average

Create monthly mean by time intervals

由 时光怂恿深爱的人放手 提交于 2019-12-07 14:06:22
问题 Sorry if this has already been posted but I looked really hard and could not find anything. I am working with monthly temperature observations for 30 years, comprising January 1960 to December 1989. It looks like this: > head(df) date temp 1 1960-01-01 22.92235 2 1960-02-01 23.07059 3 1960-03-01 23.10941 4 1960-04-01 20.78353 5 1960-05-01 17.45176 6 1960-06-01 17.31765 First, what I need to do is to average all januaries, februaries, marches and etc for the whole period. Then, I would like to

prevent long running averaging from overflow?

由 与世无争的帅哥 提交于 2019-12-07 09:46:44
问题 suppose I want to calculate average value of a data-set such as class Averager { float total; size_t count; float addData (float value) { this->total += value; return this->total / ++this->count; } } sooner or later the total or count value will overflow, so I make it doesn't remember the total value by : class Averager { float currentAverage; size_t count; float addData (float value) { this->currentAverage = (this->currentAverage*count + value) / ++count; return this->currentAverage; } } it

Cumulative count of values in R

由 感情迁移 提交于 2019-12-07 09:44:21
问题 I hope you are doing very well. I would like to know how to calculate the cumulative sum of a data set with certain conditions. A simplified version of my data set would look like: t id A 22 A 22 R 22 A 41 A 98 A 98 A 98 R 98 A 46 A 46 R 46 A 46 A 46 A 46 R 46 A 46 A 12 R 54 A 66 R 13 A 13 A 13 A 13 A 13 R 13 A 13 Would like to make a new data set where, for each value of "id", I would have the cumulative number of times that each id appears , but when t=R I need to restart the counting e.g.

Python, Pandas: average every 2 rows together

由 别等时光非礼了梦想. 提交于 2019-12-07 04:44:14
问题 pretty basic question, but was wondering: What is the 'proper' way to average every 2 rows together in pandas Dataframe, and thus end up with only half the number of rows? Note that this is different than the rolling_mean since it reduces the number of entries. 回答1: A fast way to do it: >>> s = pd.Series(range(10)) >>> s 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 >>> ((s + s.shift(-1)) / 2)[::2] 0 0.5 2 2.5 4 4.5 6 6.5 8 8.5 The "proper way" I guess would be something like: >> a = s.index.values

calculate the average of three encrypted numbers

由 核能气质少年 提交于 2019-12-07 03:54:22
问题 Is possible to calculate average of three encrypted integer? No constrain on the method of encrypting. The point of this is just to hide the three numbers and find average. 回答1: What you seem to be looking for is called Homomorphic Encryption: an encryption scheme which allows you to perform operations on encrypted data, with the encrypted result as the outcome. Such a scheme would allow you to give encrypted data to a 3rd party, which could then do computations on it for you without knowing

Average Time to Reply to Message

由 梦想与她 提交于 2019-12-06 21:36:44
Is it possible to calculate the average time to reply to a message just with the following columns: id | ref | client | admin | date | message id is the unique message number ref is the message reference number, which is not unique (searching for ref, ordering by date will show a conversation) client is ID of client, if it is a client message, else 0 if not a client admin is ID of admin, if it is an admin message, else 0 if not a client date is set up using datetime being the time of the message message being the message sent Example Data: 1 | 1 | 1 | 0 | 2011-11-07 01:00:00 | ABC 2 | 1 | 1 |

MapReduce - how do I calculate relative values (average, top k and so)?

由 送分小仙女□ 提交于 2019-12-06 21:28:30
I'm looking for a way to calculate "global" or "relative" values during a MapReduce process - an average, sum, top etc. Say I have a list of workers, with their IDs associated with their salaries (and a bunch of other stuff). At some stage of the processing, I'd like to know who are the workers who earn the top 10% of salaries. For that I need some "global" view of the values, which I can't figure out. If I have all values sent into a single reducer, it has that global view, but then I loose concurrency, and it seems awkward. Is there a better way? (The framework I'd like to use is Google's,

“circular” mean in R

由 只谈情不闲聊 提交于 2019-12-06 19:46:11
问题 Given a dataset of months, how do I calculate the "average" month, taking into account that months are circular? months = c(1,1,1,2,3,5,7,9,11,12,12,12) mean(months) ## [1] 6.333333 In this dummy example, the mean should be in January or December. I see that there are packages for circular statistics, but I'm not sure whether they suit my needs here. 回答1: I think months <- c(1,1,1,2,3,5,7,9,11,12,12,12) library("CircStats") conv <- 2*pi/12 ## months -> radians Now convert from months to

Calculate AVERAGE from 2 columns for each row in SQL

由 给你一囗甜甜゛ 提交于 2019-12-06 14:52:23
As an example I will have this table from MySQL Id | Name | Grade 1 | Grade 2 | Average 1. | Jack | 9 | 10 | 2. | Jimmy | 9 | 8 | 2. | Emmy | 9 | 7 | So, in the Average field from this table, I need to calculate the AVERAGE from the Grade 1 and Grade 2 fields. I tried a lot of possiblities which I know they are wrong like: UPDATE table_name SET Average=AVG(Grade 1 + Grade 2) I there a way to do this? Can anyone help me? Thanks! You need to add the fields together and divide by the number of fields. If your Average field is of DECIMAL type you don't really even need to specify the ROUND

How to get average value from a hashmap in MongoDB?

由 允我心安 提交于 2019-12-06 12:26:09
问题 I have a time data in my Mongo database. Each document equal a minute and contain 60 seconds as objects with value for each. How to get average value of all seconds in one minute? A document looking like that: { "_id" : ObjectId("55575e4062771c26ec5f2287"), "timestamp" : "2015-05-16T18:12:00.000Z", "values" : { "0" : "26.17", "1" : "26.17", "2" : "26.17", ... "58" : "24.71", "59" : "25.20" } } 回答1: You could take two approaches here: Changing the schema and use the aggregation framework to
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