Calling: load - metrics=["total_revenue"], tickers=["NVDA", "AMD"] DataFrame 120 OK nvda_amd_fundamentals (202 rows)

Calling: compute - code="df = df_120[df_120['period_type'] == 'annual']\ndf['year'] = df['date'].str[:4].astype(int)\ndf = df[df['year'] >= 2021]\nresult = df.pivot(index='year', columns='ticker', values='total_revenue').reset_index()\n", dataframe_ids=[120] DataFrame 121 OK result_121 (6 rows)

Calling: load - tickers=["NVDA", "AMD"], metrics=["total_revenue"] DataFrame 122 OK nvda_amd_fundamentals_105328_43121e (202 rows)

Calling: compute - dataframe_ids=[122], code="df = df_122[df_122['period_type'] == 'quarterly'].copy()\ndf['date'] = pd.to_datetime(df['date'])\ndf = df.sort_values(['ticker', 'date'])\ndf['ttm_revenue'] = df.groupby('ticker')['total_revenue'].rolling(4).sum().reset_index(0, drop=True)\ndf['year'] = df['date'].dt.year\n# Keep the latest available TTM for each year to represent that year's \"end\" figure\ndf_latest = df.groupby(['ticker', 'year'])['ttm_revenue'].last().reset_index()\nresult = df_latest.pivot(index='year', columns='ticker', values='ttm_revenue').reset_index()\nresult = result[result['year'] >= 2021]\n" DataFrame 123 OK result_123 (6 rows)

Calling: show_chart - dataframe_id=123, title="Nvidia vs AMD TTM Revenue (2021–2026)", series=["AMD", "NVDA"], x="year" Blob 53 DataFrame 123 OK result_123 (6 rows)