Ortalama kullanarak eksik değerleri atama ve doldurma

Analiz yapmak istediğinde, büyük olasılıkla kendi verilerini kullanacaksın. Veri kümelerinde genellikle bazı eksik değerler bulunur. Bu egzersizde, bu eksik değerleri atama (impute etme) pratiği yapacaksın. Eksik değerleri atamak önemlidir; çünkü analizimizde eksik değerlerin engel olmasını istemeyiz.

pandas pd takma adıyla ve NumPy np takma adıyla yüklendi. Senin için dataset adlı bir pandas DataFrame'i yüklendi. İçinde bazı eksik değerler bulunan "Total Current Liabilities" sütunu var.

Bu egzersiz

Python ile Finansal Tabloları Analiz Etme

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Impute missing value using average non-missing values by company
impute_by_company = dataset.____

# Impute missing value using average non-missing values by industry
impute_by_comp_type = dataset.____

print(impute_by_company)
print(impute_by_comp_type)

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python ile Finansal Tabloları Analiz Etme

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.7+

Kursa Ücretsiz Başlayın

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: Nakit akış tablosu Exercise 2: Nakit akışı kalemlerini doğru bölüme ata Exercise 3: Net nakit akışını hesapla Exercise 4: Nakit akış tablosundan finansal oranlar Exercise 5: Finansal tabloları birleştirme Exercise 6: Nakit akış tablosu oranlarını hesapla Exercise 7: Ortalama kullanarak eksik değerleri atama ve doldurma

Geçerli Egzersiz

Exercise 8: Yüzdeliklerle eksik değer atama Exercise 9: Finansal tabloları birleştirme ve eksik değerleri doldurma Exercise 10: Karşılaştırma için finansal oranları görselleştirme Exercise 11: Nakit akışı oranlarını görselleştirme

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!