Yahoo Finance (Website) ======================= This section provides examples of available methods from Yahoo Finance (Website) source. All the data is extracted from the web pages of Yahoo Finance through web scraping. .. important:: In each result dataframe, only the first 5 rows of the DataFrame are shown at most to keep the documentation concise. The actual DataFrame returned by each function may contain more rows. Income Statement ---------------- Retrieves the company's income statement data including revenue, expenses, and profit metrics for multiple periods. Returns a **DataFrame**. .. code-block:: python from stockdex import Ticker ticker = Ticker(ticker="NVDA") result = ticker.yahoo_web_income_stmt **Results:** +---------------------+---------------+---------------+---------------+---------------+---------------+ | | TTM | 1/31/2025 | 1/31/2024 | 1/31/2023 | 1/31/2022 | +=====================+===============+===============+===============+===============+===============+ | Total Revenue | 165,218,000 | 130,497,000 | 60,922,000 | 26,974,000 | 26,914,000 | +---------------------+---------------+---------------+---------------+---------------+---------------+ | Cost of Revenue | 49,818,000 | 32,639,000 | 16,621,000 | 11,618,000 | 9,439,000 | +---------------------+---------------+---------------+---------------+---------------+---------------+ | Gross Profit | 115,400,000 | 97,858,000 | 44,301,000 | 15,356,000 | 17,475,000 | +---------------------+---------------+---------------+---------------+---------------+---------------+ | Operating Expense | 19,420,000 | 16,405,000 | 11,329,000 | 9,779,000 | 7,434,000 | +---------------------+---------------+---------------+---------------+---------------+---------------+ | Operating Income | 95,980,000 | 81,453,000 | 32,972,000 | 5,577,000 | 10,041,000 | +---------------------+---------------+---------------+---------------+---------------+---------------+ Balance Sheet ----------------- Retrieves the company's balance sheet data showing assets, liabilities, and equity for multiple reporting periods. Returns a **DataFrame**. .. code-block:: python from stockdex import Ticker ticker = Ticker(ticker="MSFT") result = ticker.yahoo_web_balance_sheet **Results:** +-------------------------------------------+---------------+---------------+---------------+---------------+ | | 6/30/2025 | 6/30/2024 | 6/30/2023 | 6/30/2022 | +===========================================+===============+===============+===============+===============+ | Total Assets | 619,003,000 | 512,163,000 | 411,976,000 | 364,840,000 | +-------------------------------------------+---------------+---------------+---------------+---------------+ | Total Liabilities Net Minority Interest | 275,524,000 | 243,686,000 | 205,753,000 | 198,298,000 | +-------------------------------------------+---------------+---------------+---------------+---------------+ | Total Equity Gross Minority Interest | 343,479,000 | 268,477,000 | 206,223,000 | 166,542,000 | +-------------------------------------------+---------------+---------------+---------------+---------------+ | Total Capitalization | 383,631,000 | 311,165,000 | 248,213,000 | 213,574,000 | +-------------------------------------------+---------------+---------------+---------------+---------------+ | Common Stock Equity | 343,479,000 | 268,477,000 | 206,223,000 | 166,542,000 | +-------------------------------------------+---------------+---------------+---------------+---------------+ Cash Flow Statement -------------------- Retrieves the company's cash flow statement data showing operating, investing, and financing cash flows for multiple periods. Returns a **DataFrame**. .. code-block:: python from stockdex import Ticker ticker = Ticker(ticker="AAPL") result = ticker.yahoo_web_cashflow **Results:** +-------------------------------------+----------------+----------------+----------------+----------------+---------------+ | | TTM | 9/30/2024 | 9/30/2023 | 9/30/2022 | 9/30/2021 | +=====================================+================+================+================+================+===============+ | Operating Cash Flow | 108,565,000 | 118,254,000 | 110,543,000 | 122,151,000 | 104,038,000 | +-------------------------------------+----------------+----------------+----------------+----------------+---------------+ | Investing Cash Flow | 19,227,000 | 2,935,000 | 3,705,000 | -22,354,000 | -14,545,000 | +-------------------------------------+----------------+----------------+----------------+----------------+---------------+ | Financing Cash Flow | -118,158,000 | -121,983,000 | -108,488,000 | -110,749,000 | -93,353,000 | +-------------------------------------+----------------+----------------+----------------+----------------+---------------+ | End Cash Position | 36,269,000 | 29,943,000 | 30,737,000 | 24,977,000 | 35,929,000 | +-------------------------------------+----------------+----------------+----------------+----------------+---------------+ | Income Tax Paid Supplemental Data | 44,204,000 | 26,102,000 | 18,679,000 | 19,573,000 | 25,385,000 | +-------------------------------------+----------------+----------------+----------------+----------------+---------------+ Options (Calls) ----------------------- Retrieves available call options data for the ticker including contract details, prices, and trading information. Returns a **DataFrame**. .. code-block:: python from stockdex import Ticker ticker = Ticker(ticker="MSFT") result = ticker.yahoo_web_calls **Results:** +---+---------------------+-----------------------+--------+------------+--------+--------+--------+----------+--------+---------------+--------------------+ | | Contract Name | Last Trade Date (EDT) | Strike | Last Price | Bid | Ask | Change | % Change | Volume | Open Interest | Implied Volatility | +===+=====================+=======================+========+============+========+========+========+==========+========+===============+====================+ | 0 | MSFT251010C00270000 | 10/1/2025 1:24 PM | 270 | 248.96 | 245.8 | 249.35 | 0.46 | 0.19% | 1 | 3 | 206.45% | +---+---------------------+-----------------------+--------+------------+--------+--------+--------+----------+--------+---------------+--------------------+ | 1 | MSFT251010C00290000 | 9/11/2025 10:10 AM | 290 | 212.25 | 226.1 | 229.3 | 0 | 0.00% | - | 2 | 195.90% | +---+---------------------+-----------------------+--------+------------+--------+--------+--------+----------+--------+---------------+--------------------+ | 2 | MSFT251010C00355000 | 9/25/2025 9:30 AM | 355 | 153.84 | 161 | 164.45 | 0 | 0.00% | - | 7 | 134.38% | +---+---------------------+-----------------------+--------+------------+--------+--------+--------+----------+--------+---------------+--------------------+ | 3 | MSFT251010C00380000 | 10/1/2025 1:24 PM | 380 | 138.65 | 136.05 | 139.55 | 0 | 0.00% | 3 | 6 | 115.72% | +---+---------------------+-----------------------+--------+------------+--------+--------+--------+----------+--------+---------------+--------------------+ | 4 | MSFT251010C00385000 | 10/2/2025 11:49 AM | 385 | 128.18 | 130.85 | 134.45 | 0 | 0.00% | 6 | 5 | 105.08% | +---+---------------------+-----------------------+--------+------------+--------+--------+--------+----------+--------+---------------+--------------------+ Options (Puts) ----------------------- Retrieves available put options data for the ticker including contract details, prices, and trading information. Returns a **DataFrame**. .. code-block:: python from stockdex import Ticker ticker = Ticker(ticker="TSLA") result = ticker.yahoo_web_puts **Results:** +---+---------------------+-----------------------+--------+------------+-----+------+--------+----------+--------+---------------+--------------------+ | | Contract Name | Last Trade Date (EDT) | Strike | Last Price | Bid | Ask | Change | % Change | Volume | Open Interest | Implied Volatility | +===+=====================+=======================+========+============+=====+======+========+==========+========+===============+====================+ | 0 | TSLA251010P00100000 | 10/3/2025 3:54 PM | 100 | 0.01 | 0 | 0.01 | 0 | 0.00% | 15 | 371 | 325.00% | +---+---------------------+-----------------------+--------+------------+-----+------+--------+----------+--------+---------------+--------------------+ | 1 | TSLA251010P00110000 | 9/25/2025 1:21 PM | 110 | 0.02 | 0 | 0.01 | 0 | 0.00% | 3 | 17 | 300.00% | +---+---------------------+-----------------------+--------+------------+-----+------+--------+----------+--------+---------------+--------------------+ | 2 | TSLA251010P00120000 | 10/3/2025 11:12 AM | 120 | 0.01 | 0 | 0.01 | -0.03 | -75.00% | 2 | 12 | 287.50% | +---+---------------------+-----------------------+--------+------------+-----+------+--------+----------+--------+---------------+--------------------+ | 3 | TSLA251010P00130000 | 10/2/2025 9:31 AM | 130 | 0.01 | 0 | 0.01 | 0 | 0.00% | 3 | 544 | 268.75% | +---+---------------------+-----------------------+--------+------------+-----+------+--------+----------+--------+---------------+--------------------+ | 4 | TSLA251010P00135000 | 10/3/2025 11:12 AM | 135 | 0.01 | 0 | 0.01 | 0 | 0.00% | 1 | 61 | 262.50% | +---+---------------------+-----------------------+--------+------------+-----+------+--------+----------+--------+---------------+--------------------+ Description ----------- Retrieves the company's business description and overview. Returns a **string**. .. code-block:: python from stockdex import Ticker ticker = Ticker(ticker="TSLA") result = ticker.yahoo_web_description **Results:** .. code-block:: text Tesla, Inc. designs, develops, manufactures, leases, and sells electric vehicles, and energy generation and storage systems in the United States, China, and internationally. The company operates in two segments, Automotive; and Energy Generation and Storage. The Automotive segment offers electric vehicles, as well as sells automotive regulatory credits; and non-warranty after-sales vehicle, used vehicles, body shop and parts, supercharging, retail merchandise, and vehicle insurance services. This segment also provides sedans and sport utility vehicles through direct and used vehicle sales, a network of Tesla Superchargers, and in-app upgrades; purchase financing and leasing services; services for electric vehicles through its company-owned service locations and Tesla mobile service technicians; and vehicle limited warranties and extended service plans. The Energy Generation and Storage segment engages in the design, manufacture, installation, sale, and leasing of solar energy generation and energy storage products, and related services to residential, commercial, and industrial customers and utilities through its website, stores, and galleries, as well as through a network of channel partners. This segment also provides services and repairs to its energy product customers, including under warranty; and various financing options to its residential customers. The company was formerly known as Tesla Motors, Inc. and changed its name to Tesla, Inc. in February 2017. Tesla, Inc. was incorporated in 2003 and is headquartered in Austin, Texas. Key Executives ---------------- Retrieves information about the company's key executives including names, titles, compensation, and birth years. Returns a **DataFrame**. .. code-block:: python from stockdex import Ticker ticker = Ticker(ticker="TSLA") result = ticker.yahoo_web_key_executives **Results:** from stockdex import Ticker +---+----------------------+--------------------------------------------------------------+---------+-----------+-----------+ | | Name | Title | Pay | Exercised | Year Born | +===+======================+==============================================================+=========+===========+===========+ | 0 | Mr. Elon R. Musk | Co-Founder, Technoking of Tesla, CEO & Director | -- | -- | 1971 | +---+----------------------+--------------------------------------------------------------+---------+-----------+-----------+ | 1 | Mr. Vaibhav Taneja | Chief Financial Officer | 306.85k | 9.65M | 1978 | +---+----------------------+--------------------------------------------------------------+---------+-----------+-----------+ | 2 | Mr. Xiaotong Zhu | Senior Vice President of APAC & Global Vehicle Manufacturing | 518.25k | -- | 1980 | +---+----------------------+--------------------------------------------------------------+---------+-----------+-----------+ | 3 | Travis Axelrod | Head of Investor Relations | -- | -- | -- | +---+----------------------+--------------------------------------------------------------+---------+-----------+-----------+ | 4 | Mr. Brandon Ehrhart | General Counsel & Corporate Secretary | -- | -- | -- | +---+----------------------+--------------------------------------------------------------+---------+-----------+-----------+ Corporate Governance -------------------- Retrieves the company's corporate governance score and related information. Returns a **string**. .. code-block:: python from stockdex import Ticker ticker = Ticker(ticker="NVDA") result = ticker.yahoo_web_corporate_governance **Results:** .. code-block:: text NVIDIA Corporation’s ISS Governance QualityScore as of October 1, 2025 is 8. The pillar scores are Audit: 5; Board: 10; Shareholder Rights: 8; Compensation: 4. Major Holders ---------------- Retrieves information about major shareholders including percentage of shares held by insiders and institutions. Returns a **DataFrame**. .. code-block:: python from stockdex import Ticker ticker = Ticker(ticker="NVDA") result = ticker.yahoo_web_major_holders **Results:** +---+--------+---------------------------------------+ | | 0 | 1 | +===+========+=======================================+ | 0 | 4.33% | % of Shares Held by All Insider | +---+--------+---------------------------------------+ | 1 | 68.96% | % of Shares Held by Institutions | +---+--------+---------------------------------------+ | 2 | 72.08% | % of Float Held by Institutions | +---+--------+---------------------------------------+ | 3 | 6,743 | Number of Institutions Holding Shares | +---+--------+---------------------------------------+ Top Institutional Holders -------------------------- Retrieves detailed information about the largest institutional shareholders including holdings, dates, and values. Returns a **DataFrame**. .. code-block:: python from stockdex import Ticker ticker = Ticker(ticker="AAPL") result = ticker.yahoo_web_top_institutional_holders **Results:** +---+-------------------------------------------------------------+---------+---------------+-------+-----------------+ | | Holder | Shares | Date Reported | % Out | Value | +===+=============================================================+=========+===============+=======+=================+ | 0 | VANGUARD INDEX FUNDS-Vanguard Total Stock Market Index Fund | 480.28M | Jun 30, 2025 | 3.24% | 123,922,796,029 | +---+-------------------------------------------------------------+---------+---------------+-------+-----------------+ | 1 | VANGUARD INDEX FUNDS-Vanguard 500 Index Fund | 423.95M | Jun 30, 2025 | 2.86% | 109,387,814,300 | +---+-------------------------------------------------------------+---------+---------------+-------+-----------------+ | 2 | Fidelity Concord Street Trust-Fidelity 500 Index Fund | 189.64M | Aug 31, 2025 | 1.28% | 48,931,770,439 | +---+-------------------------------------------------------------+---------+---------------+-------+-----------------+ | 3 | SPDR S&P 500 ETF TRUST | 180.39M | Aug 31, 2025 | 1.22% | 46,543,192,706 | +---+-------------------------------------------------------------+---------+---------------+-------+-----------------+ | 4 | iShares Trust-iShares Core S&P 500 ETF | 179.73M | Aug 31, 2025 | 1.21% | 46,373,298,412 | +---+-------------------------------------------------------------+---------+---------------+-------+-----------------+ Top Mutual Fund Holders -------------------------- Retrieves detailed information about the largest mutual fund shareholders including holdings and ownership percentages. Returns a **DataFrame**. .. code-block:: python from stockdex import Ticker ticker = Ticker(ticker="AAPL") result = ticker.yahoo_web_top_mutual_fund_holders **Results:** +---+--------+---------------------------------------+---------------+------------+-------+ | | holder | shares | date_reported | percentage | value | +===+========+=======================================+===============+============+=======+ | 0 | 1.97% | % of Shares Held by All Insider | | | | +---+--------+---------------------------------------+---------------+------------+-------+ | 1 | 63.63% | % of Shares Held by Institutions | | | | +---+--------+---------------------------------------+---------------+------------+-------+ | 2 | 64.91% | % of Float Held by Institutions | | | | +---+--------+---------------------------------------+---------------+------------+-------+ | 3 | 6,949 | Number of Institutions Holding Shares | | | | +---+--------+---------------------------------------+---------------+------------+-------+ Summary Information ------------------- Retrieves basic market data and summary statistics for the stock including market state and price information. Returns a **DataFrame**. .. code-block:: python from stockdex import Ticker ticker = Ticker(ticker="AAPL") result = ticker.yahoo_web_summary **Results:** +------------------------------+-------------------+ | | 0 | +==============================+===================+ | marketState | | +------------------------------+-------------------+ | postMarketTime | | +------------------------------+-------------------+ | regularMarketPreviousClose | 257.13 | +------------------------------+-------------------+ | regularMarketOpen | 254.66 | +------------------------------+-------------------+ | regularMarketDayRange | 253.96 - 259.24 | +------------------------------+-------------------+ Valuation Measures ------------------- Retrieves key valuation metrics including market cap, enterprise value, and various financial ratios over time. Returns a **DataFrame**. .. code-block:: python from stockdex import Ticker ticker = Ticker(ticker="PLTR") result = ticker.yahoo_web_valuation_measures **Results:** +----------------------------+-------------+---------------+---------------+----------------+---------------+---------------+ | | Current | 6/30/2025 | 3/31/2025 | 12/31/2024 | 9/30/2024 | 6/30/2024 | +============================+=============+===============+===============+================+===============+===============+ | Market Cap | 443.75B | 323.33B | 199.16B | 176.88B | 84.44B | 56.41B | +----------------------------+-------------+---------------+---------------+----------------+---------------+---------------+ | Enterprise Value | 437.98B | 318.14B | 194.16B | 172.57B | 80.70B | 52.76B | +----------------------------+-------------+---------------+---------------+----------------+---------------+---------------+ | Trailing P/E | 623.50 | 592.70 | 444.21 | 378.15 | 218.82 | 211.08 | +----------------------------+-------------+---------------+---------------+----------------+---------------+---------------+ | Forward P/E | 217.39 | 250.00 | 156.25 | 158.73 | 88.50 | 76.92 | +----------------------------+-------------+---------------+---------------+----------------+---------------+---------------+ | PEG Ratio (5yr expected) | 3.62 | 4.32 | 3.03 | 3.24 | 1.92 | 1.94 | +----------------------------+-------------+---------------+---------------+----------------+---------------+---------------+ Financial Highlights -------------------- Retrieves key financial metrics and ratios including profit margins, returns, and fiscal year information. Returns a **DataFrame**. .. code-block:: python from stockdex import Ticker ticker = Ticker(ticker="AAPL") result = ticker.yahoo_web_financial_highlights **Results:** +------------------------------+-------------+ | Criteria | Value | +==============================+=============+ | Fiscal Year Ends | 9/28/2024 | +------------------------------+-------------+ | Most Recent Quarter (mrq) | 6/28/2025 | +------------------------------+-------------+ | Profit Margin | 24.30% | +------------------------------+-------------+ | Operating Margin (ttm) | 29.99% | +------------------------------+-------------+ | Return on Assets (ttm) | 24.55% | +------------------------------+-------------+ Trading Information -------------------- Retrieves trading-related metrics including beta, 52-week highs/lows, and performance comparisons. Returns a **DataFrame**. .. code-block:: python from stockdex import Ticker ticker = Ticker(ticker="AAPL") result = ticker.yahoo_web_trading_information **Results:** +----------------------------+-----------+ | Criteria | Value | +============================+===========+ | Beta (5Y Monthly) | 1.09 | +----------------------------+-----------+ | 52 Week Change 3 | 16.39% | +----------------------------+-----------+ | S&P 500 52-Week Change 3 | 17.90% | +----------------------------+-----------+ | 52 Week High 3 | 260.10 | +----------------------------+-----------+ | 52 Week Low 3 | 169.21 | +----------------------------+-----------+ Full Name --------- Retrieves the company's full corporate name. Returns a **string**. .. code-block:: python from stockdex import Ticker ticker = Ticker(ticker="TSLA") result = ticker.yahoo_web_full_name **Results:** .. code-block:: text Tesla Earnings Estimate ----------------- Retrieves analyst earnings estimates for current and future quarters/years including high, low, and average estimates. Returns a **DataFrame**. .. code-block:: python from stockdex import Ticker ticker = Ticker(ticker="NVDA") result = ticker.yahoo_web_earnings_estimate **Results:** +---+-----------------+-------------------------+----------------------+---------------------+------------------+ | | Currency in USD | Current Qtr. (Oct 2025) | Next Qtr. (Jan 2026) | Current Year (2026) | Next Year (2027) | +===+=================+=========================+======================+=====================+==================+ | 0 | No. of Analysts | 37 | 36 | 49 | 51 | +---+-----------------+-------------------------+----------------------+---------------------+------------------+ | 1 | Avg. Estimate | 1.24 | 1.41 | 4.5 | 6.35 | +---+-----------------+-------------------------+----------------------+---------------------+------------------+ | 2 | Low Estimate | 1.14 | 1.24 | 4.14 | 4.91 | +---+-----------------+-------------------------+----------------------+---------------------+------------------+ | 3 | High Estimate | 1.34 | 1.81 | 5 | 7.5 | +---+-----------------+-------------------------+----------------------+---------------------+------------------+ | 4 | Year Ago EPS | 0.81 | 0.89 | 2.99 | 4.5 | +---+-----------------+-------------------------+----------------------+---------------------+------------------+ Revenue Estimate ----------------- Retrieves analyst revenue estimates for current and future quarters/years including high, low, and average estimates. Returns a **DataFrame**. .. code-block:: python from stockdex import Ticker ticker = Ticker(ticker="NVDA") result = ticker.yahoo_web_revenue_estimate **Results:** +---+-----------------+-------------------------+----------------------+---------------------+------------------+ | | Currency in USD | Current Qtr. (Oct 2025) | Next Qtr. (Jan 2026) | Current Year (2026) | Next Year (2027) | +===+=================+=========================+======================+=====================+==================+ | 0 | No. of Analysts | 38 | 37 | 53 | 58 | +---+-----------------+-------------------------+----------------------+---------------------+------------------+ | 1 | Avg. Estimate | 54.6B | 61.09B | 206.45B | 274.51B | +---+-----------------+-------------------------+----------------------+---------------------+------------------+ | 2 | Low Estimate | 53.46B | 56.53B | 193.52B | 226.15B | +---+-----------------+-------------------------+----------------------+---------------------+------------------+ | 3 | High Estimate | 58.34B | 75.31B | 224.96B | 325.98B | +---+-----------------+-------------------------+----------------------+---------------------+------------------+ | 4 | Year Ago Sales | 35.08B | 39.33B | 130.5B | 206.45B | +---+-----------------+-------------------------+----------------------+---------------------+------------------+ Earnings History ----------------- Retrieves historical earnings data showing estimated vs. actual earnings and surprise percentages for past quarters. Returns a **DataFrame**. .. code-block:: python from stockdex import Ticker ticker = Ticker(ticker="NVDA") result = ticker.yahoo_web_earnings_history **Results:** +---+-----------------+------------+-----------+-----------+-----------+ | | Currency in USD | 10/31/2024 | 1/31/2025 | 4/30/2025 | 7/31/2025 | +===+=================+============+===========+===========+===========+ | 0 | EPS Est. | 0.75 | 0.85 | 0.75 | 1.01 | +---+-----------------+------------+-----------+-----------+-----------+ | 1 | EPS Actual | 0.81 | 0.89 | 0.81 | 1.05 | +---+-----------------+------------+-----------+-----------+-----------+ | 2 | Difference | 0.06 | 0.04 | 0.06 | 0.04 | +---+-----------------+------------+-----------+-----------+-----------+ | 3 | Surprise % | 8.52% | 5.25% | 8.02% | 4.05% | +---+-----------------+------------+-----------+-----------+-----------+ EPS Trend --------- Retrieves trends in earnings per share estimates showing how analyst expectations have changed over time. Returns a **DataFrame**. .. code-block:: python from stockdex import Ticker ticker = Ticker(ticker="AAPL") result = ticker.yahoo_web_eps_trend **Results:** +---+------------------+-------------------------+----------------------+---------------------+------------------+ | | Currency in USD | Current Qtr. (Sep 2025) | Next Qtr. (Dec 2025) | Current Year (2025) | Next Year (2026) | +===+==================+=========================+======================+=====================+==================+ | 0 | Current Estimate | 1.76 | 2.48 | 7.37 | 7.99 | +---+------------------+-------------------------+----------------------+---------------------+------------------+ | 1 | 7 Days Ago | 1.76 | 2.49 | 7.37 | 8 | +---+------------------+-------------------------+----------------------+---------------------+------------------+ | 2 | 30 Days Ago | 1.76 | 2.47 | 7.38 | 7.96 | +---+------------------+-------------------------+----------------------+---------------------+------------------+ | 3 | 60 Days Ago | 1.76 | 2.47 | 7.37 | 7.95 | +---+------------------+-------------------------+----------------------+---------------------+------------------+ | 4 | 90 Days Ago | 1.65 | 2.42 | 7.17 | 7.81 | +---+------------------+-------------------------+----------------------+---------------------+------------------+ EPS Revisions ------------- Retrieves information about recent analyst earnings per share estimate revisions showing upward and downward changes. Returns a **DataFrame**. .. code-block:: python from stockdex import Ticker ticker = Ticker(ticker="AAPL") result = ticker.yahoo_web_eps_revisions **Results:** +---+-------------------+-------------------------+----------------------+---------------------+------------------+ | | Currency in USD | Current Qtr. (Sep 2025) | Next Qtr. (Dec 2025) | Current Year (2025) | Next Year (2026) | +===+===================+=========================+======================+=====================+==================+ | 0 | Up Last 7 Days | 2 | 2 | 2 | 2 | +---+-------------------+-------------------------+----------------------+---------------------+------------------+ | 1 | Up Last 30 Days | 3 | 3 | 4 | 5 | +---+-------------------+-------------------------+----------------------+---------------------+------------------+ | 2 | Down Last 7 Days | -- | -- | -- | -- | +---+-------------------+-------------------------+----------------------+---------------------+------------------+ | 3 | Down Last 30 Days | 1 | 2 | 2 | 2 | +---+-------------------+-------------------------+----------------------+---------------------+------------------+ Growth Estimates ---------------- Retrieves analyst growth estimates for the company compared to market benchmarks like the S&P 500. Returns a **DataFrame**. .. code-block:: python from stockdex import Ticker ticker = Ticker(ticker="AAPL") result = ticker.yahoo_web_growth_estimates **Results:** +---+---------+--------------+-----------+--------------+-----------+ | | Symbol | Current Qtr. | Next Qtr. | Current Year | Next Year | +===+=========+==============+===========+==============+===========+ | 0 | AAPL | 7.45% | 3.66% | 9.60% | 8.35% | +---+---------+--------------+-----------+--------------+-----------+ | 1 | S&P 500 | 6.89% | 6.07% | 9.10% | 14.19% | +---+---------+--------------+-----------+--------------+-----------+