categorical-data

I'm performing a cluster analysis on categorical data, hence using k-modes approach. My data is shaped as a preference survey: How do you like hair and eyes? The respondent can pick up an answers from a fixed (multiple choice) set of 4 possibility. I therefore get the dummies, apply k-modes, attach the clusters back to the initial df and then plot them in 2D with pca. My code looks like: import numpy as np import pandas as pd from kmodes import kmodes df_dummy = pd.get_dummies(df) #transform into numpy array x = df_dummy.reset_index().values km = kmodes.KModes(n_clusters=3, init='Huang', n

In SPSS, it is (relatively) easy to create a cross tab with multiple variables using the factors (or values) as the table heading. So, something like the following (made up data, etc.). Q1, Q2, and Q3 each have either a 1, a 2, or a 3 for each person. I just left these as numbers, but they could be factors, neither seemed to help solve the problem. 1 (very Often) 2 (Rarely) 3 (Never) Q1. Likes it 12 15 13 Q2. Recommends it 22 11 10 Q3. Used it 22 12 9 In SPSS, one can even request row, column, or total percentages. I've tried table(), ftable(), xtab(), CrossTable() from gmodels, and CrossTable

Binary one-hot (also known as one-of-K) coding lies in making one binary column for each distinct value for a categorical variable. For example, if one has a color column (categorical variable) that takes the values 'red', 'blue', 'yellow', and 'unknown' then a binary one-hot coding replaces the color column with binaries columns 'color=red', 'color=blue', and 'color=yellow'. I begin with data in a pandas data-frame and I want to use this data to train a model with scikit-learn. I know two ways to do the binary one-hot coding, none of them satisfactory to me. Pandas and get_dummies in the

I have a dataset that contains both categorical (nominal and ordinal) and numerical attributes. I want to calculate the (dis)similarity matrix across my observations using these mixed attributes. Using the daisy() function of the cluster package in R, I can easily get a dissimilarity matrix as follows: if(!require("cluster")) { install.packages("cluster"); require("cluster") } data(flower) as.matrix(daisy(flower, metric = "gower")) This uses the gower metric to deal with the nominal variables. Is there a Python equivalent of the daisy() function in R? Or maybe any other module function that

This question already has an answer here: For each row return the column name of the largest value 7 answers I have a table full of binary variables that I would like to condense down to categorical variables. Very simplistically, I have is a data frame like this: data <- data.frame(id=c(1,2,3,4,5,6,7,8,9), red=c("1","0","0","0","1","0","0","0","0"),blue=c("0","1","1","1","0","1","1","1","0"),yellow=c("0","0","0","0","0","0","0","0","1")) data id red blue yellow 1 1 1 0 0 2 2 0 1 0 3 3 0 1 0 4 4 0 1 0 5 5 1 0 0 6 6 0 1 0 7 7 0 1 0 8 8 0 1 0 9 9 0 0 1 And what I would like to get back would be:

I am using R-studio and am using kaggle's forest cover data and keep getting an error when trying to use the knn3 function in caret. here is my code: library(caret) train <- read.csv("C:/data/forest_cover/train.csv", header=T) trainingRows <- createDataPartition(train$Cover_Type, p=0.8, list=F) head(trainingRows) train_train <- train[trainingRows,] train_test <- train[-trainingRows,] knnfit <- knn3(train_train[,-56], train_train$Cover_Type) This last line gives me this in the console: Error in knn3.matrix(x, y = y, k = k, ...) : y must be a factor As the error message states, y must be a

I have a quite big data frame in R with two columns. I am trying to make out of the Code column ( factor type with 858 levels) the dummy variables. The problem is that the R Studio always crashed when I am trying to do that. > str(d) 'data.frame': 649226 obs. of 2 variables: $ User: int 210 210 210 210 269 317 317 317 317 326 ... $ Code : Factor w/ 858 levels "AA02","AA03",..: 164 494 538 626 464 496 435 464 475 163 ... The User column is not unique, meaning that there can be several rows with the same User . Doesn't matter if in the end the amount of rows remains the same or the rows with the