Note

Click here to download the full example code

This example shows how to use `fill_between`

to color the area
between two lines.

```
import matplotlib.pyplot as plt
import numpy as np
```

The parameters *y1* and *y2* can be a scalar, indicating a horizontal
boundary a the given y-values. If only *y1* is given, *y2* defaults to 0.

```
x = np.arange(0.0, 2, 0.01)
y1 = np.sin(2 * np.pi * x)
y2 = 0.8 * np.sin(4 * np.pi * x)
fig, (ax1, ax2, ax3) = plt.subplots(3, 1, sharex=True, figsize=(6, 6))
ax1.fill_between(x, y1)
ax1.set_title('fill between y1 and 0')
ax2.fill_between(x, y1, 1)
ax2.set_title('fill between y1 and 1')
ax3.fill_between(x, y1, y2)
ax3.set_title('fill between y1 and y2')
ax3.set_xlabel('x')
fig.tight_layout()
```

A common application for `fill_between`

is the indication of
confidence bands.

`fill_between`

uses the colors of the color cycle as the fill
color. These may be a bit strong when applied to fill areas. It is
therefore often a good practice to lighten the color by making the area
semi-transparent using *alpha*.

```
# sphinx_gallery_thumbnail_number = 2
N = 21
x = np.linspace(0, 10, 11)
y = [3.9, 4.4, 10.8, 10.3, 11.2, 13.1, 14.1, 9.9, 13.9, 15.1, 12.5]
# fit a linear curve an estimate its y-values and their error.
a, b = np.polyfit(x, y, deg=1)
y_est = a * x + b
y_err = x.std() * np.sqrt(1/len(x) +
(x - x.mean())**2 / np.sum((x - x.mean())**2))
fig, ax = plt.subplots()
ax.plot(x, y_est, '-')
ax.fill_between(x, y_est - y_err, y_est + y_err, alpha=0.2)
ax.plot(x, y, 'o', color='tab:brown')
```

Out:

```
[<matplotlib.lines.Line2D object at 0x7f188bc74fd0>]
```

The parameter *where* allows to specify the x-ranges to fill. It's a boolean
array with the same size as *x*.

Only x-ranges of contiguous *True* sequences are filled. As a result the
range between neighboring *True* and *False* values is never filled. This
often undesired when the data points should represent a contiguous quantity.
It is therefore recommended to set `interpolate=True`

unless the
x-distance of the data points is fine enough so that the above effect is not
noticeable. Interpolation approximates the actual x position at which the
*where* condition will change and extends the filling up to there.

```
x = np.array([0, 1, 2, 3])
y1 = np.array([0.8, 0.8, 0.2, 0.2])
y2 = np.array([0, 0, 1, 1])
fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True)
ax1.set_title('interpolation=False')
ax1.plot(x, y1, 'o--')
ax1.plot(x, y2, 'o--')
ax1.fill_between(x, y1, y2, where=(y1 > y2), color='C0', alpha=0.3)
ax1.fill_between(x, y1, y2, where=(y1 < y2), color='C1', alpha=0.3)
ax2.set_title('interpolation=True')
ax2.plot(x, y1, 'o--')
ax2.plot(x, y2, 'o--')
ax2.fill_between(x, y1, y2, where=(y1 > y2), color='C0', alpha=0.3,
interpolate=True)
ax2.fill_between(x, y1, y2, where=(y1 <= y2), color='C1', alpha=0.3,
interpolate=True)
fig.tight_layout()
```

Note

Similar gaps will occur if *y1* or *y2* are masked arrays. Since missing
values cannot be approximated, *interpolate* has no effect in this case.
The gaps around masked values can only be reduced by adding more data
points close to the masked values.

The same selection mechanism can be applied to fill the full vertical height of the axes. To be independent of y-limits, we add a transform that interprets the x-values in data coorindates and the y-values in axes coordinates.

The following example marks the regions in which the y-data are above a given threshold.

Out:

```
<matplotlib.collections.PolyCollection object at 0x7f188bd22760>
```

The use of the following functions, methods and classes is shown in this example:

```
import matplotlib
matplotlib.axes.Axes.fill_between
matplotlib.pyplot.fill_between
matplotlib.axes.Axes.get_xaxis_transform
```

Out:

```
<function _AxesBase.get_xaxis_transform at 0x7f18a5a7aee0>
```

Keywords: matplotlib code example, codex, python plot, pyplot Gallery generated by Sphinx-Gallery