python - plotting a histogram on a Log scale with Matplotlib

Python - plotting a histogram on a Log scale with Matplotlib

You can plot a histogram on a logarithmic scale using Matplotlib by setting the scale of the x-axis or y-axis to logarithmic. Here's how you can do it:

import numpy as np import matplotlib.pyplot as plt # Generate some sample data (exponential distribution) data = np.random.exponential(scale=1, size=1000) # Plot the histogram on a logarithmic scale plt.hist(data, bins=50, log=True) # Set log=True for logarithmic scale plt.xlabel('Value') plt.ylabel('Frequency') plt.title('Histogram on Log Scale') plt.grid(True) plt.show()

In this example:

  • We generate some sample data using NumPy's random.exponential function to create an exponential distribution.
  • We plot the histogram using Matplotlib's hist function, setting log=True to use a logarithmic scale on the y-axis.
  • The bins parameter specifies the number of bins for the histogram.
  • We add labels to the axes, a title to the plot, and enable grid lines for better visualization.
  • Finally, we display the plot using plt.show().

You can also set log=True for the x-axis by specifying it as a parameter inside the plt.hist() function call if you want a logarithmic scale on the x-axis instead.

Examples

  1. Plot a histogram on a linear scale with Matplotlib in Python

    • Description: Create a basic histogram using Matplotlib with data on a linear scale before transitioning to a log scale.
    • Code:
      import matplotlib.pyplot as plt import numpy as np data = np.random.exponential(scale=1, size=1000) plt.hist(data, bins=30, edgecolor='black') plt.title('Histogram on Linear Scale') plt.xlabel('Value') plt.ylabel('Frequency') plt.show()

  2. Convert a Matplotlib histogram to a log scale in Python

    • Description: Transform an existing histogram plotted with Matplotlib into a log scale representation for better visualization.
    • Code:
      import matplotlib.pyplot as plt import numpy as np data = np.random.exponential(scale=1, size=1000) plt.hist(data, bins=30, edgecolor='black') plt.yscale('log') plt.title('Histogram on Log Scale') plt.xlabel('Value') plt.ylabel('Frequency') plt.show()

  3. Customize bin edges for a log scale histogram in Matplotlib

    • Description: Adjust bin edges in a Matplotlib histogram plotted on a log scale to better suit the distribution of data.
    • Code:
      import matplotlib.pyplot as plt import numpy as np data = np.random.exponential(scale=1, size=1000) plt.hist(data, bins=np.logspace(np.log10(0.1), np.log10(data.max()), 30), edgecolor='black') plt.xscale('log') plt.title('Histogram on Log Scale with Custom Bins') plt.xlabel('Value') plt.ylabel('Frequency') plt.show()

  4. Plot multiple histograms on a log scale in Matplotlib

    • Description: Visualize multiple datasets with histograms on a log scale using Matplotlib in Python for comparative analysis.
    • Code:
      import matplotlib.pyplot as plt import numpy as np data1 = np.random.exponential(scale=1, size=1000) data2 = np.random.exponential(scale=0.5, size=1000) plt.hist(data1, bins=30, edgecolor='black', alpha=0.5, label='Data 1') plt.hist(data2, bins=30, edgecolor='black', alpha=0.5, label='Data 2') plt.yscale('log') plt.title('Multiple Histograms on Log Scale') plt.xlabel('Value') plt.ylabel('Frequency') plt.legend() plt.show()

  5. Overlay a histogram and a line plot on a log scale with Matplotlib

    • Description: Combine a histogram and a line plot on a log scale using Matplotlib in Python for visualizing data distribution and trends.
    • Code:
      import matplotlib.pyplot as plt import numpy as np data = np.random.exponential(scale=1, size=1000) x = np.linspace(0.1, 10, 100) y = np.exp(-x) plt.hist(data, bins=30, edgecolor='black', alpha=0.5, label='Histogram') plt.plot(x, y, '-r', label='Exponential Decay') plt.yscale('log') plt.title('Histogram and Line Plot on Log Scale') plt.xlabel('Value') plt.ylabel('Frequency') plt.legend() plt.show()

  6. Adjust histogram bar colors and transparency on a log scale in Matplotlib

    • Description: Customize colors and transparency of histogram bars plotted on a log scale using Matplotlib in Python.
    • Code:
      import matplotlib.pyplot as plt import numpy as np data = np.random.exponential(scale=1, size=1000) plt.hist(data, bins=30, edgecolor='black', alpha=0.7, color='blue') plt.yscale('log') plt.title('Histogram on Log Scale with Custom Colors') plt.xlabel('Value') plt.ylabel('Frequency') plt.show()

  7. Plot a cumulative histogram on a log scale with Matplotlib

    • Description: Visualize cumulative distribution using a histogram on a log scale with Matplotlib in Python.
    • Code:
      import matplotlib.pyplot as plt import numpy as np data = np.random.exponential(scale=1, size=1000) plt.hist(data, bins=30, edgecolor='black', cumulative=True, alpha=0.7) plt.yscale('log') plt.title('Cumulative Histogram on Log Scale') plt.xlabel('Value') plt.ylabel('Cumulative Frequency') plt.show()

  8. Plot a 2D histogram on a log scale with Matplotlib

    • Description: Visualize 2D histogram data on a log scale using Matplotlib in Python for analyzing relationships between two variables.
    • Code:
      import matplotlib.pyplot as plt import numpy as np x = np.random.normal(size=1000) y = np.random.normal(size=1000) plt.hist2d(x, y, bins=30, cmap='Blues') plt.colorbar() plt.xscale('log') plt.yscale('log') plt.title('2D Histogram on Log Scale') plt.xlabel('X Values') plt.ylabel('Y Values') plt.show()

  9. Plot a stacked histogram on a log scale with Matplotlib

    • Description: Visualize stacked histograms on a log scale using Matplotlib in Python to compare contributions of different categories.
    • Code:
      import matplotlib.pyplot as plt import numpy as np data1 = np.random.exponential(scale=1, size=1000) data2 = np.random.exponential(scale=0.5, size=1000) plt.hist([data1, data2], bins=30, stacked=True, edgecolor='black', label=['Data 1', 'Data 2']) plt.yscale('log') plt.title('Stacked Histogram on Log Scale') plt.xlabel('Value') plt.ylabel('Frequency') plt.legend() plt.show()

  10. Plot a density histogram on a log scale with Matplotlib

    • Description: Visualize density histograms on a log scale using Matplotlib in Python for smoother representation of data distributions.
    • Code:
      import matplotlib.pyplot as plt import numpy as np data = np.random.exponential(scale=1, size=1000) plt.hist(data, bins=30, density=True, edgecolor='black', alpha=0.7) plt.yscale('log') plt.title('Density Histogram on Log Scale') plt.xlabel('Value') plt.ylabel('Density') plt.show()

More Tags

resources dot-matrix grid rpa android-edittext ubuntu-16.04 android-sharedpreferences calendar jvm angular2-modal

More Programming Questions

More Math Calculators

More General chemistry Calculators

More Biochemistry Calculators

More Stoichiometry Calculators