Simplify code using skrub TableReport and TableVectorizer

[ArturoAmorQ]

• Add a notebook + video to show how all the pandas code in the Visual inspection of data subsection can be simplified using skrub.TableReport:
• Replace ColumnTransformer with skrub.TableVectorizer starting from the Using numerical and categorical variables together notebook
  • In the same notebook, section Fitting a more powerful model, replace OrinalEncoder by skrub.ToCategorical.
  • Explicitly mention that TableVectorizer makes the column selection automatically by using its dtype
  • Introduce concept of "low/high cardinality" and demonstrate effect of cardinality_threshold on the "native-country" column in the Adult Census dataset.
  • Update visualizing scikit-learn pipelines video to use TableVectorizer (with scikit-learn version >= 1.8)
  • Modify wrap-up quizzes that use the Ames Housing dataset i.e. M1, M4 and M5 to select subset of numerical columns with pandas
• Redo the datasets description using TableReport