categorical-data

I have tried passing the dtype parameter with read_csv as dtype={n: pandas.Categorical} but this does not work properly (the result is an Object). The manual is unclear . In version 0.19.0 you can use parameter dtype='category' in read_csv : data = 'col1,col2,col3\na,b,1\na,b,2\nc,d,3' df = pd.read_csv(pd.compat.StringIO(data), dtype='category') print (df) col1 col2 col3 0 a b 1 1 a b 2 2 c d 3 print (df.dtypes) col1 category col2 category col3 category dtype: object If want specify column for category use dtype with dictionary: df = pd.read_csv(pd.compat.StringIO(data), dtype={'col1':

What is the difference between the two? It seems that both create new columns, which their number is equal to the number of unique categories in the feature. Then they assign 0 and 1 to data points depending on what category they are in. A simple example which encodes an array using LabelEncoder, OneHotEncoder, LabelBinarizer is shown below. I see that OneHotEncoder needs data in integer encoded form first to convert into its respective encoding whihc is not required incase of LabelBinarizer. from numpy import array from numpy import argmax from sklearn.preprocessing import LabelEncoder from

This question already has an answer here: Manually setting group colors for ggplot2 1 answer This is my sample data table1 xaxis yaxis ae work 1 5 35736 Attending_Education Working 2 6 72286 Attending_Education Working 3 7 133316 Attending_Education Working 4 8 252520 Attending_Education Working 5 9 228964 Attending_Education Working 6 10 504676 Attending_Education Working This is the code i had used. p<-ggplot(table1,aes(x=table1$xaxis,y=table1$yaxis)) Economic_Activity<-factor(table1$work) Education_Status<-factor(table1$ae) p<-p+geom_point(aes(colour=Education_Status,shape=Economic_Activity

In my dataframe I have some categorical columns with over 100 different categories. I want to rank the categories by the most frequent. I keep the first 9 most frequent categories and the less frequent categories rename them automatically by: OTHER Example: Here my df : print(df) Employee_number Jobrol 0 1 Sales Executive 1 2 Research Scientist 2 3 Laboratory Technician 3 4 Sales Executive 4 5 Research Scientist 5 6 Laboratory Technician 6 7 Sales Executive 7 8 Research Scientist 8 9 Laboratory Technician 9 10 Sales Executive 10 11 Research Scientist 11 12 Laboratory Technician 12 13 Sales