woodwork 0.0.2

a two-dimensional data object with labeled axes and typing information

Woodwork provides you with a common DataTable object to use with Featuretools, EvalML, and general ML. A DataTable object contains the physical, logical, and semantic data types present in the data. In addition, it can store metadata about the data.

Installation

Install with pip:

python -m pip install woodwork

Example

Below is an example of using Woodwork. In this example, a sample dataset of order items is used to create a Woodwork DataTable, specifying the LogicalType for three of the columns.

>> import woodwork as ww
>> from woodwork.logical_types import Datetime, Categorical, NaturalLanguage

>> data = ww.demo.load_retail(nrows=100)

>> dt = ww.DataTable(data, name='retail')
>> dt.set_logical_types({
    'quantity': 'Double',
    'customer_name': 'Categorical',
    'country': 'Categorical'
})
>> dt.types
                Physical Type     Logical Type Semantic Tag(s)
Data Column                                                   
order_id                Int64      WholeNumber       {numeric}
product_id           category      Categorical      {category}
description            string  NaturalLanguage              {}
quantity              float64           Double       {numeric}
order_date     datetime64[ns]         Datetime              {}
unit_price            float64           Double       {numeric}
customer_name        category      Categorical      {category}
country              category      Categorical      {category}
total                 float64           Double       {numeric}

We now have created a Woodwork DataTable 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 of Boolean or a semantic tag of numeric we can simply select those columns and access a dataframe containing just those columns:

>> filtered_df = dt.select(include=['Boolean', 'numeric']).to_pandas()
>> filtered_df
    order_id  quantity  unit_price   total  cancelled
0     536365       6.0      4.2075  25.245      False
1     536365       6.0      5.5935  33.561      False
2     536365       8.0      4.5375  36.300      False
3     536365       6.0      5.5935  33.561      False
4     536365       6.0      5.5935  33.561      False
..       ...       ...         ...     ...        ...
95    536378       6.0      4.2075  25.245      False
96    536378     120.0      0.6930  83.160      False
97    536378      24.0      0.9075  21.780      False
98    536378      24.0      0.9075  21.780      False
99    536378      24.0      0.9075  21.780      False

As 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 the Woodwork documentation for more detail on working with Woodwork tables.

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