Determine tipos de motor em colisões com fauna

Desde 1992, a Federal Aviation Administration acompanha colisões com fauna envolvendo aeronaves comerciais e militares. Esses dados ajudam a prevenir acidentes catastróficos e reduzem o impacto sobre espécies ameaçadas.

Alguns dados sobre colisões com fauna na Califórnia foram carregados na sessão. Para cada colisão, a FAA registra o tipo de motor da aeronave envolvida.

A lista engine_types_labels contém descrições dos cinco tipos de motores.
O array do NumPy engine_types contém os inteiros de 0 a 4, indicando o tipo de motor, em que o inteiro corresponde ao índice na lista engine_types_labels.

Crie um histograma da frequência de colisões por tipo de motor da aeronave.

Este exercicio faz parte do curso

Python para usuários de MATLAB

exercicio interativo prático

Tente este exercicio completando este código de exemplo.

# Create the histogram with one bin for each unique value in engine_types
plt.____()
plt.____(____, bins = [0,1,2,3,4,5])

# Show the plot

Editar e Executar Código

Este exercicio faz parte do curso

Python para usuários de MATLAB

InicianteNível de habilidade

4.8+

Comece o curso gratuitamente

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: Mergulhando nos arrays do NumPy Exercise 2: Previsão ao longo do tempo Exercise 3: Quanto mais caros são os abacates orgânicos?Exercise 4: Indexando arrays do NumPy Exercise 5: Obtendo colunas de arrays do NumPy Exercise 6: Tráfego de bicicletas ao longo da semana Exercise 7: Filtrando arrays com indexação Booleana Exercise 8: Listas Exercise 9: Explorando listas Exercise 10: Criando arrays do NumPy a partir de listas Exercise 11: Operando em listas e arrays Exercise 12: Personalizando gráficos Exercise 13: Cores, estilos de linha e legendas Exercise 14: Codificando dados em cor e tamanho Exercise 15: Determine tipos de motor em colisões com fauna

Exercicio Atual

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!