Import Data and handle it

The BI class can import data from a csv file or from a dictionary for data that can’t be stored in a tabular format.

Import tabular data from a csv file

Python

m.data(data_path, sep=';') 

R

m$data(data_path,  sep=';')

Import non tabular data

First you need to create your own dictionary with the data.

Python

m.data_on_model = dict(
    ID1 = Value1,
    ID2 = Value2, 
)

R

keys <- c("ID1","ID2")
values <- list(Value1,Value2)
data = py_dict(keys, values, convert = TRUE)
m$data_on_model=data

Handle data

For tabular data, you can use some functions to manipulate the data:

• Perform one-hot encoding OHE: One-hot encoding is a technique that converts categorical variables into binary variables. This is useful when you have a large number of categories and want to use them as features in a model.

• Create index encoding for categorical columns index: Index encoding is a technique that assigns a unique integer value to each category in a categorical variable. This is useful when you have a large number of categories and want to use them as features in a model.

• Scale scale: Standardize the data by subtracting the mean and dividing the result by the standard deviation X_i = \frac{X_i - \mu}{\sigma}.