apply

Scala: companion objects and “new” keyword

由 徘徊边缘 提交于 2019-12-05 03:00:29
In my recent posts about using or omitting a "new" keyword in Scala ( "new" keyword in Scala ) I was told that the omission comes from the fact that certain classes have companion objects defined with apply method on them. My question is: are we able to tell or is there any general rule to distinguish which classes/objects have a companion object and apply method? Thanks in advance and sorry of it's a stupid question, but coming from a Java background it is a bit confusing. In the Scala API documentation , you see a little icon in the list on the left side. If you click on that, you go to the

Convert for loop to apply

由 十年热恋 提交于 2019-12-05 02:28:04
问题 In R, how do you replace the following code using functions like apply , lapply , rapply , do.call , etc.? u <- 10:12 slist <- list() for (i in 1:length(u)) { p <- combn(u, i) for (j in 1:ncol(p)) { s <- paste(p[,j], collapse=",") slist[[s]] <- 0 } } For this part: for (j in 1:ncol(p)) { s <- paste(p[,j], collapse=",") I tried something like: s <- apply(p, 2, function(x) paste(x, collapse=",")) Which works. But then for that slist[[s]] <- 0 part inside that same for-loop, I don't know what to

MDN bind why concat arguments when calling apply

由 Deadly 提交于 2019-12-05 01:24:18
问题 MDN specifies a polyfill bind method for those browsers without a native bind method: https://developer.mozilla.org/en-US/docs/JavaScript/Reference/Global_Objects/Function/bind This code has the following line: aArgs.concat(Array.prototype.slice.call(arguments)) Which is passed as the args to the apply method on the function: fToBind.apply(this instanceof fNOP && oThis ? this : oThis, aArgs.concat(Array.prototype.slice.call(arguments))); However, this line actually repeats the arguments, so

Using Apply in Pandas Lambda functions with multiple if statements

由 ╄→尐↘猪︶ㄣ 提交于 2019-12-05 00:50:37
问题 I'm trying to infer a classification according to the size of a person in a dataframe like this one: Size 1 80000 2 8000000 3 8000000000 ... I want it to look like this: Size Classification 1 80000 <1m 2 8000000 1-10m 3 8000000000 >1bi ... I understand that the ideal process would be to apply a lambda function like this: df['Classification']=df['Size'].apply(lambda x: "<1m" if x<1000000 else "1-10m" if 1000000<x<10000000 else ...) I checked a few posts regarding multiple ifs in a lambda

Convert a python dataframe with multiple rows into one row using python pandas?

由 China☆狼群 提交于 2019-12-05 00:25:18
问题 Having the following dataframe, df = pd.DataFrame({'device_id' : ['0','0','1','1','2','2'], 'p_food' : [0.2,0.1,0.3,0.5,0.1,0.7], 'p_phone' : [0.8,0.9,0.7,0.5,0.9,0.3] }) print(df) output: device_id p_food p_phone 0 0 0.2 0.8 1 0 0.1 0.9 2 1 0.3 0.7 3 1 0.5 0.5 4 2 0.1 0.9 5 2 0.7 0.3 How to achieve this transformation? df2 = pd.DataFrame({'device_id' : ['0','1','2'], 'p_food_1' : [0.2,0.3,0.1], 'p_food_2' : [0.1,0.5,0.7], 'p_phone_1' : [0.8,0.7,0.9], 'p_phone_2' : [0.9,0.5,0.3] }) print(df2)

Compare every 2 rows and show mismatches in R

由 眉间皱痕 提交于 2019-12-04 22:25:37
I have searched a lot and tried on my own too, but couldn't find solution for this particular problem. For every 2 rows ('key' is common), I have to find mismatches in every column and highlight them in an organized way like below. The output should be in the following format: COLUMN_NAME is not matching for records below: PRINT COMPLETE RECORDS ... COLUMN_NAME is not matching for records below: PRINT COMPLETE RECORDS ... COLUMN_NAME is not matching for records below: PRINT COMPLETE RECORDS ... Input Data (it's a data frame): key V1 V2 V3 V4 V5 a1 1 2 3 4 5 a1 1 3 9 4 5 a5 2 1 4 7 5 a5 2 1 4 7

R: Webscraping a list of URLs to get a DataFrame

由 青春壹個敷衍的年華 提交于 2019-12-04 19:25:45
I can see the correct data, but cannot put it on a Data Frame (It appears as a list of elements). I think the problem is my understanding of the apply family functions. Any hint is welcome. Here is a similar question, but I think it is better to post mine as it contains more details: Webscraping content across multiple pages using rvest package library(rvest) library(lubridate) library(dplyr) urls <- list("http://simple.ripley.com.pe/tv-y-video/televisores/ver-todo-tv", "http://simple.ripley.com.pe/tv-y-video/televisores/ver-todo-tv?page=2&orderBy=seq", "http://simple.ripley.com.pe/tv-y-video

R - How to vectorize with apply family function and avoid while/for loops in this case?

由 时光怂恿深爱的人放手 提交于 2019-12-04 19:23:43
In this case (more details could be found in this question: Count how many observations in the rest of the dat fits multiple conditions? (R) ) This is a dataset called event, containing thousands of events (observations) and I selected several rows to show you the data structure. It contains the "STATEid", "date" of occurrence, and geographical coordinates in two variables "LON" "LAT". I am writing to calculate a new variable (column) for each row. This new variable should be: "Given any specific incident, count the rest of the dataset and calculate the number of events that's happened in the

Applying pnorm to columns of a data frame

由 若如初见. 提交于 2019-12-04 19:00:14
问题 I'm trying to normalize some data which I have in a data frame. I want to take each value and run it through the pnorm function along with the mean and standard deviation of the column the value lives in. Using loops, here's how I would write out what I want to do: #example data hist_data <- data.frame( matrix( rnorm( 200,mean=5,sd=.5 ),nrow=20 ) ) n <- dim( hist_data )[2] #columns=10 k <- dim( hist_data )[1] #rows =20 #set up the data frame which we will populate with a loop normalized <-

Conditional calculating Maximum value in the column

由 大城市里の小女人 提交于 2019-12-04 18:33:24
I have the following table: Class x2 x3 x4 A 14 45 53 A 8 18 17 A 16 49 20 B 78 21 48 B 8 18 5 I need for each "Class" (A and B) find the maximum value in column "X3", keep that row and delete other rows. The output should be in format like: Class x2 x3 x4 A 14 49 20 B 78 21 48 Please, ask me questions if something unclear in my problem. Thank you! A base R approach could be: mydf[as.logical(with(mydf, ave(x3, Class, FUN = function(x) x == max(x)))), ] # Class x2 x3 x4 # 3 A 16 49 20 # 4 B 78 21 48 However, note that if there are multiple values tied for max , it would return multiple rows for
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