In [1]:

%%capture [--no-stdout]
!pip install fhir-aggregator-client --no-cache-dir --quiet

%%capture [--no-stdout] !pip install fhir-aggregator-client --no-cache-dir --quiet

In [2]:

%%capture [--no-stdout]
import numpy as np
import pandas as pd
from itables import init_notebook_mode, show
init_notebook_mode(connected=True)
import itables.options as opt

opt.classes="display nowrap compact"
opt.buttons=["copyHtml5", "csvHtml5", "excelHtml5"]
opt.maxBytes=0

%env FHIR_BASE= https://google-fhir.fhir-aggregator.org
!fq vocabulary vocabulary.tsv --fhir-base-url $FHIR_BASE
df = pd.read_csv('vocabulary.tsv', sep='\t').fillna('')

%%capture [--no-stdout] import numpy as np import pandas as pd from itables import init_notebook_mode, show init_notebook_mode(connected=True) import itables.options as opt opt.classes="display nowrap compact" opt.buttons=["copyHtml5", "csvHtml5", "excelHtml5"] opt.maxBytes=0 %env FHIR_BASE= https://google-fhir.fhir-aggregator.org !fq vocabulary vocabulary.tsv --fhir-base-url $FHIR_BASE df = pd.read_csv('vocabulary.tsv', sep='\t').fillna('')

Vocabulary filtering examples¶

It's easy enough to get a straightforward table response - just utilize Pandas to filter appropriately to your interest. Some examples below:

Exploring a study¶

Finding the vocabulary specific study is easy - filter according to the desired identifier in the research_study_identifiers column.

In [3]:

scratch_df = df[df['research_study_identifiers'].str.contains('1KG')]
scratch_df

scratch_df = df[df['research_study_identifiers'].str.contains('1KG')] scratch_df

Out[3]:

Loading ITables v2.5.2 from the internet... (need help?)

Exploring available conditions¶

Or query the vocab table for the available condition codes of the various patients in each research study.

In [4]:

scratch_df = df[df['path'].str.contains('Condition.code')]
scratch_df

scratch_df = df[df['path'].str.contains('Condition.code')] scratch_df

Out[4]:

Loading ITables v2.5.2 from the internet... (need help?)