Rappresentare il traffico in bicicletta

Estrarre valori da un array è utile se ti serve informazione su un solo elemento, ma per avere una panoramica dell’andamento nei tuoi dati servono i grafici! In questo esercizio, esplorerai come il traffico ciclistico sul Fremont Bridge varia durante l’anno tracciando array NumPy con matplotlib.

Sono caricati due array NumPy:

daily_riders contiene il numero medio di ciclisti giornalieri per ogni mese del 2016.
month contiene il mese corrispondente (1-12) dell’anno per ciascun elemento di daily_riders.

È ora di visualizzare questi dati!

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Python per utenti MATLAB

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# Import pyplot as plt

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Questo esercizio fa parte del corso

Python per utenti 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: Benvenuto in Python!Exercise 2: Calcoli di base Exercise 3: Prevedere un investimento Exercise 4: Tipi di variabili Exercise 5: Metodi e pacchetti Exercise 6: Manipolare le stringhe con i metodi Exercise 7: Uso dei pacchetti Python Exercise 8: Array e grafici Exercise 9: Primi passi con gli array NumPy Exercise 10: Rappresentare il traffico in bicicletta

Esercizio in corso

Exercise 11: Cosa predice la longevità degli animali?

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: 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!