Ratio's berekenen per sector

In deze oefening beoordeel je hoe de current ratio en de debt-to-equity ratio van Tech- en FMCG-bedrijven gemiddeld en in de tijd van elkaar verschillen.

De ratio van een sector geeft ons gemiddeld een algemeen beeld van de financiële gezondheid van die sector. De ratio van een sector in de tijd helpt ons juist trends te begrijpen: blijft de ratio redelijk stabiel of schommelt die door de tijd heen? Gaat het de goede kant op of verslechtert het?

balance_sheet is voor je geladen als een pandas DataFrame. De current ratio staat in de kolom curr_ratio en de debt-to-equity ratio staat in de kolom debt_to_equity. pandas is geladen met de alias pd.

Deze oefening maakt deel uit van de cursus

Financiële overzichten analyseren in Python

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Oefeninstructies

Selecteer de bedrijven waarvoor de kolom comp_type gelijk is aan tech of fmcg.
Gebruik .groupby() om de gemiddelde current ratio en debt-to-equity ratio te berekenen, gegroepeerd per bedrijfstype (aangegeven door de kolom "comp_type").
Gebruik .groupby() om de gemiddelde current ratio en debt-to-equity ratio te berekenen per zowel "comp_type" als "Year".

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Choose the tech and fmcg companies
tech_fmcg = balance_sheet.loc[balance_sheet[____].isin(____)]

# Compute the average debt-to-equity ratio and current ratio by industry
groupby_industry = tech_fmcg.____(____)[["current_ratio", "debt_to_equity"]].____

# Compute the average debt-to-equity ratio and current ratio by industry and over time
groupby_industry_time = tech_fmcg.____(____)[["current_ratio", "debt_to_equity"]].____

print(groupby_industry)
print(groupby_industry_time)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Financiële overzichten analyseren in Python

SkillTag.level.intermediateSkillTag.label

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Begin de cursus gratis

In this chapter, you will learn how to read and interpret a balance sheet and compute and use financial ratios to evaluate a company's performance using information from the balance sheet. Additionally, you'll gain hands-on experience using the powerful pandas data manipulation package to analyze a company's financial ratios and compare them to its peers in the industry.

Exercise 1: Introductie tot de balans Exercise 2: Koppel balansposten aan hun categorie Exercise 3: Relatie tussen activa, passiva en eigen vermogen van aandeelhouders Exercise 4: Oefen met de hoofdvergelijking van de boekhouding Exercise 5: Financiële ratio's uit de balans Exercise 6: Koppel de ratio aan de juiste familie Exercise 7: Bereken de current ratio Exercise 8: Solvabiliteitsratio's Exercise 9: Financiële kengetallen berekenen met pandas Exercise 10: Bereken current ratio en debt-to-equity ratio Exercise 11: Relatief verschil per sector berekenen Exercise 12: Ratio's berekenen per sector

Huidige oefening

In this chapter, you'll learn how to read and interpret an income statement, calculate key financial ratios, and even create your own functions in Python to automate repetitive tasks. You'll also gain experience using seaborn, a powerful data visualization library, to produce figures to help you understand a company's financial performance.

Exercise 1: Introduction to the income statement Exercise 2: Compute net income Exercise 3: Computing ratios from the income statement Exercise 4: Ratios from the income statement and balance sheet Exercise 5: Updating the user-defined function Exercise 6: Updating user-defined function to do subtraction Exercise 7: Computing ratios with user-defined function Exercise 8: Computing multiple ratios with the user-defined function Exercise 9: Visualizing ratios for within-company analysis Exercise 10: Plotting ratios Exercise 11: Plotting ratios in one figure

In this chapter, you’ll analyze the cash flow statement and use its information to compute and interpret financial ratios. Using Seaborn to create informative plots that compare financial ratios across different companies, you'll build on your existing knowledge of Python and data visualization. By the end of this chapter, you'll have the skills to extract insights from cash flow statements using Python and handle messy, real-world data sets with missing data.

Exercise 1: Cash flow statement Exercise 2: Assign cash flow entries to the correct part Exercise 3: Compute net cash flow Exercise 4: Financial ratios from the cash flow statement Exercise 5: Merging financial statements Exercise 6: Compute cash flow statement ratios Exercise 7: Imputing and filling in missing values using averages Exercise 8: Imputing missing values with percentiles Exercise 9: Merging financial statements and filling missing values Exercise 10: Visualizing financial ratios for comparison Exercise 11: Plotting cash flow ratios

In this chapter, you'll learn about the different financial ratios that measure a company's profitability, how to compute them, and how to compare them across other companies. You'll build upon your knowledge of the Seaborn library to create visualizations that help you analyze profitability ratios and identify trends.

Exercise 1: A glimpse into the financial ratios of some industries Exercise 2: Using margins in pivot tables and making a bar plot Exercise 3: Preparing data for a facet grid plot Exercise 4: Making a facet grid plot Exercise 5: User-defined function to make plots Exercise 6: Profitability metrics Exercise 7: Relationship between operating margin and debt-to-equity for real estate companies Exercise 8: Practice with sns.regplot()Exercise 9: Updating the user-defined function for plotting Exercise 10: More on analyzing profitability Exercise 11: Practice making heat maps Exercise 12: Broad analysis Exercise 13: Well done!