Fietstrafiek plotten

Waarden uit een array halen is handig als je maar één element nodig hebt, maar om een overzicht van patronen in je data te krijgen, heb je grafieken nodig! In deze oefening ga je bekijken hoe het fietsverkeer over de Fremont Bridge door het jaar heen varieert door NumPy-arrays te plotten met matplotlib.

Twee NumPy-arrays zijn ingeladen:

daily_riders bevat het gemiddelde aantal dagelijkse fietsers per maand in 2016.
month bevat de bijbehorende maand (1-12) van het jaar voor elk element in daily_riders.

Tijd om deze data te plotten!

Deze oefening maakt deel uit van de cursus

Python voor MATLAB-gebruikers

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# Import pyplot as plt

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Deze oefening maakt deel uit van de cursus

Python voor MATLAB-gebruikers

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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: Welkom bij Python!Exercise 2: Eenvoudige berekeningen Exercise 3: Een investering voorspellen Exercise 4: Typen variabelen Exercise 5: Methoden en pakketten Exercise 6: Strings bewerken met methods Exercise 7: Python-packages gebruiken Exercise 8: Arrays & plotten Exercise 9: Aan de slag met NumPy-arrays Exercise 10: Fietstrafiek plotten

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Exercise 11: Wat voorspelt de levensduur van dieren?

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!