incorporating p-value into box or violin plot - matplotlib

Question

I have made a violin plot with the code below (see pic for plot). I'm wondering if it's possible to get p-values of the differences between samples on the x axis. This could be any statistical test that shows a p-value, so if there is a global shift in the violin plot, the difference could be seen.
violin plot
Edit:
For clarity, was hoping to add something like this to show pvals between samples:
for i,p in enumerate(pvals):
if p>=0.05:
displaystring = r'n.s.'
elif p<0.0001:
displaystring = r'***'
elif p<0.001:
displaystring = r'**'
else:
displaystring = r'*'
Python code for making violin plot:
#!/usr/bin/env python
"""
Usage: Run script in ~/snakemake_eclip/scripts, use help function to see which parameters are needed.
This script takes in the all_reads_matrix made by merge_matrix.py and creates a violin plot.
"""
import pandas as pd
import argparse
import matplotlib.pyplot as plt
import os
import seaborn as sns
import numpy as np
plt.switch_backend('agg')
from scipy import stats
import numpy as np
def make_violin(in_matrix, save_path):
df = pd.read_csv(str(in_matrix), index_col=False)
# remove outliers
Q1 = df.quantile(0.25)
Q3 = df.quantile(0.75)
IQR = Q3 - Q1
df = df[~((df < (Q1 - 1.5 * IQR)) | (df > (Q3 + 1.5 * IQR))).any(axis=1)]
# drop zeros
df = df[(df != 0).all(1)]
df = df.iloc[:, 1:].transform(lambda x: np.log(x / x.sum()))
print(df)
plt.figure(figsize=(20, 10), dpi=300)
sns.violinplot(data=df)
plt.plot()
plt.title("Read Counts of Individual ENSG")
plt.xlabel("Samples")
plt.ylabel("Log Transformed Normalized Read Count")
plt.savefig(os.path.join(str(save_path), 'all_reads_matrix_violin_plot_norm_log.pdf'))
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Create a violin plot from all_reads_matrix.csv')
parser.add_argument("--in_matrix",
help='name of input matrix')
parser.add_argument("--save_path",
help='path to save')
# parse out arguments
args = parser.parse_args()
# mutate matrix columns
make_violin(args.in_matrix, args.save_path)