Tag: one-hot-encoding

What should be the format of one-hot-encoded features for scikit-learn?

one-hot-encoding python python-3.x scikit-learn

I am trying to use the regressor/classifiers of scikit-learn library. I am a bit confused about the format of the one-hot-encoded features since I can send dataframe or numpy arrays to the model. Say I have categorical features named ‘a’, ‘b’ and ‘c’. Should I give them in separate columns (with pandas.get_dummies()), like below: a b c 1 1 1

How to use get_dummies or one hot encoding to encode a categorical feature with multiple elements?

encoding one-hot-encoding pandas python

I’m working on a dataset which has a feature called categories. The data for each observation in that feature consists of semi-colon delimited list eg. Rows categories Row 1 “categorya;categoryb;categoryc” Row 2 “categorya;categoryb” Row 3 “categoryc” Row 4 “categoryb;categoryc” If I try pd.get_dummies(df,columns=[‘categories’]) I get back columns with the entirety of the data as the column named e.g a column

sklearn.compose.make_column_transformer(): using SimpleImputer() and OneHotEncoder() in one step on one dataframe column

imputation one-hot-encoding pipeline python scikit-learn

I have a dataframe containing a column with categorical variables, which also includes NaNs. I’d like to to use sklearn.compose.make_column_transformer() to prepare the df in a clean way. I tried to impute nan values and OneHotEncode the column with the following code: Running the transformer on my training data raises ValueError: Input contains NaN The desired output would be something

OneHotEncoding Protein Sequences

bioinformatics one-hot-encoding python scikit-learn

I have an original dataframe of sequences listed below and am trying to use one-hot encoding and then store these in a new dataframe, I am trying to do it with the following code but am not able to store because I get the following output afterwards: Code: but get error Answer You get that strange array because it treats

OneHotEncoder categorical_features deprecated, how to transform specific column

categorical-data machine-learning one-hot-encoding python

I need to transform the independent field from string to arithmetical notation. I am using OneHotEncoder for the transformation. My dataset has many independent columns of which some are as: I have to encode the Country column like I succeed to get the desire transformation via using OneHotEncoder as Now I’m getting the depreciation message to use categories=’auto’. If I

Convert a 2d matrix to a 3d one hot matrix numpy

numpy one-hot-encoding python vectorization

I have np matrix and I want to convert it to a 3d array with one hot encoding of the elements as third dimension. Is there a way to do with without looping over each row eg should be made into Answer Approach #1 Here’s a cheeky one-liner that abuses broadcasted comparison – Sample run – For 0-based indexing, it