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One method for using personalized arrows in Python to move through various plots is to use the pyplot module from the matplotlib library. Here are the general steps to follow:

1. Import the necessary modules:

import matplotlib.pyplot as plt
from matplotlib.widgets import Button

1. Create the first plot using plt.subplots() and save it in a variable:

fig, ax = plt.subplots()
# plot something on ax

1. Create a function that will be called when the arrow buttons are clicked. The function should update the plot accordingly:

def update_plot(button):
    # update the plot based on which button was pressed

1. Create the arrow buttons using the Button class from matplotlib.widgets, and connect each button to the update_plot function:

left_arrow = Button(ax_left_arrow, '<')
left_arrow.on_clicked(update_plot)

right_arrow = Button(ax_right_arrow, '>')
right_arrow.on_clicked(update_plot)

1. Show the plot using plt.show().

Here is a more detailed example:

import matplotlib.pyplot as plt
from matplotlib.widgets import Button

# create the first plot
fig, ax = plt.subplots()
ax.plot([1,2,3,4], [1,4,9,16])

# define the update function
def update_plot(button):
    current_axis = plt.gca()
    current_axis.cla() # clear the current plot
    if button == left_arrow:
        current_axis.plot([1,2,3,4], [1,4,9,16])
    else:
        current_axis.plot([5,6,7,8], [25,36,49,64])
    plt.draw() # re-draw the plot

# create the arrow buttons
ax_left_arrow = plt.axes([0.4, 0.01, 0.1, 0.05])
left_arrow = Button(ax_left_arrow, '<')
left_arrow.on_clicked(update_plot)

ax_right_arrow = plt.axes([0.5, 0.01, 0.1, 0.05])
right_arrow = Button(ax_right_arrow, '>')
right_arrow.on_clicked(update_plot)

# show the plot
plt.show()

This example creates a simple line plot, and when the left arrow button is pressed, it re-plots the same data. When the right arrow button is pressed, it plots a different set of data. Note that we first get the current axes using plt.gca(), then call cla() to clear the current axis, then re-plot the data using plot(), and finally re-draw the plot using plt.draw().