Matplotlib - Use a loop to plot n charts Python

Matplotlib - Use a loop to plot n charts Python

In Python, using Matplotlib, you can create multiple plots using a loop. The approach depends on whether you want to create separate figures for each plot or multiple subplots within a single figure. Here's how you can do both:

1. Separate Figures for Each Plot

If you want each plot in a separate window, you can create a new figure inside the loop:

import matplotlib.pyplot as plt import numpy as np # Example data n_plots = 5 # Number of plots x = np.linspace(0, 10, 100) for i in range(n_plots): y = np.sin(x + i) # Different data for each plot plt.figure() # Create a new figure plt.plot(x, y) plt.title(f'Plot {i+1}') plt.xlabel('x') plt.ylabel('y') plt.show()

2. Multiple Subplots in a Single Figure

If you prefer multiple subplots in one figure, use plt.subplots:

import matplotlib.pyplot as plt import numpy as np # Example data n_plots = 5 # Number of plots x = np.linspace(0, 10, 100) # Create subplots fig, axs = plt.subplots(n_plots, 1, figsize=(10, 5 * n_plots)) # Adjust figsize as needed for i in range(n_plots): y = np.sin(x + i) # Different data for each plot axs[i].plot(x, y) axs[i].set_title(f'Plot {i+1}') axs[i].set_xlabel('x') axs[i].set_ylabel('y') plt.tight_layout() # Adjusts subplot params so that subplots fit into the figure area plt.show()

In this example, n_plots subplots are created vertically stacked (n_plots, 1 in plt.subplots). You can adjust the layout by changing the subplot grid size. For instance, plt.subplots(2, 3) would create 2 rows and 3 columns of subplots.

Tips:

  • Adjust figsize to ensure that the plots are well-sized and readable.
  • The tight_layout() function adjusts the spacing between subplots to prevent labels, titles, etc., from overlapping.
  • Customize each subplot as needed, just like you would with a single plot.
  • For more complex layouts, consider using GridSpec for fine-tuned control over subplot placement and size.

  1. Matplotlib Loop to Plot Multiple Charts:

    import matplotlib.pyplot as plt for i in range(5): plt.plot(range(10), [x*i for x in range(10)]) plt.title(f'Chart {i+1}') plt.show()

    Use a loop in Matplotlib to plot multiple charts with different data.

  2. Python Matplotlib Plot Multiple Charts in a Loop:

    import matplotlib.pyplot as plt import numpy as np data = np.random.rand(5, 10) for i in range(5): plt.plot(data[i], label=f'Chart {i+1}') plt.legend() plt.show()

    Plot multiple charts in Matplotlib using a loop with random data.

  3. Iterative Plotting with Matplotlib in Python:

    import matplotlib.pyplot as plt for i in range(3): plt.scatter(range(10), [x*i for x in range(10)], label=f'Chart {i+1}') plt.legend() plt.show()

    Iteratively create scatter plots in Matplotlib with a loop.

  4. Generate Multiple Plots in Matplotlib Using a Loop:

    import matplotlib.pyplot as plt import numpy as np for i in range(3): plt.plot(np.sin(np.linspace(0, 2*np.pi, 100)*(i+1)), label=f'Chart {i+1}') plt.legend() plt.show()

    Generate multiple plots with different sine wave frequencies using a loop in Matplotlib.

  5. Loop Through Data to Create Multiple Plots with Matplotlib:

    import matplotlib.pyplot as plt data = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] for i, series in enumerate(data): plt.plot(series, label=f'Chart {i+1}') plt.legend() plt.show()

    Loop through data to create multiple plots in Matplotlib.

  6. Matplotlib Subplot Loop for Multiple Charts:

    import matplotlib.pyplot as plt import numpy as np data = np.random.rand(3, 10) for i in range(3): plt.subplot(3, 1, i+1) plt.plot(data[i]) plt.title(f'Chart {i+1}') plt.tight_layout() plt.show()

    Use subplots in Matplotlib to create multiple charts in a loop.

  7. Dynamic Chart Generation with Matplotlib in Python:

    import matplotlib.pyplot as plt chart_count = 4 data = [range(10) for _ in range(chart_count)] for i in range(chart_count): plt.plot(data[i], label=f'Chart {i+1}') plt.legend() plt.show()

    Dynamically generate charts in Matplotlib using a loop with variable chart count.

  8. Creating n Plots with a Loop in Matplotlib:

    import matplotlib.pyplot as plt import numpy as np n = 3 data = np.random.rand(n, 10) for i in range(n): plt.plot(data[i], label=f'Chart {i+1}') plt.legend() plt.show()

    Create 'n' plots in Matplotlib using a loop with random data.

  9. Python Matplotlib Loop Through Data to Create Charts:

    import matplotlib.pyplot as plt data = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] for i, series in enumerate(data): plt.plot(series, label=f'Chart {i+1}') plt.legend() plt.show()

    Loop through data to dynamically create charts in Matplotlib.

  10. Using Loops to Generate Plots in Matplotlib:

    import matplotlib.pyplot as plt import numpy as np for i in range(3): x = np.linspace(0, 2*np.pi, 100) y = np.sin(x + np.pi*i/2) plt.plot(x, y, label=f'Chart {i+1}') plt.legend() plt.show()

    Generate multiple plots with varying sine wave phases using a loop in Matplotlib.

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