1
import os
2
from typing import List
3
4
import cohere
5
import langchain
6
import langchain_core
7
import langchain_experimental
8
import pandas as pd
9
from langchain.agents import Tool
10
from langchain_core.pydantic_v1 import BaseModel, Field
11
from langchain_experimental.utilities import PythonREPL

1
# Uncomment if you need to install the following packages
2
# !pip install --quiet langchain langchain_experimental cohere --upgrade

1
# versions
2
print('cohere version:', cohere.__version__)
3
print('langchain version:', langchain.__version__)
4
print('langchain_core version:', langchain_core.__version__)
5
print('langchain_experimental version:', langchain_experimental.__version__)

cohere version: 5.5.1
langchain version: 0.2.0
langchain_core version: 0.2.0
langchain_experimental version: 0.0.59

1
COHERE_API_KEY = os.environ["COHERE_API_KEY"]
2
CHAT_URL= "https://api.cohere.ai/v1/chat"
3
COHERE_MODEL = 'command-a-03-2025'
4
co = cohere.Client(api_key=COHERE_API_KEY)

1
income_statement = pd.read_csv('income_statement.csv')
2
balance_sheet = pd.read_csv('balance_sheet.csv')

1
income_statement.head(2)

1
balance_sheet.head(2)

1
python_repl = PythonREPL()
2
python_tool = Tool(
3
    name="python_repl",
4
    description="Executes python code and returns the result. The code runs in a static sandbox without interactive mode, so print output or save output to a file.",
5
    func=python_repl.run,
6
)
7
python_tool.name = "python_interpreter"
8
9
class ToolInput(BaseModel):
10
    code: str = Field(description="Python code to execute.")
11
python_tool.args_schema = ToolInput
12
13
def run_python_code(code: str) -> dict:
14
    """
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    Function to run given python code
16
    """
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    input_code = ToolInput(code=code)
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    return {'python_answer': python_tool.func(input_code.code)}
19
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functions_map = {
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    "run_python_code": run_python_code,
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}
23
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tools = [
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    {
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        "name": "run_python_code",
27
        "description": "given a python code, runs it",
28
        "parameter_definitions": {
29
            "code": {
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                "description": "executable python code",
31
                "type": "str",
32
                "required": True
33
            }
34
        }
35
    },]

1
def cohere_agent(
2
    message: str,
3
    preamble: str,
4
    tools: List[dict],
5
    force_single_step=False,
6
    verbose: bool = False,
7
) -> str:
8
    """
9
    Function to handle multi-step tool use api.
10
11
    Args:
12
        message (str): The message to send to the Cohere AI model.
13
        preamble (str): The preamble or context for the conversation.
14
        tools (list of dict): List of tools to use in the conversation.
15
        verbose (bool, optional): Whether to print verbose output. Defaults to False.
16
17
    Returns:
18
        str: The final response from the call.
19
    """
20
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    counter = 1
22
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    response = co.chat(
24
        model=COHERE_MODEL,
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        message=message,
26
        preamble=preamble,
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        tools=tools,
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        force_single_step=force_single_step,
29
    )
30
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    if verbose:
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        print(f"\nrunning 0th step.")
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        print(response.text)
34
35
    while response.tool_calls:
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        tool_results = []
37
38
        if verbose:
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            print(f"\nrunning {counter}th step.")
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        for tool_call in response.tool_calls:
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            output = functions_map[tool_call.name](**tool_call.parameters)
43
            outputs = [output]
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            tool_results.append({"call": tool_call, "outputs": outputs})
45
46
            if verbose:
47
                print(
48
                    f"= running tool {tool_call.name}, with parameters: {tool_call.parameters}"
49
                )
50
                print(f"== tool results: {outputs}")
51
52
        response = co.chat(
53
            model=COHERE_MODEL,
54
            message="",
55
            chat_history=response.chat_history,
56
            preamble=preamble,
57
            tools=tools,
58
            force_single_step=force_single_step,
59
            tool_results=tool_results,
60
        )
61
62
        if verbose:
63
            print(response.text)
64
65
            counter += 1
66
67
    return response.text
68
69
70
# test
71
output = cohere_agent("can you use python to answer 1 + 1", None, tools, verbose=True)

running 0th step.
I will use Python to answer this question.
running 1th step.
= running tool run_python_code, with parameters: {'code': 'print(1 + 1)'}
== tool results: [{'python_answer': '2\n'}]
The answer is **2**.

1
question_dict ={
2
    'q1': ['what is the highest value of cost of goods and service?',169559000000],
3
    'q2': ['what is the largest gross profit margin?',0.3836194330595236],
4
    'q3': ['what is the minimum ratio of operating income loss divided by non operating income expense?',35.360599]
5
}

1
preamble = """
2
You are an expert who answers the user's question. You are working with a pandas dataframe in Python. The name of the dataframe is `income_statement.csv`.
3
Here is a preview of the dataframe:
4
{head_df}
5
""".format(head_df=income_statement.head(3).to_markdown())
6
7
print(preamble)

1
for qsn,val in question_dict.items():
2
    print(f'question:{qsn}')
3
    question = val[0]
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    answer = val[1]
5
    output = cohere_agent(question, preamble, tools, verbose=True)
6
    print(f'GT Answer:{val[1]}')
7
    print('-'*50)

question:q1
running 0th step.
I will use Python to find the highest value of 'CostOfGoodsAndServicesSold' in the 'income_statement.csv' dataframe.
running 1th step.
= running tool run_python_code, with parameters: {'code': 'import pandas as pd\n\ndf = pd.read_csv(\'income_statement.csv\')\n\n# Find the highest value of \'CostOfGoodsAndServicesSold\'\nhighest_cost = df[\'CostOfGoodsAndServicesSold\'].max()\n\nprint(f"The highest value of \'CostOfGoodsAndServicesSold\' is {highest_cost}")'}
== tool results: [{'python_answer': "The highest value of 'CostOfGoodsAndServicesSold' is 169559000000.0\n"}]
The highest value of 'CostOfGoodsAndServicesSold' is 169559000000.0.
GT Answer:169559000000
--------------------------------------------------
question:q2
running 0th step.
I will write and execute Python code to find the largest gross profit margin.
running 1th step.
= running tool run_python_code, with parameters: {'code': 'import pandas as pd\n\ndf = pd.read_csv(\'income_statement.csv\')\n\n# Calculate gross profit margin\ndf[\'GrossProfitMargin\'] = df[\'GrossProfit\'] / df[\'RevenueFromContractWithCustomerExcludingAssessedTax\'] * 100\n\n# Find the largest gross profit margin\nlargest_gross_profit_margin = df[\'GrossProfitMargin\'].max()\n\nprint(f"The largest gross profit margin is {largest_gross_profit_margin:.2f}%")'}
== tool results: [{'python_answer': 'The largest gross profit margin is 38.36%\n'}]
The largest gross profit margin is 38.36%.
GT Answer:0.3836194330595236
--------------------------------------------------
question:q3
running 0th step.
I will use Python to find the minimum ratio of operating income loss divided by non-operating income expense.
running 1th step.
= running tool run_python_code, with parameters: {'code': 'import pandas as pd\n\ndf = pd.read_csv("income_statement.csv")\n\n# Calculate the ratio of operating income loss to non-operating income expense\ndf["OperatingIncomeLossRatio"] = df["OperatingIncomeLoss"] / df["NonoperatingIncomeExpense"]\n\n# Find the minimum ratio\nmin_ratio = df["OperatingIncomeLossRatio"].min()\n\nprint(f"The minimum ratio of operating income loss to non-operating income expense is: {min_ratio:.2f}")'}
== tool results: [{'python_answer': 'The minimum ratio of operating income loss to non-operating income expense is: 35.36\n'}]
The minimum ratio of operating income loss to non-operating income expense is 35.36.
GT Answer:35.360599
--------------------------------------------------

1
question_dict ={
2
    'q1': ['what is the ratio of the largest stockholders equity to the smallest revenue'],
3
}

1
# get the largest stockholders equity
2
x = balance_sheet['StockholdersEquity'].astype(float).max()
3
print(f"The largest stockholders equity value is: {x}")
4
5
# get the smallest revenue
6
y = income_statement['RevenueFromContractWithCustomerExcludingAssessedTax'].astype(float).min()
7
print(f"The smallest revenue value is: {y}")
8
9
# compute the ratio
10
ratio = x/y
11
print(f"Their ratio is: {ratio}")

The largest stockholders equity value is: 134047000000.0
The smallest revenue value is: 53809000000.0
Their ratio is: 2.4911631883142227

1
preamble = """
2
You are an expert who answers the user's question in complete sentences. You are working with two pandas dataframe in Python. Ensure your output is a string.
3
4
Here is a preview of the `income_statement.csv` dataframe:
5
{table_1}
6
7
Here is a preview of the `balance_sheet.csv` dataframe:
8
{table_2}
9
""".format(table_1=income_statement.head(3).to_markdown(),table_2=balance_sheet.head(3).to_markdown())
10
11
12
print(preamble)

You are an expert who answers the user's question in complete sentences. You are working with two pandas dataframe in Python. Ensure your output is a string.
Here is a preview of the `income_statement.csv` dataframe:
|    |   Unnamed: 0 | index                 |   RevenueFromContractWithCustomerExcludingAssessedTax |   CostOfGoodsAndServicesSold |   GrossProfit |   ResearchAndDevelopmentExpense |   SellingGeneralAndAdministrativeExpense |   OperatingExpenses |   OperatingIncomeLoss |   NonoperatingIncomeExpense |   IncomeLossFromContinuingOperationsBeforeIncomeTaxesExtraordinaryItemsNoncontrollingInterest |   IncomeTaxExpenseBenefit |   NetIncomeLoss |   EarningsPerShareBasic |   EarningsPerShareDiluted |   WeightedAverageNumberOfSharesOutstandingBasic |   WeightedAverageNumberOfDilutedSharesOutstanding |
|---:|-------------:|:----------------------|------------------------------------------------------:|-----------------------------:|--------------:|--------------------------------:|-----------------------------------------:|--------------------:|----------------------:|----------------------------:|----------------------------------------------------------------------------------------------:|--------------------------:|----------------:|------------------------:|--------------------------:|------------------------------------------------:|--------------------------------------------------:|
|  0 |            0 | 2017-10-01-2018-09-29 |                                          265595000000 |                  1.63756e+11 |  101839000000 |                      1.4236e+10 |                               1.6705e+10 |          3.0941e+10 |            7.0898e+10 |                   2.005e+09 |                                                                                    7.2903e+10 |                1.3372e+10 |     59531000000 |                    3    |                      2.98 |                                     1.98215e+10 |                                       2.00004e+10 |
|  1 |            1 | 2018-09-30-2018-12-29 |                                           84310000000 |                nan           |   32031000000 |                    nan          |                             nan          |        nan          |          nan          |                 nan         |                                                                                  nan          |              nan          |     19965000000 |                    1.05 |                      1.05 |                                   nan           |                                     nan           |
|  2 |            2 | 2018-09-30-2019-09-28 |                                          260174000000 |                  1.61782e+11 |   98392000000 |                      1.6217e+10 |                               1.8245e+10 |          3.4462e+10 |            6.393e+10  |                   1.807e+09 |                                                                                    6.5737e+10 |                1.0481e+10 |     55256000000 |                    2.99 |                      2.97 |                                     1.84713e+10 |                                       1.85957e+10 |
Here is a preview of the `balance_sheet.csv` dataframe:
|    |   Unnamed: 0 | index      |   CashAndCashEquivalentsAtCarryingValue |   MarketableSecuritiesCurrent |   AccountsReceivableNetCurrent |   InventoryNet |   NontradeReceivablesCurrent |   OtherAssetsCurrent |   AssetsCurrent |   MarketableSecuritiesNoncurrent |   PropertyPlantAndEquipmentNet |   OtherAssetsNoncurrent |   AssetsNoncurrent |        Assets |   AccountsPayableCurrent |   OtherLiabilitiesCurrent |   ContractWithCustomerLiabilityCurrent |   CommercialPaper |   LongTermDebtCurrent |   LiabilitiesCurrent |   LongTermDebtNoncurrent |   OtherLiabilitiesNoncurrent |   LiabilitiesNoncurrent |   Liabilities |   CommitmentsAndContingencies |   CommonStocksIncludingAdditionalPaidInCapital |   RetainedEarningsAccumulatedDeficit |   AccumulatedOtherComprehensiveIncomeLossNetOfTax |   StockholdersEquity |   LiabilitiesAndStockholdersEquity |
|---:|-------------:|:-----------|----------------------------------------:|------------------------------:|-------------------------------:|---------------:|-----------------------------:|---------------------:|----------------:|---------------------------------:|-------------------------------:|------------------------:|-------------------:|--------------:|-------------------------:|--------------------------:|---------------------------------------:|------------------:|----------------------:|---------------------:|-------------------------:|-----------------------------:|------------------------:|--------------:|------------------------------:|-----------------------------------------------:|-------------------------------------:|--------------------------------------------------:|---------------------:|-----------------------------------:|
|  0 |            0 | 2017-09-30 |                            nan          |                  nan          |                   nan          |    nan         |                 nan          |         nan          |   nan           |                    nan           |                   nan          |            nan          |      nan           | nan           |             nan          |               nan         |                            nan         |        nan        |           nan         |        nan           |             nan          |                 nan          |            nan          | nan           |                           nan |                                   nan          |                         nan          |                                        nan        |         134047000000 |                      nan           |
|  1 |            1 | 2018-09-29 |                            nan          |                  nan          |                   nan          |    nan         |                 nan          |         nan          |   nan           |                    nan           |                   nan          |            nan          |      nan           | nan           |             nan          |               nan         |                            nan         |        nan        |           nan         |        nan           |             nan          |                 nan          |            nan          | nan           |                           nan |                                   nan          |                         nan          |                                        nan        |         107147000000 |                      nan           |
|  2 |            2 | 2019-09-28 |                              4.8844e+10 |                    5.1713e+10 |                     2.2926e+10 |      4.106e+09 |                   2.2878e+10 |           1.2352e+10 |     1.62819e+11 |                      1.05341e+11 |                     3.7378e+10 |              3.2978e+10 |        1.75697e+11 |   3.38516e+11 |               4.6236e+10 |                 3.772e+10 |                              5.522e+09 |          5.98e+09 |             1.026e+10 |          1.05718e+11 |               9.1807e+10 |                   5.0503e+10 |              1.4231e+11 |   2.48028e+11 |                             0 |                                     4.5174e+10 |                           4.5898e+10 |                                         -5.84e+08 |          90488000000 |                        3.38516e+11 |

1
for qsn,val in question_dict.items():
2
    print(f'question:{qsn}')
3
    question = val[0]
4
    output = cohere_agent(question, preamble, tools, verbose=True)

question:q1
running 0th step.
I will use the provided code to find the ratio of the largest stockholders equity to the smallest revenue.
running 1th step.
= running tool run_python_code, with parameters: {'code': 'import pandas as pd\n\n# Read the CSV files into dataframes\nincome_statement = pd.read_csv(\'income_statement.csv\')\nbalance_sheet = pd.read_csv(\'balance_sheet.csv\')\n\n# Find the smallest revenue\nsmallest_revenue = income_statement[\'RevenueFromContractWithCustomerExcludingAssessedTax\'].min()\n\n# Find the largest stockholders equity\nlargest_stockholders_equity = balance_sheet[\'StockholdersEquity\'].max()\n\n# Calculate the ratio\nratio = largest_stockholders_equity / smallest_revenue\nprint(f"The ratio of the largest stockholders equity to the smallest revenue is {ratio:.2f}")'}
== tool results: [{'python_answer': 'The ratio of the largest stockholders equity to the smallest revenue is 2.49\n'}]
The ratio of the largest stockholders equity to the smallest revenue is 2.49.

1
preamble = """
2
You are an expert who answers the user's question. You are working with a pandas dataframe in Python. The name of the dataframe is `income_statement.csv`.
3
"""
4
5
question1 = "what is the highest value of cost of goods and service?"
6
7
output = cohere_agent(question1, preamble, tools, verbose=True)

running 0th step.
I will use Python to find the highest value of 'Cost of Goods and Service' in the 'income_statement.csv' file.
running 1th step.
= running tool run_python_code, with parameters: {'code': 'import pandas as pd\n\ndf = pd.read_csv(\'income_statement.csv\')\n\n# Find the highest value of \'Cost of Goods and Service\'\nhighest_cost = df[\'Cost of Goods and Service\'].max()\n\nprint(f"The highest value of \'Cost of Goods and Service\' is: {highest_cost}")'}
== tool results: [{'python_answer': "KeyError('Cost of Goods and Service')"}]
Sorry, there is no column named 'Cost of Goods and Service' in the 'income_statement.csv' file.

1
preamble = """
2
You are an expert who answers the user's question. You are working with a pandas dataframe in Python. The name of the dataframe is `income_statement.csv`.
3
If you run into error, keep trying until you fix it. You may need to view the data to understand the error.
4
"""
5
6
question1 = "what is the highest value of cost of goods and service?"
7
8
output = cohere_agent(question1, preamble, tools, verbose=True)

running 0th step.
I will use Python to find the highest value of cost of goods and service.
running 1th step.
= running tool run_python_code, with parameters: {'code': 'import pandas as pd\n\ndf = pd.read_csv(\'income_statement.csv\')\n\n# Find the highest value of \'Cost of Goods and Services\'\nhighest_cost = df[\'Cost of Goods and Services\'].max()\n\nprint(f"The highest value of \'Cost of Goods and Services\' is: {highest_cost}")'}
== tool results: [{'python_answer': "KeyError('Cost of Goods and Services')"}]
I encountered an error. I will now view the data to understand the error.
running 2th step.
= running tool run_python_code, with parameters: {'code': "import pandas as pd\n\ndf = pd.read_csv('income_statement.csv')\n\nprint(df.columns)"}
== tool results: [{'python_answer': "Index(['Unnamed: 0', 'index',\n       'RevenueFromContractWithCustomerExcludingAssessedTax',\n       'CostOfGoodsAndServicesSold', 'GrossProfit',\n       'ResearchAndDevelopmentExpense',\n       'SellingGeneralAndAdministrativeExpense', 'OperatingExpenses',\n       'OperatingIncomeLoss', 'NonoperatingIncomeExpense',\n       'IncomeLossFromContinuingOperationsBeforeIncomeTaxesExtraordinaryItemsNoncontrollingInterest',\n       'IncomeTaxExpenseBenefit', 'NetIncomeLoss', 'EarningsPerShareBasic',\n       'EarningsPerShareDiluted',\n       'WeightedAverageNumberOfSharesOutstandingBasic',\n       'WeightedAverageNumberOfDilutedSharesOutstanding'],\n      dtype='object')\n"}]
I found that the column name is 'CostOfGoodsAndServicesSold'. I will now use this to find the highest value.
running 3th step.
= running tool run_python_code, with parameters: {'code': 'import pandas as pd\n\ndf = pd.read_csv(\'income_statement.csv\')\n\n# Find the highest value of \'Cost of Goods and Services\'\nhighest_cost = df[\'CostOfGoodsAndServicesSold\'].max()\n\nprint(f"The highest value of \'Cost of Goods and Services\' is: {highest_cost}")'}
== tool results: [{'python_answer': "The highest value of 'Cost of Goods and Services' is: 169559000000.0\n"}]
The highest value of 'Cost of Goods and Services' is 169559000000.0.

1
def view_csv_data(path: str) -> dict:
2
    """
3
    Function to view the head, tail and shape of a given csv file.
4
    """
5
    df = pd.read_csv(path)
6
7
    return {
8
        "head": df.head().to_string(),
9
        "tail": df.tail().to_string(),
10
        "shape": str(df.shape),
11
    }
12
13
functions_map = {
14
    "run_python_code": run_python_code,
15
    "view_csv_data": view_csv_data
16
}
17
18
tools = [
19
    {
20
        "name": "run_python_code",
21
        "description": "given a python code, runs it",
22
        "parameter_definitions": {
23
            "code": {
24
                "description": "executable python code",
25
                "type": "str",
26
                "required": True
27
            }
28
        }
29
    },
30
    {
31
        "name": "view_csv_data",
32
        "description": "give path to csv data and get head, tail and shape of the data",
33
        "parameter_definitions": {
34
            "path": {
35
                "description": "path to csv",
36
                "type": "str",
37
                "required": True
38
            }
39
        }
40
    },
41
]

1
preamble = """
2
You are an expert who answers the user's question. You are working with a pandas dataframe in Python. The name of the dataframe is `income_statement.csv`.
3
Always view the data first to write flawless code.
4
"""
5
6
question1 = "what is the highest value of cost of goods and service?"
7
8
output = cohere_agent(question1, preamble, tools, verbose=True)

running 0th step.
I will first view the data and then write and execute Python code to find the highest value of cost of goods and service.
running 1th step.
= running tool view_csv_data, with parameters: {'path': 'income_statement.csv'}
== tool results: [{'head': '   Unnamed: 0                  index  RevenueFromContractWithCustomerExcludingAssessedTax  CostOfGoodsAndServicesSold   GrossProfit  ResearchAndDevelopmentExpense  SellingGeneralAndAdministrativeExpense  OperatingExpenses  OperatingIncomeLoss  NonoperatingIncomeExpense  IncomeLossFromContinuingOperationsBeforeIncomeTaxesExtraordinaryItemsNoncontrollingInterest  IncomeTaxExpenseBenefit  NetIncomeLoss  EarningsPerShareBasic  EarningsPerShareDiluted  WeightedAverageNumberOfSharesOutstandingBasic  WeightedAverageNumberOfDilutedSharesOutstanding\n0           0  2017-10-01-2018-09-29                                         265595000000                1.637560e+11  101839000000                   1.423600e+10                            1.670500e+10       3.094100e+10         7.089800e+10               2.005000e+09                                                                                 7.290300e+10             1.337200e+10    59531000000                   3.00                     2.98                                   1.982151e+10                                     2.000044e+10\n1           1  2018-09-30-2018-12-29                                          84310000000                         NaN   32031000000                            NaN                                     NaN                NaN                  NaN                        NaN                                                                                          NaN                      NaN    19965000000                   1.05                     1.05                                            NaN                                              NaN\n2           2  2018-09-30-2019-09-28                                         260174000000                1.617820e+11   98392000000                   1.621700e+10                            1.824500e+10       3.446200e+10         6.393000e+10               1.807000e+09                                                                                 6.573700e+10             1.048100e+10    55256000000                   2.99                     2.97                                   1.847134e+10                                     1.859565e+10\n3           3  2018-12-30-2019-03-30                                          58015000000                         NaN   21821000000                            NaN                                     NaN                NaN                  NaN                        NaN                                                                                          NaN                      NaN    11561000000                   0.62                     0.61                                            NaN                                              NaN\n4           4  2019-03-31-2019-06-29                                          53809000000                         NaN   20227000000                            NaN                                     NaN                NaN                  NaN                        NaN                                                                                          NaN                      NaN    10044000000                   0.55                     0.55                                            NaN                                              NaN', 'tail': '    Unnamed: 0                  index  RevenueFromContractWithCustomerExcludingAssessedTax  CostOfGoodsAndServicesSold   GrossProfit  ResearchAndDevelopmentExpense  SellingGeneralAndAdministrativeExpense  OperatingExpenses  OperatingIncomeLoss  NonoperatingIncomeExpense  IncomeLossFromContinuingOperationsBeforeIncomeTaxesExtraordinaryItemsNoncontrollingInterest  IncomeTaxExpenseBenefit  NetIncomeLoss  EarningsPerShareBasic  EarningsPerShareDiluted  WeightedAverageNumberOfSharesOutstandingBasic  WeightedAverageNumberOfDilutedSharesOutstanding\n6            6  2019-09-29-2019-12-28                                          91819000000                         NaN   35217000000                            NaN                                     NaN                NaN                  NaN                        NaN                                                                                          NaN                      NaN    22236000000                   1.26                     1.25                                            NaN                                              NaN\n7            7  2019-09-29-2020-09-26                                         274515000000                1.695590e+11  104956000000                   1.875200e+10                            1.991600e+10       3.866800e+10         6.628800e+10                803000000.0                                                                                 6.709100e+10             9.680000e+09    57411000000                   3.31                     3.28                                   1.735212e+10                                     1.752821e+10\n8            8  2019-12-29-2020-03-28                                          58313000000                         NaN   22370000000                            NaN                                     NaN                NaN                  NaN                        NaN                                                                                          NaN                      NaN    11249000000                   0.64                     0.64                                            NaN                                              NaN\n9            9  2020-03-29-2020-06-27                                          59685000000                         NaN   22680000000                            NaN                                     NaN                NaN                  NaN                        NaN                                                                                          NaN                      NaN    11253000000                   0.65                     0.65                                            NaN                                              NaN\n10          10  2020-06-28-2020-09-26                                          64698000000                         NaN   24689000000                            NaN                                     NaN                NaN                  NaN                        NaN                                                                                          NaN                      NaN    12673000000                   0.74                     0.73                                            NaN                                              NaN', 'shape': '(11, 17)'}]
The column name is 'CostOfGoodsAndServicesSold'. I will now write and execute Python code to find the highest value in this column.
running 2th step.
= running tool run_python_code, with parameters: {'code': "import pandas as pd\n\ndf = pd.read_csv('income_statement.csv')\n\nprint(df['CostOfGoodsAndServicesSold'].max())"}
== tool results: [{'python_answer': '169559000000.0\n'}]
The highest value of cost of goods and services is 169559000000.0.