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At a glance

Useful for: Looking at which columns are populated across datasets.

API Documentation: completeness_chart()

What is needed to generate the chart? A linker with some data.

Worked Example

from splink.duckdb.linker import DuckDBLinker
import splink.duckdb.comparison_library as cl
import splink.duckdb.comparison_template_library as ctl
from splink.duckdb.blocking_rule_library import block_on
from splink.datasets import splink_datasets
import logging, sys

# Split a simple dataset into two, separate datasets which can be linked together.
df_l = df.sample(frac=0.5)
df_r = df.drop(df_l.index)

settings = {
    "link_type": "link_only",
    "blocking_rules_to_generate_predictions": [
    "comparisons": [
        ctl.date_comparison("dob", cast_strings_to_date=True),
        cl.exact_match("city", term_frequency_adjustments=True),
        ctl.email_comparison("email", include_username_fuzzy_level=False),

linker = DuckDBLinker([df_l, df_r], settings, input_table_aliases=["df_left", "df_right"])

linker.completeness_chart(cols=["first_name", "surname", "dob", "city", "email"])

What the chart shows

The completeness_chart shows the proportion of populated (non-null) values in the columns of multiple datasets.

What the chart tooltip shows

The tooltip shows a number of values based on the panel that the user is hovering over, including:

  • The dataset and column name
  • The count and percentage of non-null values in the column for the relelvant dataset.

How to interpret the chart

Each panel represents the percentage of non-null values in a given dataset-column combination. The darker the panel, the lower the percentage of non-null values.

Actions to take as a result of the chart

Only choose features that are sufficiently populated across all datasets in a linkage model.