You can also just get the missing columns and add them to the test dataset: # Get missing columns in the training test missing_cols = set( train.columns ) – set( test.columns ) # Add a missing column in test set with default value equal to 0 for c in missing_cols: test[c] = 0 # Ensure … Read more
You can try : df = pd.get_dummies(df, columns=[‘type’])
TL;DR: pd.get_dummies(cat.astype(pd.CategoricalDtype(categories=categories))) Older pandas: pd.get_dummies(cat.astype(‘category’, categories=categories)) is there a way to pass to get_dummies (or an equivalent function) the names of the categories, so that, for the categories that don’t appear in a given dataframe, it’d just create a column of 0s? Yes, there is! Pandas has a special type of Series just for categorical … Read more
For machine learning, you almost definitely want to use sklearn.OneHotEncoder. For other tasks like simple analyses, you might be able to use pd.get_dummies, which is a bit more convenient. Note that sklearn.OneHotEncoder has been updated in the latest version so that it does accept strings for categorical variables, as well as integers. The crux of … Read more
See the relevel() function. Here is an example: set.seed(123) x <- rnorm(100) DF <- data.frame(x = x, y = 4 + (1.5*x) + rnorm(100, sd = 2), b = gl(5, 20)) head(DF) str(DF) m1 <- lm(y ~ x + b, data = DF) summary(m1) Now alter the factor b in DF by use of the … Read more