DONALDSON COMPANY, INC.: diluted weighted-average shares

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 2008-08-01 to 2025-07-31. The SEC response was captured on 2026-09-19.

Selected filing history

Related financial histories

• DONALDSON COMPANY, INC.: total assets
• DONALDSON COMPANY, INC.: total liabilities
• DONALDSON COMPANY, INC.: stockholders equity
• DONALDSON COMPANY, INC.: cash and cash equivalents
• DONALDSON COMPANY, INC.: net income or loss
• DONALDSON COMPANY, INC.: operating cash flow
• DONALDSON COMPANY, INC.: capital expenditure payments
• DONALDSON COMPANY, INC.: revenue
• DONALDSON COMPANY, INC.: financing cash flow
• DONALDSON COMPANY, INC.: investing cash flow
• DONALDSON COMPANY, INC.: retained earnings or deficit
• DONALDSON COMPANY, INC.: basic weighted-average shares
• DONALDSON COMPANY, INC.: basic earnings per share
• DONALDSON COMPANY, INC.: diluted earnings per share
• DONALDSON COMPANY, INC.: income tax expense or benefit
• DONALDSON COMPANY, INC.: net property, plant and equipment
• DONALDSON COMPANY, INC.: share-based compensation expense
• DONALDSON COMPANY, INC.: operating income or loss
• DONALDSON COMPANY, INC.: current assets
• DONALDSON COMPANY, INC.: interest expense
• DONALDSON COMPANY, INC.: current liabilities
• DONALDSON COMPANY, INC.: current accounts payable
• DONALDSON COMPANY, INC.: goodwill carrying amount
• DONALDSON COMPANY, INC.: net finite-lived intangible assets
• DONALDSON COMPANY, INC.: net current accounts receivable
• DONALDSON COMPANY, INC.: common-stock repurchase payments
• DONALDSON COMPANY, INC.: operating expenses
• DONALDSON COMPANY, INC.: net inventory
• DONALDSON COMPANY, INC.: gross profit
• DONALDSON COMPANY, INC.: cost of revenue
• DONALDSON COMPANY, INC.: selling, general and administrative expense
• DONALDSON COMPANY, INC.: research and development expense

Inspect the source

Entity

DONALDSON COMPANY, INC. / CIK 0000029644

Captured

2026-09-19T14:46:01.649Z

SEC response SHA-256

7e4152bc111786d994e8eb06fa0875d706e3c7b490a2f480de780c9e35f27862

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.

• Get an API key and run your first validation
• Connect the MCP server to your coding assistant
• Inspect the ALPHAC engine on GitHub
• Read the MCP server source and integration examples

import json
from urllib.request import urlopen

with urlopen("https://canlicapital.com/company-data/0000029644.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"])))