Koppel balansposten aan hun categorie

Activa worden door het bedrijf gebruikt om positieve economische waarde te creëren. Ze leveren dus voordelen op voor het bedrijf. Passiva zijn toekomstige verplichtingen die het bedrijf moet afbetalen. Activa en passiva kun je verder opdelen:

Vlottende activa zijn activa die doorgaans binnen één jaar voordelen opleveren.
Kortlopende passiva zijn verplichtingen die binnen één jaar moeten worden afgelost.
Langlopende activa leveren voordelen op over een periode van meer dan één jaar.
Langlopende passiva worden over een periode van meer dan één jaar afgelost.

In deze oefening zijn accounts_receivable, accounts_payable, short_term_loans, long_term_loans, inventory, long_term_investments en property_plant_equipment al voor je ingeladen.

Je gaat bepalen welke balansposten vlottende of langlopende activa en passiva zijn. Vervolgens tel je de balansposten binnen de juiste categorie bij elkaar op om de totale vlottende en langlopende activa en passiva te berekenen.

Deze oefening maakt deel uit van de cursus

Financiële overzichten analyseren in Python

Cursus bekijken

Oefeninstructies

Tel de totale vlottende activa op.
Tel de totale langlopende activa op.
Tel de totale kortlopende passiva op.
Tel de totale langlopende passiva op.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

accounts_receivable = 1298
accounts_payable = 500
short_term_loans = 3357
long_term_loans = 8000
inventory = 5420
long_term_investments = 7892
property_plant_equipment = 9840

# Add up total current assets
total_current_assets = ____

# Add up total non-current assets
total_non_current_assets = ____

# Add up total current liabilities
total_current_liab = ____

# Add up total non-current liabilities
total_non_current_liab = ____

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Financiële overzichten analyseren in Python

SkillTag.level.intermediateSkillTag.label

4.7+

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

Huidige oefening

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

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!