categorical-data

Background : I have a categorical variable, X , with four levels that I fit as separate dummy variables. Thus, there are three total dummy variables representing x=1, x=2, x=3 (x=0 is baseline). Problem/issue : I want to be able to calculate the value of a linear combination (i.e. using SAS as a calculator) of these dummy variables. For example, 2*B1 + 2*B2 + B3. In Stata, this can be done using the lincom command, which uses the stored beta estimates to calculate linear combinations of the parameters. In SAS in a procedure such as PROC GLM, I think I should use the ESTIMATE statement, but I'm

I have a multiply-imputed model from R's mice package in which there are lots of factor variables. For example: library(mice) library(Hmisc) # turn all the variables into factors fake = nhanes fake$age = as.factor(nhanes$age) fake$bmi = cut2(nhanes$bmi, g=3) fake$chl = cut2(nhanes$chl, g=3) head(fake) age bmi hyp chl 1 1 <NA> NA <NA> 2 2 [20.4,25.5) 1 [187,206) 3 1 <NA> 1 [187,206) 4 3 <NA> NA <NA> 5 1 [20.4,25.5) 1 [113,187) 6 3 <NA> NA [113,187) imput = mice(nhanes) # big model fit1 = glm.mids((hyp==2) ~ age + bmi + chl, data=imput, family = binomial) I want to test the significance of each

First thanks for reading me and thanks a lot if you can give any clue to help me solving this. As I'm new to Scikit-learn, don't hesitate to provide any advice that can help me to improve the process and make it more professional. My goal is to classify data between two categories. I would like to find a solution that would give me the most precise result. At the moment, I'm still looking for the most suitable algorithm and data preprocessing. In my data I have 24 values : 13 are nominal, 6 are binarized and the others are continuous. Here is an example of a line "RENAULT";"CLIO III";"CLIO III

I have some artist names in data['artist'] that I would like to convert to a categorical column via: x = data['artist'].astype('category').cat.codes x.dtype Returns: dtype('int32') I am getting negative numbers which suggests some sort of overflow situation. So, I'd like to use np.int64 instead but I can't find documentation on how to accomplish this. x = data['artist'].astype('category').cat.codes.astype(np.int64) x.dtype Gives dtype('int64') but it is clear that the int32 gets converted to int64 and so the negative value is still present x = data['artist'].astype('category').cat.codes.astype

I have a dataset with some categorical string columns and I want to represent them in double type. I used StringIndexer for this convertion and It works but when I tried it in another dataset that has NULL values it gave java.lang.NullPointerException error and did not work. For better understanding here is my code: for(col <- cols){ out_name = col ++ "_" var indexer = new StringIndexer().setInputCol(col).setOutputCol(out_name) var indexed = indexer.fit(df).transform(df) df = (indexed.withColumn(col, indexed(out_name))).drop(out_name) } So how can I solve this NULL data problem with

I am using the seaborn module to produce a plot similar to the example below. import pandas as pd import matplotlib.pyplot as plt import numpy as np import seaborn as sns location = "/global/scratch/umalmonj/WRF/juris/golden_hourly_manual_obs.csv" df = pd.read_csv(location,usecols= ["Year","Month","Day","Time","Weather"],parse_dates=[["Year","Month","Day","Time"]]) I have a df that looks like: Year_Month_Day_Time Weather 0 2010-01-01 00:00:00 NaN 1 2010-01-01 01:00:00 NaN 2 2010-01-01 02:00:00 NaN .. 7 2010-01-01 07:00:00 Snow 8 2010-01-01 08:00:00 Snow 9 2010-01-01 09:00:00 Snow Showers .. 18

I am trying to plot a few lines (not a bar plot, as in this case ). My y values are float , whereas x values are categorical data . How to do this in matplotlib ? My values: data1=[5.65,7.61,8.17,7.60,9.54] data2=[7.61,16.17,16.18,19.54,19.81] data3=[29.55,30.24,31.51,36.40,35.47] My categories: x_axis=['A','B','C','D','E'] The code I am using, which does not give me what I want: import matplotlib.pyplot as plt fig=plt.figure() #Creates a new figure ax1=fig.add_subplot(111) #Plot with: 1 row, 1 column, first subplot. line1 = ax1.plot(str(x_axis), data1,'ko-',label='line1') #Plotting data1