Explorer un DataFrame pandas

Le DataFrame pandas est l’une des structures de données les plus importantes (et les plus pratiques) en Python, en particulier pour la Data Science. Non seulement sa structure tabulaire est familière, mais le package pandas propose plusieurs moyens d’explorer rapidement un DataFrame. Dans cet exercice, vous utiliserez un (très) petit ensemble des méthodes et attributs disponibles dans pandas pour explorer le DataFrame avocados.

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<cours>Python pour les utilisateurs de MATLAB</cours>

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Exercice interactif pratique

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# Print the first five rows of avocados
print(avocados.____())

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Cet exercice fait partie du cours

<cours>Python pour les utilisateurs de MATLAB</cours>

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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: Dictionnaires Exercise 2: Quelles clés ?Exercise 3: Extraire des données Exercise 4: Modifier des dictionnaires Exercise 5: Introduction aux DataFrames Exercise 6: Explorer un DataFrame pandas

Exercice actuel

Exercise 7: Tracer des données depuis un DataFrame Exercise 8: Accéder aux DataFrames pandas Exercise 9: Accéder aux lignes et aux colonnes Exercise 10: Les multiples usages de .iloc Exercise 11: Créer des DataFrames pandas Exercise 12: À partir d’un fichier CSV Exercise 13: À partir d’un dictionnaire de listes Exercise 14: À partir d’une liste de dictionnaires

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!