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Python 2022-01-24 08:45:46
get dummies pandas
note: dummies = pd.get_dummies(df[['column_1']], drop_first=True) df = pd.concat([df.drop(['column_1'],axis=1), dummies],axis=1) note:for more that one coloum keep ading in the list dummies = pd.get_dummies(df[['column_1', 'column_2','column_3']], drop... Add solution -
Python 2021-10-25 05:25:09
pd.get_dummies
note: dummies = pd.get_dummies(df[['column_1']], drop_first=True) df = pd.concat([df.drop(['column_1'],axis=1), dummies],axis=1) note:for more that one coloum keep ading in the list dummies = pd.get_dummies(df[['column_1', 'column_2','column_3']], drop... Add solution -
Python 2021-10-24 07:00:15
how to use one hot encoding in python
# Basic syntax: df_onehot = pd.get_dummies(df, columns=['col_name'], prefix=['one_hot']) # Where: # - get_dummies creates a one-hot encoding for each unique categorical # value in the column named col_name # - The prefix is added at the beginning of each... Add solution -
Python 2021-10-14 08:53:05
pandas.get_dummies
note: dummies = pd.get_dummies(df[['column_1']], drop_first=True) df = pd.concat([df.drop(['column_1'],axis=1), dummies],axis=1) note:for more that one coloum keep ading in the list dummies = pd.get_dummies(df[['column_1', 'column_2','column_3']], drop... Add solution