Matplotlib

Introduction to Matplotlib

In this lesson, we will create our first basic plots using the matplotlib library.

Let's start with some data.

stock_price = [
    112.2, 113.0, 115.5, 114.5, 113.2, 113.7,
    110.0, 112.5, 113.3, 114.9, 114.5, 115.0
]

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Let's say this data represents a stock's price over an entire year.

Just by looking at the raw data, we can't really say much about it.

It would be much better if we had a graphical representation of it, like the ones we see in the news about financial markets.

Luckily, there's a great library for data visualization in Python: matplotlib.

Just as we did with NumPy, we need to first install matplotlib and then import it using the import keyword:

import matplotlib.pyplot as plt

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This allows us to use the pyplot module from matplotlib under the alias plt.

Let's create our first plot:

import matplotlib.pyplot as plt
 
stock_price = [
    112.2, 113.0, 115.5, 114.5, 113.2, 113.7,
    110.0, 112.5, 113.3, 114.9, 114.5, 115.0
]
 
plt.plot(stock_price)
plt.show()

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Output

So, what happened here?

We used two functions from the matplotlib.pyplot module:

plt.plot() to create a basic line plot.

And plt.show() to display the plot.

As you can see, just by passing our stock_price list to plt.plot(), we already get a nice-looking line plot with 2 axis.

But wait a second, how can we have a 2-dimensional figure if we just passed a single list of elements?

By default, if you don't provide data for the horizontal axis, matplotlib assumes it is a sequence of integers starting from 0.

Indeed, if we look at the plot again, you can see that the line starts at 0 on the horizontal (or x) axis.

Let's add some more meaningful data on the x-axis:

import matplotlib.pyplot as plt
 
months = (
    'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
    'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'
)
 
stock_price = [
    112.2, 113.0, 115.5, 114.5, 113.2, 113.7,
    110.0, 112.5, 113.3, 114.9, 114.5, 115.0
]
 
# months on x-axis, stock_price on y-axis
plt.plot(months, stock_price)
 
plt.show()

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Output

Much better!

However, just by looking at the plot, it's not really obvious what it shows.

What's missing are some labels and a title.

Adding a title to our plot is simple:

plt.title('Title')

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To add labels to our axes, we use:

plt.xlabel('x-label')
plt.ylabel('y-label')

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Let's see this in action:

import matplotlib.pyplot as plt
 
months = (
    'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
    'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'
)
 
stock_price = [
    112.2, 113.0, 115.5, 114.5, 113.2, 113.7,
    110.0, 112.5, 113.3, 114.9, 114.5, 115.0
]
 
plt.plot(months, stock_price)
# add title and lables
plt.title('Yearly Stock Price')
plt.xlabel('Months')
plt.ylabel('Stock Price')
 
plt.show()

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Output

Awesome! That's it for now.