Tesla için kâr tahmini

Önceki egzersizde olduğu gibi, Tesla gelir tablosu veri kümesinin adı income_statement. Önceki egzersizde öğrendiklerimizi kullanarak şimdi 2018 Tahmin verisini içeren yeni bir sütun ekleyeceğiz ve başlığını "Forecast" olarak atayacağız.

Bu egzersizde, filtered_income_statement sadece 'Revenue' satırını gösterecek şekilde ayarlamak istiyoruz.

Unutma, TTM sütunu 2018 tahmini için kullanacağımız en güncel 12 aylık değeri gösterir. Şu ana kadar 2018 için elimizdeki bilgi:

Pazar talebi analizi, Model 3 satışlarındaki artış nedeniyle gelirin 2018’de 13.000’e yükseleceğini öngörüyor.

Bu egzersiz

Python ile Finansal Tahminleme

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Yalnızca revenue_metric satırı için filtrelenmiş bir gelir tablosu oluştur.
columns özniteliğinin uzunluğunu (len()) kullanarak filtered_income_statement'ın sütun sayısını al.
filtered_income_statement içine yeni bir sütun ekle.
- Onu satırın sonuna yerleştir (konum olarak n_cols kullan).
- Sütun adı olarak 'Forecast' kullan.
- Değer olarak 13000 ekle.
Sonucu yazdır.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

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)

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python ile Finansal Tahminleme

kursunun bir parçasıdır

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Kursa Ücretsiz Başlayın

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: Finansal tablolara giriş Exercise 2: Brüt kârı hesaplama Exercise 3: Net kârı hesaplama Exercise 4: Net kâr ve brüt kâr içindeki unsurlar Exercise 5: Satışları ve Satılan Malların Maliyeti (COGS) Hesaplama Exercise 6: Satışların hesaplanması Exercise 7: İndirimle satış tahmini Exercise 8: COGS hesaplama Exercise 9: Başabaş noktasını hesaplama Exercise 10: Ham tahmin veri kümeleriyle çalışma Exercise 11: Tesla gelir tablosu Exercise 12: Tesla için kâr tahmini

Geçerli Egzersiz

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