Bar Chart Keywords¶

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

Width and Height¶

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

1. Default Width¶

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¶

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¶

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¶

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

2. Named Colors¶

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

3. Individual Colors¶

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¶

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

2. Edge Width¶

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

3. No Edge¶

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

Alpha (Transparency)¶

The alpha parameter sets bar transparency.

1. Uniform Alpha¶

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

2. Overlapping Bars¶

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¶

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)¶

x = np.arange(5)

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

2. Edge Alignment¶

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

3. Comparison¶

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)¶

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

2. Left Offset (Horizontal)¶

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

3. Building Stacked Bars¶

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¶

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

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

2. Asymmetric Error¶

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¶

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¶

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

2. Multiple Series¶

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¶

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¶

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

2. Available Patterns¶

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¶

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¶

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¶

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¶

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()