code: def onehot(data, categories=categories):
      ordinalencoder = OneHotEncoder()
      onehot = ordinalencoder.fit_transform(data[categories])
  
      columns = []
      for i, values in enumerate(ordinalencoder.categories_):
          for j in values:
              columns.append(str(categories[i] + "_" + j))
  
      return pd.DataFrame(onehot.toarray(), columns=columns)
  
file_path: fairlearn/examples/plot_intersectional_bias.py
imports: import numpy
  import pandas
  import matplotlib.pyplot
  import sklearn.metrics
  import seaborn
  from sklearn.datasets import fetch_openml
  from sklearn.preprocessing import OneHotEncoder, MinMaxScaler
  from sklearn.linear_model import LogisticRegression
  from fairlearn.metrics import MetricFrame, false_positive_rate
label_desc: Use image node_id 2 for calling a global function with example usage: onehot(data, categories) and returns: pd