Calling: load - metrics=["total_revenue", "cost_of_revenue", "gross_profit", "research_and_development", "selling_general_and_administrative", "operating_expenses", "operating_income", "net_income"], start_date="2024-01-01", tickers=["MSFT"], end_date="2026-06-12" DataFrame 224 OK msft_fundamentals (11 rows)

Calling: compute - dataframe_ids=[224], code="df = df_224\nannual_df = df[df['period_type'] == 'annual'].sort_values('date')\nquarterly_df = df[df['period_type'] == 'quarterly'].sort_values('date')\nprint(\"Annual:\\n\", annual_df[['date', 'total_revenue', 'operating_income', 'net_income']])\nprint(\"Quarterly:\\n\", quarterly_df[['date', 'total_revenue', 'operating_income', 'net_income']])\nresult = annual_df\n" DataFrame 225 OK result_225 (17 rows)

Calling: compute - code="df = df_224.sort_values(['period_type', 'date'])\nresult = df\n", dataframe_ids=[224] DataFrame 226 OK result_226 (11 rows)

Calling: compute - code="df = df_224.sort_values('date')\nresult = df\nprint(df[['date', 'period_type', 'total_revenue']])\n", dataframe_ids=[224] DataFrame 227 OK result_227 (23 rows)

Calling: compute - dataframe_ids=[224], code="print(df_224.to_string())\nresult = df_224\n" DataFrame 228 OK result_228 (23 rows)

Calling: compute - dataframe_ids=[224], code="import pandas as pd\npd.set_option('display.max_rows', 100)\npd.set_option('display.max_columns', 20)\nprint(df_224.sort_values(['period_type', 'date']).to_string())\nresult = df_224\n" DataFrame 229 OK result_229 (23 rows)

Calling: compute - dataframe_ids=[224], code="import pandas as pd\ndf = df_224\ndates = df.sort_values('date')[['date', 'period_type']].drop_duplicates()\nresult = dates\n" DataFrame 230 OK result_230 (11 rows)

Calling: compute - dataframe_ids=[230], code="result = df_230.tail(2)\n" DataFrame 231 OK result_231 (2 rows)

Calling: compute - code="import pandas as pd\ndf = df_224\n\n# Let's check what rows we have for quarterly\nq_df = df[df['period_type'] == 'quarterly'].copy()\nq_df['date'] = pd.to_datetime(q_df['date'])\nq_df = q_df.sort_values('date')\n\n# Let's print out the quarters to verify\nprint(q_df[['date', 'total_revenue', 'cost_of_revenue', 'gross_profit', 'research_and_development', 'selling_general_and_administrative', 'operating_income', 'net_income']])\n\n# Let's calculate TTM ended March 31, 2026\nttm_dates = ['2025-06-30', '2025-09-30', '2025-12-31', '2026-03-31']\nttm_df = q_df[q_df['date'].isin(ttm_dates)]\nprint(f\"Number of quarters for TTM: {len(ttm_df)}\")\n\n# Let's calculate 9M ended March 31, 2026 (FY2026 Q1-Q3)\nfy26_9m_dates = ['2025-09-30', '2025-12-31', '2026-03-31']\nfy26_9m_df = q_df[q_df['date'].isin(fy26_9m_dates)]\n\n# Let's calculate 9M ended March 31, 2025 (FY2025 Q1-Q3)\nfy25_9m_dates = ['2024-09-30', '2024-12-31', '2025-03-31']\nfy25_9m_df = q_df[q_df['date'].isin(fy25_9m_dates)]\n\n# Create a summary of these\nsummary = pd.DataFrame()\nsummary['Metric'] = ['total_revenue', 'cost_of_revenue', 'gross_profit', 'research_and_development', 'selling_general_and_administrative', 'operating_income', 'net_income']\n\n# Compute sums\nsummary['TTM_March_2026'] = [ttm_df[m].sum() for m in summary['Metric']]\nsummary['9M_FY2026'] = [fy26_9m_df[m].sum() for m in summary['Metric']]\nsummary['9M_FY2025'] = [fy25_9m_df[m].sum() for m in summary['Metric']]\n\n# Also get FY2025 (Full Year) from annual rows\nfy25_annual = df[(df['period_type'] == 'annual') & (df['date'] == '2025-06-30')]\nif not fy25_annual.empty:\n summary['FY2025_Annual'] = [fy25_annual[m].values[0] for m in summary['Metric']]\nelse:\n summary['FY2025_Annual'] = 0\n\nresult = summary\n", dataframe_ids=[224] DataFrame 232 OK result_232 (20 rows)

Calling: compute - dataframe_ids=[232], code="print(df_232.to_string())\nresult = df_232\n" Error: Python error: ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/pandas/io/parsers/c_parser_wrapper.py", line 215, in read chunks = self._reader.read_low_memory(nrows) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "pandas/_libs/parsers.pyx", line 832, in pandas._libs.parsers.TextReader.read_low_memory File "pandas/_libs/parsers.pyx", line 897, in pandas._libs.parsers.TextReader._read_rows File "pandas/_libs/parsers.pyx", line 868, in pandas._libs.parsers.TextReader._tokenize_rows File "pandas/_libs/parsers.pyx", line 885, in pandas._libs.parsers.TextReader._check_tokenize_status File "pandas/_libs/parsers.pyx", line 2084, in pandas._libs.parsers.raise_parser_error pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 14, saw 5

Calling: compute - code="import pandas as pd\ndf = df_224.copy()\ndf['date'] = pd.to_datetime(df['date'])\n\nq_df = df[df['period_type'] == 'quarterly'].sort_values('date')\n\n# Periods\nttm_dates = ['2025-06-30', '2025-09-30', '2025-12-31', '2026-03-31']\nfy26_9m_dates = ['2025-09-30', '2025-12-31', '2026-03-31']\nfy25_9m_dates = ['2024-09-30', '2024-12-31', '2025-03-31']\n\n# Dataframes\nttm_q = q_df[q_df['date'].isin(pd.to_datetime(ttm_dates))]\nfy26_9m_q = q_df[q_df['date'].isin(pd.to_datetime(fy26_9m_dates))]\nfy25_9m_q = q_df[q_df['date'].isin(pd.to_datetime(fy25_9m_dates))]\n\nmetrics = ['total_revenue', 'cost_of_revenue', 'gross_profit', 'research_and_development', 'selling_general_and_administrative', 'operating_expenses', 'operating_income', 'net_income']\n\nsummary = pd.DataFrame({'Metric': metrics})\nsummary['TTM_March_2026'] = summary['Metric'].apply(lambda m: ttm_q[m].sum())\nsummary['9M_FY2026'] = summary['Metric'].apply(lambda m: fy26_9m_q[m].sum())\nsummary['9M_FY2025'] = summary['Metric'].apply(lambda m: fy25_9m_q[m].sum())\n\nfy25_annual = df[(df['period_type'] == 'annual') & (df['date'] == '2025-06-30')]\nif not fy25_annual.empty:\n summary['FY2025_Annual'] = summary['Metric'].apply(lambda m: fy25_annual[m].values[0])\nelse:\n summary['FY2025_Annual'] = 0.0\n\nresult = summary\n", dataframe_ids=[224] DataFrame 233 OK result_233 (8 rows)

Calling: compute - code="import pandas as pd\n\n# Let's create the Revenue to Net Income Bridge for 9M FY26 (in Millions)\nbridge_9m = pd.DataFrame([\n {\"Item\": \"Total Revenue\", \"Value\": 241832.0, \"Type\": \"Starting\"},\n {\"Item\": \"Cost of Revenue\", \"Value\": -76849.0, \"Type\": \"Expense\"},\n {\"Item\": \"Gross Profit\", \"Value\": 164983.0, \"Type\": \"Subtotal\"},\n {\"Item\": \"Research & Development (R&D)\", \"Value\": -25565.0, \"Type\": \"Expense\"},\n {\"Item\": \"Selling, General & Administrative\", \"Value\": -5669.0, \"Type\": \"Expense\"},\n {\"Item\": \"Other Operating Expenses\", \"Value\": -19115.0, \"Type\": \"Expense\"},\n {\"Item\": \"Operating Income\", \"Value\": 114634.0, \"Type\": \"Subtotal\"},\n {\"Item\": \"Interest, Taxes & Non-Operating (Net)\", \"Value\": -16651.0, \"Type\": \"Expense\"},\n {\"Item\": \"Net Income\", \"Value\": 97983.0, \"Type\": \"Final\"}\n])\n\n# Let's create the Operating Income YoY Bridge (9M FY25 to 9M FY26)\nyoy_bridge = pd.DataFrame([\n {\"Driver\": \"Operating Income (9M FY25)\", \"Impact\": 94205.0, \"Type\": \"Starting\"},\n {\"Driver\": \"Revenue Growth Impact\", \"Impact\": 36549.0, \"Type\": \"Positive\"},\n {\"Driver\": \"Cost of Revenue Increase\", \"Impact\": -13032.0, \"Type\": \"Negative\"},\n {\"Driver\": \"R&D Expense Increase\", \"Impact\": -1906.0, \"Type\": \"Negative\"},\n {\"Driver\": \"SG&A Expense Increase\", \"Impact\": -439.0, \"Type\": \"Negative\"},\n {\"Driver\": \"Other OpEx Increase\", \"Impact\": -743.0, \"Type\": \"Negative\"}, # (19115 - 18372) = 743\n {\"Driver\": \"Operating Income (9M FY26)\", \"Impact\": 114634.0, \"Type\": \"Final\"}\n])\n\n# Save these to display\n# We will return the first one as result, we can create another dataframe for the second one\nresult = bridge_9m\n", dataframe_ids=[233] DataFrame 234 OK result_234 (9 rows)

Calling: compute - dataframe_ids=[233], code="import pandas as pd\nyoy_bridge = pd.DataFrame([\n {\"Driver\": \"Operating Income (9M FY25)\", \"Impact\": 94205.0, \"Type\": \"Starting\"},\n {\"Driver\": \"Revenue Growth Impact\", \"Impact\": 36549.0, \"Type\": \"Positive\"},\n {\"Driver\": \"Cost of Revenue Increase\", \"Impact\": -13032.0, \"Type\": \"Negative\"},\n {\"Driver\": \"R&D Expense Increase\", \"Impact\": -1906.0, \"Type\": \"Negative\"},\n {\"Driver\": \"SG&A Expense Increase\", \"Impact\": -439.0, \"Type\": \"Negative\"},\n {\"Driver\": \"Other OpEx Increase\", \"Impact\": -743.0, \"Type\": \"Negative\"},\n {\"Driver\": \"Operating Income (9M FY26)\", \"Impact\": 114634.0, \"Type\": \"Final\"}\n])\nresult = yoy_bridge\n" DataFrame 235 OK result_235 (7 rows)

Calling: show - dataframe_id=234 Blob 98 DataFrame 234 OK result_234 (9 rows)

Calling: show - dataframe_id=235 Blob 99 DataFrame 235 OK result_235 (7 rows)

Calling: show_chart - title="Microsoft Operating Income YoY Bridge (9M FY25 to 9M FY26)", dataframe_id=235, x="Driver", chart_type="bar", series=["Impact"] Blob 100 DataFrame 235 OK result_235 (7 rows)