Package: woodwork
Woodwork provides a common typing namespace for using your existing DataFrames in Featuretools, EvalML, and general ML. A Woodwork
DataFrame stores the physical, logical, and semantic data types present in the data. In addition, it can store metadata about the data, allowing you to store specific information you might need for your application.InstallationInstall with pip:python-mpipinstallwoodworkor from the conda-forge channel onconda:condainstall-cconda-forgewoodworkAdd-onsUpdate checker- Receive automatic notifications of new Woodwork releasespython-mpipinstall"woodwork[updater]"ExampleBelow is an example of using Woodwork. In this example, a sample dataset of order items is used to create a WoodworkDataFrame, specifying theLogicalTypefor five of the columns.importpandasaspdimportwoodworkaswwdf=pd.read_csv("https://oss.alteryx.com/datasets/online-retail-logs-2018-08-28.csv")df.ww.init(name='retail')df.ww.set_types(logical_types={'quantity':'Integer','customer_name':'PersonFullName','country':'Categorical','order_id':'Categorical','description':'NaturalLanguage',})df.wwPhysical Type     Logical Type Semantic Tag(s)
Column
order_id                category      Categorical    ['category']
product_id              category      Categorical    ['category']
description               string  NaturalLanguage              []
quantity                   int64          Integer     ['numeric']
order_date        datetime64[ns]         Datetime              []
unit_price               float64           Double     ['numeric']
customer_name             string   PersonFullName              []
country                 category      Categorical    ['category']
total                    float64           Double     ['numeric']
cancelled                   bool          Boolean              []We now have initialized Woodwork on the DataFrame with the specified logical types assigned. For columns that did not have a specified logical type value, Woodwork has automatically inferred the logical type based on the underlying data. Additionally, Woodwork has automatically assigned semantic tags to some of the columns, based on the inferred or assigned logical type.If we wanted to do further analysis on only the columns in this table that have a logical type ofBooleanor a semantic tag ofnumericwe can simply select those columns and access a dataframe containing just those columns:filtered_df=df.ww.select(include=['Boolean','numeric'])filtered_dfquantity  unit_price   total  cancelled
0          6      4.2075  25.245      False
1          6      5.5935  33.561      False
2          8      4.5375  36.300      False
3          6      5.5935  33.561      False
4          6      5.5935  33.561      False
..       ...         ...     ...        ...
95         6      4.2075  25.245      False
96       120      0.6930  83.160      False
97        24      0.9075  21.780      False
98        24      0.9075  21.780      False
99        24      0.9075  21.780      FalseAs you can see, Woodwork makes it easy to manage typing information for your data, and provides simple interfaces to access only the data you need based on the logical types or semantic tags. Please refer to theWoodwork documentationfor more detail on working with a Woodwork DataFrame.SupportThe Woodwork community is happy to provide support to users of Woodwork. Project support can be found in four places depending on the type of question:For usage questions, useStack Overflowwith thewoodworktag.For bugs, issues, or feature requests start aGithub issue.For discussion regarding development on the core library, useSlack.For everything else, the core developers can be reached by email atopen_source_support@alteryx.comBuilt at AlteryxWoodworkis an open source project built byAlteryx. To see the other open source projects we’re working on visitAlteryx Open Source. If building impactful data science pipelines is important to you or your business, please get in touch.