I have a pandas dataframe with a categorical series that has missing categories. In the example shown below, group has the categories “a”, “b”, and “c”, but there are no cases of “c” in the dataframe. The resulting pivoted dataframe has columns a and b. I expected a c column containing all missing value as well. How can I pivot
Tag: categorical-data
extract new columns and fill values based on categorical values data frame in python
I have a data frame where one column is categorical strings and the next one is the values corresponding to it: I want to create new columns based on df.status column, and fill empty ones with np.nan, requires pivot on multiple columns: I am looking for an efficient solution that works for large data frames. Answer You want:
How to reverse Label Encoder from sklearn for multiple columns?
I would like to use the inverse_transform function for LabelEncoder on multiple columns. This is the code I use for more than one columns when applying LabelEncoder on a dataframe: Is there a way to modify the code and change it so that it be used to inverse the labels from the encoder? Thanks Answer In order to inverse transform
OneHotEncoder categorical_features deprecated, how to transform specific column
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
pandas Categorical error: “Cannot setitem on a Categorical with a new category, set the categories first”
I have the following df data frame in pandas: What I want to do is to order the data frame by the following days’ order: To do so, I used the following code: When I run the code, I get this error: I have not found enough documentation to resolve this. Can you help me? Thanks! Answer df[[‘weekday’]] returns a