Übe mit sns.regplot()

In dieser Übung prüfst du, ob es eine Beziehung zwischen der operativen Marge und dem Verschuldungsgrad (debt-to-equity ratio) für alle Unternehmen in unserem Datensatz gibt. Zuerst erstellst du ein Streudiagramm, um die Beziehung zu analysieren, und kannst eine relativ klare positive Beziehung zwischen Verschuldungsgrad und operativer Marge erkennen. Anschließend verwendest du sns.regplot, um eine Ausgleichsgerade zu erhalten und die Hypothese zu bestätigen oder zu verwerfen, dass zwischen den beiden Kennzahlen eine positive Beziehung besteht.

Das pandas-DataFrame dataset ist geladen; Verschuldungsgrad und operative Marge sind bereits berechnet. pandas und seaborn sind mit den Aliassen pd bzw. sns geladen.

Diese Übung ist Teil des Kurses

<Kurs>Finanzberichte mit Python analysieren</Kurs>

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Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

# Make the scatterplot
sns.___
plt.show()
plt.close()

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Diese Übung ist Teil des Kurses

<Kurs>Finanzberichte mit Python analysieren</Kurs>

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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: Introduction to the balance sheet Exercise 2: Match balance sheet items to their class Exercise 3: Relation of assets, liabilities, and shareholders' equity Exercise 4: Practice using the main equation of accounting Exercise 5: Financial ratios from the balance sheet Exercise 6: Match the ratio to its family Exercise 7: Compute the current ratio Exercise 8: Leverage ratios Exercise 9: Computing financial ratios using pandas Exercise 10: Compute current and debt-to-equity ratios Exercise 11: Compute relative difference by industry Exercise 12: Compute ratios by industry

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: Ein Blick auf die Finanzkennzahlen einiger Branchen Exercise 2: Verwendung von Margins in Pivot-Tabellen und Erstellen eines Balkendiagramms Exercise 3: Daten für ein Facet-Grid-Diagramm vorbereiten Exercise 4: Ein FacetGrid-Diagramm erstellen Exercise 5: Benutzerdefinierte Funktion zum Erstellen von Plots Exercise 6: Profitabilitätskennzahlen Exercise 7: Zusammenhang zwischen Operating Margin und Debt-to-Equity bei Immobilienunternehmen Exercise 8: Übe mit sns.regplot()

Aktuelle Übung

Exercise 9: Aktualisierung der benutzerdefinierten Funktion zum Plotten Exercise 10: Mehr zur Analyse der Profitabilität Exercise 11: Übe das Erstellen von Heatmaps Exercise 12: Breite Analyse Exercise 13: Gut gemacht!