00_mdna.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nnilayy/SmolGPT/blob/main/00_mdna.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setting up"
      ],
      "metadata": {
        "id": "7F0T-8wAgBqZ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "LiJAvscJnxWt"
      },
      "outputs": [],
      "source": [
        "!pip install sec-parser tiktoken jupyter_black sec_downloader"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install tiktoken jupyter_black sec_downloader"
      ],
      "metadata": {
        "id": "_7srV4rgqTx5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "95gC_YN_nrgA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "8e2d29ca-2c49-46c2-f4c0-b498987d479f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "                <script type=\"application/javascript\" id=\"jupyter_black\">\n",
              "                (function() {\n",
              "                    if (window.IPython === undefined) {\n",
              "                        return\n",
              "                    }\n",
              "                    var msg = \"WARNING: it looks like you might have loaded \" +\n",
              "                        \"jupyter_black in a non-lab notebook with \" +\n",
              "                        \"`is_lab=True`. Please double check, and if \" +\n",
              "                        \"loading with `%load_ext` please review the README!\"\n",
              "                    console.log(msg)\n",
              "                    alert(msg)\n",
              "                })()\n",
              "                </script>\n",
              "                "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "from __future__ import annotations\n",
        "import sec_parser as sp\n",
        "import sec_downloader as sd\n",
        "import jupyter_black\n",
        "import tiktoken\n",
        "\n",
        "jupyter_black.load()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ticker"
      ],
      "metadata": {
        "id": "cjQvT2GZgFZP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ticker=\"CSCO\""
      ],
      "metadata": {
        "id": "rrx9HTtleIvS"
      },
      "execution_count": 207,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ticker Data"
      ],
      "metadata": {
        "id": "LuSkfA9zbpFg"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 208,
      "metadata": {
        "id": "AquoDmS1nrgB"
      },
      "outputs": [],
      "source": [
        "# Download HTML\n",
        "dl = sd.Downloader(\"alphanome.ai\", \"info@alphanome.ai\")\n",
        "html = dl.get_latest_html(\"10-Q\", ticker)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 209,
      "metadata": {
        "id": "j1vdk32XnrgC"
      },
      "outputs": [],
      "source": [
        "# Parse HTML\n",
        "elements = sp.Edgar10QParser().parse(html)\n",
        "top_level_sections = [\n",
        "    item for part in sp.TreeBuilder().build(elements) for item in part.children\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 210,
      "metadata": {
        "id": "JKlu-gjvnrgC"
      },
      "outputs": [],
      "source": [
        "# Filter MD&A section\n",
        "mdna_top_level_sections = [\n",
        "    k for k in top_level_sections if \"management\" in k.semantic_element.text.lower()\n",
        "]\n",
        "assert len(mdna_top_level_sections) == 1\n",
        "mdna_top_level_section = mdna_top_level_sections[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 211,
      "metadata": {
        "id": "c1Jv2VMNnrgC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8173c317-0152-436e-d7ed-85435c990cfe"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: '##',\n",
              " 1: '###',\n",
              " 2: '####',\n",
              " 4: '#####',\n",
              " 6: '######',\n",
              " 7: '#######',\n",
              " 8: '########'}"
            ]
          },
          "metadata": {},
          "execution_count": 211
        }
      ],
      "source": [
        "# Convert to markdown (Step 1: Get levels)\n",
        "levels = sorted(\n",
        "    {\n",
        "        k.semantic_element.level\n",
        "        for k in mdna_top_level_section.get_descendants()\n",
        "        if isinstance(k.semantic_element, sp.TitleElement)\n",
        "    }\n",
        ")\n",
        "level_to_markdown = {level: \"#\" * (i + 2) for i, level in enumerate(levels)}\n",
        "level_to_markdown"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 212,
      "metadata": {
        "id": "xpKgCgEynrgD"
      },
      "outputs": [],
      "source": [
        "# Convert to markdown (Step 2: Extract text)\n",
        "markdown = \"\"\n",
        "markdown += f\"# {mdna_top_level_section.semantic_element.text}\\n\"\n",
        "for node in mdna_top_level_section.get_descendants():\n",
        "    element = node.semantic_element\n",
        "    if isinstance(element, sp.TextElement):\n",
        "        markdown += f\"{element.text}\\n\"\n",
        "    elif isinstance(element, sp.TitleElement):\n",
        "        markdown += f\"{level_to_markdown[element.level]} {element.text}\\n\"\n",
        "    elif isinstance(element, sp.TableElement):\n",
        "        markdown += f\"[{element.get_summary()}]\\n\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 213,
      "metadata": {
        "id": "nr0WMHP3oZxH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ac831b96-e7cf-4c6c-8f64-9b9613f8e15e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# Item 2.Management’s Discussion and Analysis of Financial Condition and Results of Operations\n",
            "#### Forward-Looking Statements\n",
            "This Quarterly Report on Form 10-Q, including this Management’s Discussion and Analysis of Financial Condition and Results of Operations, contains forward-looking statements regarding future events and our future results that are subject to the safe harbors created under the Securities Act of 1933, as amended (the “Securities Act”) and the Securities Exchange Act of 1934, as amended (the “Exchange Act”). All statements other than statements of historical facts are statements that could be deemed forward-looking statements. These statements are based on current expectations, estimates, forecasts, and projections about the industries in which we operate and the beliefs and assumptions of our management. Words such as “expects,” “anticipates,” “targets,” “goals,” “projects,” “intends,” “plans,” “believes,” “momentum,” “seeks,” “estimates,” “continues,” “endeavors,” “strives,” “may,” variations of such words, and similar expressions are intended to identify such forward-looking statements. In addition, any statements that refer to projections of our future financial performance, our anticipated growth and trends in our businesses, future responses to and effects of the COVID-19 pandemic, and other characterizations of future events or circumstances are forward-looking statements. Readers are cautioned that these forward-looking statements are only predictions and are subject to risks, uncertainties, and assumptions that are difficult to predict, including those under “Part II, Item 1A. Risk Factors,” and elsewhere herein. Therefore, actual results may differ materially and adversely from those expressed in any forward-looking statements. We undertake no obligation to revise or update any forward-looking statements for any reason.\n",
            "###### OVERVIEW\n",
            "Cisco designs and sells a broad range of technologies that power the Internet. We are integrating our platforms across networking, security, collaboration, applications and the cloud. These platforms are designed to help our customers manage more users, devices and things connecting to their networks. This will enable us to provide customers with a highly secure, intelligent platform for their digital business. A summary of our results is as follows (in millions, except percentages and per-share amounts):\n",
            "[Table with 15 rows, 91 numbers, and 1143 characters.]\n",
            "Percentages may not recalculate due to rounding.NM — Not meaningful\n",
            "## Table of Contents CISCO SYSTEMS, INC.MANAGEMENT’S DISCUSSION AND ANALYSIS OF FINANCIAL CONDITION AND RESULTS OF OPERATIONS (Continued)\n",
            "In the third quarter of fiscal 2023, we delivered strong results with growth in revenue and profitability. We remain focused on delivering innovation across our technologies to assist our customers in executing on their digital transformations. Over the last several quarters, we have taken multiple steps in order to mitigate the component shortages and address the supply constraints seen industry-wide. While supply constraints for certain components still remain, we continued to see an overall improvement of such constraints in the third quarter of fiscal 2023 and, as a result, we were able to increase the delivery of products to our customers, which positively impacted product revenue. Further, we continued to make progress in the transition of our business model delivering increased software and subscriptions. We remain focused on accelerating innovation across our portfolio, and we believe that we have made continued progress on our strategic priorities. We continue to operate in a challenging macroeconomic and highly competitive environment. While the overall environment remains uncertain, we continue to aggressively invest in priority areas with the objective of driving profitable growth over the long term.Total revenue increased by 14% compared with the third quarter of fiscal 2022. Within total revenue, product revenue increased by 17% and service revenue increased by 3%. In the third quarter of fiscal 2023, total software revenue was $4.3 billion across all product areas and service, an increase of 18%. Within total software revenue, subscription revenue increased 17%. Total gross margin increased by 0.1 percentage points. Product gross margin increased by 0.9 percentage points, largely driven by favorable pricing and product mix partially offset by increased component and other costs. As a percentage of revenue, research and development, sales and marketing, and general and administrative expenses, collectively, increased by 0.7 percentage points. Operating income as a percentage of revenue decreased by 1.0 percentage points. We incurred restructuring and other charges of $87 million in the third quarter of fiscal 2023. Diluted earnings per share increased 7%, driven by an increase of 6% in net income and a decrease in diluted share count of 60 million shares.In terms of our geographic segments, revenue from the Americas increased $996 million, EMEA revenue increased by $535 million and APJC revenue increased by $205 million. From a customer market standpoint, we experienced product revenue growth in each of our customer markets. From a product category perspective, the product revenue increase of 17% was driven by growth in revenue for Secure, Agile Networks of 29%; Internet for the Future of 5%; End-to-End Security of 2%; and Optimized Application Experiences of 12%; partially offset by a product revenue decline in Collaboration of 13%.We were able to increase the delivery of products to our customers as the overall supply constraints continued to improve during the third quarter of fiscal 2023. Although total revenue increased, we saw a decline in product demand compared to the third quarter of fiscal 2022. We believe this was due to customers absorbing recently shipped products prior to placing new orders, customers adjusting to significant reductions in product lead times reducing the need for extensive advanced ordering by our customers, and macroeconomic conditions.\n",
            "## Table of Contents CISCO SYSTEMS, INC.MANAGEMENT’S DISCUSSION AND ANALYSIS OF FINANCIAL CONDITION AND RESULTS OF OPERATIONS (Continued)\n",
            "Total revenue increased 9%, with product revenue increasing 11% and service revenue increasing 2%. Total gross margin decreased 0.8 percentage points due to increased costs related to supply constraints, partially offset by favorable pricing and favorable impacts from product mix. As a percentage of revenue, research, and development, sales and marketing, and general and administrative expenses collectively increased by 0.2 percentage points. Operating income as a percentage of revenue decreased by 1.6 percentage points. We incurred restructuring and other charges of $328 million in the first nine months of fiscal 2023. Diluted earnings per share decreased 1%, driven by a decrease of 4% in net income, partially offset by a decrease in diluted share count of 93 million shares.\n",
            "#### Strategy and Priorities\n",
            "As our customers add billions of new connections to their enterprises, and as more applications move to a multicloud environment, the network becomes even more critical. Our customers are navigating change at an unprecedented pace. In this dynamic environment, we believe their priorities are to reimagine applications, power hybrid work, transform infrastructure, and secure the enterprise. Our strategy is to help our customers connect, secure, and automate to accelerate their digital agility in a cloud-first world. We are committed to driving a trusted customer experience, through our innovation, choice, and people.For additional discussion of our strategy and priorities, see Item 1. Business in our Annual Report on Form 10-K for the fiscal year ended July 30, 2022.\n",
            "#### Other Key Financial Measures\n",
            "The following is a summary of our other key financial measures for the third quarter of fiscal 2023 (in millions):\n",
            "[Table with 3 rows, 8 numbers, and 155 characters.]\n",
            "[Table with 3 rows, 8 numbers, and 188 characters.]\n",
            "## Table of Contents CISCO SYSTEMS, INC.MANAGEMENT’S DISCUSSION AND ANALYSIS OF FINANCIAL CONDITION AND RESULTS OF OPERATIONS (Continued)\n",
            "###### CRITICAL ACCOUNTING ESTIMATES\n",
            "The preparation of financial statements and related disclosures in conformity with accounting principles generally accepted in the United States requires us to make judgments, assumptions, and estimates that affect the amounts reported in the Consolidated Financial Statements and accompanying notes. Note 2 to the Consolidated Financial Statements in our Annual Report on Form 10-K for the fiscal year ended July 30, 2022, as updated as applicable in Note 2 to the Consolidated Financial Statements herein, describes the significant accounting policies and methods used in the preparation of the Consolidated Financial Statements. The accounting policies described below are significantly affected by critical accounting estimates. Such accounting policies require significant judgments, assumptions, and estimates used in the preparation of the Consolidated Financial Statements, and actual results could differ materially from the amounts reported based on these policies.\n",
            "#### Revenue Recognition\n",
            "We enter into contracts with customers that can include various combinations of products and services which are generally distinct and accounted for as separate performance obligations. As a result, our contracts may contain multiple performance obligations. We determine whether arrangements are distinct based on whether the customer can benefit from the product or service on its own or together with other resources that are readily available and whether our commitment to transfer the product or service to the customer is separately identifiable from other obligations in the contract. We classify our hardware, perpetual software licenses, and SaaS as distinct performance obligations. Term software licenses represent multiple obligations, which include software licenses and software maintenance. In transactions where we deliver hardware or software, we are typically the principal and we record revenue and costs of goods sold on a gross basis.We recognize revenue upon transfer of control of promised goods or services in a contract with a customer in an amount that reflects the consideration we expect to receive in exchange for those products or services. Transfer of control occurs once the customer has the contractual right to use the product, generally upon shipment, electronic delivery (or when the software is available for download by the customer), or once title and risk of loss has transferred to the customer. Transfer of control can also occur over time for software maintenance and services as the customer receives the benefit over the contract term. Our hardware and perpetual software licenses are distinct performance obligations where revenue is recognized upfront upon transfer of control. Term software licenses include multiple performance obligations where the term licenses are recognized upfront upon transfer of control, with the associated software maintenance revenue recognized ratably over the contract term as services and software updates are provided. SaaS arrangements do not include the right for the customer to take possession of the software during the term, and therefore have one distinct performance obligation which is satisfied over time with revenue recognized ratably over the contract term as the customer consumes the services. On our product sales, we record consideration from shipping and handling on a gross basis within net product sales. We record our revenue net of any associated sales taxes.Revenue is allocated among these performance obligations in a manner that reflects the consideration that we expect to be entitled to for the promised goods or services based on standalone selling prices (SSP). SSP is estimated for each distinct performance obligation and judgment may be required in their determination. The best evidence of SSP is the observable price of a product or service when we sell the goods separately in similar circumstances and to similar customers. In instances where SSP is not directly observable, we determine SSP using information that may include market conditions and other observable inputs. We assess relevant contractual terms in our customer contracts to determine the transaction price. We apply judgment in identifying contractual terms and determining the transaction price as we may be required to estimate variable consideration when determining the amount of revenue to recognize. Variable consideration includes potential contractual penalties and various rebate, cooperative marketing and other incentive programs that we offer to our distributors, channel partners and customers. When determining the amount of revenue to recognize, we estimate the expected usage of these programs, applying the expected value or most likely estimate and update the estimate at each reporting period as actual utilization becomes available. We also consider the customers’ right of return in determining the transaction price, where applicable. If actual credits received by distributors under these programs were to deviate significantly from our estimates, which are based on historical experience, our revenue could be adversely affected.See Note 3 to the Consolidated Financial Statements for more details.\n",
            "#### Inventory Valuation and Liability for Purchase Commitments with Contract Manufacturers and Suppliers\n",
            "Inventory is written down based on excess and obsolete inventories, determined primarily by future demand forecasts. Inventory write-downs are measured as the difference between the cost of the inventory and net realizable value, based upon assumptions about future demand, and are charged to the provision for inventory, which is a component of our cost of sales. At the point of the loss recognition, a new, lower cost basis for that inventory is established, and subsequent changes in facts and circumstances do not result in the restoration or increase in that newly established cost basis.\n",
            "## Table of Contents CISCO SYSTEMS, INC.MANAGEMENT’S DISCUSSION AND ANALYSIS OF FINANCIAL CONDITION AND RESULTS OF OPERATIONS (Continued)\n",
            "We record a liability for firm, noncancelable, and unconditional purchase commitments with contract manufacturers and suppliers for quantities in excess of our future demand forecasts consistent with the valuation of our excess and obsolete inventory.Our provision for inventory was $174 million and $72 million for the first nine months of fiscal 2023 and 2022, respectively. The provision for the liability related to purchase commitments with contract manufacturers and suppliers was $333 million and $130 million for the first nine months of fiscal 2023 and 2022, respectively. If there were to be a sudden and significant decrease in demand for our products, if there were a higher incidence of inventory obsolescence because of rapidly changing technology and customer requirements, or if supply constraints were to continue, we could be required to increase our inventory write-downs, and our liability for purchase commitments with contract manufacturers and suppliers, and accordingly our profitability, could be adversely affected. We regularly evaluate our exposure for inventory write-downs and the adequacy of our liability for purchase commitments. While supply constraints for certain components still remain, we continued to see an overall improvement of such constraints in the third quarter of fiscal 2023. We continue to manage through remaining supply constraints seen industry-wide due to shortages of certain components. For further discussion around the Supply Constraints Impacts and Risks, see “Result of Operations—Product Gross Margin—Supply Constraints Impact and Risks” and “Liquidity and Capital Resources—Inventory Supply Chain.”\n",
            "#### Loss Contingencies\n",
            "We are subject to the possibility of various losses arising in the ordinary course of business. We consider the likelihood of the incurrence of a liability, as well as our ability to reasonably estimate the amount of loss, in determining loss contingencies. An estimated loss contingency is accrued when it is probable that a liability has been incurred and the amount of loss can be reasonably estimated. We regularly evaluate information available to us to determine whether such accruals should be made or adjusted and whether new accruals are required. Third parties, including customers, have in the past and may in the future assert claims or initiate litigation related to exclusive patent, copyright, trademark, and other intellectual property rights to technologies and related standards that are relevant to us. These assertions have increased over time as a result of our growth and the general increase in the pace of patent claims assertions, particularly in the United States. If any infringement or other intellectual property claim made against us by any third party is successful, or if we fail to develop non-infringing technology or license the proprietary rights on commercially reasonable terms and conditions, our business, operating results, and financial condition could be materially and adversely affected.\n",
            "#### Goodwill and Purchased Intangible Asset Impairments\n",
            "Our methodology for allocating the purchase price relating to purchase acquisitions is determined through established valuation techniques. Goodwill represents a residual value as of the acquisition date, which in most cases results in measuring goodwill as an excess of the purchase consideration transferred plus the fair value of any noncontrolling interest in the acquired company over the fair value of net assets acquired, including contingent consideration. We perform goodwill impairment tests on an annual basis in the fourth fiscal quarter and between annual tests in certain circumstances for each reporting unit. The assessment of fair value for goodwill and purchased intangible assets is based on factors that market participants would use in an orderly transaction in accordance with the new accounting guidance for the fair value measurement of nonfinancial assets.In response to changes in industry and market conditions, we could be required to strategically realign our resources and consider restructuring, disposing of, or otherwise exiting businesses, which could result in an impairment of goodwill. There was no impairment of goodwill in each of the first nine months of fiscal 2023 and 2022.The fair value of acquired technology and patents, as well as acquired technology under development, is determined at acquisition date primarily using the income approach, which discounts expected future cash flows to present value. The discount rates used in the present value calculations are typically derived from a weighted-average cost of capital analysis and then adjusted to reflect risks inherent in the development lifecycle as appropriate. We consider the pricing model for products related to these acquisitions to be standard within the high-technology communications industry, and the applicable discount rates represent the rates that market participants would use for valuation of such intangible assets.We make judgments about the recoverability of purchased intangible assets with finite lives whenever events or changes in circumstances indicate that an impairment may exist. Recoverability of purchased intangible assets with finite lives is measured by comparing the carrying amount of the asset to the future undiscounted cash flows the asset is expected to generate. We review indefinite-lived intangible assets for impairment annually or whenever events or changes in circumstances indicate that the asset might be impaired. If the asset is considered to be impaired, the amount of any impairment is measured as the difference between the carrying value and the fair value of the impaired asset. Assumptions and estimates about future values and remaining useful lives of our purchased intangible assets are complex and subjective. They can be affected by a variety of\n",
            "## Table of Contents CISCO SYSTEMS, INC.MANAGEMENT’S DISCUSSION AND ANALYSIS OF FINANCIAL CONDITION AND RESULTS OF OPERATIONS (Continued)\n",
            "factors, including external factors such as industry and economic trends, and internal factors such as changes in our business strategy and our internal forecasts. Our ongoing consideration of all the factors described previously could result in impairment charges in the future, which could adversely affect our net income.\n",
            "#### Income Taxes\n",
            "We are subject to income taxes in the United States and numerous foreign jurisdictions. Our effective tax rates differ from the statutory rate, primarily due to the tax impact of state taxes, foreign operations, R&D tax credits, foreign-derived intangible income deductions, global intangible low-taxed income, tax audit settlements, nondeductible compensation, and international realignments. Our effective tax rate was 18.8% and 19.9% in the third quarter of fiscal 2023 and 2022, respectively, and 20.2% and 18.7% in the first nine months of fiscal 2023 and 2022, respectively.Significant judgment is required in evaluating our uncertain tax positions and determining our provision for income taxes. Although we believe our reserves are reasonable, no assurance can be given that the final tax outcome of these matters will not be different from that which is reflected in our historical income tax provisions and accruals. We adjust these reserves in light of changing facts and circumstances, such as the closing of a tax audit or the refinement of an estimate. To the extent that the final tax outcome of these matters is different than the amounts recorded, such differences will impact the provision for income taxes in the period in which such determination is made. The provision for income taxes includes the impact of reserve provisions and changes to reserves that are considered appropriate, as well as the related net interest and penalties.Significant judgment is also required in determining any valuation allowance recorded against deferred tax assets. In assessing the need for a valuation allowance, we consider all available evidence, including past operating results, estimates of future taxable income, and the feasibility of tax planning strategies. In the event that we change our determination as to the amount of deferred tax assets that can be realized, we will adjust our valuation allowance with a corresponding impact to the provision for income taxes in the period in which such determination is made.Our provision for income taxes is subject to volatility and could be adversely impacted by earnings being lower than anticipated in countries that have lower tax rates and higher than anticipated in countries that have higher tax rates; by changes in the valuation of our deferred tax assets and liabilities; by changes to foreign-derived intangible income deduction, global intangible low-tax income and base erosion and anti-abuse tax laws, regulations, or interpretations thereof; by expiration of or lapses in tax incentives; by transfer pricing adjustments, including the effect of acquisitions on our legal structure; by tax effects of nondeductible compensation; by tax costs related to intercompany realignments; by changes in accounting principles; or by changes in tax laws and regulations, treaties, or interpretations thereof, including changes to the taxation of earnings of our foreign subsidiaries, the deductibility of expenses attributable to foreign income, and the foreign tax credit rules. Significant judgment is required to determine the recognition and measurement attributes prescribed in the accounting guidance for uncertainty in income taxes. The Organisation for Economic Co-operation and Development (OECD), an international association comprised of 38 countries, including the United States, has made changes and is contemplating additional changes to numerous long-standing tax principles. There can be no assurance that these changes and any contemplated changes if finalized, once adopted by countries, will not have an adverse impact on our provision for income taxes. As a result of certain of our ongoing employment and capital investment actions and commitments, our income in certain countries was subject to reduced tax rates. Our failure to meet these commitments could adversely impact our provision for income taxes. In addition, we are subject to the continuous examination of our income tax returns by the Internal Revenue Service (IRS) and other tax authorities. We regularly assess the likelihood of adverse outcomes resulting from these examinations to determine the adequacy of our provision for income taxes. There can be no assurance that the outcomes from these continuous examinations will not have an adverse impact on our operating results and financial condition.\n",
            "## Table of Contents CISCO SYSTEMS, INC.MANAGEMENT’S DISCUSSION AND ANALYSIS OF FINANCIAL CONDITION AND RESULTS OF OPERATIONS (Continued)\n",
            "###### RESULTS OF OPERATIONS\n",
            "#### Revenue\n",
            "The following table presents the breakdown of revenue between product and service (in millions, except percentages):\n",
            "[Table with 6 rows, 36 numbers, and 428 characters.]\n",
            "We manage our business primarily on a geographic basis, organized into three geographic segments. Our revenue, which includes product and service for each segment, is summarized in the following table (in millions, except percentages):\n",
            "[Table with 8 rows, 48 numbers, and 516 characters.]\n",
            "Amounts may not sum and percentages may not recalculate due to rounding.     Total revenue increased by 14%. Product revenue increased by 17% and service revenue increased by 3%. Our total revenue reflected growth across each of our geographic segments.In the third quarter of fiscal 2022, in connection with the Russian invasion of Ukraine, Cisco announced its intention to stop business operations in Russia and Belarus. The negative impact to total revenue was approximately $200 million in the third quarter of fiscal 2022, which included committed revenue we would have otherwise recognized, charges for uncollectible receivables, and other items.In addition to the impact of macroeconomic factors, including the IT spending environment and the level of spending by government entities, revenue by segment in a particular period may be significantly impacted by several factors related to revenue recognition, including the complexity of transactions such as multiple performance obligations; the mix of financing arrangements provided to channel partners and customers; and final acceptance of the product, system, or solution, among other factors. In addition, certain customers tend to make large and sporadic purchases, and the revenue related to these transactions may also be affected by the timing of revenue recognition, which in turn would impact the revenue of the relevant segment. Total revenue increased by 9%. Product revenue increased by 11% and service revenue increased by 2%. Our total revenue reflected growth across each of our geographic segments.\n",
            "## Table of Contents CISCO SYSTEMS, INC.MANAGEMENT’S DISCUSSION AND ANALYSIS OF FINANCIAL CONDITION AND RESULTS OF OPERATIONS (Continued)\n",
            "#### Product Revenue by Segment\n",
            "The following table presents the breakdown of product revenue by segment (in millions, except percentages):\n",
            "[Table with 8 rows, 48 numbers, and 546 characters.]\n",
            "Amounts may not sum and percentages may not recalculate due to rounding.\n",
            "### Americas\n",
            "Product revenue in the Americas segment increased by 17%, with growth across all customer markets. From a country perspective, product revenue increased in the United States, Canada, Mexico and Brazil by 16%, 7%, 55% and 30%, respectively. Product revenue in the Americas segment increased by 12%, with growth across all customer markets. From a country perspective, product revenue increased in the United States, Canada, Mexico and Brazil by 11%, 10%, 29% and 29%, respectively.\n",
            "######## EMEA\n",
            "Product revenue in the EMEA segment increased by 20%, driven by growth in the public sector, enterprise and commercial markets, partially offset by a decline in the service provider market. From a country perspective, product revenue increased in Germany, the United Kingdom and France by 20%, 17% and 12%, respectively.Product revenue in the EMEA segment increased by 13%, driven by growth in the public sector, enterprise and commercial markets, partially offset by a decline in the service provider market. From a country perspective, product revenue increased in Germany, the United Kingdom and France by 21%, 8% and 23%, respectively.\n",
            "######## APJC\n",
            "Product revenue in the APJC segment increased by 15%, driven by growth across all customer markets. From a country perspective, product revenue increased in Japan, India, and Australia by 11%, 78%, and 25%, respectively. Product revenue for China was flat. Product revenue in the APJC segment increased by 6%, driven by growth in the commercial and public sector markets, partially offset by a decline in the enterprise market. Product revenue in the service provider market was flat. From a country perspective, product revenue increased in India, Australia and China by 59%, 7% and 17%, respectively, partially offset by a decline in Japan of 4%.\n",
            "## Table of Contents CISCO SYSTEMS, INC.MANAGEMENT’S DISCUSSION AND ANALYSIS OF FINANCIAL CONDITION AND RESULTS OF OPERATIONS (Continued)\n",
            "#### Product Revenue by Category\n",
            "In addition to the primary view on a geographic basis, we also prepare financial information related to product categories and customer markets for various purposes.                The following table presents product revenue by category (in millions, except percentages):\n",
            "[Table with 8 rows, 58 numbers, and 573 characters.]\n",
            "Amounts may not sum and percentages may not recalculate due to rounding.\n",
            "### Secure, Agile Networks\n",
            "The Secure, Agile Networks product category represents our core networking offerings related to switching, enterprise routing, wireless, and compute. Secure, Agile Networks revenue increased by 29%, or $1.7 billion, with growth across the portfolio except servers. Revenue grew in both campus switching and data center switching. This was primarily driven by strong growth in our Catalyst 9000 series, Nexus 9000 series and Meraki switching offerings. The increase in enterprise routing was primarily driven by growth in our Catalyst 8000 routers, SD-WAN and IoT routing offerings. Wireless had strong double-digit growth driven by our WiFi-6 products and Meraki offerings.Revenue from the Secure, Agile Networks product category increased by 18%, or $3.2 billion, with growth across the portfolio except servers. Revenue grew in both campus switching and data center switching, primarily driven by growth in our Catalyst 9000 series, Nexus 9000 series and Meraki switching offerings. Enterprise routing increased primarily driven by growth in our Catalyst 8000 series, SD-WAN and IoT routing offerings. Wireless had strong double-digit growth driven by our WiFi-6 products and Meraki offerings.\n",
            "### Internet for the Future\n",
            "The Internet for the Future product category includes our routed optical networking, public 5G, silicon and optics offerings. Revenue in our Internet for the Future product category increased by 5%, or $68 million, primarily driven by growth in our Core routing portfolio, including our Cisco 8000 series offerings. We also saw double-digit growth in the webscale provider market.Revenue in our Internet for the Future product category was flat, primarily driven by growth in our Cisco 8000 series offerings offset by declines in our Optical, Cable, and Edge portfolio. We also saw growth in the webscale provider market.\n",
            "### Collaboration\n",
            "The Collaboration product category consists of our Collaboration Devices, Meetings, Calling and Contact Center offerings. Revenue in our Collaboration product category decreased by 13%, or $147 million, primarily driven by declines in Collaboration Devices and Meetings, partially offset by growth in Calling and our Contact Center offerings.\n",
            "## Table of Contents CISCO SYSTEMS, INC.MANAGEMENT’S DISCUSSION AND ANALYSIS OF FINANCIAL CONDITION AND RESULTS OF OPERATIONS (Continued)\n",
            "Revenue in our Collaboration product category decreased by 8%, or $279 million, primarily driven by declines in Meetings and Collaboration Devices.\n",
            "### End-to-End Security\n",
            "Revenue in our End-to-End Security product category increased 2%, or $20 million primarily driven by growth in our Unified Threat Management and Zero Trust offerings.Revenue in our End-to-End Security product category increased 6%, or $156 million primarily driven by growth in our Unified Threat Management offerings.\n",
            "### Optimized Application Experiences\n",
            "The Optimized Application Experiences product category includes our full stack observability and cloud-native platforms offerings. Revenue in our Optimized Application Experiences product category increased 12%, or $21 million, driven by growth across the portfolio, including double-digit growth in our ThousandEyes offerings.Revenue in our Optimized Application Experiences product category increased 10%, or $53 million, driven by growth in our ThousandEyes offerings.\n",
            "#### Service Revenue by Segment\n",
            "The following table presents the breakdown of service revenue by segment (in millions, except percentages):\n",
            "[Table with 8 rows, 48 numbers, and 510 characters.]\n",
            "Amounts may not sum and percentages may not recalculate due to rounding. Service revenue increased 3% in the third quarter of fiscal 2023 compared with the third quarter of fiscal 2022, primarily driven by revenue growth in our solution support offerings and maintenance business, partially offset by declines in our advisory services. Service revenue increased across each of our geographic segments for the third quarter of fiscal 2023. Service revenue increased 2% in the first nine months of fiscal 2023 compared to the first nine months of fiscal 2022, primarily driven by revenue growth in our solution support offerings and maintenance business, partially offset by declines in our advisory services and software support offerings. Service revenue increased in the Americas and EMEA segments, partially offset by a decline in the APJC segment for the first nine months of fiscal 2023.\n",
            "## Table of Contents CISCO SYSTEMS, INC.MANAGEMENT’S DISCUSSION AND ANALYSIS OF FINANCIAL CONDITION AND RESULTS OF OPERATIONS (Continued)\n",
            "#### Gross Margin\n",
            "The following table presents the gross margin for products and services (in millions, except percentages):\n",
            "[Table with 4 rows, 32 numbers, and 360 characters.]\n",
            "#### Product Gross Margin\n",
            "The following table summarizes the key factors that contributed to the change in product gross margin percentage for the third quarter and first nine months of fiscal 2023, as compared with the corresponding prior year periods:\n",
            "[Table with 6 rows, 15 numbers, and 214 characters.]\n",
            "(1) Productivity includes overall manufacturing-related costs, such as component costs, warranty expense, provision for inventory, freight, logistics, shipment volume, and other items not categorized elsewhere.Product gross margin increased by 0.9 percentage points primarily driven by favorable pricing and product mix, partially offset by negative impacts from productivity, largely driven by increased costs from component and other costs. The favorable pricing was primarily driven by price increases implemented during fiscal 2022 as we ship our products. We implemented the price increases to partially offset increases in commodity and other costs.Product gross margin decreased by 0.9 percentage points primarily driven by negative impacts from productivity, largely driven by increased costs from component, freight, logistics and other costs. These impacts were partially offset by favorable pricing. The benefit we saw from favorable pricing was primarily driven by price increases implemented during fiscal 2022. We also saw benefits from product mix.\n",
            "##### Supply Constraints Impacts and Risks\n",
            "While supply constraints for certain components still remain, we continued to see an overall improvement of such constraints in the third quarter of fiscal 2023. We continue to manage through remaining supply constraints seen industry-wide due to shortages of certain components. In recent past periods, these shortages have resulted in increased costs (i.e., component and other commodity costs, freight, expedite fees, etc.) which have had a negative impact on our product gross margin and have resulted in extended lead times for us and our customers. We have taken a number of steps in order to mitigate the supply constraint related impacts including: partnering with several of our key suppliers utilizing our volume purchasing ability and extending supply coverage, including, in certain cases, revising supplier arrangements; paying significantly higher component and logistics costs to secure supply; modifying our product designs in order to leverage alternate suppliers, where possible; and continually optimizing our inventory build and customer delivery plans, among others. We believe these actions are helping us to optimize our access to critical components and meet customer demand for our products. As a result of these past actions, in order to secure supply to meet customer demand, we increased our inventory balances, inventory purchase commitments, and\n",
            "## Table of Contents CISCO SYSTEMS, INC.MANAGEMENT’S DISCUSSION AND ANALYSIS OF FINANCIAL CONDITION AND RESULTS OF OPERATIONS (Continued)\n",
            "inventory deposits and prepayments compared to prior fiscal years, which, in turn, has increased our supply chain exposure. Additionally, in certain situations, we have prepaid or made deposits with suppliers to secure future supply. We have seen supply constraints improve in the current quarter and recent past quarters. As the overall supply constraints continue to improve, we have taken and are continuing to take operational measures to work with contract manufacturers and suppliers to optimize our combined inventory and purchase commitment levels. However, our higher level of supply chain balances increases the risk of material excess and obsolete inventory and related losses if customer demand were to suddenly and significantly decrease in future periods and we are unable to further mitigate our supply chain exposures.\n",
            "#### Service Gross Margin\n",
            "Our service gross margin percentage decreased by 1.9 percentage points primarily due to higher headcount-related and delivery costs, partially offset by higher sales volume and favorable mix of service offerings.Our service gross margin normally experiences some fluctuations due to various factors such as the timing of contract initiations in our renewals, our strategic investments in headcount, and the resources we deploy to support the overall service business. Other factors include the mix of service offerings, as the gross margin from our advanced services is typically lower than the gross margin from technical support services.Service gross margin was flat primarily due to higher sales volume and favorable mix of service offerings offset by higher headcount-related and delivery costs.\n",
            "#### Gross Margin by Segment\n",
            "The following table presents the total gross margin for each segment (in millions, except percentages):\n",
            "[Table with 7 rows, 53 numbers, and 523 characters.]\n",
            "(1) The unallocated corporate items include the effects of amortization and impairments of acquisition-related intangible assets, share-based compensation expense, significant litigation settlements and other contingencies, charges related to asset impairments and restructurings, and certain other charges. We do not allocate these items to the gross margin for each segment because management does not include such information in measuring the performance of the operating segments. Amounts may not sum and percentages may not recalculate due to rounding. We experienced a gross margin percentage decrease in our Americas segment due to negative impacts from productivity, partially offset by favorable pricing and favorable product mix. Gross margin in our EMEA segment increased primarily due to favorable pricing, positive impacts from productivity improvements and favorable product mix, partially offset by lower service gross margin.The APJC segment gross margin percentage was flat primarily due to negative impacts from productivity offset by favorable product mix and favorable pricing.The gross margin percentage for a particular segment may fluctuate, and period-to-period changes in such percentages may or may not be indicative of a trend for that segment.\n",
            "## Table of Contents CISCO SYSTEMS, INC.MANAGEMENT’S DISCUSSION AND ANALYSIS OF FINANCIAL CONDITION AND RESULTS OF OPERATIONS (Continued)\n",
            "The Americas segment had a gross margin percentage decrease driven by negative impacts from productivity, partially offset by  favorable pricing and favorable product mix.The gross margin percentage decrease in our EMEA segment was primarily due to negative impacts from productivity, partially offset by favorable pricing.The APJC segment gross margin percentage decrease was driven by negative impacts from productivity and, to a lesser extent, unfavorable impacts from pricing, partially offset by favorable product mix.\n",
            "#### Research and Development (“R&D”), Sales and Marketing, and General and Administrative (“G&A”) Expenses\n",
            "R&D, sales and marketing, and G&A expenses are summarized in the following table (in millions, except percentages):\n",
            "[Table with 8 rows, 52 numbers, and 581 characters.]\n",
            "### R&D Expenses\n",
            "R&D expenses increased due to higher headcount-related expenses, higher share-based compensation expense and higher discretionary spending, partially offset by lower contracted services spending.We continue to invest in R&D in order to bring a broad range of products to market in a timely fashion. If we believe that we are unable to enter a particular market in a timely manner with internally developed products, we may purchase or license technology from other businesses, or we may partner with or acquire businesses as an alternative to internal R&D. R&D expenses increased due to higher headcount-related expenses, higher share-based compensation expense and higher discretionary spending, partially offset by lower contracted services spending and lower acquisition and divestitures related costs.\n",
            "### Sales and Marketing Expenses\n",
            "Sales and marketing expenses increased primarily due to higher headcount-related expenses, driven by higher variable compensation expense, higher share-based compensation expense and higher discretionary spending, partially offset by lower contracted services spending.Sales and marketing expenses increased primarily due to higher headcount-related expenses, higher discretionary spending and higher share-based compensation expense, partially offset by lower contracted services spending.\n",
            "### G&A Expenses\n",
            "G&A expenses increased due to higher headcount-related expenses, higher discretionary spending, higher share-based\n",
            "## Table of Contents CISCO SYSTEMS, INC.MANAGEMENT’S DISCUSSION AND ANALYSIS OF FINANCIAL CONDITION AND RESULTS OF OPERATIONS (Continued)\n",
            "compensation expense, higher acquisition and divestitures related costs and higher contracted services spending. The increase was partially offset by the impact from certain non-recurring charges recognized due to the Russia and Ukraine war in the third quarter of fiscal 2022.G&A expenses increased due to higher headcount-related expenses, higher discretionary spending and higher share-based compensation expense, partially offset by lower acquisition and divestitures related costs, lower contracted services spending and the impact from certain non-recurring charges recognized due to the Russia and Ukraine war in the first nine months of fiscal 2022.\n",
            "### Effect of Foreign Currency\n",
            "In the third quarter of fiscal 2023, foreign currency fluctuations, net of hedging, decreased the combined R&D, sales and marketing, and G&A expenses by approximately $86 million, or 1.9%, compared with the third quarter of fiscal 2022.In the first nine months of fiscal 2023, foreign currency fluctuations, net of hedging, decreased the combined R&D, sales and marketing, and G&A expenses by approximately $349 million, or 2.6%, compared with the first nine months of fiscal 2022.\n",
            "#### Amortization of Purchased Intangible Assets\n",
            "The following table presents the amortization of purchased intangible assets including impairment charges (in millions):\n",
            "[Table with 4 rows, 16 numbers, and 220 characters.]\n",
            "For each of the third quarter and first nine months of fiscal 2023, the decrease in amortization of purchased intangible assets was primarily due to certain purchased intangible assets that became fully amortized, partially offset by amortization of purchased intangibles from our recent acquisitions.\n",
            "#### Restructuring and Other Charges\n",
            "In the second quarter of fiscal 2023, we announced a restructuring plan in order to rebalance the organization and enable further investment in key priority areas. This rebalancing includes talent movement options and restructuring. Additionally, we have begun optimizing our real estate portfolio, aligned to the broader hybrid work strategy. The total pretax charges are estimated to be approximately $600 million and is expected to impact approximately 5% of our global workforce. In connection with this restructuring plan, we incurred charges of $87 million and $330 million in the third quarter and the first nine months of fiscal 2023, respectively. We expect the plan to be substantially completed by the end of the first quarter of fiscal 2024. We expect to reinvest substantially all of the cost savings from this restructuring plan in our key priority areas. As a result, the overall cost savings from this restructuring plan are not expected to be material for future periods.\n",
            "#### Operating Income\n",
            "The following table presents our operating income and our operating income as a percentage of revenue (in millions, except percentages):\n",
            "[Table with 2 rows, 12 numbers, and 200 characters.]\n",
            "Operating income increased by 9%, and operating income as a percentage of revenue decreased by 1.0 percentage points. The increase in operating income was primarily due to a revenue increase partially offset by higher operating expenses. The decrease in operating income as a percentage of revenue was primarily due to an operating expenses percentage increase.\n",
            "## Table of Contents CISCO SYSTEMS, INC.MANAGEMENT’S DISCUSSION AND ANALYSIS OF FINANCIAL CONDITION AND RESULTS OF OPERATIONS (Continued)\n",
            "Operating income increased by 2%, and operating income as a percentage of revenue decreased by 1.6 percentage points. The increase in operating income was primarily due to a revenue increase, partially offset by a gross margin percentage decrease (driven by negative impacts from productivity partially offset by favorable pricing and favorable product mix) and higher operating expenses. The decrease in operating income as a percentage of revenue was primarily due to a gross margin percentage decrease and operating expenses percentage increase.\n",
            "#### Interest and Other Income (Loss), Net\n",
            "Interest Income (Expense), Net   The following table summarizes interest income and interest expense (in millions):\n",
            "[Table with 3 rows, 22 numbers, and 268 characters.]\n",
            "For the each of the third quarter and first nine months of fiscal 2023, the increase in interest income was driven by higher average balance of cash and available-for-sale debt investments and higher interest rates. The increase in interest expense was primarily driven by higher interest rates, partially offset by a lower average debt balance.Other Income (Loss), Net   The components of other income (loss), net, are summarized as follows (in millions):\n",
            "[Table with 7 rows, 40 numbers, and 488 characters.]\n",
            "The decrease in our other income (loss), net was primarily driven by impairment charges and higher unrealized losses on our privately held investments, changes in net gains (losses) on available-for-sale debt investments and marketable equity investments and unfavorable impacts from foreign exchange.The decrease in our other income (loss), net was primarily driven by realized and unrealized losses and impairment charges on our privately held investments.\n",
            "#### Provision for Income Taxes\n",
            "The provision for income taxes resulted in an effective tax rate of 18.8% for the third quarter of fiscal 2023 compared with 19.9% for the third quarter of fiscal 2022. The decrease in the effective tax rate was primarily due to an increase in a U.S. foreign-derived intangible income deduction benefit driven by the capitalization and amortization of R&D expenses effective for fiscal 2023 as required by the Tax Cuts and Jobs Act, partially offset by a decrease in federal research credit benefit. The provision for income taxes resulted in an effective tax rate of 20.2% for the first nine months of fiscal 2023 compared with 18.7% for the first nine months of fiscal 2022. The increase in the effective tax rate was primarily due to an increase in net\n",
            "## Table of Contents CISCO SYSTEMS, INC.MANAGEMENT’S DISCUSSION AND ANALYSIS OF FINANCIAL CONDITION AND RESULTS OF OPERATIONS (Continued)\n",
            "discrete tax expenses, a decrease in stock based compensation windfall, and the impact of new U.S. foreign tax credit regulations effective for fiscal 2023. The increase was partially offset by a U.S. foreign-derived intangible income deduction benefit driven by the capitalization and amortization of R&D expenses effective for fiscal 2023 as required by the Tax Cuts and Jobs Act.\n",
            "## Table of Contents CISCO SYSTEMS, INC.MANAGEMENT’S DISCUSSION AND ANALYSIS OF FINANCIAL CONDITION AND RESULTS OF OPERATIONS (Continued)\n",
            "###### LIQUIDITY AND CAPITAL RESOURCES\n",
            "The following sections discuss the effects of changes in our balance sheet, our capital allocation strategy including stock repurchase program and dividends, our contractual obligations, and certain other commitments and activities on our liquidity and capital resources.\n",
            "#### Balance Sheet and Cash Flows\n",
            "Cash and Cash Equivalents and Investments  The following table summarizes our cash and cash equivalents and investments (in millions):\n",
            "[Table with 4 rows, 14 numbers, and 210 characters.]\n",
            "The net increase in cash and cash equivalents and investments in the first nine months of fiscal 2023 was primarily driven by cash provided by operating activities of $13.9 billion. This source of cash was partially offset by cash returned to stockholders in the form of repurchases of common stock of $3.0 billion and cash dividends of $4.7 billion, a net decrease of debt of $1.1 billion and capital expenditures of $0.6 billion.Beginning in fiscal 2023, we were required to capitalize and amortize R&D expenses as required by the Tax Cuts and Jobs Act. This change will result in significantly higher cash paid for income taxes. In February 2023, an IRS announcement related to the California floods (IR-2023-33) postponed our remaining fiscal 2023 U.S. federal income tax payment deadlines until October 2023. As of April 29, 2023, we have deferred approximately $1.7 billion of federal tax payments. We expect to defer an additional federal tax payment in the fourth quarter of fiscal 2023. Our cash paid for income taxes for the first quarter of fiscal 2024 will significantly increase as a result of these deferred federal tax payments.We maintain an investment portfolio of various holdings, types, and maturities. We classify our investments as short-term investments based on their nature and their availability for use in current operations. We believe the overall credit quality of our portfolio is strong, with our cash equivalents and our available-for-sale debt investment portfolio consisting primarily of high quality investment-grade securities. We believe that our strong cash and cash equivalents and investments position allows us to use our cash resources for strategic investments to gain access to new technologies, for acquisitions, for customer financing activities, for working capital needs, and for the repurchase of shares of common stock and payment of dividends as discussed below.  Securities Lending   We periodically engage in securities lending activities with certain of our available-for-sale debt investments. These transactions are accounted for as a secured lending of the securities, and the securities are typically loaned only on an overnight basis. We require collateral equal to at least 102% of the fair market value of the loaned security and that the collateral be in the form of cash or liquid, high-quality assets. We engage in these secured lending transactions only with highly creditworthy counterparties, and the associated portfolio custodian has agreed to indemnify us against collateral losses. We did not experience any losses in connection with the secured lending of securities during the periods presented. As of April 29, 2023 and July 30, 2022, we had no outstanding securities lending transactions.Free Cash Flow and Capital Allocation   As part of our capital allocation strategy, we target to return a minimum of 50% of our free cash flow annually to our stockholders through cash dividends and repurchases of common stock.We define free cash flow as net cash provided by operating activities less cash used to acquire property and equipment. The following table reconciles our net cash provided by operating activities to free cash flow (in millions):\n",
            "[Table with 3 rows, 8 numbers, and 174 characters.]\n",
            "## Table of Contents CISCO SYSTEMS, INC.MANAGEMENT’S DISCUSSION AND ANALYSIS OF FINANCIAL CONDITION AND RESULTS OF OPERATIONS (Continued)\n",
            "We expect that cash provided by operating activities may fluctuate in future periods as a result of a number of factors, including fluctuations in our operating results, the rate at which products are shipped during the quarter (which we refer to as shipment linearity), the timing and collection of accounts receivable and financing receivables, inventory and supply chain management, deferred revenue, and the timing and amount of tax and other payments. For additional discussion, see “Part II, Item 1A. Risk Factors” in this report.We consider free cash flow to be a liquidity measure that provides useful information to management and investors because of our intent to return a stated percentage of free cash flow to stockholders in the form of dividends and stock repurchases. We further regard free cash flow as a useful measure because it reflects cash that can be used to, among other things, invest in our business, make strategic acquisitions, repurchase common stock, and pay dividends on our common stock, after deducting capital investments. A limitation of the utility of free cash flow as a measure of financial performance and liquidity is that the free cash flow does not represent the total increase or decrease in our cash balance for the period. In addition, we have other required uses of cash, including repaying the principal of our outstanding indebtedness. Free cash flow is not a measure calculated in accordance with U.S. generally accepted accounting principles and should not be regarded in isolation or as an alternative for net cash provided by operating activities or any other measure calculated in accordance with such principles, and other companies may calculate free cash flow in a different manner than we do. The following table summarizes the dividends paid and stock repurchases (in millions, except per-share amounts):\n",
            "[Table with 10 rows, 51 numbers, and 487 characters.]\n",
            "On May 17, 2023, our Board of Directors declared a quarterly dividend of $0.39 per common share to be paid on July 26, 2023, to all stockholders of record as of the close of business on July 6, 2023. Future dividends will be subject to the approval of our Board of Directors.The remaining authorized amount for stock repurchases under this program is approximately $12.2 billion, with no termination date. Accounts Receivable, Net  The following table summarizes our accounts receivable, net (in millions):   April 29,2023July 30,2022Increase (Decrease)Accounts receivable, net$5,104 $6,622 $(1,518)Our accounts receivable net, as of April 29, 2023 decreased by approximately 23%, as compared with the end of fiscal 2022, primarily due to timing and amount of product and service billings in the third quarter of fiscal 2023 compared with the fourth quarter of fiscal 2022.\n",
            "## Table of Contents CISCO SYSTEMS, INC.MANAGEMENT’S DISCUSSION AND ANALYSIS OF FINANCIAL CONDITION AND RESULTS OF OPERATIONS (Continued)\n",
            "Inventory Supply Chain  The following table summarizes our inventories and inventory purchase commitments with contract manufacturers and suppliers (in millions):\n",
            "[Table with 3 rows, 20 numbers, and 266 characters.]\n",
            "The following table summarizes our inventory purchase commitments with contract manufacturers and suppliers by period (in millions):\n",
            "[Table with 4 rows, 28 numbers, and 260 characters.]\n",
            "Inventory as of April 29, 2023 increased by 35% and inventory purchase commitments with contract manufacturers and suppliers decreased by 30% from our balances at the end of fiscal 2022. The combined decrease of 19% in our inventory and inventory purchase commitments as compared with the end of fiscal 2022 was primarily due to fulfillment of customer demand as overall supply constraints continued to improve and our continued efforts to work with contract manufacturers and suppliers to optimize our inventory and purchase commitment levels.We increased our balances in prior fiscal years in order to address significant supply constraints seen industry-wide. The increases were primarily due to arrangements to secure supply and pricing for certain product components and commitments with contract manufacturers to meet customer demand and to address extended lead times, as well as advance payments with suppliers to secure future supply, as a result of the supply constraints. We have partnered with several of our key suppliers utilizing our volume purchasing and extending supply coverage, including revising supplier arrangements. As discussed, our risks of future material excess and obsolete inventory and related losses are further outlined in the Result of Operations—Product Gross Margin section.We purchase components from a variety of suppliers and use several contract manufacturers to provide manufacturing services for our products. During the normal course of business, in order to manage manufacturing lead times and help ensure adequate component supply, we enter into agreements with contract manufacturers and suppliers that allow them to procure inventory based upon criteria as defined by us or that establish the parameters defining our requirements and our commitment to securing manufacturing capacity. Our inventory purchase commitments are for short-term product manufacturing requirements as well as for commitments to suppliers to secure manufacturing capacity. Certain of our inventory purchase commitments with contract manufacturers and suppliers relate to arrangements to secure supply and pricing for certain product components for multi-year periods. A significant portion of our reported purchase commitments arising from these agreements are firm, noncancelable, and unconditional commitments. In certain instances, these agreements allow us the option to cancel, reschedule, and adjust our requirements based on our business needs prior to firm orders being placed.Inventory and supply chain management remain areas of focus as we balance the need to maintain supply chain flexibility to help ensure competitive lead times with the risk of inventory obsolescence because of supply constraints, rapidly changing technology and customer requirements. We believe the amount of our inventory and inventory purchase commitments is appropriate for our current and expected customer demand and revenue levels.Financing Receivables and Guarantees  The following table summarizes our financing receivables (in millions):\n",
            "[Table with 3 rows, 11 numbers, and 154 characters.]\n",
            "## Table of Contents CISCO SYSTEMS, INC.MANAGEMENT’S DISCUSSION AND ANALYSIS OF FINANCIAL CONDITION AND RESULTS OF OPERATIONS (Continued)\n",
            "Financing Receivables  Our financing arrangements include loans and leases. Our loan receivables include customer financing for purchases of our hardware, software and services (including technical support and advanced services), and also may include additional funds for other costs associated with network installation and integration of our products and services. Lease receivables include sales-type leases. Arrangements related to leases are generally collateralized by a security interest in the underlying assets. Financing receivables decreased by 14%, as compared with the end of fiscal 2022.Financing Guarantees  In the normal course of business, third parties may provide financing arrangements to our customers and channel partners under financing programs. The financing arrangements provided by third parties are related to leases and loans and typically have terms of up to three years. In some cases, we provide guarantees to third parties for these lease and loan arrangements. The financing arrangements to channel partners consist of revolving short-term financing provided by third parties, with payment terms generally ranging from 60 to 90 days. In certain instances, these financing arrangements result in a transfer of our receivables to the third party. The receivables are derecognized upon transfer, as these transfers qualify as true sales, and we receive payments for the receivables from the third party based on our standard payment terms. The volume of channel partner financing was $23.4 billion and $20.3 billion for the first nine months of fiscal 2023 and 2022, respectively. These financing arrangements facilitate the working capital requirements of the channel partners, and in some cases, we guarantee a portion of these arrangements. The balance of the channel partner financing subject to guarantees was $1.8 billion and $1.4 billion as of April 29, 2023 and July 30, 2022, respectively. We could be called upon to make payments under these guarantees in the event of nonpayment by the channel partners. Historically, our payments under these arrangements have been immaterial. Where we provide a guarantee, we defer the revenue associated with the channel partner financing arrangement in accordance with revenue recognition policies, or we record a liability for the fair value of the guarantees. In either case, the deferred revenue is recognized as revenue when the guarantee is removed. As of April 29, 2023, the total maximum potential future payments related to these guarantees was approximately $177 million, of which approximately $40 million was recorded as deferred revenue.\n",
            "### Borrowings\n",
            "Senior Notes  The following table summarizes the principal amount of our senior notes (in millions):\n",
            "[Table with 11 rows, 35 numbers, and 336 characters.]\n",
            "Interest is payable semiannually on each class of the senior fixed-rate notes, each of which is redeemable by us at any time, subject to a make-whole premium. We were in compliance with all debt covenants as of April 29, 2023.Commercial Paper  We have a short-term debt financing program in which up to $10.0 billion is available through the issuance of commercial paper notes. We use the proceeds from the issuance of commercial paper notes for general corporate purposes. We had no commercial paper notes outstanding as of April 29, 2023 and $0.6 billion in commercial paper outstanding as of July 30, 2022. Credit Facility On May 13, 2021, we entered into a 5-year credit agreement with certain institutional lenders that provides for a $3.0 billion unsecured revolving credit facility that is scheduled to expire on May 13, 2026. As of April 29, 2023, we were in compliance with the required interest coverage ratio and the other covenants, and we had not borrowed any funds under the credit agreement. On April 18, 2023, we entered into an amendment to the credit agreement to replace the LIBOR index with Term SOFR.Any advances under the 5-year credit agreement will accrue interest at rates that are equal to, based on certain conditions, either (a) with respect to loans in U.S. dollars, (i) Term SOFR (plus a 0.10% credit spread adjustment) or (ii) the Base Rate (to be\n",
            "## Table of Contents CISCO SYSTEMS, INC.MANAGEMENT’S DISCUSSION AND ANALYSIS OF FINANCIAL CONDITION AND RESULTS OF OPERATIONS (Continued)\n",
            "defined as the highest of (x) the Bank of America prime rate, (y) the Federal Funds rate plus 0.50% and (z) Term SOFR plus 1.0%), (b) with respect to loans in Euros, EURIBOR, (c) with respect to loans in Yen, TIBOR and (d) with respect to loans in Pounds Sterling, SONIA, plus a margin that is based on our senior debt credit ratings as published by Standard & Poor’s Financial Services, LLC and Moody’s Investors Service, Inc., provided that in no event will the interest rate be less than 0.0%. We will pay a quarterly commitment fee during the term of the 5-year credit agreement which may vary depending on our senior debt credit ratings. In addition, the 5-year credit agreement incorporates certain sustainability-linked metrics. Specifically, our applicable interest rate and commitment fee are subject to upward or downward adjustments if we achieve, or fail to achieve, certain specified targets based on two key performance indicator metrics: (i) social impact and (ii) foam reduction. We may also, upon the agreement of either the then-existing lenders or additional lenders not currently parties to the agreement, increase the commitments under the credit facility by up to an additional $2.0 billion and, at our option, extend the maturity of the facility for an additional year up to two times. The credit agreement requires that we comply with certain covenants, including that we maintain an interest coverage ratio as defined in the agreement.Remaining Performance Obligations   The following table presents the breakdown of remaining performance obligations (in millions):\n",
            "[Table with 6 rows, 20 numbers, and 214 characters.]\n",
            "Total remaining performance obligations as of April 29, 2023 increased 2% compared to the end of fiscal 2022. Remaining performance obligations for product increased by 4% compared to the end of fiscal 2022. Remaining performance obligations for service was flat. We expect approximately 53% of total remaining performance obligations to be recognized as revenue over the next 12 months.Deferred Revenue   The following table presents the breakdown of deferred revenue (in millions):\n",
            "[Table with 7 rows, 20 numbers, and 220 characters.]\n",
            "Total deferred revenue increased 4% compared to the end of fiscal 2022. The increase in deferred product revenue was primarily due to increased deferrals related to our recurring software offerings. The increase in deferred service revenue of 4% was driven by the impact of contract renewals, partially offset by amortization of deferred service revenue.\n",
            "#### Contractual Obligations\n",
            "####### Transition Tax Payable\n",
            "The income tax payable outstanding as of April 29, 2023 for the U.S. transition tax on accumulated earnings for foreign subsidiaries was $5.5 billion. Approximately $1.4 billion is payable in less than one year and $4.1 billion is payable between 1 to 3 years. For our Contractual Obligations see our Annual Report on Form 10-K for the fiscal year ended July 30, 2022.\n",
            "## Table of Contents CISCO SYSTEMS, INC.MANAGEMENT’S DISCUSSION AND ANALYSIS OF FINANCIAL CONDITION AND RESULTS OF OPERATIONS (Continued)\n",
            "#### Other Commitments\n",
            "In connection with our acquisitions, we have agreed to pay certain additional amounts contingent upon the achievement of certain agreed-upon technology, development, product, or other milestones or the continued employment with us of certain employees of the acquired entities. See Note 14 to the Consolidated Financial Statements.We also have certain funding commitments primarily related to our privately held investments, some of which may be based on the achievement of certain agreed-upon milestones or are required to be funded on demand. The funding commitments were $0.3 billion and $0.4 billion as of April 29, 2023 and July 30, 2022, respectively.In the ordinary course of business, we have privately held investments and provide financing to certain customers. Certain of these investments are considered to be variable interest entities. We evaluate on an ongoing basis our privately held investments and customer financings, and we have determined that as of April 29, 2023 there were no material unconsolidated variable interest entities.On an ongoing basis, we reassess our privately held investments and customer financings to determine if they are variable interest entities and if we would be regarded as the primary beneficiary pursuant to the applicable accounting guidance. As a result of this ongoing assessment, we may be required to make additional disclosures or consolidate these entities. Because we may not control these entities, we may not have the ability to influence these events.We provide financing guarantees, which are generally for various third-party financing arrangements extended to our channel partners. We could be called upon to make payments under these guarantees in the event of nonpayment by the channel partners. See the previous discussion of these financing guarantees under “Financing Receivables and Guarantees.”\n",
            "#### Liquidity and Capital Resource Requirements\n",
            "Based on past performance and current expectations, we believe our cash and cash equivalents, investments, cash generated from operations, and ability to access capital markets and committed credit lines will satisfy, through at least the next 12 months, our liquidity requirements, both in total and domestically, including the following: working capital needs (including inventory and other supply related payments), capital expenditures, investment requirements, stock repurchases, cash dividends, contractual obligations, commitments, principal and interest payments on debt, pending acquisitions, future customer financings, and other liquidity requirements associated with our operations. We expect increased payments related to inventory and other supply related payments through at least the next 12 months. There are no other transactions, arrangements, or relationships with unconsolidated entities or other persons that are reasonably likely to materially affect the liquidity and the availability of, as well as our requirements for, capital resources.\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(markdown)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tables"
      ],
      "metadata": {
        "id": "Vv9P7sxrbjE8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 214,
      "metadata": {
        "id": "mznnlQ1TijwR"
      },
      "outputs": [],
      "source": [
        "from sec_parser.semantic_elements.table_element.table_element import TableElement\n",
        "\n",
        "mdna_tables = []\n",
        "\n",
        "for section in mdna_top_level_sections:\n",
        "    for descendant in section.get_descendants():\n",
        "        if isinstance(descendant.semantic_element, TableElement):\n",
        "            mdna_tables.append(descendant.semantic_element)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Output"
      ],
      "metadata": {
        "id": "fxkmif0VYVZb"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 215,
      "metadata": {
        "id": "hOrimpJQOQ1G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a6e60d2-abbe-4bfc-95da-beee73e19b79"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 [Table with 15 rows, 91 numbers, and 1143 characters.]\n",
            "1 [Table with 3 rows, 8 numbers, and 155 characters.]\n",
            "2 [Table with 3 rows, 8 numbers, and 188 characters.]\n",
            "3 [Table with 6 rows, 36 numbers, and 428 characters.]\n",
            "4 [Table with 8 rows, 48 numbers, and 516 characters.]\n",
            "5 [Table with 8 rows, 48 numbers, and 546 characters.]\n",
            "6 [Table with 8 rows, 58 numbers, and 573 characters.]\n",
            "7 [Table with 8 rows, 48 numbers, and 510 characters.]\n",
            "8 [Table with 4 rows, 32 numbers, and 360 characters.]\n",
            "9 [Table with 6 rows, 15 numbers, and 214 characters.]\n",
            "10 [Table with 7 rows, 53 numbers, and 523 characters.]\n",
            "11 [Table with 8 rows, 52 numbers, and 581 characters.]\n",
            "12 [Table with 4 rows, 16 numbers, and 220 characters.]\n",
            "13 [Table with 2 rows, 12 numbers, and 200 characters.]\n",
            "14 [Table with 3 rows, 22 numbers, and 268 characters.]\n",
            "15 [Table with 7 rows, 40 numbers, and 488 characters.]\n",
            "16 [Table with 4 rows, 14 numbers, and 210 characters.]\n",
            "17 [Table with 3 rows, 8 numbers, and 174 characters.]\n",
            "18 [Table with 10 rows, 51 numbers, and 487 characters.]\n",
            "19 [Table with 3 rows, 20 numbers, and 266 characters.]\n",
            "20 [Table with 4 rows, 28 numbers, and 260 characters.]\n",
            "21 [Table with 3 rows, 11 numbers, and 154 characters.]\n",
            "22 [Table with 11 rows, 35 numbers, and 336 characters.]\n",
            "23 [Table with 6 rows, 20 numbers, and 214 characters.]\n",
            "24 [Table with 7 rows, 20 numbers, and 220 characters.]\n"
          ]
        }
      ],
      "source": [
        "import re\n",
        "\n",
        "# Your code to find matches\n",
        "pattern = r\"\\[Table with \\d+ rows, \\d+ numbers, and \\d+ characters.]\"\n",
        "matches = re.findall(pattern, markdown)\n",
        "for i, match in zip(range(len(matches)),matches):\n",
        "    print(str(i)+\" \"+match)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from bs4 import BeautifulSoup\n",
        "html=mdna_tables[index].html_tag.get_source_code()\n",
        "soup=BeautifulSoup(html,'html.parser')\n",
        "soup"
      ],
      "metadata": {
        "id": "YZaYC6eJyiT4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sec_parser.semantic_elements.table_element.table_parser import TableParser\n",
        "index=1\n",
        "table=mdna_tables[index].html_tag.get_source_code()\n",
        "# df=TableParser(table).parse_as_df()\n",
        "# df"
      ],
      "metadata": {
        "id": "cW35nCl0gW1N"
      },
      "execution_count": 240,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from io import StringIO\n",
        "\n",
        "def basic_preprocessing(html: str) -> pd.DataFrame:\n",
        "  tables = pd.read_html(StringIO(html), flavor=\"lxml\")\n",
        "  if len(tables) == 0:\n",
        "      msg = \"No tables found\"\n",
        "      raise ValueError(msg)\n",
        "  table = tables[0]\n",
        "  table = table.dropna(how=\"all\")\n",
        "  table.columns = pd.Index(table.iloc[0].tolist())\n",
        "  table = table[1:]\n",
        "  table = table.dropna(axis=1, how=\"all\")\n",
        "  first_row = list(table.columns)\n",
        "  new_df = pd.DataFrame([first_row], columns=table.columns)\n",
        "  table = pd.concat([new_df, table], ignore_index=True)\n",
        "  table.columns = pd.Index(\n",
        "      list(range(1, len(table.columns) + 1)),\n",
        "      dtype=str,\n",
        "  )\n",
        "  table = table.fillna(\"\")\n",
        "  return table.astype(str)\n",
        "\n",
        "def remove_blank_columns(df: pd.DataFrame) -> pd.DataFrame:\n",
        "    columns_to_remove = []\n",
        "    for i, _ in enumerate(df.columns):\n",
        "        if i < len(df.columns) - 1:\n",
        "            current_column = df.columns[i]\n",
        "            next_column = df.columns[i + 1]\n",
        "            non_empty_rows = df[\n",
        "                (df[current_column] != \"\") & (df[next_column] != \"\")\n",
        "            ]\n",
        "            if (\n",
        "                non_empty_rows[current_column] == non_empty_rows[next_column]\n",
        "            ).all():\n",
        "                if (df[current_column] == \"\").sum() >= (\n",
        "                    df[next_column] == \"\"\n",
        "                ).sum():\n",
        "                    columns_to_remove.append(current_column)\n",
        "                else:\n",
        "                    columns_to_remove.append(next_column)\n",
        "    return df.drop(columns=columns_to_remove)\n",
        "\n",
        "\n",
        "\n",
        "# def merge_columns_by_marker(table: pd.DataFrame, marker: str) -> pd.DataFrame:\n",
        "#     marker_columns = [col for col in table.columns if any(str(value) == marker for value in table[col])]\n",
        "\n",
        "#     for current_column in marker_columns:\n",
        "#         current_column_index = table.columns.get_loc(current_column)\n",
        "\n",
        "#         if current_column_index < len(table.columns) - 1:\n",
        "#             next_column = table.columns[current_column_index + 1]\n",
        "\n",
        "#             non_marker_rows = table[(table[current_column] != marker) & (table[next_column] != marker)]\n",
        "#             non_empty_non_marker_rows = non_marker_rows[(non_marker_rows[current_column] != \"\") & (non_marker_rows[next_column] != \"\")]\n",
        "#             marker_rows = table.drop(non_empty_non_marker_rows.index)\n",
        "#             marker_rows_indices = marker_rows.index\n",
        "\n",
        "#             if (non_empty_non_marker_rows[current_column] == non_empty_non_marker_rows[next_column]).all():\n",
        "#                 table.loc[marker_rows_indices, current_column] = (table.loc[marker_rows_indices, current_column].str.cat(table.loc[marker_rows_indices, next_column], sep=\"\", na_rep=\"\").str.strip())\n",
        "#                 table = table.drop(columns=[next_column])\n",
        "\n",
        "#     return table\n",
        "\n",
        "\n",
        "\n",
        "# def merge_columns_by_marker(table: pd.DataFrame, merge_marker: str) -> pd.DataFrame:\n",
        "#     marker_columns = [col for col in table.columns if any(str(value) == merge_marker for value in table[col])]\n",
        "\n",
        "#     for current_column in marker_columns:\n",
        "#         current_column_index = table.columns.get_loc(current_column)\n",
        "\n",
        "#         if current_column_index > 0:  # Check if current column is not the first column\n",
        "#             previous_column = table.columns[current_column_index - 1]\n",
        "\n",
        "#             non_marker_rows = table[(table[current_column] != merge_marker) & (table[previous_column] != merge_marker)]\n",
        "#             non_empty_non_marker_rows = non_marker_rows[(non_marker_rows[current_column] != \"\") & (non_marker_rows[previous_column] != \"\")]\n",
        "#             marker_rows = table.drop(non_empty_non_marker_rows.index)\n",
        "#             marker_rows_indices = marker_rows.index\n",
        "\n",
        "#             if (non_empty_non_marker_rows[current_column] == non_empty_non_marker_rows[previous_column]).all():\n",
        "#                 table.loc[marker_rows_indices, previous_column] = (table.loc[marker_rows_indices, previous_column].str.cat(table.loc[marker_rows_indices, current_column], sep=\"\", na_rep=\"\").str.strip())\n",
        "#                 table = table.drop(columns=[current_column])\n",
        "\n",
        "#     return table\n",
        "\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "def merge_columns_by_markers(table: pd.DataFrame, markers: list) -> pd.DataFrame:\n",
        "    for marker in markers:\n",
        "        marker_columns = [col for col in table.columns if any(str(value) == marker for value in table[col])]\n",
        "\n",
        "        for current_column in marker_columns:\n",
        "            current_column_index = table.columns.get_loc(current_column)\n",
        "\n",
        "            if marker == '$' and current_column_index < len(table.columns) - 1:\n",
        "                neighbor_column = table.columns[current_column_index + 1]\n",
        "                left_column=current_column\n",
        "                right_column=neighbor_column\n",
        "\n",
        "            elif (marker == '%' or marker == ')') and current_column_index > 0:\n",
        "                neighbor_column = table.columns[current_column_index - 1]\n",
        "                left_column=neighbor_column\n",
        "                right_column=current_column\n",
        "            else:\n",
        "                continue  # Skip if no neighbor column in the specified direction\n",
        "\n",
        "            non_marker_rows = table[(table[current_column] != marker) & (table[neighbor_column] != marker)]\n",
        "            non_empty_non_marker_rows = non_marker_rows[(non_marker_rows[current_column] != \"\") & (non_marker_rows[neighbor_column] != \"\")]\n",
        "            marker_rows = table.drop(non_empty_non_marker_rows.index)\n",
        "            marker_rows_indices = marker_rows.index\n",
        "\n",
        "            if (non_empty_non_marker_rows[current_column] == non_empty_non_marker_rows[neighbor_column]).all():\n",
        "                table.loc[marker_rows_indices, current_column] = (table.loc[marker_rows_indices, current_column].str.cat(table.loc[marker_rows_indices, neighbor_column], sep=\"\", na_rep=\"\").str.strip())\n",
        "                table = table.drop(columns=[neighbor_column])\n",
        "\n",
        "    return table"
      ],
      "metadata": {
        "id": "uj0KOJW9X2zU"
      },
      "execution_count": 291,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%timeit\n",
        "based_df=basic_preprocessing(table)\n",
        "based_df=remove_blank_columns(based_df)\n",
        "based_df=merge_columns_by_markers(based_df,[\"$\",\"%\",\")\"])\n",
        "based_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SWfPIQOscK9n",
        "outputId": "f89c47c4-2262-4460-ae48-abd55a9d2295"
      },
      "execution_count": 295,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "31.9 ms ± 1.1 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "table=TABLE_10Q_CP_0000016875_20_000032__001\n",
        "based_df=basic_preprocessing(table)\n",
        "based_df=remove_blank_columns(based_df)\n",
        "based_df=merge_columns_by_markers(based_df,[\"$\",\"%\",\")\"])\n",
        "based_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "_N7imzJAXg-v",
        "outputId": "228a882c-ee02-4fbe-e177-da25265d5c5b"
      },
      "execution_count": 297,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                   1  \\\n",
              "0                                                      \n",
              "1                  (in millions of Canadian dollars)   \n",
              "2                                         Net income   \n",
              "3  Net gain (loss) in foreign currency translatio...   \n",
              "4  Change in derivatives designated as cash flow ...   \n",
              "5  Change in pension and post-retirement defined ...   \n",
              "6     Other comprehensive income before income taxes   \n",
              "7       Income tax (expense) recovery on above items   \n",
              "8                Other comprehensive income (Note 7)   \n",
              "9                               Comprehensive income   \n",
              "\n",
              "                                         4  \\\n",
              "0  For the three months ended September 30   \n",
              "1                                     2020   \n",
              "2                                     $598   \n",
              "3                                       16   \n",
              "4                                        3   \n",
              "5                                       44   \n",
              "6                                       63   \n",
              "7                                     )(29   \n",
              "8                                       34   \n",
              "9                                     $632   \n",
              "\n",
              "                                         7  \\\n",
              "0  For the three months ended September 30   \n",
              "1                                     2019   \n",
              "2                                     $618   \n",
              "3                                      )(8   \n",
              "4                                        2   \n",
              "5                                       20   \n",
              "6                                       14   \n",
              "7                                        3   \n",
              "8                                       17   \n",
              "9                                     $635   \n",
              "\n",
              "                                       10  \\\n",
              "0  For the nine months ended September 30   \n",
              "1                                    2020   \n",
              "2                                   $1642   \n",
              "3                                    )(18   \n",
              "4                                       6   \n",
              "5                                     134   \n",
              "6                                     122   \n",
              "7                                    )(16   \n",
              "8                                     106   \n",
              "9                                   $1748   \n",
              "\n",
              "                                       13  \n",
              "0  For the nine months ended September 30  \n",
              "1                                    2019  \n",
              "2                                   $1776  \n",
              "3                                      23  \n",
              "4                                       8  \n",
              "5                                      61  \n",
              "6                                      92  \n",
              "7                                    )(41  \n",
              "8                                      51  \n",
              "9                                   $1827  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4692b187-f2b8-4c11-9e1c-a542a5de0027\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>1</th>\n",
              "      <th>4</th>\n",
              "      <th>7</th>\n",
              "      <th>10</th>\n",
              "      <th>13</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td></td>\n",
              "      <td>For the three months ended September 30</td>\n",
              "      <td>For the three months ended September 30</td>\n",
              "      <td>For the nine months ended September 30</td>\n",
              "      <td>For the nine months ended September 30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(in millions of Canadian dollars)</td>\n",
              "      <td>2020</td>\n",
              "      <td>2019</td>\n",
              "      <td>2020</td>\n",
              "      <td>2019</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Net income</td>\n",
              "      <td>$598</td>\n",
              "      <td>$618</td>\n",
              "      <td>$1642</td>\n",
              "      <td>$1776</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Net gain (loss) in foreign currency translatio...</td>\n",
              "      <td>16</td>\n",
              "      <td>)(8</td>\n",
              "      <td>)(18</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Change in derivatives designated as cash flow ...</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Change in pension and post-retirement defined ...</td>\n",
              "      <td>44</td>\n",
              "      <td>20</td>\n",
              "      <td>134</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Other comprehensive income before income taxes</td>\n",
              "      <td>63</td>\n",
              "      <td>14</td>\n",
              "      <td>122</td>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Income tax (expense) recovery on above items</td>\n",
              "      <td>)(29</td>\n",
              "      <td>3</td>\n",
              "      <td>)(16</td>\n",
              "      <td>)(41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Other comprehensive income (Note 7)</td>\n",
              "      <td>34</td>\n",
              "      <td>17</td>\n",
              "      <td>106</td>\n",
              "      <td>51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Comprehensive income</td>\n",
              "      <td>$632</td>\n",
              "      <td>$635</td>\n",
              "      <td>$1748</td>\n",
              "      <td>$1827</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4692b187-f2b8-4c11-9e1c-a542a5de0027')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4692b187-f2b8-4c11-9e1c-a542a5de0027 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4692b187-f2b8-4c11-9e1c-a542a5de0027');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8a80b21f-43ff-46b1-b64d-475ab33f446b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8a80b21f-43ff-46b1-b64d-475ab33f446b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8a80b21f-43ff-46b1-b64d-475ab33f446b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 297
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "def assign_same_value(df):\n",
        "    first_column = df.columns[0]\n",
        "    for index, row in df.iterrows():\n",
        "        if row[first_column] == '' and all(value == row.iloc[1] for value in row.iloc[1:]):\n",
        "            value_to_assign = row.iloc[1]\n",
        "            df.loc[index, first_column] = value_to_assign\n",
        "    return df\n",
        "\n",
        "\n",
        "def clean_repeating_rows(df):\n",
        "  same_values_mask = df.apply(lambda row: all(value == '' or value == row.iloc[0] for value in row), axis=1)\n",
        "  df.loc[same_values_mask, df.columns[1:]] = ''\n",
        "  return df\n",
        "\n",
        "\n",
        "def row_splitter(df):\n",
        "    df['Marked'] = (df.apply(lambda row: any(bool(re.search(r'\\$\\s*\\(\\s*[\\d,]+\\s*\\)|\\$\\s*[\\d,]+|\\(\\s*[\\d,]+\\s*%\\s*\\)|[\\d,]+\\s*%',\n",
        "                                                            str(cell).replace(\" \", \"\"))) for cell in row), axis=1))\n",
        "    first_true_index = df.index[df['Marked']].min()\n",
        "    df=df.drop(columns=\"Marked\")\n",
        "    header_rows = df.iloc[:first_true_index, :]\n",
        "    df_remaining = df.iloc[first_true_index:, :]\n",
        "    return header_rows, df_remaining"
      ],
      "metadata": {
        "id": "jBuCkR8Yx_HZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# df=assign_same_value(df)\n",
        "# df=clean_repeating_rows(df)\n",
        "header_rows,df_remaining=row_splitter(df)\n",
        "header_rows"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "Zn2K3yjJ7ftV",
        "outputId": "0e4926de-7b1f-4afd-82c3-28caf481c688"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         1             2              3             4  \\\n",
              "0  Paid subscribers(2) at:                                              \n",
              "1            (in millions)  July 1, 2023  April 1, 2023  July 2, 2022   \n",
              "2                  Disney+                                              \n",
              "\n",
              "                                5                              6  \n",
              "0         % Change Better (Worse)        % Change Better (Worse)  \n",
              "1  July 1, 2023 vs. April 1, 2023  July 1, 2023 vs. July 2, 2022  \n",
              "2                                                                 "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1e0c0120-1647-4400-bbb5-fe1690899125\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Paid subscribers(2) at:</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td>% Change Better (Worse)</td>\n",
              "      <td>% Change Better (Worse)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(in millions)</td>\n",
              "      <td>July 1, 2023</td>\n",
              "      <td>April 1, 2023</td>\n",
              "      <td>July 2, 2022</td>\n",
              "      <td>July 1, 2023 vs. April 1, 2023</td>\n",
              "      <td>July 1, 2023 vs. July 2, 2022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Disney+</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1e0c0120-1647-4400-bbb5-fe1690899125')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1e0c0120-1647-4400-bbb5-fe1690899125 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1e0c0120-1647-4400-bbb5-fe1690899125');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-3973dfb3-e8c9-45c7-a2b4-5e8ec74cdf41\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3973dfb3-e8c9-45c7-a2b4-5e8ec74cdf41')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3973dfb3-e8c9-45c7-a2b4-5e8ec74cdf41 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 326
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_remaining=assign_same_value(df_remaining)\n",
        "df_remaining=clean_repeating_rows(df_remaining)\n",
        "df_remaining"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "my_pbr--C28x",
        "outputId": "f352db0f-71f8-4fbf-97d5-e67488e7949f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               1      2      3     4       5  \\\n",
              "3                     Domestic (U.S. and Canada)   46.0   46.3  44.5   (1) %   \n",
              "4   International (excluding Disney+ Hotstar)(3)   59.7   58.6  49.2     2 %   \n",
              "5                                Disney+ Core(4)  105.7  104.9  93.6     1 %   \n",
              "6                                Disney+ Hotstar   40.4   52.9  58.4  (24) %   \n",
              "7                                          ESPN+   25.2   25.3  22.8     — %   \n",
              "8                                           Hulu                               \n",
              "9                                      SVOD Only   44.0   43.7  42.2     1 %   \n",
              "10                                Live TV + SVOD    4.3    4.4   4.0   (2) %   \n",
              "11                                 Total Hulu(4)   48.3   48.2  46.2     — %   \n",
              "\n",
              "         6  \n",
              "3      3 %  \n",
              "4     21 %  \n",
              "5     13 %  \n",
              "6   (31) %  \n",
              "7     11 %  \n",
              "8           \n",
              "9      4 %  \n",
              "10     8 %  \n",
              "11     5 %  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2b66b2fd-6262-4957-b938-8cb102189f76\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Domestic (U.S. and Canada)</td>\n",
              "      <td>46.0</td>\n",
              "      <td>46.3</td>\n",
              "      <td>44.5</td>\n",
              "      <td>(1) %</td>\n",
              "      <td>3 %</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>International (excluding Disney+ Hotstar)(3)</td>\n",
              "      <td>59.7</td>\n",
              "      <td>58.6</td>\n",
              "      <td>49.2</td>\n",
              "      <td>2 %</td>\n",
              "      <td>21 %</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Disney+ Core(4)</td>\n",
              "      <td>105.7</td>\n",
              "      <td>104.9</td>\n",
              "      <td>93.6</td>\n",
              "      <td>1 %</td>\n",
              "      <td>13 %</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Disney+ Hotstar</td>\n",
              "      <td>40.4</td>\n",
              "      <td>52.9</td>\n",
              "      <td>58.4</td>\n",
              "      <td>(24) %</td>\n",
              "      <td>(31) %</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>ESPN+</td>\n",
              "      <td>25.2</td>\n",
              "      <td>25.3</td>\n",
              "      <td>22.8</td>\n",
              "      <td>— %</td>\n",
              "      <td>11 %</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Hulu</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>SVOD Only</td>\n",
              "      <td>44.0</td>\n",
              "      <td>43.7</td>\n",
              "      <td>42.2</td>\n",
              "      <td>1 %</td>\n",
              "      <td>4 %</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Live TV + SVOD</td>\n",
              "      <td>4.3</td>\n",
              "      <td>4.4</td>\n",
              "      <td>4.0</td>\n",
              "      <td>(2) %</td>\n",
              "      <td>8 %</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Total Hulu(4)</td>\n",
              "      <td>48.3</td>\n",
              "      <td>48.2</td>\n",
              "      <td>46.2</td>\n",
              "      <td>— %</td>\n",
              "      <td>5 %</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2b66b2fd-6262-4957-b938-8cb102189f76')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2b66b2fd-6262-4957-b938-8cb102189f76 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2b66b2fd-6262-4957-b938-8cb102189f76');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ace22cb6-d6e9-432f-ba5d-e25a82b6a79c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ace22cb6-d6e9-432f-ba5d-e25a82b6a79c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ace22cb6-d6e9-432f-ba5d-e25a82b6a79c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 328
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Under Work\n"
      ],
      "metadata": {
        "id": "RqbjQWROsOqu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Blank Rows"
      ],
      "metadata": {
        "id": "G608E98f301G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "def blank_rows_df(df):\n",
        "  first_column=pd.DataFrame(df.iloc[:,0])\n",
        "  empty_rows=first_column[first_column.iloc[:,0]== '']\n",
        "  rows = []\n",
        "\n",
        "  for index in empty_rows.index:\n",
        "      if index==0:\n",
        "        rows.append(index)\n",
        "      elif index - 1 in empty_rows.index:\n",
        "          rows.append(index)\n",
        "      else:\n",
        "        break\n",
        "  return df.iloc[rows]"
      ],
      "metadata": {
        "id": "Ur9UdST2yGlu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## is_Multi_Indexable"
      ],
      "metadata": {
        "id": "y0K2MEyK33lZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def multi_indexer(df):\n",
        "  heading_rows_df=blank_rows_df(df)\n",
        "  num_rows=len(heading_rows_df)\n",
        "  rows_list = [heading_rows_df.iloc[i].tolist() for i in range(num_rows)]\n",
        "  tupled_indices = list(zip(*rows_list))\n",
        "  level_names = [f\"lvl-{i}\" for i in range(num_rows)]\n",
        "  multi_index_columns=pd.MultiIndex.from_tuples(tupled_indices, names=level_names)\n",
        "  df.columns=multi_index_columns\n",
        "  df=df.iloc[num_rows:]\n",
        "  df=df.reset_index(drop=True)\n",
        "  return df"
      ],
      "metadata": {
        "id": "qZoS-C0RLYji"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "better_df=multi_indexer(df)\n",
        "better_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 756
        },
        "id": "xtZViPs4Qeu6",
        "outputId": "65b5873e-1822-41b1-9f89-884ac7b3d81b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "lvl-0                                                \\\n",
              "lvl-1                                                 \n",
              "lvl-2                                                 \n",
              "0                                      Net revenues   \n",
              "1                                Operating expenses   \n",
              "2                                  Operating income   \n",
              "3                                  Operating margin   \n",
              "4                       Other income (expense), net   \n",
              "5                                Income tax expense   \n",
              "6                                Effective tax rate   \n",
              "7                                 Net income (loss)   \n",
              "8               Net income (loss) per diluted share   \n",
              "9      Net cash provided by operating activities(1)   \n",
              "\n",
              "lvl-0                     Three Months Ended September 30,  \\\n",
              "lvl-1                                                 2023   \n",
              "lvl-2 (In millions, except percentages and per share data)   \n",
              "0                                                  $7418     \n",
              "1                                                   6250     \n",
              "2                                                  $1168     \n",
              "3                                                    16%     \n",
              "4                                                    $73     \n",
              "5                                                    221     \n",
              "6                                                    18%     \n",
              "7                                                  $1020     \n",
              "8                                                  $0.93     \n",
              "9                                                  $1259     \n",
              "\n",
              "lvl-0                                                       \\\n",
              "lvl-1                                                 2022   \n",
              "lvl-2 (In millions, except percentages and per share data)   \n",
              "0                                                  $6846     \n",
              "1                                                   5728     \n",
              "2                                                  $1118     \n",
              "3                                                    16%     \n",
              "4                                                   $460     \n",
              "5                                                    248     \n",
              "6                                                    16%     \n",
              "7                                                  $1330     \n",
              "8                                                  $1.15     \n",
              "9                                                  $1755     \n",
              "\n",
              "lvl-0                          Percent Increase/(Decrease)  \\\n",
              "lvl-1                          Percent Increase/(Decrease)   \n",
              "lvl-2 (In millions, except percentages and per share data)   \n",
              "0                                                     8%     \n",
              "1                                                     9%     \n",
              "2                                                     4%     \n",
              "3                                                     **     \n",
              "4                                                  (84)%     \n",
              "5                                                  (11)%     \n",
              "6                                                     **     \n",
              "7                                                  (23)%     \n",
              "8                                                  (19)%     \n",
              "9                                                  (28)%     \n",
              "\n",
              "lvl-0                      Nine Months Ended September 30,  \\\n",
              "lvl-1                                                 2023   \n",
              "lvl-2 (In millions, except percentages and per share data)   \n",
              "0                                                 $21745     \n",
              "1                                                  18445     \n",
              "2                                                  $3300     \n",
              "3                                                    15%     \n",
              "4                                                   $318     \n",
              "5                                                    774     \n",
              "6                                                    21%     \n",
              "7                                                  $2844     \n",
              "8                                                  $2.55     \n",
              "9                                                  $2229     \n",
              "\n",
              "lvl-0                                                       \\\n",
              "lvl-1                                                 2022   \n",
              "lvl-2 (In millions, except percentages and per share data)   \n",
              "0                                                 $20135     \n",
              "1                                                  17542     \n",
              "2                                                  $2593     \n",
              "3                                                    13%     \n",
              "4                                                 $(337)     \n",
              "5                                                    758     \n",
              "6                                                    34%     \n",
              "7                                                  $1498     \n",
              "8                                                  $1.29     \n",
              "9                                                  $4222     \n",
              "\n",
              "lvl-0                          Percent Increase/(Decrease)  \n",
              "lvl-1                          Percent Increase/(Decrease)  \n",
              "lvl-2 (In millions, except percentages and per share data)  \n",
              "0                                                     8%    \n",
              "1                                                     5%    \n",
              "2                                                    27%    \n",
              "3                                                     **    \n",
              "4                                                   194%    \n",
              "5                                                     2%    \n",
              "6                                                     **    \n",
              "7                                                    90%    \n",
              "8                                                    98%    \n",
              "9                                                  (47)%    "
            ],
            "text/html": [
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              "    <tr>\n",
              "      <th>lvl-0</th>\n",
              "      <th></th>\n",
              "      <th colspan=\"2\" halign=\"left\">Three Months Ended September 30,</th>\n",
              "      <th>Percent Increase/(Decrease)</th>\n",
              "      <th colspan=\"2\" halign=\"left\">Nine Months Ended September 30,</th>\n",
              "      <th>Percent Increase/(Decrease)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lvl-1</th>\n",
              "      <th></th>\n",
              "      <th>2023</th>\n",
              "      <th>2022</th>\n",
              "      <th>Percent Increase/(Decrease)</th>\n",
              "      <th>2023</th>\n",
              "      <th>2022</th>\n",
              "      <th>Percent Increase/(Decrease)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lvl-2</th>\n",
              "      <th></th>\n",
              "      <th>(In millions, except percentages and per share data)</th>\n",
              "      <th>(In millions, except percentages and per share data)</th>\n",
              "      <th>(In millions, except percentages and per share data)</th>\n",
              "      <th>(In millions, except percentages and per share data)</th>\n",
              "      <th>(In millions, except percentages and per share data)</th>\n",
              "      <th>(In millions, except percentages and per share data)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Net revenues</td>\n",
              "      <td>$7418</td>\n",
              "      <td>$6846</td>\n",
              "      <td>8%</td>\n",
              "      <td>$21745</td>\n",
              "      <td>$20135</td>\n",
              "      <td>8%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Operating expenses</td>\n",
              "      <td>6250</td>\n",
              "      <td>5728</td>\n",
              "      <td>9%</td>\n",
              "      <td>18445</td>\n",
              "      <td>17542</td>\n",
              "      <td>5%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Operating income</td>\n",
              "      <td>$1168</td>\n",
              "      <td>$1118</td>\n",
              "      <td>4%</td>\n",
              "      <td>$3300</td>\n",
              "      <td>$2593</td>\n",
              "      <td>27%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Operating margin</td>\n",
              "      <td>16%</td>\n",
              "      <td>16%</td>\n",
              "      <td>**</td>\n",
              "      <td>15%</td>\n",
              "      <td>13%</td>\n",
              "      <td>**</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Other income (expense), net</td>\n",
              "      <td>$73</td>\n",
              "      <td>$460</td>\n",
              "      <td>(84)%</td>\n",
              "      <td>$318</td>\n",
              "      <td>$(337)</td>\n",
              "      <td>194%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Income tax expense</td>\n",
              "      <td>221</td>\n",
              "      <td>248</td>\n",
              "      <td>(11)%</td>\n",
              "      <td>774</td>\n",
              "      <td>758</td>\n",
              "      <td>2%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Effective tax rate</td>\n",
              "      <td>18%</td>\n",
              "      <td>16%</td>\n",
              "      <td>**</td>\n",
              "      <td>21%</td>\n",
              "      <td>34%</td>\n",
              "      <td>**</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Net income (loss)</td>\n",
              "      <td>$1020</td>\n",
              "      <td>$1330</td>\n",
              "      <td>(23)%</td>\n",
              "      <td>$2844</td>\n",
              "      <td>$1498</td>\n",
              "      <td>90%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Net income (loss) per diluted share</td>\n",
              "      <td>$0.93</td>\n",
              "      <td>$1.15</td>\n",
              "      <td>(19)%</td>\n",
              "      <td>$2.55</td>\n",
              "      <td>$1.29</td>\n",
              "      <td>98%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Net cash provided by operating activities(1)</td>\n",
              "      <td>$1259</td>\n",
              "      <td>$1755</td>\n",
              "      <td>(28)%</td>\n",
              "      <td>$2229</td>\n",
              "      <td>$4222</td>\n",
              "      <td>(47)%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
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              "        document.querySelector('#df-3381d66d-3cb4-4e30-8e26-ace087b6f89f button.colab-df-convert');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3381d66d-3cb4-4e30-8e26-ace087b6f89f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3f7bfcd2-d7ee-45ba-8713-e41d769d66f7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## General Multi-Indexer"
      ],
      "metadata": {
        "id": "h8jqvUW5L84s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.iloc[:, 1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YrkFDmjmMBGb",
        "outputId": "ddff6012-4fc9-45b1-c2b7-02725ddfe065"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                      Three Months Ended September 30,\n",
              "1                                                  2023\n",
              "2     (In millions, except percentages and per share...\n",
              "3                                                 $7418\n",
              "4                                                  6250\n",
              "5                                                 $1168\n",
              "6                                                   16%\n",
              "7                                                   $73\n",
              "8                                                   221\n",
              "9                                                   18%\n",
              "10                                                $1020\n",
              "11                                                $0.93\n",
              "12                                                $1259\n",
              "Name: 2, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-bxdUaltMu9y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def function_name(df):\n",
        "  heading_rows_df=blank_rows_df(df)\n",
        "    if len(temp_df)<=1:\n",
        "      return df\n",
        "    else:\n",
        "      if condition-1:\n",
        "        return multi_indexer(df)\n",
        "      if condition-2:\n",
        "        return row_merger(df)"
      ],
      "metadata": {
        "id": "lyRX9oH1RzCc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def is_multi_indexable(temp_df):\n",
        "  if len(temp_df)<=1:\n",
        "    return False\n",
        "  else:"
      ],
      "metadata": {
        "id": "rR4xSdmm2KLL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Hide\n"
      ],
      "metadata": {
        "id": "NZeuqEUcoaXs"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d2FdgVIEmEvG"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "table_html = mdna_tables[index].html_tag.get_source_code()\n",
        "df = pd.read_html(table_html)[0]\n",
        "df.head(100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L0zYMSyCm9Ym"
      },
      "outputs": [],
      "source": [
        "from bs4 import BeautifulSoup\n",
        "table_html = mdna_tables[index].html_tag.get_source_code()\n",
        "soup = BeautifulSoup(table_html, 'html.parser')\n",
        "soup"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Preprocessing Functions"
      ],
      "metadata": {
        "id": "UcN2SDerYUge"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def basic_preprocessing(soup):\n",
        "  df = pd.read_html(str(soup))[0]\n",
        "  df = df.dropna(how='all')\n",
        "  df.columns = df.iloc[0]\n",
        "  df = df[1:]\n",
        "  df = df.dropna(axis=1, how='all')\n",
        "  first_row = [col for col in df.columns]\n",
        "  new_df=pd.DataFrame([first_row],columns=df.columns)\n",
        "  df = pd.concat([new_df,df], ignore_index=True)\n",
        "  df.columns = range(1, len(df.columns) + 1)\n",
        "  df = df.fillna('')\n",
        "  df=df.astype(str)\n",
        "  return df\n",
        "\n",
        "def remove_blank_columns(df):\n",
        "    columns_to_remove = []\n",
        "    for i in range(len(df.columns)):\n",
        "        if i < len(df.columns)-1:\n",
        "            col1 = df.columns[i]\n",
        "            col2 = df.columns[i + 1]\n",
        "            non_empty_rows = df[(df[col1] != '') & (df[col2] != '')]\n",
        "            if (non_empty_rows[col1] == non_empty_rows[col2]).all():\n",
        "                if (df[col1] == '').sum() >= (df[col2] == '').sum():\n",
        "                    columns_to_remove.append(col1)\n",
        "                else:\n",
        "                    columns_to_remove.append(col2)\n",
        "    df.drop(columns=columns_to_remove, inplace=True)\n",
        "    return df\n",
        "\n",
        "\n",
        "def merge_dollar_columns(df):\n",
        "    for i, _ in enumerate(df.columns):\n",
        "      if i < len(df.columns)-1:\n",
        "        col1 = df.columns[i]\n",
        "        col2 = df.columns[i + 1]\n",
        "\n",
        "        # Ignore rows with '$' symbol\n",
        "        non_dollar_rows = df[(df[col1] != '$') & (df[col2] != '$')]\n",
        "        non_dollar_rows=non_dollar_rows[(non_dollar_rows[col1] != '') & (non_dollar_rows[col2] != '')]\n",
        "        dollar_rows=df.drop(non_dollar_rows.index)\n",
        "        dollar_rows_indices=dollar_rows.index\n",
        "\n",
        "        # Check if remaining rows contain the same value\n",
        "        if (non_dollar_rows[col1] == non_dollar_rows[col2]).all():\n",
        "            df.loc[dollar_rows_indices, col1] = (df.loc[dollar_rows_indices, col1]\n",
        "                                                 .str.cat(df.loc[dollar_rows_indices, col2], sep='', na_rep='')\n",
        "                                                 .str.strip())\n",
        "            df.drop(columns=[col2], inplace=True)\n",
        "    return df\n",
        "\n",
        "\n",
        "def merge_percentage_columns(df):\n",
        "    for i, _ in enumerate(df.columns):\n",
        "      if i < len(df.columns)-1:\n",
        "        col1 = df.columns[i]\n",
        "        col2 = df.columns[i + 1]\n",
        "\n",
        "        # Ignore rows with '%' symbol\n",
        "        non_dollar_rows = df[(df[col1] != '%') & (df[col2] != '%')]\n",
        "        non_percentage_rows=non_dollar_rows[(non_dollar_rows[col1] != '') & (non_dollar_rows[col2] != '')]\n",
        "        percentage_rows=df.drop(non_percentage_rows.index)\n",
        "        percentage_rows_indices=percentage_rows.index\n",
        "\n",
        "        # Check if remaining rows contain the same value\n",
        "        if (non_percentage_rows[col1] == non_percentage_rows[col2]).all():\n",
        "          df.loc[percentage_rows_indices, col1] = df.loc[percentage_rows_indices, col1].str.cat(df.loc[percentage_rows_indices, col2], sep='', na_rep='').str.strip()\n",
        "          df.drop(columns=[col2], inplace=True)\n",
        "    return df"
      ],
      "metadata": {
        "id": "pOQifBY17HyH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Parsed Tables"
      ],
      "metadata": {
        "id": "HlRwgCBzYZAp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_cleaned=basic_preprocessing(soup)\n",
        "df_cleaned=remove_blank_columns(df_cleaned)\n",
        "df_cleaned=merge_dollar_columns(df_cleaned)\n",
        "df_cleaned=merge_percentage_columns(df_cleaned)\n",
        "df_cleaned.columns = range(1, len(df_cleaned.columns) + 1)\n",
        "df_cleaned.head(100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "vLsg_wsZQXRd",
        "outputId": "2c9b76fa-1884-4a2c-942c-878b2ac61097"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               1      2      3      4      5\n",
              "0       Gross margin percentage:                            \n",
              "1                       Products  35.4%  34.5%  36.5%  36.8%\n",
              "2                       Services  70.5%  71.5%  70.8%  72.2%\n",
              "3  Total gross margin percentage  44.5%  43.3%  43.8%  43.6%"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>5</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Gross margin percentage:</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Products</td>\n",
              "      <td>35.4%</td>\n",
              "      <td>34.5%</td>\n",
              "      <td>36.5%</td>\n",
              "      <td>36.8%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Services</td>\n",
              "      <td>70.5%</td>\n",
              "      <td>71.5%</td>\n",
              "      <td>70.8%</td>\n",
              "      <td>72.2%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Total gross margin percentage</td>\n",
              "      <td>44.5%</td>\n",
              "      <td>43.3%</td>\n",
              "      <td>43.8%</td>\n",
              "      <td>43.6%</td>\n",
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              "  </tbody>\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8e99a0ef-ae0f-4f76-a33d-47bcdf943b07 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8e99a0ef-ae0f-4f76-a33d-47bcdf943b07');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-2ad3ed2f-6c54-4ffc-a185-1a4224327f49\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2ad3ed2f-6c54-4ffc-a185-1a4224327f49')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2ad3ed2f-6c54-4ffc-a185-1a4224327f49 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "json_data = df_cleaned.to_json(orient='records')\n",
        "json_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "id": "BMA5QBwtfVn0",
        "outputId": "84581a32-8f57-410b-bed2-52faa8330cba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'[{\"1\":\"\",\"2\":\"Three months ended June 30,\",\"3\":\"Three months ended June 30,\",\"4\":\"Six months ended June 30,\",\"5\":\"Six months ended June 30,\"},{\"1\":\"(in\\\\u00a0millions)\",\"2\":\"2023\",\"3\":\"2022\",\"4\":\"2023\",\"5\":\"2022\"},{\"1\":\"Interest expense\",\"2\":\"$552\",\"3\":\"$556\",\"4\":\"$1105\",\"5\":\"$1104\"},{\"1\":\"Interest income\",\"2\":\"(98)\",\"3\":\"(24)\",\"4\":\"(197)\",\"5\":\"(33)\"},{\"1\":\"Interest expense, net\",\"2\":\"$454\",\"3\":\"$532\",\"4\":\"$908\",\"5\":\"$1071\"},{\"1\":\"Net foreign exchange loss\",\"2\":\"$37\",\"3\":\"$47\",\"4\":\"$72\",\"5\":\"$72\"},{\"1\":\"Other expense, net\",\"2\":\"1412\",\"3\":\"1533\",\"4\":\"3216\",\"5\":\"757\"}]'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 418
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2TlEVkwmnrgE"
      },
      "outputs": [],
      "source": [
        "# GPT3_ENCODING = \"cl100k_base\"\n",
        "\n",
        "\n",
        "# def num_tokens_from_string(string: str, encoding_name: str) -> int:\n",
        "#     \"\"\"Returns the number of tokens in a text string.\"\"\"\n",
        "#     encoding = tiktoken.get_encoding(encoding_name)\n",
        "#     num_tokens = len(encoding.encode(string))\n",
        "#     return num_tokens\n",
        "\n",
        "\n",
        "# num_tokens_from_string(markdown, GPT4_ENCODING)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sec_parser.semantic_elements.table_element import TableElement\n",
        "from sec_parser.processing_engine.html_tag import HtmlTag\n",
        "from bs4 import BeautifulSoup\n",
        "\n",
        "# # Let's assume you have some HTML for a table\n",
        "# html = \"\"\"\n",
        "# <table>\n",
        "#     <tr>\n",
        "#         <td>1</td>\n",
        "#         <td>2</td>\n",
        "#     </tr>\n",
        "#     <tr>\n",
        "#         <td>3</td>\n",
        "#         <td>4</td>\n",
        "#     </tr>\n",
        "# </table>\n",
        "# \"\"\"\n",
        "\n",
        "# # Parse the HTML using BeautifulSoup\n",
        "# soup = BeautifulSoup(html, \"html.parser\")\n",
        "\n",
        "# # Create an HtmlTag from the BeautifulSoup object\n",
        "# html_tag = HtmlTag(soup.table)\n",
        "\n",
        "# # Create a TableElement from the HtmlTag\n",
        "# table_element = TableElement(html_tag)\n",
        "# table_element\n",
        "\n",
        "\n",
        "\n",
        "mdna_tables[0].to_dict()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "42A5dVlebZ10",
        "outputId": "c6b60452-8759-4ef3-9a19-8acb9d5404e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'cls_name': 'TableElement',\n",
              " 'tag_name': 'div',\n",
              " 'text_preview': 'Production LocationV...[339]...adsterIn development',\n",
              " 'html_preview': '<div style=\"margin-t...[8790]...></tr></table></div>',\n",
              " 'html_hash': '3682c14c',\n",
              " 'metrics': {'rows': 8, 'numbers': 2}}"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/alphanome-ai/sec-parser\n",
        "%cd \"/content/sec-parser\""
      ],
      "metadata": {
        "id": "5E7omU_MdBv3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install poetry\n",
        "!poetry export -f requirements.txt --output requirements.txt\n",
        "!pip install -r \"/content/sec-parser/requirements.txt\""
      ],
      "metadata": {
        "id": "m5gumiRudcyS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from bs4 import BeautifulSoup\n",
        "\n",
        "def colorize_html_background_inline(input_html):\n",
        "    soup = BeautifulSoup(input_html, 'html.parser')\n",
        "\n",
        "    # Define background colors\n",
        "    bg_colors = {\n",
        "        'h1': 'background-color: #ff0000;',  # Red\n",
        "        'h2': 'background-color: #ff0000;',  # Red\n",
        "        'p': 'background-color: #0000ff;',  # Blue\n",
        "        'table': 'background-color: #00ff00;',  # Green\n",
        "        # Add more mappings for other semantic elements as needed\n",
        "    }\n",
        "\n",
        "    # Apply background colors to semantic elements\n",
        "    for element, bg_style in bg_colors.items():\n",
        "        for tag in soup.find_all(element):\n",
        "            if 'style' in tag.attrs:\n",
        "                tag['style'] += f' {bg_style}'\n",
        "            else:\n",
        "                tag['style'] = bg_style\n",
        "\n",
        "    # Return the colored HTML\n",
        "    return str(soup)"
      ],
      "metadata": {
        "id": "ul2sTqm6E0xU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "html_input = \"\"\"\n",
        "<html>\n",
        "  <head>\n",
        "    <title>Sample HTML</title>\n",
        "  </head>\n",
        "  <body>\n",
        "    <h1>This is a Heading 1</h1>\n",
        "    <p>Lorem ipsum dolor sit amet...</p>\n",
        "    <h2>This is a Heading 2</h2>\n",
        "    <table border=\"1\">\n",
        "      <tr>\n",
        "        <td>Table Cell 1</td>\n",
        "        <td>Table Cell 2</td>\n",
        "      </tr>\n",
        "    </table>\n",
        "  </body>\n",
        "</html>\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "srM15i6KFNU9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "colored_html = colorize_html_background_inline(html_input)\n",
        "print(colored_html)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-9d09vc0FLnn",
        "outputId": "234e7091-f376-4851-d3cc-35a8954b1ccf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "<html>\n",
            "<head>\n",
            "<title>Sample HTML</title>\n",
            "</head>\n",
            "<body>\n",
            "<h1 style=\"background-color: #ff0000;\">This is a Heading 1</h1>\n",
            "<p style=\"background-color: #0000ff;\">Lorem ipsum dolor sit amet...</p>\n",
            "<h2 style=\"background-color: #ff0000;\">This is a Heading 2</h2>\n",
            "<table border=\"1\" style=\"background-color: #00ff00;\">\n",
            "<tr>\n",
            "<td>Table Cell 1</td>\n",
            "<td>Table Cell 2</td>\n",
            "</tr>\n",
            "</table>\n",
            "</body>\n",
            "</html>\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##"
      ],
      "metadata": {
        "id": "bRtjrZKbL5LJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "TABLE_10Q_CP_0000016875_20_000032__001 = \"\"\"\n",
        "<table cellpadding=\"0\" cellspacing=\"0\" style=\"font-family:Times New Roman;font-size:10pt;width:100%;border-collapse:collapse;text-align:left;\"><tbody><tr><td colspan=\"13\"></td></tr><tr><td style=\"width:60%;\"></td><td style=\"width:1%;\"></td><td style=\"width:8%;\"></td><td style=\"width:1%;\"></td><td style=\"width:1%;\"></td><td style=\"width:8%;\"></td><td style=\"width:1%;\"></td><td style=\"width:1%;\"></td><td style=\"width:8%;\"></td><td style=\"width:1%;\"></td><td style=\"width:1%;\"></td><td style=\"width:8%;\"></td><td style=\"width:1%;\"></td></tr><tr><td style=\"vertical-align:bottom;padding-left:2px;padding-top:2px;padding-bottom:2px;padding-right:2px;\"><div style=\"overflow:hidden;font-size:10pt;\"><span style=\"font-family:inherit;font-size:10pt;\">&nbsp;</span></div></td><td colspan=\"6\" style=\"vertical-align:bottom;border-bottom:1px solid #000000;padding-left:2px;padding-top:2px;padding-bottom:2px;\"><div style=\"text-align:center;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;font-weight:bold;\">For the three months ended September 30</span></div></td><td colspan=\"6\" style=\"vertical-align:bottom;border-bottom:1px solid #000000;padding-left:2px;padding-top:2px;padding-bottom:2px;\"><div style=\"text-align:center;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;font-weight:bold;\">For the nine months ended September 30</span></div></td></tr><tr><td style=\"vertical-align:top;border-bottom:2px solid #000000;padding-left:2px;padding-top:2px;padding-bottom:2px;padding-right:2px;\"><div style=\"text-align:left;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;\">(in millions of Canadian dollars)</span></div></td><td colspan=\"3\" style=\"vertical-align:bottom;border-bottom:2px solid #000000;padding-left:2px;padding-top:2px;padding-bottom:2px;padding-right:2px;\"><div style=\"text-align:center;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;font-weight:bold;\">2020</span></div></td><td colspan=\"3\" style=\"vertical-align:bottom;border-bottom:2px solid #000000;padding-left:2px;padding-top:2px;padding-bottom:2px;padding-right:2px;\"><div style=\"text-align:center;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;\">2019</span></div></td><td colspan=\"3\" style=\"vertical-align:bottom;border-bottom:2px solid #000000;padding-left:2px;padding-top:2px;padding-bottom:2px;padding-right:2px;\"><div style=\"text-align:center;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;font-weight:bold;\">2020</span></div></td><td colspan=\"3\" style=\"vertical-align:bottom;border-bottom:2px solid #000000;padding-left:2px;padding-top:2px;padding-bottom:2px;padding-right:2px;\"><div style=\"text-align:center;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;\">2019</span></div></td></tr><tr><td style=\"vertical-align:middle;background-color:#cceeff;padding-left:2px;padding-top:2px;padding-bottom:2px;padding-right:2px;border-top:2px solid #000000;\"><div style=\"text-align:left;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;\">Net income</span></div></td><td style=\"vertical-align:bottom;padding-left:2px;padding-top:2px;padding-bottom:2px;background-color:#cceeff;border-top:2px solid #000000;\"><div style=\"text-align:left;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;font-weight:bold;\">$</span></div></td><td style=\"vertical-align:bottom;background-color:#cceeff;padding-top:2px;padding-bottom:2px;border-top:2px solid #000000;\"><div style=\"text-align:right;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;font-weight:bold;\"><span><ix:nonfraction id=\"d742040e880-wk-Fact-3D6A129E4F7E11A9FC47268C162C2C40\" name=\"us-gaap:NetIncomeLoss\" contextref=\"FD2020Q3QTD\" unitref=\"iso4217_CAD\" decimals=\"-6\" scale=\"6\" format=\"ixt:numdotdecimal\">598</ix:nonfraction></span></span></div></td><td style=\"vertical-align:bottom;background-color:#cceeff;border-top:2px solid #000000;\"><div style=\"text-align:left;font-size:10pt;\"><span style=\"font-family:inherit;font-size:10pt;\"><br></span></div></td><td style=\"vertical-align:bottom;padding-left:2px;padding-top:2px;padding-bottom:2px;background-color:#cceeff;border-top:2px solid #000000;\"><div style=\"text-align:left;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;\">$</span></div></td><td style=\"vertical-align:bottom;background-color:#cceeff;padding-top:2px;padding-bottom:2px;border-top:2px solid #000000;\"><div style=\"text-align:right;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;\"><span><ix:nonfraction id=\"d742040e894-wk-Fact-CB1B6FD42F35C73D08B9268C164DF9E2\" name=\"us-gaap:NetIncomeLoss\" contextref=\"FD2019Q3QTD\" unitref=\"iso4217_CAD\" decimals=\"-6\" scale=\"6\" format=\"ixt:numdotdecimal\">618</ix:nonfraction></span></span></div></td><td style=\"vertical-align:bottom;background-color:#cceeff;border-top:2px solid #000000;\"><div style=\"text-align:left;font-size:10pt;\"><span style=\"font-family:inherit;font-size:10pt;\"><br></span></div></td><td style=\"vertical-align:bottom;padding-left:2px;padding-top:2px;padding-bottom:2px;background-color:#cceeff;border-top:2px solid #000000;\"><div style=\"text-align:left;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;font-weight:bold;\">$</span></div></td><td style=\"vertical-align:bottom;background-color:#cceeff;padding-top:2px;padding-bottom:2px;border-top:2px solid #000000;\"><div style=\"text-align:right;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;font-weight:bold;\"><span><ix:nonfraction id=\"d742040e908-wk-Fact-760561D0617E2A60BA84268C16359E69\" name=\"us-gaap:NetIncomeLoss\" contextref=\"FD2020Q3YTD\" unitref=\"iso4217_CAD\" decimals=\"-6\" scale=\"6\" format=\"ixt:numdotdecimal\">1,642</ix:nonfraction></span></span></div></td><td style=\"vertical-align:bottom;background-color:#cceeff;border-top:2px solid #000000;\"><div style=\"text-align:left;font-size:10pt;\"><span style=\"font-family:inherit;font-size:10pt;\"><br></span></div></td><td style=\"vertical-align:bottom;padding-left:2px;padding-top:2px;padding-bottom:2px;background-color:#cceeff;\"><div style=\"text-align:left;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;\">$</span></div></td><td style=\"vertical-align:bottom;background-color:#cceeff;padding-top:2px;padding-bottom:2px;\"><div style=\"text-align:right;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;\"><span><ix:nonfraction id=\"d742040e923-wk-Fact-96A31269D8012275E97E268C1690257F\" name=\"us-gaap:NetIncomeLoss\" contextref=\"FD2019Q3YTD\" unitref=\"iso4217_CAD\" decimals=\"-6\" scale=\"6\" format=\"ixt:numdotdecimal\">1,776</ix:nonfraction></span></span></div></td><td style=\"vertical-align:bottom;background-color:#cceeff;\"><div style=\"text-align:left;font-size:10pt;\"><span style=\"font-family:inherit;font-size:10pt;\"><br></span></div></td></tr><tr><td style=\"vertical-align:middle;padding-left:12px;padding-top:2px;padding-bottom:2px;padding-right:2px;\"><div style=\"text-align:left;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;\">Net gain (loss) in foreign currency translation adjustments, net of hedging activities</span></div></td><td colspan=\"2\" style=\"vertical-align:bottom;padding-left:2px;padding-top:2px;padding-bottom:2px;\"><div style=\"text-align:right;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;font-weight:bold;\"><span><ix:nonfraction id=\"d742040e938-wk-Fact-8EF64D4AB5FB77A51497268C162CC754\" name=\"us-gaap:OtherComprehensiveIncomeForeignCurrencyTransactionAndTranslationAdjustmentBeforeTaxPortionAttributableToParent\" contextref=\"FD2020Q3QTD\" unitref=\"iso4217_CAD\" decimals=\"-6\" scale=\"6\" format=\"ixt:numdotdecimal\">16</ix:nonfraction></span></span></div></td><td style=\"vertical-align:bottom;\"><div style=\"text-align:left;font-size:10pt;\"><span style=\"font-family:inherit;font-size:10pt;\"><br></span></div></td><td colspan=\"2\" style=\"vertical-align:bottom;padding-left:2px;padding-top:2px;padding-bottom:2px;\"><div style=\"text-align:right;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;\"><span>(<ix:nonfraction id=\"d742040e952-wk-Fact-82F9DCF8DF77557A88DE268C162CD183\" name=\"us-gaap:OtherComprehensiveIncomeForeignCurrencyTransactionAndTranslationAdjustmentBeforeTaxPortionAttributableToParent\" contextref=\"FD2019Q3QTD\" unitref=\"iso4217_CAD\" decimals=\"-6\" scale=\"6\" sign=\"-\" format=\"ixt:numdotdecimal\">8</ix:nonfraction></span></span></div></td><td style=\"vertical-align:bottom;padding-right:2px;padding-top:2px;padding-bottom:2px;\"><div style=\"text-align:left;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;\">)</span></div></td><td colspan=\"2\" style=\"vertical-align:bottom;padding-left:2px;padding-top:2px;padding-bottom:2px;\"><div style=\"text-align:right;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;font-weight:bold;\"><span>(<ix:nonfraction id=\"d742040e967-wk-Fact-11E0588AB5CB01AF405B268C1635A90A\" name=\"us-gaap:OtherComprehensiveIncomeForeignCurrencyTransactionAndTranslationAdjustmentBeforeTaxPortionAttributableToParent\" contextref=\"FD2020Q3YTD\" unitref=\"iso4217_CAD\" decimals=\"-6\" scale=\"6\" sign=\"-\" format=\"ixt:numdotdecimal\">18</ix:nonfraction></span></span></div></td><td style=\"vertical-align:bottom;padding-right:2px;padding-top:2px;padding-bottom:2px;\"><div style=\"text-align:left;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;font-weight:bold;\">)</span></div></td><td colspan=\"2\" style=\"vertical-align:bottom;padding-left:2px;padding-top:2px;padding-bottom:2px;\"><div style=\"text-align:right;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;\"><span><ix:nonfraction id=\"d742040e982-wk-Fact-4F15035DBBF815457F23268C16230BF7\" name=\"us-gaap:OtherComprehensiveIncomeForeignCurrencyTransactionAndTranslationAdjustmentBeforeTaxPortionAttributableToParent\" contextref=\"FD2019Q3YTD\" unitref=\"iso4217_CAD\" decimals=\"-6\" scale=\"6\" format=\"ixt:numdotdecimal\">23</ix:nonfraction></span></span></div></td><td style=\"vertical-align:bottom;\"><div style=\"text-align:left;font-size:10pt;\"><span style=\"font-family:inherit;font-size:10pt;\"><br></span></div></td></tr><tr><td style=\"vertical-align:middle;background-color:#cceeff;padding-left:12px;padding-top:2px;padding-bottom:2px;padding-right:2px;\"><div style=\"text-align:left;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;\">Change in derivatives designated as cash flow hedges</span></div></td><td colspan=\"2\" style=\"vertical-align:bottom;background-color:#cceeff;padding-left:2px;padding-top:2px;padding-bottom:2px;\"><div style=\"text-align:right;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;font-weight:bold;\"><span><ix:nonfraction id=\"d742040e1003-wk-Fact-C4BDAD8CAFC63B5D2BE8268C162C8C18\" name=\"us-gaap:OtherComprehensiveIncomeLossCashFlowHedgeGainLossAfterReclassificationBeforeTax\" contextref=\"FD2020Q3QTD\" unitref=\"iso4217_CAD\" decimals=\"-6\" scale=\"6\" format=\"ixt:numdotdecimal\">3</ix:nonfraction></span></span></div></td><td style=\"vertical-align:bottom;background-color:#cceeff;\"><div style=\"text-align:left;font-size:10pt;\"><span style=\"font-family:inherit;font-size:10pt;\"><br></span></div></td><td colspan=\"2\" style=\"vertical-align:bottom;background-color:#cceeff;padding-left:2px;padding-top:2px;padding-bottom:2px;\"><div style=\"text-align:right;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;\"><span><ix:nonfraction id=\"d742040e1017-wk-Fact-AEDCEDD9EDBC1058BD93268C1635E834\" name=\"us-gaap:OtherComprehensiveIncomeLossCashFlowHedgeGainLossAfterReclassificationBeforeTax\" contextref=\"FD2019Q3QTD\" unitref=\"iso4217_CAD\" decimals=\"-6\" scale=\"6\" format=\"ixt:numdotdecimal\">2</ix:nonfraction></span></span></div></td><td style=\"vertical-align:bottom;background-color:#cceeff;\"><div style=\"text-align:left;font-size:10pt;\"><span style=\"font-family:inherit;font-size:10pt;\"><br></span></div></td><td colspan=\"2\" style=\"vertical-align:bottom;background-color:#cceeff;padding-left:2px;padding-top:2px;padding-bottom:2px;\"><div style=\"text-align:right;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;font-weight:bold;\"><span><ix:nonfraction id=\"d742040e1031-wk-Fact-3CBC8509783503A8ED8F268C1661B275\" name=\"us-gaap:OtherComprehensiveIncomeLossCashFlowHedgeGainLossAfterReclassificationBeforeTax\" contextref=\"FD2020Q3YTD\" unitref=\"iso4217_CAD\" decimals=\"-6\" scale=\"6\" format=\"ixt:numdotdecimal\">6</ix:nonfraction></span></span></div></td><td style=\"vertical-align:bottom;background-color:#cceeff;\"><div style=\"text-align:left;font-size:10pt;\"><span style=\"font-family:inherit;font-size:10pt;\"><br></span></div></td><td colspan=\"2\" style=\"vertical-align:bottom;background-color:#cceeff;padding-left:2px;padding-top:2px;padding-bottom:2px;\"><div style=\"text-align:right;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;\"><span><ix:nonfraction id=\"d742040e1045-wk-Fact-0E1C8BCC58832708BB1B268C168324E2\" name=\"us-gaap:OtherComprehensiveIncomeLossCashFlowHedgeGainLossAfterReclassificationBeforeTax\" contextref=\"FD2019Q3YTD\" unitref=\"iso4217_CAD\" decimals=\"-6\" scale=\"6\" format=\"ixt:numdotdecimal\">8</ix:nonfraction></span></span></div></td><td style=\"vertical-align:bottom;background-color:#cceeff;\"><div style=\"text-align:left;font-size:10pt;\"><span style=\"font-family:inherit;font-size:10pt;\"><br></span></div></td></tr><tr><td style=\"vertical-align:middle;border-bottom:1px solid #000000;padding-left:12px;padding-top:2px;padding-bottom:2px;padding-right:2px;\"><div style=\"text-align:left;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;\">Change in pension and post-retirement defined benefit plans</span></div></td><td colspan=\"2\" style=\"vertical-align:bottom;border-bottom:1px solid #000000;padding-left:2px;padding-top:2px;padding-bottom:2px;\"><div style=\"text-align:right;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;font-weight:bold;\"><span><ix:nonfraction id=\"d742040e1066-wk-Fact-C6EB30760AAEB1B1BA11268C163D70B0\" name=\"us-gaap:OtherComprehensiveIncomeDefinedBenefitPlansAdjustmentBeforeTaxPortionAttributableToParent\" contextref=\"FD2020Q3QTD\" unitref=\"iso4217_CAD\" decimals=\"-6\" scale=\"6\" sign=\"-\" format=\"ixt:numdotdecimal\">44</ix:nonfraction></span></span></div></td><td style=\"vertical-align:bottom;border-bottom:1px solid #000000;\"><div style=\"text-align:left;font-size:10pt;\"><span style=\"font-family:inherit;font-size:10pt;\"><br></span></div></td><td colspan=\"2\" style=\"vertical-align:bottom;border-bottom:1px solid #000000;padding-left:2px;padding-top:2px;padding-bottom:2px;\"><div style=\"text-align:right;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;\"><span><ix:nonfraction id=\"d742040e1080-wk-Fact-B4FCF227E9E8F55F0DB4268C162CA9FD\" name=\"us-gaap:OtherComprehensiveIncomeDefinedBenefitPlansAdjustmentBeforeTaxPortionAttributableToParent\" contextref=\"FD2019Q3QTD\" unitref=\"iso4217_CAD\" decimals=\"-6\" scale=\"6\" sign=\"-\" format=\"ixt:numdotdecimal\">20</ix:nonfraction></span></span></div></td><td style=\"vertical-align:bottom;border-bottom:1px solid #000000;\"><div style=\"text-align:left;font-size:10pt;\"><span style=\"font-family:inherit;font-size:10pt;\"><br></span></div></td><td colspan=\"2\" style=\"vertical-align:bottom;border-bottom:1px solid #000000;padding-left:2px;padding-top:2px;padding-bottom:2px;\"><div style=\"text-align:right;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;font-weight:bold;\"><span><ix:nonfraction id=\"d742040e1094-wk-Fact-FDFE3DB054D34AD61F15268C165548D9\" name=\"us-gaap:OtherComprehensiveIncomeDefinedBenefitPlansAdjustmentBeforeTaxPortionAttributableToParent\" contextref=\"FD2020Q3YTD\" unitref=\"iso4217_CAD\" decimals=\"-6\" scale=\"6\" sign=\"-\" format=\"ixt:numdotdecimal\">134</ix:nonfraction></span></span></div></td><td style=\"vertical-align:bottom;border-bottom:1px solid #000000;\"><div style=\"text-align:left;font-size:10pt;\"><span style=\"font-family:inherit;font-size:10pt;\"><br></span></div></td><td colspan=\"2\" style=\"vertical-align:bottom;border-bottom:1px solid #000000;padding-left:2px;padding-top:2px;padding-bottom:2px;\"><div style=\"text-align:right;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;\"><span><ix:nonfraction id=\"d742040e1108-wk-Fact-DCB2273CAB14365EA2C2268C162CD5FA\" name=\"us-gaap:OtherComprehensiveIncomeDefinedBenefitPlansAdjustmentBeforeTaxPortionAttributableToParent\" contextref=\"FD2019Q3YTD\" unitref=\"iso4217_CAD\" decimals=\"-6\" scale=\"6\" sign=\"-\" format=\"ixt:numdotdecimal\">61</ix:nonfraction></span></span></div></td><td style=\"vertical-align:bottom;border-bottom:1px solid #000000;\"><div style=\"text-align:left;font-size:10pt;\"><span style=\"font-family:inherit;font-size:10pt;\"><br></span></div></td></tr><tr><td style=\"vertical-align:middle;background-color:#cceeff;padding-left:2px;padding-top:2px;padding-bottom:2px;padding-right:2px;border-top:1px solid #000000;\"><div style=\"padding-bottom:6px;padding-top:6px;text-align:left;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;\">Other comprehensive income before income taxes</span></div></td><td colspan=\"2\" style=\"vertical-align:bottom;background-color:#cceeff;padding-left:2px;padding-top:2px;padding-bottom:2px;\"><div style=\"text-align:right;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;font-weight:bold;\"><span><ix:nonfraction id=\"d742040e1129-wk-Fact-E719CB56721AF5D2E444268C169025FB\" name=\"us-gaap:OtherComprehensiveIncomeLossBeforeTaxPortionAttributableToParent\" contextref=\"FD2020Q3QTD\" unitref=\"iso4217_CAD\" decimals=\"-6\" scale=\"6\" format=\"ixt:numdotdecimal\">63</ix:nonfraction></span></span></div></td><td style=\"vertical-align:bottom;background-color:#cceeff;\"><div style=\"text-align:left;font-size:10pt;\"><span style=\"font-family:inherit;font-size:10pt;\"><br></span></div></td><td colspan=\"2\" style=\"vertical-align:bottom;background-color:#cceeff;padding-left:2px;padding-top:2px;padding-bottom:2px;border-top:1px solid #000000;\"><div style=\"text-align:right;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;\"><span><ix:nonfraction id=\"d742040e1143-wk-Fact-D70B0E5857C2E151A05D268C162C20F5\" name=\"us-gaap:OtherComprehensiveIncomeLossBeforeTaxPortionAttributableToParent\" contextref=\"FD2019Q3QTD\" unitref=\"iso4217_CAD\" decimals=\"-6\" scale=\"6\" format=\"ixt:numdotdecimal\">14</ix:nonfraction></span></span></div></td><td style=\"vertical-align:bottom;background-color:#cceeff;border-top:1px solid #000000;\"><div style=\"text-align:left;font-size:10pt;\"><span style=\"font-family:inherit;font-size:10pt;\"><br></span></div></td><td colspan=\"2\" style=\"vertical-align:bottom;background-color:#cceeff;padding-left:2px;padding-top:2px;padding-bottom:2px;\"><div style=\"text-align:right;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;font-weight:bold;\"><span><ix:nonfraction id=\"d742040e1157-wk-Fact-6CB231651BEF5D665250268C1623A861\" name=\"us-gaap:OtherComprehensiveIncomeLossBeforeTaxPortionAttributableToParent\" contextref=\"FD2020Q3YTD\" unitref=\"iso4217_CAD\" decimals=\"-6\" scale=\"6\" format=\"ixt:numdotdecimal\">122</ix:nonfraction></span></span></div></td><td style=\"vertical-align:bottom;background-color:#cceeff;\"><div style=\"text-align:left;font-size:10pt;\"><span style=\"font-family:inherit;font-size:10pt;\"><br></span></div></td><td colspan=\"2\" style=\"vertical-align:bottom;background-color:#cceeff;padding-left:2px;padding-top:2px;padding-bottom:2px;border-top:1px solid #000000;\"><div style=\"text-align:right;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;\"><span><ix:nonfraction id=\"d742040e1171-wk-Fact-4D31160244DEB4CA615A268C163DFF56\" name=\"us-gaap:OtherComprehensiveIncomeLossBeforeTaxPortionAttributableToParent\" contextref=\"FD2019Q3YTD\" unitref=\"iso4217_CAD\" decimals=\"-6\" scale=\"6\" format=\"ixt:numdotdecimal\">92</ix:nonfraction></span></span></div></td><td style=\"vertical-align:bottom;background-color:#cceeff;border-top:1px solid #000000;\"><div style=\"text-align:left;font-size:10pt;\"><span style=\"font-family:inherit;font-size:10pt;\"><br></span></div></td></tr><tr><td style=\"vertical-align:middle;padding-left:2px;padding-top:2px;padding-bottom:2px;padding-right:2px;\"><div style=\"text-align:left;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;\">Income tax (expense) recovery on above items</span></div></td><td colspan=\"2\" style=\"vertical-align:bottom;border-bottom:1px solid #000000;padding-left:2px;padding-top:2px;padding-bottom:2px;\"><div style=\"text-align:right;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;font-weight:bold;\"><span>(<ix:nonfraction id=\"d742040e1192-wk-Fact-7E556294B8C7A840EB0E268C163D9789\" name=\"us-gaap:OtherComprehensiveIncomeLossTaxPortionAttributableToParent1\" contextref=\"FD2020Q3QTD\" unitref=\"iso4217_CAD\" decimals=\"-6\" scale=\"6\" format=\"ixt:numdotdecimal\">29</ix:nonfraction></span></span></div></td><td style=\"vertical-align:bottom;border-bottom:1px solid #000000;padding-right:2px;padding-top:2px;padding-bottom:2px;\"><div style=\"text-align:left;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;font-weight:bold;\">)</span></div></td><td colspan=\"2\" style=\"vertical-align:bottom;border-bottom:1px solid #000000;padding-left:2px;padding-top:2px;padding-bottom:2px;\"><div style=\"text-align:right;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;\"><span><ix:nonfraction id=\"d742040e1207-wk-Fact-BCB65CB569842B29F18F268C16834193\" name=\"us-gaap:OtherComprehensiveIncomeLossTaxPortionAttributableToParent1\" contextref=\"FD2019Q3QTD\" unitref=\"iso4217_CAD\" decimals=\"-6\" scale=\"6\" sign=\"-\" format=\"ixt:numdotdecimal\">3</ix:nonfraction></span></span></div></td><td style=\"vertical-align:bottom;border-bottom:1px solid #000000;\"><div style=\"text-align:left;font-size:10pt;\"><span style=\"font-family:inherit;font-size:10pt;\"><br></span></div></td><td colspan=\"2\" style=\"vertical-align:bottom;border-bottom:1px solid #000000;padding-left:2px;padding-top:2px;padding-bottom:2px;\"><div style=\"text-align:right;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;font-weight:bold;\"><span>(<ix:nonfraction id=\"d742040e1221-wk-Fact-2B94915C76EFABCE83C9268C1683EEDE\" name=\"us-gaap:OtherComprehensiveIncomeLossTaxPortionAttributableToParent1\" contextref=\"FD2020Q3YTD\" unitref=\"iso4217_CAD\" decimals=\"-6\" scale=\"6\" format=\"ixt:numdotdecimal\">16</ix:nonfraction></span></span></div></td><td style=\"vertical-align:bottom;border-bottom:1px solid #000000;padding-right:2px;padding-top:2px;padding-bottom:2px;\"><div style=\"text-align:left;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;font-weight:bold;\">)</span></div></td><td colspan=\"2\" style=\"vertical-align:bottom;border-bottom:1px solid #000000;padding-left:2px;padding-top:2px;padding-bottom:2px;\"><div style=\"text-align:right;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;\"><span>(<ix:nonfraction id=\"d742040e1236-wk-Fact-2D003962DC30F86220AC268C165584F7\" name=\"us-gaap:OtherComprehensiveIncomeLossTaxPortionAttributableToParent1\" contextref=\"FD2019Q3YTD\" unitref=\"iso4217_CAD\" decimals=\"-6\" scale=\"6\" format=\"ixt:numdotdecimal\">41</ix:nonfraction></span></span></div></td><td style=\"vertical-align:bottom;border-bottom:1px solid #000000;padding-right:2px;padding-top:2px;padding-bottom:2px;\"><div style=\"text-align:left;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;\">)</span></div></td></tr><tr><td style=\"vertical-align:middle;border-bottom:1px solid #000000;background-color:#cceeff;padding-left:2px;padding-top:2px;padding-bottom:2px;padding-right:2px;border-top:1px solid #000000;\"><div style=\"text-align:left;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;\">Other comprehensive income (Note 7)</span></div></td><td colspan=\"2\" style=\"vertical-align:bottom;border-bottom:1px solid #000000;background-color:#cceeff;padding-left:2px;padding-top:2px;padding-bottom:2px;\"><div style=\"text-align:right;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;font-weight:bold;\"><span><ix:nonfraction id=\"d742040e1258-wk-Fact-DF2AB8FCA3E589FE8EF4268C168EA36E\" name=\"us-gaap:OtherComprehensiveIncomeLossNetOfTaxPortionAttributableToParent\" contextref=\"FD2020Q3QTD\" unitref=\"iso4217_CAD\" decimals=\"-6\" scale=\"6\" format=\"ixt:numdotdecimal\">34</ix:nonfraction></span></span></div></td><td style=\"vertical-align:bottom;border-bottom:1px solid #000000;background-color:#cceeff;\"><div style=\"text-align:left;font-size:10pt;\"><span style=\"font-family:inherit;font-size:10pt;\"><br></span></div></td><td colspan=\"2\" style=\"vertical-align:bottom;border-bottom:1px solid #000000;background-color:#cceeff;padding-left:2px;padding-top:2px;padding-bottom:2px;border-top:1px solid #000000;\"><div style=\"text-align:right;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;\"><span><ix:nonfraction id=\"d742040e1272-wk-Fact-4293B31D3A08549A2528268C16900287\" name=\"us-gaap:OtherComprehensiveIncomeLossNetOfTaxPortionAttributableToParent\" contextref=\"FD2019Q3QTD\" unitref=\"iso4217_CAD\" decimals=\"-6\" scale=\"6\" format=\"ixt:numdotdecimal\">17</ix:nonfraction></span></span></div></td><td style=\"vertical-align:bottom;border-bottom:1px solid #000000;background-color:#cceeff;border-top:1px solid #000000;\"><div style=\"text-align:left;font-size:10pt;\"><span style=\"font-family:inherit;font-size:10pt;\"><br></span></div></td><td colspan=\"2\" style=\"vertical-align:bottom;border-bottom:1px solid #000000;background-color:#cceeff;padding-left:2px;padding-top:2px;padding-bottom:2px;\"><div style=\"text-align:right;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;font-weight:bold;\"><span><ix:nonfraction id=\"d742040e1286-wk-Fact-D988D7EF662E68EDC196268C1690CAE4\" name=\"us-gaap:OtherComprehensiveIncomeLossNetOfTaxPortionAttributableToParent\" contextref=\"FD2020Q3YTD\" unitref=\"iso4217_CAD\" decimals=\"-6\" scale=\"6\" format=\"ixt:numdotdecimal\">106</ix:nonfraction></span></span></div></td><td style=\"vertical-align:bottom;border-bottom:1px solid #000000;background-color:#cceeff;\"><div style=\"text-align:left;font-size:10pt;\"><span style=\"font-family:inherit;font-size:10pt;\"><br></span></div></td><td colspan=\"2\" style=\"vertical-align:bottom;border-bottom:1px solid #000000;background-color:#cceeff;padding-left:2px;padding-top:2px;padding-bottom:2px;border-top:1px solid #000000;\"><div style=\"text-align:right;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;\"><span><ix:nonfraction id=\"d742040e1300-wk-Fact-5FF006F7DC9770AB9B0F268C16832980\" name=\"us-gaap:OtherComprehensiveIncomeLossNetOfTaxPortionAttributableToParent\" contextref=\"FD2019Q3YTD\" unitref=\"iso4217_CAD\" decimals=\"-6\" scale=\"6\" format=\"ixt:numdotdecimal\">51</ix:nonfraction></span></span></div></td><td style=\"vertical-align:bottom;border-bottom:1px solid #000000;background-color:#cceeff;border-top:1px solid #000000;\"><div style=\"text-align:left;font-size:10pt;\"><span style=\"font-family:inherit;font-size:10pt;\"><br></span></div></td></tr><tr><td style=\"vertical-align:middle;border-bottom:2px solid #000000;padding-left:2px;padding-top:2px;padding-bottom:2px;padding-right:2px;\"><div style=\"text-align:left;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;font-weight:bold;\">Comprehensive income</span></div></td><td style=\"vertical-align:bottom;border-bottom:2px solid #000000;padding-left:2px;padding-top:2px;padding-bottom:2px;\"><div style=\"text-align:left;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;font-weight:bold;\">$</span></div></td><td style=\"vertical-align:bottom;border-bottom:2px solid #000000;padding-top:2px;padding-bottom:2px;\"><div style=\"text-align:right;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;font-weight:bold;\"><span><ix:nonfraction id=\"d742040e1326-wk-Fact-6DA351E677DC0D5FD391268C168B94DB\" name=\"us-gaap:ComprehensiveIncomeNetOfTax\" contextref=\"FD2020Q3QTD\" unitref=\"iso4217_CAD\" decimals=\"-6\" scale=\"6\" format=\"ixt:numdotdecimal\">632</ix:nonfraction></span></span></div></td><td style=\"vertical-align:bottom;border-bottom:2px solid #000000;\"><div style=\"text-align:left;font-size:10pt;\"><span style=\"font-family:inherit;font-size:10pt;\"><br></span></div></td><td style=\"vertical-align:bottom;border-bottom:2px solid #000000;padding-left:2px;padding-top:2px;padding-bottom:2px;border-top:1px solid #000000;\"><div style=\"text-align:left;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;\">$</span></div></td><td style=\"vertical-align:bottom;border-bottom:2px solid #000000;padding-top:2px;padding-bottom:2px;border-top:1px solid #000000;\"><div style=\"text-align:right;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;\"><span><ix:nonfraction id=\"d742040e1340-wk-Fact-0A433E51D426926BCEE0268C1635D4C8\" name=\"us-gaap:ComprehensiveIncomeNetOfTax\" contextref=\"FD2019Q3QTD\" unitref=\"iso4217_CAD\" decimals=\"-6\" scale=\"6\" format=\"ixt:numdotdecimal\">635</ix:nonfraction></span></span></div></td><td style=\"vertical-align:bottom;border-bottom:2px solid #000000;border-top:1px solid #000000;\"><div style=\"text-align:left;font-size:10pt;\"><span style=\"font-family:inherit;font-size:10pt;\"><br></span></div></td><td style=\"vertical-align:bottom;border-bottom:2px solid #000000;padding-left:2px;padding-top:2px;padding-bottom:2px;\"><div style=\"text-align:left;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;font-weight:bold;\">$</span></div></td><td style=\"vertical-align:bottom;border-bottom:2px solid #000000;padding-top:2px;padding-bottom:2px;\"><div style=\"text-align:right;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;font-weight:bold;\"><span><ix:nonfraction id=\"d742040e1354-wk-Fact-D8DE420A377FDC9771FD268C1690DDF9\" name=\"us-gaap:ComprehensiveIncomeNetOfTax\" contextref=\"FD2020Q3YTD\" unitref=\"iso4217_CAD\" decimals=\"-6\" scale=\"6\" format=\"ixt:numdotdecimal\">1,748</ix:nonfraction></span></span></div></td><td style=\"vertical-align:bottom;border-bottom:2px solid #000000;\"><div style=\"text-align:left;font-size:10pt;\"><span style=\"font-family:inherit;font-size:10pt;\"><br></span></div></td><td style=\"vertical-align:bottom;border-bottom:2px solid #000000;padding-left:2px;padding-top:2px;padding-bottom:2px;border-top:1px solid #000000;\"><div style=\"text-align:left;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;\">$</span></div></td><td style=\"vertical-align:bottom;border-bottom:2px solid #000000;padding-top:2px;padding-bottom:2px;border-top:1px solid #000000;\"><div style=\"text-align:right;font-size:9pt;\"><span style=\"font-family:Arial;font-size:9pt;\"><span><ix:nonfraction id=\"d742040e1369-wk-Fact-3000F1CD0547D05A4C25268C162CA6F5\" name=\"us-gaap:ComprehensiveIncomeNetOfTax\" contextref=\"FD2019Q3YTD\" unitref=\"iso4217_CAD\" decimals=\"-6\" scale=\"6\" format=\"ixt:numdotdecimal\">1,827</ix:nonfraction></span></span></div></td><td style=\"vertical-align:bottom;border-bottom:2px solid #000000;border-top:1px solid #000000;\"><div style=\"text-align:left;font-size:10pt;\"><span style=\"font-family:inherit;font-size:10pt;\"><br></span></div></td></tr></tbody></table>\n",
        "\"\"\"\n"
      ],
      "metadata": {
        "id": "uJzrkoXfXVWh"
      },
      "execution_count": 156,
      "outputs": []
    }
  ],
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      "include_colab_link": true
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    "kernelspec": {
      "display_name": "Python 3",
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    "language_info": {
      "codemirror_mode": {
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      "file_extension": ".py",
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      "pygments_lexer": "ipython3",
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