Creating and Modifying Resources

Creating resources

Resources, such as projects, dataset, and attribute configurations, can be created through their respective collections. Each create function takes in a dictionary that conforms to the Tamr Public Docs for creating that resource type:

spec = {
    "name": "project",
    "description": "Mastering Project",
    "type": "DEDUP",
    "unifiedDatasetName": "project_unified_dataset"
project = tamr.projects.create(spec)

Using specs

These dictionaries can also be created using spec classes.

Each Resource has a corresponding ResourceSpec which can be used to build an instance of that resource by specifying the value for each property.

The spec can then be converted to a dictionary that can be passed to create.

For instance, to create a project:

spec = (
    .with_description("Mastering Project")
project = tamr.projects.create(spec.to_dict())

Calling with_* on a spec creates a new spec with the same properties besides the modified one. The original spec is unaltered, so it could be used multiple times:

base_spec = (
    .with_description("Mastering Project")

specs = []
for name in project_names:
    spec = (
        .with_unified_dataset_name(name + "_unified_dataset")

projects = [tamr.projects.create(spec.to_dict()) for spec in specs]

Creating a dataset

Datasets can be created as described above, but the dataset’s schema and records must then be handled separately.

To combine all of these steps into one, DatasetCollection has a convenience function create_from_dataframe that takes a Pandas DataFrame. This makes it easy to create a Tamr dataset from a CSV:

import pandas as pd

df = pd.read_csv("my_data.csv", dtype=str)      # string is the recommended data type
dataset = tamr.datasets.create_from_dataframe(df, primary_key_name="primary key name", dataset_name="My Data")

This will create a dataset called “My Data” with the specified primary key, an attribute for each column of the DataFrame, and the DataFrame’s rows as records.

Modifying a resource

Certain resources can also be modified using specs.

After getting a spec corresponding to a resource and modifying some properties, the updated resource can be committed to Tamr with the put function:

updated_dataset = (
    .with_description("Modified description")

Each spec class has many properties that can be changed, but refer to the Public Docs for which properties will actually be updated in Tamr. If an immutable property is changed in the update request, the new value will simply be ignored.