Calling: search_sec_compressed_filing - year="2025", ticker="INTU", search_patterns=["segment revenue", "Small Business & Self-Employed Group revenue", "Consumer Group revenue", "ProConnect Group revenue"]

Calling: compute - dataframe_ids=[], code="import pandas as pd\n\ndata = {\n 'Fiscal Year': [2023, 2024, 2025],\n 'Global Business Solutions': [8038, 9533, 11077],\n 'Consumer': [4135, 4445, 4870],\n 'Credit Karma': [1634, 1708, 2263],\n 'ProTax': [561, 599, 621]\n}\n\ndf = pd.DataFrame(data)\nresult = df\n" Error: dataframe_ids is required

Calling: compute - dataframe_ids=[], code="import pandas as pd\n\ndata = {\n 'Fiscal Year': [2023, 2024, 2025],\n 'Global Business Solutions': [8038, 9533, 11077],\n 'Consumer': [4135, 4445, 4870],\n 'Credit Karma': [1634, 1708, 2263],\n 'ProTax': [561, 599, 621]\n}\n\nresult = pd.DataFrame(data)\n" Error: dataframe_ids is required

Calling: load - metrics=["market_cap"], tickers=["INTU"] DataFrame 90 OK intu_overview (1 rows)

Calling: compute - code="import pandas as pd\n\ndata = {\n 'Fiscal Year': [2023, 2024, 2025],\n 'Global Business Solutions': [8038, 9533, 11077],\n 'Consumer': [4135, 4445, 4870],\n 'Credit Karma': [1634, 1708, 2263],\n 'ProTax': [561, 599, 621]\n}\n\nresult = pd.DataFrame(data)\n", dataframe_ids=[90] DataFrame 91 OK result_91 (3 rows)

Calling: show - dataframe_id=91 Blob 20 DataFrame 91 OK result_91 (3 rows)

Calling: show_chart - dataframe_id=91, title="Intuit Segment Revenue (Millions USD)", x="Fiscal Year", chart_type="bar", series=["Global Business Solutions", "Consumer", "Credit Karma", "ProTax"] Blob 21 DataFrame 91 OK result_91 (3 rows)

Calling: compute - dataframe_ids=[91], code="import pandas as pd\n\n# Original data\ndata = {\n 'Fiscal Year': [2023, 2024, 2025],\n 'Global Business Solutions': [8038, 9533, 11077],\n 'Consumer': [4135, 4445, 4870],\n 'Credit Karma': [1634, 1708, 2263],\n 'ProTax': [561, 599, 621]\n}\ndf = pd.DataFrame(data)\n\n# Transpose to get segments as index\ndf_melted = df.melt(id_vars=['Fiscal Year'], var_name='Segment', value_name='Revenue')\nresult = df_melted.pivot(index='Segment', columns='Fiscal Year', values='Revenue').reset_index()\n" DataFrame 92 OK result_92 (4 rows)

Calling: show_chart - x="Segment", chart_type="bar", dataframe_id=92, title="Intuit Segment Revenue by Fiscal Year (Millions USD)", series=["2023", "2024", "2025"] Blob 22 DataFrame 92 OK result_92 (4 rows)

Calling: compute - dataframe_ids=[], code="import pandas as pd\n\n# Data extracted from the SEC filing results provided in the previous turn\ndata = {\n 'Segment': ['Global Business Solutions', 'Consumer', 'Credit Karma', 'ProTax'],\n '2023 Margin': ['74%', '81%', '26%', '87%'],\n '2024 Margin': ['75%', '79%', '24%', '87%'],\n '2025 Margin': ['76%', '78%', '37%', '86%']\n}\n\nresult = pd.DataFrame(data)\n" Error: dataframe_ids is required

Calling: compute - code="import pandas as pd\n\n# Data extracted from the SEC filing results provided in the previous turn\ndata = {\n 'Segment': ['Global Business Solutions', 'Consumer', 'Credit Karma', 'ProTax'],\n '2023 Margin': ['74%', '81%', '26%', '87%'],\n '2024 Margin': ['75%', '79%', '24%', '87%'],\n '2025 Margin': ['76%', '78%', '37%', '86%']\n}\n\nresult = pd.DataFrame(data)\n", dataframe_ids=[92] DataFrame 93 OK result_93 (4 rows)

Calling: show - dataframe_id=93 Blob 23 DataFrame 93 OK result_93 (4 rows)

Calling: compute - dataframe_ids=[], code="import pandas as pd\n\n# Data from previous steps\n# Segments: Global Business Solutions, Consumer, Credit Karma, ProTax\n# Years: 2023, 2024, 2025\n\nrevenue_data = {\n 'Segment': ['Global Business Solutions', 'Consumer', 'Credit Karma', 'ProTax'],\n '2023_Rev': [8038, 4135, 1634, 561],\n '2024_Rev': [9533, 4445, 1708, 599],\n '2025_Rev': [11077, 4870, 2263, 621]\n}\n\nmargin_data = {\n 'Segment': ['Global Business Solutions', 'Consumer', 'Credit Karma', 'ProTax'],\n '2023_Margin_Pct': [0.74, 0.81, 0.26, 0.87],\n '2024_Margin_Pct': [0.75, 0.79, 0.24, 0.87],\n '2025_Margin_Pct': [0.76, 0.78, 0.37, 0.86]\n}\n\n# Calculate Operating Profit = Revenue * Margin\nprofit_data = {\n 'Segment': ['Global Business Solutions', 'Consumer', 'Credit Karma', 'ProTax'],\n '2023_Profit': [8038*0.74, 4135*0.81, 1634*0.26, 561*0.87],\n '2024_Profit': [9533*0.75, 4445*0.79, 1708*0.24, 599*0.87],\n '2025_Profit': [11077*0.76, 4870*0.78, 2263*0.37, 621*0.86]\n}\n\n# Create a combined dataframe for each segment\n# I need to create 4 separate dataframes, one for each segment, to chart them.\n\ndf_gbs = pd.DataFrame({\n 'Year': [2023, 2024, 2025],\n 'Revenue': [8038, 9533, 11077],\n 'Operating Profit': [8038*0.74, 9533*0.75, 11077*0.76]\n})\n\ndf_consumer = pd.DataFrame({\n 'Year': [2023, 2024, 2025],\n 'Revenue': [4135, 4445, 4870],\n 'Operating Profit': [4135*0.81, 4445*0.79, 4870*0.78]\n})\n\ndf_ck = pd.DataFrame({\n 'Year': [2023, 2024, 2025],\n 'Revenue': [1634, 1708, 2263],\n 'Operating Profit': [1634*0.26, 1708*0.24, 2263*0.37]\n})\n\ndf_protax = pd.DataFrame({\n 'Year': [2023, 2024, 2025],\n 'Revenue': [561, 599, 621],\n 'Operating Profit': [561*0.87, 599*0.87, 621*0.86]\n})\n\n# Returning these as separate results is tricky because compute only returns one result.\n# I will store them as a combined dataframe and then reference in show_chart or just display the data for now.\n# Actually I can't return 4 separate dataframes. I will just create one large one and use it.\n\ncombined = pd.DataFrame({\n 'Year': [2023, 2024, 2025]*4,\n 'Segment': ['Global Business Solutions']*3 + ['Consumer']*3 + ['Credit Karma']*3 + ['ProTax']*3,\n 'Revenue': [8038, 9533, 11077, 4135, 4445, 4870, 1634, 1708, 2263, 561, 599, 621],\n 'Operating Profit': [8038*0.74, 9533*0.75, 11077*0.76, 4135*0.81, 4445*0.79, 4870*0.78, 1634*0.26, 1708*0.24, 2263*0.37, 561*0.87, 599*0.87, 621*0.86]\n})\n\nresult = combined\n" Error: dataframe_ids is required

Calling: compute - dataframe_ids=[91], code="import pandas as pd\n\ncombined = pd.DataFrame({\n 'Year': [2023, 2024, 2025]*4,\n 'Segment': ['Global Business Solutions']*3 + ['Consumer']*3 + ['Credit Karma']*3 + ['ProTax']*3,\n 'Revenue': [8038, 9533, 11077, 4135, 4445, 4870, 1634, 1708, 2263, 561, 599, 621],\n 'Operating Profit': [8038*0.74, 9533*0.75, 11077*0.76, 4135*0.81, 4445*0.79, 4870*0.78, 1634*0.26, 1708*0.24, 2263*0.37, 561*0.87, 599*0.87, 621*0.86]\n})\n\n# Filter for each segment to allow individual charting\ndf_gbs = combined[combined['Segment'] == 'Global Business Solutions'].copy()\ndf_consumer = combined[combined['Segment'] == 'Consumer'].copy()\ndf_ck = combined[combined['Segment'] == 'Credit Karma'].copy()\ndf_protax = combined[combined['Segment'] == 'ProTax'].copy()\n\n# Since I can only return one result, I'll store the combined one. \n# I will use this to generate charts.\nresult = combined\n" DataFrame 94 OK result_94 (12 rows)

Calling: compute - code="import pandas as pd\ndata = {'Year': [2023, 2024, 2025], 'Revenue': [8038, 9533, 11077], 'Operating Profit': [8038*0.74, 9533*0.75, 11077*0.76]}\nresult = pd.DataFrame(data)\n", dataframe_ids=[94] DataFrame 95 OK result_95 (3 rows)

Calling: compute - code="import pandas as pd\ndata = {'Year': [2023, 2024, 2025], 'Revenue': [4135, 4445, 4870], 'Operating Profit': [4135*0.81, 4445*0.79, 4870*0.78]}\nresult = pd.DataFrame(data)\n", dataframe_ids=[94] DataFrame 96 OK result_96 (3 rows)

Calling: compute - dataframe_ids=[94], code="import pandas as pd\ndata = {'Year': [2023, 2024, 2025], 'Revenue': [1634, 1708, 2263], 'Operating Profit': [1634*0.26, 1708*0.24, 2263*0.37]}\nresult = pd.DataFrame(data)\n" DataFrame 97 OK result_97 (3 rows)

Calling: compute - dataframe_ids=[94], code="import pandas as pd\ndata = {'Year': [2023, 2024, 2025], 'Revenue': [561, 599, 621], 'Operating Profit': [561*0.87, 599*0.87, 621*0.86]}\nresult = pd.DataFrame(data)\n" DataFrame 98 OK result_98 (3 rows)

Calling: show_chart - title="Global Business Solutions: Revenue vs. Operating Profit", dataframe_id=95, x="Year", series=["Revenue", "Operating Profit"] Blob 24 DataFrame 95 OK result_95 (3 rows)

Calling: show_chart - series=["Revenue", "Operating Profit"], x="Year", dataframe_id=96, title="Consumer: Revenue vs. Operating Profit"

Calling: show_chart - x="Year", dataframe_id=97, title="Credit Karma: Revenue vs. Operating Profit", series=["Revenue", "Operating Profit"]

Calling: show_chart - dataframe_id=98, x="Year", title="ProTax: Revenue vs. Operating Profit", series=["Revenue", "Operating Profit"]