barplot normalization and ordering of groups (x-axis)

Question

Below is what I needed to do to get to the part where I attempt to implement seaborn's barplot.

import matplotlib.pyplot as plt 
import seaborn as sns 
import pandas as pd 
import statsmodels.api as sm 
import numpy as np

da = pd.read_csv("nhanes_2015_2016.csv")

da["DMDMARTL"] = da.DMDMARTL.fillna("Missing")
da["DMDMARTLdescript"] = da.DMDMARTL.replace({1: "Married", 2: "Widowed", 3: "Divorced", 4: "Separated", 5: "Never married", 
                             6: "Living with partner",       77: "Refused", 99: "Don't know"})

da["RIAGENDRx"] = da.RIAGENDR.replace({1: "Male", 2: "Female"})

da["agegrp"] = pd.cut(da.RIDAGEYR, [10, 20, 30, 40, 50, 60, 70, 80])

I pieced together bits of code here and there and arrived at what I have below.

y = "prop"
dx = da.loc[~da.RIAGENDRx.isin(["Male"]), :]
plt.figure(figsize=(12, 5))
prop_df = (dx["agegrp"]
       .groupby(dx["DMDMARTLdescript"])
       .value_counts(normalize=True)
       .rename(y)
       .reset_index())
sns.barplot(x="agegrp", y=y, hue="DMDMARTLdescript", data=prop_df)

The result of running the code above is the following

I have following issues with the plot it generates.

1. Although I have asked each age group to be normalized `(normalized = True), based on the image, it's fairly obvious that the sum of the bars in each age group exceeds 1.

2. The age groups are ordered along the x axis in a somewhat arbitrary way. I am not sure how to order them in the numerical order.

(the csv file is publicly available here github link.)