Unable to get Business Segment Data

[amismithmoritz]

Hi All,
I have previously used edgartools to get more detailed information from the financials in a relatively straightforward manner. I was able to access the sections using "concept", but now that information appears to not be a dim_ so no column exist, but is label. Can anyone point me a direction on how I could get that information?
Thanks!

Python 3.13.5
edgartools 5.11.2

bax = Company('BAX')
filings = bax.get_filings(form="10-Q")

Sort filings by date

filings_sorted = sorted(filings, key=lambda x: x.filing_date, reverse=True)

revenue = xbrl.facts.query().by_concept('Revenue').to_dataframe()
revenue['period_start'] = pd.to_datetime(revenue['period_start'])
revenue['period_end'] = pd.to_datetime(revenue['period_end'])
revenue = revenue.drop_duplicates()

# Filter for actual numeric revenue entries
revenue = revenue[
    (revenue['concept'].str.contains('Revenue')) &
    (revenue['numeric_value'].notnull()) &
    (revenue['balance'].str.contains('credit'))
    (revenue['dim_us-gaap_StatementBusinessSegmentsAxis'].notnull())
]

# Select relevant columns
revenue_filtered = revenue[[
    'dim_srt_ProductOrServiceAxis',
    'dim_srt_StatementGeographicalAxis',
    'dim_us-gaap_StatementBusinessSegmentsAxis',
    'numeric_value',
    'period_start',
    'period_end',
]]

[dgunning]

Hi! Thanks for reporting this.

Starting in recent versions of edgartools, dimension columns are excluded from DataFrame output by default to produce cleaner output that matches the face of SEC filings. The dim_* columns still exist — they just need to be explicitly requested.

Quick Fix

Add .with_dimensions() to your query chain:

revenue = xbrl.facts.query().by_concept("Revenue").with_dimensions().to_dataframe()

This will restore all the dim_* columns (dim_us-gaap_StatementBusinessSegmentsAxis, dim_srt_ProductOrServiceAxis, etc.) in the output.

Updated Example for Your Use Case

from edgar import Company
import pandas as pd

bax = Company("BAX")
filing = bax.get_filings(form="10-Q").latest()
xbrl = filing.xbrl()

# Use with_dimensions() to include dim_* columns
revenue = (xbrl.facts.query()
    .by_concept("Revenue")
    .with_dimensions()
    .to_dataframe())

# Filter for segment data
revenue = revenue[
    (revenue["concept"].str.contains("Revenue")) &
    (revenue["numeric_value"].notnull()) &
    (revenue["dim_us-gaap_StatementBusinessSegmentsAxis"].notnull())
]

Alternative: Filter by Dimension Directly

You can also use by_dimension() which automatically includes dimension columns:

# Get revenue broken down by business segment
revenue_by_segment = (xbrl.facts.query()
    .by_concept("Revenue")
    .by_dimension("StatementBusinessSegmentsAxis")
    .to_dataframe())

This filters to only facts that have the business segment dimension, and includes structured columns like dimension, member, and member_label for easier analysis.

New Structured Dimension Columns

In addition to the dim_* columns, recent versions also provide structured dimension fields:

• dimension — the dimension axis name (e.g., us-gaap:StatementBusinessSegmentsAxis)
• member — the member value (e.g., bax:RenalCareMember)
• member_label — human-readable label (e.g., Renal Care)
• full_dimension_label — formatted as "Business Segments: Renal Care"

These can be more convenient than the dim_* columns for analysis.

Hope this helps! Let me know if you have any other questions.

[amismithmoritz]

Thanks for the quick response.

The first quick fix you suggested did not work for me.
`from edgar import Company
import pandas as pd

bax = Company("BAX")
filing = bax.get_filings(form="10-Q").latest()
xbrl = filing.xbrl()

Use with_dimensions() to include dim_* columns

revenue = (xbrl.facts.query()
.by_concept("Revenue")
.with_dimensions()
.to_dataframe())

Filter for segment data

revenue = revenue[
(revenue["concept"].str.contains("Revenue")) &
(revenue["numeric_value"].notnull()) &
(revenue["dim_us-gaap_StatementBusinessSegmentsAxis"].notnull())
]`
Gave the error:

---

AttributeError Traceback (most recent call last)
Cell In[10], line 11
6 xbrl = filing.xbrl()
8 # Use with_dimensions() to include dim_* columns
9 revenue = (xbrl.facts.query()
10 .by_concept("Revenue")
---> 11 .with_dimensions()
12 .to_dataframe())
14 # Filter for segment data
15 revenue = revenue[
16 (revenue["concept"].str.contains("Revenue")) &
17 (revenue["numeric_value"].notnull()) &
18 (revenue["dim_us-gaap_StatementBusinessSegmentsAxis"].notnull())
19 ]

AttributeError: 'FactQuery' object has no attribute 'with_dimensions'

The alternative fix appears to work.

[dgunning]

Hi @amismithmoritz — apologies for the confusion! The with_dimensions() method was added in v5.14.0, so it's not available on your v5.11.2. Upgrading should fix it:

pip install --upgrade edgartools

After upgrading, both approaches will work:

# Option 1: Include all dim_* columns without filtering
revenue = (xbrl.facts.query()
    .by_concept("Revenue")
    .with_dimensions()
    .to_dataframe())

# Option 2: Filter to a specific dimension (also includes dim_* columns automatically)
revenue = (xbrl.facts.query()
    .by_concept("Revenue")
    .by_dimension("StatementBusinessSegmentsAxis")
    .to_dataframe())

In the meantime, glad to hear by_dimension() is working for you — that's been available since earlier versions.

[amismithmoritz]

Hi dgunning,
That worked perfectly!!! Thanks so much. This has been a great tool for me!