving_av  -  d30_moving_av）/ d30_moving_av 
 

使用这些简单的工具，相当多的灵活性，你多强调过去的趋势，以及你想平滑多少（或不平滑）尖峰。

I have a Django application in which I need to implement a simple trending/ranking algorithm. I'm very lost as a :

I have two models, Book and Reader. Every night, new books are added to my database. The number of readers for each book are updated too every night i.e. One book will have multiple reader statistic records (one record for each day).

Over a given period (past week, past month or past year), I would like to list the most popular books, what algorithm should I use for this?

The popularity doesn't need to be realtime in any way because the reader count for each book is only updated daily.

I found one article which was referenced in another SO post that showed how they calculated trending Wikipedia articles but the post only showed how the current trend was calculated.

As someone pointed out on SO, it is a very simple baseline trend algorithm and only calculates the slope between two data points so I guess it shows the trend between yesterday and today.

I'm not looking for a uber complex trending algorithm like those used on Hacker News, Reddit, etc.

I have only two data axes, reader count and date.

Any ideas on what and how I should implement. For someone who's never worked with anything statistics/algorithm related, this seems to be a very daunting undertaking.

Thanks in advance everyone.

Probably the simplest possible trending "algorithm" I can think of is the n-day moving average. I'm not sure how your data is structured, but say you have something like this:

books = {'Twilight': [500, 555, 580, 577, 523, 533, 556, 593],
         'Harry Potter': [650, 647, 653, 642, 633, 621, 625, 613],
         'Structure and Interpretation of Computer Programs': [1, 4, 15, 12, 7, 3, 8, 19]
        }

A simple moving average just takes the last n values and averages them:

def moving_av(l, n):
    """Take a list, l, and return the average of its last n elements.
    """
    observations = len(l[-n:])
    return sum(l[-n:]) / float(observations)

The slice notation simply grabs the tail end of the list, starting from the nth to last variable. A moving average is a fairly standard way to smooth out any noise that a single spike or dip could introduce. The function could be used like so:

book_scores = {}
for book, reader_list in books.iteritems():
    book_scores[book] = moving_av(reader_list, 5)

You'll want to play around with the number of days you average over. And if you want to emphasize recent trends you can also look at using something like a weighted moving average.

If you wanted to focus on something that looks less at absolute readership and focuses instead on increases in readership, simply find the percent change in the 30-day moving average and 5-day moving average:

d5_moving_av = moving_av(reader_list, 5)
d30_moving_av = moving_av(reader_list, 30)
book_score = (d5_moving_av - d30_moving_av) / d30_moving_av

With these simple tools you have a fair amount of flexibility in how much you emphasize past trends and how much you want to smooth out (or not smooth out) spikes.