Preparare i dati per un grafico con FacetGrid

In questo esercizio, preparerai i dati per creare questo grafico: This plot shows the gross margin of tech companies, FMCG companies, the average tech company, and the average FMCG companies over many years. Nel prossimo esercizio, metterai in pratica la creazione del grafico. Questo grafico è utile per osservare:

Il margine lordo delle aziende tech è più disperso rispetto a quello delle aziende FMCG
Anno per anno, quanto è redditizia un’azienda rispetto alla media del suo settore.

pandas è stato caricato con l’alias pd e seaborn con l’alias sns. Un DataFrame di pandas chiamato dataset è già caricato e contiene una colonna gross_margin già calcolata.

Questo esercizio fa parte del corso

Analizzare i bilanci con Python

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Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Subset tech and fmcg companies
subset_dat = dataset.loc[____]

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Questo esercizio fa parte del corso

Analizzare i bilanci con Python

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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: Uno sguardo ai rapporti finanziari di alcuni settori Exercise 2: Uso dei margini nelle tabelle pivot e creazione di un grafico a barre Exercise 3: Preparare i dati per un grafico con FacetGrid

Esercizio in corso

Exercise 4: Creare un facet grid plot Exercise 5: Funzione definita dall’utente per creare i grafici Exercise 6: Metriche di redditività Exercise 7: Relazione tra margine operativo e rapporto debito/capitale proprio per le società immobiliari Exercise 8: Esercitati con sns.regplot()Exercise 9: Aggiornare la funzione definita dall'utente per il plotting Exercise 10: Approfondire l’analisi della redditività Exercise 11: Esercitati a creare heat map Exercise 12: Analisi ampia Exercise 13: Ben fatto!