Cosa predice la longevità degli animali?

Mettiamo insieme tutto e portiamo un po' di codice MATLAB in Python.

Un collega ti ha inviato la sua analisi in MATLAB dei dati di "AnAge: The Animal Ageing and Longevity Database." Questo database contiene dati sulla longevità per oltre 4000 vertebrati, insieme ad altre informazioni correlate alla longevità.

Hai caricato i dati in Python e ora vuoi capire la relazione tra la longevità massima (memorizzata nell'array NumPy max_longevity) degli animali e il numero di cucciolate che producono ogni anno (in litters_per_year). Hai già importato pyplot e lo hai assegnato alla variabile plt.

Hai il loro codice MATLAB che traccia questa relazione, ma devi portarlo in Python.

Questo esercizio fa parte del corso

Python per utenti MATLAB

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Istruzioni dell'esercizio

Converti i commenti nella sintassi di Python.
Sostituisci le funzioni di plotting di MATLAB con le corrispondenti funzioni di pyplot.
Mostra il grafico.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

% Initialize the figure
figure

% Make the scatter plot
scatter(litters_per_year, max_longevity)

% Add axis labels
xlabel('Litters/Clutches per year')
ylabel('Maximum longevity (yrs)')

____.____()

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Python per utenti MATLAB

InicianteNível de habilidade

4.8+

Inizia il corso gratis

This chapter gets you started moving from MATLAB to Python. You'll learn about some of the similarities and differences between MATLAB and Python, how to use methods and packages, and be introduced to the popular NumPy package.

Exercise 1: Benvenuto in Python!Exercise 2: Calcoli di base Exercise 3: Prevedere un investimento Exercise 4: Tipi di variabili Exercise 5: Metodi e pacchetti Exercise 6: Manipolare le stringhe con i metodi Exercise 7: Uso dei pacchetti Python Exercise 8: Array e grafici Exercise 9: Primi passi con gli array NumPy Exercise 10: Rappresentare il traffico in bicicletta Exercise 11: Cosa predice la longevità degli animali?

Esercizio in corso

In this chapter, you will build on your new NumPy knowledge. You will dive into NumPy arrays, the Python analog to matrices by performing mathematical operations and indexing. You will also begin to explore another important Python data structure, the list, and then round out the chapter by making customs plots of your arrays using Matplotlib.

Exercise 1: Diving into NumPy arrays Exercise 2: Forecasting over time Exercise 3: How much more do organic avocados cost?Exercise 4: Indexing NumPy arrays Exercise 5: Getting columns from NumPy arrays Exercise 6: Bike traffic throughout the week Exercise 7: Filtering arrays with Boolean indexing Exercise 8: Lists Exercise 9: List exploration Exercise 10: Making NumPy arrays from lists Exercise 11: Operating on lists and arrays Exercise 12: Customizing plots Exercise 13: Colors, linestyles, and legends Exercise 14: Encoding data in color & size Exercise 15: Determine engine types in wildlife strikes

This chapter introduces some powerful Python data structures: the dictionary and the pandas DataFrame. You will learn to create dictionaries by setting key-value pairs, and view then how to view and modify your dictionary. Then you will be introduced to one of the most important packages in the Pythonista's toolbox, pandas. Specifically, you'll focus on the pandas' structure, the DataFrame, which organizes tabular data in an easily accessible way. Lastly, you'll learn how to transform different data types into DataFrames to make your data easier to work with.

Exercise 1: Dictionaries Exercise 2: Which keys?Exercise 3: Get data out Exercise 4: Modifying dictionaries Exercise 5: Introduction to DataFrames Exercise 6: Explore a pandas DataFrame Exercise 7: Plot data from a DataFrame Exercise 8: Accessing pandas DataFrames Exercise 9: Accessing rows and columns Exercise 10: The many flavors of .iloc Exercise 11: Creating pandas DataFrames Exercise 12: From a CSV file Exercise 13: From a dictionary of lists Exercise 14: From a list of dictionaries

You'll finish the course by controlling your Python flow. You will learn how to iterate through different Python data structures using for loops. You will also learn about Python contingencies using if, else, and elif and the Python comparison operators (greater than, less than, etc.) that will decide which lines of your code will be executed. Lastly, you'll circle back to NumPy arrays by using Python comparison operators to filter your arrays.

Exercise 1: Looping through data Exercise 2: Looping through lists Exercise 3: Overlaying multiple plots on a figure Exercise 4: Comparisons and control flow Exercise 5: Counting different types of aircraft Exercise 6: Finding certain rows in a DataFrame Exercise 7: Filtering data Exercise 8: Boolean indexing for quick stats Exercise 9: Booleans for the win!Exercise 10: Boolean indexing and Matplotlib fun Exercise 11: Well done!