Viva i booleani!

È il momento dello scontro diretto! In questo esercizio confronterai l'iterazione riga per riga su un DataFrame di pandas con l'indicizzazione booleana, testa a testa.

Per prima cosa, itera sulle righe del DataFrame wildlife_strikes e stampa le seguenti informazioni sull'impatto se ha coinvolto una garzetta nivea (Snowy egret), un uccello migratore nativo del Nord e Sud America:

Data dell'incidente (colonna INCIDENT_DATE)
Aeroporto (colonna AIRPORT)
Tipo di velivolo (colonna ATYPE)
Gravità del danno (colonna DAMAGE)

Poi, confronta questo metodo con l'indicizzazione booleana e stampa le stesse informazioni sul DataFrame sottoinsieme.

Questo esercizio fa parte del corso

Python per utenti MATLAB

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Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Loop through wildlife_strikes and print snowy egret data
for i, row in wildlife_strikes.____():
    if row['SPECIES']=='Snowy egret':
        print(row['INCIDENT_DATE'],
              row['AIRPORT'],
              row['ATYPE'],
              row['DAMAGE'])

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Questo esercizio fa parte del corso

Python per utenti MATLAB

InicianteNível de habilidade

4.8+

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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: 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: Scorrere i dati con i loop Exercise 2: Iterare sulle liste Exercise 3: Sovrapporre più grafici in una figura Exercise 4: Confronti e controllo di flusso Exercise 5: Contare i diversi tipi di aeromobili Exercise 6: Trovare determinate righe in un DataFrame Exercise 7: Filtrare i dati Exercise 8: Indicizzazione booleana per statistiche rapide Exercise 9: Viva i booleani!

Esercizio in corso

Exercise 10: Indicizzazione booleana e divertimento con Matplotlib Exercise 11: Ben fatto!