Mengimputasi nilai hilang dengan persentil

Dalam latihan ini, Anda akan terus berlatih mengimputasi nilai yang hilang. Berbeda dengan latihan sebelumnya, kali ini Anda akan menggunakan persentil sebagai pengganti rata-rata untuk menghitung imputasi. Menggunakan persentil adalah cara yang baik untuk mendapatkan imputasi yang konservatif. Mengimputasi nilai hilang dalam sebuah kolom menggunakan persentil melibatkan langkah-langkah berikut:

Hapus nilai yang hilang dari kolom yang menjadi fokus.
Kemudian hitung, misalnya persentil ke-70 dari angka-angka pada kolom setelah nilai hilangnya dihapus.
Nilai terburuk persentil ke-70 bergantung pada kolom tempat Anda menghitung persentil:
- Misalnya, jumlah aset yang besar dianggap baik, sehingga jumlah aset yang rendah lebih buruk. Nilai terburuk persentil ke-70 untuk aset sebenarnya adalah persentil ke-30 aset.
- Secara analog, jumlah liabilitas yang tinggi dianggap buruk. Jadi nilai terburuk persentil ke-70 untuk liabilitas sama dengan persentil ke-70 liabilitas itu sendiri.

pandas telah dimuat dengan alias pd dan NumPy telah dimuat dengan alias np. Sebuah DataFrame pandas bernama dataset telah disiapkan untuk Anda. DataFrame ini memiliki kolom "Total Current Liabilities" yang mengandung beberapa nilai hilang.

Latihan ini adalah bagian dari kursus

Menganalisis Laporan Keuangan dengan Python

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Impute missing value with 70th percentile non-missing values of company
impute_by_company = ___

# Impute missing value with 70th percentile non-missing values of industry
impute_by_comp_type = ____

print(impute_by_company)
print(impute_by_comp_type)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Menganalisis Laporan Keuangan dengan Python

SkillTag.level.intermediateSkillTag.label

4.7+

Mulai Kursus 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: 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: Laporan arus kas Exercise 2: Tetapkan entri arus kas ke bagian yang benar Exercise 3: Hitung arus kas bersih Exercise 4: Rasio keuangan dari laporan arus kas Exercise 5: Menggabungkan laporan keuangan Exercise 6: Hitung rasio laporan arus kas Exercise 7: Mengimputasi dan mengisi nilai hilang menggunakan rata-rata Exercise 8: Mengimputasi nilai hilang dengan persentil

Latihan Saat Ini

Exercise 9: Menggabungkan laporan keuangan dan mengisi nilai yang hilang Exercise 10: Memvisualisasikan rasio keuangan untuk perbandingan Exercise 11: Memplot rasio arus kas

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!