Comparing plotting directly from a Pandas DataFrame (df.plot()) and using Matplotlib's plt.plot() reveals trade-offs in code complexity and customization. While plt.plot() provides explicit and itemized control over stylization, it typically requires more code than the high-level abstractions of Pandas.

To simplify styling in Matplotlib without writing verbose customization code, built-in style sheets can be applied.

To list all available styles:

import matplotlib.pyplot as plt
print(plt.style.available)

To apply a specific style (such as 'fivethirtyeight') to a plot:

from matplotlib import pyplot as plt
plt.style.use('fivethirtyeight')

x = [1, 2, 3, 4]
y = [1, 4, 3, 2]
y_2 = [5, 4, 1, 6]

plt.plot(x, y, label='Thing One')
plt.plot(x, y_2, label='Thing Two')
plt.xlabel('Time')
plt.ylabel('Number of things')
plt.title('Basic Plot')
plt.legend()
plt.tight_layout()
plt.show()