Calcola i rapporti per settore

In questo esercizio valuterai in che modo, in media e nel tempo, il current ratio e il debt-to-equity ratio delle aziende Tech e FMCG differiscono.

Il rapporto di un settore, in media, ci dà un’idea generale della sua situazione finanziaria. Il rapporto di un settore nel tempo invece ci aiuta a capirne l’andamento: il rapporto è relativamente stabile o fluttua nel tempo? Sta migliorando o peggiorando?

balance_sheet è stato caricato per te come DataFrame di pandas. Il current ratio è nella colonna curr_ratio e il debt-to-equity ratio è nella colonna debt_to_equity. pandas è stato importato con l’alias pd.

Questo esercizio fa parte del corso

Analizzare i bilanci con Python

Visualizza il corso

Istruzioni dell'esercizio

Seleziona le aziende per cui la colonna comp_type è tech o fmcg.
Usa .groupby() per calcolare il current ratio medio e il debt-to-equity ratio medio raggruppando per tipo di azienda (indicato dalla colonna "comp_type").
Usa .groupby() per calcolare il current ratio medio e il debt-to-equity ratio medio raggruppando sia per "comp_type" sia per "Year".

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# 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)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Analizzare i bilanci con Python

IntermediárioNível de habilidade

4.7+

Inizia il corso 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: Introduzione allo stato patrimoniale Exercise 2: Abbina le voci di bilancio alla loro categoria Exercise 3: Relazione tra attività, passività e patrimonio netto Exercise 4: Metti in pratica l'equazione fondamentale della contabilità Exercise 5: Indici finanziari dallo stato patrimoniale Exercise 6: Abbina ciascun rapporto alla sua famiglia Exercise 7: Calcolare il current ratio Exercise 8: Indici di leva finanziaria Exercise 9: Calcolare gli indici finanziari con pandas Exercise 10: Calcola il current ratio e il rapporto debito/patrimonio netto Exercise 11: Calcola la differenza relativa per settore Exercise 12: Calcola i rapporti per settore

Esercizio in corso

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!