melt

I have the following R data.table (though this should scale with a data.frame too). The goal is to reshape this data.table to plot as a scatterplot in ggplot2 . I therefore need to reshape this data.table to have one "factor" column to color the points: > library(data.table) > dt ID x_A y_A x_B y_B 1: 05AC 0.81 3 0.92 2.05 2: 01BA 0.41 5 0.63 1.8 3: Z1AC 0.41 5 0.58 1.8 4: B2BA 0.21 6.5 1.00 1.8 .... I believe the correct output needs to be of the form: ID type x y 05AC A 0.81 3 05AC B 0.92 2.05 01BA A 0.41 5 01BA B 0.63 1.8 Z1AC A 0.41 5 Z1AC B 0.58 1.8 B2BA A 0.21 6.5 B2BA B 1.00 1.8 Is

I have a data set which is in wide format like this Index Country Variable 2000 2001 2002 2003 2004 2005 0 Argentina var1 12 15 18 17 23 29 1 Argentina var2 1 3 2 5 7 5 2 Brazil var1 20 23 25 29 31 32 3 Brazil var2 0 1 2 2 3 3 I want to reshape my data to long so that year, var1, and var2 become new columns Index Country year var1 var2 0 Argentina 2000 12 1 1 Argentina 2001 15 3 2 Argentina 2002 18 2 .... 6 Brazil 2000 20 0 7 Brazil 2001 23 1 I got my code to work when I only had one variable by writing df=(pd.melt(df,id_vars='Country',value_name='Var1', var_name='year')) I cant figure out how

I would like to melt several groups of columns of a dataframe into multiple target columns. Similar to questions Python Pandas Melt Groups of Initial Columns Into Multiple Target Columns and pandas dataframe reshaping/stacking of multiple value variables into seperate columns . However I need to do this explicitly by column name, rather than by index location. import pandas as pd df = pd.DataFrame([('a','b','c',1,2,3,'aa','bb','cc'), ('d', 'e', 'f', 4, 5, 6, 'dd', 'ee', 'ff')], columns=['a_1', 'a_2', 'a_3','b_1', 'b_2', 'b_3','c_1', 'c_2', 'c_3']) df Original Dataframe: id a_1 a_2 a_3 b_1 b_2

I have a dataframe which can be generated from the code as given below df = pd.DataFrame({'person_id' :[1,2,3],'date1': ['12/31/2007','11/25/2009','10/06/2005'],'val1': [2,4,6],'date2': ['12/31/2017','11/25/2019','10/06/2015'],'val2':[1,3,5],'date3': ['12/31/2027','11/25/2029','10/06/2025'],'val3':[7,9,11]}) I followed the below solution to convert it from wide to long pd.wide_to_long(df, stubnames=['date', 'val'], i='person_id', j='grp').sort_index(level=0) Though this works with sample data as shown below, it doesn't work with my real data which has more than 200 columns. Instead of person

Consider the following code: set.seed(1) M = matrix(rnorm(9), ncol = 3) dimnames(M) = list(LETTERS[1:3], LETTERS[1:3]) print(M) A B C A -0.6264538 1.5952808 0.4874291 B 0.1836433 0.3295078 0.7383247 C -0.8356286 -0.8204684 0.5757814 melt(M) Var1 Var2 value 1 A A -0.6264538 2 B A 0.1836433 3 C A -0.8356286 4 A B 1.5952808 5 B B 0.3295078 6 C B -0.8204684 7 A C 0.4874291 8 B C 0.7383247 9 C C 0.5757814 If i call melt using a data.frame , i get a different result: DF = data.frame(M) melt(DF) variable value 1 A -0.6264538 2 A 0.1836433 3 A -0.8356286 4 B 1.5952808 5 B 0.3295078 6 B -0.8204684 7 C