# Matplotlib Line Plot – A Helpful Illustrated Guide

The line plot is the most iconic of all the plots. To draw one in matplotlib, use the plt.plot() function and pass it a list of numbers used as the y-axis values. Per default, the x-axis values are the list indexes of the passed line. Matplotlib automatically connects the points with a blue line per default. You can change the line type and marker size with additional arguments.

Syntax of plt.plot():

plot([x], y, [fmt], *, data=None, **kwargs)

Example Calls:

>>> plot(x, y)        # plot x and y using default line style and color
>>> plot(x, y, 'bo')  # plot x and y using blue circle markers
>>> plot(y)           # plot y using x as index array 0..N-1
>>> plot(y, 'r+')     # ditto, but with red plusses

## Minimal Line Plot Examples

The minimal example is the following:

import matplotlib.pyplot as plt
plt.plot([0, 1, 2, 3])
plt.ylabel('line plot')
plt.show()

The output generated by these four lines of code is the following simple line plot:

So far, so good! Here’s a bit more advanced example using the seaborn styling for the plot:

import matplotlib.pyplot as plt
import numpy as np

# I prefer this style to the default
plt.style.use('seaborn')

# Square numbers from 0-10 inclusive
squared = [x**2 for x in range(11)]
plt.plot(squared)
plt.show()

Like scatter plots, line plots show the relationship between two variables. Unlike scatter plots, they are often used to measure how a variable changes over time. Thus we would use a line plot to show how the stock market has performed rather than a scatter plot.

Line plots are excellent for time series data because we can put time on the x-axis and whatever we are measuring on the y-axis.

Let’s look at a classic example – the US stock market.

## Matplotlib Line Plot Stock Market Example

This plot shows the S&P 500 index over 2019 using matplotlib’s default settings. The S&P 500 tracks the top 500 US stocks and so is a reflection of the stock market overall.

I’ve split the data into two NumPy arrays. One for the S&P 500 values (e.g. sp2019) and one for the dates (e.g. dates2019).

Note: the dates only include business days because the stock market is only open on business days.

# First 5 business days in 2019
>>> bus2019[:5]
[numpy.datetime64('2019-01-01'),
numpy.datetime64('2019-01-02'),
numpy.datetime64('2019-01-03'),
numpy.datetime64('2019-01-04'),
numpy.datetime64('2019-01-07')]

# First 5 S&P 500 values in 2019
# It contains some missing values (NaN - Not a Number)
>>> sp2019[:5]
array([[    nan],
[2510.03],
[2447.89],
[2531.94],
[2549.69]])

There are gaps in the plot because of the missing values. But the data is good enough for our purposes.

To plot this, we pass sp2019 to plt.plot() and then call plt.show().

plt.plot(sp2019)
plt.show()

Great. It shows the S&P 500 values on the y-axis but what are the numbers on the x-axis?

If you only pass a list or NumPy array, matplotlib uses the list indexes for the x-axis values.

>>> len(sp2019)
250

As there are 250 values in sp2019, the x-axis ranges from 0 to 250.

In this case, it would be better if we had dates on the x-axis. To do this, we pass two arguments to plt.plot(). First the x-axis values, then the y-axis ones.

# x-axis for dates, y-axis for S&P 500 index
plt.plot(dates2019, sp2019)
plt.show()

Matplotlib spaces the dates out evenly and chooses the best level of accuracy. For this plot, it chose months. It would be annoying if it chose dates down to the day.

Finally, let’s add some axis labels and a title.

plt.plot(bus2019, sp2019)
plt.title('S&P500 Index - 2019')
plt.xlabel('Date')
plt.ylabel('Index')
plt.show()

Perfect. To save space, I will exclude the lines of code that set the axis labels and title. But make sure to include them in your plots.

## Matplotlib Line Plot Color

Color is an incredibly important part of plotting and deserves an entire article in itself. Check out the Seaborn docs for a great overview.

Color can make or break your plot. Some color schemes make it ridiculously easy to understand the data and others make it impossible.

However, one reason to change the color is purely for aesthetics.

We choose the color of points in plt.plot() with the keyword c or color. The default is blue.

You can set any color you want using an RGB or RGBA tuple (red, green, blue, alpha). Each element of these tuples is a float in [0.0, 1.0]. You can also pass a hex RGB or RGBA string such as '#1f1f1f'. However, most of the time you’ll use one of the 50+ built-in named colors. The most common are:

• 'b' or 'blue'
• 'r' or 'red'
• 'g' or 'green'
• 'k' or 'black'
• 'w' or 'white'

Here’s the plot of the S&P500 index for 2019 using different colors

For each plot, call plt.plot() with dates2019 and sp2019. Then set color (or c) to your choice

# Blue (the default value)
plt.plot(dates2019, sp2019, color='b')

# Red
plt.plot(dates2019, sp2019, color='r')

# Green
plt.plot(dates2019, sp2019, c='g')

# Black
plt.plot(dates2019, sp2019, c='k')

## Matplotlib Line Plot Multiple Lines

If you draw multiple line plots at once, matplotlib colors them differently. This makes it easy to recognize the different datasets.

Let’s plot the S&P500 index for 2018 and 2019 on one plot to compare how it performed each month. You do this by making two plt.plot() calls before calling plt.show().

plt.plot(sp2019)
plt.plot(sp2018)
plt.show()

This looks great. It’s very easy to tell the orange and blue lines apart. But there are two problems:

1. The date axis doesn’t show dates
2. We don’t know which line is for which year.

## Matplotlib x axis label

To solve the first problem, we need to rename the numbers on the x-axis. In matplotlib, they are called x-ticks and so we use the plt.xticks() function.

It accepts two arguments: plt.xticks(ticks, labels)

• ticks – a list of positions to place the ticks
• labels – a list of labels to describe each tick

In this case, the ticks are [0, 50, 100, 150, 200, 250] and the labels are the months of the year.

plt.plot(sp2019)
plt.plot(sp2018)

# Create ticks and labels
ticks = [0, 50, 100, 150, 200, 250]
labels = ['Jan', 'Mar', 'May', 'Jul', 'Sep', 'Nov']

# Pass to xticks
plt.xticks(ticks, labels)
plt.show()

Now let’s find out which line is for which year.

## Matplotlib Line Plot Legend

To add a legend we use the plt.legend() function. This is easy to use with line plots.

In each plt.plot() call, label each line with the label keyword. When you call plt.legend(), matplotlib will draw a legend with an entry for each line.

# Add label to 2019 plot
plt.plot(sp2019, label='2019')

# Add label to 2018 plot
plt.plot(sp2018, label='2018')

# Call plt.legend to display it
plt.legend()

plt.xticks(ticks, labels)
plt.show()

Perfect. We now have a finished plot. We know what all the axes represent and know which line is which. You can see that 2019 was a better year almost every month.

By default, matplotlib draws the legend in the 'best' location. But you can manually set it using the loc keyword and one of these 10, self-explanatory, strings:

• 'upper right', 'upper left', 'upper center'
• 'lower right', 'lower left', 'lower center'
• 'center right', 'center left'
• 'right' or 'center' (for some reason, 'left' is not an option)

Here are some examples of putting the legend in different locations

Best practice is to place your legend somewhere where it doesn’t obstruct the plot.

## Matplotlib Linestyle

There are several linestyles you can choose from. They are set with the linestyle or ls keyword in plt.plot().

Their syntax is intuitive and easy to remember. Here are the square numbers with all possible linestyles,

For each plot, call plt.plot(squared) and set linestyle or ls to your choice

# Solid (default)
plt.plot(squared, linestyle='-')

# Dashed
plt.plot(squared, linestyle='--')

# Dashdot
plt.plot(squared, ls='-.')

# Dotted
plt.plot(squared, ls=':')

You can also pass the linestyle names instead of the short form string. The following are equivalent:

• 'solid' or '-'
• 'dashed' or '--'
• 'dashdot' or '-.'
• 'dotted' or ':'

## Matplotlib Line Thickness

You can set the line thickness to any float value by passing it to the linewidth or lw keyword in plt.plot().

Here are the square numbers with varying line widths. Smaller numbers mean thinner lines.

plt.plot(squared, linewidth=1)

plt.plot(squared, linewidth=3.25)

plt.plot(squared, lw=10)

plt.plot(squared, lw=15.35)

## Matplotlib Line Width

You can set the line width to any float value by passing it to the linewidth or lw keyword in plt.plot().

Here are the square numbers with varying line widths

## Matplotlib Line Plot with Markers

By default, plt.plot() joins each of the values with a line and doesn’t highlight individual points. You can highlight them with the marker keyword.

There are over 30 built-in markers to choose from. Plus you can use any LaTeX expression and even define your own shapes. We’ll cover the most common ones.

Like most things in matplotlib, the syntax is intuitive. Either, the shape of the string reflects the shape of the marker, or the string is a single letter that matches the first letter of the shape.

• 'o' – circle
• '^' – triangle up
• 's' – square
• '+' – plus
• 'D' – diamond
• '$...$' – LaTeX syntax e.g. '$\pi$' makes each marker the Greek letter π.

Let’s see some examples

For each plot, call plt.plot(squared) and set marker to your choice

# Circle
plt.plot(squared, marker='o')

# Plus
plt.plot(squared, marker='+')

# Diamond
plt.plot(squared, marker='D')

# Triangle Up
plt.plot(squared, marker='^')

If you set linestyle='', you won’t plot a line, just the markers.

# Circle
plt.plot(squared, marker='o', linestyle='')

# Plus
plt.plot(squared, marker='+', linestyle='')

# Diamond
plt.plot(squared, marker='D', linestyle='')

# Triangle Up
plt.plot(squared, marker='^', linestyle='')

## Matplotlib Line Plot Format Strings

Setting the marker, linestyle and color of a plot is something you want to do all the time. So matplotlib included a quick way to do it

plt.plot(y, fmt)

# with x-axis values
plt.plot(x, y, fmt)

After passing the y-axis and/or x-axis values, you can pass fmt. It’s a string made up of three parts:

fmt = '[marker][line][color]'

Each part is optional and you can pass them in any order. You can use the short form markers, linestyles and colors we have discussed in this article. For example, 'o--g' is circle markers, dashed lines and green color.

# These are equivalent
plt.plot(x, y, 'o--g')
plt.plot(x, y, marker='o', linestyle='--', color='g')
plt.plot(x, y, marker='o', ls='--', c='g')

Here are some examples with different markers, linestyles and colors.

# Circles, dash line, red 'o--r'
plt.plot(squared, 'o--r')

# Plus, dashdot line, green '+-.g'
plt.plot(squared, '+-.g')

# Diamonds, solid line, black 'D-k'
plt.plot(squared, 'D-k')

# Triangle up, dot line, blue 'b:^'
plt.plot(squared, 'b:^')

If you don’t specify a linestyle in the format string, matplotlib won’t draw a line. This makes your plots look similar to a scatter plot. For this reason, some people prefer to use plt.plot() over plt.scatter(). The choice is up to you.

## Summary

You now know all the essentials to make professional looking and effective line plots.

You can change the color and plot multiple lines on top of each other. You can write custom labels for the axes and title. You’re able to clearly explain different lines using a legend. And you can customize the look of your plot using color, linewidth, markers and linestyles.

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## References 