Bar Chart Keywords¶

The ax.bar() and ax.barh() methods accept numerous keyword arguments to control bar appearance and behavior.

Mental Model

Bar chart keywords control the geometry and look of each rectangle: width sets thickness, color fills the bar, edgecolor outlines it, align shifts its position, and bottom offsets its base (used for stacking). Like scatter keywords, each can be a scalar for uniform styling or an array for per-bar control.

Parameter Priority

Not all keywords are equally important. Focus on these in order:

color — visual grouping and meaning (the first thing readers see)
width — spacing clarity between bars
bottom — stacking logic (accumulating series)
alpha — transparency for layering, overlap, and emphasis control

Transparency (alpha) matters more than it seems: it reveals overlap in grouped bars, reduces visual dominance of large bars, and guides attention to highlighted elements.

Width and Height¶

The width parameter controls bar thickness for vertical bars; height for horizontal bars.

1. Default Width¶

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

categories = ['A', 'B', 'C', 'D', 'E'] values = [23, 45, 56, 78, 32]

fig, ax = plt.subplots() ax.bar(categories, values) # Default width = 0.8 plt.show() ```

2. Custom Width¶

```python fig, axes = plt.subplots(1, 3, figsize=(12, 4))

for ax, width in zip(axes, [0.3, 0.6, 0.9]): ax.bar(categories, values, width=width) ax.set_title(f'width = {width}')

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

3. Variable Widths¶

```python widths = [0.5, 0.7, 0.9, 0.6, 0.4]

fig, ax = plt.subplots() ax.bar(categories, values, width=widths) plt.show() ```

Color¶

The color parameter sets bar fill color.

1. Single Color¶

python fig, ax = plt.subplots() ax.bar(categories, values, color='steelblue') plt.show()

2. Named Colors¶

python ax.bar(categories, values, color='coral') ax.bar(categories, values, color='forestgreen') ax.bar(categories, values, color='goldenrod')

3. Individual Colors¶

```python colors = ['red', 'green', 'blue', 'orange', 'purple']

fig, ax = plt.subplots() ax.bar(categories, values, color=colors) plt.show() ```

Edge Properties¶

Control bar border appearance.

1. Edge Color¶

python fig, ax = plt.subplots() ax.bar(categories, values, color='lightblue', edgecolor='navy') plt.show()

2. Edge Width¶

python ax.bar(categories, values, color='lightblue', edgecolor='navy', linewidth=2)

3. No Edge¶

python ax.bar(categories, values, edgecolor='none')

Alpha (Transparency)¶

The alpha parameter sets bar transparency.

1. Uniform Alpha¶

python fig, ax = plt.subplots() ax.bar(categories, values, alpha=0.7) plt.show()

2. Overlapping Bars¶

```python x = np.arange(5) values1 = [23, 45, 56, 78, 32] values2 = [30, 40, 50, 60, 40]

fig, ax = plt.subplots() ax.bar(x, values1, alpha=0.7, label='Series 1') ax.bar(x, values2, alpha=0.7, label='Series 2') ax.legend() plt.show() ```

3. Highlight Effect¶

```python alphas = [0.3, 0.3, 1.0, 0.3, 0.3] # Highlight third bar

fig, ax = plt.subplots() for i, (cat, val, a) in enumerate(zip(categories, values, alphas)): ax.bar(cat, val, alpha=a, color='steelblue') plt.show() ```

Alignment¶

The align parameter controls bar position relative to x coordinate.

1. Center Alignment (Default)¶

```python x = np.arange(5)

fig, ax = plt.subplots() ax.bar(x, values, align='center') ax.set_xticks(x) plt.show() ```

2. Edge Alignment¶

python fig, ax = plt.subplots() ax.bar(x, values, align='edge') ax.set_xticks(x) plt.show()

3. Comparison¶

```python fig, axes = plt.subplots(1, 2, figsize=(10, 4))

axes[0].bar(x, values, align='center', width=0.8) axes[0].set_title("align='center'") axes[0].axvline(x=2, color='red', linestyle='--')

axes[1].bar(x, values, align='edge', width=0.8) axes[1].set_title("align='edge'") axes[1].axvline(x=2, color='red', linestyle='--')

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

Bottom and Left¶

Offset bar base from zero.

1. Bottom Offset (Vertical)¶

python fig, ax = plt.subplots() ax.bar(categories, values, bottom=10) ax.axhline(y=10, color='red', linestyle='--') plt.show()

2. Left Offset (Horizontal)¶

python fig, ax = plt.subplots() ax.barh(categories, values, left=10) ax.axvline(x=10, color='red', linestyle='--') plt.show()

3. Building Stacked Bars¶

```python values1 = [23, 45, 56, 78, 32] values2 = [15, 25, 20, 30, 18]

fig, ax = plt.subplots() ax.bar(categories, values1, label='Base') ax.bar(categories, values2, bottom=values1, label='Addition') ax.legend() plt.show() ```

Error Bars¶

Add error bars with yerr and xerr.

1. Symmetric Error¶

```python errors = [3, 5, 4, 6, 3]

fig, ax = plt.subplots() ax.bar(categories, values, yerr=errors, capsize=5) plt.show() ```

2. Asymmetric Error¶

```python lower_errors = [2, 3, 2, 4, 2] upper_errors = [4, 6, 5, 8, 4]

fig, ax = plt.subplots() ax.bar(categories, values, yerr=[lower_errors, upper_errors], capsize=5) plt.show() ```

3. Error Bar Styling¶

python fig, ax = plt.subplots() ax.bar(categories, values, yerr=errors, capsize=5, error_kw={'elinewidth': 2, 'ecolor': 'red', 'capthick': 2}) plt.show()

Label¶

Add labels for legend display.

1. Label Parameter¶

python fig, ax = plt.subplots() ax.bar(categories, values, label='2024 Sales') ax.legend() plt.show()

2. Multiple Series¶

```python x = np.arange(5) width = 0.35

fig, ax = plt.subplots() ax.bar(x - width/2, values, width, label='2023') ax.bar(x + width/2, [v * 1.1 for v in values], width, label='2024') ax.set_xticks(x) ax.set_xticklabels(categories) ax.legend() plt.show() ```

3. Legend Location¶

python ax.legend(loc='upper left') ax.legend(loc='upper right') ax.legend(loc='best')

Hatch Patterns¶

Add patterns to bars for print-friendly distinction.

1. Single Pattern¶

python fig, ax = plt.subplots() ax.bar(categories, values, hatch='//') plt.show()

2. Available Patterns¶

```python fig, axes = plt.subplots(2, 4, figsize=(12, 6)) patterns = ['/', '\', '|', '-', '+', 'x', 'o', 'O']

for ax, pattern in zip(axes.flat, patterns): ax.bar(['A'], [1], hatch=pattern * 2, edgecolor='black') ax.set_title(f"hatch='{pattern}'")

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

3. Combined with Color¶

python fig, ax = plt.subplots() ax.bar(categories, values, color='lightblue', hatch='//', edgecolor='navy') plt.show()

Combining Keywords¶

Create styled bar charts with multiple parameters.

1. Professional Style¶

```python fig, ax = plt.subplots(figsize=(10, 6))

bars = ax.bar(categories, values, color='steelblue', edgecolor='navy', linewidth=1.5, alpha=0.8, width=0.6)

ax.bar_label(bars, padding=3) ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False)

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

2. Conditional Coloring¶

```python colors = ['green' if v > 50 else 'red' for v in values]

fig, ax = plt.subplots() ax.bar(categories, values, color=colors, edgecolor='black') plt.show() ```

3. Gradient Effect¶

```python import matplotlib.cm as cm

cmap = cm.get_cmap('Blues') colors = [cmap(v / max(values)) for v in values]

fig, ax = plt.subplots() ax.bar(categories, values, color=colors, edgecolor='navy') plt.show() ```

Exercises¶

Exercise 1. Create a bar chart with five categories where bars above a threshold of 50 are colored green and bars below are colored red. Use values [23, 65, 42, 78, 35]. Add a horizontal dashed line at the threshold and use edgecolor='black' and linewidth=1.5.

Solution to Exercise 1

import matplotlib.pyplot as plt

categories = ['A', 'B', 'C', 'D', 'E']
values = [23, 65, 42, 78, 35]
threshold = 50
colors = ['green' if v > threshold else 'red' for v in values]

fig, ax = plt.subplots(figsize=(8, 5))
ax.bar(categories, values, color=colors, edgecolor='black', linewidth=1.5)
ax.axhline(y=threshold, color='gray', linestyle='--', label=f'Threshold = {threshold}')
ax.legend()
ax.set_title('Conditional Bar Coloring')
plt.show()

Exercise 2. Create a bar chart with error bars. Use categories ['A', 'B', 'C', 'D'], values [40, 55, 30, 70], and asymmetric errors lower=[3, 4, 2, 5], upper=[5, 7, 4, 8]. Style the error bars with red color and cap size of 8. Use hatch pattern '//' on the bars.

Solution to Exercise 2

import matplotlib.pyplot as plt

categories = ['A', 'B', 'C', 'D']
values = [40, 55, 30, 70]
lower = [3, 4, 2, 5]
upper = [5, 7, 4, 8]

fig, ax = plt.subplots(figsize=(8, 5))
ax.bar(categories, values, hatch='//', edgecolor='black', color='lightblue',
       yerr=[lower, upper], capsize=8,
       error_kw={'ecolor': 'red', 'elinewidth': 2, 'capthick': 2})
ax.set_ylabel('Value')
ax.set_title('Bar Chart with Asymmetric Error Bars')
plt.show()

Exercise 3. Create a grouped bar chart with three series using the width and positional offset technique. Categories are ['Q1', 'Q2', 'Q3', 'Q4'] with three series: [20, 35, 30, 45], [25, 32, 34, 40], [15, 27, 28, 38]. Use width=0.25, different colors, alpha=0.85, and edgecolor='black'. Add bar labels and a legend.

Solution to Exercise 3

import matplotlib.pyplot as plt
import numpy as np

categories = ['Q1', 'Q2', 'Q3', 'Q4']
series1 = [20, 35, 30, 45]
series2 = [25, 32, 34, 40]
series3 = [15, 27, 28, 38]

x = np.arange(len(categories))
width = 0.25

fig, ax = plt.subplots(figsize=(10, 6))
b1 = ax.bar(x - width, series1, width, label='Product A', alpha=0.85, edgecolor='black')
b2 = ax.bar(x, series2, width, label='Product B', alpha=0.85, edgecolor='black')
b3 = ax.bar(x + width, series3, width, label='Product C', alpha=0.85, edgecolor='black')

ax.bar_label(b1, padding=2, fontsize=8)
ax.bar_label(b2, padding=2, fontsize=8)
ax.bar_label(b3, padding=2, fontsize=8)

ax.set_xticks(x)
ax.set_xticklabels(categories)
ax.legend()
ax.set_title('Grouped Bar Chart')
plt.tight_layout()
plt.show()