Working with Colors, Themes, and Styles

import matplotlib.pyplot as plt
import numpy as np

# Sequential palettes (for ordered/magnitude data)
fig, axes = plt.subplots(2, 3, figsize=(15, 8))

# Qualitative palettes
qualitative = {
    'tab10':     plt.cm.tab10,
    'Set2':      plt.cm.Set2,
    'Paired':    plt.cm.Paired,
}
for ax, (name, cmap) in zip(axes[0], qualitative.items()):
    colors = [cmap(i) for i in np.linspace(0, 0.9, 8)]
    ax.bar(range(8), [1]*8, color=colors)
    ax.set_title(f'{name}\n(Categorical)', fontsize=11)
    ax.set_xticks([])

# Sequential palettes
sequential = {
    'Blues':     plt.cm.Blues,
    'YlOrRd':    plt.cm.YlOrRd,
    'viridis':   plt.cm.viridis,
}
for ax, (name, cmap) in zip(axes[1], sequential.items()):
    colors = [cmap(i) for i in np.linspace(0.2, 1.0, 8)]
    ax.bar(range(8), [1]*8, color=colors)
    ax.set_title(f'{name}\n(Sequential)', fontsize=11)
    ax.set_xticks([])

plt.suptitle('Color Palette Reference', fontsize=14, fontweight='bold')
plt.tight_layout()
plt.savefig('color_palettes.png', dpi=150)
plt.show()

import matplotlib.pyplot as plt
import numpy as np

styles = ['default', 'seaborn-v0_8-whitegrid', 'ggplot', 'dark_background', 'bmh']
x = np.linspace(0, 10, 100)

fig, axes = plt.subplots(1, 5, figsize=(20, 4))
for ax, style in zip(axes, styles):
    with plt.style.context(style):
        ax.plot(x, np.sin(x), linewidth=2)
        ax.plot(x, np.cos(x), linewidth=2)
        ax.set_title(style.replace('seaborn-v0_8-', 'sns-'), fontsize=9)
        ax.set_xticks([])

plt.suptitle('Matplotlib Style Comparison', fontsize=13, fontweight='bold')
plt.tight_layout()
plt.savefig('styles.png', dpi=150)
plt.show()

# Apply globally for all charts in session
plt.style.use('seaborn-v0_8-whitegrid')

# Design system: consistent brand colors
BRAND_COLORS = {
    'primary':    '#1565C0',  # Deep blue — main data
    'secondary':  '#2E7D32',  # Deep green — positive/growth
    'danger':     '#C62828',  # Deep red — negative/alert
    'warning':    '#E65100',  # Orange — caution
    'neutral':    '#546E7A',  # Blue-grey — background data
    'highlight':  '#FFD54F',  # Amber — emphasis/callout
}

# Accessibility: color-blind safe palette (ColorBrewer)
COLORBLIND_SAFE = ['#E69F00', '#56B4E9', '#009E73', '#F0E442', '#0072B2', '#D55E00', '#CC79A7']

# Apply to a chart
fig, ax = plt.subplots(figsize=(10, 5))
depts = ['Engineering', 'Marketing', 'Sales', 'HR', 'Finance']
values = [2.8, 1.9, 2.3, 0.8, 1.5]

bars = ax.bar(depts, values, color=[BRAND_COLORS['primary'] if v == max(values)
              else BRAND_COLORS['neutral'] for v in values])
for bar, val in zip(bars, values):
    ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.03,
            f'${val:.1f}M', ha='center', fontweight='bold')

ax.set_title('Revenue by Department', fontsize=14, fontweight='bold')
ax.set_ylabel('Revenue (Million $)')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.grid(True, axis='y', alpha=0.3)
plt.tight_layout()
plt.savefig('brand_colors.png', dpi=150)
plt.show()