Computation times

17:16.478 total execution time for 18 files from all galleries:

Example	Time	Mem (MB)
Various string encoders: a sentiment analysis example (../examples/0020_text_with_string_encoders.py)	03:39.477	1270.4
Multiples tables: building machine learning pipelines with DataOps (../examples/data_ops/1120_multiple_tables.py)	03:28.953	679.5
AggJoiner on a credit fraud dataset (../examples/0070_join_aggregation.py)	02:51.874	838.6
Tuning DataOps with Optuna (../examples/data_ops/1131_optuna_choices.py)	00:57.174	566.8
Encoding: from a dataframe to a numerical matrix for machine learning (../examples/0010_encodings.py)	00:52.899	512.3
Spatial join for flight data: Joining across multiple columns (../examples/0060_multiple_key_join.py)	00:47.653	3017.7
Hyperparameter tuning with DataOps (../examples/data_ops/1130_choices.py)	00:44.965	500.6
Interpolation join: infer missing rows when joining two tables (../examples/0080_interpolation_join.py)	00:39.360	2578.9
Fuzzy joining dirty tables with the Joiner (../examples/0040_fuzzy_joining.py)	00:34.121	500.5
SquashingScaler: Robust numerical preprocessing for neural networks (../examples/0100_squashing_scaler.py)	00:29.233	499.7
Using PyTorch (via skorch) in DataOps (../examples/data_ops/1160_pytorch.py)	00:28.828	527.4
Subsampling for faster development (../examples/data_ops/1140_subsampling.py)	00:21.662	537.4
Getting Started with skrub (tutorials/0000_getting_started.py)	00:21.429	503.7
Hands-On with Column Selection and Transformers (../examples/0090_apply_to_cols.py)	00:15.630	499.7
Tutorial: Using Data Ops to build a machine-learning pipeline (tutorials/1110_data_ops_intro.py)	00:15.090	519.5
Handling datetime features with the DatetimeEncoder (../examples/0030_datetime_encoder.py)	00:12.319	504.4
Use case: developing locally and deploying to production (../examples/data_ops/1150_use_case.py)	00:12.190	543.4
Deduplicating misspelled categories (../examples/0050_deduplication.py)	00:03.621	500.1