Computation times

15:46.229 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)	03:35.401	1384.7
Multiples tables: building machine learning pipelines with DataOps (../examples/data_ops/1120_multiple_tables.py)	03:11.428	678.6
AggJoiner on a credit fraud dataset (../examples/0070_join_aggregation.py)	02:47.012	729.2
Tuning DataOps with Optuna (../examples/data_ops/1131_optuna_choices.py)	01:04.544	613.5
Encoding: from a dataframe to a numerical matrix for machine learning (../examples/0010_encodings.py)	00:55.308	517.0
SquashingScaler: Robust numerical preprocessing for neural networks (../examples/0100_squashing_scaler.py)	00:32.401	519.2
Spatial join for flight data: Joining across multiple columns (../examples/0060_multiple_key_join.py)	00:30.899	2410.2
Hyperparameter tuning with DataOps (../examples/data_ops/1130_choices.py)	00:30.751	518.4
Fuzzy joining dirty tables with the Joiner (../examples/0040_fuzzy_joining.py)	00:28.937	517.5
Interpolation join: infer missing rows when joining two tables (../examples/0080_interpolation_join.py)	00:25.130	1971.8
Using PyTorch (via skorch) in DataOps (../examples/data_ops/1160_pytorch.py)	00:20.085	537.7
Subsampling for faster development (../examples/data_ops/1140_subsampling.py)	00:15.908	528.3
Getting Started (../examples/0000_getting_started.py)	00:15.563	518.9
Hands-On with Column Selection and Transformers (../examples/0090_apply_to_cols.py)	00:15.520	516.7
Introduction to machine-learning pipelines with skrub DataOps (../examples/data_ops/1110_data_ops_intro.py)	00:13.657	517.3
Handling datetime features with the DatetimeEncoder (../examples/0030_datetime_encoder.py)	00:11.271	520.6
Use case: developing locally and deploying to production (../examples/data_ops/1150_use_case.py)	00:08.825	617.5
Deduplicating misspelled categories (../examples/0050_deduplication.py)	00:03.590	517.0