Computation times

15:31.054 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)	03:26.713	1315.5
Multiples tables: building machine learning pipelines with DataOps (../examples/data_ops/1120_multiple_tables.py)	02:59.671	631.7
AggJoiner on a credit fraud dataset (../examples/0070_join_aggregation.py)	02:11.141	750.4
Encoding: from a dataframe to a numerical matrix for machine learning (../examples/0010_encodings.py)	00:54.709	577.2
Tuning DataOps with Optuna (../examples/data_ops/1131_optuna_choices.py)	00:54.034	566.7
Spatial join for flight data: Joining across multiple columns (../examples/0060_multiple_key_join.py)	00:47.427	2966.4
Hyperparameter tuning with DataOps (../examples/data_ops/1130_choices.py)	00:43.372	566.6
Interpolation join: infer missing rows when joining two tables (../examples/0080_interpolation_join.py)	00:38.884	2590.4
Fuzzy joining dirty tables with the Joiner (../examples/0040_fuzzy_joining.py)	00:32.790	566.9
Using PyTorch (via skorch) in DataOps (../examples/data_ops/1160_pytorch.py)	00:25.910	594.3
SquashingScaler: Robust numerical preprocessing for neural networks (../examples/0100_squashing_scaler.py)	00:25.348	566.8
Subsampling for faster development (../examples/data_ops/1140_subsampling.py)	00:19.932	572.2
Getting Started (../examples/0000_getting_started.py)	00:16.457	566.5
Hands-On with Column Selection and Transformers (../examples/0090_apply_to_cols.py)	00:14.566	567.5
Introduction to wrangling pipelines for machine-learning skrub DataOps (../examples/data_ops/1110_data_ops_intro.py)	00:13.784	566.7
Handling datetime features with the DatetimeEncoder (../examples/0030_datetime_encoder.py)	00:11.506	571.7
Use case: developing locally and deploying to production (../examples/data_ops/1150_use_case.py)	00:10.410	662.7
Deduplicating misspelled categories (../examples/0050_deduplication.py)	00:04.400	567.0