Ortalama ve medyanı hesaplama

Bu bölümde, nu3 tarafından hazırlanan 2018 Food Carbon Footprint Index kaynağındaki food_consumption veri kümesiyle çalışacaksın. food_consumption veri kümesi, her bir country ve yiyecek kategorisi için kişi başına yıllık tüketilen yiyecek miktarını kilogram cinsinden (consumption) ve bunun kilogram karbondioksit, yani CO₂ olarak ölçülen karbon ayak izini (co2_emissions) içerir.

Bu egzersizde, pandas ve numpy becerilerini kullanarak ABD ve Belçika'daki yiyecek tüketimini karşılaştırmak için merkez ölçülerini hesaplayacaksın.

pandas senin için pd takma adıyla içe aktarıldı ve food_consumption önceden yüklendi.

Bu egzersiz, kursun bir parçasıdır

Python ile İstatistiğe Giriş

Kursa Göz Atın

Egzersiz talimatları

numpy'yi np takma adıyla içe aktar.
food_consumption içinden country değeri 'USA' olan satırları alt kümeye ayır.
Senin için zaten oluşturulmuş olan usa_consumption DataFrame'inde yiyecek consumption değerlerinin ortalamasını hesapla.
usa_consumption DataFrame'inde yiyecek consumption değerlerinin medyanını hesapla.

Uygulamalı etkileşimli egzersiz

Bu egzersizi bu örnek kodu tamamlayarak deneyin.

# Import numpy with alias np
____

# Subset country for USA: usa_consumption
usa_consumption = ____

# Calculate mean consumption in USA
print(____)

# Calculate median consumption in USA
print(____)

Kodu Düzenle ve Çalıştır

Bu egzersiz, kursun bir parçasıdır

Python ile İstatistiğe Giriş

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başla

Summary statistics gives you the tools you need to boil down massive datasets to reveal the highlights. In this chapter, you'll explore summary statistics including mean, median, and standard deviation, and learn how to accurately interpret them. You'll also develop your critical thinking skills, allowing you to choose the best summary statistics for your data.

Exercise 1: İstatistik nedir?Exercise 2: Betimsel ve çıkarımsal istatistik Exercise 3: Veri türü sınıflandırması Exercise 4: Merkez ölçüleri Exercise 5: Ortalama ve medyanı hesaplama

Geçerli egzersiz

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In this chapter, you'll learn how to quantify the strength of a linear relationship between two variables, and explore how confounding variables can affect the relationship between two other variables. You'll also see how a study’s design can influence its results, change how the data should be analyzed, and potentially affect the reliability of your conclusions.

Exercise 1: Correlation Exercise 2: Guess the correlation Exercise 3: Relationships between variables Exercise 4: Correlation caveats Exercise 5: What can't correlation measure?Exercise 6: Transforming variables Exercise 7: Does sugar improve happiness?Exercise 8: Confounders Exercise 9: Design of experiments Exercise 10: Study types Exercise 11: Longitudinal vs. cross-sectional studies Exercise 12: Congratulations!