A partir de uma lista de dicionários

É comum acabar com uma lista de dicionários que contém dados que você quer analisar. Esse padrão aparece bastante ao consultar dados de um banco como MySQL, MongoDB ou uma API REST. Cada dicionário é um registro, com um conjunto semelhante de chaves, que viram as colunas do DataFrame.

Neste exercício, você vai pegar uma lista de dados, age_records, em que cada registro é um dicionário (são 4219 dicionários), e convertê-la em um DataFrame do pandas.

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Python para usuários de MATLAB

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Tente este exercicio completando este código de exemplo.

# View the data type of age_records
print(____(age_records))

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Este exercicio faz parte do curso

Python para usuários de 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: 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: Dicionários Exercise 2: Quais chaves?Exercise 3: Obter dados Exercise 4: Modificando dicionários Exercise 5: Introdução aos DataFrames Exercise 6: Explore um DataFrame do pandas Exercise 7: Plotar dados de um DataFrame Exercise 8: Acessando DataFrames do pandas Exercise 9: Acessando linhas e colunas Exercise 10: Os muitos sabores de .iloc Exercise 11: Criando DataFrames do pandas Exercise 12: A partir de um arquivo CSV Exercise 13: A partir de um dicionário de listas Exercise 14: A partir de uma lista de dicionários

Exercicio Atual

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!