melt

问题 This question already has answers here : Reshaping multiple sets of measurement columns (wide format) into single columns (long format) (7 answers) Closed 4 years ago . I am trying to add a time-varying predictor to a long-form dataframe using reshape2::melt but I was wondering if there was a faster way to do it. Here is the toy data in wide form. There are three measures of an outcome variable (session1, session2, and session3) taken at different visits/time points. The duration between

问题 Pretty new to stackoverflow, please bear with me if the format looks odd.. I have a big set of data with 100+ columns of data structured like: countrya countryb year variable1 variable2 ...... varaible100 I want to have the 100 variables separated into 100 new dataframes and save them into csvs. Below is the code I have for creating 1 new csv. dfm1=pd.melt(df, id_vars=['countrya','countryb','year'], value_vars=['variable1'], value_name='variable1') dfm1.drop('variable',axis=1) dfm1.to_csv(

问题 Pretty new to stackoverflow, please bear with me if the format looks odd.. I have a big set of data with 100+ columns of data structured like: countrya countryb year variable1 variable2 ...... varaible100 I want to have the 100 variables separated into 100 new dataframes and save them into csvs. Below is the code I have for creating 1 new csv. dfm1=pd.melt(df, id_vars=['countrya','countryb','year'], value_vars=['variable1'], value_name='variable1') dfm1.drop('variable',axis=1) dfm1.to_csv(

问题 I have a data frame which I need to transform. I need to change the rows into unique columns based on the value of a column. ex: The Input DataFrame | column_1 | column_2 | ----------------------- | A | B | | A | C | | B | E | | B | C | | C | F | | C | G | The Output DataFrame | column_1 | column_2 | column_3 | ---------------------------------- | A | B | C | | B | E | C | | C | F | G | The final DataFrame should have all the unique values in column_1 and the values from column_2 from input

问题 I have data of this wide format that I want to convert to long format Cond Construct Line Plant Tube_shoot weight_shoot Tube_root weight_root 1 Standard NA NA 2 199.95 - - 2 Cd0 IIF 43.1 1 3 51.87 4 10.39 3 Cd0 IIF 43.1 2 5 81.80 6 15.05 4 Cd0 IIF 43.1 3 7 101.56 8 16.70 What I basically want is to keep Tube_shoot and weight_shoot together, i.e. treat these two columns as one for melt. But because I can only use id.vars=c("Cond","Construct","Line","Plant") the result is not quite what I want.

问题 I have data of this wide format that I want to convert to long format Cond Construct Line Plant Tube_shoot weight_shoot Tube_root weight_root 1 Standard NA NA 2 199.95 - - 2 Cd0 IIF 43.1 1 3 51.87 4 10.39 3 Cd0 IIF 43.1 2 5 81.80 6 15.05 4 Cd0 IIF 43.1 3 7 101.56 8 16.70 What I basically want is to keep Tube_shoot and weight_shoot together, i.e. treat these two columns as one for melt. But because I can only use id.vars=c("Cond","Construct","Line","Plant") the result is not quite what I want.

问题 I have a very large data set that requires individual one-way ANOVAs for multiple species on 4 different treatments with several measurements. Usually I just make separate excel spread sheets for each species and run ANOVAs on each, looping through each measurement column, but this is very time consuming. Is it possible to use a single spread sheet and run ANOVAs on the melted data? Or perhaps there is another reshape option I could use? Data: structure(list(Species = c("A", "A", "A", "A", "A

问题 Is it possible to end up with a categorical variable column after a melt operation in pandas? If I set up the data like this: import pandas as pd import numpy as np df = pd.DataFrame( np.random.randn(3, 5), columns=["A", "B", "C", "D", "E"] ) df["id"] = range(1, 4) df | | A | B | C | D | E | id | |----|-----------|-----------|-----------|-----------|------------|------| | 0 | -0.406174 | -0.686917 | -0.172913 | -0.273074 | -0.0246714 | 1 | | 1 | 0.323783 | -1.7731 | 1.57581 | -1.15671 | -1

问题 I'm melting some data and don't want to provide an id.var parameter to melt. The data melts fine, but I get the "No id variables; using all as measure variables" Is there a way to prevent that message from coming up, or a way to say id.var=default or something like that? An iris example using dplyr: > dt <- iris %>% summarize_at(c("Sepal.Length","Sepal.Width"), funs(mean)) > dt Sepal.Length Sepal.Width 1 5.843333 3.057333 > melt(dt, value.name="Mean") No id variables; using all as measure

问题 How can I melt a pandas data frame using multiple variable names and values? I have the following data frame that changes its shape in a for loop. In one of the for loop iterations, it looks like this: ID Cat Class_A Class_B Prob_A Prob_B 1 Veg 1 2 0.9 0.1 2 Veg 1 2 0.8 0.2 3 Meat 1 2 0.6 0.4 4 Meat 1 2 0.3 0.7 5 Veg 1 2 0.2 0.8 I need to melt it in such a way that it looks like this: ID Cat Class Prob 1 Veg 1 0.9 1 Veg 2 0.1 2 Veg 1 0.8 2 Veg 2 0.2 3 Meat 1 0.6 3 Meat 2 0.4 4 Meat 1 0.3 4