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One of OakVar's main aims is easy integration of genomic variant analysis results with AI/ML frameworks.

The first step toward that goal is the conversion of analysis results into DataFrames.

OakVar provides the following method to convert the analysis results in SQLite tables into Polars DataFrame.

import oakvar as ov
df = ov.get_df_from_db("oakvar_example.vcf.sqlite")

This will load the content of variant table into df as a Polars DataFrame.

If you want to use Pandas DataFrame instead,

df.to_pandas(use_pyarrow_extension_array = True)

will produce a Pandas version of the same DataFrame.

Back to get_df_from_db method, you can specify which table to load and a SQL expression for filtering the content.

df = ov.get_df_from_db(
    "oakvar_example.vcf.sqlite", table_name="sample"

will load the sample table into df as a DataFrame.

df = ov.get_df_from_db(
    sql="base__so='MIS' and clinvar__sig like '%Pathogenic%'"

will load the variant table, but with the filter for the variants whose consequence is missense and whose ClinVar clinical significance include Pathogenic.