Plot Different Data Frame in Single Line Plot

I have Different Data Frame with same length let's say 10 rows with different values. The First Data frame is Static, however rest of them are user input let's say user want 2 data frame then total it would be three data frame with same length. I would like to plot values for all three of them in one line graph in python.

import pandas as pd
import matplotlib.pyplot as plt

L1 = pd.DataFrame([20,19,18,17,16,15])
K1 = pd.DataFrame([15,14,13,11,18,21])
k2 = pd.DataFrame([10,15,16,21,22,25])

fig1 = plt.figure()
ax1 = fig1.add_subplot(111)
ax1.plot(L1[0])
ax1.plot(k1[0])
ax1.plot(k2[0])
fig1.savefig(Path + "abc.png")

I tried above but it just plot only L1 and K1 when I run it in a loop. How can I plot all three data frames in one figure?

when you create a subplots set number of rows and columns plt.sublots(nbrow, nbcol)
this will return two objects: figure and axis of main frame each axis being a subplot

import pandas as pd
import matplotlib.pyplot as plt


L1 = pd.DataFrame([20,19,18,17,16,15])
K1 = pd.DataFrame([15,14,13,11,18,21])
k2 = pd.DataFrame([10,15,16,21,22,25])
fig, axs = plt.subplots(3, 1)
axs[0].plot(L1.values)
axs[1].plot(K1.values)
axs[2].plot(k2.values)
axs[0].set_ylabel('YLabel 0')
axs[1].set_ylabel('YLabel 1')
axs[2].set_ylabel('YLabel 2')
fig.align_ylabels()
plt.show()

Since you just have numbers, I don't see the reason to use a pandas DataFrame, so I converted them to numpy arrays. Then it's just a matter of plotting each of the arrays in a single subplot.

import matplotlib.pyplot as plt
import numpy as np

L1 = np.array([20,19,18,17,16,15])
K1 = np.array([15,14,13,11,18,21])
k2 = np.array([10,15,16,21,22,25])

fig, ax = plt.subplots()
ax.plot(L1, label="L1")
ax.plot(K1, label="K1")
ax.plot(k2, label="k2")
ax.legend()
ax.set_xlabel("index")