melt

I cannot figure out how to do "reverse melt" using Pandas in python. This is my starting data import pandas as pd from StringIO import StringIO origin = pd.read_table(StringIO('''label type value x a 1 x b 2 x c 3 y a 4 y b 5 y c 6 z a 7 z b 8 z c 9''')) origin Out[5]: label type value 0 x a 1 1 x b 2 2 x c 3 3 y a 4 4 y b 5 5 y c 6 6 z a 7 7 z b 8 8 z c 9 This is the output I would like to have: label a b c x 1 2 3 y 4 5 6 z 7 8 9 I'm sure there is an easy way to do this, but I don't know how. there are a few ways; using .pivot : >>> origin.pivot(index='label', columns='type')['value'] type a

Assuming the following DataFrame: key.0 key.1 key.2 topic 1 abc def ghi 8 2 xab xcd xef 9 How can I combine the values of all the key.* columns into a single column 'key', that's associated with the topic value corresponding to the key.* columns? This is the result I want: topic key 1 8 abc 2 8 def 3 8 ghi 4 9 xab 5 9 xcd 6 9 xef Note that the number of key.N columns is variable on some external N. Alexander You can melt your dataframe: >>> keys = [c for c in df if c.startswith('key.')] >>> pd.melt(df, id_vars='topic', value_vars=keys, value_name='key') topic variable key 0 8 key.0 abc 1 9 key

I am using the patterns() argument in data.table::melt() to melt data that has columns that have several easily-defined patterns. It is working, but I'm not seeing how I can create a character index variable instead of the default numeric breakdown. For example, in A the dog and cat columns are numbered... take a look at at the "variable" column: A = data.table(idcol = c(1:5), dog_1 = c(1:5), cat_1 = c(101:105), dog_2 = c(6:10), cat_2 = c(106:110), dog_3 = c(11:15), cat_3 = c(111:115)) head(melt(A, measure = patterns("^dog", "^cat"), value.name = c("dog", "cat"))) idcol variable dog cat 1: 1 1

This question already has an answer here: Reshaping multiple sets of measurement columns (wide format) into single columns (long format) 7 answers I need to reshape my wide table into long format but keeping multiple fields for each record, for example: dw <- read.table(header=T, text=' sbj f1.avg f1.sd f2.avg f2.sd blabla A 10 6 50 10 bA B 12 5 70 11 bB C 20 7 20 8 bC D 22 8 22 9 bD ') # Now I want to melt this table, keeping both AVG and SD as separate fields for each measurement, to get something like this: # sbj var avg sd blabla # A f1 10 6 bA # A f2 50 10 bA # B f1 12 5 bB # B f2 70 11