Computation times

14:24.340 total execution time for 16 files from all galleries:

Example	Time	Mem (MB)
Multiples tables: building machine learning pipelines with DataOps (../examples/data_ops/12_multiple_tables.py)	04:10.441	0.0
Various string encoders: a sentiment analysis example (../examples/02_text_with_string_encoders.py)	03:40.613	0.0
AggJoiner on a credit fraud dataset (../examples/07_join_aggregation.py)	02:32.708	0.0
Encoding: from a dataframe to a numerical matrix for machine learning (../examples/01_encodings.py)	00:49.450	0.0
Hyperparameter tuning with DataOps (../examples/data_ops/13_choices.py)	00:47.640	0.0
SquashingScaler: Robust numerical preprocessing for neural networks (../examples/10_squashing_scaler.py)	00:28.496	0.0
Spatial join for flight data: Joining across multiple columns (../examples/06_multiple_key_join.py)	00:27.502	0.0
Interpolation join: infer missing rows when joining two tables (../examples/08_interpolation_join.py)	00:17.915	0.0
Fuzzy joining dirty tables with the Joiner (../examples/04_fuzzy_joining.py)	00:14.591	0.0
Subsampling for faster development (../examples/data_ops/14_subsampling.py)	00:13.383	0.0
Getting Started (../examples/00_getting_started.py)	00:11.405	0.0
Hands-On with Column Selection and Transformers (../examples/09_apply_to_cols.py)	00:10.343	0.0
Introduction to machine-learning pipelines with skrub DataOps (../examples/data_ops/11_data_ops_intro.py)	00:08.112	0.0
Use case: developing locally and deploying to production (../examples/data_ops/15_use_case.py)	00:05.756	0.0
Handling datetime features with the DatetimeEncoder (../examples/03_datetime_encoder.py)	00:04.789	0.0
Deduplicating misspelled categories (../examples/05_deduplication.py)	00:01.198	0.0