Generate Python Matplotlib Visualizations with AI

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

# Sample quarterly sales data (in thousands of USD) for three products
quarters = ['Q1', 'Q2', 'Q3', 'Q4']
widget_a = [45, 52, 61, 70]
widget_b = [38, 41, 55, 63]
widget_c = [30, 48, 50, 58]

# Color-blind friendly palette (from Wong, 2011 / Okabe-Ito)
colors = ['#0072B2',  # blue
          '#E69F00',  # orange
          '#009E73']  # green

# Set up bar positions
x = np.arange(len(quarters))  # label locations
width = 0.25                  # width of each bar

# Create the figure and axis
fig, ax = plt.subplots(figsize=(10, 6))

# Plot each product's bars, offset by width
bars_a = ax.bar(x - width, widget_a, width, label='Widget A', color=colors[0])
bars_b = ax.bar(x,         widget_b, width, label='Widget B', color=colors[1])
bars_c = ax.bar(x + width, widget_c, width, label='Widget C', color=colors[2])

# Helper function to annotate each bar with its value
def add_value_labels(bars):
    for bar in bars:
        height = bar.get_height()
        ax.annotate(f'{height}',
                    xy=(bar.get_x() + bar.get_width() / 2, height),
                    xytext=(0, 3),              # 3 points vertical offset
                    textcoords='offset points',
                    ha='center', va='bottom',
                    fontsize=9)

# Apply labels to all bar groups
add_value_labels(bars_a)
add_value_labels(bars_b)
add_value_labels(bars_c)

# Titles, axis labels, ticks, legend, and styling
ax.set_title('Quarterly Sales Comparison by Product', fontsize=14, fontweight='bold')
ax.set_xlabel('Quarter', fontsize=12)
ax.set_ylabel('Sales (in thousands of USD)', fontsize=12)
ax.set_xticks(x)
ax.set_xticklabels(quarters)
ax.legend(title='Product', loc='upper left')
ax.yaxis.grid(True, linestyle='--', alpha=0.6)
ax.set_axisbelow(True)

# Add a little headroom so labels don't get clipped
ax.set_ylim(0, max(max(widget_a), max(widget_b), max(widget_c)) * 1.15)

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

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

# Sample data: quarterly sales for three products
quarters = ['Q1', 'Q2', 'Q3', 'Q4']
widget_a = [45000, 52000, 48000, 61000]
widget_b = [38000, 41000, 55000, 59000]
widget_c = [52000, 48000, 51000, 54000]

# Color-blind friendly palette (Okabe-Ito)
colors = ['#E69F00', '#56B4E9', '#009E73']

# Set up the figure and axis
fig, ax = plt.subplots(figsize=(10, 6))

# Define bar positions
x = np.arange(len(quarters))
bar_width = 0.25

# Create grouped bars
bars1 = ax.bar(x - bar_width, widget_a, bar_width, label='Widget A', color=colors[0])
bars2 = ax.bar(x, widget_b, bar_width, label='Widget B', color=colors[1])
bars3 = ax.bar(x + bar_width, widget_c, bar_width, label='Widget C', color=colors[2])

# Function to add value labels on top of each bar
def add_value_labels(bars):
    for bar in bars:
        height = bar.get_height()
        ax.text(bar.get_x() + bar.get_width() / 2, height,
                f'${height:,.0f}',
                ha='center', va='bottom', fontsize=9, fontweight='bold')

# Add value labels to all bar groups
add_value_labels(bars1)
add_value_labels(bars2)
add_value_labels(bars3)

# Customize the chart
ax.set_xlabel('Quarter', fontsize=12, fontweight='bold')
ax.set_ylabel('Sales ($)', fontsize=12, fontweight='bold')
ax.set_title('Quarterly Sales Comparison by Product', fontsize=14, fontweight='bold', pad=20)
ax.set_xticks(x)
ax.set_xticklabels(quarters)
ax.legend(loc='upper left', frameon=True, shadow=True)

# Format y-axis to display currency
ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: f'${x/1000:.0f}K'))

# Add grid for better readability
ax.grid(axis='y', alpha=0.3, linestyle='--')
ax.set_axisbelow(True)

# Adjust layout to prevent label cutoff
plt.tight_layout()

# Display the chart
plt.show()
```

This code creates a professional grouped bar chart with the following features:

- **Grouped bars**: Three bars per quarter for easy side-by-side comparison
- **Color-blind friendly palette**: Uses the Okabe-Ito color scheme (orange, blue, green) that's accessible to people with color vision deficiency
- **Value labels**: Dollar amounts displayed on top of each bar for precise reading
- **Professional styling**: Includes grid lines, formatted y-axis with currency symbols, bold labels, and a legend
- **Sample data**: Realistic quarterly sales figures for three widget products
- **Self-contained**: Runs immediately without external data files

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

# Sample quarterly sales data
quarters = ['Q1', 'Q2', 'Q3', 'Q4']
widget_a = [120, 135, 150, 165]
widget_b = [100, 145, 140, 155]
widget_c = [90, 110, 130, 145]

# Set up bar positions
x = np.arange(len(quarters))
bar_width = 0.24

# Create the figure and axis
fig, ax = plt.subplots(figsize=(10, 6))

# Color-blind friendly palette (Okabe-Ito inspired)
colors = ['#0072B2', '#E69F00', '#009E73']

# Plot grouped bars
bars_a = ax.bar(x - bar_width, widget_a, width=bar_width, label='Widget A', color=colors[0])
bars_b = ax.bar(x,             widget_b, width=bar_width, label='Widget B', color=colors[1])
bars_c = ax.bar(x + bar_width, widget_c, width=bar_width, label='Widget C', color=colors[2])

# Add titles and labels
ax.set_title('Quarterly Sales Comparison by Product', fontsize=14, pad=12)
ax.set_xlabel('Quarter', fontsize=12)
ax.set_ylabel('Sales (Units)', fontsize=12)
ax.set_xticks(x)
ax.set_xticklabels(quarters)
ax.legend()

# Add a light horizontal grid for readability
ax.yaxis.grid(True, linestyle='--', alpha=0.4)
ax.set_axisbelow(True)

# Function to add value labels above each bar
def add_value_labels(bars):
    for bar in bars:
        height = bar.get_height()
        ax.text(
            bar.get_x() + bar.get_width() / 2,
            height + 2,
            f'{int(height)}',
            ha='center',
            va='bottom',
            fontsize=10
        )

# Apply value labels to each product's bars
add_value_labels(bars_a)
add_value_labels(bars_b)
add_value_labels(bars_c)

# Adjust layout to prevent clipping
plt.tight_layout()

# Display the chart
plt.show()
```

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

# --- 1. Sample Data ---
# Define the categories (quarters) and the data for each product.
quarters = ['Q1', 'Q2', 'Q3', 'Q4']
sales_data = {
    'Widget A': [250, 265, 280, 290],
    'Widget B': [220, 230, 245, 270],
    'Widget C': [180, 200, 210, 240],
}
products = list(sales_data.keys())
product_sales = np.array(list(sales_data.values()))

# --- 2. Setup for Plotting ---
# Calculate the position for each group of bars on the x-axis.
x = np.arange(len(quarters))  # The label locations [0, 1, 2, 3]
bar_width = 0.25  # The width of each individual bar
spacing = 0.05 # A small space between bars in a group

# Create the figure and axes objects for the plot.
# Using layout='constrained' helps automatically adjust plot elements.
fig, ax = plt.subplots(figsize=(12, 7), layout='constrained')

# --- 3. Color Palette ---
# Use a color-blind friendly palette (from Paul Tol's "Vibrant" scheme).
colors = ['#0077BB', '#EE7733', '#009988']

# --- 4. Plotting the Bars ---
# Loop through each product to plot its bars.
for i, product in enumerate(products):
    # Calculate the offset for each bar within a group.
    # The central bar is at x, others are offset by bar_width + spacing.
    offset = bar_width * (i - 1) + spacing * (i - 1)
    
    # Plot the bars for the current product.
    rects = ax.bar(x + offset, product_sales[i], bar_width, label=product, color=colors[i])
    
    # Add value labels on top of each bar.
    ax.bar_label(rects, padding=3, fmt='%d', fontsize=9)

# --- 5. Customization and Styling ---
# Add a descriptive title and clear axis labels.
ax.set_title('Quarterly Sales Comparison by Product', fontsize=16, fontweight='bold')
ax.set_xlabel('Quarter', fontsize=12)
ax.set_ylabel('Sales (in thousands)', fontsize=12)

# Set the x-axis ticks to be in the center of each group of bars.
ax.set_xticks(x, quarters)

# Add a legend to identify the products.
ax.legend(loc='upper left', ncols

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

# Define quarters and realistic sample sales data (in thousands of units)
quarters = ['Q1', 'Q2', 'Q3', 'Q4']
sales_a = [100, 120, 110, 130]  # Widget A
sales_b = [90,  100, 95,  105]  # Widget B
sales_c = [80,  90,  85,  100]  # Widget C

# Color-blind friendly palette (blue, orange, green)
colors = ['#0072B2', '#D55E00', '#009E73']

# Set up the bar positions and width for grouping
x = np.arange(len(quarters))
width = 0.25  # Width of each bar

# Create figure and axis
fig, ax = plt.subplots(figsize=(10, 6))

# Plot grouped bars for each product
bars_a = ax.bar(x - width, sales_a, width, label='Widget A', color=colors[0])
bars_b = ax.bar(x, sales_b, width, label='Widget B', color=colors[1])
bars_c = ax.bar(x + width, sales_c, width, label='Widget C', color=colors[2])

# Add value labels on top of each bar
for i, v in enumerate(sales_a):
    ax.text(i - width, v + 2, f'{v}', ha='center', va='bottom', fontweight='bold')
for i, v in enumerate(sales_b):
    ax.text(i, v + 2, f'{v}', ha='center', va='bottom', fontweight='bold')
for i, v in enumerate(sales_c):
    ax.text(i + width, v + 2, f'{v}', ha='center', va='bottom', fontweight='bold')

# Customize the plot: titles, labels, legend, grid, and styling
ax.set_xlabel('Quarters', fontsize=12, fontweight='bold')
ax.set_ylabel('Sales (thousands of units)', fontsize=12, fontweight='bold')
ax.set_title('Quarterly Sales Comparison for Widgets A, B, and C', fontsize=14, fontweight='bold', pad=20)
ax.set_xticks(x)
ax.set_xticklabels(quarters)
ax.legend(loc='upper left')
ax.grid(axis='y', linestyle='--', alpha=0.7)

# Adjust layout to prevent label cutoff
plt.tight_layout()

# Display the plot
plt.show()
```