python - How to fill matplotlib bars with a gradient?

Python - How to fill matplotlib bars with a gradient?

Filling matplotlib bars with a gradient involves creating a custom color map and applying it to the bars in your plot. Matplotlib doesn't have built-in support for gradient-filled bars, but you can achieve this effect by generating a colormap that transitions smoothly between colors and using it to color each bar individually.

Here's a step-by-step guide to create a bar plot with gradient-filled bars using matplotlib:

Step 1: Import Libraries

First, import the necessary libraries:

import numpy as np import matplotlib.pyplot as plt from matplotlib.colors import LinearSegmentedColormap

Step 2: Generate Sample Data

Create some sample data for the bar plot:

categories = ['Category 1', 'Category 2', 'Category 3'] values = [20, 35, 25]

Step 3: Define a Custom Color Map

Define a custom colormap that transitions between two colors. You can adjust the colors and the number of segments (N) to control the gradient effect:

def create_colormap(color1, color2, N=100): return LinearSegmentedColormap.from_list('custom_cmap', [color1, color2], N=N)

Step 4: Create the Bar Plot

Create the bar plot and fill each bar with a gradient:

# Define colors for the gradient color1 = 'blue' color2 = 'cyan' # Create a custom colormap cmap = create_colormap(color1, color2) # Create figure and axis fig, ax = plt.subplots() # Plot the bars with gradient colors bars = ax.bar(categories, values, color=cmap(np.linspace(0, 1, len(categories)))) # Add labels, title, etc. ax.set_xlabel('Categories') ax.set_ylabel('Values') ax.set_title('Bar Plot with Gradient-Filled Bars') # Show the plot plt.show()

Explanation:

  • Custom Colormap: create_colormap() function creates a linear segmented colormap (LinearSegmentedColormap) that transitions smoothly between color1 and color2. Adjust the colors and the number of segments (N) as needed.

  • Plotting Bars: bars = ax.bar(categories, values, color=cmap(np.linspace(0, 1, len(categories)))) uses the custom colormap to fill each bar with a gradient color.

  • Visualization: Customize the plot with labels, titles, and other aesthetics as required.

Customization Tips:

  • Adjust color1 and color2 to change the starting and ending colors of the gradient.
  • Experiment with different colors and colormap configurations (N) to achieve the desired gradient effect.

This approach provides a flexible way to create bar plots with gradient-filled bars using matplotlib, allowing you to enhance visualizations with smooth color transitions. Adjust the code and parameters to fit your specific data and design requirements.

Examples

  1. Matplotlib bar chart with linear gradient fill

    • Description: Creating a bar chart in Matplotlib where each bar is filled with a linear gradient color scheme.
    • Code:
      import matplotlib.pyplot as plt from matplotlib import cm import numpy as np # Sample data categories = ['Category A', 'Category B', 'Category C', 'Category D'] values = [10, 20, 15, 25] # Generate linear gradient colors colors = cm.viridis(np.linspace(0, 1, len(categories))) # Create bar chart with gradient fill fig, ax = plt.subplots() bars = ax.bar(categories, values, color=colors) # Add colorbar for reference cbar = plt.colorbar(cm.ScalarMappable(cmap='viridis'), ax=ax) cbar.set_label('Gradient Intensity') plt.show()

  2. Matplotlib stacked bar chart with gradient fill

    • Description: Generating a stacked bar chart in Matplotlib where each segment of the bars is filled with a gradient color scheme.
    • Code:
      import matplotlib.pyplot as plt from matplotlib import cm import numpy as np # Sample data categories = ['Category A', 'Category B', 'Category C'] values1 = [10, 15, 20] values2 = [5, 10, 15] # Generate linear gradient colors colors1 = cm.Blues(np.linspace(0.2, 0.8, len(categories))) colors2 = cm.Greens(np.linspace(0.2, 0.8, len(categories))) # Create stacked bar chart with gradient fill fig, ax = plt.subplots() bars1 = ax.bar(categories, values1, color=colors1) bars2 = ax.bar(categories, values2, bottom=values1, color=colors2) # Add colorbar for reference cbar1 = plt.colorbar(cm.ScalarMappable(cmap='Blues'), ax=ax) cbar1.set_label('Gradient Intensity (Category A)') cbar2 = plt.colorbar(cm.ScalarMappable(cmap='Greens'), ax=ax) cbar2.set_label('Gradient Intensity (Category B)') plt.show()

  3. Matplotlib horizontal bar chart with gradient fill

    • Description: Creating a horizontal bar chart in Matplotlib with bars filled using a gradient color scheme.
    • Code:
      import matplotlib.pyplot as plt from matplotlib import cm import numpy as np # Sample data categories = ['Category A', 'Category B', 'Category C', 'Category D'] values = [10, 20, 15, 25] # Generate linear gradient colors colors = cm.cividis(np.linspace(0, 1, len(categories))) # Create horizontal bar chart with gradient fill fig, ax = plt.subplots() bars = ax.barh(categories, values, color=colors) # Add colorbar for reference cbar = plt.colorbar(cm.ScalarMappable(cmap='cividis'), ax=ax) cbar.set_label('Gradient Intensity') plt.show()

  4. Matplotlib grouped bar chart with gradient fill

    • Description: Generating a grouped bar chart in Matplotlib where each group of bars is filled with a gradient color scheme.
    • Code:
      import matplotlib.pyplot as plt from matplotlib import cm import numpy as np # Sample data categories = ['Category A', 'Category B', 'Category C'] values1 = [10, 15, 20] values2 = [5, 10, 15] # Generate linear gradient colors colors1 = cm.Oranges(np.linspace(0.2, 0.8, len(categories))) colors2 = cm.Purples(np.linspace(0.2, 0.8, len(categories))) # Create grouped bar chart with gradient fill fig, ax = plt.subplots() bar_width = 0.35 x = np.arange(len(categories)) bars1 = ax.bar(x - bar_width/2, values1, bar_width, label='Group 1', color=colors1) bars2 = ax.bar(x + bar_width/2, values2, bar_width, label='Group 2', color=colors2) # Add legend and colorbar for reference ax.legend() cbar1 = plt.colorbar(cm.ScalarMappable(cmap='Oranges'), ax=ax) cbar1.set_label('Gradient Intensity (Group 1)') cbar2 = plt.colorbar(cm.ScalarMappable(cmap='Purples'), ax=ax) cbar2.set_label('Gradient Intensity (Group 2)') plt.show()

  5. Matplotlib bar chart with custom gradient fill

    • Description: Creating a bar chart in Matplotlib with bars filled using a custom-defined gradient color scheme.
    • Code:
      import matplotlib.pyplot as plt from matplotlib import cm import numpy as np # Sample data categories = ['Category A', 'Category B', 'Category C', 'Category D'] values = [10, 20, 15, 25] # Define custom gradient colors custom_colors = ['#FF5733', '#FFC300', '#C70039', '#900C3F'] colors = [custom_colors[i % len(custom_colors)] for i in range(len(categories))] # Create bar chart with custom gradient fill fig, ax = plt.subplots() bars = ax.bar(categories, values, color=colors) plt.show()

  6. Matplotlib bar chart with radial gradient fill

    • Description: Implementing a bar chart in Matplotlib where each bar has a radial gradient color fill.
    • Code:
      import matplotlib.pyplot as plt from matplotlib import cm import numpy as np # Sample data categories = ['Category A', 'Category B', 'Category C', 'Category D'] values = [10, 20, 15, 25] # Generate radial gradient colors colors = cm.spring(np.linspace(0, 1, len(categories))) # Create bar chart with radial gradient fill fig, ax = plt.subplots() bars = ax.bar(categories, values, color=colors) # Add colorbar for reference cbar = plt.colorbar(cm.ScalarMappable(cmap='spring'), ax=ax) cbar.set_label('Gradient Intensity') plt.show()

  7. Matplotlib bar chart with stepped gradient fill

    • Description: Creating a bar chart in Matplotlib with bars filled using a stepped gradient color scheme.
    • Code:
      import matplotlib.pyplot as plt from matplotlib import cm import numpy as np # Sample data categories = ['Category A', 'Category B', 'Category C', 'Category D'] values = [10, 20, 15, 25] # Generate stepped gradient colors colors = cm.cool(np.linspace(0, 1, len(categories))) # Create bar chart with stepped gradient fill fig, ax = plt.subplots() bars = ax.bar(categories, values, color=colors) # Add colorbar for reference cbar = plt.colorbar(cm.ScalarMappable(cmap='cool'), ax=ax) cbar.set_label('Gradient Intensity') plt.show()

  8. Matplotlib bar chart with logarithmic gradient fill

    • Description: Implementing a bar chart in Matplotlib where each bar is filled with a logarithmic gradient color scheme.
    • Code:
      import matplotlib.pyplot as plt from matplotlib import cm import numpy as np # Sample data categories = ['Category A', 'Category B', 'Category C', 'Category D'] values = [10, 20, 15, 25] # Generate logarithmic gradient colors colors = cm.summer(np.logspace(0.1, 1, len(categories))) # Create bar chart with logarithmic gradient fill fig, ax = plt.subplots() bars = ax.bar(categories, values, color=colors) # Add colorbar for reference cbar = plt.colorbar(cm.ScalarMappable(cmap='summer'), ax=ax) cbar.set_label('Gradient Intensity (Log Scale)') plt.show()

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