Calling: load - tickers=["MSFT"], end_date="2026-06-10", start_date="2018-01-01", metrics=["total_shareholder_equity", "total_assets", "total_liabilities"] DataFrame 219 OK msft_fundamentals (41 rows)

Calling: compute - name="msft_book_value_summary", dataframe_ids=[219], code="import pandas as pd\ndf = df_219.copy()\ndf['date'] = pd.to_datetime(df['date'])\n# annuals\nannuals = df[df['period_type'] == 'annual'].sort_values('date').copy()\n# latest quarterly if it is after the last annual\nlatest_q = df[df['period_type'] == 'quarterly'].sort_values('date').iloc[-1]\nlast_annual_date = annuals['date'].max()\nif latest_q['date'] > last_annual_date:\n # Append as latest\n latest_df = pd.DataFrame([latest_q])\n latest_df['period_type'] = 'quarterly (latest)'\n result = pd.concat([annuals, latest_df], ignore_index=True)\nelse:\n result = annuals\n\n# Let's extract year or date format\nresult['period'] = result.apply(lambda row: row['date'].strftime('%y') if 'annual' in row['period_type'] else row['date'].strftime('%y-%m (%b)'), axis=1)\nresult = result[['period', 'total_shareholder_equity', 'total_assets', 'total_liabilities']]\nresult = result.set_index('period').T\nresult = result.reset_index()\n" DataFrame 220 OK msft_book_value_summary (3 rows)

Calling: compute - dataframe_ids=[219], name="msft_book_value_formatted", code="import pandas as pd\ndf = df_219.copy()\ndf['date'] = pd.to_datetime(df['date'])\n\nannuals = df[df['period_type'] == 'annual'].sort_values('date').copy()\nlatest_q = df[df['period_type'] == 'quarterly'].sort_values('date').iloc[-1]\nlast_annual_date = annuals['date'].max()\n\nif latest_q['date'] > last_annual_date:\n latest_df = pd.DataFrame([latest_q])\n latest_df['period_label'] = 'Q3-26' # Since June is FY end, March 2026 is Q3-26\n annuals['period_label'] = annuals['date'].dt.strftime('FY%y')\n combined = pd.concat([annuals, latest_df], ignore_index=True)\nelse:\n annuals['period_label'] = annuals['date'].dt.strftime('FY%y')\n combined = annuals\n\n# Reorder/select columns\ncombined = combined[['period_label', 'total_shareholder_equity', 'total_assets', 'total_liabilities']]\n\n# Melt or Pivot to have periods as columns\ncombined_transposed = combined.set_index('period_label').T\n\n# Convert values to Billions\nfor col in combined_transposed.columns:\n combined_transposed[col] = combined_transposed[col] / 1e9\n\ncombined_transposed = combined_transposed.reset_index()\ncombined_transposed['index'] = combined_transposed['index'].map({\n 'total_shareholder_equity': 'Total Shareholder Equity (Book Value)',\n 'total_assets': 'Total Assets',\n 'total_liabilities': 'Total Liabilities'\n})\nresult = combined_transposed\n" DataFrame 221 OK msft_book_value_formatted (3 rows)

Calling: show - dataframe_id=221 Blob 96 DataFrame 221 OK msft_book_value_formatted (3 rows)

Calling: load - end_date="2026-06-10", tickers=["MSFT"], metrics=["total_revenue", "net_income", "operating_cashflow", "capital_expenditures", "long_term_debt", "short_term_debt", "cash_and_cash_equivalents_at_carrying_value"], start_date="2020-01-01" DataFrame 222 OK msft_fundamentals_134014_e48b53 (31 rows)

Calling: compute - name="msft_ttm_last_5_years", code="import pandas as pd\ndf = df_222.copy()\ndf['date'] = pd.to_datetime(df['date'])\n\n# Fill NaNs with 0\ndf = df.fillna(0)\n\n# Calculate Total Debt\ndf['total_debt'] = df['short_term_debt'] + df['long_term_debt']\n\n# Let's verify we have quarterly rows for TTM calculations\nquarterly = df[df['period_type'] == 'quarterly'].sort_values('date').copy()\n\n# TTM is trailing 4 quarters. \n# Flow metrics to sum over 4 quarters: total_revenue, net_income, operating_cashflow, capital_expenditures\n# Balance sheet metrics to take latest value: total_debt, cash_and_cash_equivalents_at_carrying_value\n\nflow_metrics = ['total_revenue', 'net_income', 'operating_cashflow', 'capital_expenditures']\nbalance_metrics = ['total_debt', 'cash_and_cash_equivalents_at_carrying_value']\n\n# Calculate TTM values for quarterly data\nfor m in flow_metrics:\n quarterly[m + '_ttm'] = quarterly[m].rolling(window=4).sum()\n\n# For balance sheet, TTM is just the current quarter's value\nfor m in balance_metrics:\n quarterly[m + '_ttm'] = quarterly[m]\n\n# Filter to get the periods we want.\n# We want the last 5 years of TTM data. Let's look at the dates.\n# MSFT fiscal year ends June 30.\n# The user wants \"last 5 years TTM\" or \"last TTM last 5 years\".\n# Let's provide the TTM as of June 2021, June 2022, June 2023, June 2024, June 2025, and the latest TTM (March 2026).\n# Note that June quarterly TTM is equivalent to the annual value.\ntarget_dates = ['2021-06-30', '2022-06-30', '2023-06-30', '2024-06-30', '2025-06-30', '2026-03-31']\ntarget_dates_dt = [pd.to_datetime(d) for d in target_dates]\n\nselected_ttm = quarterly[quarterly['date'].isin(target_dates_dt)].copy()\n\n# Let's format the period label\ndef format_label(d):\n if d.month == 6:\n return f\"FY{d.strftime('%y')} TTM\"\n else:\n return f\"LTM ({d.strftime('%b-%y')})\"\n\nselected_ttm['period_label'] = selected_ttm['date'].apply(format_label)\n\n# Keep only the columns we need\noutput_cols = ['period_label', 'total_revenue_ttm', 'net_income_ttm', 'operating_cashflow_ttm', 'capital_expenditures_ttm', 'total_debt_ttm', 'cash_and_cash_equivalents_at_carrying_value_ttm']\nresult_df = selected_ttm[output_cols].copy()\n\n# Rename columns\nresult_df.columns = ['Period', 'Revenue', 'Net Income', 'Operating Cash Flow', 'CapEx', 'Total Debt', 'Cash & Cash Equiv.']\n\n# Transpose\nresult_df = result_df.set_index('Period').T\n\n# Convert to Billions and format\nfor col in result_df.columns:\n result_df[col] = result_df[col] / 1e9\n\nresult = result_df.reset_index()\n", dataframe_ids=[222] DataFrame 223 OK msft_ttm_last_5_years (6 rows)

Calling: show - dataframe_id=223 Blob 97 DataFrame 223 OK msft_ttm_last_5_years (6 rows)