Note

Click here to download the full example code

# Filling the area between lines¶

This example shows how to use `fill_between`

to color the area
between two lines.

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

## Basic usage¶

The parameters *y1* and *y2* can be scalars, indicating a horizontal
boundary at 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()
```

## Example: Confidence bands¶

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*.

```
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 0x7f1c8dfc4a90>]
```

## Selectively filling horizontal regions¶

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.

## Selectively marking horizontal regions across the whole Axes¶

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.

```
fig, ax = plt.subplots()
x = np.arange(0, 4 * np.pi, 0.01)
y = np.sin(x)
ax.plot(x, y, color='black')
threshold = 0.75
ax.axhline(threshold, color='green', lw=2, alpha=0.7)
ax.fill_between(x, 0, 1, where=y > threshold,
color='green', alpha=0.5, transform=ax.get_xaxis_transform())
```

Out:

```
<matplotlib.collections.PolyCollection object at 0x7f1c8e240c70>
```

References

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

**Total running time of the script:** ( 0 minutes 1.495 seconds)

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