Laporan laba rugi Tesla

Himpunan data untuk laporan laba rugi Tesla bernama income_statement.

Himpunan data ini memiliki kolom terakhir bernama Trailing Twelve Months (TTM), yaitu 12 bulan data terbaru yang tersedia. Kita akan menggunakannya untuk menghitung prakiraan tahun 2018 untuk Tesla.

Kita hanya memerlukan baris 'Revenue', 'Gross profit', 'Total operating expenses', dan 'Net income', jadi kita akan membuat laporan laba rugi tersaring agar hanya menampilkan baris-baris ini. Kode penyaringannya menggunakan pola berikut.

dataframe[dataframe.columnname.isin(list_of_categories)]

Latihan ini adalah bagian dari kursus

Peramalan Keuangan dengan Python

Petunjuk latihan

Lihat income_statement di shell.
Buat daftar bernama interesting_metrics yang berisi baris-baris yang kita perlukan.
Gunakan metode .isin() untuk menyaring laporan laba rugi pada baris di mana metric ada di dalam interesting_metrics.
Lihat hasilnya.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Choose some interesting metrics
interesting_metrics = ['____', '____', '____', '____']

# Filter for rows containing these metrics
filtered_income_statement = income_statement[income_statement.____.____(____)]

# See the result
print(filtered_income_statement)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Peramalan Keuangan dengan Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

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: Pengantar laporan keuangan Exercise 2: Menghitung laba kotor Exercise 3: Menghitung laba bersih Exercise 4: Unsur dalam laba bersih & laba kotor Exercise 5: Menghitung penjualan & Harga Pokok Penjualan (COGS)Exercise 6: Menghitung penjualan Exercise 7: Membuat proyeksi penjualan dengan diskon Exercise 8: Menghitung COGS Exercise 9: Menghitung titik impas Exercise 10: Bekerja dengan himpunan data prakiraan mentah Exercise 11: Laporan laba rugi Tesla

Latihan Saat Ini

Exercise 12: Membuat prakiraan laba untuk Tesla

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