Compute ratios by industry

In this exercise, you will assess how the current ratio and debt-to-equity ratio of Tech and FMCG companies are different on average and over time.

An industry's ratio, on average gives us an overall idea of the industry's financial condition. On the other hand, an industry's ratio over timehelps us understand its trends: is the ratio relatively stable or fluctuating over time? Is it improving or worsening?

balance_sheet has been loaded for you as a pandas DataFrame. The current ratio is provided in a column called curr_ratio, and the debt-to-equity ratio is provided in a column called debt_to_equity. pandas has been loaded with the alias pd.

Cet exercice fait partie du cours

Analyzing Financial Statements in Python

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Instructions

Select those companies whose column comp_type is tech or fmcg.
Use .groupby() to compute the average current ratio and debt-to-equity ratio grouped by company type (denoted by the column "comp_type").
Use .groupby() to compute the average current ratio and debt-to-equity ratio by both "comp_type" and "Year".

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# 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)

Modifier et exécuter le code

Cet exercice fait partie du cours

Analyzing Financial Statements in Python

IntermédiaireNiveau de compétence

4.7+

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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

Exercice en cours

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!