The data parameter is the input data that you want to binarize. It can be either a pandas DataFrame or a DataFrameGroupBy object.
required
n_bins
int
The n_bins parameter specifies the number of bins to use when binarizing numeric data. It is used in the create_recipe function to determine the number of bins for each numeric column. pd.qcut() is used to bin the numeric data.
4
thresh_infreq
float
The thresh_infreq parameter is a float that represents the threshold for infrequent categories. Categories that have a frequency below this threshold will be grouped together and labeled with the name specified in the name_infreq parameter. By default, the threshold is set to 0.01.
0.01
name_infreq
str
The name_infreq parameter is used to specify the name that will be assigned to the category representing infrequent values in a column. This is applicable when performing binarization on non-numeric columns. By default, the name assigned is β-OTHERβ.
'-OTHER'
one_hot
bool
The one_hot parameter is a boolean flag that determines whether or not to perform one-hot encoding on the categorical variables after binarization. If one_hot is set to True, the categorical variables will be one-hot encoded, creating binary columns for each unique category.
True
Returns
Type
Description
The function binarize returns the transformed data after applying various data preprocessing
steps such as converting non-numeric columns to categorical, replacing boolean columns with integers, fixing low cardinality numeric data, fixing high skew numeric data, and creating a recipe for binarization.
See Also
correlate() : Calculates the correlation between a target variable and all other variables in a pandas DataFrame.
Examples
# NON-TIMESERIES EXAMPLE ----import pandas as pdimport numpy as npimport pytimetk as tk# Set a random seed for reproducibilitynp.random.seed(0)# Define the number of rows for your DataFramenum_rows =200# Create fake data for the columnsdata = {'Age': np.random.randint(18, 65, size=num_rows),'Gender': np.random.choice(['Male', 'Female'], size=num_rows),'Marital_Status': np.random.choice(['Single', 'Married', 'Divorced'], size=num_rows),'City': np.random.choice(['New York', 'Los Angeles', 'Chicago', 'Houston', 'Miami'], size=num_rows),'Years_Playing': np.random.randint(0, 30, size=num_rows),'Average_Income': np.random.randint(20000, 100000, size=num_rows),'Member_Status': np.random.choice(['Bronze', 'Silver', 'Gold', 'Platinum'], size=num_rows),'Number_Children': np.random.randint(0, 5, size=num_rows),'Own_House_Flag': np.random.choice([True, False], size=num_rows),'Own_Car_Count': np.random.randint(0, 3, size=num_rows),'PersonId': range(1, num_rows +1), # Add a PersonId column as a row count'Client': np.random.choice(['A', 'B'], size=num_rows) # Add a Client column with random values 'A' or 'B'}# Create a DataFramedf = pd.DataFrame(data)# Binarize the datadf_binarized = df.binarize(n_bins=4, thresh_infreq=0.01, name_infreq="-OTHER", one_hot=True)df_binarized.glimpse()
