data-manipulation

I know how to drop columns from a data frame using Python. But for my problem the data set is vast, the columns I want to drop are grouped together or are basically singularly spread out across the column heading axis. Is there a shorter way to slice or drop all the columns with fewer lines of code rather than to write it out like how I have done. The way I have done it here works but I would like a more summarized way. The flight_data_copy_final is the variable in which it should be stored. Here's my code: from IPython.display import display flight_data_copy_version1 = flight_data_copy.drop

The following content is stored in a file: chrome.exe 512 Console 0 73,780 K chrome.exe 800 Console 0 11,052 K chrome.exe 1488 Console 0 92,720 K chrome.exe 1600 Console 0 32,344 K chrome.exe 2240 Console 0 35,132 K chrome.exe 2360 Console 0 21,276 K chrome.exe 3524 Console 0 66,732 K chrome.exe 3924 Console 0 23,524 K Is there a way to extract the 5th column with the Windows command line? Something like the UNIX cut command. for /f "tokens=5 delims= " %i in (file.txt) DO echo %i If you're familiar with the GNU cut utility, you might be better off using the Win32 port: http://gnuwin32

All, I have a question that I fear might be too pedestrian to ask here, but searching for it elsewhere is leading me astray. I may not be using the right search terms. I have a panel data frame (country-year) in R with some missing values on a given variable. I'm trying to impute them with the value from another vector in another data frame. Here's an illustration of what I am trying to do. Assume Data is the data frame of interest, which has missing values on a given vector that I'm trying to impute from another donor data frame. It looks like this. country year x 70 1920 9.234 70 1921 9.234

Lets say I have a data frame with the following structure: DF <- data.frame(x = 0:4, y = 5:9) > DF x y 1 0 5 2 1 6 3 2 7 4 3 8 5 4 9 what is the most efficient way to turn 'DF' into a data frame with the following structure: w x y 1 0 5 1 1 6 2 1 6 2 2 7 3 2 7 3 3 8 4 3 8 4 4 9 Where w is a length 2 window rolling through the dataframe 'DF.' The length of the window should be arbitrary, i.e a length of 3 yields w x y 1 0 5 1 1 6 1 2 7 2 1 6 2 2 7 2 3 8 3 2 7 3 3 8 3 4 9 I am a bit stumped by this problem, because the data frame can also contain an arbitrary number of columns, i.e. w,x,y,z etc.

I have two JavaScript arrays orig (the original array of objects) and update (the updated orig array of objects) that have the same length and contain objects, and I want to output the differences between the each pair of objects. Example: var orig = [{enabled:"true", name:"Obj1", id:3},{enabled:"true", name:"Obj2", id:4}]; var update = [{enabled:"true", name:"Obj1", id:3}, {enabled:"true", name:"Obj2-updated", id:4}]; The output should be: name:"Obj2-updated" I implemented something but it needs optimization... for(var prop=0; prop<orig.length; prop++) { for(prop=0; prop<update.length; prop++

I have the following data Date Col1 Col2 2014-01-01 123 12 2014-01-01 123 21 2014-01-01 124 32 2014-01-01 125 32 2014-01-02 123 34 2014-01-02 126 24 2014-01-02 127 23 2014-01-03 521 21 2014-01-03 123 13 2014-01-03 126 15 Now, I want to count unique values in Col1 for the each date (that did not repeat in previous date), and add to the previous count. For example, Date Count 2014-01-01 3 i.e. 123,124,125 2014-01-02 5 (2 + above 3) i.e. 126, 127 2014-01-03 6 (1 + above 5) i.e. 521 only lukeA library(dplyr) df %.% arrange(Date) %.% filter(!duplicated(Col1)) %.% group_by(Date) %.% summarise(Count

For the following data ds <- read.table(header = TRUE, text =" id year attend 1 2007 1 1 2008 1 1 2009 1 1 2010 1 1 2011 1 8 2007 3 8 2008 NA 8 2009 3 8 2010 NA 8 2011 3 9 2007 2 9 2008 3 9 2009 3 9 2010 5 9 2011 5 10 2007 4 10 2008 4 10 2009 2 10 2010 NA 10 2011 NA ") ds<- ds %>% dplyr::mutate(time=year-2000) print(ds) How would I write a dplyr::filter() command to keep only the ids that don't have a single NA? So only subjects with ids 1 and 9 should stay after the filter. Robert Krzyzanowski Use filter in conjunction with base::ave ds %>% dplyr::filter(ave(!is.na(attend), id, FUN = all)) To