Graph bar graph for overstory data

import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
# %matplotlib inline needed to view graphs in jupyter notebook

# import and prepare data
Overstory=pd.read_csv("OverstoryData.csv", header = 0,                  
                     dtype={'ws': 'str', 'plot':'str','year':object, 
                'tree':'str', 'history':object,
              'species':'category', 'sp_cd':'category',
              'dbh_cm':np.float64, 'tr_ht_m':np.float64
              }                    
                    )

# create table of averages and sums per plot, history, ws, and year
pl_s =pd.DataFrame(Overstory
             .groupby(['year', 'ws', 'history','plot'])
             .agg(
             dbh_avg=pd.NamedAgg(column="dbh_cm", aggfunc="mean"),
             tr_ct=pd.NamedAgg(column="dbh_cm", aggfunc="count"),
             tr_ht_avg=pd.NamedAgg(column="tr_ht_m", aggfunc="mean"),
             BA_sum=pd.NamedAgg(column="BA", aggfunc="sum"),
             BM_kg_pl=pd.NamedAgg(column="biomass", aggfunc="sum")
             
              )
)

#2.471 acres=1ha
#1plot=1/10 acre
# 1cm^2 = 0.0001m^2
#1kg=0.001megagram

#create new columns with calculations
#kg/plot convert biomass  ((biomass*10)*2.471))/1000) Mg/ha
pl_s['BM_Mg_ha']=((pl_s.BM_kg_pl*10*2.471)/1000)

# cm^2/plot basal area to ((ba*10)*2.471))/10000 = m^2/ha
pl_s['BA_m2_ha']=((pl_s.BA_sum*10*2.471)/10000)

#tree count (tr_ct*10)*2.471) = trees/ha
pl_s['tr_ct_ha']=(pl_s.tr_ct*10*2.471)


#create table of averages 
ws_sum =pd.DataFrame(pl_s
             .groupby(['year', 'ws', 'history'])
             .agg(
             dbh_Tavg=pd.NamedAgg(column="dbh_avg", aggfunc="mean"),
             tr_ht_Tavg=pd.NamedAgg(column="tr_ht_avg", aggfunc="mean"),
             tr_ct_ha=pd.NamedAgg(column="tr_ct_ha", aggfunc="mean"),
             BA_avg=pd.NamedAgg(column="BA_m2_ha", aggfunc="mean"),
             BM_avg=pd.NamedAgg(column="BM_Mg_ha", aggfunc="mean")
             
              )
)
ws_sum.round(2) #round to 2 decimals


#change numerical 1 and string 1 to "Live Biomass", 2 to "Necromass"
ws_sum=ws_sum.reset_index(drop=False)
ws_sum.history=ws_sum['history'].replace({1:"Live Biomass", '2':"Necromass", '2?':"Necromass",
                                          '11':"Live Biomass",
                                      '1':"Live Biomass", 2:"Necromass"})

#select only history with live and necromass and years 2018 and 2022
t=ws_sum.loc[((ws_sum['history']=='Live Biomass')|
          (ws_sum['history']=='Necromass'))]
t2=pd.DataFrame(t.loc[((t['year']=='2018')|
         (t['year']=='2022'))])

#plot graph and save png file
baG=sns.catplot(x="year", y='tr_ht_Tavg', hue="history", palette="mako",col="ws", kind="bar", data=t2)
baG.set_axis_labels("", "Average Tree hieght (m)")
plt.savefig('tr_ht_bar.png')