def test_ordinal_encoder_features_names_out_pandas():
"""Check feature names out is same as the input."""
pd = pytest.importorskip("pandas")
names = ["b", "c", "a"]
X = pd.DataFrame([[1, 2, 3]], columns=names)
enc = OrdinalEncoder().fit(X)
feature_names_out = enc.get_feature_names_out()
assert_array_equal(names, feature_names_out)
# `sparse=False` is used in the `OneHotEncoder` for didactic purposes, namely
# easier visualization of the data.
#
# Sparse matrices are efficient data structures when most of your matrix
# elements are zero. They won't be covered in detail in this course. If you
# want more details about them, you can look at
# [this](https://scipy-lectures.org/advanced/scipy_sparse/introduction.html#why-sparse-matrices).
# ```
# %% [markdown]
# We see that encoding a single feature will give a NumPy array full of zeros
# and ones. We can get a better understanding using the associated feature
# names resulting from the transformation.
# %%
feature_names = encoder.get_feature_names_out(input_features=["education"])
education_encoded = pd.DataFrame(education_encoded, columns=feature_names)
education_encoded
# %% [markdown]
# As we can see, each category (unique value) became a column; the encoding
# returned, for each sample, a 1 to specify which category it belongs to.
#
# Let's apply this encoding on the full dataset.
# %%
print(f"The dataset is composed of {data_categorical.shape[1]} features")
data_categorical.head()
# %%
data_encoded = encoder.fit_transform(data_categorical)
