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.

This exercise is part of the course

Financial Forecasting in Python

Exercise instructions

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.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

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)

Edit and Run Code

This exercise is part of the course

Financial Forecasting in Python

IntermediateSkill Level

4.8+

Start Course for Free

In this chapter, we will learn the basics of financial statements, with a specific focus on the income statement, which provides details on our sales, costs, and profits. We will learn how to calculate profitability metrics and finish off what we have learned by building our profit forecast for Tesla!

Exercise 1: Introduction to financial statements Exercise 2: Calculating gross profit Exercise 3: Calculating net profit Exercise 4: Elements within net profit & gross profit Exercise 5: Calculating sales & Cost of Goods Sold (COGS)Exercise 6: Calculating sales Exercise 7: Forecasting sales with a discount Exercise 8: Calculating COGS Exercise 9: Calculating the break-even point Exercise 10: Working with raw forecast datasets Exercise 11: Tesla income statement Exercise 12: Forecasting profit for Tesla

Current Exercise

In this chapter, we will learn a bit more about the balance sheet, covering assets and liabilities and specific ratios to help evaluate the financial health and efficiency of a company, as well as how these ratios can assist us in building a great forecast.

Exercise 1: Introduction to the balance sheet Exercise 2: Calculating accounts receivable (debtors)Exercise 3: Bad debts Exercise 4: Calculating accounts payable (creditors)Exercise 5: Understanding accounts payable and receivable Exercise 6: Balance sheet efficiency ratios - Part 1 Exercise 7: Debtor days ratio Exercise 8: Days payable outstanding Exercise 9: Balance sheet efficiency ratios - Part 2 Exercise 10: Days in inventory and asset turnover ratio Exercise 11: Understanding ratios Exercise 12: Calculating balance sheet ratios for Ford Exercise 13: Forecasting the balance sheet for Ford

We have gotten a basic understanding of income statements and balance sheets. However, consolidating data for forecasting is complex, so in this chapter, we will look at some basic tools to help solve some of the complexities specifically relating to finance - working with dates and different financial periods, and formatting our raw data into the correct format for financial forecasting.

Exercise 1: Financial periods and how to work with them Exercise 2: Converting quarters into months Exercise 3: Merging months into quarters Exercise 4: The datetime library Exercise 5: Working with the datetime library Exercise 6: Converting date formats - simple Exercise 7: Converting date formats - explicit Exercise 8: Tips and tricks when working with datasets Exercise 9: Working with datasets - month totals Exercise 10: Working with datasets - combining datasets Exercise 11: Working with datasets - exporting data

In this chapter, we will be exploring two more aspects to creating a good forecast. First, we will look at assumptions, what drives them and what happens when an assumption changes? Next, we will look at variances, as a forecast is built at one point in time, but what happens when the actual results do not correspond to our forecast? We need to build a sensitive forecast that can be sensitive to changes in both assumptions and take into account variances, and this is what we will explore in this chapter.

Exercise 1: Building sensitive forecast models Exercise 2: Weighted probability Exercise 3: Market sentiment Exercise 4: Dependencies and sensitivity Exercise 5: Assigning dependencies for sales and COGS Exercise 6: Building a sensitivity analysis for gross profit Exercise 7: Assigning dependencies for expenses Exercise 8: Build a sensitivity analysis for the net profit Exercise 9: Working with variances in the forecast Exercise 10: Building an alternate forecast Exercise 11: Building a gap analysis between forecasts Exercise 12: Setting dependencies for Netflix Exercise 13: Calculating an alternative forecast for Netflix