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CENCORA, INC.: gross profit

Gross profit for CENCORA, INC. Inspect selected reporting periods, original units and SEC filing links; download the financial history.

All CENCORA, INC. financial histories

What this measure means

Revenue less the costs directly attributed to the goods or services sold. It precedes other operating expenses and is not net income.

Exact concept: us-gaap:GrossProfit. Each value covers an annual-duration reporting interval, shown with both start and end dates. Different units remain separate; no currency conversion or interpolation is applied.

Coverage of this history

Selected reporting periods run from 2006-10-01 to 2025-09-30. The SEC response was captured on 2026-09-20.

Selected filing history

Gross profit in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
2024-10-012025-09-3011,478,539,000USD2025-11-2510-K · 0001140859-25-000131
2023-10-012024-09-309,910,029,000USD2025-11-2510-K · 0001140859-25-000131
2022-10-012023-09-308,959,493,000USD2025-11-2510-K · 0001140859-25-000131
2021-10-012022-09-308,296,367,000USD2024-11-2610-K · 0001140859-24-000177
2020-10-012021-09-306,943,228,000USD2023-11-2110-K · 0001140859-23-000197
2019-10-012020-09-305,191,884,000USD2022-11-2210-K · 0001140859-22-000098
2018-10-012019-09-305,138,312,000USD2021-11-2310-K · 0001140859-21-000058
2017-10-012018-09-304,612,317,000USD2020-11-1910-K · 0001140859-20-000050
2016-10-012017-09-304,546,002,000USD2019-11-1910-K · 0001140859-19-000040
2015-10-012016-09-304,272,606,000USD2018-11-2010-K · 0001140859-18-000053
2014-10-012015-09-303,529,313,000USD2017-11-2110-K · 0001140859-17-000047
2013-10-012014-09-302,982,366,000USD2016-11-2210-K · 0001140859-16-000022
2012-10-012013-09-302,507,819,000USD2015-11-2410-K · 0001047469-15-008939
2011-10-012012-09-302,634,686,000USD2014-11-2510-K · 0001047469-14-009555
2010-10-012011-09-302,458,977,000USD2013-11-2610-K · 0001047469-13-010867
2009-10-012010-09-302,325,050,000USD2012-11-2710-K · 0001047469-12-010807
2008-10-012009-09-302,100,075,000USD2011-11-2210-K · 0000950123-11-100028
2007-10-012008-09-302,047,002,000USD2010-11-2310-K · 0000950123-10-108165
2006-10-012007-09-302,219,059,000USD2009-11-2510-K · 0000950123-09-066012

Related financial histories

Inspect the source

Entity
CENCORA, INC. / CIK 0001140859
Captured
2026-09-20T07:41:57.997Z
SEC response SHA-256
3dc134357c7d7cb1acfe534c4f2086a82e61b4c9367c091507c66b9efb8c863c

Current SEC company facts · Download the original response snapshot (gzip) · Download the selected JSON

Latest-filed annual-report facts per unit and reporting period at capture time. Duration facts cover 300 to 400 days. This selection can include restatements and is not a point-in-time backtest dataset. Missing concepts are omitted, never zero-filled. Values retain original units and are not currency converted. Extended concepts require compatible unit shapes and at least three reporting ends with changing values within one unit. Constant or incompatible added histories are omitted.

Public company accounting reference, not market prices, returns, an investment recommendation, or ALPHAC performance. Validate a separately constructed return series with the validation API; accounting values are not returns.

Use this in research

A financial period ends before its results become public. Use the filing date as a minimum availability boundary, inspect amendments, and retain the original filing vintage when testing historical signals. This latest-filed selection can contain information unavailable at the time.

These pages do not supply prices, total-return histories, corporate-action adjustments or a tradable universe. Build those inputs separately before evaluating a strategy. A profitable backtest can still reflect selection bias or costs that were left out.

Research methodology · Execution and cost assumptions · Check backtest overfitting

Build with the open-source tools

Use these accounting records as inspectable inputs. When you have constructed a return series, the validation tools can help test its statistical evidence and preserve the result with its limitations.

Read the published dataset with Python
import json
from urllib.request import urlopen

with urlopen("https://canlicapital.com/company-data/0001140859.json") as response:
    record = json.load(response)
print(record["fetched_at"])
print(record["policy"])
for concept in record["concepts"]:
    print(concept["tag"], next(iter(concept["observations"])))