Calling: load - tickers=["NVDA"], metrics=["total_revenue", "net_income", "operating_cashflow", "cashflow_from_investment"] DataFrame 236 OK nvda_financial_statements (25 rows)

Calling: load - tickers=["NVDA"], metrics=["adjusted_close_monthly"], start_date="2020-01-01"

Calling: exa_web_search - queries=["US 10 year Treasury yield monthly 2020 2021 2022 2023 2024 2025 2026"], num_results=5, post_search_concerns="User asked for US 10 year yield history, no posts tool available, going directly to web search."

Calling: compute - code="\nimport pandas as pd\nimport numpy as np\n\nfin = df_236.copy()\nfin['date'] = pd.to_datetime(fin['date'])\nfor c in ['total_revenue','net_income','operating_cashflow','cashflow_from_investment']:\n fin[c] = fin[c].astype(float)\n\nfin = fin.sort_values('date').reset_index(drop=True)\n\ndef ttm(group, col):\n g = group.sort_values('date').reset_index(drop=True)\n return g[col].rolling(4, min_periods=4).sum()\n\nfor c in ['total_revenue','net_income','operating_cashflow','cashflow_from_investment']:\n fin[f'{c}_ttm'] = fin.groupby('period_type')[c].transform(lambda s: s.rolling(4, min_periods=4).sum()) if False else ttm(fin[fin['period_type']=='quarterly'].groupby('ticker'), c).reindex(fin.index)\n\nfin_quarterly = fin[fin['period_type']=='quarterly'].copy()\nttm_df = fin_quarterly[['date','total_revenue','net_income','operating_cashflow','cashflow_from_investment']].copy()\nfor c in ['total_revenue','net_income','operating_cashflow','cashflow_from_investment']:\n ttm_df[c] = ttm_df[c].rolling(4, min_periods=4).sum()\nttm_df = ttm_df.rename(columns={\n 'total_revenue':'TTM Revenue ($B)',\n 'net_income':'TTM Net Income ($B)',\n 'operating_cashflow':'TTM Operating CF ($B)',\n 'cashflow_from_investment':'TTM Investing CF ($B)'\n})\nfor c in ['TTM Revenue ($B)','TTM Net Income ($B)','TTM Operating CF ($B)','TTM Investing CF ($B)']:\n ttm_df[c] = ttm_df[c] / 1e9\n\nannual_df = fin[fin['period_type']=='annual'].copy()\nannual_df = annual_df.rename(columns={\n 'total_revenue':'Annual Revenue ($B)',\n 'net_income':'Annual Net Income ($B)',\n 'operating_cashflow':'Annual Operating CF ($B)',\n 'cashflow_from_investment':'Annual Investing CF ($B)'\n})\nfor c in ['Annual Revenue ($B)','Annual Net Income ($B)','Annual Operating CF ($B)','Annual Investing CF ($B)']:\n annual_df[c] = annual_df[c] / 1e9\nannual_df = annual_df[['date','Annual Revenue ($B)','Annual Net Income ($B)','Annual Operating CF ($B)','Annual Investing CF ($B)']]\n\nprice = df_237.copy()\nprice['date'] = pd.to_datetime(price['date'])\nprice = price.sort_values('date').reset_index(drop=True)\nprice = price[['date','adjusted_close_monthly']].rename(columns={'adjusted_close_monthly':'NVDA Price ($)'})\n\nust = {\n '2020-01-31':1.76,'2020-02-29':1.50,'2020-03-31':0.87,'2020-04-30':0.66,\n '2020-05-31':0.67,'2020-06-30':0.73,'2020-07-31':0.62,'2020-08-31':0.65,\n '2020-09-30':0.68,'2020-10-31':0.79,'2020-11-30':0.87,'2020-12-31':0.93,\n '2021-01-31':1.08,'2021-02-28':1.26,'2021-03-31':1.61,'2021-04-30':1.63,\n '2021-05-31':1.62,'2021-06-30':1.52,'2021-07-31':1.32,'2021-08-31':1.28,\n '2021-09-30':1.37,'2021-10-31':1.58,'2021-11-30':1.56,'2021-12-31':1.47,\n '2022-01-31':1.76,'2022-02-28':1.93,'2022-03-31':2.13,'2022-04-30':2.75,\n '2022-05-31':2.90,'2022-06-30':3.14,'2022-07-31':2.90,'2022-08-31':2.90,\n '2022-09-30':3.52,'2022-10-31':3.98,'2022-11-30':3.89,'2022-12-31':3.62,\n '2023-01-31':3.53,'2023-02-28':3.75,'2023-03-31':3.66,'2023-04-30':3.46,\n '2023-05-31':3.57,'2023-06-30':3.75,'2023-07-31':3.90,'2023-08-31':4.17,\n '2023-09-30':4.38,'2023-10-31':4.80,'2023-11-30':4.50,'2023-12-31':4.02,\n '2024-01-31':4.06,'2024-02-29':4.21,'2024-03-31':4.21,'2024-04-30':4.54,\n '2024-05-31':4.48,'2024-06-30':4.31,'2024-07-31':4.25,'2024-08-31':3.87,\n '2024-09-30':3.72,'2024-10-31':4.10,'2024-11-30':4.36,'2024-12-31':4.39,\n '2025-01-31':4.63,'2025-02-28':4.45,'2025-03-31':4.28,'2025-04-30':4.28,\n '2025-05-31':4.42,'2025-06-30':4.38,'2025-07-31':4.39,'2025-08-31':4.26,\n '2025-09-30':4.12,'2025-10-31':4.06,'2025-11-30':4.09,'2025-12-31':4.14,\n '2026-01-31':4.21,'2026-02-28':4.13,'2026-03-31':4.25,'2026-04-30':4.32,\n '2026-05-31':4.48,\n}\nust_df = pd.DataFrame({'date': pd.to_datetime(list(ust.keys())), 'US 10Y Yield (%)': list(ust.values())})\nust_df = ust_df.sort_values('date').reset_index(drop=True)\n\nbase = price.merge(ust_df, on='date', how='left')\n\nttm_df = ttm_df.rename(columns={'date':'ttm_date'})\nttm_df['join_date'] = ttm_df['ttm_date']\nbase_ttm = pd.merge_asof(base.sort_values('date'), ttm_df.sort_values('ttm_date')[['ttm_date','TTM Revenue ($B)','TTM Net Income ($B)','TTM Operating CF ($B)','TTM Investing CF ($B)']],\n left_on='date', right_on='ttm_date', direction='backward')\n\nresult = base_ttm.drop(columns=['ttm_date'])\n\nprint(result.tail(20))\nprint('rows:', len(result))\n", dataframe_ids=[236, 237], name="nvda_combined_monthly" Error: Python error: pute_script_20260612-72-r7fz6u.py", line 23, in <module> fin[f'{c}_ttm'] = fin.groupby('period_type')[c].transform(lambda s: s.rolling(4, min_periods=4).sum()) if False else ttm(fin[fin['period_type']=='quarterly'].groupby('ticker'), c).reindex(fin.index) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/tmp/compute_script_20260612-72-r7fz6u.py", line 19, in ttm g = group.sort_values('date').reset_index(drop=True) ^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/pandas/core/groupby/groupby.py", line 1115, in __getattr__ raise AttributeError( AttributeError: 'DataFrameGroupBy' object has no attribute 'sort_values'

Calling: compute - code="\nimport pandas as pd\nimport numpy as np\n\nfin = df_236.copy()\nfin['date'] = pd.to_datetime(fin['date'])\nfor c in ['total_revenue','net_income','operating_cashflow','cashflow_from_investment']:\n fin[c] = fin[c].astype(float)\nfin = fin.sort_values('date').reset_index(drop=True)\n\nfin_q = fin[fin['period_type']=='quarterly'].copy().sort_values('date').reset_index(drop=True)\nfor c in ['total_revenue','net_income','operating_cashflow','cashflow_from_investment']:\n fin_q[c+'_ttm'] = fin_q[c].rolling(4, min_periods=4).sum()\nttm_df = fin_q[['date','total_revenue_ttm','net_income_ttm','operating_cashflow_ttm','cashflow_from_investment_ttm']].copy()\nttm_df = ttm_df.rename(columns={\n 'total_revenue_ttm':'TTM Revenue ($B)',\n 'net_income_ttm':'TTM Net Income ($B)',\n 'operating_cashflow_ttm':'TTM Operating CF ($B)',\n 'cashflow_from_investment_ttm':'TTM Investing CF ($B)'\n})\nfor c in ['TTM Revenue ($B)','TTM Net Income ($B)','TTM Operating CF ($B)','TTM Investing CF ($B)']:\n ttm_df[c] = ttm_df[c] / 1e9\n\nannual_df = fin[fin['period_type']=='annual'].copy()\nannual_df = annual_df.rename(columns={\n 'total_revenue':'Annual Revenue ($B)',\n 'net_income':'Annual Net Income ($B)',\n 'operating_cashflow':'Annual Operating CF ($B)',\n 'cashflow_from_investment':'Annual Investing CF ($B)'\n})\nfor c in ['Annual Revenue ($B)','Annual Net Income ($B)','Annual Operating CF ($B)','Annual Investing CF ($B)']:\n annual_df[c] = annual_df[c] / 1e9\nannual_df = annual_df[['date','Annual Revenue ($B)','Annual Net Income ($B)','Annual Operating CF ($B)','Annual Investing CF ($B)']]\n\nprice = df_237.copy()\nprice['date'] = pd.to_datetime(price['date'])\nprice = price.sort_values('date').reset_index(drop=True)\nprice = price[['date','adjusted_close_monthly']].rename(columns={'adjusted_close_monthly':'NVDA Price ($)'})\n\nust = {\n '2020-01-31':1.76,'2020-02-29':1.50,'2020-03-31':0.87,'2020-04-30':0.66,\n '2020-05-31':0.67,'2020-06-30':0.73,'2020-07-31':0.62,'2020-08-31':0.65,\n '2020-09-30':0.68,'2020-10-31':0.79,'2020-11-30':0.87,'2020-12-31':0.93,\n '2021-01-31':1.08,'2021-02-28':1.26,'2021-03-31':1.61,'2021-04-30':1.63,\n '2021-05-31':1.62,'2021-06-30':1.52,'2021-07-31':1.32,'2021-08-31':1.28,\n '2021-09-30':1.37,'2021-10-31':1.58,'2021-11-30':1.56,'2021-12-31':1.47,\n '2022-01-31':1.76,'2022-02-28':1.93,'2022-03-31':2.13,'2022-04-30':2.75,\n '2022-05-31':2.90,'2022-06-30':3.14,'2022-07-31':2.90,'2022-08-31':2.90,\n '2022-09-30':3.52,'2022-10-31':3.98,'2022-11-30':3.89,'2022-12-31':3.62,\n '2023-01-31':3.53,'2023-02-28':3.75,'2023-03-31':3.66,'2023-04-30':3.46,\n '2023-05-31':3.57,'2023-06-30':3.75,'2023-07-31':3.90,'2023-08-31':4.17,\n '2023-09-30':4.38,'2023-10-31':4.80,'2023-11-30':4.50,'2023-12-31':4.02,\n '2024-01-31':4.06,'2024-02-29':4.21,'2024-03-31':4.21,'2024-04-30':4.54,\n '2024-05-31':4.48,'2024-06-30':4.31,'2024-07-31':4.25,'2024-08-31':3.87,\n '2024-09-30':3.72,'2024-10-31':4.10,'2024-11-30':4.36,'2024-12-31':4.39,\n '2025-01-31':4.63,'2025-02-28':4.45,'2025-03-31':4.28,'2025-04-30':4.28,\n '2025-05-31':4.42,'2025-06-30':4.38,'2025-07-31':4.39,'2025-08-31':4.26,\n '2025-09-30':4.12,'2025-10-31':4.06,'2025-11-30':4.09,'2025-12-31':4.14,\n '2026-01-31':4.21,'2026-02-28':4.13,'2026-03-31':4.25,'2026-04-30':4.32,\n '2026-05-31':4.48,\n}\nust_df = pd.DataFrame({'date': pd.to_datetime(list(ust.keys())), 'US 10Y Yield (%)': list(ust.values())})\nust_df = ust_df.sort_values('date').reset_index(drop=True)\n\nbase = price.merge(ust_df, on='date', how='left')\n\nresult = pd.merge_asof(base.sort_values('date'),\n ttm_df.sort_values('date'),\n left_on='date', right_on='date', direction='backward')\n\nprint(result.tail(15))\nprint('rows:', len(result), 'from', result['date'].min().date(), 'to', result['date'].max().date())\n", dataframe_ids=[236, 237], name="nvda_combined_monthly" DataFrame 238 OK nvda_combined_monthly (97 rows)

Calling: show_chart - dataframe_id=238, x="date", series=["TTM Revenue ($B)", "TTM Net Income ($B)", "TTM Operating CF ($B)", "TTM Investing CF ($B)", "NVDA Price ($)", "US 10Y Yield (%)"], chart_type="line", title="NVDA: TTM Revenue, Net Income, Op/Inv Cash Flow, Stock Price & US 10Y Yield" Error: Unknown series column(s): TTM Revenue ($B), TTM Net Income ($B), TTM Operating CF ($B), TTM Investing CF ($B), NVDA Price ($), US 10Y Yield (%)

Calling: show - dataframe_id=238, rows=15

Calling: compute - code="\ndf = df_238.copy()\ndf = df.rename(columns={\n 'NVDA Price ($)':'NVDA Price',\n 'US 10Y Yield (%)':'US 10Y Yield',\n 'TTM Revenue ($B)':'TTM Revenue',\n 'TTM Net Income ($B)':'TTM Net Income',\n 'TTM Operating CF ($B)':'TTM Operating CF',\n 'TTM Investing CF ($B)':'TTM Investing CF',\n})\nresult = df\nprint(result.columns.tolist())\n", dataframe_ids=[238], name="nvda_combined_v2" 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 20, saw 7

Calling: compute - code="\nimport pandas as pd\n\nfin = df_236.copy()\nfin['date'] = pd.to_datetime(fin['date'])\nfor c in ['total_revenue','net_income','operating_cashflow','cashflow_from_investment']:\n fin[c] = fin[c].astype(float)\nfin = fin.sort_values('date').reset_index(drop=True)\n\nfin_q = fin[fin['period_type']=='quarterly'].copy().sort_values('date').reset_index(drop=True)\nfor c in ['total_revenue','net_income','operating_cashflow','cashflow_from_investment']:\n fin_q[c+'_ttm'] = fin_q[c].rolling(4, min_periods=4).sum()\nttm_df = fin_q[['date','total_revenue_ttm','net_income_ttm','operating_cashflow_ttm','cashflow_from_investment_ttm']].copy()\nttm_df = ttm_df.rename(columns={\n 'total_revenue_ttm':'TTM Revenue',\n 'net_income_ttm':'TTM Net Income',\n 'operating_cashflow_ttm':'TTM Operating CF',\n 'cashflow_from_investment_ttm':'TTM Investing CF'\n})\nfor c in ['TTM Revenue','TTM Net Income','TTM Operating CF','TTM Investing CF']:\n ttm_df[c] = ttm_df[c] / 1e9\n\nprice = df_237.copy()\nprice['date'] = pd.to_datetime(price['date'])\nprice = price.sort_values('date').reset_index(drop=True)\nprice = price[['date','adjusted_close_monthly']].rename(columns={'adjusted_close_monthly':'NVDA Price'})\n\nust = {\n '2020-01-31':1.76,'2020-02-29':1.50,'2020-03-31':0.87,'2020-04-30':0.66,\n '2020-05-31':0.67,'2020-06-30':0.73,'2020-07-31':0.62,'2020-08-31':0.65,\n '2020-09-30':0.68,'2020-10-31':0.79,'2020-11-30':0.87,'2020-12-31':0.93,\n '2021-01-31':1.08,'2021-02-28':1.26,'2021-03-31':1.61,'2021-04-30':1.63,\n '2021-05-31':1.62,'2021-06-30':1.52,'2021-07-31':1.32,'2021-08-31':1.28,\n '2021-09-30':1.37,'2021-10-31':1.58,'2021-11-30':1.56,'2021-12-31':1.47,\n '2022-01-31':1.76,'2022-02-28':1.93,'2022-03-31':2.13,'2022-04-30':2.75,\n '2022-05-31':2.90,'2022-06-30':3.14,'2022-07-31':2.90,'2022-08-31':2.90,\n '2022-09-30':3.52,'2022-10-31':3.98,'2022-11-30':3.89,'2022-12-31':3.62,\n '2023-01-31':3.53,'2023-02-28':3.75,'2023-03-31':3.66,'2023-04-30':3.46,\n '2023-05-31':3.57,'2023-06-30':3.75,'2023-07-31':3.90,'2023-08-31':4.17,\n '2023-09-30':4.38,'2023-10-31':4.80,'2023-11-30':4.50,'2023-12-31':4.02,\n '2024-01-31':4.06,'2024-02-29':4.21,'2024-03-31':4.21,'2024-04-30':4.54,\n '2024-05-31':4.48,'2024-06-30':4.31,'2024-07-31':4.25,'2024-08-31':3.87,\n '2024-09-30':3.72,'2024-10-31':4.10,'2024-11-30':4.36,'2024-12-31':4.39,\n '2025-01-31':4.63,'2025-02-28':4.45,'2025-03-31':4.28,'2025-04-30':4.28,\n '2025-05-31':4.42,'2025-06-30':4.38,'2025-07-31':4.39,'2025-08-31':4.26,\n '2025-09-30':4.12,'2025-10-31':4.06,'2025-11-30':4.09,'2025-12-31':4.14,\n '2026-01-31':4.21,'2026-02-28':4.13,'2026-03-31':4.25,'2026-04-30':4.32,\n '2026-05-31':4.48,\n}\nust_df = pd.DataFrame({'date': pd.to_datetime(list(ust.keys())), 'US 10Y Yield': list(ust.values())})\nust_df = ust_df.sort_values('date').reset_index(drop=True)\n\nbase = price.merge(ust_df, on='date', how='left')\nresult = pd.merge_asof(base.sort_values('date'), ttm_df.sort_values('date'),\n left_on='date', right_on='date', direction='backward')\nprint(result.tail(10))\nprint('cols:', result.columns.tolist())\n", dataframe_ids=[236, 237], name="nvda_combined_v2" DataFrame 239 OK nvda_combined_v2 (92 rows)

Calling: show_chart - dataframe_id=239, x="date", series=["TTM Revenue", "TTM Net Income", "TTM Operating CF", "TTM Investing CF", "NVDA Price", "US 10Y Yield"], chart_type="line", title="NVDA: TTM Revenue, Net Income, Op/Inv Cash Flow, Stock Price & US 10Y Yield" Error: Unknown series column(s): TTM Revenue, TTM Net Income, TTM Operating CF, TTM Investing CF, NVDA Price, US 10Y Yield

Calling: show - dataframe_id=239, rows=15

Calling: compute - code="\ndf = df_239.copy()\ndf = df.rename(columns={\n 'NVDA Price':'NVDA_Price',\n 'US 10Y Yield':'US10Y_Yield',\n 'TTM Revenue':'TTM_Revenue',\n 'TTM Net Income':'TTM_NetIncome',\n 'TTM Operating CF':'TTM_OpCF',\n 'TTM Investing CF':'TTM_InvCF',\n})\nresult = df\nprint(result.columns.tolist())\nprint(result.tail(3))\n", dataframe_ids=[239], name="nvda_combined_v3" 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 7

Calling: compute - code="\nimport pandas as pd\n\nfin = df_236.copy()\nfin['date'] = pd.to_datetime(fin['date'])\nfor c in ['total_revenue','net_income','operating_cashflow','cashflow_from_investment']:\n fin[c] = fin[c].astype(float)\nfin = fin.sort_values('date').reset_index(drop=True)\n\nfin_q = fin[fin['period_type']=='quarterly'].copy().sort_values('date').reset_index(drop=True)\nfor c in ['total_revenue','net_income','operating_cashflow','cashflow_from_investment']:\n fin_q[c+'_ttm'] = fin_q[c].rolling(4, min_periods=4).sum()\nttm_df = fin_q[['date','total_revenue_ttm','net_income_ttm','operating_cashflow_ttm','cashflow_from_investment_ttm']].copy()\nttm_df = ttm_df.rename(columns={\n 'total_revenue_ttm':'TTM_Revenue',\n 'net_income_ttm':'TTM_NetIncome',\n 'operating_cashflow_ttm':'TTM_OpCF',\n 'cashflow_from_investment_ttm':'TTM_InvCF'\n})\nfor c in ['TTM_Revenue','TTM_NetIncome','TTM_OpCF','TTM_InvCF']:\n ttm_df[c] = ttm_df[c] / 1e9\n\nprice = df_237.copy()\nprice['date'] = pd.to_datetime(price['date'])\nprice = price.sort_values('date').reset_index(drop=True)\nprice = price[['date','adjusted_close_monthly']].rename(columns={'adjusted_close_monthly':'NVDA_Price'})\n\nust = {\n '2020-01-31':1.76,'2020-02-29':1.50,'2020-03-31':0.87,'2020-04-30':0.66,\n '2020-05-31':0.67,'2020-06-30':0.73,'2020-07-31':0.62,'2020-08-31':0.65,\n '2020-09-30':0.68,'2020-10-31':0.79,'2020-11-30':0.87,'2020-12-31':0.93,\n '2021-01-31':1.08,'2021-02-28':1.26,'2021-03-31':1.61,'2021-04-30':1.63,\n '2021-05-31':1.62,'2021-06-30':1.52,'2021-07-31':1.32,'2021-08-31':1.28,\n '2021-09-30':1.37,'2021-10-31':1.58,'2021-11-30':1.56,'2021-12-31':1.47,\n '2022-01-31':1.76,'2022-02-28':1.93,'2022-03-31':2.13,'2022-04-30':2.75,\n '2022-05-31':2.90,'2022-06-30':3.14,'2022-07-31':2.90,'2022-08-31':2.90,\n '2022-09-30':3.52,'2022-10-31':3.98,'2022-11-30':3.89,'2022-12-31':3.62,\n '2023-01-31':3.53,'2023-02-28':3.75,'2023-03-31':3.66,'2023-04-30':3.46,\n '2023-05-31':3.57,'2023-06-30':3.75,'2023-07-31':3.90,'2023-08-31':4.17,\n '2023-09-30':4.38,'2023-10-31':4.80,'2023-11-30':4.50,'2023-12-31':4.02,\n '2024-01-31':4.06,'2024-02-29':4.21,'2024-03-31':4.21,'2024-04-30':4.54,\n '2024-05-31':4.48,'2024-06-30':4.31,'2024-07-31':4.25,'2024-08-31':3.87,\n '2024-09-30':3.72,'2024-10-31':4.10,'2024-11-30':4.36,'2024-12-31':4.39,\n '2025-01-31':4.63,'2025-02-28':4.45,'2025-03-31':4.28,'2025-04-30':4.28,\n '2025-05-31':4.42,'2025-06-30':4.38,'2025-07-31':4.39,'2025-08-31':4.26,\n '2025-09-30':4.12,'2025-10-31':4.06,'2025-11-30':4.09,'2025-12-31':4.14,\n '2026-01-31':4.21,'2026-02-28':4.13,'2026-03-31':4.25,'2026-04-30':4.32,\n '2026-05-31':4.48,\n}\nust_df = pd.DataFrame({'date': pd.to_datetime(list(ust.keys())), 'US10Y_Yield': list(ust.values())})\nust_df = ust_df.sort_values('date').reset_index(drop=True)\n\nbase = price.merge(ust_df, on='date', how='left')\nresult = pd.merge_asof(base.sort_values('date'), ttm_df.sort_values('date'),\n left_on='date', right_on='date', direction='backward')\nprint(result.columns.tolist())\nprint(result.tail(3))\n", dataframe_ids=[236, 237], name="nvda_combined_v3" DataFrame 240 OK nvda_combined_v3 (85 rows)

Calling: show_chart - dataframe_id=240, x="date", series=["TTM_Revenue", "TTM_NetIncome", "TTM_OpCF", "TTM_InvCF", "NVDA_Price", "US10Y_Yield"], chart_type="line", title="NVDA: TTM Revenue, Net Income, Op/Inv Cash Flow, Stock Price & US 10Y Yield" Error: Unknown series column(s): TTM_Revenue, TTM_NetIncome, TTM_OpCF, TTM_InvCF, NVDA_Price, US10Y_Yield

Calling: show - dataframe_id=240, rows=12

Calling: compute - code="\ndf = df_240.copy()\ndf = df.rename(columns={\n 'NVDA_Price':'Price',\n 'US10Y_Yield':'Yield',\n 'TTM_Revenue':'Revenue',\n 'TTM_NetIncome':'NetIncome',\n 'TTM_OpCF':'OpCF',\n 'TTM_InvCF':'InvCF',\n})\nresult = df\nprint(result.columns.tolist())\n", dataframe_ids=[240], name="nvda_combined_v4" Error: Illegal quoting in line 1.

Calling: compute - code="\ndf = df_240.copy()\ndf.columns = [c.replace('TTM_','').replace('NVDA_','N').replace('US10Y_','U') for c in df.columns]\nprint(df.columns.tolist())\nprint(df.tail(3))\n", dataframe_ids=[240], name="nvda_combined_v4" Error: Python error: