39813c572463211d05f35a0dcac56ba7bd07c725,examples/plot_investigating_dirty_categories.py,,,#,17

Before Change

//     "Employee Position Title"].sample(n=n_observations)?
categorical_encoder = CategoricalEncoder(encoding="onehot-dense")
one_hot_encoded = categorical_encoder.fit_transform(
    sorted_values[:n_obs].reshape(-1, 1))
f3, ax3 = plt.subplots(figsize=(6, 6))
cax3 = ax3.matshow(one_hot_encoded[:n_obs, :n_categories])
f3.colorbar(cax3)

After Change

// encoding simply a subset of the observations
n_obs = 20
employee_position_titles = values["Employee Position Title"].head(
    n_obs).to_frame()
categorical_encoder = CategoricalEncoder(encoding="onehot-dense")
one_hot_encoded = categorical_encoder.fit_transform(employee_position_titles)

Instances