### Introduction

*Matplotlib* is one of the most widely used data visualization libraries in Python. From simple to complex visualizations, it's the go-to library for most.

In this tutorial, we'll take a look at *how to plot multiple line plots in Matplotlib* - on the same `Axes`

or `Figure`

.

If you'd like to read more about plotting line plots in general, as well as customizing them, make sure to read our guide on Plotting Lines Plots with Matplotlib.

### Plot Multiple Line Plots in Matplotlib

Depending on the style you're using, OOP or MATLAB-style, you'll either use the `plt`

instance, or the `ax`

instance to plot, with the same approach.

To plot multiple line plots in Matplotlib, you simply repeatedly call the `plot()`

function, which will apply the changes to the same `Figure`

object:

```
import matplotlib.pyplot as plt
x = [1, 2, 3, 4, 5, 6]
y = [2, 4, 6, 5, 6, 8]
y2 = [5, 3, 7, 8, 9, 6]
fig, ax = plt.subplots()
ax.plot(x, y)
ax.plot(x, y2)
plt.show()
```

Without setting any customization flags, the default colormap will apply, drawing both line plots on the same `Figure`

object, and adjusting the color to differentiate between them:

Now, let's generate some random sequences using NumPy, and customize the line plots a tiny bit by setting a specific color for each, and labeling them:

```
import matplotlib.pyplot as plt
import numpy as np
line_1 = np.random.randint(low = 0, high = 50, size = 50)
line_2 = np.random.randint(low = -15, high = 100, size = 50)
fig, ax = plt.subplots()
ax.plot(line_1, color = 'green', label = 'Line 1')
ax.plot(line_2, color = 'red', label = 'Line 2')
ax.legend(loc = 'upper left')
plt.show()
```

We don't have to supply the X-axis values to a line plot, in which case, the values from `0..n`

will be applied, where `n`

is the last element in the data you're plotting. In our case, we've got two sequences of data - `line_1`

and `line_2`

, which will both be plotted on the same X-axis.

While plotting, we've assigned colors to them, using the `color`

argument, and labels for the legend, using the `label`

argument. This results in:

### Plot Multiple Line Plots with Different Scales

Sometimes, you might have two datasets, fit for line plots, but their values are significantly different, making it hard to compare both lines. For example, if `line_1`

had an exponentially increasing sequence of numbers, while `line_2`

had a linearly increasing sequence - surely and quickly enough, `line_1`

would have values so much larger than `line_2`

, that the latter fades out of view.

Let's use NumPy to make an exponentially increasing sequence of numbers, and plot it next to another line on the same `Axes`

, linearly:

```
import matplotlib.pyplot as plt
import numpy as np
linear_sequence = np.linspace(0, 10, 10)
# [0, 1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 10]
exponential_sequence = np.exp(linear_sequence)
# [1.00e+00, 3.03e+00, 9.22e+00, 2.80e+01, 8.51e+01, 2.58e+02, 7.85e+02, 2.38e+03, 7.25e+03, 2.20e+04]
fig, ax = plt.subplots()
ax.plot(linear_sequence)
ax.plot(exponential_sequence)
plt.show()
```

Running this code results in:

The exponential growth in the `exponential_sequence`

goes out of proportion very fast, and it looks like there's absolutely no difference in the `linear_sequence`

, since it's so minuscule relative to the exponential trend of the other sequence.

Now, let's plot the `exponential_sequence`

on a logarithmic scale, which will produce a visually straight line, since the Y-scale will exponentially increase. If we plot it on a logarithmic scale, and the `linear_sequence`

just increases by the same constant, we'll have two overlapping lines and we will only be able to see the one plotted after the first.

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Let's change up the `linear_sequence`

a bit to make it observable once we plot both:

```
import matplotlib.pyplot as plt
import numpy as np
# Sequences
linear_sequence = [1, 2, 3, 4, 5, 6, 7, 10, 15, 20]
exponential_sequence = np.exp(np.linspace(0, 10, 10))
fig, ax = plt.subplots()
# Plot linear sequence, and set tick labels to the same color
ax.plot(linear_sequence, color='red')
ax.tick_params(axis='y', labelcolor='red')
# Generate a new Axes instance, on the twin-X axes (same position)
ax2 = ax.twinx()
# Plot exponential sequence, set scale to logarithmic and change tick color
ax2.plot(exponential_sequence, color='green')
ax2.set_yscale('log')
ax2.tick_params(axis='y', labelcolor='green')
plt.show()
```

This time around, we'll have to use the OOP interface, since we're creating a new `Axes`

instance. One `Axes`

has one scale, so we create a new one, in the same position as the first one, and set its scale to a logarithmic one, and plot the exponential sequence.

This results in:

We've also changed the tick label colors to match the color of the line plots themselves, otherwise, it'd be hard to distinguish which line is on which scale.

### Plot Multiple Line Plots with Multiple Y-Axis

Finally, we can apply the same scale (linear, logarithmic, etc), but have different values on the Y-axis of each line plot. This is achieved through having multiple Y-axis, on different `Axes`

objects, in the same position.

For example, the `linear_sequence`

won't go above 20 on the Y-axis, while the `exponential_sequence`

will go up to 20000. We can plot them both *linearly*, simply by plotting them on different `Axes`

objects, in the same position, each of which set the Y-axis ticks automatically to accommodate for the data we're feeding in:

```
import matplotlib.pyplot as plt
import numpy as np
# Sequences
linear_sequence = [1, 2, 3, 4, 5, 6, 7, 10, 15, 20]
exponential_sequence = np.exp(np.linspace(0, 10, 10))
fig, ax = plt.subplots()
# Plot linear sequence, and set tick labels to the same color
ax.plot(linear_sequence, color='red')
ax.tick_params(axis='y', labelcolor='red')
# Generate a new Axes instance, on the twin-X axes (same position)
ax2 = ax.twinx()
# Plot exponential sequence, set scale to logarithmic and change tick color
ax2.plot(exponential_sequence, color='green')
ax2.tick_params(axis='y', labelcolor='green')
plt.show()
```

We've again created another `Axes`

in the same position as the first one, so we can plot on the same place in the `Figure`

but different `Axes`

objects, which allows us to set values for each Y-axis individually.

Without setting the Y-scale to logarithmic this time, both will be plotted linearly:

### Conclusion

In this tutorial, we've gone over how to plot multiple Line Plots on the same `Figure`

or `Axes`

in Matplotlib and Python. We've covered how to plot on the same `Axes`

with the same scale and Y-axis, as well as how to plot on the same `Figure`

with different and identical Y-axis scales.

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