<jupyter_start><jupyter_text>The following lines of code are examples of ways in which the user can manipulate and visualize the downloaded data<jupyter_code># create a dataframe with the downloaded data to allow for easy manipulation
financial_data=pd.DataFrame(ret["IBM"]["is"]["annualReports"])
financial_data=((financial_data.set_index("fiscalDateEnding")).dropna())
financial_data=financial_data.drop(['nonInterestIncome','reportedCurrency','investmentIncomeNet'],axis=1)
financial_data_2=financial_data
financial_data_2

financial_data_2=financial_data_2.replace("None",0)
financial_data_2=financial_data_2.astype('int32')
financial_data_2['Gross Profit Margin']=financial_data_2['grossProfit']/financial_data_2['totalRevenue']
financial_data_2.dtypes
financial_data_2.hvplot.line(x='fiscalDateEnding', y=['Gross Profit Margin'],rot=90, title="Gross Profit Margin")
    

%matplotlib inline
financial_data_2.hvplot.bar(x='fiscalDateEnding', y=['totalRevenue', 'costOfRevenue', 'grossProfit'],rot=90, title='Income Statement Analysis'
    
)<jupyter_output><empty_output>
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