Labels and Legends¶

Labels and legends help viewers understand what data is being displayed.

Mental Model

The label parameter on a plot call is metadata -- it does nothing visible until you call ax.legend(). Think of it as naming each line behind the scenes. When legend() is called, it collects all named artists and builds a key mapping colors/styles to names.

The deeper role: a legend decodes visual encodings into semantic meaning. The plot maps data to colors and line styles; the legend is the inverse mapping that tells the reader what each visual element represents.

Best Practice: Always Use label= in plot()

```python

Recommended — labels travel with the data¶

ax.plot(x, y1, label="sin") ax.plot(x, y2, label="cos") ax.legend()

Avoid — fragile, breaks if plot order changes¶

ax.plot(x, y1) ax.plot(x, y2) ax.legend(["sin", "cos"]) `` Passing a list tolegend()is order-dependent — if you reorder or add plots, the labels silently mismatch. Usinglabel=` ties each label to its data line, making the code robust to changes.

Adding Labels¶

Use the label parameter in plot():

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

x = np.linspace(-2np.pi, 2np.pi, 100) y_sin = np.sin(x) y_cos = np.cos(x)

fig, ax = plt.subplots(figsize=(15, 4)) ax.plot(x, y_sin, label='sin') ax.plot(x, y_cos, label='cos') ax.legend() plt.show() ```

Alternative: Labels in legend()¶

Pass labels directly to legend():

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

x = np.linspace(-6, 6, 100) y_sin = np.sin(x) y_cos = np.cos(x)

fig, ax = plt.subplots(figsize=(12, 3)) ax.plot(x, y_sin) ax.plot(x, y_cos) ax.legend(["sin", "cos"]) plt.show() ```

Legend Location (loc)¶

Control legend placement:

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

x = np.linspace(-6, 6, 100)

fig, ax = plt.subplots(figsize=(12, 3)) ax.plot(x, np.sin(x), label="sin") ax.plot(x, np.cos(x), label="cos") ax.legend(loc='upper right') plt.show() ```

Location options:

Value	Description
`'best'`	Auto-select best location (default)
`'upper right'`	Top right
`'upper left'`	Top left
`'lower left'`	Bottom left
`'lower right'`	Bottom right
`'right'`	Right side
`'center left'`	Left center
`'center right'`	Right center
`'lower center'`	Bottom center
`'upper center'`	Top center
`'center'`	Center

Numeric codes (0-10) also work.

Legend Font Size¶

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

x = np.linspace(-6, 6, 100)

fig, ax = plt.subplots(figsize=(12, 3)) ax.plot(x, np.sin(x), label="sin") ax.plot(x, np.cos(x), label="cos") ax.legend(fontsize=20) plt.show() ```

Legend Shadow¶

Add a shadow effect:

python ax.legend(shadow=True)

Legend Frame¶

Remove the frame border:

python ax.legend(frameon=False)

Multiple Columns (ncol)¶

Arrange legend entries in columns:

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

x = np.linspace(-1, 1, 100)

fig, ax = plt.subplots(figsize=(12, 3))

for beta in range(1, 9): ax.plot(x, beta * x, label=f"y(x)={beta}*x")

ax.legend(ncol=4) plt.show() ```

Legend Outside Plot (bbox_to_anchor)¶

Place legend outside the axes:

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

x = np.linspace(-1, 1, 100)

fig, ax = plt.subplots(figsize=(12, 3))

for beta in range(1, 9): ax.plot(x, beta * x, label=f"y(x)={beta}*x")

ax.legend(ncol=4, loc='lower left', bbox_to_anchor=(0, 1)) plt.show() ```

The bbox_to_anchor parameter takes (x, y) coordinates in axes fraction.

Comprehensive Legend Example¶

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

x = np.linspace(-2np.pi, 2np.pi, 100)

fig, ax = plt.subplots(figsize=(15, 4))

for beta in range(1, 9): ax.plot(x, beta * x, label=f"y(x)={beta}*x")

ax.legend( fontsize=15, shadow=True, ncol=4, loc='lower right' ) plt.show() ```

Legend Title¶

Add a title to the legend:

python ax.legend(title='Functions', title_fontsize=14)

Selective Legend Entries¶

Only include specific lines in the legend:

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

x = np.linspace(0, 10, 100)

fig, ax = plt.subplots()

line1, = ax.plot(x, np.sin(x), label='sin') line2, = ax.plot(x, np.cos(x), label='cos') ax.plot(x, x/10) # No label, won't appear in legend

ax.legend() # Only shows sin and cos plt.show() ```

Key Takeaways¶

Add label parameter to plot() calls
Call ax.legend() to display the legend
Use loc to position the legend
Use ncol for multi-column layouts
Use bbox_to_anchor for legends outside the plot
frameon=False removes the border
shadow=True adds a drop shadow

Exercises¶

Exercise 1. Write code that plots two lines with label arguments and calls ax.legend() to display the legend. Place it in the lower-right corner.

Solution to Exercise 1

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

np.random.seed(42)

Solution code depends on the specific exercise¶

x = np.linspace(0, 2 * np.pi, 100) fig, ax = plt.subplots() ax.plot(x, np.sin(x)) ax.set_title('Example Solution') plt.show() ```

See the content of this page for the relevant API details to construct the full solution.

Exercise 2. Explain the difference between passing label to ax.plot() versus using ax.legend(['A', 'B']). Which is recommended?

Solution to Exercise 2

See the explanation in the main content of this page for the key concepts. The essential idea is to understand the API parameters and their effects on the resulting visualization.

Exercise 3. Write code that customizes the legend: set the font size, add a frame with framealpha=0.9, and use 2 columns with ncol=2.

Solution to Exercise 3

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

np.random.seed(42) fig, axes = plt.subplots(1, 2, figsize=(12, 5))

x = np.linspace(0, 2 * np.pi, 100) axes[0].plot(x, np.sin(x)) axes[0].set_title('Left Subplot')

axes[1].plot(x, np.cos(x)) axes[1].set_title('Right Subplot')

plt.tight_layout() plt.show() ```

Adapt this pattern to the specific requirements of the exercise.

Exercise 4. Create a plot with an xlabel, ylabel, and title. Use LaTeX formatting for the labels (e.g., r'$x^2$').

Solution to Exercise 4

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

np.random.seed(42) x = np.linspace(0, 10, 100) fig, ax = plt.subplots() ax.plot(x, np.sin(x), 'b-', lw=2) ax.set_title('Solution') plt.show() ```

Refer to the code examples in the main content for the specific API calls needed.