multiple-columns

I have a pandas dataframe as follows: ticker account value date aa assets 100,200 20121231, 20131231 bb liabilities 50, 150 20141231, 20131231 I would like to split df['value'] and df['date'] so that the dataframe looks like this: ticker account value date aa assets 100 20121231 aa assets 200 20131231 bb liabilities 50 20141231 bb liabilities 150 20131231 Would greatly appreciate any help. You can first split columns, create Series by stack and remove whitespaces by strip : s1 = df.value.str.split(',', expand=True).stack().str.strip().reset_index(level=1, drop=True) s2 = df.date.str.split(',',

I'm trying to apply a function to a group of columns in a large data.table without referring to each one individually. a <- data.table( a=as.character(rnorm(5)), b=as.character(rnorm(5)), c=as.character(rnorm(5)), d=as.character(rnorm(5)) ) b <- c('a','b','c','d') with the MWE above, this: a[,b=as.numeric(b),with=F] works, but this: a[,b[2:3]:=data.table(as.numeric(b[2:3])),with=F] doesn't work. What is the correct way to apply the as.numeric function to just columns 2 and 3 of a without referring to them individually. (In the actual data set there are tens of columns so it would be

I'm trying to remove the first two columns (of which I'm not interested in) from a DbgView log file. I can't seem to find an example that prints from column 3 onwards until the end of the line. Note that each line has variable number of columns. Marcin ...or a simpler solution: cut -f 3- INPUTFILE just add the correct delimiter (-d) and you got the same effect. awk '{for(i=3;i<=NF;++i)print $i}' awk '{ print substr($0, index($0,$3)) }' solution found here: http://www.linuxquestions.org/questions/linux-newbie-8/awk-print-field-to-end-and-character-count-179078/ Dennis Williamson Jonathan

I have df : df = pd.DataFrame({'a':[7,8,9], 'b':[1,3,5], 'c':[5,3,6]}) print (df) a b c 0 7 1 5 1 8 3 3 2 9 5 6 Then rename first value by this : df.columns.values[0] = 'f' All seems very nice: print (df) f b c 0 7 1 5 1 8 3 3 2 9 5 6 print (df.columns) Index(['f', 'b', 'c'], dtype='object') print (df.columns.values) ['f' 'b' 'c'] If select b it works nice: print (df['b']) 0 1 1 3 2 5 Name: b, dtype: int64 But if select a it return column f : print (df['a']) 0 7 1 8 2 9 Name: f, dtype: int64 And if select f get keyerror. print (df['f']) #KeyError: 'f' print (df.info()) #KeyError: 'f' What is