Pandas Workflow

Connecting To Tamr

Connecting to a Tamr instance:

import os
import pandas as pd
from tamr_unify_client import Client
from tamr_unify_client.auth import UsernamePasswordAuth

username = os.environ['TAMR_USERNAME']
password = os.environ['TAMR_PASSWORD']

auth = UsernamePasswordAuth(username, password)
tamr = Client(auth)

Load dataset as Dataframe

Loading: In Memory

Loading a dataset as a pandas dataframe is possible via the from_records() method that pandas provides. An example is shown below:

my_dataset = tamr.datasets.by_name("my_tamr_dataset")
df = pd.DataFrame.from_records(my_dataset.records())

This will construct a pandas dataframe based on the records that are streamed in, and stored in the pandas dataframe. Once all records have been loaded, you will be able to interact with the dataframe normally.

Note that as values are typically represented inside arrays within Tamr, the values will be encapsulated lists inside the dataframe. You can use traditional methods in pandas to deal with this; for example by calling .explode(), or extracting specific elements.

Loading: Streaming

When working with large datasets it is sometimes better not to work in memory, but to iterate through a dataset, rather than load the entire dataset at once. Since dataset.records() is a generator, this can easily be done as follows:

output = []
for record in dataset.records():
    single_record_df = pd.DataFrame.from_records(record)

Custom Generators

In order to customise the data loaded into the pandas dataframe, it is possible to customise the generator object dataset.records() by wrapping it in a different generator.

For example, it is possible to automatically flatten all lists with a length of one, and apply this to the dataset.records() generator as follows:

def unlist(lst):
    If object is a list of length one, return first element. 
    Otherwise, return original object. 
    if isinstance(lst, list) and len(lst) is 1:
        return lst[0]
        return lst

def dataset_to_pandas(dataset):
    Incorporates basic unlisting for easy transfer between Tamr and Pandas. 
    for record in dataset.records():
        for key in record:
            record[key] = unlist(record[key])
        yield record

df = pd.DataFrame.from_records(dataset_to_pandas(my_dataset))

Similarly, it is possible to filter to extracting only certain attributes, by specifying this in the generator:

def filter_dataset_to_pandas(dataset, colnames):
    Filter the dataset to only the primary key and the columns specified as a list in colnames. 
    assert isinstance(colnames, list)
    colnames = dataset.key_attribute_names + colnames if dataset.key_attribute_names[0] not in colnames else colnames
    for record in dataset.records():
        yield {k: unlist(v) for k, v in record.items() if k in colnames}

df = pd.DataFrame.from_records(filter_dataset_to_pandas(my_dataset, ['City', 'new_attr']))

Note that upserting these records back to the original Tamr Dataset would overwite the existing records and attributes, and cause loss of the data stored in the removed attributes.

Upload Dataframe as Dataset

Create New Dataset

To create a new dataset and upload data, the convenience function datasets.create_from_dataframe() can be used. Note that Tamr will throw an error if columns aren’t generally formatted as strings. (The exception being geospatial columns. For that, see the geospatial examples.)

To format values as strings while preserving null information, specify dtype=object when creating a dataframe from a csv file.

df = pd.read_csv("my_file.csv", dtype=object)

Creating the dataset is as easy as calling:

tamr.datasets.create_from_dataframe(df, 'primaryKey', 'my_new_dataset')

For an already-existing dataframe, the columns can be converted to strings using:

df = df.astype(str)

Note, however, that converting this way will cause any NaN or None values to become strings like 'nan' that will persist into the created Tamr dataset.

Changing Values

Making Changes: In Memory

When making changes to a dataset that was loaded as a dataframe, changes can be pushed back to Tamr using the dataset.upsert_from_dataframe() method as follows:

df = pd.DataFrame.from_records(my_dataset.records())
df['column'] = 'new_value'
my_dataset.upsert_from_dataframe(df, primary_key_name='primary_key')

Making Changes: Streaming

For larger datasets it might be better to stream the data and apply changes while iterating through the dataset. This way the full dataset does not need to be loaded into memory.

for record in dataset.records():
    single_record_df = pd.DataFrame.from_records(record)
    single_record_df['column_to_change'] = 'new_value'
    dataset.upsert_from_dataframe(single_record_df, primary_key_name='primary_key')

Adding Attributes

When making changes to dataframes, new dataframe columns are not automatically created as attributes when upserting records to Tamr. In order for these changes to be recorded, these attributes first need to be created.

One way of creating these for source datasets automatically would be as follows:

def add_missing_attributes(dataset, df):
    Detects any attributes in the dataframe that aren't in the dataset and attempts to add them (as strings).
    existing_attributes = [ for att in dataset.attributes]
    new_attributes = [att for att in df.columns.to_list() if att not in existing_attributes]
    if not new_attributes:
    for new_attribute in new_attributes:
        attr_spec = {"name": new_attribute,
                     "type": {"baseType": "ARRAY", "innerType": {"baseType": "STRING"}},

add_missing_attributes(my_dataset, df)


When running into errors upon loading dataset.records() into a pandas dataframe, it is good to consider the following steps. To extract a single record, the following code can be used to provide a minimal reproducible example:

record = next(dataset.records())


Tamr allows for more variety in attribute names and contents than pandas does. In most cases pandas can load data correctly, but it is possible to modify the parsing using a custom generator as shown above. An example below changes an attribute name, and extracts only the first element:

def custom_parser(dataset):
    for record in dataset.records():
        record['pandas_column_name'] = record.pop('dataset_attribute_name')
        record['first_element_of_column'] = record['multi_value_column'][0]
        yield record

df = pd.DataFrame.from_records(custom_parser(dataset))