how to find 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

da = pd.read_csv("C:/Users/eli/Desktop/YtPruboBEemdqA7UJJ_tgg_63e179e3722f4ef783f58ff6e395feb7_nhanes_2015_2016.csv")

da["RIAGENDRx"] = da.RIAGENDR.replace({1: "Male", 2: "Female"})
print(da.loc[da.RIAGENDRx=="Female", ["BMXLEG", "BMXARML"]].dropna().corr())
print(da.loc[da.RIAGENDRx=="Male", ["BMXLEG", "BMXARML"]].dropna().corr())

da.BMXLEG.dropna(inplace=True)
da.BMXARML.dropna(inplace=True)
df=da.loc[:, ["BMXARML", "BMXLEG"]].fillna(0)
da = da.corr()

########################################################

# 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()