Bekerja dengan himpunan data - menggabungkan himpunan data

Dalam contoh ini, Anda akan bekerja dengan dua himpunan data, df1 dan df2. Anda dapat mencetak keduanya di konsol untuk melihat isinya. Anda akan melihat bahwa keduanya menggunakan pemformatan tanggal yang berbeda.

Lebih spesifik, df1 menyatakan bulan dengan nama (misalnya 02-Feb-18), sedangkan df2 menyatakan bulan secara numerik (misalnya 06/01/2018). Selain itu, df1 menggunakan tanda hubung (-) sebagai pemisah, sedangkan df2 menggunakan garis miring (/) sebagai pemisah.

Kita akan menggabungkan kedua himpunan data ini untuk membentuk proyeksi terkonsolidasi untuk kuartal tersebut. Untuk melakukannya, kita perlu mengurai format tanggal yang berbeda pada df1 dan df2.

Latihan ini adalah bagian dari kursus

Peramalan Keuangan dengan Python

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

____ = {'Jan': 0, 'Feb': 0, 'Mar': 0}
calendar = {'01': '____', '02': '____', '03': '____'}

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: 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

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: Periode keuangan dan cara mengolahnya Exercise 2: Mengonversi kuartal menjadi bulan Exercise 3: Menggabungkan bulan menjadi triwulan Exercise 4: Pustaka datetime Exercise 5: Bekerja dengan pustaka datetime Exercise 6: Mengonversi format tanggal - sederhana Exercise 7: Mengonversi format tanggal - eksplisit Exercise 8: Tips dan trik saat bekerja dengan himpunan data Exercise 9: Bekerja dengan himpunan data - total bulanan Exercise 10: Bekerja dengan himpunan data - menggabungkan himpunan data

Latihan Saat Ini

Exercise 11: Bekerja dengan himpunan data - mengekspor 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