Los muchos sabores de .iloc

Hay múltiples formas de indexar un DataFrame de pandas — quizá demasiadas. En este ejercicio, vas a explorar los muchos usos — o trampas — del indexador .iloc. Esto es algo que probablemente te dará algún quebradero de cabeza en tu aventura con Python, así que es bueno saber desde el principio a qué te enfrentas.

En concreto, en este ejercicio aprenderás a usar .iloc de tres formas ligeramente distintas que devuelven resultados drásticamente diferentes.

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Python para usuarios de MATLAB

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# Access first row and first column
iloc_1 = animals.____[____,____]
print(iloc_1)
print(type(iloc_1))

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Este ejercicio forma parte del curso

Python para usuarios 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: Diccionarios Exercise 2: ¿Qué claves?Exercise 3: Extrae datos Exercise 4: Modificar diccionarios Exercise 5: Introducción a los DataFrames Exercise 6: Explora un DataFrame de pandas Exercise 7: Representa datos de un DataFrame Exercise 8: Acceder a DataFrames de pandas Exercise 9: Acceder a filas y columnas Exercise 10: Los muchos sabores de .iloc

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Exercise 11: Crear DataFrames de pandas Exercise 12: A partir de un archivo CSV Exercise 13: A partir de un diccionario de listas Exercise 14: A partir de una lista de diccionarios

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!