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Prologis, Inc.: diluted weighted-average shares

Diluted weighted-average shares for Prologis, Inc. Inspect selected reporting periods, original units and SEC filing links; download the financial history.

All Prologis, Inc. financial histories

What this measure means

Weighted-average shares used for diluted earnings per share. Potential shares are included under the applicable dilution rules, not simply added to outstanding shares.

Exact concept: us-gaap:WeightedAverageNumberOfDilutedSharesOutstanding. 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 2009-01-01 to 2025-12-31. The SEC response was captured on 2026-09-20.

Selected filing history

Diluted weighted-average shares in original reported units, latest-filed observation per period
Period startPeriod endValueUnitFiledSource filing
2025-01-012025-12-31956,832,000shares2026-02-1310-K · 0001193125-26-051453
2024-01-012024-12-31953,590,000shares2026-02-1310-K · 0001193125-26-051453
2023-01-012023-12-31951,791,000shares2026-02-1310-K · 0001193125-26-051453
2022-01-012022-12-31811,608,000shares2025-02-1410-K · 0000950170-25-021272
2021-01-012021-12-31764,762,000shares2024-02-1310-K · 0000950170-24-014539
2020-01-012020-12-31754,414,000shares2023-02-1410-K · 0001564590-23-001902
2019-01-012019-12-31654,903,000shares2022-02-0910-K · 0001564590-22-004436
2018-01-012018-12-31590,239,000shares2021-02-1110-K · 0001564590-21-005312
2017-01-012017-12-31552,300,000shares2020-02-1110-K · 0001564590-20-004096
2016-01-012016-12-31546,666,000shares2019-02-1310-K · 0001564590-19-002872
2015-01-012015-12-31533,944,000shares2018-02-1510-K · 0001564590-18-002228
2014-01-012014-12-31506,391,000shares2017-02-1510-K · 0001564590-17-001559
2013-01-012013-12-31491,546,000shares2016-02-1910-K · 0001564590-16-012933
2012-01-012012-12-31461,848,000shares2015-02-2510-K · 0001193125-15-062622
2011-01-012011-12-31371,730,000shares2014-02-2610-K · 0001193125-14-070012
2010-01-012010-12-31219,515,000shares2013-02-2810-K · 0001193125-13-081863
2009-01-012009-12-31179,966,000shares2012-02-2910-K · 0001193125-12-087643

Related financial histories

Inspect the source

Entity
Prologis, Inc. / CIK 0001045609
Captured
2026-09-20T05:16:30.429Z
SEC response SHA-256
42b1e16ac75fb70de369040e3e511da28ae2ad9bbe32c9c6bcd4affa074e55d1

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/0001045609.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"])))