Snippet Code Writer For Data Visualizati

Here's a Python code snippet using `Matplotlib` to create a clear and visually appealing bar chart for "Sales Data by Region." This code assumes we have a dataset with sales data for each region, but if any data is missing, it will handle that gracefully. ```python import matplotlib.pyplot as plt import numpy as np import pandas as pd # Sample sales data by region, including a NaN value to simulate missing data data = { 'Region': ['North', 'South', 'East', 'West', 'Central'], 'Sales': [15000, np.nan, 12000, 18000, 14000] } # Convert the data to a DataFrame for easier manipulation df = pd.DataFrame(data) # Handle missing data by filling NaN values with 0 (or use an average, median, etc.) df['Sales'] = df['Sales'].fillna(0) # Set up figure and axis for a clear layout plt.figure(figsize=(10, 6)) # Bar chart for sales by region plt.bar(df['Region'], df['Sales'], color=['#4c72b0', '#55a868', '#c44e52', '#8172b2', '#ccb974']) # Add labels and title to make the chart clear plt.title('Sales by Region', fontsize=16, fontweight='bold') plt.xlabel('Region', fontsize=12) plt.ylabel('Sales ($)', fontsize=12) # Add value labels on top of each bar for clarity for index, value in enumerate(df['Sales']): plt.text(index, value + 500, f"${int(value):,}", ha='center', fontsize=10) # Display gridlines to improve readability of sales values plt.grid(axis='y', linestyle='--', alpha=0.7) # Optimize layout to avoid overlapping and make the chart easier to read plt.tight_layout() # Show the plot plt.show() ``` ### Explanation 1. **Data Creation**: We create a sample dictionary with "Region" and "Sales" data. One region has a `NaN` value to demonstrate missing data handling. 2. **Missing Data Handling**: We fill any missing sales values with `0` (or you could replace with an average, median, etc.). 3. **Chart Setup**: We use a bar chart, as it is suitable for comparing categorical data (sales across regions). 4. **Custom Colors**: Each bar has a distinct color for visual appeal. 5. **Labels and Titles**: Clear labels and a bold title enhance readability. 6. **Value Labels**: Values are added on top of each bar to show exact sales figures. 7. **Gridlines**: Horizontal gridlines improve readability. 8. **Layout Optimization**: `plt.tight_layout()` is used to avoid any text overlap, ensuring a clean look.