How to generate probability density and cumulative density plots using seaborn in python?

import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
x = np.array([260.58,
              42.21,
              88.53,
              103.9,
              120.23,
              215.66,
              180.39,
              112.66,
              637.18,
              72.09,
              49.23,
              67.06,
              58.01,
              56.26,
              104.77,
              166.93,
              50.87,
              51.24,
              93.91,
              44.54,
              61.1,
              108.49,
              144.94,
              88.73,
              139.22,
              40.18,
              53.84,
              100.36,
              235.53])

'''Same plot PDF and CDF'''
plt.rcParams["figure.figsize"] = [12.00, 6.50]
plt.rcParams["figure.autolayout"] = True
fig,axes=plt.subplots(1,2)

sns.kdeplot(x,ax=axes[0],color='green')
axes[0].set_title('TITTLE')
axes[0].set_ylabel("Y-AXIS TITLE")
axes[0].set_xlabel("X-AXIS TITLE")
ax = axes[0].lines[0]
xs = ax.get_xdata()
ys = ax.get_ydata()
middle = x.mean()
sdev = x.std()
left = middle - sdev
right = middle + sdev
axes[0].vlines(middle, 0, np.interp(middle, xs, ys),ls=':',color='blue')

axes[0].fill_between(xs, 0, ys, where=(left <= xs) & (xs <= right), facecolor="red", alpha=0.2)
axes[0].fill_between(xs, 0, ys, alpha=0.2, color='yellow')
legend_entries = [
    'Curve',
    f'Mean of X-AXIS DATA: {middle:.2f}ms',
    f"Standard Deviation of X-AXIS DATA: {sdev:.2f}ms"
]
axes[0].legend(legend_entries)



sns.ecdfplot(x,ax=axes[1],color='green')
axes[1].set_title("TITTLE")
axes[1].set_ylabel("Y-AXIS TITLE")
axes[1].set_xlabel("X-AXIS TITLE")
plt.legend(labels=["Curve",])
plt.show()# if you want to save the image, please comment this line
# to save the image
#plt.savefig("output3")