In Matplotlib, you can reduce the blank space around a chart using different methods. Here's how to use each method:
1. Using subplots_adjust(): The `subplots_adjust()` function allows you to manually adjust the spacing between subplots in a Matplotlib figure. You can use it to control the margins and spacing around your chart.

    import matplotlib.pyplot as plt

    # Create a simple plot
    plt.plot([1, 2, 3, 4], [1, 4, 9, 16])

    # Adjust the spacing using subplots_adjust()
    plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.1)

    # Show the plot
    plt.show()

In this example, `left, right, top, and bottom` parameters control the spacing on the left, right, top, and bottom sides of the plot, respectively. Adjust these values as needed to reduce the blank space.
2. Using tight_layout(): The `tight_layout()` function automatically adjusts the spacing between subplots to minimize overlaps and maximize the use of available space. It is a convenient way to ensure that your plots fit nicely within the figure without manually specifying spacing.

    import matplotlib.pyplot as plt

    # Create a simple plot
    plt.plot([1, 2, 3, 4], [1, 4, 9, 16])

    # Use tight_layout() to automatically adjust spacing
    plt.tight_layout()

    # Show the plot
    plt.show()

In this example, you don't need to specify margins or spacing explicitly. `Tight_layout()` will automatically calculate and adjust the spacing between the plot and the figure edges.
Both `subplots_adjust()` and `tight_layout()` have their uses, but tight_layout() is particularly convenient when you want to quickly optimize the layout of your plots without specifying margins manually. However, you may still use `subplots_adjust()` for more precise control when needed.
3. bbox_inches: You can use the `bbox_inches` parameter of the `savefig()` function in Matplotlib to remove any whitespace around the edges of the saved figure. Setting `bbox_inches` to `tight` will ensure that the saved figure contains only the area covered by your plot without any excess blank space [2]. Here's an example of how to use it:

    import matplotlib.pyplot as plt

    # Create a simple plot
    plt.plot([1, 2, 3, 4], [1, 4, 9, 16])

    # Save the figure with 'tight' bounding box
    plt.savefig('my_plot.png', bbox_inches='tight')

    # Show the plot (optional)
    plt.show()

After running this code, you will have a saved image file (`my_plot.png`) with reduced blank space around the chart.
4. Set Axis Limits: You can control the range of values displayed on the x and y axes using `set_xlim()` and `set_ylim()`.

    import matplotlib.pyplot as plt

    # Create a figure and axis
    fig, ax = plt.subplots()

    # Set custom x and y axis limits
    ax.set_xlim(0, 10)
    ax.set_ylim(0, 100)

    # Plot your data

    # Show the plot
    plt.show()

5. Adjust Tick Positions and Labels: Customize the positions and labels of tick marks on the axes using `set_xticks()` and `set_xticklabels()` for the x-axis and their counterparts for the y-axis.

    import matplotlib.pyplot as plt

    # Create a figure and axis
    fig, ax = plt.subplots()

    # Customize tick positions and labels
    ax.set_xticks([0, 1, 2, 3, 4])
    ax.set_xticklabels(['A', 'B', 'C', 'D', 'E'])

    # Plot your data

    # Show the plot
    plt.show()