Calcolare media e mediana

In questo capitolo lavorerai con l'insieme di dati food_consumption tratto dal 2018 Food Carbon Footprint Index di nu3. L'insieme di dati food_consumption contiene il numero di chilogrammi di cibo consumati per persona all'anno in ciascun country e categoria alimentare (consumption), e la relativa impronta di carbonio (co2_emissions) misurata in chilogrammi di anidride carbonica, ovvero CO₂.

In questo esercizio calcolerai misure di centro per confrontare il consumo di cibo negli Stati Uniti e in Belgio usando le tue competenze con pandas e numpy.

pandas è importato come pd e food_consumption è già caricato.

Questo esercizio fa parte del corso

Introduzione alla statistica in Python

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Istruzioni dell'esercizio

Importa numpy con l'alias np.
Sottoinsieme food_consumption per ottenere le righe in cui country è 'USA'.
Calcola la media del consumption alimentare nel DataFrame usa_consumption, che è già stato creato per te.
Calcola la mediana del consumption alimentare nel DataFrame usa_consumption.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# 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(____)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Introduzione alla statistica in Python

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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: Che cos'è la statistica?Exercise 2: Statistica descrittiva e inferenziale Exercise 3: Classificazione dei tipi di dati Exercise 4: Misure di tendenza centrale Exercise 5: Calcolare media e mediana

Esercizio in corso

Exercise 6: Media, mediana e forma della distribuzione Exercise 7: Misure di dispersione Exercise 8: Varianza e deviazione standard Exercise 9: Quartili, quantili e quintili Exercise 10: Individuare gli outlier usando l'IQR

In this chapter, you'll learn how to generate random samples and measure chance using probability. You'll work with real-world sales data to calculate the probability of a salesperson being successful. Finally, you’ll use the binomial distribution to model events with binary outcomes.

Exercise 1: What are the chances?Exercise 2: With or without replacement?Exercise 3: Calculating probabilities Exercise 4: Sampling deals Exercise 5: Discrete distributions Exercise 6: Creating a probability distribution Exercise 7: Identifying distributions Exercise 8: Expected value vs. sample mean Exercise 9: Continuous distributions Exercise 10: Which distribution?Exercise 11: Data back-ups Exercise 12: Simulating wait times Exercise 13: The binomial distribution Exercise 14: Simulating sales deals Exercise 15: Calculating binomial probabilities Exercise 16: How many sales will be won?

It’s time to explore one of the most important probability distributions in statistics, normal distribution. You’ll create histograms to plot normal distributions and gain an understanding of the central limit theorem, before expanding your knowledge of statistical functions by adding the Poisson, exponential, and t-distributions to your repertoire.

Exercise 1: The normal distribution Exercise 2: Distribution of Amir's sales Exercise 3: Probabilities from the normal distribution Exercise 4: Simulating sales under new market conditions Exercise 5: Which market is better?Exercise 6: The central limit theorem Exercise 7: Visualizing sampling distributions Exercise 8: The CLT in action Exercise 9: The mean of means Exercise 10: The Poisson distribution Exercise 11: Identifying lambda Exercise 12: Tracking lead responses Exercise 13: More probability distributions Exercise 14: Distribution dragging and dropping Exercise 15: Modeling time between leads Exercise 16: The t-distribution

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!