.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/00_getting_started.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. or to run this example in your browser via JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_00_getting_started.py: Getting Started =============== This guide showcases the features of ``skrub``, an open-source package that aims at bridging the gap between tabular data sources and machine-learning models. Much of ``skrub`` revolves around vectorizing, assembling, and encoding tabular data, to prepare data in a format that shallow or classic machine-learning models understand. .. GENERATED FROM PYTHON SOURCE LINES 14-31 Downloading example datasets ---------------------------- The :obj:`~skrub.datasets` module allows us to download tabular datasets and demonstrate ``skrub``'s features. .. note:: You can control the directory where the datasets are stored by: - setting in your environment the ``SKRUB_DATA_DIRECTORY`` variable to an absolute directory path, - using the parameter ``data_directory`` in fetch functions, which takes precedence over the envar. By default, the datasets are stored in a folder named "skrub_data" in the user home folder. .. GENERATED FROM PYTHON SOURCE LINES 34-39 .. code-block:: Python from skrub.datasets import fetch_employee_salaries dataset = fetch_employee_salaries() employees_df, salaries = dataset.X, dataset.y .. GENERATED FROM PYTHON SOURCE LINES 40-41 Explore all the available datasets in :ref:`downloading_a_dataset_ref`. .. GENERATED FROM PYTHON SOURCE LINES 44-51 Generating an interactive report for a dataframe ------------------------------------------------- The :class:`~skrub.Cleaner` allows to clean the dataframe, parsing nulls, dates, and dropping columns with too many nulls. To quickly get an overview of a dataframe's contents, use the :class:`~skrub.TableReport`. .. GENERATED FROM PYTHON SOURCE LINES 53-58 .. code-block:: Python from skrub import Cleaner, TableReport employees_df = Cleaner().fit_transform(employees_df) TableReport(employees_df) .. raw:: html

Please enable javascript

The skrub table reports need javascript to display correctly. If you are displaying a report in a Jupyter notebook and you see this message, you may need to re-execute the cell or to trust the notebook (button on the top right or "File > Trust notebook").



.. GENERATED FROM PYTHON SOURCE LINES 59-70 You can use the interactive display above to explore the dataset visually. .. note:: You can see a few more `example reports`_ online. We also provide an experimental online demo_ that allows you to select a CSV or parquet file and generate a report directly in your web browser, without installing anything. .. _example reports: https://skrub-data.org/skrub-reports/examples/ .. _demo: https://skrub-data.org/skrub-reports/ .. GENERATED FROM PYTHON SOURCE LINES 73-75 It is also possible to tell skrub to replace the default pandas & polars displays with ``TableReport``. .. GENERATED FROM PYTHON SOURCE LINES 75-82 .. code-block:: Python from skrub import set_config set_config(use_tablereport=True) employees_df .. raw:: html

Please enable javascript

The skrub table reports need javascript to display correctly. If you are displaying a report in a Jupyter notebook and you see this message, you may need to re-execute the cell or to trust the notebook (button on the top right or "File > Trust notebook").



.. GENERATED FROM PYTHON SOURCE LINES 83-84 This setting can easily be reverted: .. GENERATED FROM PYTHON SOURCE LINES 84-89 .. code-block:: Python set_config(use_tablereport=False) employees_df .. raw:: html
gender department department_name division assignment_category employee_position_title date_first_hired year_first_hired
0 F POL Department of Police MSB Information Mgmt and Tech Division Records... Fulltime-Regular Office Services Coordinator 1986-09-22 1986
1 M POL Department of Police ISB Major Crimes Division Fugitive Section Fulltime-Regular Master Police Officer 1988-09-12 1988
2 F HHS Department of Health and Human Services Adult Protective and Case Management Services Fulltime-Regular Social Worker IV 1989-11-19 1989
3 M COR Correction and Rehabilitation PRRS Facility and Security Fulltime-Regular Resident Supervisor II 2014-05-05 2014
4 M HCA Department of Housing and Community Affairs Affordable Housing Programs Fulltime-Regular Planning Specialist III 2007-03-05 2007
... ... ... ... ... ... ... ... ...
9223 F HHS Department of Health and Human Services School Based Health Centers Fulltime-Regular Community Health Nurse II 2015-11-03 2015
9224 F FRS Fire and Rescue Services Human Resources Division Fulltime-Regular Fire/Rescue Division Chief 1988-11-28 1988
9225 M HHS Department of Health and Human Services Child and Adolescent Mental Health Clinic Serv... Parttime-Regular Medical Doctor IV - Psychiatrist 2001-04-30 2001
9226 M CCL County Council Council Central Staff Fulltime-Regular Manager II 2006-09-05 2006
9227 M DLC Department of Liquor Control Licensure, Regulation and Education Fulltime-Regular Alcohol/Tobacco Enforcement Specialist II 2012-01-30 2012

9228 rows × 8 columns



.. GENERATED FROM PYTHON SOURCE LINES 90-96 Easily building a strong baseline for tabular machine learning -------------------------------------------------------------- The goal of ``skrub`` is to ease tabular data preparation for machine learning. The :func:`~skrub.tabular_learner` function provides an easy way to build a simple but reliable machine-learning model, working well on most tabular data. .. GENERATED FROM PYTHON SOURCE LINES 99-107 .. code-block:: Python from sklearn.model_selection import cross_validate from skrub import tabular_learner model = tabular_learner("regressor") results = cross_validate(model, employees_df, salaries) results["test_score"] .. rst-class:: sphx-glr-script-out .. code-block:: none array([0.91129818, 0.88013711, 0.91451364, 0.92117174, 0.92487738]) .. GENERATED FROM PYTHON SOURCE LINES 108-114 To handle rich tabular data and feed it to a machine-learning model, the pipeline returned by :func:`~skrub.tabular_learner` preprocesses and encodes strings, categories and dates using the :class:`~skrub.TableVectorizer`. See its documentation or :ref:`sphx_glr_auto_examples_01_encodings.py` for more details. An overview of the chosen defaults is available in :ref:`end_to_end_pipeline`. .. GENERATED FROM PYTHON SOURCE LINES 117-128 Assembling data --------------- ``Skrub`` allows imperfect assembly of data, such as joining dataframes on columns that contain typos. ``Skrub``'s joiners have ``fit`` and ``transform`` methods, storing information about the data across calls. The :class:`~skrub.Joiner` allows fuzzy-joining multiple tables, each row of a main table will be augmented with values from the best match in the auxiliary table. You can control how distant fuzzy-matches are allowed to be with the ``max_dist`` parameter. .. GENERATED FROM PYTHON SOURCE LINES 130-132 In the following, we add information about countries to a table containing airports and the cities they are in: .. GENERATED FROM PYTHON SOURCE LINES 134-158 .. code-block:: Python import pandas as pd from skrub import Joiner airports = pd.DataFrame( { "airport_id": [1, 2], "airport_name": ["Charles de Gaulle", "Aeroporto Leonardo da Vinci"], "city": ["Paris", "Roma"], } ) # notice the "Rome" instead of "Roma" capitals = pd.DataFrame( {"capital": ["Berlin", "Paris", "Rome"], "country": ["Germany", "France", "Italy"]} ) joiner = Joiner( capitals, main_key="city", aux_key="capital", max_dist=0.8, add_match_info=False, ) joiner.fit_transform(airports) .. raw:: html
airport_id airport_name city capital country
0 1 Charles de Gaulle Paris Paris France
1 2 Aeroporto Leonardo da Vinci Roma Rome Italy


.. GENERATED FROM PYTHON SOURCE LINES 159-165 Information about countries have been added, even if the rows aren't exactly matching. It's also possible to augment data by joining and aggregating multiple dataframes with the :class:`~skrub.AggJoiner`. This is particularly useful to summarize information scattered across tables, for instance adding statistics about flights to the dataframe of airports: .. GENERATED FROM PYTHON SOURCE LINES 167-186 .. code-block:: Python from skrub import AggJoiner flights = pd.DataFrame( { "flight_id": range(1, 7), "from_airport": [1, 1, 1, 2, 2, 2], "total_passengers": [90, 120, 100, 70, 80, 90], "company": ["DL", "AF", "AF", "DL", "DL", "TR"], } ) agg_joiner = AggJoiner( aux_table=flights, main_key="airport_id", aux_key="from_airport", cols=["total_passengers"], # the cols to perform aggregation on operations=["mean", "std"], # the operations to compute ) agg_joiner.fit_transform(airports) .. raw:: html
airport_id airport_name city total_passengers_mean total_passengers_std
0 1 Charles de Gaulle Paris 103.333333 15.275252
1 2 Aeroporto Leonardo da Vinci Roma 80.000000 10.000000


.. GENERATED FROM PYTHON SOURCE LINES 187-191 For joining multiple auxiliary tables on a main table at once, use the :class:`~skrub.MultiAggJoiner`. See other ways to join multiple tables in :ref:`assembling`. .. GENERATED FROM PYTHON SOURCE LINES 194-207 Encoding data ------------- When a column contains categories with variations and typos, it can be encoded using one of ``skrub``'s encoders, such as the :class:`~skrub.GapEncoder`. The :class:`~skrub.GapEncoder` creates a continuous encoding, based on the activation of latent categories. It will create the encoding based on combinations of substrings which frequently co-occur. For instance, we might want to encode a column ``X`` that contains information about cities, being either Madrid or Rome : .. GENERATED FROM PYTHON SOURCE LINES 209-227 .. code-block:: Python from skrub import GapEncoder X = pd.Series( [ "Rome, Italy", "Rome", "Roma, Italia", "Madrid, SP", "Madrid, spain", "Madrid", "Romq", "Rome, It", ], name="city", ) enc = GapEncoder(n_components=2, random_state=0) # 2 topics in the data enc.fit(X) .. raw:: html 
GapEncoder(n_components=2, random_state=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.


.. GENERATED FROM PYTHON SOURCE LINES 228-229 The :class:`~skrub.GapEncoder` has found the following two topics: .. GENERATED FROM PYTHON SOURCE LINES 231-233 .. code-block:: Python enc.get_feature_names_out() .. rst-class:: sphx-glr-script-out .. code-block:: none ['city: madrid, spain, sp', 'city: italia, italy, romq'] .. GENERATED FROM PYTHON SOURCE LINES 234-237 Which correspond to the two cities. Let's see the activation of each topic depending on the rows of ``X``: .. GENERATED FROM PYTHON SOURCE LINES 239-242 .. code-block:: Python encoded = enc.fit_transform(X).assign(original=X) encoded .. raw:: html
city: madrid, spain, sp city: italia, italy, romq original
0 0.052257 13.547743 Rome, Italy
1 0.050202 3.049798 Rome
2 0.063282 15.036718 Roma, Italia
3 12.047028 0.052972 Madrid, SP
4 16.547818 0.052182 Madrid, spain
5 6.048861 0.051139 Madrid
6 0.050019 3.049981 Romq
7 0.053193 9.046807 Rome, It


.. GENERATED FROM PYTHON SOURCE LINES 243-247 The higher the activation, the closer the row to the latent topic. These columns can now be understood by a machine-learning model. The other encoders are presented in :ref:`encoding`. .. GENERATED FROM PYTHON SOURCE LINES 250-261 Next steps ---------- We have briefly covered pipeline creation, vectorizing, assembling, and encoding data. We presented the main functionalities of ``skrub``, but there is much more to it ! Please refer to our :ref:`user_guide` for a more in-depth presentation of ``skrub``'s concepts, or visit our `examples `_ for more illustrations of the tools that we provide ! .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 8.493 seconds) .. _sphx_glr_download_auto_examples_00_getting_started.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/skrub-data/skrub/main?urlpath=lab/tree/notebooks/auto_examples/00_getting_started.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../lite/lab/index.html?path=auto_examples/00_getting_started.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: 00_getting_started.ipynb <00_getting_started.ipynb>` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: 00_getting_started.py <00_getting_started.py>` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: 00_getting_started.zip <00_getting_started.zip>` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_