Groupby value_counts giving keyerror - pandas

Question

I am trying to plot countries whose scale has changes over time.
this is the dataset i am using :'https://www.kaggle.com/datasets/whenamancodes/the-global-hunger-index'
wasting = pd.read_csv('/kaggle/input/the-global-hunger-index/share-of-children-with-a-weight-too-low-for-their-height-wasting.csv')
# rename the column
wasting.rename(columns={'Prevalence of wasting, weight for height (% of children under 5)':'Wasting'},inplace=True)
#create new column with pd.cut
bins = [0,9.9,19.99,34.99,49.99,np.inf]
labels = ['Low','Moderate','Serious','Alarming','Extremely Alarming']
wasting['W_Scale'] = pd.cut(wasting['Wasting'],bins=bins,labels=labels,right=False).astype('category')
wasting.head()
wasting.isna().sum()
#selecting countries with w_scale greater than 1
wasting_entity_scale = wasting.groupby('Entity').filter(lambda x: x['W_Scale'].nunique()>1)
wasting_entity_scale = wasting_entity_scale.groupby(['Year','Entity'])['W_Scale'].value_counts().reset_index(name='count')
wasting_entity_scale = wasting_entity_scale[wasting_entity_scale['count']>0]
wasting_entity_scale = wasting_entity_scale.reset_index(drop=True)
#until this point everything is fine.
traces = {}
for i, (loc, d) in enumerate(wasting_entity_scale.groupby("Entity")):
# use meta so that we know which country a trace belongs to
fig = px.histogram(
d, x="Year", y="Entity", color="level_2"
).update_traces(meta=loc, visible=(i == 0))
traces[loc] = fig.data
l = fig.layout
# integrate all the traces
fig = go.Figure([t for a in traces.values() for t in a]).update_layout(l)
# now buuld menu using meta to know which traces should be visible per country
fig.update_layout(
updatemenus=[
{
"active": 0,
"buttons": [
{
"label": c,
"method": "update",
"args": [
{"visible": [t.meta == c for t in fig.data]},
{"title": c},
],
}
for c in traces.keys()
],
}
]
)
when i try to plot it, it shows this error:
KeyError: 'Serious'
Can someone please teach me what is it that i am doing wrong.
Thank you.