correlation coefficiant and p in Python

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats


df_no_nulls_diesel = df_diesel[["Years Automobile", "Price BGN", "Kilometers", "Horsepower"]].dropna().copy()
df_no_nulls_diesel.corr()

g = sns.lmplot(x = "Years Automobile", y = "Price BGN", data = df_no_nulls_diesel, height=12, aspect=1)
g.fig.suptitle('Diesel engines: Years Automobile vs Price BGN')
plt.show()

g = sns.heatmap(np.round(df_no_nulls_diesel.corr(), 2), annot = True).set(title='Diesel engines: Correlation Analysis')
g
plt.show()

df_no_nulls_diesel.mpg.corr(df_no_nulls_diesel.years)


########################################################
# jointplot returns a jointgrid, which we need to assign to a variable in order to add an annotation
# This line is almost like the original, but it seems that fill is needed explicitly now.
# And most importantly, ".annotate" is not just deprecated. It's gone.
jg = sns.jointplot(x='BMXLEG', y='BMXARML', data=da, kind='kde', fill=True)

# To get the correlation, we need to consider only the records with NA values for either measurement.
da_no_nulls = da[['BMXLEG', 'BMXARML']].dropna()
pearsonr, p = stats.pearsonr(da_no_nulls.BMXLEG, da_no_nulls.BMXARML)
pearson_str = f'pearsonr = {pearsonr:.2f}; p = {p}'

# Placing the annotation somewhere readable requires that we find the max of the axes
jg.ax_joint.text(
    jg.ax_joint._axes.xaxis.get_data_interval()[1],
    jg.ax_joint._axes.yaxis.get_data_interval()[1],
    pearson_str,
    horizontalalignment='right')
plt.show()