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Forecasting profit for Tesla

As in the previous exercise, the dataset for the Tesla income statement is named income_statement. Using what we have learned in the previous exercise, we will now append a new column with 2018 Forecast data, which we will assign the header "Forecast".

For this exercise, we would like to set the filtered_income_statement to only show the row 'Revenue'.

Remember, the TTM column is the most recent 12-month value that we will use for the 2018 forecast. Thus far, we have the following information for 2018:

  • The market demand analysis predicts the revenue to increase to 13,000 in 2018 due to increased sales of Model 3.

Deze oefening maakt deel uit van de cursus

Financial Forecasting in Python

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Oefeninstructies

  • Create a filtered income statement only for the revenue_metric row.
  • Get the number of columns of filtered_income_statement, using the length (len()) of the columns attribute.
  • Insert a new column in filtered_income_statement.
    • Locate it at the end of the row (use n_cols as the location).
    • Use 'Forecast' as the column name.
    • Insert the value 13000.
  • Print the result.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

revenue_metric = ['Revenue']

# Filter for rows containing the revenue metric
filtered_income_statement = ____[____.____.____(____)]

# Get the number of columns in filtered_income_statement
n_cols = ____(filtered_income_statement.____)

# Insert a column in the correct position containing the column 'Forecast'
filtered_income_statement.insert(____, '____', ___) 

# See the result
print(filtered_income_statement)
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