Esplorare le liste

Una caratteristica unica delle liste è che possono contenere ogni tipo di elemento: numeri, stringhe, e persino funzioni e array NumPy. Questo è molto utile per raggruppare elementi invece di creare una variabile per ciascuno.

In questo esercizio vedrai alcuni dei tipi di elementi che una lista può contenere, esplorando cosa c'è dentro la lista things.

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Python per utenti MATLAB

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# Print the first element of "things"
print(____)

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Questo esercizio fa parte del corso

Python per utenti MATLAB

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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: Welcome to Python!Exercise 2: Basic calculations Exercise 3: Forecasting an investment Exercise 4: Types of variables Exercise 5: Methods and packages Exercise 6: Manipulating strings with methods Exercise 7: Using Python packages Exercise 8: Arrays & plotting Exercise 9: Getting started with NumPy arrays Exercise 10: Plotting bicycle traffic Exercise 11: What predicts animal longevity?

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: Immersione negli array NumPy Exercise 2: Previsioni nel tempo Exercise 3: Quanto costano in più gli avocado biologici?Exercise 4: Indicizzazione degli array NumPy Exercise 5: Estrarre colonne da array NumPy Exercise 6: Traffico in bici durante la settimana Exercise 7: Filtrare array con indicizzazione booleana Exercise 8: Liste Exercise 9: Esplorare le liste

Esercizio in corso

Exercise 10: Creare array NumPy dalle liste Exercise 11: Operare su liste e array Exercise 12: Personalizzare i grafici Exercise 13: Colori, stili di linea e legende Exercise 14: Codificare i dati con colore e dimensione Exercise 15: Determina i tipi di motore negli impatti con la fauna selvatica

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!